Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Kazemian, Negin
Doctor of Philosophy - PhD
University of British Columbia
The intricate nexus between humans, environmental factors, and the gut microbiota plays a pivotal role in determining the overall health of the host. Our gut microbiota's composition is a dynamic tapestry woven from threads of both early-life events and adult experiences, each contributing uniquely to our overall health and well-being. Thus, the overall aim of this thesis was to investigate the role of the gut microbiome in health and disease states. The gut microbiota’s role in cholesterol metabolism (cholesterol to coprostanol conversion), is not fully understood. Therefore, I first aimed to explore the gut’s environment, encompassing mucus, microbiome, and metabolites to explore its relationship with cholesterol metabolism among healthy participants. High-converters had increased Akkermansia muciniphila. Importantly, the causal role of A. muciniphila in conversion was confirmed. Non-converters showed reduced mucus glycan sialylation and fecal bile acid desulfation, similar to trends observed in inflammatory bowel disease (IBD). Next, I aimed to gain a holistic understanding of the impact of adverse childhood experiences (ACEs) on gut microbiota. ACEs can impact physical and mental health outcomes in adulthood; however, studies assessing how ACEs may impact gut microbiota are lacking. High ACE individuals displayed distinct gut microbiota differences potentially impacting intestinal tissue integrity. They also experienced a higher prevalence of diarrhea, possibly linked to stress on the gastrointestinal system. Lastly, I aimed to better understand the underlying mechanisms for fecal microbiota transplantation (FMT) and optimize microbial therapeutics for recurrent Clostridioides difficile infections (rCDI). Studies often have a uni-kingdom focus and few studies have examined the role of gut mycobiome and virome on FMT efficacy. Thus, I aimed to investigate whether FMT super-donors exist or whether the gut microbiomes of both donors and recipients are implicated in FMT success. Our results suggested that FMT outcome is not only dependent on the ecological structure of the recipients but also on the interactions between the donor and recipient microbiomes. In summary, our integrated studies underscore the multi-faceted factors involved in microbiome-mediated mechanisms in health and disease states. Insights gathered can pave the way for innovative therapeutic interventions against diseases and microbiota-related conditions.
INVESTIGATING THE HUMAN MICROBIOME: DYNAMIC INTERACTIONS AND THERAPEUTIC POTENTIALS IN HEALTH AND DISEASE STATES by Negin Kazemian M.Sc., The University of British Columbia Okanagan, 2017 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) April 2024 © Negin Kazemian, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Investigating the human microbiome: Dynamic Interactions and therapeutic potentials in health and disease states submitted by Negin Kazemian in partial fulfillment of the requirements of the degree of Doctor of Philosophy. Dr. Sepideh Pakpour, School of Engineering, UBC Okanagan Supervisor Dr. Kirk Bergstrom, Irving K. Barber School of Arts and Sciences, UBC Okanagan Supervisory Committee Member Dr. Fatemeh Hendijani Fard, Irving K. Barber School of Arts and Sciences, UBC Okanagan Supervisory Committee Member Laura Grieneisen, Irving K. Barber School of Arts and Sciences, UBC Okanagan University Examiner Dr. Katrine Whiteson, School of Biological Sciences, University of California, Irvine External Examiner iii Abstract The intricate nexus between humans, environmental factors, and the gut microbiota plays a pivotal role in determining the overall health of the host. Our gut microbiota's composition is a dynamic tapestry woven from threads of both early-life events and adult experiences, each contributing uniquely to our overall health and well-being. Thus, the overall aim of this thesis was to investigate the role of the gut microbiome in health and disease states. The gut microbiota’s role in cholesterol metabolism (cholesterol to coprostanol conversion), is not fully understood. Therefore, I first aimed to explore the gut’s environment, encompassing mucus, microbiome, and metabolites to explore its relationship with cholesterol metabolism among healthy participants. High-converters had increased Akkermansia muciniphila. Importantly, the causal role of A. muciniphila in conversion was confirmed. Non-converters showed reduced mucus glycan sialylation and fecal bile acid desulfation, similar to trends observed in inflammatory bowel disease (IBD). Next, I aimed to gain a holistic understanding of the impact of adverse childhood experiences (ACEs) on gut microbiota. ACEs can impact physical and mental health outcomes in adulthood; however, studies assessing how ACEs may impact gut microbiota are lacking. High ACE individuals displayed distinct gut microbiota differences potentially impacting intestinal tissue integrity. They also experienced a higher prevalence of diarrhea, possibly linked to stress on the gastrointestinal system. Lastly, I aimed to better understand the underlying mechanisms for fecal microbiota transplantation (FMT) and optimize microbial therapeutics for recurrent Clostridioides difficile infections (rCDI). Studies often have a uni-kingdom focus and few studies have examined the role of gut mycobiome and virome on FMT efficacy. Thus, I aimed to investigate whether FMT super-donors exist or whether the gut microbiomes of both donors and recipients are implicated in FMT success. Our results suggested iv that FMT outcome is not only dependent on the ecological structure of the recipients but also on the interactions between the donor and recipient microbiomes. In summary, our integrated studies underscore the multi-faceted factors involved in microbiome-mediated mechanisms in health and disease states. Insights gathered can pave the way for innovative therapeutic interventions against diseases and microbiota-related conditions. v Lay Summary Our gut environment is complex, intricate, and home to a community of microbes influencing our health. This study aimed to assess the gut environment in health and disease states. First, I aimed to assess how the gut environment is involved in converting cholesterol to coprostanol–a conversion thought to decrease high cholesterol. I found a specific bacterium (Akkermansia muciniphila) that can aid with this conversion. Next, I assessed if difficult childhood experiences can have long-term effects on the gut. I confirmed that childhood experiences can affect the microbial community and their functions. Finally, to optimize fecal microbiota transplantation (FMT: transferring gut microbes from a healthy person to a patient), I assessed the gut bacteria, fungi and viruses. I showed it’s important to consider all microorganisms and both donors' and patients’ gut environment. The findings of this thesis could lead to new ways to treat diseases by targeting our gut. vi Preface Most of the results of this Ph.D. thesis have been published in peer-reviewed scientific journals or have been submitted for peer review. I was responsible for preparing the first draft of each paper. All tasks were done under the supervision of Dr. Sepideh Pakpour, who provided guidance on the study design, experiments, data analysis, as well as the revision of our publications. The chapters presented in this thesis are derived from my doctoral research as listed below. Chapter 1 presents the introduction and literature review of my doctoral research topics. A version of Chapter 1 has been published as a review journal article: Kazemian N, Mahmoudi M, Halperin F, Wu JC, Pakpour S. (2020). Gut microbiota and cardiovascular disease: Opportunities and challenges. Microbiome, 8(1), 36. N. Kazemian and Dr. Morteza Mahmoudi wrote the first draft of the manuscript. The conceptualization was carried out by Dr. Sepideh Pakpour and N. Kazemian. All members provided critical review of the manuscript and editing. A preliminary version of this work was accepted as a symposium presentation (N. Kazemian and S. Pakpour. (2019) “Gut microbiota and cardiovascular diseases: Challenges and opportunities”, New Horizons in Cardiovascular Health: An Academic-Industry Symposium, Kelowna, BC, Canada, September 2019). A version of Chapter 2 has been submitted as a journal article: Kazemian N, Karimianghadim R, Golzan SA, NN, Irungu JDW, Coombs GB, Low J, Little JP, Zandberg WF, Bergstrom K, Pakpour S. (2024). The role of Akkermansia muciniphila in luminal cholesterol metabolism: Exploring the gut microbiome, mucus, and metabolite dynamics. Submitted to Gut. This research was conducted at the University of British Columbia, Okanagan campus, under the supervision of Dr. Sepideh Pakpour. The work in this chapter was approved by UBC Clinical vii Research Ethics Board under the certificate number H18-01489 (GMCVD). Under the supervision of my committee members, I aided with the study design, obtained study ethics, recruited participants, and collected samples (saliva and stool samples), conducted lab experiments, collected and conducted statistical analyses, and wrote the manuscript. Dr. Philip Ainslie’s student (Geoff Coombs) aided with the blood sample collection of participants and Dr. Jonathan Little’s student (Jonathan Low) aided with the blood sample lipid analysis. Dr. Wesley Zandberg’s student (Nitin and Jackline Irungu) aided with the HPLC-MS analysis of stool mucin samples: glycomics and sialic acid analysis. Amir Golzan aided with the in vitro experiments and Ramin Karimian aided with the bioinformatics. Dr. Kirk Bergstrom supervised and provided continuous expert support for the stool mucin assessment. Dr. Sepideh Pakpour was involved in the supervision of the project in all aspects and provided a critical review of the version presented here. This work was wholly drafted by N. Kazemian and all members provided a critical review of the manuscript and editing. A preliminary version of this work was accepted as a conference presentation (Kazemian N, Karimianghadim R, Golzan SA, NN, Irungu JDW, Coombs GB, Low J, Little JP, Zandberg WF, Bergstrom K, Pakpour S. (2024) “Unraveling the interplay of gut microbiome, metabolites, and mucus in luminal cholesterol metabolism”, UBC Okanagan Interdisciplinary Student Health Conference, UBC, Kelowna, BC, Canada, March 2024). A version of Chapter 3 has been published as a journal article: Kazemian N, Zhou T, Chalasani N, Narayan A, Cedeno Laurent JG, Olvera Alvarez HA, Pakpour S. (2024). Long-term impact of childhood adversity on the gut microbiome of nursing students. International Journal of Environmental Research and Public Health, 21(1), 68. For this collaborative project, I had the opportunity to travel to the University of Texas at El Paso (UTEP), where patient recruitment viii and sample collection took place under the supervision of Dr. Hector A. Olvera Alvarez. During my week at UTEP, I was able to conduct the laboratory work required for stool sample preparation prior to 16S rRNA sequencing. The sequencing results along with the metadata were then sent to N. Kazemian and Dr. Sepideh Pakpour. Under the supervision of my supervisor, I analyzed the data and wrote the manuscript. Dr. Apurva Narayan’s students, Tony Zhou and Naveen Chalasani, aided with the data analysis. All members provided a critical review of the manuscript and editing. A version of Chapter 4 has been published as a journal article: Kazemian N, Ramezankhani M, A Sehgal, Khalid FM, Kalkhoran AHZ, Narayan A, Wong GKS, Kao D, Pakpour S. (2020). The trans-kingdom battle between donor and recipient gut microbiome influences fecal microbiota transplantation outcome. Scientific Reports, 10(1), 18439. The work in this chapter was approved by UBC Clinical Research Ethics Board under the certificate number H21-03481 (NEWS). This was a collaborative study with the University of Alberta, where patient recruitment and sample collection took place under the supervision of Dr. Dina Kao. N. Kazemian and Dr. Sepideh Pakpour wrote the first draft of the manuscript. M. Ramezankhani, A. Sehgal, and Dr. Narayan aided with the data analysis. FM. Khalid and AHZ. Kalkhoran contributed to the metagenomic sequence handling and processing. All authors read and approved the final manuscript. A preliminary version of this work was accepted as a conference presentation (N. Kazemian, M. Ramezankhani, A. Sehgal, FM. Khalid, GKS. Wong, D. Kao, and S. Pakpour. (2019) “Key role of donor and patient gut microbiome in predicant fecal microbiota transplantation treatment outcomes”, Biology Graduate Symposium (BGS), UBC, Kelowna, BC, Canada, September 2019). ix Table of Contents Abstract ---------------------------------------------------------------------------------------------------------------- iii Lay Summary --------------------------------------------------------------------------------------------------------- v Preface ----------------------------------------------------------------------------------------------------------------- vi Table of Contents --------------------------------------------------------------------------------------------------- ix List of Tables --------------------------------------------------------------------------------------------------------- xi List of Figures ------------------------------------------------------------------------------------------------------- xii Nomenclature ------------------------------------------------------------------------------------------------------- xvi Acknowledgements ------------------------------------------------------------------------------------------------ xix Dedication ----------------------------------------------------------------------------------------------------------- xxii Chapter 1: Introduction ------------------------------------------------------------------------------------------- 1 1.1 Anatomy of the Intestines -------------------------------------------------------------------------------------------------------- 1 1.2 Intestinal Microbiota -------------------------------------------------------------------------------------------------------------- 5 1.2.1 Functions of gut microbiota ----------------------------------------------------------------------------------------------- 6 1.3 Factors Influencing the Gut Microbiome ----------------------------------------------------------------------------------- 12 1.3.1 Infancy ----------------------------------------------------------------------------------------------------------------------- 12 1.3.2 Childhood ------------------------------------------------------------------------------------------------------------------- 13 1.3.3 Age, sex, and ethnicity ---------------------------------------------------------------------------------------------------- 16 1.3.4 Lifestyle ---------------------------------------------------------------------------------------------------------------------- 18 1.3.5 Antibiotic treatment ------------------------------------------------------------------------------------------------------- 22 1.4 Gut Microbiome and Disease States ----------------------------------------------------------------------------------------- 24 1.4.1 The gut-brain axis ---------------------------------------------------------------------------------------------------------- 26 1.4.2 The gut-heart axis ---------------------------------------------------------------------------------------------------------- 27 1.4.3 Microbiome-based therapeutics: Fecal microbiota transplantation (FMT) ----------------------------------- 33 1.5 Thesis Outline, Objectives, and Hypotheses ------------------------------------------------------------------------------- 34 Chapter 2: The Role of Akkermansia muciniphila in Luminal Cholesterol Metabolism: Exploring the Gut Microbiome, Mucus, and Metabolite Dynamics ---------------------- 39 2.1 Introduction ------------------------------------------------------------------------------------------------------------------------ 39 2.2 Materials and Methods ---------------------------------------------------------------------------------------------------------- 42 2.3 Results ------------------------------------------------------------------------------------------------------------------------------ 57 2.4 Discussion -------------------------------------------------------------------------------------------------------------------------- 74 2.5 Summary --------------------------------------------------------------------------------------------------------------------------- 79 Chapter 3: Long-Term Impact of Childhood Adversity on the Gut Microbiome of Nursing Students ----------------------------------------------------------------------------------------------------- 81 3.1 Introduction ------------------------------------------------------------------------------------------------------------------------ 81 3.2 Materials and Methods ---------------------------------------------------------------------------------------------------------- 84 3.3 Results ------------------------------------------------------------------------------------------------------------------------------ 91 x 3.4 Discussion -------------------------------------------------------------------------------------------------------------------------- 98 3.5 Summary -------------------------------------------------------------------------------------------------------------------------- 102 Chapter 4: The Trans-Kingdom Battle Between Donor and Recipient Gut Microbiome Influences Fecal Microbiota Transplantation Outcome ----------------------------------- 104 4.1 Introduction ----------------------------------------------------------------------------------------------------------------------- 104 4.2 Materials and Methods --------------------------------------------------------------------------------------------------------- 106 4.3 Results ----------------------------------------------------------------------------------------------------------------------------- 110 4.4 Discussion ------------------------------------------------------------------------------------------------------------------------ 117 4.5 Summary -------------------------------------------------------------------------------------------------------------------------- 121 Chapter 5: Concluding Remarks --------------------------------------------------------------------------- 123 5.1 Limitations and Future Outlook --------------------------------------------------------------------------------------------- 126 Bibliography ------------------------------------------------------------------------------------------------------- 130 Appendices --------------------------------------------------------------------------------------------------------- 152 Appendix A Supplementary table of PERMANOVA tests conducted from Chapter 2 ----------------------------- 152 Appendix B Stool and serum lipid comparisons of healthy participants from Chapter 2 --------------------------- 153 Appendix C Stool SCFA assessment from Chapter 2 ---------------------------------------------------------------------- 154 Appendix D Sequencing and pipeline comparisons, and strain analysis from Chapter 2 --------------------------- 155 Appendix E Top bacterial taxa from metagenomic sequencing of Chapter 2 ----------------------------------------- 156 Appendix F Stool mucus assessment compared between converters from Chapter 2 ------------------------------- 157 Appendix G Supplementary table for clinical characteristics of rCDI patients receiving FMT from Chapter 4 -------------------------------------------------------------------------------------------------------------------------------------------- 158 Appendix H The intra-variability of features of donor 1 and the corresponding recipients from Chapter 4 --- 159 Appendix I The intra-variability of features of donor 3 and the corresponding recipients from Chapter 4 ----- 160 xi List of Tables Table 1. Summary of the sample population. .............................................................................................................. 91 Table A.1 PERMANOVA results along with their respective r2 and p-adjusted values……………………………………….…152 Table F.1. Clinical characteristics of rCDI patients receiving FMT……………………………………………………………….……158 xii List of Figures Figure 1. Anatomy of the intestines. Anatomy of the small and large intestine, as well as the anatomical cross-section of the gut demonstrating the organization of various tissue layers. Figure created using BioRender.com. ..... 5 Figure 2. Factors influencing the gut microbiome. ACEs, adverse childhood experiences. Figure created using BioRender.com. .......................................................................................................................................................... 23 Figure 3. Cholesterol, gut microbiota, and CAD. Exogenous and endogenous sources of luminal cholesterol (A). The multifaceted mechanisms involved in CAD development (B). The gut microbiota can directly (via metabolites) and indirectly (via the immune system) lead to CAD. VLDL, very low-density lipoprotein; IDL, intermediate-density lipoproteins; LDL, low-density lipoprotein; LDL-R, low-density lipoprotein receptor; HDL, high-density lipoprotein; LRP, low-density lipoprotein receptor-related protein; TICE, transintestinal cholesterol excretion; CVD, cardiovascular disease; CAD, coronary artery disease .................................................................. 31 Figure 4. Multifaceted mechanisms affecting CAD. Exogenous and endogenous sources of luminal cholesterol and diet, and the gut microbiota mechanisms involved in affecting the immune system and CAD development. GLP-1, glucagon-like peptide 1; TMA, trimethylamine; PAMP, pathogen-associated molecular patterns; SCFA, short chain fatty acid; Treg, regulatory T cells; LPS, lipopolysaccharides; LDL-R, low-density lipoprotein receptor; TMAO, trimethylamine-N-oxide; CYP7A1, cholesterol 7-alpha-hydroxylase; FMO, flavin-containing monooxygenase; FGFR4, fibroblast growth factor receptor 4; FGF19, fibroblast growth factor receptor 19; NPC1L1, Niemann-Pick C1-like 1; ABCG5/8, G5/ATP-binding-cassette transporter G8, ABCB1a/b, TP-binding cassette transporter B1; TLR4, toll-like receptor 4; FXR, farnesoid X receptor; NOD2, nucleotide binding oligomerization domain containing 2; GPCR41/43/109A, G protein-coupled receptors 41/43/109A .............................................................. 32 Figure 5. Stool lipid profiling and gut microbiota composition of converter groups. Principal Component Analysis (PCA) with feature importance for stool lipid composition (A). Shannon diversity at the genus level (B). Beta diversity density plot with Bray-Curtis dissimilarity (C). ROC curve for random forest (D). The top bacterial taxa differentiating converter groups, where the size of the ellipses represents the average Mean Decrease in Impurity (MDI) (D), and LEfSe analysis with histogram of the LDA scores (log10) calculated for taxa with differential abundances in non- and high-converters (E). The high-converter group is represented in green (1.0) and the non-converter group is represented in yellow (0.0). Overlapping bacterial taxa from random forest and LEfSe compared between converters using the Mann-Whitney U test and FDR correction (G). Network analysis of the correlation between conversion rate, stool lipids, and bacterial taxa (H). Red links indicate negative and green links indicate positive associations with the width indicating correlation magnitude. Red and green nodes represent bacteria and stool lipids, respectively. * p = < 0.05 ............................................................................................... 61 Figure 6. Serum cholesterol and stool bile acid profiling and their relationship with each other and bacterial taxa. Principal Component Analysis (PCA) with feature importance for serum lipid composition (A). Network analysis of the correlation between conversion rate, stool lipids, serum lipids, and bacterial taxa differentiating non- and high-converters (B). PCA with feature importance for stool BA composition (C). Total BA composition, total primary ratio, total secondary ratio, total conjugated ratio, and total sulfated ratio of stool BAs compared between non- and high-converters (D). Network analysis of the correlation between conversion rate, stool lipids, bacterial taxa, and stool total BAs (E). Red links indicate negative and green links indicate positive associations with the width indicating correlation magnitude. Red, green, and purple nodes represent bacteria, stool lipids, and serum lipids, respectively. * p = < 0.05 ............................................................................................................ 63 Figure 7. Serum bile acid among converter groups and their relationship with stool and serum lipids, stool bile acids, and bacterial taxa. Principal Component Analysis (PCA) with feature importance for serum BA composition (A). Total BA composition, total primary ratio, total secondary ratio, total conjugated ratio, and total sulfated ratio of serum BAs compared between non- and high-converters (B). Correlation network analysis of conversion rate, stool and serum lipids and BAs, and bacterial taxa (C). Red links indicate negative and green links indicate positive associations with the width indicating correlation magnitude. Purple, green, red, brown, and blue nodes xiii represent bacteria, stool lipids, serum lipids, serum BAs, and stool BAs, respectively. * p = < 0.05, ** p = < 0.01 ............................................................................................................................................................................. 65 Figure 8. Metagenomic sequencing and in vitro assay assessing Akkermansia muciniphila’s ability to convert cholesterol into coprostanol and desulfate bile acids. Random forest and LEfSe analysis using the Kraken2 pipeline to identify the top bacterial taxa that can differentiate non- and high-converters (A-i), as well as the top bacterial taxa from 16S analysis identified in the Kraken2 pipeline (A-ii). The red bacterial taxa were significantly increased in high-converters. A. muciniphila compared between non- and high-converters (B) (* p-adjusted < 0.05). In vitro assay to assess A. muciniphila’s ability to convert cholesterol to coprostanol (C-i). The effect of various concentrations of cholesterol and coprostanol (0.01, 0.02, and 0.04%) on the growth of A. muciniphila (C-ii). After incubation at 37 °C, the optical density was measured (OD600) at 3-minute intervals over 48 hrs (performed in triplicate). Coprostanol quantification of media samples containing A. muciniphila exposed to various concentrations of cholesterol for 72 hrs (C-iii). Controls (NC-0, 0.01, 0.02, and 0.04% of cholesterol) contained no A. muciniphila. In vitro assay to assess A. muciniphila’s ability to desulfate BAs (D-i). The effect of various concentrations of sulfated BAs (cholic acid 3-sulfate and chenodeoxycholic 3-sulfate; 5, 10, and 20 μmol/ml) on the growth of A. muciniphila (D-ii). Sulfated and desulfated BA quantification of media samples containing A. muciniphila exposed to various concentrations of cholic acid 3-sulfate and chenodeoxycholic 3-sulfate for 72 hrs (D-ii). Controls (NC) contained sulfated BAs but no A. muciniphila. Error bars indicate standard deviation. * p < 0.05. CDCA, chenodeoxycholic acid; CA, cholic acid. Parts of figure created with BioRender.com. ....................... 70 Figure 9. Mucus assessment and glycomics. MUC2:FISH-stained images of fecal mucin compared between non- and high-converters (A). Mucus thickness along the distance of fecal periphery compared between non- and high-converters (B-i). Protein quantification of extracted fecal-adherent mucus samples compared between converters (B-ii). Alcian blue staining of fecal-adherent mucin-purified from stool, where the arrow indicates high molecular weight MUC2. (B-iii). Principal component analysis (PCA) with feature importance for six distinct glycan classes (C), and relative abundance of glycan classes compared between converters (D). PCA with feature importance for all individual glycans (E). Top glycans from PCA compared between non- and high-converters (F). PCA with feature importance for sialic acids (G). Sialic acids compared between non- and high-converters (H). Heatmap of sialic acids and bacterial taxa differentiating non- and high-converters (I). * p = < 0.05, ** p = < 0.01 .............. 74 Figure 10. Multifaceted mechanisms involved in luminal and systemic cholesterol dynamics compared between non- and high-converters. Distinct lipid profiles and gut community structures compared between non- and high-converter groups reveal the potential for non-converters to develop hypercholesterolemia and CVD, as well as IBD. The distinct dominant bacterial taxa among converters also reveal the metabolite variations with non-converters exhibiting a diminished capacity to desulfate fecal bile acids, perhaps due to reduced gut microbiota with BSH activity, leading to decreased sulfated bile acids in serum and potentially increased inflammation. Assessing the mucin glycans revealed decreased sialylation in non-converters. Overall, the disturbed gut environment of non-converters shows the potential gut barrier dysfunction which can lead to bacterial translocation and increased inflammation. Abbreviations: LDL, Low-Density Lipoprotein Cholesterol; HDL, High-Density Lipoprotein Cholesterol; TG, triglyceride; BAs, bile acids; 1° BAs, primary bile acids; 2° BAs, secondary bile acids; CDCA, chenodeoxycholic acid; CA, cholic acid; LCA, lithocholic acid; DCA, deoxycholic acid; T/G, taurine/glycine; BSH, bile salt hydrolase; SCFAs, short chain fatty acids. Figure created using BioRender.com. ...................................... 78 Figure 11. Factors moderating the effects of ACEs on the mental and physical health of individuals. ..................... 82 Figure 12. Significant features differentiating low and high ACE groups. Factors including A) diarrhea episodes within two weeks prior to sample collection, B) waist-to-hip ratio, C) triglyceride (mg/dL), and D) depression index PHQ9 were compared between ACE groups. Significant differences were determined using the Mann-Whitney U test and FDR correction. * p-adjusted < 0.05. ACE, adverse childhood experience. ............................... 92 Figure 13. Gut microbial diversity (genus-level). A) The alpha diversity (Shannon’s diversity index) and B) the beta diversity density plot (Bray-Curtis dissimilarity) for ACE groups were determined. Significant differences were determined using the t-test and ANOSIM for alpha and beta diversity, respectively. ACE, adverse childhood experience. .................................................................................................................................................................. 93 xiv Figure 14. RF and LEfSe analysis characterizing the significant bacterial taxa in high and low ACE groups. The implemented RF model led to A) the ROC curve displaying an AUC of 61% (p > 0.05), B) the top ten most important bacteria differentiating ACE groups, where the size of the ellipses represents the average Mean Decrease in Impurity (MDI), C) Histogram of the LDA scores (log10) calculated for taxa with differential abundances in high and low ACE individuals. The high ACE group is represented in green (1.0) and the low ACE group is represented in yellow (0.0), and D) overlapping bacterial taxa from both random forest and LEfSe compared between ACE groups using the Mann-Whitney U test and FDR correction. * p-adjusted < 0.05; ** p-adjusted < 0.01. ACE, adverse childhood experience. ............................................................................................................. 95 Figure 15. RF and LEfSe analysis characterizing the significant biochemical pathways (via PICRUSt2) in high and low ACE groups. The implemented RF model led to A) the ROC curve displaying an AUC of 52% (p > 0.05), B) the top ten most important pathways differentiating ACE groups, where the size of the ellipses represents the average Mean Decrease in Impurity (MDI), C) Histogram of the LDA scores (log10) calculated for pathways with differential abundances in high and low ACE individuals. The high ACE group is represented in green (1.0), and the low ACE group is represented in yellow (0.0), and D) overlapping pathways from both RF and LEfSe compared between ACE groups using Mann-Whitney U test and FDR correction. ** p-adjusted < 0.01. ACE, adverse childhood experience. .................................................................................................................................. 97 Figure 16. The experimental data structure of stool samples collected. Samples were collected from 4 donors (D1 to D4) and 17 patients one week prior to FMT (pre-FMT) and patients one week following FMT (post-FMT). For D1 and D3, multiple independent samples were taken for different patients. Green line indicates a successful FMT outcome and a red line indicates a failed FMT outcome. ......................................................................................... 107 Figure 17. Gut microbial diversity of FMT donors and recipients. The α-diversity (Shannon index) of (A) bacteria, (B) archaea, (C) fungi, (D) protozoa, and (E) viruses of donors, recipients pre- and post-FMT for successful and failed FMT outcomes of rCDI patients. Significant differences were determined using the Kruskal-Wallis and Wilcoxon signed-rank tests for unpaired and paired (i.e. when analysing pre- and post-FMT of recipients) samples, respectively, followed by Bonferroni post-hoc correction. Adjusted p-values were defined at *p<0.05, **p<0.01, ***p<0.001, and ****p<0.0001. The Beta diversity was calculated for all microorganisms (F) using the Bray-Curtis dissimilarity and analyzed using ANOSIM. ................................................................................................... 112 Figure 18. Gut microbial diversity of bile acid metabolizers, SCFA producers, and sporulators. The α-diversity (Shannon index) of (A) bile acid metabolizers, (B) SCFA producers, and (C) sporulating bacteria of donors, recipients pre- and post-FMT for successful and failed FMT outcomes. Significant differences were determined using the Kruskal-Wallis and Wilcoxon signed-rank tests for unpaired and paired samples, respectively, followed by the Bonferroni post-hoc correction. Adjusted p-values were defined at *p<0.05, **p<0.01, ***p<0.001, and ****p<0.0001. The Beta diversity was also calculated using the Bray-Curtis distance-based density plots (D, E, F) and analyzed using ANOSIM. ................................................................................................................................... 114 Figure 19. Bile acid metabolizers of FMT donors and recipients. Heatmap of bile acid metabolizers of (A) donors (D1-D4), (B) recipients pre-FMT, and (C) recipients post-FMT for successful and failed FMT outcomes. The dendrogram shows clustering based on the relative abundances. The heatmap color (blue to red, corresponding to low to high) represents the row z-score of the relative abundance values. Each sub-block under heatmaps represent a different time point for the corresponding donor. .................................................................................................. 115 Figure 20. Machine learning model predicting FMT outcome. The random forest model utilized led to (A) the ROC curve displaying an AUC of 98%, (B) the top 9 most important microbes (Blue ellipses represent bacteria; Yellow ellipse represent fungi) involved in FMT success prediction where the size of the ellipses represents the feature importance magnitudes (average Mean Decrease in Impurity), and (C) the relative abundance (shown on logarithmic scale) for the top 9 features of donor, and recipient samples pre- and post-FMT. ............................... 117 Figure 21. Multifaceted mechanisms affecting FMT treatment outcome. FMT treatment outcome of (A) successful FMT recipients, and (B) failed FMT recipients. A successful treatment outcome includes the repair of the disturbed gut microbial ecosystem by transient colonization of nexus species followed by secondary succession of bile acid metabolizers, sporulators, and short chain fatty acid producers. A failed treatment outcome may be due to the xv presence of fungal and bacteria genera including Yarrowia and Wigglesworthia in recipients, minimizing the establishment of repair or successful secondary colonization for functional ecosystem restoration. ....................... 120 Figure B.1. Stool lipids (A) and serum lipids (B) compared between converter groups. * p-adjusted < 0.05, ** p-adjusted < 0.01, *** p-adjusted < 0.001, **** p-adjusted < 0.0001…………………………………….................................153 Figure C.1. Stool SCFAs among converter groups and their relationship with stool and serum lipids and bile acids, and bacterial taxa. Principal Component Analysis (PCA) with feature importance for stool SCFA composition (A). SCFAs compared between converter groups (B). Network analysis of stool SCFAs, conversion rate, stool and serum lipids and BAs, and bacterial taxa (C). Red links indicate negative and green links indicate positive associations with the width indicating correlation magnitude. * p-adjusted < 0.05, ** p-adjusted < 0.01…………………...............154 Figure D.1. Rarefaction plot (A) and phylogenetic tree (B) comparing 16S rRNA sequencing with shotgun sequencing using the Kraken2 and MetaPhlAn3 pipelines. Phylogenetic tree comparing the A. muciniphila strains present in our samples, the purchased ATCC strain, and the reference genome from StrainPhlAn analysis (C)…..155 Figure E.1. Top bacterial taxa from metagenomic sequencing using the Kraken 2 database compared between non- and high-converters. * p-adjusted < 0.05……………………………………………………………………………………………………….……156 Figure F.1. Tiled cross-section of stool samples stained with fluorescence in-situ hybridization (FISH).…………….157 Figure H.1. The intra-variability of relative abundance of the top 9 features of donor 1 (D1) samples and their corresponding recipients, pre- and post-FMT leading to successful (A and B) and failed (C and D) FMT outcomes………………………………………………………………………………………………………………………………………………………………159 Figure I.1. The intra-variability of relative abundance of the top 9 features of donor 3 (D3) samples and their corresponding recipients, pre- and post-FMT leading to successful (A and B) and failed (C and D) FMT outcomes..…………………………………………………………………………………………..….……………………………………………………………160 xvi Nomenclature Acronyms ABCB1a/b ATP-binding cassette transporter B1 member 1a and 1b ABCG5/G8 ATP-binding cassette (ABC) transporters G5 and G8 ACE Adverse childhood experience AMP Antimicrobial peptide ANOSIM Analysis of similarities ANS Autonomic nervous system ASV Amplicon sequence variant AUC Area under the curve BA Bile acid BMI Body mass index Bracken Bayesian reestimation of abundance with KrakEN BSH Bile salt hydrolase BSN Bachelor of science in nursing CA Cholic acid CAD Coronary artery disease CDC Centers for disease control and prevention CDCA Chenodeoxycholic acid CDI Clostridiodes difficile infection CNS Central nervous system COPD Obstructive Pulmonary Disease CRH Corticotropin-releasing hormone CRP C-reactive protein CVD Cardiovascular disease CYP7A1 Cholesterol 7-alpha-hydroxylase DCA Deoxycholic acid ELS Early life stress ENS Enteric nervous system FDR False discovery rate FISH Fluorescence in situ hybridization FMO Flavin containing monooxygenase FMT Fecal microbiota transplantation FOXP3 Forkhead box P3 FXR Farnesoid X receptor Fuc Fucose GAL Galactose GI Gastrointestinal xvii GalNAc N-acetylgalactosamine GlcNAc N-acetylglucosamine GLP-1 Glucagon-like peptide 1 GPR109A G protein-coupled receptors 109A GPR41 G protein-coupled receptors 41 GPR43 G protein-coupled receptors 43 HDL High-density lipoprotein HPA Hypothalamic pituitary adrenal IBD Inflammatory bowel disease IBS Irritable bowel syndrome IDL Intermediate-density lipoproteins IEC Intestinal epithelial cell IL-18 Interleukin-18 IL-1B Interleukin-1B IL-6 Interleukin-6 iTOL Interactive tree of life KNN K-nearest neighbour LCA Lithocholic acid LDL Low-density lipoprotein LDL-R Low-density lipoprotein receptor LEfSe The linear discriminant analysis effect size LOO Leave one out LPS Lipopolysaccharides LRP Low-density lipoprotein receptor-related protein MAMP Microbial-associated molecular patterns MDI Mean decrease in impurity Neu5Ac 5-N-acetylneuraminic acid NEWS Nurse engagement and wellness study NGS Next-generation sequencing NLR NOD-like receptor NPC1L1 Niemann-Pick C1-like 1 OD Optical density OTU Operational taxonomic units PAMP Pathogen-associated molecular patterns PCA Principal component analysis PCR Polymerase chain reaction PERMANOVA Permutational multivariate analysis of variance PHQ9 Patient health questionnaires PLD Phospholipase D PRR Pattern recognition receptors xviii QIIME Quantitative insights into microbial ecology rCDI Recurrent Clostridiodes difficile infection RF Random forest ROC Receiver operating characteristics SCFA Short chain fatty acid TC Total cholesterol TG Triglyceride TGR5 G protein-coupled bile acid receptor TICE Transintestinal cholesterol efflux TLR Toll-like receptor TLR-4 Toll-like receptor 4 TMA Trimethylamine TMAO Trimethylamine-N-oxide TNF-a Tumor necrosis factor alpha Treg Regulatory T cells UDCA Ursodeoxycholic acid UTEP University of Texas at El Paso VLDL Very low-density lipoprotein xix Acknowledgements Embarking on the journey towards a Ph.D. is a voyage marked by relentless waves of challenges and the unpredictable winds of discovery. This journey, arduous and enduring, is not one to be sailed alone. The support of many individuals in my life – mentors, peers, family, and friends – is what guided me on this journey. Firstly, I would like to express my deepest gratitude to my supervisor, Dr. Sepideh Pakpour, for her continuous guidance during my studies. Meeting you as an undergraduate student was a turning point in my life and what sparked my interest in research in the first place. Your unwavering guidance and insightful mentorship have been the bedrock of my journey. Thank you for illuminating paths I never knew existed and gently steering me through the challenges of research. Your patience, expertise, and encouragement have been indispensable to my growth as a scholar and as an individual. I will forever treasure the wisdom and lessons gained from this enriching journey as I continue to navigate the vast tapestry of life’s experiences. I would like to also acknowledge our project collaborators. Thank you to Dr. Hector Olvera Alvarez for allowing me to visit your lab at the University of Texas at El Paso. Conducting those experiments with the stool samples collected from nursing students is a fun memory that I will forever cherish. Thank you for introducing me to a branch of research that I had never explored before (adverse childhood experiences and their effect on health). Thank you to Dr. Dina Kao at the University of Alberta for giving us access to your sample data and introducing me to therapies such as fecal microbiota transplantation (FMT). A special thank you to Dr. Morteza Mahmoudi for collaborating with us and guiding me with the writing of our published review paper. Thank you to Dr. Wesley Zandberg and his team for aiding with the mucin glycomics and sialic acid analysis. I would also like to highlight the immense support of xx my advisory committee, Dr. Kirk Bergstrom and Dr. Fatemeh Fard. Your collective wisdom, critical insights, and constructive feedback have been instrumental in shaping both my research and my perspectives. Dr. Bergstrom, I appreciate the window of opportunity that you opened for me into the gut mucus world. Thank you for allowing me to learn from you and work alongside you in the lab. Dr. Fard, I am grateful for all the valuable and insightful comments in our group meetings, as well as our discussions at the reading groups that you allowed me to attend with your team. A heartfelt thank you to my friends and colleagues who were my guiding stars and cheerleaders along the way. Thank you Dr. Kinga Vojnits for your friendship and mentorship. I enjoyed every aspect of lab management and couldn’t have done it without your support. Thank you Amirhossein Golzan for being my right hand and aiding with experiments. I thoroughly enjoyed our discussions while we did our anaerobic chamber experiments. Thank you Ramin Karimianghadim for patiently guiding me and aiding me immensely with the python programming language that I was struggling with. My entire BMR family, I learned from all of you, and you will aways have a special place in my heart. Thank you for always inspiring me with all the amazing work that you all do. A special thank you to Melika Edalat and Harleen Tiwana for being wonderful friends – you both know how much you mean to me. I would like to acknowledge the funding sources that made my Ph.D. work possible. I was honoured to receive the Alexander Graham Bell Natural Sciences and Engineering Research Council of Canada (NSERC) Scholarship which aided with 3 years of my research. I was honoured to be a University Graduate Fellow for 3 years, as well as receiving the UBC Okanagan Graduate Research Scholarship for 2 years. xxi To my amazing family, thank you for all your love and encouragement. Thank you to my amazing sister, Rojin Kazemian, for always being there for me. You’re my inspiration, always. For my great parents who have sacrificed so much in their lives for a better future for their daughters, thank you for allowing me to pursue my passion, listening to my stories about the highs and lows of my research, and always cheering me on, especially when I failed. Thank you for your unwavering support throughout my life. I love you unconditionally. Lastly, a special thank you to my loving husband. I know it seemed as though this journey of mine would never end but you’ve supported me every step of the way. Thank you for your patience and for giving me the space to pursue my passions. You were the silent force behind every page of this thesis. In moments of doubt, it was your faith in me that rekindled my own. I am eternally grateful, and I love you with all my heart. Negin Kazemian University of British Columbia April 2024 xxii Dedication This thesis is dedicated to my loving parents, my beautiful sister, and my love. We did it. Fahimeh Tajzadeh & Hossein Kazemian Rojin Kazemian Reuben Mann 1 | P a g e Chapter 1: Introduction The overall goal of this thesis was to investigate the role of gut microbiome in health and disease states. In this chapter, I first provide an overview of the structure and function of the intestinal tract and the gut microbiota. Next, I introduce factors influencing the gut microbiome with an emphasis on adverse childhood experiences, followed by the role of the gut microbiome in disease states. This section dives deeper into the gut-heart and gut-brain axis, as well as hurdles in the field such as causality versus correlation in gut microbiome studies. Lastly, I will discuss how we can utilize the gut microbiota as therapeutic frontiers. I will be reviewing the existing literature and highlighting any knowledge gaps in the field. I then conclude by describing how this thesis attempts to fill in these gaps by outlining the specific goals and objectives of each chapter. 1.1 Anatomy of the Intestines The human intestines play a pivotal role in the overall digestion and absorption of nutrients essential for the survival and functioning of the body. Anatomically, this multilayered organ is categorized into the small intestine (~7 meters long; 2.5 cm in diameter) and the large intestine (1.5 meters long; 7.62 cm in diameter) (Irving & Catchpole, 1992). The small intestine is further subdivided into three parts, namely the duodenum, jejunum, and ileum from the proximal (at the stomach) to the distal portion (Campbell, Berry, & Liang, 2019) (Figure 1). Once the partially digested food from the stomach, known as chyme, enters the small intestine, a series of coordinated muscular contractions further aid with breaking down the ingested food. Here, tiny finger-like projections known as villi and microvilli can increase the surface area and allow for the efficient absorption of digested nutrients (Gelberg, 2014). The large intestine is 2 | P a g e comprised of the cecum, colon (ascending, transverse, descending, and sigmoid colon), rectum, and anal canal (Irving & Catchpole, 1992) (Figure 1). The primary function of the large intestine is to absorb water, electrolytes, and vitamins from the remaining indigestible food matter received from the small intestine and to transport the waste material toward elimination (Nigam, Knight, & Williams, 2019; Welch, Ottinger, & Welch, 1980). The large intestine houses a significant amount of gut microbiota which further contributes to digestion. The function of the intestines varies from their proximal origin at the stomach through their distal point at the anus; however, the overall structure of the tract remains relatively similar (Figure 1). The outer region of the intestine is made up of two smooth muscle layers known as muscularis externa, which play a vital role in the mechanical processes of digestion and movement of materials along the digestive tract (i.e., peristalsis) (Volk & Lacy, 2017). Between these two muscle layers is a critical component of the enteric nervous system (ENS) known as the myenteric plexus, which is often referred to as the “second brain” of the body due to its significant role in regulating gastrointestinal (GI) functions (Costa, Brookes, & Hennig, 2000). This intricate network of neurons and ganglia can communicate with the central nervous system (CNS) via the vagus nerve and is responsible for coordinating and controlling the muscular contractions of the digestive organs, ensuring efficient digestion and absorption of nutrients, vascular blood flow, as well as the elimination of waste (Costa, Brookes, & Hennig, 2000; Yoo & Mazmanian, 2017). The next layer, known as the submucosa, is composed of connective tissue, lymphatic vessels, nerves, and glands. One of the key functions of the submucosa is to provide structural support. This layer also contains another branch of the ENS known as the submucosal plexus, which further extends into the next layer, the mucosal layer (Fleming, Ehsan, Moore, & Levin, 3 | P a g e 2020). This forms a direct channel of communication with the intestinal epithelial cells (IECs). The muscularis mucosa of the mucosal region is composed of a thin layer of smooth muscle cells, further aiding with the expansion and contraction of the mucosa, which also assists in expelling mucus from the mucosal glands. This layer is followed by the lamina propria, epithelium, and lastly, the mucus layer lining the lumen wall. The gut mucus is a heterogeneous mixture composed mainly of water (Hansson, 2020; Johansson & Hansson, 2016), electrolytes, lipids, and proteins (Bansil & Turner, 2018). The mucus layer is the primary physical defence barrier of the host, segregating internal organs such as the gut from microorganisms but providing a ubiquitous source of carbon and energy for commensal microbes (König et al., 2016). The mucin glycans’ binding affinity to water leads to the moisturizing and lubricating properties of the mucus layer. Thus, protecting the epithelial cells from dehydration and mechanical stress during the passage of luminal content and peristalsis (Cone, 2009; Johansson, Sjövall, & Hansson, 2013). Moreover, the mucus can remove debris and bacteria and can act as a surface cleaner (Hansson, 2019). In addition, the intestinal mucus layer forms a diffusion barrier for the diffusion of small molecules such as ions, water, nutrients, and gases to reach the enterocytes (Johansson & Hansson, 2016). The luminal surface of the intestinal epithelium is made up of IECs which constitute barrier surfaces that separate hosts from the external environment (Kong, Zhang, & Zhang, 2018). Covering ~400 m2 of surface area with a single layer of cells organized into crypts and villi, this mucosal surface is constantly renewed by pluripotent intestinal epithelial stem cells residing in the base of crypts (Crosnier, Stamataki, & Lewis, 2006; Van der Flier & Clevers, 2009). A significant contrast between the small and large intestines lies in the makeup of their epithelial barrier. In the small intestine, IECs can differentiate into seven cell lineages, including 4 | P a g e enterocytes, Paneth cells, goblet cells, enteroendocrine cells, Tuft cells, M cells, and cup cells. In contrast, the colon is typically composed of only enterocytes, goblet cells, and Tuft cells. Goblet cells, mostly abundant in the distal colon and rectum (Specian & Oliver, 1991), are responsible for secreting glycosylated proteins known as mucin including the mucin MUC2 (van Klinken et al., 1999). The lumen contains a two-tiered mucus layer, while the small intestine contains a single layer. In the small intestine, the mucosal layer is loose and porous allowing for food particles to reach the IEC. Although this might allow microbes to penetrate the mucosal layer, the Paneth cells, only found in the small intestine (Clevers & Bevins, 2013), synthesize and secrete antimicrobial peptides (AMPs) and proteins and keep microbes away from the IECs (Ermund, Schütte, Johansson, Gustafsson, & Hansson, 2013). The large intestine includes two mucus layers, an inner attached dense mucus that is void of bacteria (50–100 μm thick) and an outer non-attached less dense mucus that is the habitat for commensal bacteria (800 μm thick) (Johansson et al., 2008). Overall, mucosal integrity is vital for maintaining health and has gained increasing attention from the scientific community, which has led to a plethora of research in this field, highlighting that this ‘slimy’ feature in the gut should not be overlooked. 5 | P a g e Figure 1. Anatomy of the intestines. Anatomy of the small and large intestine, as well as the anatomical cross-section of the gut demonstrating the organization of various tissue layers. Figure created using BioRender.com. 1.2 Intestinal Microbiota The intestines are inhabited by a myriad of bacteria, archaea, viruses, fungi, and protists. These microorganisms are referred to as the microbiota, and the catalogue of these microbes and their genes is referred to as the microbiome (Ursell, Metcalf, Parfrey, & Knight, 2012). While bacteria are the most abundant component, research has had a narrow lens on the bacterial compositions and their influence on human health and diseases. Conversely, the scientific exploration of the human fungal community (mycobiome), viral (virome), and archaeal microbiota (archaeome) remains relatively underdeveloped due to unique methodological challenges (Matijašić et al., 2020). However, it's important to note that the field of microbiome 6 | P a g e research is rapidly evolving. As technology advances and our understanding deepens, we can expect more comprehensive insights into the entire spectrum of microorganisms inhabiting the gut microbiome and their impact on human health. The human gut microbiota is dominated by Firmicutes (60-80%), Bacteroides (15-25%), Actinobacteria, Proteobacteria, Fusobacteria, and Verrucomicrobia, which comprise 98% of the gut microbiome (Arumugam et al., 2011; Laterza, Rizzatti, Gaetani, Chiusolo, & Gasbarrini, 2016). The Firmicutes phylum is composed of more than 200 different genera such as Lactobacillus, Bacillus, Clostridium, Enterococcus, and Ruminococcus (Rinninella et al., 2019). Clostridium genera represent 95% of the Firmicutes phyla (Rinninella et al., 2019). Bacteroidetes consist of predominant genera such as Bacteroides and Prevotella (Rinninella et al., 2019). The Actinobacteria phylum is proportionally less abundant and mainly represented by the Bifidobacterium genus (Arumugam et al., 2011). About 500-1000 species of bacteria colonize the human intestine with a high degree of intra- and inter-individual variation (Kerimi, Kraut, da Encarnacao, & Williamson, 2020; Olsson et al., 2022). The bacterial composition also varies longitudinally along the intestinal tract due to the varying environments of the intestines. In the small intestine, the transit time is short and bile concentration is high, while the colon has slower flow rates and milder pH, as well as larger microbial communities (Milani et al., 2017). 1.2.1 Functions of gut microbiota The gut microbiota plays multiple critical roles in the maintenance of their host health, including helping host nutrition and energy harvest, intestinal epithelial homeostasis (Hooper, Midtvedt, & Gordon, 2002; Nicholson et al., 2012), drug metabolism and toxicity (Jia, Li, Zhao, & Nicholson, 2008), immune system response (Hooper, Littman, & Macpherson, 2012), and 7 | P a g e protection from pathogens (Quigley, 2013). One of the unique relationships that has been vastly studied includes the crosstalk between the gut microbiome and the immune system, which share a dynamic and reciprocal relationship that is fundamental to human health. The immune system has developed to do more than just combat pathogens, as it has adapted to tolerate the presence of beneficial microbiota that live in symbiosis with their host (Sansonetti & Medzhitov, 2009). For example, this is demonstrated by the fact that regulatory T (Treg) cells are capable of recognizing not just the body’s own antigens but also those from harmless commensal organisms, preventing pathological immune responses (Lathrop et al., 2011). Thus, the immune system tolerates commensal bacteria, yet it can respond to the same microbiota during the disruption of the intestinal barrier or other homeostatic perturbations (Hand et al., 2012). Pattern recognition receptors (PRRs) located on IECs can also detect microbial-associated molecular patterns (MAMPs) leading to inflammatory responses (Negi, Das, Pahari, Nadeem, & Agrewala, 2019). For example, PRRs such as toll-like receptors (TLRs) and NOD-like receptors (NLR) can recognize components of bacterial cells (Franchi, Warner, Viani, & Nuñez, 2009). Thus, the translocation of bacteria across the epithelium is detected which activates the immune system by recruiting phagocytic neutrophils (Schwab et al., 2014). Therefore, regulatory 'checks and balances' are essential for maintaining the host's health, as any compromise in the intestinal barrier's integrity and the immune system's equilibrium can lead to inflammatory bowel disease (IBD) and a variety of other chronic health conditions (Zheng, Liwinski, & Elinav, 2020). In addition, these microorganisms can also generate microbial products such as uremic toxins (Mahmoodpoor, Saadat, Barzegari, Ardalan, & Vahed, 2017), bile acids (BAs) (Tang & Hazen, 2014), trimethylamine-N-oxide (TMAO) (Allayee & Hazen, 2015), short chain fatty acids (SCFAs) (Tan et al., 2015), lipopolysaccharides (LPS) (Candelli et al., 2021), nitric oxide, 8 | P a g e vitamin K (Fernandez & Hill, 1975), vitamin B complex (Wan et al., 2022), gut hormones (Afsar, Vaziri, Aslan, Tarim, & Kanbay, 2016), and neurotransmitters, which can alter host metabolism and affect bodily functions in health and disease states. BAs and SCFAs are two of the most common metabolites that are vastly studied in health and disease states. BAs formed by the rate-limiting enzyme cholesterol 7-alpha-hydroxylase (CYP7A1) (Chiang, 2009), are the main metabolites of cholesterol in the liver that help in the absorption of fats, nutrients, and lipophilic vitamins (Ferrell, Boehme, Li, & Chiang, 2016) and also the regulation of lipids, glucose, and energy metabolism (Broeders et al., 2015; Li et al., 2012). Primary BAs are conjugated to amino acids taurine or glycine to form bile salts that are secreted into bile and stored in the gallbladder until they are released into the small intestine where they emulsify fats and forms micelles which are absorbed into enterocytes (Ferrell, Boehme, Li, & Chiang, 2016). In the gut, the primary BAs such as cholic acid (CA) and chenodeoxycholic acid (CDCA) become deconjugated by the gut microbiota and bile salt hydrolase (BSH) to form secondary BAs, including deoxycholic acid (DCA), lithocholic acid (LCA), and ursodeoxycholic acid (UDCA) (Ferrell, Boehme, Li, & Chiang, 2016; Kumar, Mason, Brooker, & O'Brien, 2012). All conjugated and unconjugated BAs in the lumen can be reabsorbed (95%) and transported back to the liver, except for UDCA and LCA, which are mostly excreted in feces (Chiang, 2009). Primary (deconjugated by gut microbiota) and secondary BAs can also affect the immune system by the inhibition of NF-κb-dependent transcription of pro-inflammatory cytokines via farnesoid X receptor (FXR) and the membrane G protein-coupled bile acid receptor Gpbar-1 (aka TGR5) receptors (van den Munckhof et al., 2018). SCFAs are a microbial-derived metabolite that are formed due to the fermentation of complex carbohydrates (Macfarlane & Macfarlane, 2011; Nutting, Islam, & Daugirdas, 1991) 9 | P a g e affecting a range of host processes such as host-microbe signalling, energy utilization, and control of colonic pH with consequent effects on the microbiota composition and gut motility (Musso, Gambino, & Cassader, 2011). The most abundant SCFAs are acetate, propionate, and butyrate (Macfarlane & Macfarlane, 2011). Bacteroidetes phylum members can yield acetate and butyrate, whereas Firmicutes phylum can lead to butyrate (Macfarlane & Macfarlane, 2003). SCFAs are also positively correlated with Alistipes putredinis spp., Roseburia, Eubacterium rectale, and Faecali prausnitzii (David et al., 2014b). Furthermore, they play an integral part in maintaining the intestinal barrier integrity by regulating the expression of tight junction proteins (Krishnan, Alden, & Lee, 2015). SCFAs can also affect the immune system, exerting an anti-inflammatory impact (Sandek et al., 2012) through the activation of G protein-coupled receptors 41 (GPR41), 43 (GPR43), and 109A (GPR109A) (Pluznick et al., 2013) via induction of Treg cells controlled by the forkhead box P3 (Foxp3) promoter (Arpaia et al., 2013). In addition, they can produce anti-inflammatory gut hormones such as glucagon-like peptide 1 (GLP-1) (Tolhurst et al., 2012). Although SCFAs have many positive effects, their production can also shift the bacterial balance and lead to inflammation by activating the toll-like receptor 4 (TLR4) (Normén, Laerke, Jensen, Langkilde, & Andersson, 2001). Therefore, their role in the immune system needs to be further investigated. Dietary choline, betaine, phosphatidylcholine, lecithin, and L-carnitine (Koeth et al., 2013; Wang et al., 2011) are involved in the production of TMAO, a risk factor for coronary artery disease (CAD) development (Wang et al., 2011) that come from many sources, including red meat, egg, fish, brassica vegetables, peanuts, and soybean (Falony, Vieira-Silva, & Raes, 2015). Specifically, increased TMAO levels have been associated with an increased risk of death and non-fatal myocardial infarction or stroke (Tang et al., 2013). The gut microbiota also play a 10 | P a g e role in TMAO production via (a) choline production and (b) the intermediate molecule trimethylamine (TMA) production. Only recently has the gut microbiota’s ability to produce choline via phospholipase D (PLD) enzyme been found (Chittim, Martinez Del Campo, & Balskus, 2019). The microbiome-derived TMA molecule can pass into host circulation and travel to hepatocytes, where it is metabolized to TMAO (Zeisel & Warrier, 2017) by flavin containing monooxygenase (FMO) enzyme encoded by the FMO gene in the liver, kidney, and other tissues (Phillips et al., 1995). High TMAO production can consequently affect lipids and lead to a 43% higher CAD risk (Koeth et al., 2013; Wang et al., 2011). TMAO is also associated with C-reactive protein (CRP) and endothelial dysfunction in the setting of increased gut permeability and is related to increased serum levels of LPS endotoxin (Al-Obaide et al., 2017). The gut microbiota can heavily influence TMAO production. Healthy individuals have high TMAO producing microbes and a ratio of 2:1 for Firmicutes to Bacteroidetes (Ley, Turnbaugh, Klein, & Gordon, 2006). TMA production has been found in 102 genomes covering 36 species, and TMA producers include Firmicutes, Proteobacteria, Actinobacteria, and absent in Bacteroidetes (Falony, Vieira-Silva, & Raes, 2015). Firmicutes including Anaerococcus, Clostridium, Desulfitobacterium, Enterococcus, Streptococcus, and Proteobacteria including Dseulfovibrio, Enterobacter, Escherichia, Klebsiella, Proteus, Pseudomonas, Actinobacter, and Citrobacter have been associated with TMA production (Al-Obaide et al., 2017). One study found that 8 species from Firmicutes and Proteobacteria consumed > 60% of choline for TMA production: Anaerococcus hydrogenalis, Clostridium asparagiforme, C. hathawayi, C. sporogenes, Escherichia fergusonii, Proteus penneri, Providencia rettgeri, and Edwardsiella tarda (Liu, Niu, & Zhang, 2015). Other gut microbiota associated with higher TMAO production include Sporobacter, Prevotella (Falony, Vieira-Silva, & Raes, 2015), and Ruminococcus 11 | P a g e gnavus (Wang et al., 2015), which are associated with atherosclerotic CAD. The gut microbiome-derived TMAO can also affect our immune system by activating TXNIP-NLRP3 inflammasomes (Sun et al., 2016), leading to the expression of inflammatory markers such as tumor necrosis factor alpha (TNF-α), interleukin 6 (IL-6) (Al-Obaide et al., 2017; Rohrmann, Linseisen, Allenspach, von Eckardstein, & Müller, 2016), interleukin 18 (IL-18), and interleukin 1 beta (IL-1B) (Sun et al., 2016). Certain gut microbiota have long been known to possess the ability to convert absorbable cholesterol to coprostanol, a reduced non-absorbable sterol, which is excreted in feces (Dam, 1934; Illman, Storer, & Topping, 1993; Lichtenstein, 2009). Coprostanol production in humans starts during the second half of the first year of life (Midtvedt & Midtvedt, 1993) and is sex-dependent, with young women being high converters compared to young males (Benno et al., 2011). Furthermore, currently, the rate of microbial cholesterol-to-coprostanol conversion in human populations is believed to be bimodal, with high converters showing almost complete cholesterol conversion and low converters with coprostanol representing less than one third of the fecal neutral sterols content (Midtvedt & Midtvedt, 1993; Veiga et al., 2005). To date, isolated cholesterol-reducing strains have been limited to the genera of Eubacterium (E. coprostanoligenes) and Bacteroides (Bacteroides sp. strain D8) (Gerard et al., 2007; Ren, Li, Schwabacher, Young, & Beitz, 1996), but many remain to be uncovered. Using animal models, the oral administration of E. coprostanoligenes resulted in a significant decrease in plasma cholesterol concentration in dietary-induced hypercholesterolemic rabbits that lasted for at least 34 days after the last bacterial feeding (Li, Baumann, Meling, Sell, & Beitz, 1996). For human models, there have been many studies on cholesterol metabolism in the gut (Gerard et al., 2007; Kenny et al., 2020; Li, Baumann, Meling, Sell, & Beitz, 1996; Midtvedt & Midtvedt, 1993; 12 | P a g e Midtvedt et al., 1990; Rosenfeld, Fukushima, Hellman, & Gallagher, 1954), and an inverse relationship between the human serum cholesterol and coprostanol/cholesterol ratio in the human feces has been suggested (Dam, 1934; Kenny et al., 2020; Lye, Rusul, & Liong, 2010; Tahri, Grill, & Schneider, 1997). However, these studies employed very small sample sizes with a limited variation of sample populations lacking diverse demographic backgrounds and included unsuccessful attempts to isolate specific microbial strains responsible for the coprostanol/cholesterol conversion. In addition, the genes or enzymes involved in the cholesterol conversion to coprostanol in the gut remain unknown (Gerard, 2013). 1.3 Factors Influencing the Gut Microbiome 1.3.1 Infancy Gut microbiota development is a complex process that begins even before birth and is influenced by various factors throughout our lives (Figure 2). Traditionally, it was thought that the human fetus develops in a sterile environment (Mackie, Sghir, & Gaskins, 1999); however, this notion has been challenged by studies suggesting the presence of a prenatal microbiota (Jiménez et al., 2008; Stinson, Boyce, Payne, & Keelan, 2019). Birth gestational age is a major determinant of gut microbiota colonization. Preterm babies often have a less diverse gut microbiota with a higher presence of potentially harmful bacteria from the Enterobacteriaceae family of the Proteobacteria phylum and reduced levels of Bifidobacterium, Bacteroides, and Atopobium (Arboleya et al., 2012; Butel et al., 2007). The mode of delivery has a profound impact on the infant’s gut microbiota colonization with vaginally delivered infants acquiring microbial communities resembling their mother’s vaginal microbiota dominated by Prevotella, Sneathia, and Lactobacillus (Dominguez-Bello et al., 2010). In contrast, infants delivered via 13 | P a g e cesarian-section are more likely to be colonized by skin-associated bacteria such as Staphylococcus, Propionibacterium, and Corynebacterium spp. (Dominguez-Bello et al., 2010), as well as decreased Bacteroides species (Azad et al., 2013). In addition to the host’s own genetics that can shape the microbiota (Goodrich et al., 2016), breastfeeding is another influential factor since breast milk contains > 700 bacterial species (Cabrera-Rubio et al., 2012). Human milk not only provides the infant with nutrients but also beneficial bacteria and oligosaccharides that serve as prebiotics (Ma et al., 2020; Underwood et al., 2015). In a study by Azad et al. (2013), formula-fed infants had an increased richness of species in their fecal microbiota with an overrepresentation of Clostridiodes difficile. Once infants start ingesting solid foods the gut microbiota further diversifies with shifts away from a dominance of Bifidobacterium to a prevalence of Bacteroidetes and Firmicutes (Homann et al., 2021; Palmer, Bik, DiGiulio, Relman, & Brown, 2007). 1.3.2 Childhood As children grow, their gut microbiota continues to change and is influenced by various factors such as the environment. Rothschild et al. (2018) demonstrated that the gut microbiome is predominantly shaped by environmental factors and that host genetics plays a minor role (Rothschild et al., 2018). For example, children in rural environments tend to develop a more diverse microbiota, which has been linked with the hygiene hypothesis that suggests that reduced exposure to microbes at an early age may impact the development of the immune system, potentially leading to a higher incidence of allergies and autoimmune diseases (Okada, Kuhn, Feillet, & Bach, 2010). 14 | P a g e Adverse childhood experiences (ACEs) are another factor that has been recently explored for its effects on human health, as well as the gut microbiome. ACEs refer to a variety of negative experiences that children may encounter before the age of 18, including various forms of abuse, neglect, or family dysfunction, and that may have detrimental health implications in adulthood (Felitti et al., 1998). Different types of ACEs that have been identified include experiencing bullying, discrimination, violence, or displacement in the foster care system (Cronholm et al., 2015; Wade et al., 2016); having a parent who has been relocated to a war zone; experiencing violence in the community or the sudden loss of or separation from a loved one; or being a victim of social isolation or theft (Finkelhor, Shattuck, Turner, & Hamby, 2013; Karatekin & Hill, 2019). The Centers for Disease Control and Prevention (CDC) currently outlines ten central ACE categories which include three types of abuse (physical, sexual, and emotional), two types of neglect (physical and emotional), and five types of family dysfunction (living with a family member who has a history of substance abuse; living with a mother who has experienced domestic violence; residing with a family member who has a history of mental illness or depression; losing a parent through either abandonment or divorce; and living with a family member who has been incarcerated) (CDC, 2021). Social, behavioural, and environmental factors may also contribute to the level of distress experienced by an individual. For example, individuals who have lower socioeconomic status may experience greater stress due to inadequate access to social support systems or services (Lantz et al., 1998). Consequently, poor socioeconomic status or limited household income may cause individuals to live in an environment that has a greater risk of exposure to more hazardous or stressful conditions, such as crime, drug use, noise pollution, and discrimination (Baum, Garofalo, & Yali, 1999). Lower socioeconomic status has also been linked to a variety of adverse 15 | P a g e health behaviours that may contribute to chronic stress such as poor diet and nutrition (Janssen, Boyce, Simpson, & Pickett, 2006), poor sleep quality (Gellis et al., 2005), and reduced physical activity (Janssen, Boyce, Simpson, & Pickett, 2006). ACEs have been associated with a variety of health risk behaviours such as alcoholism, drug abuse, smoking, physical inactivity, numerous sexual partners, and sexually transmitted diseases (Felitti et al., 1998). Exposure to ACEs can also increase mental health conditions in adolescence and adulthood (McLaughlin et al., 2012). Individuals with a history of ACE, for example, are more likely to develop various psychiatric disorders in adolescence, including behavioural, fear, distress, and substance use disorders, compared to individuals who have not been exposed to ACEs (McLaughlin et al., 2012). In addition, stressful or traumatic experiences may disrupt or alter a child’s emotional development leading to poor coping strategies such as emotional suppression or avoidance, as well as certain internalizing symptoms often associated with anxiety and depression (McLaughlin & Hatzenbuehler, 2009). In a study performed by Chapman et al. (2004), researchers found that ACE scores were associated with the likelihood of developing chronic depressive disorder in adulthood (Chapman et al., 2004). In addition, women who had experienced emotional abuse in childhood were nearly three times more likely to have a history of depressive disorder compared to women who had not experienced emotional abuse during childhood (Chapman et al., 2004). ACEs are associated with numerous poor health outcomes in adulthood including ischemic heart disease, cancer, stroke, diabetes, chronic lung disease (Felitti et al., 1998), liver and kidney disease (Dong, Dube, Felitti, Giles, & Anda, 2003), and autoimmune diseases (e.g. Addison’s disease, celiac disease, rheumatoid arthritis, irritable bowel disease, or multiple sclerosis) (Dube et al., 2009). Recently, ACEs and their relationship with the gut microbiome 16 | P a g e have been investigated in children, as well as pregnant women (Hantsoo et al., 2019; Zhong et al., 2019). Flannery et al. (2020) demonstrated that caregiving behaviors can modify children's (ages 5-7) gut microbiome, influencing both taxonomic and functional compositions (Flannery et al., 2020). Pregnant women with high ACEs also had increased Prevotella compared to low ACE individuals, as well as increased inflammatory markers such as IL-6, TNF-α, and CRP (Hantsoo et al., 2019). Interestingly, multiple rodent studies have also revealed that exposure to early life stress (ELS), the functional equivalent of ACE in rodents, has been linked to increased intestinal permeability, bacterial translocation across the gut epithelial barrier, and alterations in the composition of the gut microbiota (De Palma et al., 2015; Moussaoui et al., 2016; O'Mahony et al., 2009). These changes may elicit an immune response and affect the overall body system. Hence, further investigation into the link between ACEs and the gut-brain axis could yield a critical understanding of the pathways by which early-life stress influences health and overall well-being. 1.3.3 Age, sex, and ethnicity Age plays a significant role in shaping the gut microbiota throughout an individual’s lifespan. By 2030, 1 billion individuals are projected to be over 65 years old (Childs, Li, & van Deursen, 2018). As we move into adulthood, the gut microbiota stabilizes and becomes more resilient to changes; however, in elderly individuals, the diversity of the gut microbiota often decreases (Garcia-Pena, Alvarez-Cisneros, Quiroz-Baez, & Friedland, 2017). This may be due to the accumulation of disorders, changes in diet, a decrease in exercise and mobility, and the use of certain medications (Claesson et al., 2012; van Tongeren, Slaets, Harmsen, & Welling, 2005). These alterations in the gut microbiome can impact the overall health of older adults, potentially influencing the aging process and susceptibility to age-related diseases. However, contradictory 17 | P a g e findings have also been found suggesting no significant differences in the gut microbial structure of participants from various age groups (Odamaki et al., 2016; Takagi et al., 2019). Overall, it is safe to conclude that aging is associated with increased gut dysbiosis and is inversely associated with gut microbial diversity (Kim & Jazwinski, 2018). In addition, ageing has also been associated with reduced BA synthesis (Bertolotti et al., 2007), as well as the reduction of genes involved in SCFA production (Rampelli et al., 2013). Aging affects the immune system, with systemic inflammation being one of the hallmarks of aging and one of the causes of increased risk for many age-associated diseases including CAD, diabetes, and cancers (Chmielewski, 2018). Furthermore, aging is modulated by a positive feedback loop in which chronic systemic inflammation in older people is associated with developing age-related diseases which then lead to increased inflammatory responses through these conditions as well (Chmielewski, 2018). Sex can also influence the composition and function of the gut microbiota. Beyond environmental and social differences between men and women (e.g., occupational hazards, lifestyle, social stresses, and access to healthcare) that can contribute to gender differences in disease development, sex chromosomes, and sex hormones can also contribute to sex- and gender-related differences in CAD (Harvey, Coffman, & Miller, 2015; Regitz-Zagrosek & Kararigas, 2017). Sex-differences are also associated with the overall gut microbiota structure (Chen et al., 2016; Takagi et al., 2019). For example, in a study by Takagi et al., significant increases in genera Prevotella, Megamonas, Fusobacterium, Megasphaera, Bifidobacterium, Ruminoccocus and Akkermansia were found in males and females, respectively (Takagi et al., 2019). However, males and females did not differ significantly in their microbial diversity (Takagi et al., 2019). 18 | P a g e Ethnicity differences can capture biological variations derived from social, economic, and cultural differences, human genetic variation, and biogeographical ancestry divergences, as well as lifestyle and dietary differences (Brooks, Priya, Blekhman, & Bordenstein, 2018). In addition, ethnicity and dietary differences are associated with variations in microbial composition and abundance (Biswas et al., 2013; Chen et al., 2016; Fettweis et al., 2014) and even more strongly with gut microbiota than other factors such as genetics (Rothschild et al., 2018), age, sex, and body mass index (BMI) (Brooks, Priya, Blekhman, & Bordenstein, 2018). For example, comparative studies of the microbiome in rural and urban areas in healthy individuals have reported that populations residing in non-Western and/or rural areas have a higher bacterial diversity when compared with populations in America and Europe (Filippo et al., 2010; Yatsunenko et al., 2012). In another study by Deschasaux et al., there was a higher gut microbial diversity observed within the Dutch population and the smallest diversity in South Asians, with Ghanaians, Turks, and Africans in the middle (Deschasaux et al., 2018). Increased Firmicutes and decreased Bacteroidetes were also observed in the Dutch population, while increased Actinobacteria was observed in the South Asian populations (Deschasaux et al., 2018). In a study analyzing the samples from 1,673 individuals in the United States, 12 microbial genera and families varied by ethnicity (Brooks, Priya, Blekhman, & Bordenstein, 2018). For these reasons, the inclusion of demographic factors such as age, sex, and ethnicity are a must for studies in the era of precision. 1.3.4 Lifestyle While the microbiome of a healthy individual is relatively stable, gut microbial dynamics can be influenced by lifestyle habits such as diet, daily exercise, smoking, drinking, and sleeping quality. Diet is a powerful modulator of the gut microbiome, exerting its influence by shaping 19 | P a g e the microbial community's composition and function. The nutrients we consume serve as substrates for microbial metabolism, thereby favouring the growth of certain species over others. For example, one fourth of our bodily cholesterol comes from dietary intake (Goldstein & Brown, 1990; Voet & Voet, 2010). This has led to a growing debate on whether dietary cholesterol can affect CAD development. For example, beneficial modifications of gut microbiota caused by the Mediterranean diet have been shown to ameliorate obesity, inflammation, CAD, and other related metabolic alterations (Krznarić, Vranešić Bender, & Meštrović, 2019; Widmer, Flammer, Lerman, & Lerman, 2015). This diet puts greater emphasis on fruits, vegetables, and legumes and has been associated with increased SCFA levels (Garcia-Mantrana, Selma-Royo, Alcantara, & Collado, 2018). In addition, diet can affect the immune system by shifting inflammatory responses that are linked with diseases. A study by Wilck et al. showed that high salt intake affects the gut microbiome, particularly by depleting Lactobacillus murinus and increasing Th17 cells and hypertension (Wilck et al., 2017). In addition, Westernized diet composed of high unsaturated fat can lead to increased Bacteroidetes and decreased Firmicutes and Bilophila wadsworthia (sulfite reducing microorganisms), compared to a diet composed of high saturated fat that can lead to increased low-density lipoprotein (LDL) cholesterol (Berger, Raman, Vishwanathan, Jacques, & Johnson, 2015) and B. wadsworthia, which is associated with dyslipidemia and increased inflammation (David et al., 2014b; Devkota et al., 2012; Qin et al., 2012). High protein and high fat diets have also been associated with increased Ruminococcus and decreased Bacteroidetes, Clostridium coccoides, Bifidobacterium, E. rectale, Akkermansia muciniphila (Cani et al., 2007; Everard et al., 2013; Yoshimoto et al., 2013) and increased BA concentration in feces. TMAO and SCFA production can also vary, with omnivores producing more TMAO compared to vegans (Koeth et al., 2013), and high fiber diets 20 | P a g e leading to higher SCFAs (Filippo et al., 2010; Qin et al., 2012) and increased gut bacterial diversity (Filippo et al., 2010). The notion of diet influencing cholesterol in the body is a continuing debate that requires further research. Our diet can also have anti-inflammatory effects. Anthocyanin in our diet (e.g., blueberries), for example, is an antioxidant that can affect the gut microbiota by increasing their diversity, which may reduce inflammatory responses (Shondelmyer, Knight, Sanivarapu, Ogino, & Vanamala, 2018). Pre- and pro-biotics have also been thoroughly investigated and shown to improve the gut environment by intestinal barrier enhancement, regulation of immune functions, and the prevention of pathogenic infections (Gareau, Sherman, & Walker, 2010). They have been associated with decreased inflammation (Kekkonen et al., 2008) and increased SCFA, Bacteroidetes, Bifidiobacterium, and decreased Firmicutes (Yadav, Lee, Lloyd, Walter, & Rane, 2013). Although pre- and probiotic usage is increasing in popularity, questions remain with respect to specific immune and physiological effects they may have on health and disease and thus further studies are needed. Thus, dietary choices are instrumental in maintaining a balanced and healthy gut microbiota, which is crucial for overall well-being. Daily exercise has been found to have a positive impact on the gut microbiome. Daily exercise increases gut microbial diversity and increases Firmicutes such as Clostridiales, Roseburia, Lachnospiraceae, and Erysipelotrichaceae, which can increase SCFAs (Dziewiecka et al., 2022), increasing tight junction proteins in the colon epithelia and aiding with intestinal barrier integrity, as well as inhibiting inflammatory markers. Moderate exercise has positive effects on the health of average athletes, such as a reduction in inflammation and intestinal permeability and an improvement in body composition (Cook et al., 2013). However, intensive exercise can have the opposite effect and increase the epithelial wall permeability, and decrease 21 | P a g e gut mucus thickness, which may enable pathogens to enter the bloodstream (Ribeiro, Petriz, Marques, Kamilla, & Franco, 2021). Furthermore, the benefits of exercise on the gut microbiota are not just limited to the changes in bacterial populations. Physical activity can also influence the gut environment itself by altering the pH level and gut transit time (Langøy, Smith, Wold, Samdal, & Haug, 2019). Habits such as smoking and drinking alcohol can also have significant impacts on the gut microbiome. Cigarette smoking, a well-known risk factor for many diseases (e.g., asthma, Chronic Obstructive Pulmonary Disease (COPD), and cancer) can decrease the diversity of gut microbiota and increase pathogenic bacteria such as Prevotella (Antinozzi et al., 2022). Increased smoking rates can also affect the integrity of the gut lining and increase the risk of GI diseases such as IBD (Benjamin et al., 2012; Chen et al., 2016). Excessive drinking of alcohol is another global problem. Alcohol consumption, particularly in excessive amounts, can similarly disrupt the gut microbiome and decrease the abundance of Bacteroidetes and increase the abundance of Proteobacteria (Mutlu et al., 2012). Studies have also shown that drinking can disrupt the intestinal barrier through increased oxidative stress in the intestines which can disrupt the gut barrier (Purohit et al., 2008). Excessive drinking has also been associated with diseases including alcoholic liver disease and cirrhosis (Chen et al., 2016). Sleep is a universal physiological need in all species and is essential for the maintenance and recovery of various physiological functions of the body. In addition, sleep plays a crucial role in maintaining the health and balance of the gut microbiome. Disruptions in sleep patterns, such as those caused by sleep deprivation or irregular sleep schedules, can lead to significant changes in the composition and function of gut bacteria via the gut-brain axis (Han, Yuan, & Zhang, 2022; Smith et al., 2019). Growing evidence has reported the positive correlations 22 | P a g e between sleep efficiency and the diversity of the gut microbiome leading to increased Blautia and Ruminococcus, as well as decreased Prevotella spp (Grosicki, Riemann, Flatt, Valentino, & Lustgarten, 2020). Decreased sleep quality has also been associated with weakened gut barrier function, leading to increased gut permeability and increased systemic inflammation. Furthermore, poor sleep can impact the production of hormones and neurotransmitters that regulate digestion and immune function, indirectly affecting the gut microbiome (Kim, Jeong, & Hong, 2015). Chronic sleep disturbances have been linked with GI disorders, such as IBD (Kinnucan, Rubin, & Ali, 2013). This bidirectional relationship between sleep and the gut microbiome highlights the importance of maintaining regular, restorative sleep for overall gut health and well-being. 1.3.5 Antibiotic treatment Antibiotics are commonly prescribed drugs that have saved millions of lives from infections but can have a profound impact on the gut microbiome decreasing taxonomic richness and diversity, as well as affecting functional attributes (Dethlefsen, Huse, Sogin, & Relman, 2008; Dethlefsen & Relman, 2011). Antibiotics can inhibit the growth of bacteria, which, while effective against pathogenic bacteria, can also affect beneficial gut bacteria (Jakobsson et al., 2010). This can also lead to the emergence of antibiotic-resistant strains (de la Cochetiere et al., 2005). For example, this broad-spectrum impact can disrupt the gut ecosystem, paving the way for opportunistic pathogens such as Clostridiodes difficile to proliferate, which can lead to recurrent C. difficile infection (rCDI). Vancomycin, for example, is the common antibiotic used for C. difficile infection (CDI) and can decrease Bacteroidetes and Faecalibacterium, while increasing Proteobacteria species (Isaac et al., 2017). Oral antibiotics have also been associated with an increased risk of colon cancer (Zhang & Chen, 2019). In addition, animal studies of mice 23 | P a g e (8-10 weeks) have shown that antibiotics can lead to decreased gut mucus thickness and heighten the susceptibility to pathogenic infiltration and subsequent intestinal inflammation (Wlodarska et al., 2011). Therefore, the usage of antibiotics requires appropriate guidance. The design and development of antibiotic alternatives are crucial, as well as methods to conserve and restore the gut ecosystem after antibiotic-associated perturbation. Figure 2. Factors influencing the gut microbiome. ACEs, adverse childhood experiences. Figure created using BioRender.com. 24 | P a g e 1.4 Gut Microbiome and Disease States Currently, we are observing a surge of scientific research attempting to establish connections between the gut microbiome and a broad array of health conditions. An imbalance in the composition of the gut microbiome, often termed ‘gut dysbiosis’, has been vastly studied and linked with many diseases (Hooks & O'Malley, 2017). However, studying the gut microbiome presents unique challenges due to its complex and dynamic nature. The diversity of microbial species, their functions, and their interactions with each other and the host add layers of complexity to this field of research. In addition, as previously mentioned in section 1.3, the microbiome is influenced by a myriad of factors, making it difficult to isolate specific causes or effects. There are also many methodological hurdles related to experimental design, experimental models (i.e., human or animal studies), ethical issues, sampling methods, and sample analysis. In vitro models using bioreactors and microfluidic microfluidic systems such as the “organ-on-a-chip” have also been used to assess the gut microbiome and can remove the host variability and host altogether (Amato, Maurice, Guillemin, & Giles-Vernick, 2019; Bein et al., 2018). But when it comes to human and animal studies, other methodological and biases can exist and arise at several levels such as technical, experimental, analytical, interpretative, and at the publication stage. For example, sample collection (i.e., stool samples) can also lead to many challenges, with no standard protocol and consensus available for quality assurance and downstream analysis. The gut microbiome contains distinct microbial consortia in saliva, upper GI tract, lower GI tract, and fecal samples (Vasapolli et al., 2019). The upper GI has shown increased Gemella, Veillonella, Neisseria, Fusobacterium, Streptococcus, Prevotella, Pseudomonas, and Actinomyces, while the lower GI has shown increased Faecalibacterium, Ruminococcus, and Bacteroides (Vasapolli et al., 2019), which can produce methodology 25 | P a g e challenges. In addition, the composition of fecal bacterial communities can be affected by factors including experimental design and procedures such as collection, storage, and DNA extraction (Panek et al., 2018). It has been shown that the fecal microbiome is not representative of the mucosal microbiome, and it is crucial to move beyond the monolithic “stool-centric” viewpoint (Vasapolli et al., 2019). In addition to the type of samples, longitudinal sampling can increase our understanding of the steady state, but certainly relay a burden on the patients. Finally, within the last decade, the surge of gut microbiome studies can be attributed to the development of cost-effective high throughput next-generation sequencing (NGS) technology and “omics” data such as human genomic, metabolomic, and proteomic data (Costea et al., 2017). For example, studies often conduct 16S rRNA sequencing rather than shotgun metagenomics which can aid in stepping away from a uni-kingdom perspective, as well as providing strain-level taxonomic classifications. However, high-throughput sequencing can also fail to provide information on transcription, translation, or enzymatic activity (Koh & Bäckhed, 2020). In addition, a significant proportion of the data cannot be assigned a function due to a lack of close matches in reference databases (Zhu et al., 2010), specifically viral data. One of the most significant hurdles in gut microbiome studies and when observing associations between the microbiome and certain health conditions is moving from correlation to establishing causation (Fischbach, 2018; Koh & Bäckhed, 2020). For example, varied gut microbiota composition has been associated with cardiovascular disease (CVD), yet a definitive cause-effect relationship between these changes and CVD has been difficult to pinpoint, especially in humans. Aiming to uncover specific changes in the microbiome causing health outcomes requires carefully designed controlled experiments, often using animal models, which may not fully replicate human biology. These obstacles necessitate the development of 26 | P a g e innovative and multidisciplinary research and technologies to better understand the causal relationships with the gut microbiome and their potential impact on health and disease states. 1.4.1 The gut-brain axis When it comes to studies associating the gut microbiome with various diseases, many often utilize the various gut axes and bidirectional communicational networks in their discussions to support their findings such as the gut-brain, gut-lung, gut-liver, gut-heart, and gut-oral axes. For example, under the gut-brain axis umbrella, chronic stress, depression, and anxiety and their effects on the gut microbiome have vastly been explored. Increased levels of Butyricimonas and decreased Clostridiales have been associated with stress (Qu et al., 2017; Toh et al., 2021) and depression (Yang et al., 2017). Many pathways have been proposed to mediate the communication between the gut and brain including interactions between the autonomic nervous system (ANS), ENS, CNS, immune system, and endocrine system (Carabotti, Scirocco, Maselli, & Severi, 2015). Gut microbiota can also communicate with the hypothalamic pituitary adrenal (HPA) axis, which can coordinate stress responses (Mayer, 2000). Signal molecules generated in HPA are distributed in the body and can affect gut microbiota composition. Gut microbiota metabolites, such as neuropeptides and SCFAs, also play a key role in the gut-brain axis by interacting with the CNS, activating microglia, which modulate HPA activity and, in turn, cytokine release (Rea, Dinan, & Cryan, 2016). As previously discussed in section 1.3.2, ACEs can also increase depression rates in adulthood and have been recently explored for their relationship with the gut microbiome in children and pregnant women. The lifetime consequences of ACEs can be especially significant for those encountering elevated stress levels in their professional occupations (Huerta-Franco et 27 | P a g e al., 2013). For example, the nursing profession inherently involves greater exposure to stress beginning as early as nursing school. Nursing students can face heavy academic workloads, learn complex skills in fast-paced environments, and have long and irregular work hours (Jimenez, Navia-Osorio, M., & Diaz, 2010; Lavoie-Tremblay, Sanzone, Aubé, & Paquet, 2021). Rella et al. (2009) conducted a study investigating fatigue and burnout among nursing students and found that 20% of graduates reported serious fatigue/stress and depression (Rella, Winwood, & Lushingtoon, 2009). The presence of ACEs in their lives can exacerbate this risk even further as studies have shown associations of high ACEs with increased burnout severity and depressive symptoms among nursing students (McKee-Lopez, Robbins, Provencio-Vasquez, & Olvera, 2019). Although research on stress among nursing students and nurses is abundant, no studies have examined the long-lasting impact of ACE and considered the relationship between ACEs, gut microbiota and its functions, and the overall health of nursing students. Further exploration of the connection between ACEs and the gut-brain axis may provide valuable insights into the mechanisms through which stress in childhood affects health and well-being across the lifespan. 1.4.2 The gut-heart axis High serum cholesterol (hypercholesterolemia) is a well-documented risk factor for the most prevalent form CVD known as CAD (Goldstein et al., 1973; Lee et al., 2015), which is one of the leading causes of morbidity and mortality worldwide (Lynch et al., 2017). The buildup of cholesterol-containing deposits (plaque) inside the artery walls can lead to atherosclerosis (Mailer, Gistera, Polyzos, Ketelhuth, & Hansson, 2017), which is expected to cause 12 million coronary deaths annually by 2030 (Cassar, Holmes, Rihal, & Gersh, 2009). Cholesterol in the body originates from two sources and is synthesized de novo in the liver or can enter our body via our diet and cholesterol-rich foods. About one fourth of the cholesterol in the body comes 28 | P a g e from dietary intake (exogenous) and the rest is synthesized de novo (endogenous) via the mevalonate pathway (Goldstein & Brown, 1990; Voet & Voet, 2010). The cholesterol synthesized within the body is classified as either high-density lipoproteins (HDL) cholesterol or LDL cholesterol, the latter of which can enter the circulatory system and becomes a key marker of CAD (Cerqueira et al., 2016). By contrast, HDL cholesterol is inversely associated with CAD (Kannel, Dawber, Friedman, Glennon, & McNamara, 1964) and has anti-atherogenic functions by exerting anti-inflammatory and anti-oxidative effects and promoting reverse cholesterol transport (RCT), which can eliminate LDL cholesterol (Rosenson et al., 2013). However, HDL may lose its anti-atherogenic properties and becomes pro-atherogenic (dysfunctional) under conditions such as inflammation, diabetes, and oxidative stress (Rosenson et al., 2013). Moreover, elevated LDL cholesterol is a risk factor for CAD (Catapano et al., 2016), which may be due to the uptake of LDL cholesterol particles by macrophages that leads to foam cells and atherosclerosis (Sorci-Thomas & Thomas, 2016). The gut lumen plays an eminent role in controlling the body’s cholesterol balance and is responsible for exogenous intake via cholesterol absorption (Morgan, Mooney, Wilkinson, Pickles, & Mc Auley, 2016). Luminal cholesterol can come from different sources and is mainly derived from (i) our diet, (ii) bile via the hepatobiliary pathway (Grundy, 2016), and (iii) de novo cholesterol via the transintestinal cholesterol efflux (TICE) pathway (Le May et al., 2013; van der Velde, Brufau, & Groen, 2010) (Figure 3A). In the liver, cholesterol is metabolized into BA and is secreted into bile via the hepatobiliary pathway where the ATP-binding cassette transporter, G5/ATP-binding-cassette transporter G8 (ABCG5/G8), plays a key role in cholesterol efflux from hepatocytes into bile (Yu et al., 2002). TICE is an alternative route to the hepatobiliary pathway, where cholesterol from the blood can directly enter enterocytes through 29 | P a g e LDL receptors (LDL-R) and is effluxed by ABCG5/G8 and the ATP-binding cassette transporter B1 (ABCB1a/b) into the lumen (Le May et al., 2013). The cholesterol content of the lumen is then either absorbed into enterocytes via Niemann-Pick C1-like 1 (NPC1L1) and incorporated into chylomicrons for entry into the circulatory system (Morgan, Mooney, Wilkinson, Pickles, & Mc Auley, 2016), or is reduced by gut microbiota to poorly absorbable coprostanol (5B-Cholestan-3B-ol) (Gerard et al., 2004; Midtvedt & Midtvedt, 1993; Rosenfeld, Fukushima, Hellman, & Gallagher, 1954), which is mostly excreted. Aside from the complex interplay of numerous cholesterol sources in the body, many other factors can affect cholesterol balance and CAD development including our gut microbiota. Recently, the gut microbiome’s involvement in the pathogenesis of CVD due to its varied effects on the body, particularly the immune system has been explored, which is known as the gut-heart axis (Bui et al., 2023). Associations between an altered gut microbiome composition and CVD can take place through direct (via metabolites) and indirect pathways (via the immune system) (Figure 3B) (Matey-Hernandez et al., 2018; Nakaya & Ikewaki, 2018). Susceptibility to atherosclerosis, for example, has been demonstrated to be transferable by microbiota transplantation in murine models (Gregory et al., 2015). To date, many infectious agents have been linked to atherosclerosis including Helicobacter pylori, Cytomegalovirus, Hepatitis C virus, Chlamydia pneumoniae, and Porphyromonas gingivalis (Libby, 2017). Interestingly, a study by Mitra et al. showed that microbiota displayed differences between symptomatic and asymptomatic atherosclerotic plaques, with asymptomatic plaques having an increased abundance of host microbiome-associated families including Porphyromonadaceae, Bacteroidaceae, Micrococcaceae, and Streptococcaceae (Mitra et al., 2015). In contrast, symptomatic atherosclerotic plaques contained an increased abundance of pathogenic 30 | P a g e microbiome families including Helicobacteraceae, Neisseraceae, and Thiotrichaceae (Mitra et al., 2015). Moreover, gut microbiota dysbiosis as a result of the disruption to the overall state of gut microbiota has been associated with increased inflammation, which is linked with the development of atherosclerosis (Chistiakov, Bobryshev, Kozarov, Sobenin, & Orekhov, 2015). Recently, alterations in the gut microbiota and its metabolites have also been associated with hypertension and vascular dysfunction (Kim et al., 2018). Heart failure has also been associated with specific gut microbial species such as increased Escherichia coli, Klebsiella penumoniae, and Streptococcus viridans (Tang, Kitai, & Hazen, 2017). One study has shown that patients with symptomatic stroke and transient ischemic attack have an altered gut microbiota with increased opportunistic pathogens including Enterobacter, Megasphaera, Oscillibacter, and Desulfovibrio (Yin et al., 2015). Furthermore, the gut microbiota has the capacity to contribute to substantial variation in blood lipid composition (Fu et al., 2015), which can affect CAD development. For example, Firmicutes such as Lactobacillus reuteri is associated with higher HDL (Taranto, Medici, Perdigon, Ruiz Holgado, & Valdez, 1998), whereas the genus Eggerthella is associated with decreased HDL cholesterol (Fu et al., 2015). However, the causal relationship between the gut microbiome and CAD development remains unclear. Cholesterol regulation in the body is a complex mechanism with factors that are intertwined in a multifaceted system (Figure 4). Therefore, further studies are needed to understand the underlying mechanisms and identify which microbial strains or their metabolites are responsible for CAD development. 31 | P a g e A) B) Figure 3. Cholesterol, gut microbiota, and CAD. Exogenous and endogenous sources of luminal cholesterol (A). The multifaceted mechanisms involved in CAD development (B). The gut microbiota can directly (via metabolites) and indirectly (via the immune system) lead to CAD. VLDL, very low-density lipoprotein; IDL, intermediate-density lipoproteins; LDL, low-density lipoprotein; LDL-R, low-density lipoprotein receptor; HDL, high-density lipoprotein; LRP, low-density lipoprotein receptor-related protein; TICE, transintestinal cholesterol excretion; CVD, cardiovascular disease; CAD, coronary artery disease 32 | P a g e Figure 4. Multifaceted mechanisms affecting CAD. Exogenous and endogenous sources of luminal cholesterol and diet, and the gut microbiota mechanisms involved in affecting the immune system and CAD development. GLP-1, glucagon-like peptide 1; TMA, trimethylamine; PAMP, pathogen-associated molecular patterns; SCFA, short chain fatty acid; Treg, regulatory T cells; LPS, lipopolysaccharides; LDL-R, low-density lipoprotein receptor; TMAO, trimethylamine-N-oxide; CYP7A1, cholesterol 7-alpha-hydroxylase; FMO, flavin-containing monooxygenase; FGFR4, fibroblast growth factor receptor 4; FGF19, fibroblast growth factor receptor 19; NPC1L1, Niemann-Pick C1-like 1; ABCG5/8, G5/ATP-binding-cassette transporter G8, ABCB1a/b, TP-binding cassette transporter B1; TLR4, toll-like receptor 4; FXR, farnesoid X receptor; NOD2, nucleotide binding oligomerization domain containing 2; GPCR41/43/109A, G protein-coupled receptors 41/43/109A 33 | P a g e 1.4.3 Microbiome-based therapeutics: Fecal microbiota transplantation (FMT) The gut microbiome is associated with various diseases; however, its causal relationship in the development of CDI has been established. As mentioned in section 1.3.5, antibiotics are the primary treatment method for CDI which can lead to rCDI (Bajaj et al., 2018; Pamer, 2016). Fecal microbiota transplantation (FMT) is a novel treatment that is highly effective in the management of rCDI and holds promise in several dysbiosis-associated diseases (Bajaj et al., 2017; Van Schooneveld et al., 2013). FMT for rCDI treatment has a high success rate with 85% efficacy (Van Schooneveld et al., 2013). For this treatment, the distal gut microbial communities from a healthy individual are transferred to a patient’s intestinal tract (Drekonja et al., 2015), which can aid with the restoration of a disturbed microbial ecosystem and the reinstatement of lost microbial functional networks. However, the logistics of selecting and screening donors as well as optimizing the efficacy of this treatment are the main constraints of the therapeutic uses of FMT (Cammarota et al., 2019). In order to optimize FMT it is crucial to understand how microbial ecosystems reassemble over time after FMT and which microbial strains are the determining factors in this dynamic process. However, the current scientific lens has mainly had a uni-kingdom major focus on bacteria, leading to the proposition of the existence of FMT “super-donors”. For example, an ideal donor having increased Lachnospiraceae, Ruminococcaceae, and Clostridium scindens has been known to increase FMT success (Taur et al., 2018), which are also positively associated with secondary BAs that inhibit CDI germination (Bajaj et al., 2018). Furthermore, FMT can aid with the restoration of SCFAs in patients (Seekatz et al., 2018). Beyond the gut bacteria and metabolites involved in FMT treatment, only a few studies have explored the role of gut mycobiome and virome on FMT efficacy, which emphasizes that it is fundamental to include the 34 | P a g e relative contribution of all domains. Furthermore, it is important to note that donors are not the only key players as the pre-existing microbiome in recipients also plays a significant role (Smillie et al., 2018). Thus, we must consider the microbiome-associated ecosystem heterogeneity in both donors and recipients. To fully understand the role of gut microbiota in FMT and to guide therapeutic interventions for rCDI, it is critical that we elucidate the interconnected bodily factors that work together to affect gut microbiota and disease development. A better understanding of such mechanisms can lead to personalized microbial-based strategies for the restoration of the gut ecosystem. 1.5 Thesis Outline, Objectives, and Hypotheses In this section, I will present the proposed research questions of this thesis, contextualized by the existing knowledge gaps in the field. The overall aim of this thesis was to investigate the role of the gut microbiome in health (i.e., its involvement in luminal cholesterol metabolism) and disease states (i.e., ACE effects and FMT treatment for rCDI). As mentioned in section 1.4.2, cholesterol dysregulation has been linked to a spectrum of diseases, notably hypercholesterolemia. Emerging research has unveiled the gut microbiota’s role in luminal cholesterol metabolism through the reduction of cholesterol to coprostanol, a non-absorbable sterol excreted in feces. While previous research has centred on identifying gut microbiota driving this conversion among non-, low-, and high-converters, no reports have holistically assessed the gut environment encompassing the gut mucus, microbiome and metabolites in the lumen and serum and their relationship with cholesterol metabolism. In addition, while studies have found associations between the gut microbiome and serum lipids, the impact of cholesterol metabolism on serum sterol contents remains unknown. Therefore, in 35 | P a g e Chapter 2, I utilized a multidisciplinary approach to elucidate the complex interactions within the gut environment in relation to cholesterol metabolism among healthy individuals. First, I sought to assess the gut microbiome between individuals categorized as non- and high-converters of cholesterol to coprostanol. I hypothesize that several members of Lachnospiraceae and Ruminococcaceae (Antharam et al., 2013), as well as members of Bacteroides, Eubacterium, and Clostridium cluster IV, have cholesterol reductase activity (Freier, Beitz, Li, & Hartman, 1994; Gerard et al., 2007; Kenny et al., 2020). As such, select gut microbiota may show an ability to reduce cholesterol to coprostanol in an in vitro model. Second, I aimed to uncover the metabolic changes occurring within the gut in response to differing cholesterol conversion efficiencies, with a primary focus on identifying specific metabolic profiles of BAs and SCFAs. Since changes at the functional level and metabolites such as SCFAs and BAs are implicated in hypercholesterolemia and CVD development (Gerard, 2013), I hypothesize that the gut microbiota modulating SCFA production and BA ratios, if unbalanced and in an unhealthy state, could lead to reduced SCFAs and secondary BAs in non-converters. This could in turn increase primary bile acids such as CDA, activate FXR, downregulate bile acid production, and thus increase cholesterol in serum (Lefebvre, Cariou, Lien, Kuipers, & Staels, 2009). Third, I aimed to explore the relationships between gut lipid components (i.e., cholesterol and BAs), and serum lipid and BAs. I hypothesize that there is an inverse relationship between the serum cholesterol and coprostanol/cholesterol ratio in the gut regulated by microbiota (Kenny et al., 2020). In the case of high-converter populations, the endogenous and exogenous luminal cholesterol are mostly converted to poorly absorbable coprostanol, which would be excreted from the body. In contrast, in low- or non-converter individuals, cholesterol remains intact within the lumen where half is re-absorbed (Gerard, 2013) resulting in increased serum cholesterol. Furthermore, I 36 | P a g e examined potential causal relationships between identified bacterial taxa and significantly changed metabolites and their roles in influencing cholesterol conversion. Lastly, for the first time, I aimed to probe the architecture and glycan composition of gut mucus structures and compare them across converter groups, aiming to explore whether mucus structure and glycan composition change in response to different cholesterol conversion levels. I hypothesize that non-converters have a disturbed gut barrier integrity leading to thinner fecal-adherent mucus, as well as altered mucus O-glycosylation patterns (i.e., similar to patterns observed in IBD patients) (Robbe Masselot et al., 2023). Next, as discussed in sections 1.3.1 and 1.3.2, the early years of childhood play a crucial role in shaping the gut microbiome and have been associated with poor cardiovascular health in adulthood. ACEs encompassing negative, stressful, and potentially traumatic events during childhood, can impact physical and mental health outcomes in adulthood. For example, studies suggest ACE can have short-term effects on children’s gut microbiome. However, no one has assessed the longitudinal effects of ACE and whether the burden of these events experienced during childhood could have long-lasting impacts on the physical and mental health of adults. In addition, the lifetime consequences of ACEs can be especially significant for those encountering elevated stress levels in their professional occupations such as nursing students (discussed in section 1.4.1). Thus, in Chapter 3, I aimed to examine the impact of ACE exposure on the mental and physical health of adult nursing students. Specifically, I sought to determine whether ACE exposure has long-term effects on the gut microbiota and its function during adulthood. Lastly, I aimed to identify potential factors moderating the effects of ACEs on the gut microbiome, which could in turn serve as interventional targets to mitigate the lifelong consequences of ACE. I hypothesize that the high-ACE group will have a disturbed gut ecosystem leading to changes in 37 | P a g e specific gut microbiota, as well as changes at the functional level. Specifically, high-ACE individuals may have increased Prevotella, as previous research has shown that pregnant women with high-ACE had increased Prevotella compared to low-ACE individuals, as well as increased inflammatory markers such as IL-6, TNF-α, and CRP (Hantsoo et al., 2019). In addition, multiple rodent studies have also revealed that exposure to early life stress (ELS), the functional equivalent of ACE in rodents, has been linked to increased intestinal permeability, bacterial translocation across the gut epithelial barrier, and alterations in the composition of the gut microbiota (De Palma et al., 2015; Moussaoui et al., 2016; O'Mahony et al., 2009). These changes may elicit an immune response and affect the overall body system. In addition, I hypothesized that changes in the gut microbiota composition could exacerbate the impact of ACEs on nursing students' mental health. In a study performed by Chapman et al. (2004), researchers found that ACE scores were associated with the likelihood of developing chronic depressive disorder in adulthood (Chapman et al., 2004). Furthermore, I hypothesize that positive lifestyle habits (e.g., improved diet, physical activity, and sleep) will aid in combatting the negative effects of ACE since these factors can also affect the gut microbiome (Davies et al., 2014; Muegge et al., 2011). Next, as discussed in section 1.4.3, there is a well-documented disease in the microbiome field that links a microbial biomarker with causation in a disease known as C. difficile infection (CDI), which with antibiotic treatment can often lead to rCDI. FMT is a novel treatment with high efficacy rates that has been utilized for the management of rCDI and holds promise for several dysbiosis-associated diseases. There have been several studies focusing on understanding the underlying mechanisms in FMT treatment for rCDI. However, the current scientific lens has mainly had a uni-kingdom major focus on bacteria, leading to the proposition of the existence of 38 | P a g e FMT “super-donors”. Thus, in Chapter 4, I aimed to assess the gut microbiome (via metagenomic sequencing) of donor samples prior to FMT and patients following FMT to further understand the factors involved in FMT treatment efficacy. To this end, I specifically investigated whether FMT super-donors exists for rCDI treatment, or whether the donor-recipient compatibility and short-term fluctuations in the gut microbiomes (a combination of bacteria, fungi, archaea, and viruses) of both donors and recipients have profound implications in FMT success. Previous research has shown that bacteriophages alone weren’t sufficient to eradicate CDI. Thus, I hypothesize that it is essential to consider all domains of life and consider both donors and patients in order to optimize FMT efficacy. Lastly, in Chapter 5, I consolidate the findings from the previous chapters, outlining the overarching conclusions, and outline potential avenues for future research. 39 | P a g e Chapter 2: The Role of Akkermansia muciniphila in Luminal Cholesterol Metabolism: Exploring the Gut Microbiome, Mucus, and Metabolite Dynamics 2.1 Introduction The gut environment is a dynamic and intricate ecosystem, sculpted by numerous interacting elements such as the gut mucus, microbiota, and metabolites, all of which contribute to its overall state and function (Thursby & Juge, 2017). Over the years, this multifaceted environment has garnered attention for its role in cholesterol metabolism through the conversion of cholesterol to poorly absorbable coprostanol (5β-cholestan-3β-ol) which is excreted in feces (Gerard et al., 2004; Gerard et al., 2007; Kenny et al., 2020; Midtvedt & Midtvedt, 1993; Rosenfeld, Fukushima, Hellman, & Gallagher, 1954). The efficiency of cholesterol to coprostanol conversion among individuals varies leading to non-, low-, and high-converters (Wilkins & Hackman, 1974). These conversion patterns were initially suggested to be equally distributed with respect to sex and independent of age (Veiga et al., 2005; Wilkins & Hackman, 1974). However, this understanding is contradicted by a study by Benno et al. (2011), which examined cholesterol to coprostanol conversion in 663 healthy subjects and revealed an age- and sex-dependent pattern. Specifically, they found reduced non- and low-converters in the oldest male age group (Benno et al., 2011), suggesting that conversion patterns might vary with age and sex. Luminal cholesterol's journey, involving numerous factors influencing systemic cholesterol levels, is impacted by the complex network of gut microbiota. The conversion of luminal cholesterol into coprostanol by the gut microbiome can play a crucial role in the body's cholesterol absorption, distribution, and excretion. Specifically, it has been hypothesized that increasing coprostanol production (i.e., being a high-converter) could potentially lower serum 40 | P a g e cholesterol levels (Jia, Zou, Han, Bae, & Jeon, 2023). However, direct evidence from human studies is scarce. A study by Sekimoto et al. (1983) observed an inverse relationship between serum cholesterol levels and the coprostanol-to-cholesterol ratio in the stool of a small cohort of healthy and hospitalized patients (Sekimoto, Shimada, Makanishi, Nakano, & Katayama, 1983). Recent discoveries have also shed light on the role of cholesterol dehydrogenases, encoded by the intestinal sterol metabolism A (ismA) genes, in a group of uncultured microorganisms, including members of cluster IV Clostridium (Kenny et al., 2020). Interestingly, the presence of IsmA-encoding bacteria has been linked to lower levels of total serum cholesterol (Kenny et al., 2020). However, studies directly connecting specific bacterial taxa to blood lipid levels remain limited. For instance, a study by Fu et al. (2015) identified certain bacterial taxa contributing to variations in blood lipid levels (Fu et al., 2015), and in vitro assays have demonstrated the cholesterol-lowering potential of taxa including Lactobacillus, Bifidobacterium, Enterococcus, and Streptococcus (Lye, Rusul, & Liong, 2010). To date, isolated strains for direct cholesterol to coprostanol conversion have been limited to the genera of Eubacterium coprostanoligenes and Bacteroides sp. strain D8 (Gerard et al., 2007; Ren, Li, Schwabacher, Young, & Beitz, 1996), yet efforts to elucidate the impact of luminal cholesterol to coprostanol conversion on serum sterol contents have remained elusive. Thus, more research is needed to fully understand the direct effects of coprostanol conversion and the influence of specific bacterial taxa on serum cholesterol levels. The gut represents a dynamically interrelated system; however, it's still not fully understood how the conversion of cholesterol to coprostanol impacts other aspects of sterol metabolism and broader systemic effects. The gut microbiome possesses other extensive metabolic capabilities that have been associated with both luminal and systemic cholesterol 41 | P a g e levels. For example, the gut microbiota can influence systemic cholesterol levels via microbial-derived metabolite production including bile acids (BAs), Short chain fatty acids (SCFAs), and trimethylamine-N-oxide production (TMAO). BAs are the main metabolites of cholesterol in the liver and aid in the absorption of fats, nutrients, and lipophilic vitamins, as well as playing a key role in regulating lipid, glucose, and energy metabolism (Li et al., 2012). Dysregulated BA metabolism has been linked with hypercholesterolemia, diabetes, and certain types of cancers (Li et al., 2012). Thus, monitoring and understanding BA ratios including primary, secondary, and conjugated BAs can be a valuable tool in diagnosing and managing a range of health conditions. However, previous cholesterol metabolism studies have largely centred on BAs in the stool, overlooking the connection between BAs in stool and serum. SCFAs, primarily acetate, propionate, and butyrate, are another vital metabolite and can influence various host functions including host-microbiome signalling, energy utilization, and regulation of colonic pH with consequent effects on microbiota composition and gut motility (Tan et al., 2015). Using an animal model, Fushimi et al. (2006) demonstrated that ingestion of SCFAs decreased plasma cholesterol levels in mice by reducing cholesterol synthesis rates in both the liver and small intestine (Fushimi et al., 2006). Due to SCFA's ability to inhibit cholesterol synthesis, they have been proposed to play a protective role in the onset of hypercholesterolemia and cardiovascular disease (CVD) (Chambers, Preston, Frost, & Morrison, 2018). Finally, as we delve deeper into the role of gut microbiota in cholesterol metabolism, the gut mucus layer has come to the forefront as an essential area of focus. Despite its significance, studies have not yet determined if differences in the mucus layer exist between individuals with varying cholesterol to coprostanol conversion rates. The gut mucus, an essential host-derived barrier between, and nutrient source for, the host and the diverse microorganisms residing in the 42 | P a g e gut, is crucial in health and disease management (König et al., 2016). Mucins, especially the heavily glycosylated protein mucin 2 (MUC2), constitute the major protein in the mucosal layer (König et al., 2016). MUC2 is comprised mainly of glycans, > 80% by weight, which consists primarily of >100 structures containing combinations of monosaccharides including galactose (Gal), N-acetylglucosamine (GlcNAc), N-acetylgalactosamine (GalNAc), fucose (Fuc), and sialic acids (5-N-acetylneuraminic acid, Neu5Ac) (Bergstrom & Xia, 2013). The mucosal integrity of the gut, in terms of synthesis, thickness, viscosity, and glycosylation patterns, is fundamental in preventing various diseases (Bergstrom & Xia, 2013). Mucus glycosylation alterations have been associated with conditions such as colitis, colonic cancer, and inflammatory bowel disease (IBD (Bergstrom & Xia, 2013). Thus, expanding research scopes to include the gut mucus layer is vital for a more comprehensive understanding of cholesterol metabolism, which could uncover crucial insights into the different metabolic capabilities of individuals, linking the health of the gut to systemic health and disease outcomes. These gaps underscore the need for more comprehensive research to unravel the complex interplay between gut microbiota, cholesterol and sterol metabolism, gut mucus and its wider implications on gut and systemic health. 2.2 Materials and Methods 2.2.1 Participant recruitment A total of 31 healthy male and female participants were recruited at the University of British Columbia Okanagan campus. All participants provided written informed consent. This study was approved by review boards from the University of British Columbia and all research methods were performed in accordance with the relevant guidelines and regulations. 43 | P a g e Inclusion criteria included men and women aged ≥ 18 and no history of hypercholesterolemia or coronary artery disease. Individuals were excluded from participating if they were unable to provide informed consent or were unwilling to participate, use of oral intravenous antibiotics or chemotherapy within the previous 6 months, pregnancy, history of alcohol or drug abuse or alcohol consumption ≥ 14 drinks/week, self-reported acute infectious disease (cold/flu, gastroenteritis, etc.), self-reported chronic inflammatory or autoimmune disease (inflammatory bowel disease, eczema, psoriasis, type II diabetes, etc.), and triglyceride ≥ 400 mg/dL. Demographic and collected health end-points included sex, ethnicity, age, overall health, medical history, and daily habits. 2.2.2 Biological sample collection Participants donated stool samples using a self-collected stool kit. Samples were stored at 4 ºC and transported to the Biomedical Microbiome Research Centre at the University of British Columbia Okanagan within 24 hrs. Stool samples were then aliquoted (approximately 1 g of stool) into individual Para-Pak sampling vials containing preservatives described below, assigned a code for de-identification, and stored at -80 ºC until further analysis. Blood samples (5 ml) were also collected from participants after overnight fasting. Serum tubes were left to clot at room temperature and centrifuged at 1,500 g for 10 min at 4 ºC and stored at -20 ºC until further analysis. All participants also completed a dietary questionnaire at the time of sample collection, containing questions pertaining to health symptoms, diet, and well-being. 2.2.3 Lipid analysis Serum samples were analyzed for total cholesterol (TC), High-Density Lipoprotein Cholesterol (HDL), Low-Density Lipoprotein Cholesterol (LDL), non-HDL, Chol/HDL ratio, 44 | P a g e and triglyceride (TG) using the AFINION 2 analyzer (Abbott, Illinois, United States). Stool samples in tubes with no preservatives containing silica desiccant beads (5 g) were used for lipid analysis. Cholesterol, lanosterol, 7-dehydrocholesterol, 7-α hydroxycholesterol, desmosterol, cholestanol, lathosterol, coprostanol, β-sitosterol, campesterol, and stigmasterol were quantified in lyophilized stool samples by LC-MS/MS at Metabolon, Inc. (Durham, NC, USA). Quantification was performed through comparison with internal standards as described previously (Evans et al., 2014). For cholesterol to coprostanol conversion rate, the results were expressed as the percentage of coprostanol in the total amount of cholesterol and coprostanol present. For classification, the participants were split into three main groups: non-converters (< 5% conversion rate), low-converters (between 5% and < 40% conversion rate), and high-converters (at least 40% conversion rate) (Benno et al., 2011). 2.2.4 Bile acid and SCFA analysis Stool samples in tubes with no preservatives containing silica desiccant beads (5 g) were used for SCFA and BA analysis. Stool and serum samples were shipped to the UVIC Genome BC Proteomic Centre (Victoria, BC, Canada) where they were analyzed by ultra-high-performance liquid chromatography-multiple reaction monitoring mass spectrometry (UPLC-MRM/MS) using an Agilent 1290 system coupled to a Sciex 4000 QTRAP mass spectrometer. The MS instrument was operated in MRM mode with negative-ion detection. A Waters BEH C18 LC column (2.1*150 mm, 1.7 μm) was used and the mobile phase was (A) 0.01% formic acid in water and (B) 0.01% formic acid in acetonitrile for binary solvent gradient elution. Detailed sample preparation, quantification procedures, and UPLC and MS operation parameters were previously described (Han et al., 2015). 45 | P a g e 2.2.5 Microbiome analysis 2.2.5.1 16S rRNA sequencing Stool samples in Para-Pak vials containing 5 mL of RNAlater preservative were shipped to Integrated Microbiome Resource (Dalhousie University, Halifax, NS, Canada) for bacterial DNA extraction and sequencing. Following the extraction of DNA from each stool sample using the Qiagen PowerFecal DNA stool kit, the V4 region of the 16S rRNA bacterial gene was amplified by polymerase chain reaction (PCR) using the universal bacterial primers 515F (forward primer) and 806R (reverse primer) as described previously (Comeau, Douglas, & Langille, 2017). Equal amounts of DNA from each purified PCR product were pooled together to generate an aggregated library for downstream processing. Illumina MiSeq sequencing was performed following standard Illumina MiSeq operation procedures. Demultiplexed, paired-end reads were generated for each sample using the default parameters on the Illumina MiSeq control software and imported into Quantitative Insights into Microbial Ecology (QIIME) version 2 (2021.8). A total of 14,704,739 raw reads were obtained, with an average of 241,061 reads per sample. A minimum quality control score of 25 was selected: 80 bases were trimmed from the 5’ end of each read, and each read was truncated from the 3’ end at position n = 188 using the DADA2 denoise-paired method in QIIME 2. The resulting total frequency of amplicon sequence variants (ASVs) was 13,153,145, with an average frequency of 215,625 ASVs per sample. Taxonomy was assigned to each ASV using a pre-trained Naïve Bayes classifier and the q2-feature-classifier plugin. This classifier was trained on the Greengenes 13_8 99% OTUs from the 515F/806R region of sequences. Similar sequences were collapsed into single replicate sequences, or operational taxonomic units (OTUs), to the genus level. Subsequently, taxa 46 | P a g e abundances were normalized by the total number of reads sequenced in each sample. All singletons and doubletons were removed from the normalized OTU table. 2.2.5.2 Metagenomic data collection A subset of the stool samples were shipped to Integrated Microbiome Resource (Dalhousie University, Halifax, NS, Canada) for DNA extraction and sequencing. DNA from stool samples was extracted using the Qiagen PowerFecal DNA stool kit. Shotgun sequencing for metagenomics at 2X depth (generating ~12 million raw reads) was applied using the Nextera Flex Kit, and Illumina NextSeq 2000 platform was performed as previously described (Comeau, Douglas, & Langille, 2017). The raw reads were preprocessed using the Kneaddata (version 0.10) pipeline, which also removes host-related sequences and trims the sequences using a human genome database. In the KneadData pipeline, the Trimmomatic (version 0.39) tool was used for trimming the read pre-processing, employing the subsequent options: SLIDINGWINDOW:4:25 (quality-based trailing trimming with a window space of 4 bases and average quality threshold of 25), HEADCROP:15 (removing first 15 low-quality bases), and MINLEN:60 (eliminating reads shorter than a 60 bases threshold). For microbial composition analysis, Kraken2 and MetaPhlAn3 pipelines were used separately. For Kraken2, Kraken standard database was built (13-Mar-2022) with default parameter values (--kmer-len: 35, --minimizer-len: 31, and --minimizer-spaces: 7). For MetaphlAn3 (version 3.0.14) the -t parameter was set as “rel_ab_w_read_stats” to produce the estimated relative abundance of features. Using the Bowtie2 tool, it maps the reads to a database (version 30) of marker genes (unique clade-specific marker genes obtained from ~17,000 reference genomes of bacteria, archaea, viruses, and eukaryotes). The outcome of the Kraken2 and MetaPhlAn3 pipelines were separated for bacteria and the abundances of different microbial genera were obtained by collapsing detected 47 | P a g e taxonomies to the genus level and summing features within the same genera. Taxa abundances were normalized by the total number of reads sequenced in each sample. All singletons, doubletons, and features (bacteria) present in < 0.01% of samples were removed from the normalized table. 2.2.5.3 Generating rarefaction plot and phylogenetic tree Rarefaction curves (plots of the number of genera as a function of the number of sequences per sample) were performed for comparison of genus richness between shotgun sequencing using the Kraken2 and MetaPhlAn3 databases, as well as 16S rRNA sequencing. Subsequently, the union of all genera that were identified by either 16S rRNA gene or shotgun metagenomic sequencing (Kraken2 and MetaPhlAn3 databases) was used to generate a phylogenetic common tree using ETE, a python package used for producing the phylogenetic tree files. This tree was visualized using the Interactive Tree of Life (iTOL) (version 6.8) along with metadata that described which profiling method (either 16S, shotgun metagenomics using Kraken and MetaPhlAn databases) was able to identify the genus. 2.2.5.4 Strain-level profiling of metagenomic samples To track bacterial strains in distinct biological samples, we used StrainPhlAn 4 (version 4.0.3). Using the output of MetaPhlan3, StrainPhlAn4 identifies the presence of species’ strain in samples based on the defined thresholds: “sample_with_n_markers” and “marker_in_n_samples”. A sample was kept as long as it had at least 15 markers (parameter–sample_with_n_markers) and a marker was kept as long as it was present in 50% of the samples (parameter–marker_in_n_samples). StrainPhlAn4 considers the remaining samples as a distinct strain of the species under investigation. Strain analysis was only performed on one bacterial 48 | P a g e species in this study (A. muciniphila). The reference genome of A. muciniphila strains was also incorporated into the StrainPhlAn4 analysis, including one purchased strain (ATCC BAA-835; Cedarlane, Vancouver, BC, Canada) and another as an additional context reference genome. Subsequently, iTOL (version 6.8) was utilized for visualization and to create a phylogenetic tree in order to determine if the A. muciniphila strain in our samples was similar to the purchased strain for in vitro analysis. 2.2.5.5 In vitro analysis for causality tests A. muciniphila (ATCC BAA-835) was purchased from Cedarlane (Vancouver, BC, Canada) and cultured in an anaerobic chamber (Coy Laboratory) under anaerobic conditions (85% N2, 5% H2 and 10% CO2) at 37 °C for 48-72 hrs, as previously described (Derrien, Vaughan, Plugge, & de Vos, 2004). Cultures were washed and concentrated in pre-reduced PBS with 25% (v/v) glycerol and immediately frozen and stored at -80 °C. Thawed A. muciniphila samples were then inoculated in brain heart infusion broth (BHI, Fisher Scientific) (1:5) supplemented with 0.25% porcine gastric mucin (Type III, Millipore-Sigam) and 5 g/1L of yeast extract (VWR) at 37 °C for 24 hrs till the optical density (OD600) reached ~1.00. Media was sterilized at 121 °C for 15 min before use (mucin was filtered by a 0.22 μm water membrane prior to media addition and autoclaving). After autoclaving, 0.03% L-cysteine (Millipore-Sigma) was added to BHI-mucin broth and allowed to pre-reduce overnight. Fully grown pre-culture containing A. muciniphila was inoculated with a ratio of 1:5 into BHI-mucin broth supplemented with either cholesterol (0.01, 0.02, 0.04%) or coprostanol (0.01, 0.02, 0.04%) pre-dissolved in acetone (Fisher Scientific). In addition, separate batches were exposed to co-cultured cholic acid 3-sulfate (5, 10, 20 μmol/ml) and chenodeoxycholic acid 3- 49 | P a g e sulfate (5, 10, 20 μmol/ml) (Garcia, Kosek, Beltrán, Tomás-Barberán, & Hajslova, 2022). Four replicate cultures were used along with positive (spiked with known concentrations of lipids and BAs) and negative controls. Samples were re-supplemented with fresh media every 12 hours. Growth kinetics of A. muciniphila were measured using 200 μl aliquots dispensed into clear flat bottom 96-well microplates incubated at 37 °C in a Cerillo Stratus Kinetic microplate reader (Cerillo, Charlottesville, VA, USA) under anaerobic conditions. The OD600 was measured at 3-min intervals for 48 hrs. Results were obtained from three biological replicates. Samples were then sent to the UVIC Genome BC Proteomic Centre (Victoria, BC, Canada) where they were analyzed by UPLC-MRM/MS for cholesterol, coprostanol, cholic acid, chenodeoxycholic acid, cholic acid 3-sulfate, and chenodeoxycholic acid 3-sulfate. 2.2.6 Mucus analysis 2.2.6.1 Specimen preparation for histology assessment For fixation, small sections were carefully collected from the fecal surface and placed in Carnoy’s solution (60% Methanol; 30% Chloroform; 10% Glacial Acetic Acid) at 4 °C. Samples were then shipped to Wax-it Histology Services (Vancouver, BC, Canada) for paraffin embedding. Samples were oriented to get cross-sections along the fecal surface. 5 μm Carnoy’s fixed paraffin embedded (CFPE) sections were then collected for further analysis. 2.2.6.2 Fluorescence in situ hybridization (FISH) FISH was performed as previously described (Bergstrom et al., 2017). First, deparaffinized sections were incubated at 37 °C overnight with a Texas Red–conjugated universal bacterial probe EUB338 (5’-GCTGCCTCCCGTAGGAGT-3’; bp 337–354 within 50 | P a g e bacteria EU622773; Eurofins MWG Operon) in hybridization buffer (20 mM Tris-HCl, pH 7.4, 0.9 M NaCl, 0.1% sodium dodecyl sulfate). The sections were rinsed with wash buffer (20 mM Tris-HCl, pH 7.4, 0.9 M NaCl) and PBST (1 x PBS pH 7.0 + 0.05% Tween-20) twice each. Second, sections were incubated with Alexa Fluor 488 (Cedarlane Labs, Vancouver, BC, Canada). Slides were washed three times in dH2O and stained with 20 ng/ul DAPI for 5 min, washed again in dH2O and mounted with Fluoroshield™ (Millipore-Sigma). The sections were imaged by widefield microscopy. 2.2.6.3 Widefield and confocal imaging To tile-image the fecal sections, an Olympus IX81 microscope equipped with a motorized stage to enable tiling at 10X magnifications was used. Tiled images were stitched together using Olympus cellSens Dimension software (Version 3.1). For epifluorescent images an EVOS (ThermoScientific) microscope was used. Using the tiled cross-section images of a CPFE fecal pellet stained using FISH, mucus thickness measurements were acquired using ImageJ (Castaneda, Piedrahita-Quintero, & Garcia-Sucerquia, 2015) and then plotted using ggplot2 package. 2.2.6.4 Mucus/MUC2 isolation and protein extraction Mucus isolation from human stool samples was performed as previously described with some modifications (Bergstrom et al., 2017). For mucus isolation from fecal material, ~5 g of fresh or frozen feces were placed directly into a tube containing 5 mL of guanidium chloride (GuCl) extraction buffer (6 M GuCl, 0.1 M Tris pH 8.0, 1 mM EDTA) with 5X cOmplete ™ Protease Inhibitors (Millipore-Sigma). The solution was vortexed. All samples were then extracted overnight at 4 °C on a rotator. The samples were pelleted by centrifugation and pellets 51 | P a g e were resuspended and re-extracted with an equal amount of GuCl extraction buffer. Following re-extraction and removal of the supernatant, samples were reduced twice with 100 mM dithiothreitol suspended in GuCl extraction buffer in order to solubilize the mucin (once overnight at 37 °C, the other for 4 h at 37 °C). Soluble mucins were then alkylated with 250 mM iodoacetamide on a rotator overnight at room temperature in the dark. The reduced and alkylated mucins were dialyzed into ddH2O, and post-dialyzed samples were concentrated by centrifugation using 100 kDa MWCO dialysis cassettes (ThermoFisher). The samples were then treated with DNase I to remove potential DNA contamination and dialyzed via repeat elution and concentration in the centrifugal filtration unit. The samples were then stored at -20 °C until analyzed via electrophoretic or glycomic approaches described below. In addition, sample proteins were quantified using the DC (detergent compatible) Protein Assay Kit (Bio-Rad). 2.2.6.5 Composite agarose-polyacrylamide gel electrophoresis (Composite AgPAGE) Analysis of mucins was performed using composite Sodium Dodecyl Sulfate Urea AgPAGE (SDS-UAgPAGE). The gel was made up of a gradient of 0.5 - 1.0% agarose, 0 - 10% glycerol, and 0 - 6% acrylamide. Running buffer consisted of buffered boric acid (192 mM boric acid, 1 mM EDTA, 0.1% SDS, pH 7.6). Following electrophoreses for 4 hrs at 110 V on ice, the gels were stained by Alcian Blue (AB) solution. 2.2.6.6 Glycomic analysis by high-performance liquid chromatography-mass spectrometry (HPLC-MS) Human mucus samples, extracted and purified as described above, were prepared for glycomic analyses exactly as previously described (Bergstrom et al., 2020). In brief, samples were lyophilized into 1.5 mL screw-capped centrifuge tubes (Sarstedt). Mucin O-glycans were 52 | P a g e released by reductive β-elimination in 100 μL 50 mM NaOH containing 1 M NaBH4 at 45 °C for 16 h. Samples were cooled to 25 °C and neutralized by adding 10 μL aliquots of 2 M acetic acid—vortex-mixing after each addition—until bubbling stopped. O-glycans were recovered from the dried material by solid phase extraction (SPE). SPE was performed using 250 mg Supleco ENVI-Carb graphitic carbon cartridges (Sigma-Millipore). Cartridges were conditioned with 3 mL aqueous 80% acetonitrile (ACN) with 0.1% trifluoroacetic acid (TFA) followed by 6 mL ultrapure H2O; positive pressure at the top of the tube was used for all SPE procedures. Neutralized glycan samples were loaded onto the cartridges, followed by 200 μL ultrapure H2O used to rinse the sample tube. The cartridges were washed with 3 mL ultrapure H2O, and reduced O-glycans were eluted using four sequential 550 μL aliquots of aqueous 50% ACN with 0.1% TFA. Eluates were dried in vacuo using a SpeedVac ThermoScientific) centrifugal evaporator to approximately 50 μL which was snap frozen in liquid nitrogen and lyophilized using a Labconco freeze-dryer. Samples were redissolved in 30 μL ultrapure H2O prior to analysis. HPLC was conducted on a Vanquish (ThermoScientific) system using a Hypercarb (100 mm × 2.1 mm, 3 μm particle size; ThermoScientific) column at 50 °C. Mobile phases A and B were water and containing 5 mM ammonium formate and acetonitrile (ACN) containing 0.1% formic acid, respectively (Robb et al., 2022). The following gradient elution was employed at a flow rate of 0.25 mL/min: 0 – 5 min, 5% B; 5 – 15 min, 5 – 20% B; 15 – 20 min, 20 – 40% B; 20 – 25 min, 40 – 80% B; 25 – 27 min, 80% B; 27.0 – 27.1, 80 – 5 % B. MS was conducted in negative electrospray ionization mode using a QExactive instrument (ThermoScientific). Source parameters were as follows: sheath gas (N2) flow rate of 10 L/min; a spray voltage of -3 kV; and a capillary temperature of 250 °C. The instrument was run at a resolution of 70,000 over a scan 53 | P a g e range from 150 to 2250 m/z. All data were saved in profile mode. MS data were analyzed using TraceFinder 5.1 General Quant. A list of putative glycan formulae was generated from the known atomic composition of the following monosaccharide classes—hexose (Hex, i.e., galactose, mannose, or glucose), N-acetyl-hexosamine (HexNAc, i.e., N-acetylglucosamine or N-acetylgalactosamine), deoxyhexose (assumed to be fucose), and 5-N-acetylneuraminic acid (Neu5Ac)—plus sulfate esters. One water molecule was subtracted from each formula for each glycosidic bond or sulfate and a single, reduced reducing end (i.e., alditol) was assumed for each formula. Post-TraceFinder data processing was done using Microsoft Excel (Microsoft Corporation, Redmond, USA) as follows: raw peak areas for all putative glycans were normalized to the total glycan pool detected in each specific sample. Strict quality checks were maintained such that glycans that differed more than 10 ppm between the observed and predicted m/z for the [M-H]- ions were not included in the results. Retention times and m/z ratios were used as identifiers for glycans. Because glycan biosynthesis is competitive, with the formation of specific glycans precluding the biosynthesis of others, relative glycan abundances were compared both in a glycan-specific manner as well as by unique classes of glycans. Glycan classes considered were “Neutral” (i.e., those that contained only Hex and HexNAc residues), “Fucosylated” (i.e., neutral plus a fucose), “Sialylated” (any glycan containing Neu5Ac), “Sulfated”, “Fucosylated and sialylated”, and “Fucosylated and sulfated”. In instances where the concentration of a specific glycan was below the method detection limit, one-fifth of the value of the lowest abundance glycan within that glycan class was imputed for statistical purposes. 2.2.6.7 Sialic acid analysis by HPLC-MS Purified human mucus samples were prepared for sialic acid analysis exactly as previously described (Wylie & Zandberg, 2018). In brief, samples were freeze dried in 1.5 mL 54 | P a g e centrifuge tubes and fortified with 13C3-Neu5Ac before being subjected to hydrolysis in 2 M acetic acid (AcOH) at 80 °C for 3 h. The resulting mixture was cooled, centrifuged, and mixed with an equivalent volume of 48 mM 4,5-dimethylbenzen-1,2-diamine (DMBA) in 2 M AcOH and heated for an additional 1 h at 60 °C in the dark. After DMBA-derivatization, samples were applied to conditioned 1 mL C18 solid phase extraction (SPE) cartridges (Supelco), washed once with water (1 mL) and eluted using 2 x 0.5 mL 50% ACN. Eluates were dried using a SpeedVac and reconstituted in 100 μL 30% MeOH for HPLC-MS analysis on an Agilent 1290 Infinity system (Agilent Technologies) exactly as previously described. Neu5Ac was quantitated using a nine-point calibration curve with calibrators ranging from 5 to 25,000 ng/mL Neu5Ac (R2 > 0.999). Relative concentrations of O-acetylated sialic acids were also determined; with the exception of Neu5,9Ac2, these were putatively identified on the basis of their relative retention times and previously acquired product ion MS spectra. Four chromatically-resolved tri-O-acetylated sialic acids were detected; these are denoted as Neu5Ac3_1, _2, _3, and _4. 2.2.7 Statistical analysis Data were processed and visualized in Python 3.9 and R (version 2023.06.1) using custom scripts. Scripts used to analyze the data and produce the figures have been deposited at https://github.com/neginkaz/The-Role-of-Akkermansia-muciniphila-in-Luminal-Cholesterol-Metabolism.git. Principal Component Analysis (PCA) plots were constructed using the python library Scikit-learn. Subsequently, significant differences between groups were evaluated using permutational multivariate analysis of variance (PERMANOVA) using the pairwise Adonis from 55 | P a g e the Vegan package within R. Any p-value < 0.05 after using false discovery rate (FDR) correction was considered significant. Using 16S rRNA and metagenomic sequences, the Shannon diversity index for each sample was determined using the normalized tables and the Vegan: Community Ecology Package within R. The R packages ggplot2, preseqR, and reshape2 were used to construct a boxplot and one-way ANOVA (normality checked using Shapiro-Wilk test) was performed to determine if any significant differences in alpha diversity existed within the data. Differences among bacterial community structures between samples (beta diversity) were determined using Bray-Curtis Dissimilarity from the Vegan: Community Ecology Package within R and visualized using a density plot to identify the dissimilarity of distribution. Significant differences in beta diversity were evaluated using the ANOSIM test and any p-value < 0.05 was considered significant. To test whether certain gut microbiota were able to differentiate non- and high-converter groups, a random forest model was trained on non- and high-converter samples at the genus level using the Python library, Scikit-learn. A dimensionality reduction model was utilized to ensure that the trained RF model avoided overfitting and generalized better on the data. PCA was used to extract the features which best described the principal components. The top twenty features from this analysis were selected and used in the training process of the RF model. The Leave One Out (LOO) cross-validation method was then implemented to assess how effectively the trained classifier generalized in the event of unseen data. All measured test errors were averaged to determine the cross-validation error value. The Receiver Operating Characteristic (ROC) curve was plotted for each LOO dataset. Then, Fisher’s exact statistical significance test was utilized to compare the RF classifiers with the greatest validation scores and determine the 56 | P a g e difference between the predictive performance of the top RF candidates. The RF model was run 100 times, and the average Mean Decrease in Impurity (MDI) of the most notable features was determined. The linear discriminant analysis (LDA) effect size (LEfSe) method was also performed to identify key taxa that may contribute to the observed differences in gut microbial communities using relative abundances. The LEfSe algorithm coupled the non-parametric Kruskal-Wallis test and the unpaired Wilcoxon test with LDA scores to estimate the LDA effect size of differentially abundant bacterial OTU and functional biochemical pathways with statistical significance. Gut microbiomes were considered to be significantly different if the p-value was less than 0.05 and if the LDA score (log10) threshold was greater than 3.0. The overlapping microbial taxa from RF and LEfSe analysis were then used for further analysis and comparisons between groups. The top features from this analysis were selected and after checking the normality the Mann-Whitney U test or t-test was used to compare the features between non- and high-converter groups. Any p-values less than 0.05 after FDR correction were considered significant. Using multiple machine learning algorithms and finding consensus between their results increases the reliability, generalization, and robustness of the analysis, reducing bias and overfitting while exploring different perspectives. It allows for model selection, performance comparison, and more accurate insights into the underlying patterns and relationships within the data. Serum and stool total BAs, as well as primary, secondary, conjugated, and sulfated BA ratios, were also compared between non- and high-converter groups using relative abundances (abundances were normalized by the total number of BAs in each sample). After checking the normality the Mann-Whitney U test or t-test was used to compare the features between non- and high-converter groups and any p-value < 0.05 was considered significant. 57 | P a g e Heatmaps were constructed using the python libraries Matplotlib (version 3.7.1) and Seaborn (version 0.11.2). For heatmaps, the Spearman’s rank correlation was used using the python library SciPy (version 1.7.3). Lastly, correlation network analysis was utilized to assess the relationship between various features. The network was constructed based on Spearman's rank correlation coefficient, whose rho values were more than 0.3 and p-adjusted < 0.05 (using FDR). Based on these correlation coefficients, a correlation network was built where edges (red links indicate negative and green links indicate positive associations) connecting nodes represented the significant correlations and widths indicated correlation magnitude. The network was created using the python library, NetworkX (version 2.6.3) and visualized in Gephi (Graph Visualization and Manipulation) software (version 0.9.2). 2.3 Results 2.3.1 Stool lipid and gut microbiota profiling of cholesterol converter groups A total of 31 healthy male and female participants were recruited in this study. Our cohort consisted of 42% males and 58% females, with an average age of 40 years (SD = 15). To assess differences in stool lipid community structures across various levels of cholesterol to coprostanol conversion (non-converters: < 5%; low-converters: between 5 and < 40%; and high-converters: ³ 40%), we employed the Principal Component Analysis (PCA), which revealed that converter groups exhibited distinct stool lipid profiles (PERMANOVA, p = 0.001, Figure 5A, Appendix A.1). Specifically, high-converters had higher levels of coprostanol, cholestanol, and lathosterol than non- and low-converters, while non-converters had significantly higher levels of cholesterol, 7-dehydrocholesterol, 7-α hydroxycholesterol, beta-sitosterol, campesterol, 58 | P a g e desmosterol, and stigmasterol than high-converters (Figure 5A, Appendix B.1A). We found no significant relationship between age and sex with the conversion rates (Kruskal-Wallis, p > 0.05). Next, we sought to identify and characterize the differences in the gut microbiome between non- and high-converters using 16S rRNA sequencing. For the gut microbiome diversity analysis, significant differences in alpha diversity (Shannon diversity index) between the non- and high-converters were observed at the genus level (ANOVA, F(2,28) = 4.78, p = 0.029, Figure 5B); however non- and low-converters (ANOVA, p > 0.05, Figure 5B), and low- and high-converters (ANOVA, p > 0.05, Figure 5B) were not significantly different. Generating density plots based on the estimated Bray-Curtis dissimilarity values (beta diversity) also showed that the gut community structures varied significantly between converters (ANOSIM, p = 0.009, R = 0.234, Figure 5C). This was also evident as the peaks on the density plots did not overlap, indicating distinct differences between the groups. Bacterial genera differentiating non- and high-converters were investigated using a random forest (RF) model and LEfSe analysis (Figure 5D, E, F). Although the RF prediction model did not show significant predictive power (AUC=0.74, Fisher’s exact test; p > 0.05, Figure 5D), the overlapping results of both methods revealed specific genera of bacteria that significantly differentiate between converter groups (Figure 5G). Specifically, high-converters exhibited increased levels of Ruminococcus (Mann-Whitney U, p-adjusted = 0.032), Ruminococcaceae (Mann-Whitney U, p-adjusted = 0.037), Oscillospira (Mann-Whitney U, p-adjusted = 0.022), Akkermansia (Mann-Whitney U, p-adjusted = 0.027), Ruminococcaceae (Mann-Whitney U, p-adjusted = 0.034), and Christensenellaceae (Mann-Whitney U, p-adjusted = 0.03), as well as decreased levels of Lachnospiraceae (Mann-Whitney U, p-adjusted = 0.04), 59 | P a g e Enterobacteriaceae (Mann-Whitney U, p-adjusted = 0.027), Enterocloster (Mann-Whitney U, p-adjusted = 0.019), and Bacteroides (Mann-Whitney U, p-adjusted = 0.03) (Figure 5G). This was further confirmed using correlation network analysis (rho > 0·3 or rho < −0.3; adjusted p-value < 0.05) (Figure 5H). 60 | P a g e 61 | P a g e Figure 5. Stool lipid profiling and gut microbiota composition of converter groups. Principal Component Analysis (PCA) with feature importance for stool lipid composition (A). Shannon diversity at the genus level (B). Beta diversity density plot with Bray-Curtis dissimilarity (C). ROC curve for random forest (D). The top bacterial taxa differentiating converter groups, where the size of the ellipses represents the average Mean Decrease in Impurity (MDI) (D), and LEfSe analysis with histogram of the LDA scores (log10) calculated for taxa with differential abundances in non- and high-converters (E). The high-converter group is represented in green (1.0) and the non-converter group is represented in yellow (0.0). Overlapping bacterial taxa from random forest and LEfSe compared between converters using the Mann-Whitney U test and FDR correction (G). Network analysis of the correlation between conversion rate, stool lipids, and bacterial taxa (H). Red links indicate negative and green links indicate positive associations with the width indicating correlation magnitude. Red and green nodes represent bacteria and stool lipids, respectively. * p = < 0.05 2.3.2 Serum Lipid Dynamics: Exploring Correlations with Stool Lipids, Cholesterol Conversion Rates, and Specific Bacterial Genera We then explored the relationships between gut and serum lipid levels, including potential associations with specific bacterial taxa. Assessing differences in serum lipid community structures revealed no significant variations between converters (PERMANOVA, p > 0.05, Figure 6A, Appendix A.1). However, triglyceride levels were increased in low-converters compared to high-converters (ANOVA, p = 0.002, Appendix B.1B). To evaluate the relationship between stool and serum lipids, a correlation network analysis was conducted, which showed no significant relationships between serum and stool lipids (Figure 6B). However, certain bacterial taxa were associated with serum lipids. Notably, Oscillospira spp. (predominant in high- 62 | P a g e converters) had a negative relationship with triglycerides, while members of the Ruminococcaceae family were positively associated with HDL and negatively associated with non-HDL cholesterol (Figure 6B). 2.3.3 Comparative Analysis of Stool and Serum Bile Acids Among Cholesterol Converters: Investigating Associations with Conversion Rates, Lipid Profiles, and Predominant Bacterial Genera Given the influence of gut microbiota on luminal cholesterol metabolism (Le Roy et al., 2019) and its role in modifying metabolites such as BAs, which can impact systemic cholesterol levels, the analysis of BAs in stool and serum samples was conducted. The stool BA composition did not vary between converters (PEREMANOVA, p = > 0.05, Figure 6C, Appendix A.1). Comparing the types of BAs showed that high-converters had increased primary (Mann Whitney U, adjusted p = 0.027) and decreased sulfated (Mann Whitney U, adjusted p = 0.018) stool BA ratios compared to non-converters (Figure 6D-ii, v). However, total BAs, as well as secondary and conjugated BA ratios didn’t significantly vary between converters (Figure 6D-i, iii, iv). Correlation network analysis demonstrated that serum triglycerides were negatively associated with secondary BAs in stool (Figure 6E). In addition, high-converters exhibited positive correlations with secondary BAs, and interestingly, negative correlations with total sulfated BAs in stool (Figure 6E). Conversely, bacterial taxa that were abundant in the non-converter group (Bacteroides spp. and members of Enterobacteriaceae) were positively correlated with sulfated BAs (Figure 6E). 63 | P a g e Figure 6. Serum cholesterol and stool bile acid profiling and their relationship with each other and bacterial taxa. Principal Component Analysis (PCA) with feature importance for serum lipid 64 | P a g e composition (A). Network analysis of the correlation between conversion rate, stool lipids, serum lipids, and bacterial taxa differentiating non- and high-converters (B). PCA with feature importance for stool BA composition (C). Total BA composition, total primary ratio, total secondary ratio, total conjugated ratio, and total sulfated ratio of stool BAs compared between non- and high-converters (D). Network analysis of the correlation between conversion rate, stool lipids, bacterial taxa, and stool total BAs (E). Red links indicate negative and green links indicate positive associations with the width indicating correlation magnitude. Red, green, and purple nodes represent bacteria, stool lipids, and serum lipids, respectively. * p = < 0.05 In contrast, when investigating serum BAs, the PCA showed that serum BA composition varied between non- and high-converters (PEREMANOVA, p-adjusted = 0.003, Appendix A.1), and between low- and high-converters (PEREMANOVA, p-adjusted = 0.003) (Figure 7A). However, there was no difference between non- and low-converters (PEREMANOVA, p-adjusted > 0.05, Figure 7A, Appendix A.1). Comparing the types of serum BAs showed that high-converters had increased sulfated BAs (Mann Whitney U, adjusted p = 0.019) compared to non-converters (Figure 7B-v). However, total, primary, secondary, and conjugated BA ratios didn’t significantly vary between converter groups (Figure 7B-i, ii, iii, iv). Results of correlation network analysis demonstrated that cholestanol and members of Ruminococcaceae (both predominant in high-converters) were negatively associated with total BAs in serum while stigmasterol (predominant in non-converters) was positively associated with total BAs in serum (Figure 7C). High-converters exhibited positive correlations with sulfated BAs in serum (Figure 7C). 65 | P a g e Figure 7. Serum bile acid among converter groups and their relationship with stool and serum lipids, stool bile acids, and bacterial taxa. Principal Component Analysis (PCA) with feature importance for serum BA composition (A). Total BA composition, total primary ratio, total secondary ratio, total conjugated ratio, and total sulfated ratio of serum BAs compared between non- and high-converters (B). Correlation network analysis of conversion rate, stool and serum lipids and BAs, and bacterial taxa (C). Red links indicate negative and green links indicate positive associations with the width indicating correlation magnitude. Purple, green, red, brown, 66 | P a g e and blue nodes represent bacteria, stool lipids, serum lipids, serum BAs, and stool BAs, respectively. * p = < 0.05, ** p = < 0.01 2.3.4 Short-Chain Fatty Acids in Stool Among Cholesterol Converters: Examining Correlations with Conversion Rates, Stool and Serum Lipids, Bile Acids, and Key Bacterial Genera Next, our investigation extended to the study of short-chain fatty acids (SCFAs), amplified by their role in intestinal barrier integrity, modulating immune cell responses, influencing inflammation, and affecting serum lipid levels (Lu et al., 2022). While the PCA analysis indicated that the stool SCFA composition remains consistent across converters (PERMANOVA, p-adjusted > 0.05, Appendix C.1A, Appendix A.1), a closer examination of SCFA relative abundances revealed some differences. Specifically, propionic acid (Kruskal-Wallis, p-adjusted = 0.003) and 3-methylvaleric acid (Kruskal-Wallis, p-adjusted = 0.049) were increased in non-converters compared to high-converters (Appendix C.1B). Network analysis revealed that bacterial taxa predominant in high-converters were positively associated with most SCFAs (Appendix C.1C), while serum non-HDL cholesterol displayed negative associations with SCFAs, including isovaleric acid, 2-methylbutyric acid, and isobutyric acid (Appendix C.1C). 2.3.5 Exploring Causal Links: The Role of Identified Bacterial Taxa and Key Metabolites Next, using metagenomic sequencing, we aimed to investigate the causal relationships between specific bacterial species and the observed variations in lipid and bile profiles. This was essential to accurately identify the species and strains of bacteria associated with high-converters. After comparing detection rates, it was apparent that MetaPhlAn had several missing 67 | P a g e taxa detected at the 16S level. Thus, we selected the Kraken2 pipeline for further analysis due to its generation of increased feature count and higher genus richness, as demonstrated by our rarefaction plots and phylogenetic trees (Appendix D.1A, B). We then employed RF and LEfSe analyses to identify the key bacterial species distinguishing non- and high-converters. At the species level, Akkermansia muciniphila, Bifidobacterium adolescentis, Bacteroides ovatus, and Lachnospira eligens were identified as significant discriminators (Figure 8A-i, Appendix E.1). However, A. muciniphila was the sole species significantly increased in high-converters. We also examined the top bacterial taxa from 16S sequencing using the Kraken2 pipeline (Figure 8A-ii), confirming A. muciniphila as the primary species distinguishing high-converters (Mann-Whitney U, p-adjusted = 0.03, Figure 8B). 2.3.6 In Vitro Exploration of Causal Relationships: Assessing A. muciniphila's Ability to Produce Coprostanol and Desulfate Bile Acids Next, we aimed to explore the causal connections between A. muciniphila strains and metabolic changes. Metagenomic analysis pinpointed three key A. muciniphila strains, among which two showed a close resemblance to the strain available in our laboratory obtained from ATCC (Appendix D.1C). Thus, A. muciniphila (ATCC BAA-835) was grown in mucin BHI and exposed to various concentrations of cholesterol and coprostanol (Figure 8C-i). Assessing their optical density (OD) for 48 hrs showed that the exposure groups all had similar effects on A. muciniphila’s growth rate and didn’t vary significantly from the control group containing no cholesterol or coprostanol (Figure 8C-ii). Media were subsequently analyzed for coprostanol quantification via UPLC-MRM/MS to assess A. muciniphila’s cholesterol to coprostanol conversion capability. A. muciniphila led to increased coprostanol production with 0.04% 68 | P a g e cholesterol exposure after 48 and 72 hrs compared to control samples (Mann-Whitney U, p = 0.04, Figure 8C-iii). The ability of A. muciniphila to desulfate BAs was also assessed with the addition of various concentrations of sulfated BAs including cholic acid 3-sulfate and chenodeoxycholic acid 3-sulfate (Figure 8D-i). The sulfated BAs had no inhibitory effects on the growth rate of A. muciniphila (Figure 8D-ii). Media were also analyzed using UPLC-MRM/MS for CA and CDCA which revealed no significant BA desulfation abilities for this particular purchased strain of A. muciniphila (Figure 8D-iii). 69 | P a g e 70 | P a g e Figure 8. Metagenomic sequencing and in vitro assay assessing Akkermansia muciniphila’s ability to convert cholesterol into coprostanol and desulfate bile acids. Random forest and LEfSe analysis using the Kraken2 pipeline to identify the top bacterial taxa that can differentiate non- and high-converters (A-i), as well as the top bacterial taxa from 16S analysis identified in the Kraken2 pipeline (A-ii). The red bacterial taxa were significantly increased in high-converters. A. muciniphila compared between non- and high-converters (B) (* p-adjusted < 0.05). In vitro assay to assess A. muciniphila’s ability to convert cholesterol to coprostanol (C-i). The effect of various concentrations of cholesterol and coprostanol (0.01, 0.02, and 0.04%) on the growth of A. muciniphila (C-ii). After incubation at 37 °C, the optical density was measured (OD600) at 3-minute intervals over 48 hrs (performed in triplicate). Coprostanol quantification of media samples containing A. muciniphila exposed to various concentrations of cholesterol for 72 hrs (C-iii). Controls (NC-0, 0.01, 0.02, and 0.04% of cholesterol) contained no A. muciniphila. In vitro assay to assess A. muciniphila’s ability to desulfate BAs (D-i). The effect of various concentrations of sulfated BAs (cholic acid 3-sulfate and chenodeoxycholic 3-sulfate; 5, 10, and 20 μmol/ml) on the growth of A. muciniphila (D-ii). Sulfated and desulfated BA quantification of media samples containing A. muciniphila exposed to various concentrations of cholic acid 3-sulfate and chenodeoxycholic 3-sulfate for 72 hrs (D-ii). Controls (NC) contained sulfated BAs but no A. muciniphila. Error bars indicate standard deviation. * p < 0.05. CDCA, chenodeoxycholic acid; CA, cholic acid. Parts of figure created with BioRender.com. 2.3.7 Stool Mucus Analysis and Glycomics Profiling Given the known role of A. muciniphila as a mucus degrader and its higher abundance in high-converters, our study aimed to investigate whether significant differences existed at the mucus level between converters. Recent studies have shown that mucus is strongly adherent to 71 | P a g e feces, including in humans and therefore amenable to non-invasive imaging and biochemical characterization (Fancy et al., 2024). Thus, to visualize the mucus structure and stool microbiota, as well as assess the mucus continuity and thickness variation of samples compared between converters, we dually stained Carnoy’s fixed fecal sections for MUC2 via immunostaining, and bacteria via fluorescene in situ hybridization (FISH) using a universal bacterial probe, EUB338. MUC2:FISH staining showed a clear continuous mucus coating the fecal samples and encapsulating the microbiota (Figure 9A, Appendix F.1). However, the mucus thickness did not vary significantly among converters (Kruskal-Wallis, p > 0.05, Figure 9B-i). Fecal-adherent mucus was then extracted, semi-purified, and quantified, which showed that low-converters had increased protein concentration compared to high-converters (Kruskal-Wallis, p-adjusted = 0.003) and non-converters (Kruskal-Wallis, p-adjusted = 0.001) (Figure 9B-ii). Subsequently, using in-gel Alcian Blue (AB) staining, we visualized the human fecal MUC2 and noted high-molecular AB-positive bands, ~ 1- 2 MDa in size suggesting successful extraction of the oligosaccharide-rich MUC2 (Figure 9A-iii). MUC2 is hyper O-glycosylated, thus it is important to characterize the O-linked oligosaccharides due to their influence on the structural and functional properties of MUC2 (Bergstrom & Xia, 2022). When assessing the mucin glycans, the PCA showed that the relative abundances of six distinct glycan classes including neutral, fucosylated, sialylated, sulfated, fucosylated and sialylated, and fucosylated and sulfated varied significantly between non- and high-converters (PEREMANOVA, p-adjusted = 0.048, Appendix A.1), as well as between low- and non-converters (PEREMANOVA, p-adjusted = 0.036, Appendix A.1) (Figure 9C). However, there was no difference between non- and low-converters (PEREMANOVA, p-adjusted > 0.05, Figure 9C, Appendix A.1). Interestingly, total glycan sialylation was increased 72 | P a g e among high-converters compared to non-converters (Mann-Whitney U, p-adjusted = 0.019, Figure 9D-iii). Total neutral glycans were also increased in low-converters compared to high-converters (Kruskal-Wallis, p-adjusted = 0.008, Figure 9D-i); however, no significant differences were observed for fucosylated, sulfated, fucosylated and sialylated or fucosylated and sulfated glycan classes among converter groups (Kruskal-Wallis, p-adjusted > 0.05, Figure 9D-ii, iv, v, and vi). To observe if any unique glycans were contributing to these differences, we analyzed the relative abundances of individual glycans. The PCA for all glycans showed that the communities did not vary significantly between converters (PEREMANOVA, p-adjusted > 0.05, Figure 9E, Appendix A.1). Comparing the top glycans differentiating converters showed that one HexNAc2HexNeu5Ac was significantly increased among high-converters compared to non-converters (Mann-Whitney U, p-adjusted = 0.04, Figure 9F). Next, we wanted to assess the sialylated glycans further since they were significantly increased in high-converters (Figure 9D-iii). Sialic acids were removed from mucus samples using mild acid hydrolysis, a procedure that preserves physiologically-relevant O-acetate esters that are not amenable to detection by O-glycomic protocols. The PCA for sialic acids showed that the communities did not vary significantly between converters (PEREMANOVA, p-adjusted > 0.05, Figure 9G, Appendix A.1); however, the acetylated sialic acid Neu5,9Ac2 was significantly increased in high-converters (t-test, p-adjusted = 0.002, Figure 9H) and was positively associated with members of Ruminococcaceae (Figure 9I). The heatmap of sialic acids and top bacterial taxa also showed that Neu5Ac was positively associated with Akkermansia spp., while the tri-O-acetylated species Neu5Ac3_1, Neu5Ac3_2, Neu5Ac3_3, and Neu5Ac3_4 were negatively correlated with Akkermansia spp. (Figure 9I). 73 | P a g e 74 | P a g e Figure 9. Mucus assessment and glycomics. MUC2:FISH-stained images of fecal mucin compared between non- and high-converters (A). Mucus thickness along the distance of fecal periphery compared between non- and high-converters (B-i). Protein quantification of extracted fecal-adherent mucus samples compared between converters (B-ii). Alcian blue staining of fecal-adherent mucin-purified from stool, where the arrow indicates high molecular weight MUC2. (B-iii). Principal component analysis (PCA) with feature importance for six distinct glycan classes (C), and relative abundance of glycan classes compared between converters (D). PCA with feature importance for all individual glycans (E). Top glycans from PCA compared between non- and high-converters (F). PCA with feature importance for sialic acids (G). Sialic acids compared between non- and high-converters (H). Heatmap of sialic acids and bacterial taxa differentiating non- and high-converters (I). * p = < 0.05, ** p = < 0.01 2.4 Discussion In our investigation into the regulatory factors affecting luminal cholesterol metabolism in healthy individuals, we discovered varied rates of cholesterol to coprostanol conversion, leading to non-, low-, and high-converter groups. The prevalence of A. muciniphila, identified as a potential probiotic for mitigating metabolic diseases such as CVD (Lakshmanan, Murugesan, Al Khodor, & Terranegra, 2022; Xue et al., 2023; Yan, Sheng, & Li, 2021) and IBD (Zheng et al., 2023), was significantly greater in high-converters. This finding, confirmed by metagenomic sequencing and further demonstrated through in vitro assays, underscores the crucial role of A. muciniphila in the gut flora, particularly its ability to increase cholesterol to coprostanol conversion, thereby potentially aiding in the prevention of CVD and IBD. Further analysis of gut microbiota composition revealed that high-converters harboured beneficial taxa such as Oscillospira spp., Ruminococcaceae, and Christensenellaceae, all linked with anti-atherogenic 75 | P a g e and IBD protective properties (Earley et al., 2019; Konikoff & Gophna, 2016; Li, Lin, Vanhoutte, Woo, & Xu, 2016; Relizani et al., 2022; Sokol et al., 2008; Waters & Ley, 2019; Zhan et al., 2023). In contrast, non-converters predominantly contained bacteria like Enterobacteriaceae and Enterocloster, opportunistic pathogens linked to CVD and IBD (Figure 10) (Baldelli, Scaldaferri, Putignani, & Del Chierico, 2021; Zeng et al., 2019). This distinction of microbial populations among converter groups indicates more than just ecological diversity, but potentially points to certain metabolic pathways being more active in one group than the other. In addition, our findings indicated that high-converters exhibited reduced levels of luminal sulfated BAs, positively correlated with A. muciniphila. Sulfation of BAs enhances their solubility by adding a negative charge to BAs, reduces their absorption in the intestines, and enhances their elimination through stool and urine (Alnouti, 2009). Thus, sulfation serves as a key detoxification route for BAs. In humans, while only a small proportion of BAs in bile and serum are sulfated, more than 70% of BAs in urine are sulfated, indicating their efficient elimination (Alnouti, 2009). We hypothesize that high-converters may have an increased capacity to desulfate luminal BAs while non-converters may be lacking the necessary gut microbiota (i.e., decreased bile salt hydrolase (BSH) activity) required for desulfation and conversion to secondary BAs (Figure 10), which have been associated with decreased cholesterol, triglyceride, and inflammatory markers in the gut (Jia, Zou, Han, Bae, & Jeon, 2023; Kuno, Hirayama-Kurogi, Ito, & Ohtsuki, 2018). Interestingly, past studies have shown that IBD patients with dysbiosis had a 15% increase in fecal sulfated BAs (Duboc et al., 2013), mirroring what we observed in our non-converter groups. Similar findings have also shown that stool samples of IBD patients had decreased secondary BAs and increased sulfated BAs, highlighting 76 | P a g e the essential role of the gut microbiota in deconjugation, dehydroxylation, and desulfation of BAs (Franzosa et al., 2019). Building upon the findings of increased A. muciniphila abundance in high-converters, our study also focused on the potential changes at the mucus level. Given A. muciniphila's role as a mucus degrader, we aimed to determine if this increased bacterial activity could lead to discernible differences in the mucus composition between converters. Indeed, while our analysis showed no significant variation in gut mucus thickness and structure across converters, it revealed a distinct glycan community composition. Notably, high-converters exhibited increased glycan sialylation. Interestingly, A. muciniphila expresses enzymes called sialidases which can cleave sialic acids and utilize them as nutrient sources, or aid with enhanced adherence to cells (Juge, Tailford, & Owen, 2016). The increased sialylation observed in high-converters could potentially enhance the integrity of the gut barrier, a finding supported by Yao et al. (2022), who demonstrated that mucus sialylation is essential for maintaining its integrity and protecting against bacterial degradation (Yao et al., 2022). Yao et al. (2022) also demonstrated that decreased sialylation may cause defective mucus proteins and IBD (Yao et al., 2022), which further supports our hypothesis that non-converters may be prone to developing IBD. Finally, when assessing the serum, contrary to previous studies (Jia, Zou, Han, Bae, & Jeon, 2023; Sekimoto, Shimada, Makanishi, Nakano, & Katayama, 1983), our results did not corroborate the inverse relationship between coprostanol production and serum cholesterol levels. This could be attributed to our focus on healthy adults and the relatively narrow range of variations within this group. Nonetheless, we observed associations between certain gut bacteria and serum cholesterol. For instance, Oscillospira spp. and Ruminococcaceae, prevalent in high- 77 | P a g e converters, have been linked to beneficial effects on serum lipid profiles, potentially influencing cardiovascular health (Earley et al., 2019; Li, Lin, Vanhoutte, Woo, & Xu, 2016; Sokol et al., 2008; Waters & Ley, 2019). Conversely, the presence of bacteria associated with negative health outcomes in non-converters, such as Enterobacteriaceae and Enterocloster (Baldelli, Scaldaferri, Putignani, & Del Chierico, 2021; Zeng et al., 2019), might impact serum cholesterol levels in a way that predisposes to CVD and IBD. We observed that bacterial taxa prevalent in high-converters reduced triglycerides and non-HDL cholesterol while enhancing HDL cholesterol which has a protective effect against CVD. In addition, our data revealed an inverse relationship between sulfated BAs in the gut and in serum. Non-converters exhibited decreased serum sulfated BAs which can be a marker of underlying metabolic imbalances or liver stress, potentially leading to a higher risk of hepatobiliary diseases and associated cardiovascular conditions (Alnouti, 2009). 78 | P a g e Figure 10. Multifaceted mechanisms involved in luminal and systemic cholesterol dynamics compared between non- and high-converters. Distinct lipid profiles and gut community structures compared between non- and high-converter groups reveal the potential for non-converters to develop hypercholesterolemia and CVD, as well as IBD. The distinct dominant bacterial taxa among converters also reveal the metabolite variations with non-converters exhibiting a diminished capacity to desulfate fecal bile acids, perhaps due to reduced gut microbiota with BSH activity, leading to decreased sulfated bile acids in serum and potentially increased inflammation. Assessing the mucin glycans revealed decreased sialylation in non-converters. Overall, the disturbed gut environment of non-converters shows the potential gut barrier dysfunction which can lead to bacterial translocation and increased inflammation. 79 | P a g e Abbreviations: LDL, Low-Density Lipoprotein Cholesterol; HDL, High-Density Lipoprotein Cholesterol; TG, triglyceride; BAs, bile acids; 1° BAs, primary bile acids; 2° BAs, secondary bile acids; CDCA, chenodeoxycholic acid; CA, cholic acid; LCA, lithocholic acid; DCA, deoxycholic acid; T/G, taurine/glycine; BSH, bile salt hydrolase; SCFAs, short chain fatty acids. Figure created using BioRender.com. 2.5 Summary In conclusion, our investigation into cholesterol metabolism has revealed the pivotal roles of gut microbiota, particularly A. muciniphila, in the conversion of cholesterol to coprostanol. This study extends the existing understanding, which suggested that increased conversion might lower the risk of hypercholesterolemia and subsequently CVD. However, our findings suggest that the implications of this conversion process may extend beyond CVD, with potential links to IBD. High-converters, marked by a greater presence of A. muciniphila, not only demonstrated increased efficiency for cholesterol conversion but also exhibited significant changes in BA metabolism, indicated by increased luminal BA desulfation. These alterations suggest a unique metabolic environment with potential systemic health implications. Moreover, for the first time, we revealed that high-converters had enhanced sialylation, crucial for maintaining the integrity and functionality of the gut barrier. Our study, therefore, contributes to a more comprehensive understanding of how changes in gut microbiota and cholesterol metabolism can influence not just cardiovascular health but also conditions such as IBD, paving the way for novel therapeutic approaches. As we look to the future, our research points to the need for expanded studies with larger and more diverse populations to further understand these mechanisms. Crucial areas for further 80 | P a g e exploration include the use of animal models, gut-on-a-chip and organoid studies to dissect the specific pathways and interactions at play. These innovative approaches can simulate the human gut environment, allowing for a deeper investigation into how gut microbiota impact cholesterol metabolism. Such strategies could extend beyond targeting gut microbiota, also encompassing the manipulation of mucus glycans. Targeting these elements may offer novel approaches for managing hypercholesterolemia and reducing the risk of CVD and IBD. 81 | P a g e Chapter 3: Long-Term Impact of Childhood Adversity on the Gut Microbiome of Nursing Students 3.1 Introduction Adverse childhood experiences (ACEs) encompass abuse (emotional, physical, and sexual), neglect, and household dysfunction (Anda, Butchart, Felitti, & Brown, 2010; Cronholm et al., 2015; Felitti et al., 1998; Wade et al., 2016). Most adults have encountered at least one ACE (LaNoue, Graeber, Helitzer, & Fawcett, 2013), with prevalence at 65–87% (LaNoue, Graeber, de Hernandez, Warner, & Helitzer, 2012; M., Anda, Dube, Giles, & Felitti, 2003). ACE exposure has been associated with various health issues in adulthood, including cardiovascular disease (Dong et al., 2004; Felitti et al., 1998), diabetes (Felitti et al., 1998), cancer (Brown et al., 2010; van der Meer, van Duijn, Wolterbeek, & Tibben, 2012), depression (Chapman et al., 2004; Jewkes, Dunkle, Nduna, Jama, & Puren, 2010), obesity, irritable bowel disease (IBD) (Dube et al., 2009), sexually transmitted infections (Hillis, Anda, Felitti, Nordenberg, & Marchbanks, 2000; Jewkes, Dunkle, Nduna, Jama, & Puren, 2010), and premature mortality in adulthood (Brown & Anda, 2009). An ACE history is also associated with risky behaviors such as smoking, inactivity, unsafe sexual practices (Brown & Anda, 2009; Hillis, Anda, Felitti, Nordenberg, & Marchbanks, 2000), and substance use disorders (McLaughlin et al., 2012). Social, behavioral, and environmental factors may also moderate the effects of ACEs on the mental and physical health of individuals (Figure 11). Social support, for example, can buffer ACEs’ effects on health (Hatch, Swerbenski, & Gray, 2020), but low socioeconomic status exacerbates stress, impacting sleep, activity, diet, and nutrition (Gellis et al., 2005; Janssen, Boyce, Simpson, & Pickett, 2006). 82 | P a g e Adverse Childhood Experiences (ACEs)Adverse Health OutcomesSleepDiet MicrobiotaExerciseSocial SupportChronic StressFigure 11. Factors moderating the effects of ACEs on the mental and physical health of individuals. The gut microbiome, influenced by endogenous and exogenous factors and life events such as diet, age, stress, genetics, disease, and antibiotics, varies significantly among individuals (Cowan, Dinan, & Cryan, 2020; RodrÍguez et al., 2015). The early years of childhood play a crucial role in shaping the gut microbiome, impacting biological and behavioral outcomes throughout life (Tanaka & Nakayama, 2017). ACEs may also permanently affect the gut microbiome’s diversity and composition in adulthood. For example, past studies of rodents have shown that early life stress can alter the gut microbiome (Jašarević, Howard, Misic, Beiting, & Bale, 2017; Jašarević, Howerton, Howard, & Bale, 2015). Flannery et al. (2020) also demonstrated that caregiving behaviors can modify children’s gut microbiome, influencing both taxonomic and functional compositions (Flannery et al., 2020). In addition, a history of ACE may lead to impaired immune function (Carpenter et al., 2010; Danese, Pariante, Caspi, Taylor, & Poulton, 2007; Fagundes, Glaser, & Kiecolt-Glaser, 2013) and dysregulated hypothalamic pituitary adrenal (HPA) axis function in adulthood (Ehrlich, Ross, Chen, & Miller, 2016; 83 | P a g e Lovallo, Farag, Sorocco, Cohoon, & Vincent, 2012). Excitingly, emerging research has revealed the interconnectedness of the gut microbiota, the brain, and various aspects of the gut–brain axis (Chang, Wei, & Hashimoto, 2022; Mayer, Savidge, & Shulman, 2014). In summary, the gut microbiome’s role in regulating immune homeostasis and its symbiotic relationship with the immune system suggests its potential role as a mediator in the ACE–neuroendocrine–immune function link (Hantsoo et al., 2019; Nusslock & Miller, 2016; Zheng, Liwinski, & Elinav, 2020). The lifetime impacts of ACE can be even more critical for individuals with increased occupational stress (Huerta-Franco et al., 2013). Nursing is an occupation with an inherently increased exposure to stressors that start at nursing school. Nursing students, for example, often face a heavy academic workload, information overload, rigorous exams, and learning of complex skills in a fast-paced environment, along with long and irregular work hours while caring for patients in clinical settings (Jimenez, Navia-Osorio, M., & Diaz, 2010; Lavoie-Tremblay, Sanzone, Aubé, & Paquet, 2021). Rella et al. (2009) conducted a study investigating fatigue and burnout among nursing students and found that 20% of graduates reported serious fatigue/stress and depression (Rella, Winwood, & Lushingtoon, 2009). The presence of ACEs in their lives can exacerbate this risk even further, as studies have shown the association of high ACE with increased burnout severity and depressive symptoms among nursing students (McKee-Lopez, Robbins, Provencio-Vasquez, & Olvera, 2019). Additionally, research suggests that the gut microbiota may play a significant role in influencing the development and severity of depression (Cheung et al., 2019; Chevalier et al., 2020; Limbana, Khan, & Eskander, 2020; Petrak et al., 2022). For this reason, we hypothesized that the gut microbiota could exacerbate the impact of ACEs on nursing students’ mental health. Although research on stress among nursing students and nurses is abundant, no studies have examined the long-lasting impact of ACEs and 84 | P a g e considered the relationship between ACEs, the gut microbiota and its functions, and the overall health of nursing students. This insight could support gut–microbiome pathways to prevent depression-related challenges among nursing students, as these conditions may persist or worsen post-graduation and can contribute to increased healthcare costs and lower quality of healthcare (Gibbons, 2010; Hwang & Kim, 2022; Njim et al., 2020). To address these gaps, this study aimed to examine the impact of ACE exposure on the mental and physical health of nursing students. Specifically, we sought to determine whether ACE exposure has long-term effects on the gut microbiota and its function during adulthood. Lastly, we aimed to identify potential factors moderating the effects of ACEs on the gut microbiome, which could in turn serve as interventional targets to mitigate the lifelong consequences of ACE. 3.2 Materials and Methods 3.2.1 Study design and participant recruitment This study was part of a larger prospective cohort study. Male and female participants (n = 150) were selected from an existing cohort of nursing students enrolled in the Bachelor of Science in Nursing (BSN) program at the University of Texas at El Paso (UTEP) who participated in the Nurse Engagement and Wellness Study (NEWS), a prospective cohort study examining how social, behavioral, and environmental factors may moderate the effects of stress experienced by nursing students and new nurses (Olvera et al., 2019). Media outlets, posters, flyers, emails, and information sessions held within classes were used to recruit eligible participants, and all participants provided written informed consent at the Biobehavioral Research Laboratory at UTEP. In addition, any participant who transferred to a different 85 | P a g e university, dropped out of the BSN program, did not graduate from the BSN program, was pregnant or lactating, or had been treated with antibiotics within the month prior to providing a stool sample was excluded from this study. This study was approved by review boards from the Harvard T.H. Chan School of Public Health (16-0080), UTEP (857149-1), and the University of British Columbia (UBC) (H21-03481). All research methods were performed in accordance with the relevant guidelines and regulations. 3.2.2 Stool collection and analyses Sample collection took place from February to December 2018. Participants donated stool samples using a self-collected stool kit. This stool kit was modelled after the stool kit used by the Human Microbiome Project II and included two pre-labelled Para-Pak sampling vials. One Para-Pak vial was pre-filled with 5 mL of RNALlater preservative. The second Para-Pak vial was pre-filled with 10 mL of 20% glycerol and 2 mL of acid-washed glass beads for bacterial strain isolation and analyses. Participants placed a teaspoon-sized sample of stool (approximately 1 g) into each sampling tube, stored the samples in a designated package within their personal freezer, and then delivered the package to the Biobehavioral Research Laboratory at UTEP within 24 h. Upon arrival at the lab, the stool samples were stored at −80 °C. Once all stool samples had been processed, the samples were stored on dry ice and shipped to the Massachusetts Host-Microbiome Center at Brigham and Women’s Hospital (MGH CCIB DNA core) for bacterial DNA extraction and sequencing. Following the extraction and purification of DNA from each stool sample, the V4 region of the 16S rRNA bacterial gene was amplified by polymerase chain reaction (PCR) using the universal bacterial primers 515F (forward primer) and 806R (reverse primer) as described previously (Caporaso et al., 2011; Kozich, Westcott, Baxter, Highlander, & Schloss, 2013). Equal amounts of DNA from each purified PCR product 86 | P a g e were pooled together to generate an aggregated library for downstream processing. Illumina MiSeq sequencing was performed following standard Illumina MiSeq operation procedures. Demultiplexed, paired-end reads were generated for each sample using the default parameters of the Illumina MiSeq control software and imported into Quantitative Insights into Microbial Ecology (QIIME) version 2 (2021.8) (Bolyen et al., 2019). A total of 10,202,879 raw reads were obtained, with an average of 68,019 reads per sample, and each forward and reverse read was 250 nucleotides in length. A minimum quality control score of 20 was selected: 13 bases were trimmed from the 5′ end of each read, and each read was truncated from the 3′ end at position n = 210 using the DADA2 denoise-paired method in QIIME 2 (Callahan et al., 2016). The resulting total frequency of amplicon sequence variants (ASVs) was 7,965,440, with an average frequency of 52,103 ASVs per sample. Taxonomy was assigned to each ASV using a pre-trained naïve Bayes classifier and the q2-feature-classifier plugin. This classifier was trained on the Greengenes 13_8 99% OTUs from the 515F/806R region of sequences (Bokulich et al., 2018). Similar sequences were collapsed into single-replicate sequences, or operational taxonomic units (OTUs), to the genus level. Subsequently, taxa abundances were normalized by the total number of reads sequenced in each sample. All singletons and doubletons were removed from the normalized OTU table, which was then used to predict the microbiome’s biochemical pathways abundance (MetaCyc) using phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt2) (version 2.4.1) (Douglas et al., 2020). 3.2.3 Blood collection and analysis Blood samples were also collected from participants. Serum and plasma were separated from the blood samples by centrifugation within an hour of collection and stored at −80 °C to preserve the integrity of the components for future analysis. Serum samples were then analyzed 87 | P a g e for levels of triglycerides, high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), and glucose. Inflammatory markers were assessed via serum levels of C-reactive protein (CRP) and the inflammatory cytokines interleukin (IL)-1β, IL-6, IL-8, and tumor necrosis factor alpha (TNFα). Serum samples were analyzed in duplicate wells using the Milliplex™ MultiAnalyte Profiling (MAP) Human CVD Panel 3 pre-mixed kit (EMD Millipore Corp, Billerica, MA, USA) for CRP and the Human High-Sensitivity T Cell pre-mixed kit (EMD Millipore Corp, Billerica, MA, USA) for IL-1β, IL-6, IL-8, and TNFα. The plates were read on a Luminex 200 analyzer (Luminex Corporation, Austin, TX, USA) running Milliplex Analyst Version 5.1 software (Vigene Tech Inc. Carlisle, MA, USA) [48]. Concentrations for each biomarker were calculated with reference to a five-point best-fitting standard curve. 3.2.4 Demographic and collected health endpoints Specific information pertaining to the different study assessments performed (health endpoints, biomarkers, life stress exposure, behaviors and personal traits, social factors, indicators of engagement and performance, and environmental exposure via clinical measures, biological sample collection, and self-reports) in the NEWS has been described in detail elsewhere (Olvera et al., 2019). In short, participants were required to complete a series of self-report questionnaires including a demographic questionnaire, the Adverse Childhood Experiences Questionnaire (ACE-Q), and the PHQ9 questionnaire. All questionnaires were completed anonymously in an online format during the first 4 weeks of the participants’ BSN program. Data from incomplete questionnaires were excluded from the analysis. Each participant also provided baseline demographic information including gender, race, ethnicity, and age. 3.2.5 Childhood adversity 88 | P a g e Upon arrival at the Biobehavioral Research Laboratory at UTEP, each participant completed the ACE-Q (approximately 20 min to complete on average) to assess exposure to adverse events that occurred before the age of 18, such as childhood maltreatment and neglect, household dysfunction, and abusive parenting (Felitti et al., 1998). The Centers for Disease Control and Prevention (CDC) have expanded the original seven-item ACE-Q to address the current ten central ACE categories recognized by the CDC. These ten ACE categories include three types of abuse, two types of neglect, and five types of family dysfunction (CDC, 2021). ACE scores are calculated by adding the number of different ACE categories reported by an individual. Thus, ACE scores may range from 0 to 10 points and reflect the occurrence of an adverse event, not the severity or frequency of the event. ACE scores greater than or equal to 3 were considered “High ACE” and ACE scores less than 3 were considered “Low ACE” in this study. These parameters were based on the sample distribution. 3.2.6 Depression questionnaires Depression was measured using the patient health (PHQ9) questionnaire. The PHQ9 questionnaire (approximately <10 min to complete) consists of the nine criteria on which the diagnosis of DSM-IV depressive disorder is based. Results of the PHQ-9 were scored as follows: a score of 5–9 was considered minimal depression; 10–15 was considered mild major depression; 15–19 was considered moderate major depression; and greater than or equal to 20 was considered severe major depression. 3.2.7 Statistical analysis All collected and measured data were utilized to test whether any features were able to differentiate between ACE groups. Specifically, a dimensionality reduction model using principal 89 | P a g e component analysis (PCA) was used to compute the features which best described the principal components. The top features from this analysis were selected and, after checking normality (using the Shapiro–Wilk test), the Mann–Whitney U test was used to compare the features between ACE groups. All p-values less than 0.05 after false discovery rate (FDR) correction were considered significant. Using 16S rRNA sequences, the Shannon diversity index for each sample was determined using the normalized OTU table with the singletons and doubletons removed and the Vegan: Community Ecology Package (Oksanen et al., 2022) within R (version 2021.09.1). The R packages ggplot2 (Wickham, 2009), preseqR (Deng, Daley, & Smith, 2018), and reshape2 (Wickham, 2020) were used to construct a boxplot, and the t-test was performed to determine if any significant differences in alpha diversity existed within the data. Differences among bacterial community structures between samples (beta diversity) were determined using Bray–Curtis Dissimilarity from the Vegan: Community Ecology Package within R (version 4.0.3) (Oksanen et al., 2022). Significant differences in beta diversity were evaluated using the ANOSIM test, and any p-value less than 0.05 was considered significant. To test whether certain gut microbiota and functional biochemical pathways were predictive of ACE, a random forest (RF) model (Cutler et al., 2007) was trained on high- and low-ACE samples at the genus level, and again separately for the biochemical pathways using the Python library Scikit-learn (Pedregosa et al., 2011). A dimensionality reduction model was utilized to ensure that the trained RF model avoided overfitting and generalized better on the data (Cao, Chua, Chong, Lee, & Gu, 2003). PCA was used to compute the features which best described the principal components. The top twenty features from this analysis were selected and used in the training process for the RF model. The leave-one-out (LOO) cross-validation method 90 | P a g e was then implemented to assess how effectively the trained classifier generalized in the event of unseen data. All measured test errors were averaged to determine the cross-validation error value. The receiver operating characteristic (ROC) curve was plotted for each LOO dataset. Then, Fisher’s exact statistical significance test was utilized to compare the RF classifiers with the greatest validation scores and determine the differences between the predictive performances of the top RF candidates (Dietterich, 1998b). The RF model was run 100 times, and the average mean decrease in impurity (MDI) of the most notable features was determined (Louppe, Wehenkel, Sutera, & Geurts, 2013). The linear discriminant analysis (LDA) effect size (LEfSe) method was also performed to identify key taxa and biochemical pathways that may contribute to the observed differences in gut microbial communities using relative abundances (Segata et al., 2011). The LEfSe algorithm coupled the non-parametric Kruskal–Wallis test and the unpaired Wilcoxon test with LDA scores to estimate the LDA effect sizes of differentially abundant bacterial OTUs and functional biochemical pathways with statistical significance (Segata et al., 2011). The overlapping microbial taxa and biochemical pathways from RF and LEfSe analysis were then used for further analysis and comparisons between groups. Using multiple machine-learning algorithms and finding consensus between their results increases the reliability, generalization, and robustness of the analysis, reducing bias and overfitting. It allows for model selection, performance comparison, and more accurate insights into the underlying patterns and relationships within the data. Gut microbiomes and biochemical pathways were considered to be significantly different if the p-value was less than 0.05 and if the LDA score (log10) threshold was greater than 3.0. Lastly, network analysis was utilized to assess the relationships between various features differentiating low- and high-ACE groups with potential factors that could moderate the effects 91 | P a g e of ACEs. The k-nearest neighbor (KNN) algorithm based on Euclidean distance was used to estimate and substitute missing data (k = 5) (Batista & Monard, 2002). The network was constructed based on Spearman’s rank correlation coefficients whose rho values were more than 0.3 and p-adjusted < 0.05 (using FDR). 3.3 Results 3.3.1 Demographic characteristics and features differentiating ACE groups This study included a total of 150 adult male and female nursing students who were enrolled in the BSN program at UTEP and members of the original NEWS cohort (Olvera et al., 2019). The average participant’s age was 26 years (SD = 6.5), and the average BMI score was 26.3 (SD = 5.6) (Table 1). The majority of the study participants self-identified as female (83%), Hispanic (87%), and White (88%) (Table 1). Furthermore, 53% of the participants reported “High ACE” scores (≥3), and 47% of the participants reported “Low ACE” scores (<3) (Table 1). Table 1. Summary of the sample population. Dimension n Mean (%) SD Age - 150 26 6.5 BMI - 150 26.3 5.6 Gender Male 26 (17) - Female 124 (83) - Ethnicity Hispanic 130 (87) - Non-Hispanic 20 (13) - Race American Indian 5 (3) - Asian 6 (4) - African American 7 (5) - Native Hawaiian 0 (0) - White 132 (88) - ACE Low (< 3) 80 (53) - High (≥ 3) 70 (47) - 92 | P a g e n, number of students per group; SD, standard deviation; BMI, body mass index; ACE, adverse childhood experience. We observed some key differences between low- and high-ACE groups. For instance, having experienced diarrhea within the last two weeks prior to sample collection was among one of the factors differentiating ACE groups. Diarrhea was experienced by 16.7% of the low-ACE group compared with 34.4% of the high-ACE group (Figure 12A). Also, triglycerides were significantly decreased in individuals with high ACE (Mann–Whitney U; p-adjusted = 0.03), while waist-to-hip ratio (Mann–Whitney U; p-adjusted = 0.024) and depression (Mann–Whitney U; p-adjusted = 0.025) were significantly increased in individuals with high ACE. Inflammatory markers were not significantly different between ACE groups. Figure 12. Significant features differentiating low and high ACE groups. Factors including A) diarrhea episodes within two weeks prior to sample collection, B) waist-to-hip ratio, C) triglyceride (mg/dL), and D) depression index PHQ9 were compared between ACE groups. Significant differences were determined using the Mann-Whitney U test and FDR correction. * p-adjusted < 0.05. ACE, adverse childhood experience. 3.3.2 Gut microbiome diversity analysis 93 | P a g e At the genus level, no significant differences in alpha diversity, as measured by the Shannon diversity index, were observed between the ACE groups (t-test, p > 0.05, Figure 13A). However, when generating density plots based on the estimated Bray-Curtis dissimilarity values, significant differences in beta diversity were detected for the ACE groups (ANOSIM, p = 0.04, Figure 13B). Figure 13. Gut microbial diversity (genus-level). A) The alpha diversity (Shannon’s diversity index) and B) the beta diversity density plot (Bray-Curtis dissimilarity) for ACE groups were determined. Significant differences were determined using the t-test and ANOSIM for alpha and beta diversity, respectively. ACE, adverse childhood experience. 3.3.3 Bacteria differentiating ACE groups Based on the RF model, the following top ten most important bacteria were identified to differentiate ACE groups (listed in the order of decreasing average mean decrease in impurity (MDI)): Clostridium spp., members of Clostridiales, Butyricimonas spp., Ruminococcacea., Ruminococcus spp. Prevotella spp., Lachnospira spp., Bacteroides spp., members of 94 | P a g e Ruminococcaceae, and Bifidobacterium spp. (Figure 14B); however, the prediction model was determined to be not significant (Fisher’s exact test; p > 0.05) (Figure 14A). LEfSe analysis was also performed in comparison with RF to test which groups of bacteria differentiate low- and high-ACE individuals and are predictive of ACE. From the calculated LDA scores, certain taxa were identified for the “High ACE” group (Prevotella, Porphyromonas, Weissella, and Butyricimonas), and three taxa were identified for the “Low ACE” group (Lachnospira, Clostridiales, and Clostridium) (Figure 14C). The taxa with the greatest relative abundance (characterized by the LDA score with the highest magnitude) were the genera Prevotella and Lachnospira for the high- and low-ACE groups, respectively (Figure 14C). Random forest and LEfSe analysis revealed five overlapping taxa of bacteria that can differentiate between ACE groups, including Clostridium, Clostridiales, Butyricimonas, Prevotella, and Lachnospira (Figure 14D). High ACE individuals exhibited decreased levels of Clostridiales (Mann-Whitney U, p-adjusted = 0.042, Figure 14D-ii), and Lachnospira (Mann-Whitney U, p-adjusted = 0.041, Figure 14D-v), as well as increased levels of Prevotella (Mann-Whitney U, p-adjusted = 0.043, Figure 14D-iv) and Butyricimonas (Mann-Whitney U, p-adjusted = 0.008, Figure 14D-iii). However, there was no significant difference in Clostridium levels between the two groups, after correction (Mann-Whitney U, p-adjusted > 0.05, Figure 14D-i). 95 | P a g e Figure 14. RF and LEfSe analysis characterizing the significant bacterial taxa in high and low ACE groups. The implemented RF model led to A) the ROC curve displaying an AUC of 61% (p > 0.05), B) the top ten most important bacteria differentiating ACE groups, where the size of the ellipses represents the average Mean Decrease in Impurity (MDI), C) Histogram of the LDA scores (log10) calculated for taxa with differential abundances in high and low ACE individuals. The high ACE group is represented in green (1.0) and the low ACE group is represented in yellow (0.0), and D) overlapping bacterial taxa from both random forest and LEfSe compared between ACE groups using the Mann-Whitney U test and FDR correction. * p-adjusted < 0.05; ** p-adjusted < 0.01. ACE, adverse childhood experience. 3.3.4 Functional differences between ACE groups 96 | P a g e Based on the RF model trained on samples from both high and low ACE individuals, a PCA identified the top ten most important biochemical pathways that may predict ACE (listed in the order of decreasing average MDI) including PWY_1861 (formaldehyde assimilation II-assimilatory RuMP Cycle), PWY_5971 (palmitate biosynthesis II)), P124_PWY (Bifidobacterium shunt)), SULFATE-CYS-PWY (sulfate assimilation and cysteine biosynthesis), PENTOSE_P_PWY (pentose phosphate pathway), PWY_5505 (L-glutamate and L-glutamine biosynthesis), PWY_2941 (L-lysine biosynthesis II), PWY_5676 (acetyl-CoA fermentation to butanoate), PWY_7392 (taxadiene biosynthesis), and PWY_7377 (cob(II)yrinate a,c-diamide biosynthesis I) (Figure 15B); however, based on LOO cross-validation, the prediction model was not significant (Fisher’s exact test; p > 0.05) (Figure 15A). LEfSe analysis was also performed to assess the functional differences between low and high ACE individuals. From the calculated LDA scores, three functional pathways were identified for the “High ACE” group (PWY_1861 (formaldehyde assimilation II-assimilatory RuMP Cycle), PWY_5918 (superpathway of heme b biosynthesis from glutamate), and HEXITOLDEGSUPER_PWY (superpathway of hexitol degradation (bacteria)) (Figure 15C). Seven functional pathways were identified for the “Low ACE” group (DTDPRHAMSYN_PWY (dTDP-L-rhamnose biosynthesis), PANTO_PWY (phosphopantothenate biosynthesis I), TRPSYN_PWY (L-tryptophan biosynthesis), PWY_4984 (Urea cycle), GLYCOCAT_PWY (glycogen degradation I), PWY_7242 (D-fructuronate degradation), and PWY_5505 (L-glutamate and L-glutamine biosynthesis) (Figure 15C). The functional pathways with the greatest relative abundance (characterized by the LDA score with the highest magnitude) were PWY_1861 and PWY_5505 for high and low ACE groups, respectively (Figure 15C). 97 | P a g e Random forest and LEfSe analysis led to two overlapping pathways that can differentiate between ACE groups including PWY_1861 and PWY_5505 (Figure 15D). High ACE individuals had decreased PWY_5505 (L-glutamate and L-glutamine biosynthesis; Mann Whitney U; p-adjusted = 0.01) and increased PWY_1861 (formaldehyde assimilation II-assimilatory RuMP Cycle; Mann Whitney U; p-adjusted = 0.01) (Figure 15D). Figure 15. RF and LEfSe analysis characterizing the significant biochemical pathways (via PICRUSt2) in high and low ACE groups. The implemented RF model led to A) the ROC curve displaying an AUC of 52% (p > 0.05), B) the top ten most important pathways differentiating ACE groups, where the size of the ellipses represents the average Mean Decrease in Impurity (MDI), C) Histogram of the LDA scores (log10) calculated for pathways with differential abundances in high and low ACE individuals. The high ACE group is represented in green 98 | P a g e (1.0), and the low ACE group is represented in yellow (0.0), and D) overlapping pathways from both RF and LEfSe compared between ACE groups using Mann-Whitney U test and FDR correction. ** p-adjusted < 0.01. ACE, adverse childhood experience. Lastly, using network analysis (rho > 0·3 or rho < −0·3), we assessed the relationship between various features differentiating low and high ACE groups (i.e., bacterial taxa, biochemical pathways, diarrhea, depression, waist-to-hip ratio, triglyceride), with potential factors that could moderate the effects of ACEs (i.e., sleep, diet, and physical activity). We found that the bacterial genus of Prevotella, was negatively correlated with L-glutamate and L-glutamine biosynthesis (PWY-5505) (Spearman correlation; rho = 0.33; p-adjusted = 0.001), with no significant correlations with potential moderators. On the other hand, decreased sleep quality was found to be significantly related to depression (Spearman correlation; rho = 0.43; p-adjusted = 0.001), but not linked to any affected bacterial taxa and functions. 3.4 Discussion In our quest to investigate the long-lasting impacts of ACEs, we examined the gut microbiota and its functions in adulthood. We observed distinct differences between individuals with low and high ACE exposure in gut microbiota composition and the relative abundance of specific bacterial taxa. Notably, high-ACE individuals displayed an increased relative abundance of Butyricimonas spp. and Prevotella spp. while exhibiting decreased levels of Clostridiales and Lachnospira spp. These observations align with a previous study where pregnant women with a history of high ACE showed a higher abundance of Prevotella (Hantsoo et al., 2019). It is worth noting that an overabundance of certain Prevotella spp. has been associated with autoimmune diseases, chronic inflammatory conditions, hypertension, and heart failure (Iljazovic et al., 2021; 99 | P a g e Kummen et al., 2018; Larsen, 2017; Li et al., 2017), suggesting potential health implications of ACE-related microbial changes. Furthermore, we observed a negative correlation between Prevotella abundance and L-glutamate and L-glutamine biosynthesis, which were found to be decreased in the high-ACE group. Glutamine plays a vital role in maintaining intestinal tissue integrity by promoting enterocyte proliferation and inducing the expression of tight-junction proteins (Kim & Kim, 2017). Depletion of glutamine stores may occur during trauma, sepsis, and inflammatory bowel disease (IBD) (Kim & Kim, 2017), providing a potential link between stressful events in childhood and decreased gut health later in life. The higher prevalence of diarrhea among individuals in the high-ACE group suggests that changes in gut microbiota, particularly the increased abundance of Prevotella, may play a key role in gut health across the lifespan. Prevotella contributes to inflammation by producing lipopolysaccharide (LPS), which can trigger Toll-like receptor 4 (TLR-4), leading to low-grade inflammation and diarrhea-predominant irritable bowel syndrome (IBS) (Graham, Mullen, & Whelan, 2015; Wu et al., 2019). Increased diarrhea can also be attributed to the effects of stress on the gastrointestinal system. For instance, when the stress hormone corticotropin-releasing hormone (CRH) was injected into the hypothalamus of rats, it resulted in a significant 84% reduction in colonic transit time (Mönnikes, Schmidt, & Taché, 1993). Consequently, this accelerated passage of stool through the colon can lead to greater excretion of water and nutrients from the body, potentially contributing to an increase in diarrhea episodes (Osadchiy, Martin, & Mayer, 2019). Hence, the role of diarrhea in the connections between ACEs, the gut microbiota, and its functions warrants further investigation. We also observed increased depression among nursing students with high ACE, further highlighting the potential to explore the gut–brain axis in individuals with ACEs. Previous 100 | P a g e research has also shown a relationship between ACE and depression among nursing students (McKee-Lopez, Robbins, Provencio-Vasquez, & Olvera, 2019). A study performed by Chapman et al. (2004) observed a strong positive relationship between ACE score and the likelihood of developing chronic depressive disorder in adulthood (Chapman et al., 2004). In addition, previous research has suggested a potential relationship between the gut microbiome and depression, indicating that exploring the gut–brain axis may shed light on the mechanisms underlying the association between gut health and depressive disorders. For example, in a study examining the impact of ACE and caregiver stress on the gut microbiota in children (ages 5–7), researchers discovered that the microbial composition of the gut was significantly altered by socioeconomic risk, dysfunction in the relationship between the caregiver and the child, and dysregulation in the child’s behavioral patterns (Flannery et al., 2020). The increased level of Butyricmonas and reduced levels of Clostridiales have also been associated with stress (Qu et al., 2017; Toh et al., 2021) and depression (Yang et al., 2017). However, contrary to previous studies, our findings did not reveal a direct relationship between the gut microbiome and depression. This could be because ACEs may also affect the overall adaptiveness of the body’s immune response and lead to increased levels of pro-inflammatory molecules (Danese, Pariante, Caspi, Taylor, & Poulton, 2007; Hartwell et al., 2013). Gut microbiota metabolites, such as neuropeptides and short-chain fatty acids (SCFAs), play a key role in the gut–brain axis by interacting with the central nervous system, activating microglia, which modulate HPA activity and, in turn, cytokine release (Rea, Dinan, & Cryan, 2016). Interestingly, multiple rodent studies have revealed that exposure to early life stress (ELS), the functional equivalent of ACE in rodents, has been linked to increased intestinal permeability, bacterial translocation across the gut epithelial barrier, and alterations in the composition of the gut microbiota (De Palma et al., 2015; Moussaoui et al., 101 | P a g e 2016; O'Mahony et al., 2009). These changes may elicit an immune response and affect the overall body system. Therefore, further exploration of the connections between ACEs and the gut–brain axis may provide valuable insights into the mechanisms through which stress in childhood affects health and well-being across the lifespan. Lastly, our study explored potential moderators that might mitigate the lasting health effects of ACEs. Improving sleep quality may help combat depression among nursing students with high ACE, given the well-established link between depression and sleep disturbances (Agargun, Kara, & Solmaz, 1997; Paterson, Nutt, & Wilson, 2009). Sleep disturbances can have wide-ranging effects on gastrointestinal, psychiatric, and neurological health (Graham & Kyle, 2017; Hyun, Baek, & Lee, 2019; Khanijow, Prakash, Emsellem, Borum, & Doman, 2015). Notably, sleep quality has also been found to influence the gut microbiota, with circadian clock misalignment and sleep deprivation affecting the microbial community structure in the gut (Davies et al., 2014; Thaiss et al., 2016). Maltreated children, who are at an elevated risk of sleep problems, may also experience increased inflammation levels and metabolic disruptions in the gut, significantly impacting overall health (Baldwin & Danese, 2019). Therefore, enhancing sleep quality could serve as an intervention for high–ACE individuals, potentially reducing depressive symptoms and improving overall health. This research is the first study investigating the long-term effects of ACES on the gut microbiota and its associated functions in adult nursing students. Our comprehensive methodology allowed us to gain a holistic understanding of the impact of ACEs on the gut microbiota and its potential implications for nursing students’ well-being. The limitations of the study include the low racial and ethnic diversity of the student cohort, as 88% of the participants self-identified as White and 87% self-identified as Hispanic. Thus, additional studies with an 102 | P a g e increased number of participants and diverse demographic characteristics are needed to provide a more accurate representation of the general student population of the world. Furthermore, instances of ACEs are self-reported on the ACE questionnaire. Since everyone responds differently to chronic stress and trauma, instances of ACEs may be over- or under-reported on the ACE questionnaire. Therefore, ACE scores can only be used to demonstrate correlations or associations between childhood adversity and the gut microbiome and functional changes in adulthood. Future studies are encouraged to incorporate a longitudinal approach with the collection of comprehensive health histories, specifically of gastrointestinal disorders, to better understand the long-term impacts of ACEs. In addition, detailed data collection can serve as an extensive control for potential confounding factors in order to address the complex landscape of microbiome assessments. It is crucial to acknowledge that further research and comprehensive assessments are needed to fully understand the intricate interplay between ACE exposure, the gut microbiome, and various adverse health and mental health outcomes in adulthood. 3.5 Summary In conclusion, this study substantiates that adult nursing students with high ACE display a notable alteration in gut microbiota composition and functionality, with implications for intestinal health and potential associations with psychological well-being. Focusing on functional interactions within the gut microbiome and the metabolites produced provides a more comprehensive understanding of the intricate mechanisms underlying the relationship between ACE and adverse health outcomes. Such understanding can help inform potential therapeutic strategies to restore microbial balance and promote intestinal health in individuals with high ACE. Probiotic use may offer a promising approach to alleviate diarrhea symptoms associated with ACEs and improve overall gut health. Through further exploration of the gut–brain axis and 103 | P a g e potential interventions, we can pave the path for future healthcare approaches catered to individuals with high ACE, ultimately improving their well-being and quality of life. Collaboration and ongoing research are vital in fully unravelling ACE’s impact and devising effective strategies to promote optimal health for all those affected by early-life adversity. 104 | P a g e Chapter 4: The Trans-Kingdom Battle Between Donor and Recipient Gut Microbiome Influences Fecal Microbiota Transplantation Outcome 4.1 Introduction Antibiotics are the primary treatment method for Clostridioides difficile infections (CDI); however, the negative impact on the diversity, composition, and functionality of gut microbiota results in recurrent CDI (rCDI) (Bajaj et al., 2018; Pamer, 2016) requiring fecal microbiota transplantation (FMT). FMT is a strategy for the restoration of a disturbed microbial ecosystem and reinstatement of lost microbial functional networks. Although highly effective in the treatment of rCDI as well as promising in several other diseases (Bajaj et al., 2017; Moayyedi, 2008; Van Schooneveld et al., 2013), FMT carries infectious and non-infectious risks (Alang & Kelly, 2015; DeFilipp et al., 2019; Quera, Espinoza, Estay, & Rivera, 2014; Schwartz, Gluck, & Koon, 2013). In addition, under each specific disease scenario, it is crucial to understand how microbial ecosystems reassemble overtime after FMT and which microbial strains are the determining factors in this dynamic process. For rCDI treatment, the scientific lens in the past mainly had a uni-kingdom major focus on bacteria. It has been suggested that an ideal donor should have high Lachnospiraceae and Ruminococcaceae (Taur et al., 2018), which are also positively associated with secondary bile acids that inhibit CDI germination (Bajaj et al., 2018). Increased Clostridium scindens in donors has also shown a positive correlation with FMT efficacy and outcomes via the production of secondary bile acids (Buffie et al., 2015). Moreover, FMT restores short chain fatty acids (SCFAs) metabolism, with immune modulatory effects in rCDI patients (Seekatz et al., 2018). SCFAs and butyrate producing bacteria have been found to decrease the induction of proinflammatory cytokines and promote the differentiation of colonic Treg cells, leading to the attenuation of colitis in mice and humans (Atarashi et al., 2013; 105 | P a g e Crothers et al., 2018). In addition, anaerobic, endospore-forming Firmicutes are dominant members of gut microbiota that can produce SCFAs (Browne et al., 2016), which allow organisms to enter metabolically dormant states that aid in their survival and transmission to new hosts (Kearney et al., 2018). Thus, the oral delivery of SER-109, composed of sporulating bacteria, remains a promising therapeutic approach for rCDI treatment (Gerding et al., 2015; Khanna et al., 2016). Furthermore, a critical consideration for FMT efficacy and durability is that the microbial consortium of the donors is not the only key player. The existing endogenous microbiome in recipients can also play a significant role in determining the colonization of those exogenous species. For example, focusing on bacterial engraftment, Smillie et al. (Smillie et al., 2018) suggested that selective forces in the patient’s gut (host control), rather than input dose dependence (bacterial abundance in the donor and patient), determines bacterial abundance after FMT and, subsequently, its efficacy. In contrast, a number of studies suggest that FMT success is only dependent on the bacterial diversity and composition of the stool donor, leading to the proposition of the existence of FMT super-donors (Moayyedi et al., 2015; Paramsothy et al., 2017). Beyond the gut bacterium, more recently few studies have examined the role of gut mycobiome and virome on FMT efficacy. For example, Zuo and colleagues found a negative relationship between the abundance of fungi such as Candida albicans in donor stool and FMT efficacy (Zuo et al., 2018). Over the last decade, phages have gained increasing attention for therapeutic use due to their specificity (Wittebole, De Roock, & Opal, 2014). The reduction in the abundance of Caudovirales bacteriophages and an increase in Microviridae abundance, specifically higher abundance of Eel River basin pequenovirus as a potential Proteobacteria predator, were shown to be related to FMT efficacy in CDI patients (Bryson, Thurber, Correa, 106 | P a g e Orphan, & Vega Thurber, 2015; Zuo et al., 2017). Using targeted refined phage therapy, Nale et al. (Nale, Redgwell, Millard, & Clokie, 2018) used a cocktail of four C. difficile Myoviruses (CDHM1, 2, 5, and 6) to eradicate the CDI in a batch fermentation model, which suggests that a combination of bacteriophages may be needed to treat CDI. More recently, rCDI in five patients was prevented using sterile fecal filtrate, void of live bacteria (Ott et al., 2027). Contrary to these, a study by Meader et al. (Meader, Mayer, Steverding, Carding, & Narbad, 2013) showed that bacteriophages alone weren’t sufficient to eradicate CDI. These studies emphasize that in order to uncover mechanisms involved in FMT efficacy, it is fundamental to include the relative contribution of all domains and consider the microbiome-associated ecosystem heterogeneity in both donors and recipients. To this end, we specifically investigated whether FMT super-donors exists for rCDI treatment, or whether the donor-recipient compatibility and short-term fluctuations in the gut microbiomes (a combination of bacteria, fungi, archaea, and viruses) of both donors and recipients have profound implications in FMT success. 4.2 Materials and Methods 4.2.1 Study design and sample collection Seventeen adult male and female patients who received FMT for rCDI at the University of Alberta Hospital in Edmonton, Alberta, Canada, between October 2012 and November 2014 were included in this study (Millan et al., 2016). All research methods were performed in accordance with the relevant guidelines and regulations. Criteria for receiving FMT were 1) at least 2 recurrent episodes of mild to moderate CDI, or 2) at least 1 recurrent episode of CDI requiring hospitalization. This study was approved by the University of Alberta Health Research Ethics Board, and all participants provided written informed consent. Patients aged 35-85 were 107 | P a g e RecipientsPre-FMT Donors Pre-FMT D1 D2 D3 D4Post-FMT t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t1 t1 t2 t3 t4 t5 t1included; however, individuals were excluded from participating if they had been exposed to any form of antibiotics, antifungals, antivirals, or antiparasitics within the previous six months (Millan et al., 2016). More information pertaining to the patient characteristics, donor selection criteria, and screening processes have been described previously (Millan et al., 2016) (Appendix G.1). All participants received FMT by colonoscopy, with stool samples from unrelated donors registered with the Edmonton FMT program. After a failed FMT, each patient received FMT from the same donor or a different donor, depending on donor availability. Patients discontinued antibiotics for CDI 24 hours prior to FMT and took 4 L of polyethylene glycol-based bowel preparation (GoLYTELY) one day prior to FMT. Stool samples were collected from donors and patients one week prior to FMT (pre-FMT) as well as from patients one week following FMT (post-FMT). Figure 16 shows the number of donors and recipients, as well as the FMT treatment outcomes. It’s important to note that although some donors had provided multiple stool samples, these samples were provided at different time points (minimum of a one-week gap), which were then administered to the recipients (Figure 16). It has been perceived that the autocorrelation between microbiomes of stool samples of a given donor normally diminishes between 3-5 days (Gibbons, Kearney, Smillie, & Alm, 2017). Figure 16. The experimental data structure of stool samples collected. Samples were collected from 4 donors (D1 to D4) and 17 patients one week prior to FMT (pre-FMT) and patients one week following FMT (post-FMT). For D1 and D3, multiple independent samples were taken for 108 | P a g e different patients. Green line indicates a successful FMT outcome and a red line indicates a failed FMT outcome. 4.2.2 Metagenomic data collection DNA from stool samples were extracted using the Qiagen QIAamp DNA stool kit. Shotgun sequencing for metagenomics was applied using the Nextera XT DNA Sample Preparation Kit, and Illumina MiSeq platform was performed as previously described (Millan et al., 2016). Host DNA was detected and reads were removed by mapping with the GEM program to the human genome with inclusive parameters (Guo, Mahony, & Gifford, 2012). A custom Kraken database was built of whole genomes of bacteria, viruses, archaea, eukaryote, and viroids (Wood, Lu, & Langmead, 2019). The Bayesian Reestimation of Abundance with KrakEN (Bracken) algorithm was used (kmer length of 30 and read length of 100 bp) to compute the abundance of species in DNA sequences originating from each metagenomic sample (Lu, Breitwieser, Thielen, & Salzberg, 2017). Singletons, as well as those taxa occurring in only one or two samples, were removed and abundances of different microbial genera were obtained by collapsing detected taxonomies to the genus level and summing features within the same genera. Subsequently, taxa abundances were normalized by the total number of reads sequenced in each sample. 4.2.3 Statistical analysis The α-diversity (Shannon diversity index) of successful and failed FMT samples were compared for all organisms using the R package Vegan (Oksanen et al., 2022). In addition, α-diversity of bacteria related to bile acid metabolizers (Gerard, 2013), SCFA producing genera (Seekatz et al., 2018), and sporulators (Lagier, Cadoret, & Raoult, 2017) were compared for 109 | P a g e successful and failed FMT samples. The bacterial genera associated with these functions were extracted from previous studies (Gerard, 2013; Lagier, Cadoret, & Raoult, 2017; Seekatz et al., 2018). Significant differences in α-diversity were determined using the non-parametric Kruskal-Wallis and Wilcoxon signed-rank test for unpaired and paired samples (pre- and post-FMT samples of recipients), respectively, using Bonferroni correction to adjust the probability. Differences among community structures across samples (β-diversity) were calculated using the Bray-Curtis dissimilarity metric using the R package Vegan and visualized via density plots using custom python scripts (Oksanen et al., 2022). Significant differences in β-diversity across donors and recipients were evaluated using analysis of similarities (ANOSIM) (Clarke, 1993). Heatmap clustering graphs were constructed using the R pheatmap package to visualize the relative abundance of major bile acid producers in donors and recipients before and after FMT (Kolde, 2019). To test whether donor and recipient microbial composition can predict FMT outcome, we trained a Random Forest (RF) model on pre-treatment samples of both donors and recipients at the genus level (Cutler et al., 2007). The microbial taxa of both donors and recipients constitute the feature space of the model and the following steps were performed using the Python library, Scikit-learn (Pedregosa et al., 2011). As the features’ count outnumbers that of the test samples, a dimensionality reduction method was implemented so that the trained model avoids overfitting and generalizes better on the test data (Cao, Chua, Chong, Lee, & Gu, 2003). Thus, the Principal Component Analysis (PCA) was used to exploit the features which describe the principal components the most. The top 20 features from this analysis were selected to be employed in the training process of the RF model. In order to assess how well the trained classifier generalizes in case of unseen data, the Leave One Out (LOO) cross-validation method was employed. In this 110 | P a g e method, each data point was used once as a test data, while the classifier was trained on the remaining data points. Subsequently, the cross-validation error value was calculated by averaging all the measured test errors. For each LOO data subset, the Receiver Operating Characteristic (ROC) curve was plotted. Next, the RF classifiers with the highest validation scores were compared by implementing a statistical significance test. Herein, McNemar’s test was used to determine the statistical significance of the difference between the predictive performance of the top RF candidates (Dietterich, 1998a). The RF model identified to be the most precise was then employed to find the most important features in the FMT treatment outcome task. After running the model 100 times, the average Mean Decrease in Impurity (MDI) of the most important features were also calculated (Louppe, Wehenkel, Sutera, & Geurts, 2013). Subsequently, the Kruskal-Wallis test with the Bonferroni correction to adjust the probability was utilized to compare the relative abundance of the top important features across the samples. Lastly, in an attempt to evaluate our model’s performance and its generalizability, another independent dataset was used (Li et al., 2016; Vrieze et al., 2012). This dataset consisted of DNA extracted from 5 fecal samples from 3 donors, and 5 fecal samples from each of 10 FMT recipients: collected at day 0 (pre-FMT) and days 2, 14, 42 and 84 after FMT. 4.3 Results This study included seventeen adult male and female patients who received FMT for rCDI by colonoscopy from four unrelated donors. Stool samples were collected from donors and patients one week prior to FMT (pre-FMT), as well as from patients one week following FMT (post-FMT) (Figure 16). Among recipients, 9/17 patients were successfully treated with a single FMT (53% successful FMT), while 8 patients failed the first FMT and required a second 111 | P a g e procedure. There was no difference between the two groups in factors of age, sex, or duration of CDI (Millan et al., 2016) (Appendix G.1). We found no significant difference in alpha diversities of different organisms in stool samples provided by donors used for all patients whether the treatment outcome was successful or not (Kruskal-Wallis test, p>0.05, Figure 17A-E). For the recipients, no significant differences in alpha diversities were observed between successful and failed pre-FMT samples (Kruskal-Wallis test, p>0.05, Figure 17A-E). There was a significant increase in the bacterial (Figure 17A) and fungal (Figure 17C) alpha diversities (Shannon diversity index) in post-FMT stool samples after successful FMT (Wilcoxon test, p-value<0.001 and p-value<0.01, respectively), but not failed ones. No significant changes in this index were seen post-FMT in archaeal, protozoan, and viral diversities (Figure 17B, D, E). Results showed significant differences in beta diversities of all organisms in stool samples between recipients and donors pre-FMT (ANOSIM, R=0.920), but no significant differences were detected between successful and failed donors (ANOSIM, R=0.648), successful and failed recipients pre-FMT (ANOSIM, R=0.098), and failed recipients pre-FMT and post-FMT (ANOSIM, R=0.219) (Figure 17F). After successful FMT, the recipients’ microbiome composition resembled their donors (ANOSIM, R=0.595), while the composition of failed FMT recipients remained different compared to their donors (ANOSIM, R= 0.860) (Figure 17F). 112 | P a g e Shannon Diversity43210Failed FMTDonorsRecipients - Before - FMTRecipients - After - FMT(A)Shannon Diversity43210Successful FMT Failed FMTShannon Diversity43210Successful FMT Failed FMTShannon Diversity43210Successful FMT Failed FMT(C)Successful FMT(E) (F)00.050.100.150.200.25-0.4 -0.2 0.0 0.2 0.4 0.6DensityBray–Curtis DissimilarityDonors - Failed FMTDonoros - Successful FMTRecipients - Before Failed FMTRecipients - After Failed FMTRecipients - Before Successful FMTRecipients - After Successful FMTFungiViruses Bacteria Protozoa Alpha DiversityBeta Diversity*****All Organisms Successful FMTShannon Diversity43210(B)Failed FMTArchaea(D) Figure 17. Gut microbial diversity of FMT donors and recipients. The α-diversity (Shannon index) of (A) bacteria, (B) archaea, (C) fungi, (D) protozoa, and (E) viruses of donors, recipients pre- and post-FMT for successful and failed FMT outcomes of rCDI patients. Significant differences were determined using the Kruskal-Wallis and Wilcoxon signed-rank tests for unpaired and paired (i.e. when analysing pre- and post-FMT of recipients) samples, respectively, followed by Bonferroni post-hoc correction. Adjusted p-values were defined at *p<0.05, **p<0.01, ***p<0.001, and ****p<0.0001. The Beta diversity was calculated for all microorganisms (F) using the Bray-Curtis dissimilarity and analyzed using ANOSIM. We then isolated bacterial genera associated with bile acid metabolism suggested by Gerard et al. (Gerard, 2013) and SCFA production suggested by Seekatz et al. (Seekatz et al., 2018), as well as sporulating communities (Lagier, Cadoret, & Raoult, 2017), since it has been 113 | P a g e shown that they can impact FMT efficacy. Our results showed that donors had similar microbial alpha diversity (Kruskal-Wallis test, p>0.05, Figure 18A-C) and community structures for SCFA producers and sporulators (ANSIM, R=0.524 and R=0.582, respectively, Figure 18E-F). However, the community structure of bile acid metabolizers was significantly different between successful and failed FMT donors (ANOSIM, R=0.828) (Figure 18D). Specifically, bacterial bile acid metabolizers including Lactobacillus (associated with deconjugation and esterification of bile salts), Fusobacterium (associated with desulfation of bile salts), Pseudomonas (desulfation of bile salts), and Escherichia (oxidation and epimerization of bile salts) were significantly less abundant in unsuccessful donor samples (Figure 19A). Interestingly, intra-variability within donors pertaining to the abundance of bacterial bile acid metabolizers was observed (Figure 19A), which shows that donor composition can vary over time and affect FMT outcome. Focusing on recipients, our results showed that successful FMT is associated with the colonization of bile acid metabolizers, SCFA producers, and sporulating bacterial genera since the diversity (Wilcoxon signed-rank test, p=0.003, p=0.00014, and p=0.015, respectively) and community structures of associated bacteria within each community significantly increased after successful FMT (ANOSIM, R=0.670, R=0.759, and R=0.872, respectively) but not the failed ones (ANOSIM, R=0.091, R=0.117, and R=0.134, respectively) (Figure 18). Figure 19 also shows that no significant differences were detected between successful and failed recipients pre-FMT with the colonization of bile acid metabolizers, SCFA producers, and sporulating bacterial genera (Figure 19B). However, after successful FMT, the recipients’ microbiome functionality resembled their donors, while failed FMT recipients remained different compared to their donors (Figure 19C). 114 | P a g e Figure 18. Gut microbial diversity of bile acid metabolizers, SCFA producers, and sporulators. The α-diversity (Shannon index) of (A) bile acid metabolizers, (B) SCFA producers, and (C) sporulating bacteria of donors, recipients pre- and post-FMT for successful and failed FMT outcomes. Significant differences were determined using the Kruskal-Wallis and Wilcoxon signed-rank tests for unpaired and paired samples, respectively, followed by the Bonferroni post-hoc correction. Adjusted p-values were defined at *p<0.05, **p<0.01, ***p<0.001, and ****p<0.0001. The Beta diversity was also calculated using the Bray-Curtis distance-based density plots (D, E, F) and analyzed using ANOSIM. 115 | P a g e BacteroidesRuminococcusClostridiumBifidobacteriumListeriaLactobacillusFusobacteriumPseudomonasEscherichiaEubacteriumEggerthella−3−2−10123Donors - Failed FMTDonoros - Successful FMTRuminococcusClostridiumEubacteriumPseudomonasEscherichiaBacteroidesLactobacillusFusobacteriumBifidobacteriumListeriaEggerthellaRecipients - Before Failed FMTRecipients - Before Successful FMTRecipients - After Failed FMTRecipients - After Successful FMTRuminococcusClostridiumPseudomonasFusobacteriumBifidobacteriumListeriaLactobacillusEubacteriumBacteroidesEggerthellaEscherichia(A) (B) (C)D1 D3 D2 D1 D3 D4t1 t2 t3 t4 t5 t6 t1 t2 t1 t7 t8 t9 t10 t3 t4 t5 t1 Figure 19. Bile acid metabolizers of FMT donors and recipients. Heatmap of bile acid metabolizers of (A) donors (D1-D4), (B) recipients pre-FMT, and (C) recipients post-FMT for successful and failed FMT outcomes. The dendrogram shows clustering based on the relative abundances. The heatmap color (blue to red, corresponding to low to high) represents the row z-score of the relative abundance values. Each sub-block under heatmaps represent a different time point for the corresponding donor. Finally, we investigated whether the gut microbiome of donor and recipients before FMT can predict the treatment outcomes. The top 20 features from PCA analysis were selected and employed in the subsequent training process of a classification model, using samples from both donor and recipient pre-FMT at the genus level. Using LOO cross validation, the prediction model was significant (p=0.0099) (Figure 20A), with the most important genera being Desulfovibrio, Filifactor, Bacillus, Yarrowia, Odoribacter, Wigglesworthia, Oscillibacter, Intestinimonas, and Clostridiodes (Figure 20B). Furthermore, in order to visualize the impact of the top features on FMT efficiency, the relative abundance of such features was plotted for donor 116 | P a g e and recipient samples pre- and post-FMT (Figure 20C). Interestingly, the fungal genus of Yarrowia, as well as bacterial genus of Wigglesworthia, were significantly higher in pre-FMT failed recipients than pre-FMT successful recipients (Figure 19C, Kruskal-Wallis, p=0.001 and p=0.002, respectively). The donor samples that contributed to a successful FMT outcome had a higher abundance of Clostridiodes (p=0.002), Desulfovibrio (p=0.004), Odoribacter (p=0.002), and Oscillibacter (p=0.003) compared to failed FMT donors (Figure 20C), and intra-variability in the relative abundances of these genera for each donor was observed (Appendix H.1, I.1) when comparing successful and failed samples. It is important to note that, these genera were not detected in successful recipients post-FMT (Appendix H.1, I.1), indicating that long-term colonization of these genera in recipients may not be critical for FMT success. We then evaluated our model’s performance against an independent dataset (Van Schooneveld et al., 2013; Vrieze et al., 2012) interestingly the model identified similar top features including Odoribacter and Clostrioides; albeit with no statistically significant discriminatory powers. This was expected due to technical variation between studies which overshadowed the biological variation as well as the lack of full consistency between the two studies rooted in the difference in the average age or ethnicity of the two cohorts. 117 | P a g e Figure 20. Machine learning model predicting FMT outcome. The random forest model utilized led to (A) the ROC curve displaying an AUC of 98%, (B) the top 9 most important microbes (Blue ellipses represent bacteria; Yellow ellipse represent fungi) involved in FMT success prediction where the size of the ellipses represents the feature importance magnitudes (average Mean Decrease in Impurity), and (C) the relative abundance (shown on logarithmic scale) for the top 9 features of donor, and recipient samples pre- and post-FMT. 4.4 Discussion Despite long-term stability and plasticity of healthy and low to moderately disturbed gut systems (David et al., 2014a), severely damaged gut ecosystems are not self-renewing; therefore, FMT can help with restoring damaged systems through (a) the recreation of the original 118 | P a g e ecosystem (e.g., by autologous FMT) or (b) the construction of an entirely new and alternative ecosystem (e.g., by allogeneic FMT). In our study, we showed that the success of gut ecological recovery through FMT is dependent on several factors, including the donor gut microbiome (the presence of specific bacteria) as well as the pre-FMT recipient gut community structures and recovering habitat (the absence of specific bacteria and fungi) (Figure 20). In addition, short-term fluctuations in the gut microbiome of both donors and recipients have profound implications in FMT success by producing temporary changes or loss of function (see Appendix H.1, I.1; Figure 19). Therefore, the notion of the “super-donor” is oversimplified due to the observed short-term fluctuations, and a recipient’s microbiota may be just as important to consider when predicting treatment outcomes, especially in other dysbiotic conditions such as ulcerative colitis. Our results also showed that a trans-kingdom interaction between bacteria and fungi may be important to consider in FMT outcomes. Considering ecological theories on community construction and recovery after disturbance, we hypothesize that the first step of a successful FMT is the colonization of “nexus species” including members of Desulfovibrio, Odoribacter, Oscillibacter, and Clostridioides genera, as identified in two independent datasets (Figure 21). These are transient in the community development, but are ecosystem engineers that determine secondary succession trajectories of the ecosystem (Appendix H.1, I.1). For example, Odoribacter is a known SCFA producer (Goker et al., 2011). Thus, its presence in the donor and the initial transfer to recipients may contribute to decreased inflammation (Ríos-Covián et al., 2016). In addition, the class Clostridia includes many endospore-forming organisms that have the capacity to produce SCFAs (Paredes-Sabja, Torres, Setlow, & Sarker, 2008; Van den Abbeele et al., 2013), which can induce T regulatory cells and associated anti-inflammatory cytokines 119 | P a g e (Atarashi et al., 2013). Following a successful repair, the secondary succession of endogenous or exogenous bile acid metabolizers can restore microbial diversity (lost commensals) and a variety of ecosystem functions (Whisenant, 1999). Namely, when bile acid metabolizers colonize the repaired gut ecosystem, secondary bile acid concentrations, as pleiotropic signaling molecules in the gut, liver, and systemic circulation, increases (Trauner, Fickert, & Tilg, 2013). This process entails the germination of endogenous or exogenous sporulators such as Clostridia and other putative endospore formers, which are considered stress-resistant and are particularly adaptive to cross-host dissemination (Filippidou et al., 2015; Kearney et al., 2018). Aligned with the above hypothesized mechanism, donors that led to a failed FMT had reduced Fusobacterium and Pseudomonas genera, which are both capable of desulfating primary bile acids. When these genera exist, sulfation can reduce primary bile acid toxicity and increase secondary bile acid excretion via urine and feces (Alnouti, 2009). This reduced desulfation capacity in failed donor samples further perpetuates the already existing disturbed bile acid pool and inhibits successful secondary colonization for functional ecosystem restoration. Moreover, bacterial genera, which can dehydroxylate primary bile acids into secondary bile acids, are also known to produce SCFAs (Ríos-Covián et al., 2016). These gut microbiota associated metabolites, especially butyrate, are a main source of energy for colonocytes and can activate G-protein coupled receptors that regulate intestinal motility and inflammation (Jung, Park, Jeon, & Han, 2015; Ríos-Covián et al., 2016). Lack of such genera in donor samples may diminish the therapeutic potential of FMT. 120 | P a g e Less MucusLayerDysbiosisPathogenic CommunityMetabolic FunctionDisturbanceLess MucusLayerMaintainedMucus LayerPerturbation Clolonization of Transient Species 2o Succession Healthy StateDysbiosis HomeostasisPathogenic CommunityMetabolic FunctionColonizationResistanceMetabolicMutualismA) B) 1o bile acid2o bile acid SCFA2o bile acidSCFASporulators2o bile acid SCFASporulatorsWigglesworthia YarrowiaLess MucusLayerDysbiosisPathogenic CommunityMetabolic FunctionClostridiodesDesulfovibrioOdoribacterOscillibacterSuccessful FMTPerturbationFailed Clolonization of Transient Species Failed 2O SuccessionDisturbanceFailed FMTPerturbationFungi Wigglesworthia YarrowiaFungi Figure 21. Multifaceted mechanisms affecting FMT treatment outcome. FMT treatment outcome of (A) successful FMT recipients, and (B) failed FMT recipients. A successful treatment outcome includes the repair of the disturbed gut microbial ecosystem by transient colonization of 121 | P a g e nexus species followed by secondary succession of bile acid metabolizers, sporulators, and short chain fatty acid producers. A failed treatment outcome may be due to the presence of fungal and bacteria genera including Yarrowia and Wigglesworthia in recipients, minimizing the establishment of repair or successful secondary colonization for functional ecosystem restoration. However, interestingly, the presence of the Yarrowia and Wigglesworthia genera in pre-FMT recipients can act as a barrier for the establishment of repair or successful secondary colonization for functional ecosystem restoration (Figure 20C). This can be due to nutrient cycling and carbon uptake elevation by fungal activity. Moreover, Yarrowia lipolytica has been vastly studied as a non-conventional yeast species capable of synthesizing a group of metabolites, in particular lipases and other hydrolytic enzymes (Fabiszewska, Stolarzewicz, Zamojska, & Białecka-Florjańczyk, 2014). These opportunistic fungal pathogens can cause infections in immunocompromised and critically ill patients (Boyd, Wheless, Brady, & Ellis, 2017; Gouba & Drancourt, 2015; Zieniuk & Fabiszewska, 2019). To overcome this challenge, treatment targeted at these fungal elements prior to FMT may potentially enhance treatment efficacy. 4.5 Summary In summary, there have been a number of studies focusing on understanding the underlying mechanisms in FMT treatment, which can accordingly be used for the optimization of future treatments. In the past, the scientific lens mainly had a uni-kingdom major focus on bacteria, leading to the proposition of the existence of FMT “super-donors”. However, our preliminary study along with a growing number of studies (Lamendella et al., 2018; Meader, 122 | P a g e Mayer, Steverding, Carding, & Narbad, 2013; Nale, Redgwell, Millard, & Clokie, 2018; Ng et al., 2020; Zuo et al., 2017) support the existence of complex trans-kingdom interactions. We observed that the presence of specific bacteria in donors (Clostridioides spp., Desulfovibrio spp., Odoribacter spp. and Oscillibacter spp.) and the absence of fungi (Yarrowia spp.) and bacteria (Wigglesworthia spp.) in recipients prior to FMT could predict FMT success. Our study here suggests that FMT is not necessarily a ‘one stool fits all’ approach and that donor-recipient cross-kingdom microbiota interactions, along with their short-term fluctuations in the gut, bring profound implications in FMT success. 123 | P a g e Chapter 5: Concluding Remarks In this Ph.D. study, I endeavoured to investigate the role of the gut microbiome in health and disease states. First, by recruiting healthy participants, I aimed to delve into the gut’s environment, encompassing the mucus, microbiome, and metabolites in stool and blood to explore its relationship with cholesterol metabolism. This research highlighted the significant role that gut bacteria, especially A. muciniphila, play in converting cholesterol into coprostanol. Individuals with high-conversion rates of cholesterol to coprostanol had increased levels of A. muciniphila. By conducting an in vitro analysis, A. muciniphila’s causal role in this conversion process was confirmed. I had hypothesized that increasing coprostanol production (i.e., being a high-converter) could potentially lower serum cholesterol levels. However, by assessing stool and serum lipids, our findings did not validate this inverse relationship between stool coprostanol and serum cholesterol levels. However, certain bacterial taxa, present in high-converters, were negatively associated with blood lipid profiles. Exploring the metabolites revealed that although SCFA composition did not vary across converter groups as I had hypothesized, non-converts had decreased fecal bile acid desulfation, a pattern similar to those with IBD. In addition, I had hypothesized that mucus O-glycosylation patterns would be altered, which was confirmed with non-converters having decreased mucus glycan sialylation, a pattern also observed in IBD patients. This multi-disciplinary approach underscored that the effects of gut microbiota on cholesterol metabolism may affect other health issues beyond hypercholesterolemia and CVD, including conditions such as IBD. The early years of childhood play a crucial role in shaping the gut microbiome. Thus, I aimed to examine the impact of ACE exposure on the mental and physical health of adult nursing students to assess if ACE exposure could have long-term impacts on their overall health and 124 | P a g e well-being. Our study showed that the gut community structure of individuals with low ACE varied from those with high ACE. High ACE individuals had an increased abundance of Butyricimonas spp. and Prevotella spp., and decreased levels of Clostridiales and Lachnospira spp. Following this, we set to establish if any functional differences existed between low and high ACE individuals, which was indeed confirmed. Prevotella abundance correlated negatively with L-glutamate and L-glutamine biosynthesis, potentially impacting intestinal tissue integrity. The findings of this study supported my hypothesis that the high ACE group would have a disturbed gut ecosystem leading to changes in specific gut microbiota (specifically increased Prevotella as shown in previous studies), as well as changes at the functional level. However, inflammatory markers were not significantly different between ACE groups as hypothesized. In addition, nursing students with high ACEs reported increased depression as hypothesized and supported by previous studies; however, a direct gut microbiota-depression relationship was inconclusive. High ACE individuals also experienced a higher prevalence of diarrhea. Lastly, this study explored potential moderators that might mitigate the lasting health effects of ACEs. I showed that enhancing sleep quality could serve as an intervention for high ACE individuals, potentially reducing depressive symptoms and improving overall health. Our gut microbiota are also used as therapeutic frontiers for various diseases such as rCDI through FMT. Therefore, lastly, I aimed to assess the gut microbiome of donor samples prior to FMT and patients following FMT to further understand the factors involved in FMT treatment efficacy. Using metagenomic sequencing, I was hoping to step away from the uni-kingdom outlook that most studies have assessing FMT treatment. Taken together, the study results suggested that FMT outcome is not only dependent on the ecological structure of the recipients but also the interactions between the donor and recipient microbiomes at the 125 | P a g e taxonomical and functional levels. This was interesting to highlight, since many studies had focused on using super-donors for FMT treatments. However, it’s important to consider both donors and recipients’ gut environments as I showed that the presence of specific bacteria in donors (Clostridioides spp., Desulfovibrio spp., Odoribacter spp. and Oscillibacter spp.) and the absence of fungi (Yarrowia spp.) and bacteria (Wigglesworthia spp.) in recipients prior to FMT could predict FMT success. These results supported my hypothesis that in order to optimize FMT efficacy it’s important to consider all domains of life and both donors and patients. Our results also suggested a series of interlocked mechanisms for FMT success, including the repair of the disturbed gut ecosystem by transient colonization of nexus species followed by secondary succession of bile acid metabolizers, sporulators, and SCFA producers. The combined findings from this work suggest the multifaceted web of factors involved in microbiome-mediated mechanisms in health and disease states. Even in healthy individuals, we can see that the gut microbiome’s involvement in cholesterol metabolism is multifaceted, intertwined with factors such as their metabolites, as well as the gut mucus structure and glycans. In addition, to add to the complexity, we must consider the interaction between gut components and systemic levels of lipids and metabolites. As mentioned in Chapter 1, many factors can affect our gut microbiome. However, it was surprising to find that ACEs can have such long-lasting impacts on our health, especially our gut microbiota’s community structure and function. Harnessing our gut microbiota to manage various diseases through FMT treatment is also truly fascinating. However, interdisciplinary approaches are required to shed light on gut bacterial-mediated mechanisms to enable efficacious and high-precision treatments for all individuals. Therefore, this research underscores the necessity for a holistic and integrative scientific inquiry 126 | P a g e that embraces the complexity of the gut microbiome, aiming to unlock the full therapeutic potential of microbiome science for personalized medicine. 5.1 Limitations and Future Outlook In the first study, assessing the gut environment’s involvement in luminal cholesterol metabolism, we utilized both 16S rRNA and metagenomic sequencing. This is the first study to highlight Akkermansia muciniphila’s causal role in coprostanol production. In addition, no study had previously assessed the gut mucus and compared the between non- and high-converters. As we look to the future, our research points to the need for increased power and expanded studies with larger and more diverse populations to further understand these mechanisms. In addition, metagenomic sequencing was conducted on select samples. In the future, it would be great to assess a larger cohort using metagenomic sequencing to further validate these preliminary results. Through network analysis, many associations can be observed between gut bacteria, metabolites, and lipids with serum lipids and bile acids, however, it is important to note that these associations that I presented need further research to determine the causality. Furthermore, although SCFAs were assessed in stool samples, it would be interesting to also assess them in serum in order to see if any relationships exist between the gut and serum concerning cholesterol metabolism. Other metabolite assessments besides SCFAs and BAs, such as TMAO, should also be assessed in non- and high-converters to further validate the link between non-converters developing CVD. Assessment of lipoprotein(a) in serum, a risk factor for CVD, would also be interesting to compare between non- and high-converters, as well as assessing its relationship with gut microbiota and metabolites. In addition, it would be interesting to further interlink these studies and assess whether non-converters are perhaps more prone to develop rCDI. Furthermore, exploring the impact of luminal cholesterol and coprostanol on the germination and 127 | P a g e growth of C. difficile in rCDI would offer valuable insights. The saliva samples collected in this study can also be utilized to assess the gut-oral axis and to assess whether changes in oral microbiota can be observed between converter groups. Crucial areas for further exploration include the use of animal models, gut-on-a-chip and organoid studies to dissect the specific pathways and interactions at play. These innovative approaches can simulate the human gut environment, allowing for a deeper investigation into how gut microbiota impact cholesterol metabolism. Such strategies could extend beyond targeting gut microbiota, also encompassing the manipulation of mucus glycans. Targeting these elements may offer novel approaches for managing hypercholesterolemia and reducing the risk of CVD and IBD. The second study included the assessment of the impact of ACEs on the mental and physical health of adult nursing students. Limited studies suggest ACEs can have short-term effects on children's gut microbiome and pregnant women. This was the first study to assess the impact of ACE and considered the relationship between ACEs, gut microbiota and its functions, and the overall health of nursing students. The limitations of the study include the decreased racial and ethnic diversity of the student cohort, as 87.3% of the participants self-identified as White, and 86.7% of the participants self-identified as Hispanic. Thus, additional studies that include participants with more diverse demographic characteristics are needed to provide a more accurate representation of the general nursing student population in the world. Furthermore, instances of ACEs are self-reported on the ACE questionnaire. Since everyone responds differently to chronic stress and trauma, instances of ACEs may be over- or under-reported on the ACE questionnaire. Therefore, ACE scores can only be used to demonstrate correlations or associations between childhood adversity and gut dysbiosis in adulthood. Future studies are encouraged to incorporate a longitudinal approach with the collection of comprehensive health 128 | P a g e histories, specifically of gastrointestinal disorders, to better understand the long-term impacts of ACEs. In addition, detailed data collection can serve as an extensive control for potential confounding factors in order to address the complex landscape of microbiome assessments. It is crucial to acknowledge that further research and comprehensive assessments are needed to fully understand the intricate interplay between ACE exposure, the gut microbiome, and various adverse health and mental outcomes in adulthood. Focusing on the functional interactions within the gut microbiome and the metabolites produced provides a more comprehensive understanding of the intricate mechanisms underlying the relationship between ACE and adverse health outcomes. Moreover, the gut mucus assessment may potentially highlight significant differences between low and high ACE individuals. Such understanding can help inform potential therapeutic strategies to restore microbial balance and promote intestinal health in individuals with high ACE. Probiotic use may offer a promising approach to alleviate diarrhea symptoms associated with ACEs and improve overall gut health. Additionally, it would be intriguing to evaluate whether high ACE groups are more susceptible to developing rCDI, or if they are mostly non-converters. Through further exploration of the gut-brain axis and potential interventions, we can pave the path for future healthcare approaches catered to individuals with high ACE, ultimately improving their well-being and quality of life. Collaboration and ongoing research are vital in fully unravelling ACE's impact and devising effective strategies to promote optimal health for all those affected by early-life adversity. Lastly, I aimed to assess the gut microbiome of donors and patients undergoing FMT treatment for rCDI in an attempt to understand the factors involved in FMT treatment efficacy. This study uniquely assessed all domains of life instead of the typical uni-kingdom aspect of analysis, as well as considering the functional differences between donors and recipients. We 129 | P a g e believe such intertwined mechanisms should be considered as a critical design philosophy towards optimization of this treatment for rCDI as well as other dysbiosis-related conditions. FMT is not a ‘one stool fits all’ approach. Thus, a better understanding of such mechanisms can be fundamental key elements to develop adaptive, personalized microbial based strategies for restoration of the gut ecosystem. In the future, FMT could eventually be replaced by defined consortia of bacterial or single strains that have been selected based on their mode of action. It should be noted that this study had limitations including the small sample study cohort, as well as the lack of ethnic minorities within the sample population (88% Caucasian). This signifies the need for larger cohort studies that include patients with diverse demographic characteristics. Future studies with a larger sample population can further assess the preliminary mechanisms suggested in this study and eventually optimize FMT treatment for rCDI. 130 | P a g e Bibliography Afsar, B., Vaziri, N., Aslan, G., Tarim, K., and Kanbay, M. (2016). Gut hormones and gut microbiota: Implications for kidney function and hypertension. Journal of the American Society of Hypertension, 10(12), 954-961. Agargun, M. Y., Kara, H., and Solmaz, M. (1997). Sleep disturbances and suicidal behavior in patients with major depression. the Journal of Clinical Psychiatry, 58(6), 249-251. Al-Obaide, M. A. I., Singh, R., Datta, P., Rewers-Felkins, K. A., Salguero, M. V., Al-Obaidi, I., et al. (2017). Gut microbiota-dependent trimethylamine-N-oxide and serum biomarkers in patients with T2DM and advanced CKD. J Clin Med, 6(9), 86. Alang, N. and Kelly, C. (2015). Weight gain after fecal microbiota transplantation. Open Forum Infect Dis, 2(1), ofv004. Allayee, H. and Hazen, S. L. (2015). Contribution of gut bacteria to lipid levels. Circulation Research, 117(9), 750-754. Alnouti, Y. (2009). Bile acid sulfation: A pathway of bile acid elimination and detoxification. Toxicol. Sci., 108(2), 225-246. Amato, K. R., Maurice, C. F., Guillemin, K., and Giles-Vernick, T. (2019). Multi-disciplinarity in microbiome research: A challenge and opportunity to rethink causation, variability and scale. Bioessays, 41(10), e1900007. Anda, R. F., Butchart, A., Felitti, V. J., and Brown, D. W. (2010). Building a framework for global surveillance of the public health implications of adverse childhood experiences. American Journal of Preventive Medicine, 39(1), 93-98. Antharam, V. C., Li, E. C., Ishmael, A., Sharma, A., Mai, V., Rand, K. H., et al. (2013). Intestinal dysbiosis and depletion of butyrogenic bacteria in Clostridium difficile infection and nosocomial diarrhea. Journal of Clinical Microbiology, 51(9), 2884-2892. Antinozzi, M., Giffi, M., Sini, N., Gallè, F., Valeriani, F., De Vito, C., et al. (2022). Cigarette smoking and human gut microbiota in healthy adults: A systematic review. Biomedicines, 10(2), 510. Arboleya, S., Binetti, A., Salazar, N., Fernández, N., Solís, G., Hernández-Barranco, A., et al. (2012). Establishment and development of intestinal microbiota in preterm neonates. FEMS Microbiology Ecology, 79(31), 763-772. Arpaia, N., Campbell, C., Fan, X., Dikiy, S., van der Veeken, J., deRoos, P., et al. (2013). Metabolites produced by commensal bacteria promote peripheral regulatory T-cell generation. Nature, 504(7480), 451-455. Arumugam, M., Raes, J., Pelletier, E., Le Paslier, D., Yamada, T., Mende, D. R., et al. (2011). Enterotypes of the human gut microbiome. Nature, 473(7346), 174-80. Atarashi, K., Tanoue, T., Oshima, K., Suda, W., Nagano, Y., Nishikawa, H., et al. (2013). Treg induction by a rationally selected mixture of clostridia strains from the human microbiota. Nature 500(7461), 232-236. Azad, M. B., Konya, T., Maughan, H., Guttman, D. S., Field, C. J., Chari, R. S., et al. (2013). Gut microbiota of healthy Canadian infants: Profiles by mode of delivery and infant diet at 4 months. Canadian Medical Association Journal, 185(5), 385-394. Bajaj, J. S., Kakiyama, G., Savidge, T., Takei, H., Kassam, Z. A., Fagan, A., et al. (2018). Antibiotic-associated disruption of microbiota composition and function in cirrhosis is restored by fecal transplant Hepatology, 68(4), 1549-1558. Bajaj, J. S., Kassam, Z., Fagan, A., Gavis, E. A., Liu, E., Cox, I. J., et al. (2017). Fecal microbiota transplant from a rational stool donor improves hepatic encephalopathy: A randomized clinical trial. Hepatology, 66(6), 1727-1738. 131 | P a g e Baldelli, V., Scaldaferri, F., Putignani, L., and Del Chierico, F. (2021). The role of Enterobacteriaceae in gut microbiota dysbiosis in inflammatory bowel diseases. Microorganisms (Basel), 9(4), 697. Baldwin, J. R. and Danese, A. (2019). Pathways from childhood maltreatment to cardiometabolic disease: A research review. Adoption & Fostering, 43(3), 329-339. Bansil, R. and Turner, B. S. (2018). The biology of mucus: composition, synthesis and organization. Adv Drug Deliv Rev 20, 124, 3-15. Batista, G. E. and Monard, M. C. (2002). A study of k-nearest neighbour as an imputation method. Soft computing systems: Design, management and applications, 48, 251–260. Baum, A., Garofalo, J. P., and Yali, A. M. (1999). Socioeconomic status and chronic stress. Does stress account for SES effects on health? Ann N Y Acad Sci, 896, 131-44. Bein, A., Shin, W., Jalili-Firoozinezhad, S., Park, M. H., Sontheimer-Phelps, A., Tovaglieri, A., et al. (2018). Microfluidic organ-on-a-chip models of human intestine. Cellular and Molecular Gastroenterology and Hepatology, 5(4), 659-668. Benjamin, J. L., Hedin, C. R. H., Koutsoumpas, A., Ng, S. C., McCarthy, N. E., Prescott, N. J., et al. (2012). Smokers with active crohn's disease have a clinically relevant dysbiosis of the gastrointestinal microbiota. Inflammatory Bowel Diseases, 18(6), 1092-1100. Benno, P., Midtvedt, K., Alam, M., Collinder, E., Norin, E., and Midtvedt, T. (2011). Examination of intestinal conversion of cholesterol to coprostanol in 633 healthy subjects reveals an age- and sex-dependent pattern. Microb. Ecol. Health Dis., 17(4). Berger, S., Raman, G., Vishwanathan, R., Jacques, P. F., and Johnson, E. J. (2015). Dietary cholesterol and cardiovascular disease: a systematic review and meta-analysis. Am J Clin Nutr, 102(2), 276-294. Bergstrom, K., Fu, J., Johansson, M. E. V., Liu, X., Gao, N., Wu, Q., et al. (2017). Core 1- and 3-derived O-glycans collectively maintain the colonic mucus barrier and protect against spontaneous colitis in mice. Mucosal Immunol., 10(1), 91-103. Bergstrom, K., Shan, X., Casero, D., Batushansky, A., Lagishetty, V., Jacobs, J. P., et al. (2020). Proximal colon-derived O-glycosylated mucus encapsulates and modulates the microbiota. Science 370(6515), 467-472. Bergstrom, K. and Xia, L. (2022). The barrier and beyond: Roles of intestinal mucus and mucin-type O-glycosylation in resistance and tolerance defense strategies guiding host-microbe symbiosis. GM, 14(1), 2052699. Bergstrom, K. S. B. and Xia, L. (2013). Mucin-type O-glycans and their roles in intestinal homeostasis. Glycobiol (Oxford), 23(9), 1026-1037. Bertolotti, M., Gabbi, C., Anzivino, C., Crestani, M., Mitro, N., Del Puppo, M., et al. (2007). Age-related changes in bile acid synthesis and hepatic nuclear receptor expression. European Journal of Clinical Investigation, 37(6), 501-508. Biswas, I., Mason, M. R., Nagaraja, H. N., Camerlengo, T., Joshi, V., and Kumar, P. S. (2013). Deep sequencing identifies ethnicity-specific bacterial signatures in the oral microbiome. PLoS ONE, 8(10), e77287. Bokulich, N. A., Kaehler, B. D., Rideout, J. R., Dillon, M., Bolyen, E., Knight, R., et al. (2018). Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome, 6(1). Bolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C. C., Al-Ghalith, G. A., et al. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol., 37(8), 852-857. Boyd, A. S., Wheless, L., Brady, B. G., and Ellis, D. (2017). Cutaneous yarrowia lipolytica infection in an immunocompetent woman. JAAD Case Rep, 3(3), 219-221. 132 | P a g e Broeders, Evie P. M., Nascimento, Emmani B. M., Havekes, B., Brans, B., Roumans, Kay H. M., Tailleux, A., et al. (2015). The bile acid chenodeoxycholic acid increases human brown adipose tissue activity. Cell Metab., 22(3), 418-426. Brooks, A. W., Priya, S., Blekhman, R., and Bordenstein, S. R. (2018). Gut microbiota diversity across ethnicities in the United States. PLoS Biol, 16(12), e2006842. Brown, D. W. and Anda, R. F. (2009). Adverse childhood experiences: Origins of behaviors that sustain the HIV epidemic. AIDS, 23(16), 2231-2233. Brown, D. W., Anda, R. F., Felitti, V. J., Edwards, V. J., Malarcher, A. M., Croft, J. B., et al. (2010). Adverse childhood experiences are associated with the risk of lung cancer: A prospective cohort study. BMC Public Health, 10(1), 311. Browne, H., Forster, S., Anonye, B., Kumar, N., Neville, B., Stares, M., et al. (2016). Culturing of 'unculturable' human microbiota reveals novel taxa and extensive sporulation. Nature, 533(7604), 543. Bryson, S. J., Thurber, A. R., Correa, A. M. S., Orphan, V. J., and Vega Thurber, R. (2015). A novel sister clade to the enterobacteria microviruses (family microviridae) identified in methane seep sediments. Environ Microbiol, 17(10), 3708-3721. Buffie, C. G., Bucci, V., Stein, R. R., McKenney, P. T., Ling, L., Gobourne, A., et al. (2015). Precision microbiome restoration of bile acid-mediated resistance to Clostridium difficile. Nature, 517(7533), 205-208. Bui, T. V. A., Hwangbo, H., Lai, Y., Hong, S. B., Choi, Y., Park, H., et al. (2023). The gut-heart axis: Updated review for the roles of microbiome in cardiovascular health. Korean Circulation Journal, 53(8), 499-518. Butel, M., Suau, A., Campeotto, F., Magne, F., Aires, J., Ferraris, L., et al. (2007). Conditions of bifidobacterial colonization in preterm infants: A prospective analysis. Journal of Pediatric Gastroenterology and Nutrition, 44(5), 577-582. Cabrera-Rubio, R., Collado, M. C., Laitinen, K., Salminen, S., Isolauri, E., and Mira, A. (2012). Human milk microbiome changes over lactation and is shaped by maternal weight and mode of delivery. The American Journal of Clinical Nutrition, 96(3), 544-551. Callahan, B. J., Mcmurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., and Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581-583. Cammarota, G., Ianiro, G., Kelly, C. R., Mullish, B. H., Allegretti, J. R., Kassam, Z., et al. (2019). International consensus conference on stool banking for faecal microbiota transplantation in clinical practice. Gut, 68(12), 2111-2121. Campbell, J., Berry, J., and Liang, Y., Chapter 71 - anatomy and physiology of the small intestine, in Shackelford's surgery of the alimentary tract, C.J. Yeo, Editor. 2019, Elsevier Inc. p. 817-841. Candelli, M., Franza, L., Pignataro, G., Ojetti, V., Covino, M., Piccioni, A., et al. (2021). Interaction between lipopolysaccharide and gut microbiota in inflammatory bowel diseases. International Journal of Molecular Sciences, 22(12), 6242. Cani, P. D., Amar, J., Iglesias, M. A., Poggi, M., Knauf, C., Bastelica, D., et al. (2007). Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes, 56(7), 1761-1772. Cao, L. J., Chua, K. S., Chong, W. K., Lee, H. P., and Gu, Q. M. (2003). A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing, 55(1), 321-336. Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Lozupone, C. A., Turnbaugh, P. J., et al. (2011). Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proceedings of the National Academy of Sciences - PNAS, 108, 4516-4522. 133 | P a g e Carabotti, M., Scirocco, A., Maselli, M. A., and Severi, C. (2015). The gut-brain axis: Interactions between enteric microbiota, central and enteric nervous systems. Annals of Gastroenterology, 28(2), 203-209. Carpenter, L. L., Gawuga, C. E., Tyrka, A. R., Lee, J. K., Anderson, G. M., and Price, L. H. (2010). Association between plasma IL-6 response to acute stress and early-life adversity in healthy adults. Neuropsychopharmacology, 35, 2617–2623. Cassar, A., Holmes, D. R., Rihal, C. S., and Gersh, B. J. (2009). Chronic coronary artery disease: Diagnosis and management. Mayo Clinic Proceedings, 84(12), 1130-1146. Castaneda, R., Piedrahita-Quintero, P., and Garcia-Sucerquia, J. (2015). Image processing and computing for digital holography with ImageJ. Opt. Pura. Apl., 48, 77-84. Catapano, A. L., Graham, I., De Backer, G., Wiklund, O., Chapman, M. J., Drexel, H., et al. (2016). 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias. European Heart Journal, 37(39), 2999-3058. Centers for Disease Control and Prevention. Adverse Childhood Experiences (ACEs). 2021; Available online: https://www.cdc.gov/violenceprevention/aces/index.html. Cerqueira, N., Oliveira, E. F., Gesto, D. S., Santos-Martins, D., Moreira, C., Moorthy, H. N., et al. (2016). Cholesterol biosynthesis: A mechanistic overview. Biochemistry, 55(39), 5483-5506. Chambers, E. S., Preston, T., Frost, G., and Morrison, D. J. (2018). Role of gut microbiota-generated short-chain fatty acids in metabolic and cardiovascular health. Curr. Nutr. Rep., 7(4), 198-206. Chang, L., Wei, Y., and Hashimoto, K. (2022). Brain–gut–microbiota axis in depression: A historical overview and future directions. Brain Research Bulletin, 182, 44-56. Chapman, D. P., Whitfield, C. L., Felitti, V. J., Dube, S. R., Edwards, V. J., and Anda, R. F. (2004). Adverse childhood experiences and the risk of depressive disorders in adulthood. Journal of Affective Disorders, 82(2), 217-225. Chen, J., Ryu, E., Hathcock, M., Ballman, K., Chia, N., Olson, J. E., et al. (2016). Impact of demographics on human gut microbial diversity in a US Midwest population. PeerJ, 4, e1514. Cheung, S. G., Goldenthal, A. R., Uhlemann, A., Mann, J. J., Miller, J. M., and Sublette, M. E. (2019). Systematic review of gut microbiota and major depression. Frontiers in Psychiatry, 10, 34. Chevalier, G., Siopi, E., Guenin-Macé, L., Pascal, M., Laval, T., Rifflet, A., et al. (2020). Effect of gut microbiota on depressive-like behaviors in mice is mediated by the endocannabinoid system. Nature Communications, 11(1), 6363-15. Chiang, J. Y. (2009). Bile acids: regulation of synthesis. J Lipid Res, 50(10), 1955-1966. Childs, B. G., Li, H., and van Deursen, J. M. (2018). Senescent cells: A therapeutic target for cardiovascular disease. The Journal of Clinical Investigation, 128(4), 1217-1228. Chistiakov, D. A., Bobryshev, Y. V., Kozarov, E., Sobenin, I. A., and Orekhov, A. N. (2015). Role of gut microbiota in the modulation of atherosclerosis-associated immune response. Frontiers in Microbiology, 6, 671. Chittim, C. L., Martinez Del Campo, A., and Balskus, E. P. (2019). Gut bacterial phospholipase Ds support disease-associated metabolism by generating choline. Nat Microbiol, 4(1), 155-163. Chmielewski, P. (2018). Leukocyte count, systemic inflammation, and health status in older adults: a narrative review. Anthropol Rev, 81(1), 81-101. Claesson, M. J., Jeffery, I. B., Conde, S., Power, S. E., O'Connor, E. M., Cusack, S., et al. (2012). Gut microbiota composition correlates with diet and health in the elderly. Nature, 488(7410), 178-184. Clarke, K. R. (1993). Non-parametric multivariate analyses of changes in community structure. Austral Ecol, 18(1), 117-143. Clevers, H. C. and Bevins, C. L. (2013). Paneth cells: Maestros of the small intestine cyrpts Annual Review of Physiology 75(289-311. 134 | P a g e Comeau, A. M., Douglas, G. M., and Langille, M. G. I. (2017). Microbiome helper: A custom and streamlined workflow for microbiome research. Msystems, 2(1). Cone, R. A. (2009). Barrier properties of mucus. Adv Drug Deliv Re, 61, 75–8. Cook, M. D., Martin, S. A., Williams, C., Whitlock, K., Wallig, M. A., Pence, B. D., et al. (2013). Forced treadmill exercise training exacerbates inflammation and causes mortality while voluntary wheel training is protective in a mouse model of colitis. Brain, Behavior, and Immunity, 33, 46-56. Costa, M., Brookes, S. J. H., and Hennig, G. W. (2000). Anatomy and physiology of the enteric nervous system. Gut, 4), iv15-19. Costea, P. I., Zeller, G., Sunagawa, S., Pelletier, E., Alberti, A., Levenez, F., et al. (2017). Towards standards for human fecal sample processing in metagenomic studies. Nature Biotechnology, 35(11), 1069-1076. Cowan, C. S. M., Dinan, T. G., and Cryan, J. F. (2020). Annual Research Review: Critical windows - the microbiota-gut-brain axis in neurocognitive development. J Child Psychol Psychiatry, 61(3), 353-371. Cronholm, P. F., Forke, C. M., Wade, R., Bair-Merritt, M. H., Davis, M., Harkins-Schwarz, M., et al. (2015). Adverse childhood experiences: Expanding the concept of adversity. American Journal of Preventive Medicine, 49(3), 354-361. Crosnier, C., Stamataki, D., and Lewis, J. (2006). Organizing cell renewal in the intestine: stem cells, signals and combinatiorial control Nature Rev Genet, 7, 349-359. Crothers, J., Kassam, Z., Smith, M., Phillips, M., Vo, E., Velez, M., et al. (2018). Tu1893 - A double-blind, randomized, placebo-control pilot trial of fecal microbiota transplantation capsules from rationally selected donors in active ulcerative colitis. Gastroenterology, 154(6), S-1050-S-1051. Cutler, D. R., Edwards, T. C. J., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., et al. (2007). Random forests for classification in ecology. Ecology, 88(11), 2783-2792. Dam, H. (1934). The formation of coprosterol in the intestine: The action of intestinal bacteria on cholesterol. Biochem J, 28(3), 820-825. Danese, A., Pariante, C. M., Caspi, A., Taylor, A., and Poulton, R. (2007). Childhood maltreatment predicts adult inflammation in a life-course study. Proceedings of the National Academy of Sciences - PNAS,, 104(4), 1319-1324. David, L. A., Materna, A. C., Friedman, J., Campos-Baptista, M. I., Blackburn, M. C., Perrotta, A., et al. (2014a). Host lifestyle affects human microbiota on daily timescales. Genome Biol, 15(7), R89-R89. David, L. A., Maurice, C. F., Carmody, R. N., Gootenberg, D. B., Button, J. E., Wolfe, B. E., et al. (2014b). Diet rapidly and reproducibly alters the human gut microbiome. Nature, 505(7484), 559-563. Davies, S., Ang, J., Revell, V., Holmes, B., Mann, A., Robertson, F., et al. (2014). Effect of sleep deprivation on the human metabolome. Proceedings of the National Academy of Sciences - PNAS, 111(29), 10761-10766. de la Cochetiere, M. F., Durand, T., Lepage, P., Bourreille, A., Galmiche, J. P., and Dodre, J. (2005). Resilience of the dominant human fecal microbiota upon short-course antibiotic challenge. Journal of Clinical Microbiology, 43(11), 5588-5592. De Palma, G., Blennerhassett, P., Lu, J., Deng, Y., Park, A. J., Green, W., et al. (2015). Microbiota and host determinants of behavioural phenotype in maternally separated mice. Nature Communications, 6(1), 7735. DeFilipp, Z., Bloom, P. P., Torres Soto, M., Mansour, M. K., Sater, M. R. A., Huntley, M. H., et al. (2019). Drug-resistant E. coli bacteremia transmitted by fecal microbiota transplant. N Engl J Med, 381(21), 2043-2050. Deng, C., Daley, T., and Smith, A. preseqR: Predicting Species Accumulation Curves. 2018; Available online: https://CRAN.R-project.org/package=preseqR 135 | P a g e Derrien, M., Vaughan, E. E., Plugge, C. M., and de Vos, W. M. (2004). Akkermansia muciniphila gen. nov., sp. nov., a human intestinal mucin-degrading bacterium. Int. J. Syst. Evol. Microbiol., 54(5), 1469-1476. Deschasaux, M., Bouter, K. E., Prodan, A., Levin, E., Groen, A. K., Herrema, H., et al. (2018). Depicting the composition of gut microbiota in a population with varied ethnic origins but shared geography. Natr Med, 24(10), 1526-1531. Dethlefsen, L., Huse, S., Sogin, M. L., and Relman, D. A. (2008). The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16S rRNA sequencing. PLoS Biol, 6(11), 2383-2400. Dethlefsen, L. and Relman, D. A. (2011). Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proceedings of the National Academy of Sciences of the United States of America, 108(Supplement 1), 4554-4561. Devkota, S., Wang, Y., Musch, M. W., Leone, V., Fehlner-Peach, H., Nadimpalli, A., et al. (2012). Dietary-fat-induced taurocholic acid promotes pathobiont expansion and colitis in Il10-/- mice. Nature, 487(7405), 104-8. Dietterich, T. G. (1998a). Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Comput., 10(7), 1895-1923. Dietterich, T. G. (1998b). Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Computation, 10(7), 1895-1923. Dominguez-Bello, M. G., Costello, E. K., Contreras, M., Magris, M., Hidalgo, G., Fierer, N., et al. (2010). Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proceedings of the National Academy of Sciences - PNAS, 107(26), 11971-11975. Dong, M., Dube, S. R., Felitti, V. J., Giles, W. H., and Anda, R. F. (2003). Adverse Childhood Experiences and Self-reported Liver Disease. Archives of Internal Medicine 163(16), 1949. Dong, M., Giles, W. H., Felitti, V. J., Dube, S. R., Williams, J. E., Chapman, D. P., et al. (2004). Insights into causal pathways for ischemic heart disease: Adverse childhood experiences study. Circulation, 110(13), 1761-1766. Douglas, G. M., Maffei, V. J., Zaneveld, J. R., Yurgel, S. N., Brown, J. R., Taylor, C. M., et al. (2020). PICRUSt2 for prediction of metagenome functions. Nature Biotechnology, 38(6), 685-688. Drekonja, D., Reich, J., Gezahegn, S., Greer, N., Shaukat, A., MacDonald, R., et al. (2015). Fecal microbiota transplantation for clostridium difficile infection A systematic review. Ann Intern Med, 162(9), 630-U230. Dube, S. R., Fairweather, D., Pearson, W. S., Felitti, V. J., Anda, R. F., and Croft, J. B. (2009). Cumulative childhood stress and autoimmune diseases in adults. Psychosomatic Medicine, 71(2), 243-250. Duboc, H., Rajca, S., Rainteau, D., Benarous, D., Maubert, M. A., Quervain, E., et al. (2013). Connecting dysbiosis, bile-acid dysmetabolism and gut inflammation in inflammatory bowel diseases. Gut, 62(4), 531-539. Dziewiecka, H., Buttar, H. S., Kasperska, A., Ostapiuk–Karolczuk, J., Domagalska, M., Cichoń, J., et al. (2022). Physical activity induced alterations of gut microbiota in humans: A systematic review. BMC Sports Science, Medicine & Rehabilitation, 14(1), 1-122. Earley, H., Lennon, G., Balfe, Á., Coffey, J. C., Winter, D. C., and O'Connell, P. R. (2019). The abundance of Akkermansia muciniphila and its relationship with sulphated colonic mucins in health and ulcerative colitis. Sci. Rep., 9(1), 15683-9. Ehrlich, K. B., Ross, K. M., Chen, E., and Miller, G. E. (2016). Testing the biological embedding hypothesis: Is early life adversity associated with a later proinflammatory phenotype? Development and Psychopathology, 28(4pt2), 1273-1283. 136 | P a g e Ermund, A., Schütte, A., Johansson, M. E. V., Gustafsson, J. K., and Hansson, G. C. (2013). Studies of mucus in mouse stomach, small intestine, and colon. I. gastrointestinal mucus layers have different properties depending on location as well as over the peyer's patches. American Journal of Physiology: Gastrointestinal and Liver Physiology, 305(5), G341-G347. Evans, A. M., Bridgewater, B. R., Liu, Q., Mitchell, M. W., Robinson, R. J., Dai, H., et al. (2014). High resolution mass spectrometry improves data quantity and quality as compared to unit mass resolution mass spectrometry in high-throughput profiling metabolomics. Metabolomics, 4(2). Everard, A., Belzer, C., Geurts, L., Ouwerkerk, J. P., Druart, C., Bindels, L. B., et al. (2013). Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity. Proceedings of the National Academy of Sciences of the United States of America, 110(22), 9066-9071. Fabiszewska, A. U., Stolarzewicz, I. A., Zamojska, W. M., and Białecka-Florjańczyk, E. (2014). Carbon source impact on yarrowia lipolytica KKP 379 lipase production. Appl Biochem Microbiol, 50(4), 404-410. Fagundes, C. P., Glaser, R., and Kiecolt-Glaser, J. K. (2013). Stressful early life experiences and immune dysregulation across the lifespan. Brain, Behavior, and Immunity, 27(1), 8-12. Falony, G., Vieira-Silva, S., and Raes, J. (2015). Microbiology meets big data: The case of gut microbiota-derived trimethylamine. Annual Review of Microbiology, 69(1), 305-321. Fancy, N., Melvin, M., N., N., Kazemian, N., D’Aloisio, L., Davidson-Hunt, S., et al. (2024). Fecal-adherent mucus is a non-invasive source of primary human MUC2 for structural and functional characterization in health and disease. JBC, 300(3), 105675. Felitti, V. J., Anda, R. F., Nordenberg, D., Williamson, D. F., Spitz, A. M., Edwards, V., et al. (1998). Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. The Adverse Childhood Experiences (ACE) Study. Am J Prev Med, 14, 245–258. Fernandez, F. and Hill, M. J. (1975). Proceedings: The production of vitamin K by human intestinal bacteria. J Med Microbiol, 8(2), pix. Ferrell, J. M., Boehme, S., Li, F., and Chiang, J. Y. (2016). Cholesterol 7alpha-hydroxylase-deficient mice are protected from high-fat/high-cholesterol diet-induced metabolic disorders. J. Lipid. Res., 57(7), 1144-1154. Fettweis, J. M., Brooks, J. P., Serrano, M. G., Sheth, N. U., Girerd, P. H., Edwards, D. J., et al. (2014). Differences in vaginal microbiome in African American women versus women of European ancestry. Microbiology, 160(10), 2272-2282. Filippidou, S., Junier, P., Junier, T., Wunderlin, T., Lo, C., Li, P., et al. (2015). Under-detection of endospore-forming firmicutes in metagenomic data. Comput Struct Biotec, 13(C), 299. Filippo, C. D., Cavalieri, D., Paola, M. D., Ramazzotti, M., Poullet, J. B., Massart, S., et al. (2010). Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proceedings of the National Academy of Sciences of the United States of America, 107(33), 14691-14693. Finkelhor, D., Shattuck, A., Turner, H., and Hamby, S. (2013). Improving the adverse childhood experiences study scale. JAMA Pediatr, 167(1), 70-75. Fischbach, M. A. (2018). Microbiome: Focus on causation and mechanism. Cell, 174(4), 785-790. Flannery, J. E., Stagaman, K., Burns, A. R., Hickey, R. J., Roos, L. E., Giuliano, R. J., et al. (2020). Gut feelings begin in childhood: The gut metagenome correlates with early environment, caregiving, and behavior. Mbio, 11(1). Fleming, M. A., Ehsan, L., Moore, S. R., and Levin, D. E. (2020). The enteric nervous system and its emerging role as a therapeutic target. Gastroenterology Research and Practice, 1-13. Franchi, L., Warner, N., Viani, K., and Nuñez, G. (2009). Function of nod-like receptors in microbial recognition and host defense. Immunological Reviews, 227(1), 106-128. 137 | P a g e Franzosa, E. A., Sirota-Madi, A., Avila-Pacheco, J., Fornelos, N., Haiser, H. J., Reinker, S., et al. (2019). Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nat. Microbiol., 4(2), 293-305. Freier, T. A., Beitz, D. C., Li, L., and Hartman, P. A. (1994). Characterization of eubacterium coprostanoligenes sp. nov., a cholesterol-reducing anaerobe. Journal of Systematic Bacteriology, 44(1), 137-142. Fu, J., Bonder, M. J., Cenit, M. C., Tigchelaar-Feenstra, E., Maatman, A., Dekens, J. A. M., et al. (2015). The gut microbiome contributes to a substantial proportion of the variation in blood lipids. Circ. Res., 117(9), 817-824. Fushimi, T., Suruga, K., Oshima, Y., Fukiharu, M., Tsukamoto, Y., and Goda, T. (2006). Dietary acetic acid reduces serum cholesterol and triacylglycerols in rats fed a cholesterol-rich diet. Br. J. Nutr., 95(5), 916-924. Garcia, C. J., Kosek, V., Beltrán, D., Tomás-Barberán, F. A., and Hajslova, J. (2022). Production of new microbially conjugated bile acids by human gut microbiota. Biomol. (Basel, Switzerland), 12(5), 687. Garcia-Mantrana, I., Selma-Royo, M., Alcantara, C., and Collado, M. C. (2018). Shifts on gut microbiota associated to mediterranean diet adherence and specific dietary intakes on general adult population. Front Microbiol, 9, 890. Garcia-Pena, C., Alvarez-Cisneros, T., Quiroz-Baez, R., and Friedland, R. P. (2017). Microbiota and aging. A review and commentary. Archives of Medical Research, 48(8), 681-689. Gareau, M. G., Sherman, P. M., and Walker, W. A. (2010). Probiotics and the gut microbiota in intestinal health and disease. Nat Rev Gastroenterol Hepatol, 7(9), 503-514. Gelberg, H. B. (2014). Comparative anatomy, physiology, and mechanisms of disease production of the esophagus, stomach, and small intestine. Toxicologic Pathology, 42(1), 54-66. Gellis, L. A., Lichstein, K. L., Scarinci, I. C., Durrence, H. H., Taylor, D. J., Bush, A. J., et al. (2005). Socioeconomic status and insomnia. J Abnorm Psychol, 114(1), 111-8. Gerard, P. (2013). Metabolism of cholesterol and bile acids by the gut microbiota. Pathogens, 3(1), 14-24. Gerard, P., Beguet, F., Lepercq, P., Rigottier-Gois, L., Rochet, V., Andrieux, C., et al. (2004). Gnotobiotic rats harboring human intestinal microbiota as a model for studying cholesterol-to coprostanol conversion. FEMS Microbiol. Ecol., 47(3), 337-343. Gerard, P., Lepercq, P., Leclerc, M., Gavini, F., Raibaud, P., and Juste, C. (2007). Bacteroides sp. strain D8, the first cholesterol-reducing bacterium isolated from human feces. Appl. Environ. Microbiol., 73(18), 5742-5749. Gerding, D. N., Meyer, T., Lee, C., Cohen, S. H., Murthy, U. K., Poirier, A., et al. (2015). Administration of spores of nontoxigenic clostridium difficile strain M3 for prevention of recurrent C. difficile infection: A randomized clinical trial. JAMA, 313(17), 1719-1727. Gibbons, C. (2010). Stress, coping and burn-out in nursing students. International Journal of Nursing Studies, 47(10), 1299-1309. Gibbons, S., Kearney, S., Smillie, C., and Alm, E. (2017). Two dynamic regimes in the human gut microbiome. Plos Comput Biol, 13(2), e1005364. Goker, M., Gronow, S., Zeytun, A., Nolan, M., Lucas, S., Lapidus, A., et al. (2011). Complete genome sequence of odoribacter splanchnicus type strain (1651/6(T)). Stand Genomic Sci, 4(2), 200-209. Goldstein, J. L. and Brown, M. S. (1990). Regulation of the mevalonate pathway. Nature, 343(6257), 425-430. Goldstein, J. L., Hazzard, W. R., Schrott, H. G., Bierman, E. L., Motulsky, A. G., Levinski, M. J., et al. (1973). Hyperlipidemia in coronary heart-disease: I. Lipid-levels in 500 survivors of myocardial-infarction. Journal of Clinical Investigation, 52(7), 1533-1543. 138 | P a g e Goodrich, J., Davenport, E., Beaumont, M., Jackson, M., Knight, R., Ober, C., et al. (2016). Genetic determinants of the gut microbiome in UK twins. Cell Host & Microbe, 19(5), 731-743. Gouba, N. and Drancourt, M. (2015). Digestive tract mycobiota: A source of infection. Med Mal Infect, 45(1-2), 9-16. Graham, C., Mullen, A., and Whelan, K. (2015). Obesity and the gastrointestinal microbiota: A review of associations and mechanisms. Nutrition Reviews, 73(6), 376-385. Graham, C. D. and Kyle, S. D. (2017). A preliminary investigation of sleep quality in functional neurological disorders: Poor sleep appears common, and is associated with functional impairment. Journal of the Neurological Sciences, 378, 63-166. Gregory, J. C., Buffa, J. A., Org, E., Wang, Z., Levison, B. S., Zhu, W., et al. (2015). Transmission of atherosclerosis susceptibility with gut microbial transplantation. Journal of Biological Chemistry, 290(9), 5647-5660. Grosicki, G. J., Riemann, B. L., Flatt, A. A., Valentino, T., and Lustgarten, M. S. (2020). Self-reported sleep quality is associated with gut microbiome composition in young, healthy individuals: A pilot study. Sleep Medicine, 73, 76-81. Grundy, S. M. (2016). Does dietary cholesterol matter? Current Atherosclerosis Reports,, 18(11), 1-7. Guo, Y., Mahony, S., and Gifford, D. K. (2012). High resolution genome wide binding event finding and motif discovery reveals transcription factor spatial binding constraints. Plos Comput Biol, 8(8), e1002638-e1002638. Han, J., Liu, Y., Wang, R., Yang, J., Ling, V., and Borchers, C. H. (2015). Metabolic profiling of bile acids in human and mouse blood by LC–MS/MS in combination with phospholipid-depletion solid-phase extraction. Anal. Chem. (Washington), 87(2), 1127-1136. Han, M., Yuan, S., and Zhang, J. (2022). The interplay between sleep and gut microbiota. Brain Research Bulletin, 180, 131-146. Hand, T. W., Dos Santos, L. M., Bouladoux, N., Molloy, M. J., Pagán, A. J., Pepper, M., et al. (2012). Acute gastrointestinal infection induces long-lived microbiota-specific T cell responses. Science, 337(6101), 1553-1556. Hansson, G. C. (2019). Mucus and mucins in diseases of the intestinal and respiratory tracts. J Intern Med, 285(479–90. Hansson, G. C. (2020). Mucins and the microbiome. Ann Revi Biochem, 89(1), 769-793. Hantsoo, L., Jašarević, E., Criniti, S., Mcgeehan, B., Tanes, C., Sammel, M. D., et al. (2019). Childhood adversity impact on gut microbiota and inflammatory response to stress during pregnancy. Brain, Behavior, and Immunity, 75, 240-250. Hartwell, K. J., Moran-Santa Maria, M. M., Twal, W. O., Shaftman, S., Desantis, S. M., Mcrae-Clark, A. L., et al. (2013). Association of elevated cytokines with childhood adversity in a sample of healthy adults. Journal of Psychiatric Research, 47(5), 604-610. Harvey, R. E., Coffman, K. E., and Miller, V. M. (2015). Women-specific factors to consider in risk, diagnosis and treatment of cardiovascular disease. Women’s Health, 11(2), 239-257. Hatch, V., Swerbenski, H., and Gray, S. A. O. (2020). Family social support buffers the intergenerational association of maternal adverse childhood experiences and preschoolers' externalizing behavior. American Journal of Orthopsychiatry, 90(4), 489-501. Hillis, S. D., Anda, R. F., Felitti, V. J., Nordenberg, D., and Marchbanks, P. A. (2000). Adverse childhood experiences and sexually transmitted diseases in men and women: A retrospective study. Virus Research, 106(1), e11. Homann, C., Rossel, C. A. J., Dizzell, S., Bervoets, L., Simioni, J., Li, J., et al. (2021). Infants’ first solid foods: Impact on gut microbiota development in two intercontinental cohorts. Nutrients, 13(8), 2639. Hooks, K. B. and O'Malley, M. A. (2017). Dysbiosis and its discontents. Mbio, 8(5), e01492-17. 139 | P a g e Hooper, L. V., Littman, D. R., and Macpherson, A. J. (2012). Interactions between the microbiota and the immune system. Science, 336(6086), 1268-1273. Hooper, L. V., Midtvedt, T., and Gordon, J. I. (2002). How host-microbial interactions shape the nutrient environment of the mammalian intestine. Annual Review of Nutrition, 22, 283. Huerta-Franco, M. R., Vargas-Luna, M., Tienda, P., Delgadillo-Holtfort, I., Balleza-Ordaz, M., and Flores-Hernandez, C. (2013). Effects of occupational stress on the gastrointestinal tract. World Journal of Gastrointestinal Pathophysiology, 4(4), 108-118. Hwang, E. and Kim, J. (2022). Factors affecting academic burnout of nursing students according to clinical practice experience. BMC Medical Education, 22(1), 346. Hyun, M. K., Baek, Y., and Lee, S. (2019). Association between digestive symptoms and sleep disturbance: A cross-sectional community-based study. BMC Gastroenterology, 19(1), 34. Iljazovic, A., Roy, U., Gálvez, E. J. C., Lesker, T. R., Zhao, B., Gronow, A., et al. (2021). Perturbation of the gut microbiome by Prevotella spp. enhances host susceptibility to mucosal inflammation. Mucosal Immunology, 14(1), 113-124. Illman, R. J., Storer, G. B., and Topping, D. L. (1993). White wheat flour lowers plasma cholesterol and increases cecal steroids relative to whole wheat flour, wheat bran and wheat pollard in rats. J Nutr, 123(6), 1094. Irving, M. H. and Catchpole, B. (1992). ABC of colorectal diseases: Anatomy and physiology of the colon, rectum, and anus. BMJ, 304(6834), 1106-1108. Isaac, S., Scher, J. U., Djukovic, A., Jiménez, N., Littman, D. R., Abramson, S. B., et al. (2017). Short- and long-term effects of oral vancomycin on the human intestinal microbiota. Journal of Antimicrobial Chemotherapy, 72(1), 128-136. Jakobsson, H. E., Jernberg, C., Andersson, A. F., Sjolund-Karlsson, M., Jansson, J. K., and Engstrand, L. (2010). Short-term antibiotic treatment has differing long-term impacts on the human throat and gut microbiome. PLoS One, 5(3), e9836. Janssen, I., Boyce, W. F., Simpson, K., and Pickett, W. (2006). Influence of individual- and area-level measures of socioeconomic status on obesity, unhealthy eating, and physical inactivity in Canadian adolescents. The American Journal of Clinical Nutrition, 83(1), 139-145. Jašarević, E., Howard, C. D., Misic, A. M., Beiting, D. P., and Bale, T. L. (2017). Stress during pregnancy alters temporal and spatial dynamics of the maternal and offspring microbiome in a sex-specific manner. Scientific Reports, 7(1), 44182. Jašarević, E., Howerton, C. L., Howard, C. D., and Bale, T. L. (2015). Alterations in the vaginal microbiome by maternal stress are associated with metabolic reprogramming of the offspring gut and brain. Endocrinology, 156(9), 3265-3276. Jewkes, R. K., Dunkle, K., Nduna, M., Jama, P., and Puren, A. (2010). Associations between childhood adversity and depression, substance abuse and HIV and HSV2 incident infections in rural South African youth. Child Abuse Negl 34, 833–841. Jia, B., Zou, Y., Han, X., Bae, J., and Jeon, C. O. (2023). Gut microbiome-mediated mechanisms for reducing cholesterol levels: Implications for ameliorating cardiovascular disease. Trends Microbiol (Regular Ed.), 31(1), 76-91. Jia, W., Li, H., Zhao, L., and Nicholson, J. K. (2008). Gut microbiota: A potential new territory for drug targeting. Nature Reviews Drug Discovery, 7(2), 123-129. Jimenez, C., Navia-Osorio, M., P., and Diaz, C. V. (2010). Stress and health in novice and experienced nursing students. Journal of Advanced Nursing, 66(2), 442-455. Jiménez, E., Marín, M. L., Martín, R., Odriozola, J. M., Olivares, M., Xaus, J., et al. (2008). Is meconium from healthy newborns actually sterile? Research in Microbiology, 159(3), 187-193. Johansson, M. E. V. and Hansson, G. C. (2016). Immunological aspects of intestinal mucus and mucins. Nature Reviews. Immunology, 16(10), 639-649. 140 | P a g e Johansson, M. E. V., Phillipson, M., Petersson, J., Velcich, A., Holm, L., and Hansson, G. C. (2008). The inner of the two Muc2 mucin-dependent mucus layers in colon is devoid of bacteria. Proceedings of the National Academy of Sciences - PNAS, 105(39), 15064-15069. Johansson, M. E. V., Sjövall, H., and Hansson, G. C. (2013). The gastrointestinal mucus system in health and disease. Nat. Rev. Gastroenterol. Hepatol, 10(6), 352-361. Juge, N., Tailford, L., and Owen, C. D. (2016). Sialidases from gut bacteria: A mini-review. Biochem. Soc. Trans., 44(1), 166-175. Jung, T., Park, J. H., Jeon, W., and Han, K. (2015). Butyrate modulates bacterial adherence on LS174T human colorectal cells by stimulating mucin secretion and MAPK signaling pathway. Nutr Res Pract, 9(4), 343-349. Kannel, W. B., Dawber, T. R., Friedman, G. D., Glennon, W. E., and McNamara, P. M. (1964). Risk factors in coronary heart disease. An evaluation of several serum lipids as predictors of coronary heart disease; the Framingham Study. Annals of Internal Medicine, 61, 888. Karatekin, C. and Hill, M. (2019). Expanding the Original Definition of Adverse Childhood Experiences (ACEs). J Child Adolesc Trauma, 12(3), 289-306. Kearney, S. M., Gibbons, S. M., Poyet, M., Gurry, T., Bullock, K., Allegretti, J. R., et al. (2018). Endospores and other lysis-resistant bacteria comprise a widely shared core community within the human microbiota. ISME J, 12(10), 2403-2416. Kekkonen, R. A., Lummela, N., Karjalainen, H., Latvala, S., Tynkkynen, S., Järvenpää, S., et al. (2008). Probiotic intervention has strain-specific anti-inflammatory effects in healthy adults. World J Gastroenterol, 14(13), 2029-2036. Kenny, D. J., Plichta, D. R., Shungin, D., Koppel, N., Hall, A. B., Fu, B., et al. (2020). Cholesterol metabolism by uncultured human gut bacteria influences host cholesterol level. Cell Host Microbe, 28(2), 245-257.e6. Kerimi, A., Kraut, N. U., da Encarnacao, J. A., and Williamson, G. (2020). The gut microbiome drives inter- and intra-individual differences in metabolism of bioactive small molecules. Scientific Reports, 10(1), 19590. Khanijow, V., Prakash, P., Emsellem, H. A., Borum, M. L., and Doman, D. B. (2015). Sleep dysfunction and gastrointestinal diseases. Gastroenterology & Hepatology, 11(12), 817-825. Khanna, S., Pardi, D. S., Kelly, C. R., Kraft, C. S., Dhere, T., Henn, M. R., et al. (2016). A novel microbiome therapeutic increases gut microbial diversity and prevents recurrent Clostridium difficile infection. J Infect, 214(2), 173-181. Kim, M. and Kim, H. (2017). The roles of glutamine in the intestine and its implication in intestinal diseases. International Journal of Molecular Sciences, 18(5), 1051. Kim, S., Goel, R., Kumar, A., Qi, Y., Lobaton, G., Hosaka, K., et al. (2018). Imbalance of gut microbiome and intestinal epithelial barrier dysfunction in patients with high blood pressure. Clinical Science (London, England: 1979), 132(6), 701-718. Kim, S. and Jazwinski, S. M. (2018). The gut microbiota and healthy aging: A mini-review. Gerontology, 64(6), 513-520. Kim, T. W., Jeong, J., and Hong, S. (2015). The impact of sleep and circadian disturbance on hormones and metabolism. nternational Journal of Endocrinology, 591729-9. Kinnucan, J. A., Rubin, D. T., and Ali, T. (2013). Sleep and inflammatory bowel disease: Exploring the relationship between sleep disturbances and inflammation. Gastroenterology & Hepatology, 9(11), 718-727. Koeth, R. A., Wang, Z., Levison, B. S., Buffa, J. A., Org, E., Sheehy, B. T., et al. (2013). Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nature Medicine, 19(5), 576-585. 141 | P a g e Koh, A. and Bäckhed, F. (2020). From association to causality: The role of the gut microbiota and its functional products on host metabolism. Molecular Cell, 78(4), 584-596. Kolde, R. pheatmap: Pretty Heatmaps Cran R Project 2019 [cited 2019; Available online: https://cran.r-project.org/web/packages/pheatmap/index.html. Kong, S., Zhang, Y. H., and Zhang, W. (2018). Regulation of intestinal epithelial cells properties and functions by amino acids. BioMed Research Internationa, 2018, 1-10. König, J., Wells, J., Cani, P. D., García-Ródenas, C. L., MacDonald, T., Mercenier, A., et al. (2016). Human intestinal barrier function in health and disease. Clin. Transl. Gasteroenterol., 7(10), e196. Konikoff, T. and Gophna, U. (2016). Oscillospira : A central, enigmatic component of the human gut microbiota. Trends Microbio. (Regular Ed.), 24(7), 523-524. Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K., and Schloss, P. D. (2013). Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq illumina sequencing platform. Applied and Environmental Microbiology, 79(17), 5112-5120. Krishnan, S., Alden, N., and Lee, K. (2015). Pathways and functions of gut microbiota metabolism impacting host physiology. Curr Opin Biotechnol, 36, 137-145. Krznarić, Ž., Vranešić Bender, D., and Meštrović, T. (2019). The mediterranean diet and its association with selected gut bacteria. Current Opin Clin Nutr Metab Care, 22, 401–6. Kumar, P. S., Mason, M. R., Brooker, M. R., and O'Brien, K. (2012). Pyrosequencing reveals unique microbial signatures associated with healthy and failing dental implants. J Clin Periodontol, 39(5), 425-433. Kummen, M., Mayerhofer, C. C. K., Vestad, B., Broch, K., Awoyemi, A., Storm-Larsen, C., et al. (2018). Gut microbiota signature in heart failure defined From Profiling of 2 Independent cohorts. Journal of the American College of Cardiology, 71(10), 1184-1186. Kuno, T., Hirayama-Kurogi, M., Ito, S., and Ohtsuki, S. (2018). Reduction in hepatic secondary bile acids caused by short-term antibiotic-induced dysbiosis decreases mouse serum glucose and triglyceride levels. Sci. Rep., 8(1), 1253-15. Lagier, J., Cadoret, F., and Raoult, D. (2017). Critical microbiological view of SER-109. The Journal of Infectious Diseases, 215(1), 161-162. Lakshmanan, A. P., Murugesan, S., Al Khodor, S., and Terranegra, A. (2022). The potential impact of a probiotic: Akkermansia muciniphila in the regulation of blood pressure—the current facts and evidence. J. Transl. Med., 20(1), 1-430. Lamendella, R., Wright, J. R., Hackman, J., McLimans, C., Toole, D. R., Bernard Rubio, W., et al. (2018). Antibiotic treatments for Clostridium difficile infection are associated with distinct bacterial and fungal community structures. Msphere, 3(1), e00572-17. Langøy, A., Smith, O. R. F., Wold, B., Samdal, O., and Haug, E. M. (2019). Associations between family structure and young people's physical activity and screen time behaviors. BMC Public Health, 19(1), 433. LaNoue, M., Graeber, D., de Hernandez, B. U., Warner, T. D., and Helitzer, D. L. (2012). Direct and indirect effects of childhood adversity on adult depression. Community Ment Health J, 48( 187–192. LaNoue, M., Graeber, D. A., Helitzer, D. L., and Fawcett, J. (2013). Negative affect predicts adults’ ratings of the current, but not childhood, impact of adverse childhood events. Community Mental Health Journal, 49(5), 560-566. Lantz, P. M., House, J. S., Lepkowski, J. M., Williams, D. R., Mero, R. P., and Chen, J. (1998). Socioeconomic Factors, Health Behaviors, and Mortality. JAMA, 279(21), 1703. Larsen, J. M. (2017). The immune response toPrevotellabacteria in chronic inflammatory disease. Immunology, 151(4), 363-374. 142 | P a g e Laterza, L., Rizzatti, G., Gaetani, E., Chiusolo, P., and Gasbarrini, A. (2016). The gut microbiota and immune system relationship in human graft-versus-host disease. Mediterranean Journal of Hematology and Infectious Diseases, 8(1), e2016025. Lathrop, S. K., Bloom, S. M., Rao, S. M., Nutsch, K., Lio, C., Santacruz, N., et al. (2011). Peripheral education of the immune system by colonic commensal microbiota. Nature, 478(7368), 250-254. Lavoie-Tremblay, M., Sanzone, L., Aubé, T., and Paquet, M. (2021). Sources of stress and coping strategies among undergraduate nursing students across all years. Canadian Journal of Nursing Research, 54(3), 261-271. Le May, C., Berger, J. M., Lespine, A., Pillot, B., Prieur, X., Letessier, E., et al. (2013). Transintestinal cholesterol excretion is an active metabolic process modulated by PCSK9 and statin involving ABCB1. Arterioscler Thromb Vasc Biol, 33(7), 1484-1493. Le Roy, T., Lécuyer, E., Chassaing, B., Rhimi, M., Lhomme, M., Boudebbouze, S., et al. (2019). The intestinal microbiota regulates host cholesterol homeostasis. BMC Biol., 17(1), 94. Lee, S. H., Jeong, M. H., Cho, J. M., Kang, W. C., Hur, S. H., Jeon, D. W., et al. (2015). Prevalence of lipid abnormalities among treated patients with stable CHD: The dyslipidemia international study (DYSIS) II South Korea results. Atherosclerosis, 241(1), e131-e132. Lefebvre, P., Cariou, B., Lien, F., Kuipers, F., and Staels, B. (2009). Role of bile acids and bile acid receptors in metabolic regulation. Physiol Rev, 89(1), 147-191. Ley, R. E., Turnbaugh, P. J., Klein, S., and Gordon, J. I. (2006). Microbial ecology: Human gut microbes associated with obesity. Nature, 444(7122), 1022-1023. Li, J., Lin, S., Vanhoutte, P. M., Woo, C. W., and Xu, A. (2016). Akkermansia muciniphila protects against atherosclerosis by preventing metabolic endotoxemia-induced inflammation in apoe−/− mice. Circ. (New York, N.Y.), 133(24), 2434-2446. Li, J., Zhao, F., Wang, Y., Chen, J., Tao, J., Tian, G., et al. (2017). Gut microbiota dysbiosis contributes to the development of hypertension. Microbiome, 5(1), 14. Li, L., Baumann, C. A., Meling, D. D., Sell, J. L., and Beitz, D. C. (1996). Effect of orally administered Eubacterium coprostanoligenes ATCC 51222 on plasma cholesterol concentration in laying hens. Poult Sci J, 75(6), 743-745. Li, S. S., Zhu, A., Benes, V., Costea, P. I., Hercog, R., Hildebrand, F., et al. (2016). Durable coexistence of donor and recipient strains after fecal microbiota transplantation. Science, 352(6285), 586-589. Li, T., Francl, J. M., Boehme, S., Ochoa, A., Zhang, Y., Klaassen, C. D., et al. (2012). Glucose and insulin induction of bile acid synthesis: mechanisms and implication in diabetes and obesity. J. Biol. Chem., 287(3), 1861-1873. Libby, P. (2017). Superficial erosion and the precision management of acute coronary syndromes: Not one-size-fits-all. European Heart Journal, 38(11), 801-803. Lichtenstein, A. H. (2009). Intestinal cholesterol metabolism. Annals of Medicine, 22(1), 49-52. Limbana, T., Khan, F., and Eskander, N. (2020). Gut microbiome and depression: How microbes affect the way we think. Curēus, 12(8), e9966. Liu, T. X., Niu, H. T., and Zhang, S. Y. (2015). Intestinal Microbiota Metabolism and Atherosclerosis. Chin Med J (Engl), 128(20), 2805-2811. Louppe, G., Wehenkel, L., Sutera, A., and Geurts, P. (2013). Understanding variable importances in forests of randomized trees. NIPS, 431–439. Lovallo, W. R., Farag, N. H., Sorocco, K. H., Cohoon, A. J., and Vincent, A. S. (2012). Lifetime adversity leads to blunted stress axis reactivity: Studies from the oklahoma family health patterns project. Biological Psychiatry, 71(4), 344-349. Lu, J., Breitwieser, F. P., Thielen, P., and Salzberg, S. L. (2017). Bracken: Estimating species abundance in metagenomics data. PeerJ Comput Sci, 3, e104. 143 | P a g e Lu, Y., Zhang, Y., Zhao, X., Shang, C., Xiang, M., Li, L., et al. (2022). Microbiota-derived short-chain fatty acids: Implications for cardiovascular and metabolic disease. Front. Cardiovasc. Med., 9, 900381. Lye, H. S., Rusul, G., and Liong, M. T. (2010). Removal of cholesterol by Lactobacilli via incorporation and conversion to coprostanol. J. Dairy Sci., 93(4), 1383-1392. Lynch, A., Crowley, E., Casey, E., Cano, R., Shanahan, R., McGlacken, G., et al. (2017). The Bacteroidales produce an N-acylated derivative of glycine with both cholesterol-solubilising and hemolytic activity. Sci Rep, 7(1), 13270. M., D., Anda, R. F., Dube, S. R., Giles, W. H., and Felitti, V. J. (2003). The relationship of exposure to childhood sexual abuse to other forms of abuse, neglect, and household dysfunction during childhood. Child Abuse Negl, 27, 625–639. Ma, J., Li, Z., Zhang, W., Zhang, C., Zhang, Y., Mei, H., et al. (2020). Comparison of gut microbiota in exclusively breast-fed and formula-fed babies: A study of 91 term infants. Scientific Reports, 10(1), 15792. Macfarlane, G. T. and Macfarlane, S. (2011). Fermentation in the human large intestine: Its physiologic consequences and the potential contribution of prebiotics. J Clin Gastroenterol, S120-S127. Macfarlane, S. and Macfarlane, G. T. (2003). Regulation of short-chain fatty acid production. Proceedings of the Nutrition Society, 62(1), 67-72. Mackie, R. I., Sghir, A., and Gaskins, H. R. (1999). Developmental microbial ecology of the neonatal gastrointestinal tract. The American Journal of Clinical Nutrition, 69(5), 1035S-1045S. Mahmoodpoor, F., Saadat, Y. R., Barzegari, A., Ardalan, M., and Vahed, S. Z. (2017). The impact of gut microbiota on kidney function and pathogenesis. Biomedicine and Pharmacotherapy, 93(412-419. Mailer, R. K. W., Gistera, A., Polyzos, K. A., Ketelhuth, D. F. J., and Hansson, G. K. (2017). Hypercholesterolemia induces differentiation of regulatory T cells in the liver. Circulation Research, 120(11), 1740-1753. Matey-Hernandez, M. L., Williams, F. M. K., Potter, T., Valdes, A. M., Spector, T. D., and Menni, C. (2018). Genetic and microbiome influence on lipid metabolism and dyslipidemia. Physiol Genomics, 50(2), 117-126. Matijašić, M., Meštrović, T., Paljetak, H. Č., Perić, M., Barešić, A., and Verbanac, D. (2020). Gut microbiota beyond bacteria-mycobiome, virome, archaeome, and eukaryotic parasites in IBD. International Journal of Molecular Sciences, 21(8), 2668. Mayer, E. A. (2000). The neurobiology of stress and gastrointestinal disease. Gut, 47(6), 861-869. Mayer, E. A., Savidge, T., and Shulman, R. J. (2014). Brain-gut microbiome interactions and functional bowel disorders. Gastroenterology, 146(6), 1500-12. McKee-Lopez, G., Robbins, L., Provencio-Vasquez, E., and Olvera, H. (2019). The relationship of childhood adversity on burnout and depression among BSN students. Journal of Professional Nursing, 35(2), 112-119. McLaughlin, K. A., Greif Green, J., Gruber, M. J., Sampson, N. A., Zaslavsky, A. M., and Kessler, R. C. (2012). Childhood adversities and first onset of psychiatric disorders in a national sample of US adolescents. Arch Gen Psychiatry, 69(11), 1151-60. McLaughlin, K. A. and Hatzenbuehler, M. L. (2009). Mechanisms linking stressful life events and mental health problems in a prospective, community-based sample of adolescents. J Adolesc Health, 44(2), 153-160. Meader, E., Mayer, M. J., Steverding, D., Carding, S. R., and Narbad, A. (2013). Evaluation of bacteriophage therapy to control Clostridium difficile and toxin production in an in vitro human colon model system. Anaerobe, 22, 25-30. Midtvedt, A. C. and Midtvedt, T. (1993). Conversion of cholesterol to coprostanol by the intestinal microflora during the 1st 2 years of human life. J. Pediatr. Gastroenterol. Nutr., 17(2), 161-168. 144 | P a g e Midtvedt, T., Lingaas, E., Carlstedt-Duke, B., Höverstad, T., Midtvedt, A. C., Saxerholt, H., et al. (1990). Intestinal microbial conversion of cholesterol to coprostanol in man. Influence of antibiotics. APMIS : Acta Pathologica, Microbiologica, Et Immunologica Scandinavica, 98(9), 839. Milani, C., Duranti, S., Bottacini, F., Casey, E., Turroni, F., Mahony, J., et al. (2017). The first microbial colonizers of the human gut: Composition, activities, and health implications of the infant gut microbiota. Microbiology and Molecular Biology Reviews, 81(4). Millan, B., Park, H., Hotte, N., Mathieu, O., Burguiere, P., Tompkins, T., et al. (2016). Fecal microbial transplants reduce antibiotic-resistant genes in patients with recurrent Clostridium difficile infection. Clin Infect Dis, 62(12), 1479-1486. Mitra, S., Drautz-Moses, D. I., Alhede, M., Maw, M. T., Liu, Y., Purbojati, R. W., et al. (2015). In silico analyses of metagenomes from human atherosclerotic plaque samples. Microbiome, 3(1), 38. Moayyedi, P. (2008). The epidemiology of obesity and gastrointestinal and other diseases: An overview. Digestive Diseases and Sciences, 53(9), 2293-2299. Moayyedi, P., Surette, M. G., Kim, P. T., Libertucci, J., Wolfe, M., Onischi, C., et al. (2015). Fecal microbiota transplantation induces remission in patients with active ulcerative colitis in a randomized controlled trial. Gastroenterology, 149(1), 102-109. Mönnikes, H., Schmidt, B. G., and Taché, Y. (1993). Psychological stress-induced accelerated colonic transit in rats involves hypothalamic corticotropin-releasing factor. Gastroenterology, 104(3), 716. Morgan, A. E., Mooney, K. M., Wilkinson, S. J., Pickles, N. A., and Mc Auley, M. T. (2016). Cholesterol metabolism: A review of how ageing disrupts the biological mechanisms responsible for its regulation. Ageing Research Reviews, 27, 108-124. Moussaoui, N., Larauche, M., Biraud, M., Molet, J., Million, M., Mayer, E., et al. (2016). Limited Nesting Stress Alters Maternal Behavior and In Vivo Intestinal Permeability in Male Wistar Pup Rats. PLOS ONE, 11(5), e0155037. Muegge, B. D., Kuczynski, J., Kinghts, D., Clemente, J. C., González, A., Fontana, L., et al. (2011). Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science, 332(6032), 970-974. Musso, G., Gambino, R., and Cassader, M. (2011). Interactions between gut microbiota and host metabolism predisposing to obesity and diabetes. Annual Reviews of Medicine, 62, 361-380. Mutlu, E. A., Gillevet, P. M., Rangwala, H., Sikaroodi, M., Naqvi, A., Engen, P. A., et al. (2012). Colonic microbiome is altered in alcoholism. American Journal of Physiology: Gastrointestinal and Liver Physiology, 302(9), G966-G978. Nakaya, K. and Ikewaki, K. (2018). Microbiota and HDL metabolism. Curr Opin Lipidol, 29(1), 18-23. Nale, J. Y., Redgwell, T. A., Millard, A., and Clokie, M. R. J. (2018). Efficacy of an optimised bacteriophage cocktail to clear Clostridium difficile in a batch fermentation model. Antibiotics 7, 13. Negi, S., Das, D. K., Pahari, S., Nadeem, S., and Agrewala, J. N. (2019). Potential role of gut microbiota in induction and regulation of innate immune memory. Frontiers in Immunology, 10, 2441. Ng, S. C., Kamm, M. A., Yeoh, Y. K., Chan, P. K. S., Zuo, T., Tang, W., et al. (2020). Scientific frontiers in faecal microbiota transplantation: Joint document of asia-pacific association of gastroenterology (APAGE) and asia-pacific society for digestive endoscopy (APSDE). Gut, 69(1), 83-91. Nicholson, J. K., Holmes, E., Kinross, J., Burcelin, R., Gibson, G., Jia, W., et al. (2012). Host-gut microbiota metabolic interactions. Science, 336(6086), 1262-1267. Nigam, Y., Knight, J., and Williams, N. (2019). Gastrointestinal tract 5: The anatomy and functions of the large intestine. Nursing Times, 115(10), 50. Njim, T., Mbanga, C., Mouemba, D., Makebe, H., Toukam, L., Kika, B., et al. (2020). Determinants of depression among nursing students in cameroon: A cross-sectional analysis. BMC Nursing, 19(1), 26. 145 | P a g e Normén, L., Laerke, H. N., Jensen, B. B., Langkilde, A. M., and Andersson, H. (2001). Small-bowel absorption of D-tagatose and related effects on carbohydrate digestibility: An ileostomy study. Am J Clin Nutr, 73(1), 105-110. Nusslock, R. and Miller, G. E. (2016). Early-life adversity and physical and emotional health across the lifespan: A neuroimmune network hypothesis. Biological Psychiatry, 80(1), 23-32. Nutting, C. W., Islam, S., and Daugirdas, J. T. (1991). Vasorelaxant effects of short chain fatty acid salts in rat caudal artery. AJP- Heart and Circulatory Physiology, 261(2), H561-H567. O'Mahony, S. M., Marchesi, J. R., Scully, P., Codling, C., Ceolho, A.-M., Quigley, E. M. M., et al. (2009). Early Life Stress Alters Behavior, Immunity, and Microbiota in Rats: Implications for Irritable Bowel Syndrome and Psychiatric Illnesses. Biological Psychiatry, 65(3), 263-267. Odamaki, T., Kato, K., Sugahara, H., Hashikura, N., Takahashi, S., Xiao, J. Z., et al. (2016). Age-related changes in gut microbiota composition from newborn to centenarian: A cross-sectional study. BMC Microbiology, 16, 90. Okada, H., Kuhn, C., Feillet, H., and Bach, J.-. (2010). 'hygiene hypothesis' for autoimmune and allergic diseases: An update. Clinical and Experimental Immunology,, 160(1), 1-9. Oksanen, J., Simpson, G. L., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P., et al. Vegan: Community Ecology Package. R package version 2.4-6. 2022; Available online: https://cran.r-project.org/web/packages/vegan/index.html. Olsson, L. M., Boulund, F., Nilsson, S., Khan, M. T., Gummesson, A., Fagerberg, L., et al. (2022). Dynamics of the normal gut microbiota: A longitudinal one-year population study in Sweden. Cell Host & Microbe, 30(5), 726-739. Olvera, H. A., Provencio-Vasquez, E., Slavich, G. M., Laurent, J. G. C., Browning, M., McKee-Lopez, G., et al. (2019). Stress and health in nursing students: The nurse engagement and wellness study. Nursing Research (New York), 68(6), 453-463. Osadchiy, V., Martin, C. R., and Mayer, E. A. (2019). The Gut–Brain axis and the microbiome: Mechanisms and clinical implications. Clinical Gastroenterology and Hepatology, 17(2), 322-332. Ott, S. J., Waetzig, G. H., Rehman, A., Moltzau-Anderson, J., Bharti, R., Grasis, J. A., et al. (2027). Efficacy of sterile fecal filtrate transfer for treating patients with Clostridium difficile infection. Gastroenterology, 152(4), 799-811. Palmer, C., Bik, E. M., DiGiulio, D. B., Relman, D. A., and Brown, P. O. (2007). Development of the human infant intestinal microbiota. PloS Biology, 5(7), e177. Pamer, E. (2016). Resurrecting the intestinal microbiota to combat antibiotic-resistant pathogens. Science, 352(6285), 535-538. Panek, M., Čipčić Paljetak, H., Barešić, A., Perić, M., Matijašić, M., Lojkić, I., et al. (2018). Methodology challenges in studying human gut microbiota - effects of collection, storage, DNA extraction and next generation sequencing technologies. Scientific Reports, 8(1), 5143-13. Paramsothy, S., Kamm, M. A., Kaakoush, N. O., Walsh, A. J., van den Bogaerde, J., Samuel, D., et al. (2017). Multidonor intensive faecal microbiota transplantation for active ulcerative colitis: A randomised placebo-controlled trial. Lancet, 389(10075), 1218-1228. Paredes-Sabja, D., Torres, J. A., Setlow, P., and Sarker, M. R. (2008). Clostridium perfringens spore germination: Characterization of germinants and their receptors. J Bacteriol, 190(4), 1190-1201. Paterson, L. M., Nutt, D. J., and Wilson, S. J. (2009). NAPSAQ-1: National patient sleep assessment questionnaire in depression. International Journal of Psychiatry in Clinical Practice,, 13(1), 48-58. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: Machine learning in python. JMLR, 12(85), 2825-2830. Petrak, F., Herpertz, S., Hirsch, J., Röhrig, B., Donati-Hirsch, I., Juckel, G., et al. (2022). Gut microbiota differs in composition between adults with type 1 diabetes with or without depression and healthy control participants: A case-control study. BMC Microbiology, 22(1), 1-169. 146 | P a g e Phillips, I. R., Dolphin, C. T., Clair, P., Hadley, M. R., Hutt, A. J., McCombie, R. R., et al. (1995). The molecular biology of the flavin-containing monooxygenases of man. Chemico-Biological Interactions, 96(1), 17-32. Pluznick, J. L., Protzko, R. J., Gevorgyan, H., Peterlin, Z., Sipos, A., Han, J., et al. (2013). Olfactory receptor responding to gut microbiota-derived signals plays a role in renin secretion and blood pressure regulation. Proceedings of the National Academy of Sciences, 110(11), 4410-4415. Purohit, V., Bode, J. C., Bode, C., Brenner, D. A., Choudhry, M. A., Hamilton, F., et al. (2008). Alcohol, intestinal bacterial growth, intestinal permeability to endotoxin, and medical consequences: Summary of a symposium. Alcohol, 42(5), 349-361. Qin, J., Li, Y., Cai, Z., Li, S., Zhu, J., Zhang, F., et al. (2012). A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature, 490(7418), 55-60. Qu, Y., Yang, C., Ren, Q., Ma, M., Dong, C., and Hashimoto, K. (2017). Comparison of (R)-ketamine and lanicemine on depression-like phenotype and abnormal composition of gut microbiota in a social defeat stress model. Scientific Reports, 7(1), 15725-10. Quera, R., Espinoza, R., Estay, C., and Rivera, D. (2014). Bacteremia as an adverse event of fecal microbiota transplantation in a patient with crohn's disease and recurrent Clostridium difficile infection. J Crohn's Colitis, 8(3), 252-253. Quigley, E. M. M. (2013). Gut bacteria in health and disease. Gastroenterology & Hepatology, 9(9), 560. Rampelli, S., Candela, M., Turroni, S., Biagi, E., Collino, S., Franceschi, C., et al. (2013). Functional metagenomic profiling of intestinal microbiome in extreme ageing. Aging-US, 5(12), 902-912. Rea, K., Dinan, T. G., and Cryan, J. F. (2016). The microbiome: A key regulator of stress and neuroinflammation. Neurobiology of Stress, 4, 23-33. Regitz-Zagrosek, V. and Kararigas, G. (2017). Mechanistic pathways of wex differences in cardiovascular disease. Physiol Rev, 97(1), 1-37. Relizani, K., Le Corf, K., Kropp, C., Martin-Rosique, R., Kissi, D., Déjean, G., et al. (2022). Selection of a novel strain of Christensenella minuta as a future biotherapy for Crohn’s disease. Sci. Rep., 12, 6017. Rella, S., Winwood, P. C., and Lushingtoon, K. (2009). When does nursing burnout begin? An investigation of the fatigue experience of australian nursing students. Journal of Nursing Management, 17(7), 886-897. Ren, D., Li, L., Schwabacher, A. W., Young, J. W., and Beitz, D. C. (1996). Mechanism of cholesterol reduction to coprostanol by Eubacterium coprostanoligenes ATCC 51222. Steroids, 61(1), 33-40. Ribeiro, F. M., Petriz, B., Marques, G., Kamilla, L. H., and Franco, O. L. (2021). Is there an exercise-intensity threshold capable of avoiding the leaky gut? Frontiers in Nutrition, 8,627289. Rinninella, E., Raoul, P., Cintoni, M., Franceschi, F., Miggiano, G. A. D., Gasbarrini, A., et al. (2019). What is the healthy gut microbiota composition? A changing ecosystem across age, environment, diet, and diseases. Microorganisms (Basel), 7(1), 14. Ríos-Covián, D., Ruas-Madiedo, P., Margolles, A., Gueimonde, M., Reyes-Gavilán, d. L., and Clara G, S., N. (2016). Intestinal short chain fatty acids and their link with diet and human health. Front Microbiol, 7, 185. Robb, C. S., Hobbs, J. K., Pluvinage, B., Reintjes, G., Klassen, L., Monteith, S., et al. (2022). Metabolism of a hybrid algal galactan by members of the human gut microbiome. Nat. Chem. Biol., 18(5), 501-510. Robbe Masselot, C., Cordier, C., Marsac, B., Nachury, M., Léonard, R., and Sendid, B. (2023). Human fecal mucin glycosylation as a new biomarker in inflammatory bowel diseases. Inflammatory Bowel Diseases, 29(1), 167-171. 147 | P a g e RodrÍguez, J. M., Murphy, K., Stanton, C., Ross, R. P., Kober, O. I., Juge, N., et al. (2015). The composition of the gut microbiota throughout life, with an emphasis on early life. Microbial Ecology in Health and Disease, 26(1), 1-26050. Rohrmann, S., Linseisen, J., Allenspach, M., von Eckardstein, A., and Müller, D. (2016). Plasma concentrations of trimethylamine-N-oxide are directly associated with dairy food consumption and low-grade inflammation in a German adult population. J Nutr, 146(2), 283-289. Rosenfeld, R. S., Fukushima, D. K., Hellman, L., and Gallagher, T. F. (1954). The transformation of cholesterol to coprostanol. JBC, 211(1), 301-311. Rosenson, R. S., Brewer, H. B., Jr., Ansell, B., Barter, P., Chapman, M. J., Heinecke, J. W., et al. (2013). Translation of high-density lipoprotein function into clinical practice: Current prospects and future challenges. Circulation, 128(11), 1256-1267. Rothschild, D., Weissbrod, O., Barkan, E., Kurilshikov, A., Korem, T., Zeevi, D., et al. (2018). Environment dominates over host genetics in shaping human gut microbiota. Nature, 555(7695), 210. Sandek, A., Bjarnason, I., Volk, H. D., Crane, R., Meddings, J. B., Niebauer, J., et al. (2012). Studies on bacterial endotoxin and intestinal absorption function in patients with chronic heart failure. Int J Cardiol, 157(1), 80-85. Sansonetti, P. J. and Medzhitov, R. (2009). Learning tolerance while fighting ignorance. Cell, 138(3), 416-420. Schwab, L., Goroncy, L., Palaniyandi, S., Gautam, S., Triantafyllopoulou, A., Mocsai, A., et al. (2014). Neutrophil granulocytes recruited upon translocation of intestinal bacteria enhance graft-versus-host disease via tissue damage. Nature Medicine, 20(6), 648-654. Schwartz, M., Gluck, M., and Koon, S. (2013). Norovirus gastroenteritis after fecal microbiota transplantation for treatment of Clostridium difficile infection despite asymptomatic donors and lack of sick contacts. Am J of Gastroenterol, 108(8), 1367. Seekatz, A. M., Theriot, C. M., Rao, K., Chang, Y., Freeman, A. E., Kao, J. Y., et al. (2018). Restoration of short chain fatty acid and bile acid metabolism following fecal microbiota transplantation in patients with recurrent Clostridium difficile infection. Anaerobe, 53, 64-73. Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W. S., et al. (2011). Metagenomic biomarker discovery and explanation. Genome Biology, 12(6), R60. Sekimoto, H., Shimada, O., Makanishi, M., Nakano, T., and Katayama, O. (1983). Interrelationship between serum and fecal sterols. JPN. J. Med., 22(1), 14-20. Shondelmyer, K., Knight, R., Sanivarapu, A., Ogino, S., and Vanamala, K. P. V. (2018). Ancient Thali diet: Gut microbiota, immunity, and health. Yale J Biol Med, 91(2), 177–184. Smillie, C. S., Sauk, J., Gevers, D., Friedman, J., Sung, J., Youngster, I., et al. (2018). Strain tracking reveals the determinants of bacterial engraftment in the human gut following fecal microbiota transplantation. Cell Host Microbe, 23(2), 229-240. Smith, R. P., Easson, C., Lyle, S. M., Kapoor, R., Donnelly, C. P., Davidson, E. J., et al. (2019). Gut microbiome diversity is associated with sleep physiology in humans. PloS One, 14(10), e0222394. Sokol, H., Pigneur, B., Watterlot, L., Lakhdari, O., Bermúdez-Humarán, L. G., Gratadoux, J., et al. (2008). Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of crohn disease patients. Proc. Natl. Acad. Sci. USA, 105(43), 16731-16736. Sorci-Thomas, M. G. and Thomas, M. J. (2016). Microdomains, Inflammation, and Atherosclerosis. Circulation Research, 118(4), 679-691. Specian, R. D. and Oliver, M. G. (1991). Functional biology of intestinal goblet cells. American Journal of Physiology: Cell Physiology, 260(2), C183-C193. Stinson, L. F., Boyce, M. C., Payne, M. S., and Keelan, J. A. (2019). The not-so-sterile womb: Evidence that the human fetus is exposed to bacteria prior to birth. Frontiers in Microbiology, 10, 1124. 148 | P a g e Sun, X., Jiao, X., Ma, Y., Liu, Y., Zhang, L., He, Y., et al. (2016). Trimethylamine N-oxide induces inflammation and endothelial dysfunction in human umbilical vein endothelial cells via activating ROS-TXNIP-NLRP3 inflammasome. Biochemical and Biophysical Research Communications, 481(1-2), 63-70. Tahri, K., Grill, J. P., and Schneider, F. (1997). Involvement of trihydroxyconjugated bile salts in cholesterol assimilation by bifidobacteria. Curr Microbiol, 34(2), 79-84. Takagi, T., Naito, Y., Inoue, R., Kashiwagi, S., Uchiyama, K., Mizushima, K., et al. (2019). Differences in gut microbiota associated with age, sex, and stool consistency in healthy Japanese subjects. Gastroenterology, 54(1), 53-63. Tan, J., McKenzie, C., Potamitis, M., Thorburn, A. N., Mackay, C. R., and Macia, L. (2015). The role of short-chain fatty acids in health and disease. Adv. Imm., 121, 91-119. Tanaka, M. and Nakayama, J. (2017). Development of the gut microbiota in infancy and its impact on health in later life. Allergology International, 66(4), 515-522. Tang, W. H., Kitai, T., and Hazen, S. L. (2017). Gut microbiota in cardiovascular health and disease. Circulation Research, 120(7), 1183-1196. Tang, W. H. W. and Hazen, S. L. (2014). The contributory role of gut microbiota in cardiovascular disease. The Journal of Clinical Investigation, 124(10), 4204-4211. Tang, W. H. W., Wang, Z., Levison, B. S., Koeth, R. A., Britt, E. B., Fu, X., et al. (2013). Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. The New England Journal of Medicine, 368(17), 1575-1584. Taranto, M. P., Medici, M., Perdigon, G., Ruiz Holgado, A. P., and Valdez, G. F. (1998). Evidence for hypocholesterolemic effect of lactobacillus reuteri in hypercholesterolemic mice. J Dairy Sci., 81(9), 2336-2340. Taur, Y., Coyte, K., Schluter, J., Robilotti, E., Figueroa, C., Gjonbalaj, M., et al. (2018). Reconstitution of the gut microbiota of antibiotic-treated patients by autologous fecal microbiota transplant. Sci Transl Med, 10(460), eaap9489. Thaiss, C. A., Levy, M., Korem, T., Dohnalová, L., Shapiro, H., Jaitin, D. A., et al. (2016). Microbiota diurnal rhythmicity programs host transcriptome oscillations. Cell, 167(6), 1495-1510.e12. Thursby, E. and Juge, N. (2017). Introduction to the human gut microbiota. Biochem. J., 474(11), 1823-1836. Toh, T. S., Wei Jie Lee, J., Toh, K. Y., Ho, J. P., Fung Yen Lim, J., Tan, A. H., et al. (2021). IDDF2021-ABS-0165 Psychological well-being and sleep quality among healthy stool donors in singapore: A cross-sectional study. Gut, 70, A50-A51. Tolhurst, G., Heffron, H., Lam, Y., Parker, H., Habib, A., Diakogiannaki, E., et al. (2012). Short-chain fatty acids stimulate glucagon-like peptide-1 secretion via the G-protein-coupled receptor FFAR2. Diabetes, 61(2), 364-371. Trauner, M., Fickert, P., and Tilg, H. (2013). Bile acids as modulators of gut microbiota linking dietary habits and inflammatory bowel disease: A potentially dangerous liaison. Gastroenterology, 144(4), 844-846. Underwood, M. A., Gaerlan, S., De Leoz, M. L. A., Dimapasoc, L., Kalanetra, K. M., Lemay, D. G., et al. (2015). Human milk oligosaccharides in premature infants: Absorption, excretion, and influence on the intestinal microbiota. Pediatric Research, 78(6), 670-677. Ursell, L. K., Metcalf, J. L., Parfrey, L. W., and Knight, R. (2012). Defining the human microbiome. Virus Research, 70(8), S38-S44. Van den Abbeele, P., Belzer, C., Goossens, M., Kleerebezem, M., M., D. V. W., Thas, O., et al. (2013). Butyrate-producing clostridium cluster XIVa species specifically colonize mucins in an in vitro gut model. ISME J, 7(5), 949-961. 149 | P a g e van den Munckhof, I. C. L., Kurilshikov, A., Ter Horst, R., Riksen, N. P., Joosten, L. A. B., Zhernakova, A., et al. (2018). Role of gut microbiota in chronic low-grade inflammation as potential driver for atherosclerotic cardiovascular disease: A systematic review of human studies. Obes Rev, 19(12), 1719-1734. Van der Flier, L. G. and Clevers, H. (2009). Stem cells, self-renewal, and differentiation in the intestinal epithelium. Annu Rev Physiol, 71, 241-260. van der Meer, L., van Duijn, E., Wolterbeek, R., and Tibben, A. (2012). Adverse childhood experiences of persons at risk for huntington's disease or BRCA1/2 hereditary breast/ovarian cancer. Clinical Genetics, 81(1), 18-23. van der Velde, A. E., Brufau, G., and Groen, A. K. (2010). Transintestinal cholesterol efflux. Curr Opin Lipidol, 21(3), 167-171. van Klinken, B. J., Einerhand, A. W., Duits, L. A., Makkink, M. K., Tytgat, K. M., Renes, I. B., et al. (1999). Gastrointestinal expression and partial cDNA cloning of murine Muc2. The American Journal of Physiology, 276(1), G115-G124. Van Schooneveld, T. C., Gross, A., Kalil, A. C., Hataye, J. M., Palmore, T. N., Powers, J. H., et al. (2013). Duodenal infusion of feces for recurrent Clostridium difficile. The New England Journal of Medicine, 368(22), 2143-2145. van Tongeren, S. P., Slaets, J. P., Harmsen, H. J., and Welling, G. W. (2005). Fecal microbiota composition and frailty. Applied and Environmental Microbiology, 71(10), 6438-6442. Vasapolli, R., Schütte, K., Schulz, C., Vital, M., Schomburg, D., Pieper, D. H., et al. (2019). Analysis of transcriptionally active bacteria throughout the gastrointestinal tract of healthy individuals. Gastroenterology, 157(4), 1081-1092. Veiga, P., Juste, C., Lepercq, P., Saunier, K., Beguet, F., and Gerard, P. (2005). Correlation between faecal microbial community structure and cholesterol-to-coprostanol conversion in the human gut. FEMS Microbiol. Lett., 242(1), 81-86. Voet, D. and Voet, J. G., Biochemistry. 4th ed. 2010: Wiley & Sons. Volk, N. and Lacy, B. (2017). Anatomy and physiology of the small bowel. Gastrointestinal Endoscopy Clinics of North America, 27(1), 1-13. Vrieze, A., Van Nood, E., Holleman, F., Salojärvi, J., Kootte, R. S., Bartelsman, J. F. W. M., et al. (2012). Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome. Gastroenterology 143(4), 913-916.e7. Wade, R., Cronholm, P. F., Fein, J. A., Forke, C. M., Davis, M. B., Harkins-Schwarz, M., et al. (2016). Household and community-level adverse childhood experiences and adult health outcomes in a diverse urban population. Child Abuse & Neglect, 52, 135-145. Wan, Z., Zheng, J., Zhu, Z., Sang, L., Zhu, J., Luo, S., et al. (2022). Intermediate role of gut microbiota in vitamin B nutrition and its influences on human health. Frontiers in Nutrition, 9(1031502. Wang, Z., Klipfell, E., Bennett, B. J., Koeth, R., Levison, B. S., DuGar, B., et al. (2011). Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature, 472(7341), 57-63. Wang, Z., Roberts, A. B., Buffa, J. A., Levison, B. S., Zhu, W., Org, E., et al. (2015). Non-lethal inhibition of gut microbial trimethylamine production for the treatment of atherosclerosis. Cell, 163(7), 1585-1595. Waters, J. L. and Ley, R. E. (2019). The human gut bacteria christensenellaceae are widespread, heritable, and associated with health. BMC Biol., 17(1), 83. Welch, C. E., Ottinger, L. W., and Welch, J. P., Anatomy and Physiology of the Colon and Rectum, in Manual of Lower Gastrointestinal Surgery. 1980, Springer: New York. Whisenant, S. G., Repairing damaged wildlands: A process-oriented, landscape-scale approach. 1999, New York, USA: Cambridge University Press. 150 | P a g e Wickham, H., ggplot2: Elegant Graphics for Data Analysis, ed. Springer-Verlag. 2009, Springer-Verlag New York. Wickham, H. Reshape2: Flexibly Reshape Data: A Reboot of the Reshape Package. 2020; Available online: https://cran.r-project.org/web/packages/reshape2/index.html. Widmer, R. J., Flammer, A. J., Lerman, L. O., and Lerman, A. (2015). The mediterranean diet, its components, and cardiovascular disease. Am J Med, 128, 229–38. Wilck, N., Matus, M. G., Kearney, S. M., Olesen, S. W., Forslund, K., Bartolomaeus, H., et al. (2017). Salt-responsive gut commensal modulates TH17 axis and disease. Nature, 551(7682), 585-589. Wilkins, T. D. and Hackman, A. S. (1974). Two patterns of neutral steroid conversion in the feces of normal North Americans. Cancer Res. (Chicago, Ill.), 34(9), 2250-2254. Wittebole, X., De Roock, S., and Opal, S. M. (2014). A historical overview of bacteriophage therapy as an alternative to antibiotics for the treatment of bacterial pathogens. Virulence, 5(1), 226-235. Wlodarska, M., Willing, B., Keeney, K. M., Menendez, A., Bergstrom, K. S., Gill, N., et al. (2011). Antibiotic treatment alters the colonic mucus layer and predisposes the host to exacerbated Citrobacter rodentium-induced colitis. Infection and Immunity, 79(4), 1536-1545. Wood, D. E., Lu, J., and Langmead, B. (2019). Improved metagenomic analysis with kraken 2. Genome Biol, 20(1), 257-13. Wu, K., Sun, W., Li, N., Chen, Y., Wei, Y., and Chen, D. (2019). Small intestinal bacterial overgrowth is associated with diarrhea-predominant irritable bowel syndrome by increasing mainly Prevotella abundance. Scandinavian Journal of Gastroenterology, 54(12), 1419-1425. Wylie, A. D. and Zandberg, W. F. (2018). Quantitation of sialic acids in infant formulas by liquid Chromatography–Mass spectrometry: An assessment of different protein sources and discovery of new analogues. J. Agrig. Food Chem., 66(30), 8114-8123. Xue, C., Li, G., Gu, X., Su, Y., Zheng, Q., Yuan, X., et al. (2023). Health and disease: Akkermansia muciniphila , the shining star of the gut flora. Research (Washington), 6, 0107. Yadav, H., Lee, J. H., Lloyd, J., Walter, P., and Rane, S. G. (2013). Beneficial metabolic effects of a probiotic via butyrate-induced GLP-1 hormone secretion. Journal of Biological Chemistry, 288(35), 25088-25097. Yan, J., Sheng, L., and Li, H. (2021). Akkermansia muciniphila: Is it the holy grail for ameliorating metabolic diseases. Gut Microbes, 13(1), 1984104. Yang, C., Qu, Y., Fujita, Y., Ren, Q., Ma, M., Dong, C., et al. (2017). Possible role of the gut microbiota-brain axis in the antidepressant effects of (R)-ketamine in a social defeat stress model. Translational Psychiatry, 7(12), 1294-11. Yao, Y., Kim, G., Shafer, S., Chen, Z., Kubo, S., Ji, Y., et al. (2022). Mucus sialylation determines intestinal host-commensal homeostasis. Cell, 185(7), 1172-1188.e28. Yatsunenko, T., Rey, F. E., Manary, M. J., Trehan, I., Dominguez-Bello, M. G., Contreras, M., et al. (2012). Human gut microbiome viewed across age and geography. Nature, 486(7402), 222-227. Yin, J., Liao, S., He, Y., Wang, S., Xia, G., Liu, F., et al. (2015). Dysbiosis of gut microbiota with reduced trimethylamine-N-oxide level in patients with large-artery atherosclerotic stroke or transient ischemic attack. Journal of the American Heart Association, 4(11). Yoo, B. B. and Mazmanian, S. K. (2017). The enteric network: Interactions between the immune and nervous systems of the gut. Immunity 46(6), 910-926. Yoshimoto, S., Loo, T. M., Atarashi, K., Kanda, H., Sato, S., Oyadomari, S., et al. (2013). Obesity-induced gut microbial metabolite promotes liver cancer through senescence secretome. Nature, 499(7456), 97-101. Yu, L., Hammer, R. E., Li-Hawkins, J., Von Bergmann, K., Lutjohann, D., Cohen, J. C., et al. (2002). Disruption of Abcg5 and Abcg8 in mice reveals their crucial role in biliary cholesterol secretion. Proceedings of the National Academy of Sciences, 99(25), 16237-16242. 151 | P a g e Zeisel, S. H. and Warrier, M. (2017). Trimethylamine N-oxide, the microbiome, and heart and kidney disease. Annual Review of Nutrition, 37(1), 157-181. Zeng, X., Gao, X., Peng, Y., Wu, Q., Zhu, J., Tan, C., et al. (2019). Higher risk of stroke is correlated with increased opportunistic pathogen load and reduced levels of butyrate-producing bacteria in the gut. Front. Cell. Infect. Microbiol., 9, 4. Zhan, S., Liu, C., Meng, J., Mao, R., Tu, T., Lin, J., et al. (2023). Mucosa-associated Oscillospira sp. is related to intestinal stricture and post-operative disease course in crohn's disease. Microorganisms (Basel), 11(3), 794. Zhang, S. and Chen, D. (2019). Facing a new challenge: The adverse effects of antibiotics on gut microbiota and host immunity. Chinese Medical Journal, 132(10), 1135-1138. Zheng, D., Liwinski, T., and Elinav, E. (2020). Interaction between microbiota and immunity in health and disease. Cell Research, 30(6), 492-506. Zheng, M., Han, R., Yuan, Y., Xing, Y., Zhang, W., Liu, Y., et al. (2023). The beneficial role of probiotic Akkermansia muciniphila in inflammatory bowel disease. Gastroenterology, 164(4), S92. Zhong, H., Penders, J., Shi, Z., Ren, H., Cai, K., Fang, C., et al. (2019). Impact of early events and lifestyle on the gut microbiota and metabolic phenotypes in young school-age children. Microbiome, 7(1), 2. Zhu, H., Li, S., Cao, J., Mende, D. R., Yu, C., Xu, J., et al. (2010). A human gut microbial gene catalogue established by metagenomic sequencing. Nature, 464(7285), 59-65. Zieniuk, B. and Fabiszewska, A. (2019). Yarrowia lipolytica: A beneficious yeast in biotechnology as a rare opportunistic fungal pathogen: A minireview. World J Microb Biot, 35(1), 1-8. Zuo, T., Wong, S., Cheung, C., Lam, K., Lui, R., Cheung, K., et al. (2018). Gut fungal dysbiosis correlates with reduced efficacy of fecal microbiota transplantation in Clostridium difficile infection. Nature, 9, 1-11. Zuo, T., Wong, S. H., Lam, K., Lui, R., Cheung, K., Tang, W., et al. (2017). Bacteriophage transfer during faecal microbiota transplantation in Clostridium difficile infection is associated with treatment outcome. Gut, 67(4), 634-643. 152 | P a g e Appendices Appendix A Supplementary table of PERMANOVA tests conducted from Chapter 2 Table A.1 PERMANOVA results along with their respective r2 and p-adjusted values. Pairs R2 P-adjusted Stool lipids (Figure 5A) High vs. Low 0.631 0.003 High vs. Non 0.739 0.003 Low vs. Non 0.421 0.021 Serum lipids (Figure 6A) High vs. Low 0.030 1 High vs. Non 0.046 1 Low vs. Non 0.089 1 Stool BAs (Figure 6C) High vs. Low 0.125 0.063 High vs. Non 0.160 0.081 Low vs. Non 0.066 1 Serum BAs (Figure 7A) High vs. Low 0.183 0.003 High vs. Non 0.207 0.003 Low vs. Non 0.190 0.186 Total glycan classes (Figure 9C) High vs. Low 0.072 0.648 High vs. Non 0.178 0.048 Low vs. Non 0.508 0.036 All glycans (Figure 9E) High vs. Low 0.097 0.114 High vs. Non 0.060 0.78 Low vs. Non 0.159 0.33 Sialic acids (Figure 9G) High vs. Low 0.255 0.093 High vs. Non 0.208 0.312 Low vs. Non 0.009 1 SCFAs (Figure B.1A) High vs. Low 0.028 1 High vs. Non 0.099 0.213 Low vs. Non 0.165 0.282 Adjusted p-values are displayed, which were obtained using 999 permutations and FDR correction applied. 153 | P a g e Cholesterol (ug/g)0.0Non Low HighCoprostanol (ug/g)0.000.500.75Non Low High0.257-α Hydroxycholesterol (ug/g)5x10-5Non Low High7-Dehydrocholesterol (ug/g)Non Low HighConverter Converter Converter Converter****beta-Sitosterol (ug/g)0.1Non Low High0.0ConverterCampesterol (ug/g)0.00Non Low HighCholestanol (ug/g)0.0100.015Non Low HighDesmosterol (ug/g)Non Low HighConverter Converter ConverterA)Lanosterol (ug/g)0.0050.01Non Low High0.0075ConverterLathosteorl (ug/g)Non Low High0.004ConverterStigmasterol (ug/g)0.00Non Low HighConverter0.20.40.610x10-515x10-51x10-42x10-43x10-44x10-40.20.30.050.100.150.0200.0250.05x10-410x10-415x10-40.01250.0080.0120.0160.010.020.03*** ****** *** * * * ** **** ** ** ** ** ** ** ****Total Cholesterol (mmol/L)0.34Non Low HighLDL (mmol/L)0.180.20Non Low HighHDL (mmol/L)Non Low HighConverter Converter ConverterNon-HDL (mmol/L)0.200.24Non Low High0.22ConverterChol/HDLNon Low High2ConverterTriglyceride (mmol/L)0.04Non Low HighConverter0.350.360.380.220.240.050.100.150.26340.060.080.10**B)0.370.28***Fig. S1 Stool lipids (A) and serum lipids (B) compared between converter groups. * p-adjusted < 0.05, ** p-adjusted < 0.01, *** p-adjusted < 0.001, **** p-adjusted < 0.0001.Appendix B Stool and serum lipid comparisons of healthy participants from Chapter 2 Figure B.1 Stool lipids (A) and serum lipids (B) compared between converter groups. * p-adjusted < 0.05, ** p-adjusted < 0.01, *** p-adjusted < 0.001, **** p-adjusted < 0.0001. 154 | P a g e Appendix C Stool SCFA assessment from Chapter 2 Figure C.1 Stool SCFAs among converter groups and their relationship with stool and serum lipids and bile acids, and bacterial taxa. Principal Component Analysis (PCA) with feature importance for stool SCFA composition (A). SCFAs compared between converter groups (B). Network analysis of stool SCFAs, conversion rate, stool and serum lipids and BAs, and bacterial taxa (C). Red links indicate negative and green links indicate positive associations with the width indicating correlation magnitude. * p-adjusted < 0.05, ** p-adjusted < 0.01 155 | P a g e Appendix D Sequencing and pipeline comparisons, and strain analysis from Chapter 2 Figure D.1 Rarefaction plot (A) and phylogenetic tree (B) comparing 16S rRNA sequencing with shotgun sequencing using the Kraken2 and MetaPhlAn3 pipelines. Phylogenetic tree comparing the A. muciniphila strains present in our samples, the purchased ATCC strain, and the reference genome from StrainPhlAn analysis (C). 156 | P a g e g_AkkermansiaRelative Abundance00.0050.010g_Prevotella00.015g_Lachnospira g_BifidobacteriumA. muciniphilla B. adolescentis B. ovatus L. eligens00.010.02Non High Non High Non High Non HighRelative AbundanceRelative Abundance00.010.02Relative Abundance 00.010Relative Abundance Relative Abundance 00.060.12Relative AbundanceRelative Abundance 0.050.100.150.20 0.015* ** *Non HighRelative Abundance*B. SP-M1000.0250.0500.075g_ChristensenellaConverter Converter Converter Converter0.0052.5x10-45.0x10-47.5x10-4ConverterNon HighConverter0.01500.0100.015*00.0030.0060.009Non HighConverterNon HighConverter0.030.09Non HighConverterRelative AbundanceNon HighConverterFig. S3 Top bacterial taxa from metagenomic sequencing using the Kraken 2 database comparedbetween non- and high-converters. * p-adjusted < 0.05 Appendix E Top bacterial taxa from metagenomic sequencing of Chapter 2 Figure E.1 Top bacterial taxa from metagenomic sequencing using the Kraken 2 database compared between non- and high-converters. * p-adjusted < 0.05 157 | P a g e Appendix F Stool mucus assessment compared between converters from Chapter 2 Figure F.1 Tiled cross-section of stool samples stained with fluorescence in-situ hybridization (FISH). 158 | P a g e Appendix G Supplementary table for clinical characteristics of rCDI patients receiving FMT from Chapter 4 Table G.2 Clinical characteristics of rCDI patients receiving FMT. Among recipients, 9/17 patients were successfully treated with a single FMT (53% successful FMT), while 8/17 patients failed the first FMT and required a second procedure. There was no difference between the two groups in factors of age (p=0.7039), sex (p=1.000), or duration of rCDI pre-FMT (p=0.4237). The Shapiro-Wilk test was used to determine if data were normally distributed. If normally distributed, a Student t-test was used, and if not the nonparametric Mann–Whitney U test was performed. Numbers are given as means with range in brackets. Single FMT Repeated FMT Number of patients 9 8 Age 67.7 (35.3-84.9) 71.4 (49.5-83.8) Gender 6 male; 3 female 5 male; 3 female Average duration of rCDI pre-FMT 150.4 days (71-275) 126.2 days (53-246) 159 | P a g e Desulfovibrio-DDesulfovibrio-P-BDesulfovibrio-P-AOdoribacter-DOdoribacter-P-BOdoribacter-P-AClostridioides-DClostridioides-P-BClostridioides-P-AOscillibacter-DOscillibacter-P-BOscillibacter-P-ABacillus-DBacillus-P-BBacillus-P-AFilifactor-DFilifactor-P-BFilifactor-P-AIntestinimonas-DIntestinimonas-P-BIntestinimonas-P-AWigglesworthia-DWigglesworthia-P-BWigglesworthia-P-AYarrowia-DYarrowia-P-BYarrowia-P-A(A) D1-R1-S(B) D1-R2-SDesulfovibrio-DDesulfovibrio-P-BDesulfovibrio-P-AOdoribacter-DOdoribacter-P-BOdoribacter-P-AClostridioides-DClostridioides-P-BClostridioides-P-AOscillibacter-DOscillibacter-P-BOscillibacter-P-ABacillus-DBacillus-P-BBacillus-P-AFilifactor-DFilifactor-P-BFilifactor-P-AIntestinimonas-DIntestinimonas-P-BIntestinimonas-P-AWigglesworthia-DWigglesworthia-P-BWigglesworthia-P-AYarrowia-DYarrowia-P-BYarrowia-P-A(C) D1-R10-F(D) D1-R11-F0.0010.0020.0030.0040.0050.0060.0070.0080.0500.0600.0010.0020.0030.0040.0050.0060.0070.0080.0500.0600.0010.0020.0030.0040.0050.0060.0070.0080.0500.0600.0010.0020.0030.0040.0050.0060.0070.0080.0500.060Relative Abundance0.0000.0000.0000.000Relative Abundance)LJ 61 7KH LQWUDYDULDELOLW\ RI UHODWLYH DEXQGDQFH RI WKH WRS IHDWXUHV RI GRQRU  D1 VDPSOHV DQG WKHLU FRUUHVSRQGLQJ UHFLSLHQWV SUH DQG SRVW)07 OHDGLQJ WR VXFFHVVIXO A DQG B DQG IDLOHG C DQG D )07 RXWFRPHV Appendix H The intra-variability of features of donor 1 and the corresponding recipients from Chapter 4 Figure H.1 The intra-variability of relative abundance of the top 9 features of donor 1 (D1) samples and their corresponding recipients, pre- and post-FMT leading to successful (A and B) and failed (C and D) FMT outcomes. 160 | P a g e 0.0010.0020.0030.0040.0050.0060.0070.008Desulfovibrio-DDesulfovibrio-P-BDesulfovibrio-P-AOdoribacter-DOdoribacter-P-BOdoribacter-P-AClostridioides-DClostridioides-P-BClostridioides-P-AOscillibacter-DOscillibacter-P-BOscillibacter-P-ABacillus-DBacillus-P-BBacillus-P-AFilifactor-DFilifactor-P-BFilifactor-P-AIntestinimonas-DIntestinimonas-P-BIntestinimonas-P-AWigglesworthia-DWigglesworthia-P-BWigglesworthia-P-AYarrowia-DYarrowia-P-BYarrowia-P-A0.0500.060 (A) D2-R7-S0.0010.0020.0030.0040.0050.0060.0070.0080.0500.060 (B) D2-R8-S0.0010.0020.0030.0040.0050.0060.0070.008Desulfovibrio-DDesulfovibrio-P-BDesulfovibrio-P-AOdoribacter-DOdoribacter-P-BOdoribacter-P-AClostridioides-DClostridioides-P-BClostridioides-P-AOscillibacter-DOscillibacter-P-BOscillibacter-P-ABacillus-DBacillus-P-BBacillus-P-AFilifactor-DFilifactor-P-BFilifactor-P-AIntestinimonas-DIntestinimonas-P-BIntestinimonas-P-AWigglesworthia-DWigglesworthia-P-BWigglesworthia-P-AYarrowia-DYarrowia-P-BYarrowia-P-A0.0500.060 (C) D2-R15-F0.0010.0020.0030.0040.0050.0060.0070.0080.0500.060 (D) D2-R16-FRelative Abundance0.0000.0000.0000.000Relative Abundance)LJ 6 The intra-variability of relative abundance of the top 9 features of donor 3 (D3) samples and their corresponding recipients, pre- and post-FMT leading to successful (A and B) and failed (C and D) FMT outcomes.Appendix I The intra-variability of features of donor 3 and the corresponding recipients from Chapter 4 Figure I.1 The intra-variability of relative abundance of the top 9 features of donor 3 (D3) samples and their corresponding recipients, pre- and post-FMT leading to successful (A and B) and failed (C and D) FMT outcomes.
Thesis/Dissertation
eng
Mechanical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Pakpour, Sepideh
Investigating the human microbiome : dynamic interactions and therapeutic potentials in health and disease states
Text