Science, Faculty of
DSpace
UBCO
Dadlani, Hansika
Master of Science - MSc
University of British Columbia
High fat diets (HFDs) cause insulin resistance, oxidative stress, inflammation, all of which are linked with gut microbial dysbiosis. As a therapy, probiotics can reduce inflammation, oxidative stress and insulin resistance under such conditions. However, for these protective effects, probiotic microbes need to colonize and persist in the gut, especially under conditions of oxidative insult and inflammation. Naturally occurring probiotics have fared poorly thus far. To overcome this challenge, two probiotics, BioColoniz & BioPersist, were created in Dr. Gibson’s laboratory from Lactobacillus reuteri and Escherichia coli Nissle 1917 (EcN) parent strains respectively. BioColoniz is bio-engineered to adhere and colonize the gut while BioPersist possesses a survival advantage by expressing a gene that confers resistance to ambient oxidative insult. We hypothesized that these designer probiotics could improve insulin insensitivity induced by HFDs. C57/Bl6 mice were fed a 40% energy HFD for 6 weeks. Mice were either not gavaged or gavaged with the one of the two probiotics during the study. Subsequently, analysis of BioPersist and Biocoloniz livers revealed lowered oxidative stress biomarker nitrotyrosine. BioPersist livers also demonstrated increased antioxidant (glutathione peroxidase-4) and reduced inflammatory biomarkers (inducible nitric oxide synthase and tumor necrosis factor-α). Mice administered BioPersist also gained the least amount of weight on HFD and demonstrated improved glucose tolerance tests. Improved phosphorylation and abundance of insulin/IGF-I receptor β in both BioPersist and BioColoniz livers were also noted. Furthermore, BioPersist livers had elevated phosphorylation of Akt, a key insulin signaling molecule. Overall, we show an improvement of in vivo glucose homeostasis associated with lower inflammation and oxidative stress in the liver with designer probiotics following a high fat diet. These results indicate that bio-engineered probiotics might be a viable therapeutic option for treating clinical insulin resistance and type 2 diabetes in the future.
DESIGNER PROBIOTIC SUPPLEMENTATION OUTCOMES ON HIGH FAT DIET by Hansika Dadlani B.Tech, Vellore Institute of Technology, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Biochemistry and Molecular Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) February 2021 © Hansika Dadlani, 2021 ii The following individuals certify that they have read, and recommend to the for acceptance, the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Designer probiotic supplementation outcomes on high fat diet submitted by Hansika Dadlani in partial fulfillment of the requirements for the degree of Master of Science Dr. Sanjoy Ghosh, Faculty of Science Supervisor Dr. Susan Murch, Faculty of Science Supervisory Committee Member Dr. Michael Deyholos, Faculty of Science Supervisory Committee Member Dr. Angelina Devlin, Faculty of Medicine University Examiner iii Abstract High fat diets (HFDs) cause insulin resistance, oxidative stress, inflammation, all of which are linked with gut microbial dysbiosis. As a therapy, probiotics can reduce inflammation, oxidative stress and insulin resistance under such conditions. However, for these protective effects, probiotic microbes need to colonize and persist in the gut, especially under conditions of oxidative insult and inflammation. Naturally occurring probiotics have fared poorly thus far. To overcome this challenge, two probiotics, BioColoniz & BioPersist, were created in Dr. Gibson’s laboratory from Lactobacillus reuteri and Escherichia coli Nissle 1917 (EcN) parent strains respectively. BioColoniz is bio-engineered to adhere and colonize the gut while BioPersist possesses a survival advantage by expressing a gene that confers resistance to ambient oxidative insult. We hypothesized that these designer probiotics could improve insulin insensitivity induced by HFDs. C57/Bl6 mice were fed a 40% energy HFD for 6 weeks. Mice were either not gavaged or gavaged with the one of the two probiotics during the study. Subsequently, analysis of BioPersist and Biocoloniz livers revealed lowered oxidative stress biomarker nitrotyrosine. BioPersist livers also demonstrated increased antioxidant (glutathione peroxidase-4) and reduced inflammatory biomarkers (inducible nitric oxide synthase and tumor necrosis factor-α). Mice administered BioPersist also gained the least amount of weight on HFD and demonstrated improved glucose tolerance tests. Improved phosphorylation and abundance of insulin/IGF-I receptor β in both BioPersist and BioColoniz livers were also noted. Furthermore, BioPersist livers had elevated phosphorylation of Akt, a key insulin signaling molecule. Overall, we show an improvement of in vivo glucose homeostasis associated with lower inflammation and oxidative stress in the liver with designer probiotics following a high fat diet. These results iv indicate that bio-engineered probiotics might be a viable therapeutic option for treating clinical insulin resistance and type 2 diabetes in the future. v Lay Summary High fat diets have been liked with microbial dysbiosis, inflammation and irregular glucose metabolism which are underlying causes of many diseases. In the recent years, probiotics have garnered enormous attention and are believed to be beneficial, however, some studies have contradicted their defensive properties and claimed that probiotics fail to survive and colonize the gut. In the Gibson lab at UBC Okanagan, designer probiotics were designed to colonize and survive in the gut. I hypothesized that designer probiotics, BioColoniz and BioPersist would reverse adverse effects associated with high fat diet. We observed that BioPersist treatment during high fat feeding, significantly decreased weight gain, improved glucose metabolism and ameliorated expression of insulin receptor in the liver. Further investigation revealed that designer probiotics reduced oxidative stress and inflammatory biomarkers. These results indicate the protective and propitious role and of BioColoniz and BioPersist in a world where high fat diets are commonly consumed. vi Preface All animal experiments conducted for this research were approved by the UBC Animal Care Committee (Certificate # A15-0240). The designer probiotics, BioPersist and BioColoniz were designed by Dr. Deanna Gibson and Dr. Artem Godovannyi. Dr, Gibson, one of the inventors of the designer probiotics, BioPersist and BioColoniz; is the spouse of Dr. Sanjoy Ghosh, my primary supervisor. Both the designer probiotics (BioPersist and BioColoniz) used in this research have been previously studied by Sandeep Kaur Gill (M.Sc., UBC Okanagan) in context of Inflammatory bowel disease. The patent for the designer probiotics was issued in August 2018 in United States, Canada and Europe (Patent # PCT/CA2018/050188). In this thesis, I was responsible for all the experimental work and data analysis. Jacqueline Barnett assisted me with the animal work including mice gavages and IPGTT. Candice Quinn assisted with growing the probiotics. Euthanasia was assisted by Lisa Renaud, Lucas Starchuk, Jacqueline Barnett and Andrea Verdugo-Meza. All the serum analyses were conducted by Eve Technologies located in Calgary. vii Table of Contents ABSTRACT ...................................................................................................................... iii LAY SUMMARY .............................................................................................................. v PREFACE ......................................................................................................................... vi TABLE OF CONTENTS ............................................................................................... vii LIST OF TABLES ............................................................................................................ x LIST OF FIGURES ......................................................................................................... xi LIST OF ABBREVIATIONS ........................................................................................ xii ACKNOWLEDGEMENTS ........................................................................................... xv DEDICATION................................................................................................................ xvi CHAPTER 1: INTRODUCTION .......................................................................................... 1 1.1 High fat diets and potential consequences.......................................................... 1 1.1.1 HFDs and oxidative stress ........................................................................................ 3 1.1.2 HFDs and inflammation ........................................................................................... 4 1.2 Gut Microbiome .................................................................................................. 5 1.2.1 HFDs modulate microbial mediated inflammation .................................................. 6 1.2.2 HFDs induce insulin resistance ................................................................................ 7 1.2.2.1 Inflammation disrupts insulin signaling. ...................................................................... 10 1.3 Gut Microbiome modulation: ‘Probiotics’ ....................................................... 11 1.3.1 Challenges around probiotic supplementation ....................................................... 14 viii 1.3.2 Designer Probiotics ................................................................................................ 16 1.3.2.1 BioPersist ..................................................................................................................... 17 1.3.2.2 BioColoniz ................................................................................................................... 19 1.4 Research overview & hypothesis ...................................................................... 21 CHAPTER 2: BIOPERSIST DECREASES INFLAMMATORY MARKERS, WHILE SIMULTANEOUSLY IMPROVING INSULIN SENSITIVITY ............................................................. 22 2.1 Overview ........................................................................................................... 22 2.2 Materials & Methods ........................................................................................ 23 2.2.1 Probiotic Strains ..................................................................................................... 23 2.2.2 Murine model & diet .............................................................................................. 24 2.2.3 Intraperitoneal glucose tolerance test (IPGTT) ...................................................... 27 2.2.4 Protein Extraction ................................................................................................... 28 2.2.5 Western blots .......................................................................................................... 30 2.2.6 Probiotic Strain detection ....................................................................................... 32 2.2.7 Hormonal analysis in blood serum ......................................................................... 34 2.2.8 Statistical Analysis ................................................................................................. 34 2.3 Results ............................................................................................................... 35 2.3.1 Least weight gain was observed in BioPersist administered mice. ........................ 35 2.3.2 BioPersist improved glucose metabolism .............................................................. 37 2.3.3 BioPersist enhanced insulin signaling with increased insulin receptor β expression and phosphorylation of Akt ........................................................................................................................ 38 2.3.4 Impact of BioPersist & BioColoniz, on downstream effects of insulin ................. 40 2.3.5 BioPersist decreased inflammatory biomarkers in rodent liver ............................. 42 2.3.6 BioPersist & BioColoniz help curb oxidative stress ............................................. 43 2.3.7 BioPersist elevated appetite curbing hormones, PYY & amylin ........................... 45 ix 2.3.8 Designer probiotics do not translocate from the gut. ............................................. 48 2.4 Discussions ....................................................................................................... 50 2.4.1 Least weight gain was observed in BioPersist administered mice. ........................ 50 2.4.2 BioPersist improved glucose metabolism .............................................................. 52 2.4.3 BioPersist enhanced insulin signaling with increased insulin receptor β expression and phosphorylation of Akt ........................................................................................................................ 55 2.4.4 Impact of BioPersist & BioColoniz on downstream effects of insulin .................. 56 2.4.5 BioPersist decreased inflammatory biomarkers in rodent liver ............................. 59 2.4.6 BioPersist & BioColoniz help curb oxidative stress markers ............................... 61 2.4.7 BioPersist elevated appetite curbing hormones, PYY & amylin ........................... 63 2.4.8 Designer probiotics do not translocate from the gut. ............................................. 64 CHAPTER 3: CONCLUSION ........................................................................................... 66 3.1 Limitations of research ..................................................................................... 66 3.2 Significance of research .................................................................................... 66 3.3 Main conclusions .............................................................................................. 67 BIBLIOGRAPHY ........................................................................................................... 68 APPENDIX .................................................................................................................... 117 x List of Tables Table 2.1 Composition of the media used to cultivate BioColoniz ................................. 24 Table 2.2 Basal diet composition ...................................................................................... 26 Table 2.3 % Final diet composition of macronutrients in rodent diet .............................. 26 Table 2.4 RIPA buffer recipe ............................................................................................ 28 Table 2.5 5X SDB buffer recipe ....................................................................................... 29 Table 2.6 List of Primary Antibodies used ....................................................................... 31 Table 2.7 List of Secondary Antibodies used ................................................................... 32 Table 2.8 PCR reaction set up........................................................................................... 33 Table 2.9 Primer sequences used to detect BioPersist & BioColoniz ............................. 33 Table A.3.1 Nanodrop concentration of DNA extracted for Probiotic detection experiment................................................................................................................................... 117 xi List of Figures Figure 1.1 HFD induced Microbiome modulation causes lowered intestinal permeability which in turn leads to chronic inflammation .................................................................................. 7 Figure 1.2 Simple adaptation of the complex insulin signaling pathway ........................... 8 Figure 1.3 Proposed protective properties of probiotics ................................................... 12 Figure 1.4 Simplified representation of BioPersist design & development (Sandeep Kaur Gill, 2018) ..................................................................................................................................... 18 Figure 1.5 Tetrathionate respiration model seen in Salmonella typhimurium. ................. 19 Figure 1.6 Simplified representation of BioColoniz design & development (Sandeep Kaur Gill, 2018) ..................................................................................................................................... 20 Figure 2.1 Least weight gain observed in mice administered BioPersist ......................... 36 Figure 2.2 BioPersist improved high fat diet induced insulin resistance .......................... 37 Figure 2.3 BioPersist enhanced insulin signaling via improved insulin receptor expression and phosphorylation. ..................................................................................................................... 40 Figure 2.4 Impact of BioPersist & BioColoniz on downstream effects of insulin .......... 42 Figure 2.5 BioPersist decreased inflammatory biomarkers .............................................. 43 Figure 2.6 BioPersist & BioColoniz help curb oxidative stress ....................................... 44 Figure 2.7 BioPersist elevated appetite curbing hormones ............................................... 47 Figure 2.8 Designer probiotics do not translocate from the gut ....................................... 49 xii List of Abbreviations Akt Protein kinase B Asr Sulphite reductase CFB Bacteroidetes CVDs Cardiovascular diseases FIRM Firmicutes FOXO Forkhead box protein GAP GTPase activating protein GIP Gastric inhibitory ppetide GLP-1 Glucagon like peptide- 1 GPX Glutathione peroxidase GSH Glutathione GSK-3 Glycogen synthase kinase- 3 GST Glutathione-S-transferase GTP Guanosine-5'-triphosphate H2S Hydrogen Sulfide HFD High fat diet IBD Inflammatory bowel disease IGF-1 Insulin like growth factor-1 xiii IL-6 Interleukin- 6 INF- γ Interferon- Gamma iNOS inducible nitric oxide synthase IPGTT Intra-peritoneal glucose tolerance test IR Insulin receptor IRS- 1 Insulin receptor substrate- 1 LPS Lipopolysaccharides mTOR Mammalian target of rapamycin MUFAs Monounsaturated fatty acids Phs Thiosulphate reductase PIP-3 Phosphatidylinositol (3,4,5)-trisphosphate PUFAs Poly unsaturated fatty acids PYY Polypeptide YY RNS Reactive Nitrogen Species ROS Reactive oxygen species SFAs Saturated fatty acids T2D Type 2 diabetes TLR-4 Toll like receptor- 4 xiv TNF - α Tumor necrosis factor – Alpha TSC-2 Tuberous Sclerosis Complex 2 Ttr Tetrathionate reductase xv Acknowledgements The first person that I would like to thank is my supervisor, Dr. Sanjoy Ghosh. I came to his lab as a young, timid girl and I am leaving as a confident, independent woman. The second person is Dr. Deanna Gibson, I want to thank her for giving me the opportunity to work on her creation. Both, Dr. Ghosh and Dr. Gibson have always inspired me and will continue to do so. I want to thank them for not giving up on me when numerous things did not go as planned. In addition, I want to thank my supervisory committee members, Dr. Michael Deyholos and Dr. Susan Murch for their guidance and valuable feedback. Furthermore, I want to acknowledge all the members of Ghosh and Gibson lab members for always volunteering to help me, their constructive criticism and their constant moral support. I am very grateful to all the current and previous members of the labs, especially Jacqueline Barnett and Lucas Starchuk for their contribution towards my animal experiments and statistics respectively. And lastly, I would like to thank my family and friends. My parents, Hemant & Jyoti Dadlani are my rock and have always supported me throughout my years of education, both morally and financially. I am grateful for my loving, little doggie brother, Joey for always putting a smile on my face. A big thank you to all friends, especially Manjot, Naveen & Akash for their patience and constant care. xvi Dedication To my maa and paa… 1 Chapter 1: Introduction 1.1 High fat diets and potential consequences Diet is believed to be a major contributing factor in the rise of chronic diseases and obesity in westernized countries (Cordain et al., 2005; Kopp, 2019). Dietary fats, critical for the functioning of the human body have often been accused for the obesity epidemic and its adverse effects. Thus, regulating fat intake has been a consistent concern since the 1950s (Popkin B.M., Haines P.S., 2004, Hall, 2018). A study in 1959 reporting the influence of the type of fat consumed on insulin action encouraged researchers to understand the role of different types of dietary fats (Kinsell et al., 1959). Fats are composed of fatty acids linked to a glycerol by an ester bond. Fatty acids can be divided into three major categories based on the number of double bonds they possess. Saturated fatty acids (SFAs) have no double bonds whereas Monounsaturated fatty acids (MUFAs) and Polyunsaturated fatty acids (PUFAs) have single and multiple double bonds respectively (Hall, 2018). In addition, PUFAs can be subdivided into n-3 PUFAs and n-6 PUFAs families based on the position of double bonds with respect to the terminal methyl group (S. Lee et al., 2018). High fat diets (HFDs) have been linked to metabolic syndrome which is a metacondition disorder of metabolic origin (Eckel et al., 2005; Fraulob et al., 2010; Lasker et al., 2019). The syndrome has been associated with a higher risk of hypertension, hyperglycemia, Cardiovascular diseases (CVDs), type 2 diabetes and a constant pro-inflammatory state (Grundy, 2008; Verdugo-Meza et al., 2020). The increase in CVDs and diabetes has been shown to be a direct impact of the rise in n-6/n-3 ratio, prevalent in a typical Western diet consisting of higher n-6 PUFAs and lower n-3PUFAs (Afshin et al., 2019; de Lorgeril & Salen, 2006; Harris et al., 2006; 2 Patterson et al., 2012). Various other abnormalities such as deteriorated cardiac health (Dinicolantonio & O’Keefe, 2018), irregular mitochondrial fatty acid oxidation (Sergi et al., 2019) and weakened recovery from myocardial infraction have been linked to excess fat intake (Michel de Lorgeril & Salen, 2006). In addition, HFDs rich in n-6 PUFAs promote sedentary behavior, lowered respiratory ratio and impaired insulin sensitivity (Pither et al., 2017; C. K. Wong et al., 2015). In context of diabetes, high fat diets are known to induce insulin resistance (Costa et al., 2011; Zhigang Liu et al., 2015; Pataky et al., 2017; Srinivasan et al., 2005). There are classic studies in human subjects which support the rodent research on high fat diet and their association with the onset of non-insulin dependent diabetes (J. A. Marshall et al., 1994; Julie A. Marshall et al., 1991). Recent studies also suggest the same. A study done in non- diabetic women involving a 6 year follow up period showed that dietary fat intake was positively associated with the occurrence of type 2 Diabetes (Alhazmi et al., 2014). Another study with over 27,000 subjects reported a strong correlation between impaired glucose tolerance and dietary fat intake (Narasimhan et al., 2016). Furthermore, increased fat consumption has been associated with dysbiosis or alteration of gut microbiome in both humans and animal models (Angoa-Pérez et al., 2020; David et al., 2014). HFDs induced obese mice demonstrate intestinal dysbiosis followed by increased intestinal permeability and endotoxemia mediated inflammation (Patrice D Cani et al., 2007). This increase in inflammation was observed because of weaken gut intestinal barrier or increased intestinal permeability which has been associated with consumption of excess dietary fat (Rohr et al., 2020). Expression of vital tight junction proteins such as cludin-1, occludin and junctional 3 adhesion molecule-1 was decreased in both obese and non-obese mice post HFD feeding (Suzuki & Hara, 2010). 1.1.1 HFDs and oxidative stress Oxidative stress is an imbalance between reactive oxygen species (ROS) and antioxidants. It has been depicted to be an underlying cause in the pathogenesis of many chronic diseases including insulin resistance, arthritis, heart failure, IBD and many more (Kopp, 2019; Moylan & Reid, 2007). Over nutrition has been known to contribute to oxidative stress (Tan et al., 2018). In fact, pathways for ROS production are up regulated in HFD mice even before the onset of insulin resistance (Matsuzawa-Nagata et al., 2008). Rats fed a constant high fat diet rich in n-6 PUFAs were found to have higher expression of biomarkers such as inducible nitric oxide synthase (iNOS) coupled with an increase in 8-hydroxyguanosine, an indicator of oxidative damage to mitochondrial DNA and 3-nitrotyrosine, a footprint of reactive nitrogen species (RNS) associated oxidative damage (Ghosh et al., 2006). Oxidative stress, increased lipid peroxidation along with decreased antioxidants such as glutathione (GSH), glutathione peroxidase (GPX) and glutathione-S-transferase (GST) in hepatic, cardiac and renal tissues of high fat diet (HFD) induced obese rat models have been reported (Ballal et al., 2010; Noeman et al., 2011). High dietary fat intake has been shown to increase oxidative stress in both rodents and humans (Anderson et al., 2009; Tan et al., 2018). A study conducted in healthy subjects revealed that plasma concentration of GPX significantly decreased 2 hours after a high fat meal. In addition, a significant increase in excretion of oxidative modification products such as was observed, indicating that high fat consumption is linked to depletion of antioxidants and increased oxidative stress (Tsai et al., 2004). 4 1.1.2 HFDs and inflammation In general, inflammation is described as the body’s immune response to an irritant and generally lessens after the irritant has been destroyed however, non-resolving inflammation can lead to disease (Nathan & Ding, 2010). Tissue inflammation is a key factor contributing towards insulin resistance and chronic stages of HFD induced insulin resistance is mediated by macrophage induced inflammation (Y. S. Lee et al., 2011). HFDs not only induce adipose chronic inflammation (Gao et al., 2015) but also intestinal inflammation with high tumor necrosis factor (TNF- α) and interferon (INF- γ) levels in colonic tissues (X. Li et al., 2019; X Ma et al., 2008). In fact, studies suggest that intestinal inflammation precedes high fat western diet induced obesity, adiposity and insulin resistance (Ding et al., 2010). A possible link between intestinal inflammation and nutrition overload lies in the fact that higher endotoxin levels are found in plasma and intestinal lumen of HFD fed mice, which promote pro-inflammatory cytokine activation (Kim et al., 2012). Similar results were seen in healthy men, implying that a high fat meal induces low grade endotoxemia which eventually contributed to a postprandial inflammatory state (André et al., 2019; Erridge et al., 2007). Coupled with inflammation is intestinal dysbiosis which can have major impacts on the immunological and metabolic functions of the host (Araújo et al., 2017). High n-6 PUFA diets caused bacterial overgrowth but deplete microbes from major gut phyla. Diets rich in corn oil encouraged notably increased bacterial infiltration along with closer association of gut microbiota to intestinal surfaces as compared to normal chow diet (Ghosh et al., 2013). Despite the substantial evidence and research about the detrimental effects of overconsumption of n-6 PUFA, Canadians are presently consuming approximately 7 % energy 5 from n-PUFAs, which is far more that the required 1% energy essential for development and functioning. In 2009, Canadian Food Statistics revealed that on average population consumes 26 grams of PUFA a day, which is 42% higher that PUFA consumption in 1981(Madden et al., 2009). This significant increase mirrors the increase in number of overweight and obese children as well as adults (Twells et al., 2014). This is an issue of concern and needs attention and rectification. This thesis aims to highlight adverse effects of high fat diets rich in corn oil and propose a management strategy with novel designer probiotics. 1.2 Gut Microbiome The gut fosters a vast variety of microbiota that have major effects on both host health and disease (Hasan & Yang, 2019; Wiley et al., 2017). Microbiome along with its metabolites can affect host metabolism, nutrition absorption as well as host immunity. This has been an active area of research and at present we are aware of the 5 phyla that constitute the human gut microbiota: Bacteroidetes (CFB), Firmicutes, (FIRM) Actinobacteria, Proteobacteria, and Verrucomicrobiota. The former two, CFB and FIRM have beneficial bacteria and make up about 90% of the total bacterial species (Eckburg et al., 2005; Sender et al., 2016). Endogenous factors including host genetics and maternal effects along with exogenous factors such as diet, host environment, consumption of antibiotics and many others influence gut microbiome diversity (Hasan & Yang, 2019). Modulation of these bacterial populations can have detrimental effects and lead to chronic diseases such as obesity, insulin resistance and IBD (De Palma et al., 2017; Pflughoeft & Versalovic, 2012) . FIRM to CFB ratio changes over time with aging. Similar change is observed in obese, diabetics, intestinal bowel disease (IBD) patients and individuals with metabolic 6 ailments (Castaner et al., 2018; Ley et al., 2006). Diet plays an important role in shaping microbiome composition (David et al., 2014). Nutrition overload seems to have a drastic effect on the composition of bacterial diversity (Singh et al., 2017). Studies conducted in humans demonstrate increase in FIRM populations coupled with a decrease in CBD bacterial populations in obese individuals (Kasai et al., 2015; Koliada et al., 2017). A parallel trend is also seen in high fat diet induced alterations (Murphy et al., 2015). Metagenomic analysis of half a million sequence reads indicated extensive changes in microbiome diversity in both wildtype mice which became obese and knockout mice which did not gain weight even after switching normal chow with high-fat diet. This study demonstrated that high fat diet can alter microbiome composition independent of obesity (Hildebrandt et al., 2009). 1.2.1 HFDs modulate microbial mediated inflammation Bacteria can be classified into Gram positive or Gram negative. Firmicutes fall into the former category whereas Bacteroidetes fall into the latter (Eckburg et al., 2005). Gram negative bacteria have a thinner peptidoglycan layer composed of lipopolysaccharides (LPS) in their cell wall. Under normal conditions, presence of low amounts of LPS does not have adverse effects in the gut, however, increased intestinal permeability may lead to transfer of LPS into the circulatory system and stem a chronic state of inflammation. (Thevaranjan et al., 2017). Considerable evidence suggests that high fat diets can contribute to endotoxemia, or high levels of endotoxins such as LPS in blood plasma (P D Cani et al., 2007; Erridge et al., 2007; Singh et al., 2017). This explains the similar increase in blood circulation levels of LPS is observed in patients with diabetes and obesity (Boutagy et al., 2016; Pussinen et al., 2011). 7 Figure 1.1 HFD induced Microbiome modulation causes lowered intestinal permeability which in turn leads to chronic inflammation 1.2.2 HFDs induce insulin resistance LPS is a strong activator of toll-like receptor-4 (TLR-4), which when activated promotes transcription of genes which are involved in inflammatory responses such as can interfere with insulin signaling, increase oxidative stress and/or cause sepsis or hepatic injury (Guijarro-Muñoz et al., 2014). Thus, perennial exposure to LPS can result in a state of subclinical inflammation and initiate a state of insulin resistance via complex interrelated processes (Patrice D Cani et al., 2007; Manco et al., 2010). To understand these processes, it is important to understand insulin signaling. 8 Figure 1.2 Simple adaptation of the complex insulin signaling pathway Insulin is a vital hormone controlling energy production and growth and metabolic responses (Wilcox, 2005; Yamauchi et al., 1996). Insulin is synthesized from its precursor, proinsulin which consists of two chains connected via C-peptide (M. Liu et al., 2018). Molecular effects of insulin and IGF-1 (Insulin like growth factor) occur after a complex network of signaling pathways activated by the insulin and IGF-1 receptor respectively (Petersen & Shulman, 2018). Ligand stimulation by insulin or IGF-1 leads to a cascade of subsequent phosphorylation events. The first being recruitment and phosphorylation of receptors substrates such IRS (insulin receptor substrate) and Shc proteins which in turn causes activation of PI3K-Akt and Ras-MAPK pathways respectively. PI3K-Akt pathways plays an important role in regulating different metabolic effects such as lipid and protein synthesis, glycogen formation, gluconeogenesis etc. This pathway is initiated with the phosphorylation of PI3K, which causes generation of secondary messenger, PIP3. PIP3 activates PDK-1 which phosphorylates Akt at 9 Thr-308. However, for complete activation, phosphorylation at Ser-473 is essential and this is done via mTORC2 (Sarbassov et al., 2005). Akt is the major mediator of the metabolic effects of insulin (Boucher et al., 2014) with numerous downstream effects indicated in Figure 1.2. In the absence of insulin, FOXO1, which belongs to the Forkhead family of transcription factors, prevents the transcription of genes required for initiating adipogenesis (Armoni et al., 2006) and promotes the expression of glucose-6-phosphatase, a key enzyme in gluconeogenesis (J Nakae et al., 2001). Akt phosphorylates FOXOs causes their translocation from the nucleus to the cytoplasm. This prevents them from being transcriptionally active (Xie et al., 2012). Inactivation of FOXO prevents gluconeogenesis and promotes adipogenesis. Similarly, Akt phosphorylates and inactivates GSK-3 (Glycogen synthase kinase-3) which is known to phosphorylate and inhibit glycogen synthase, an important enzyme in glycogen synthesis. Inhibition of GSK-3 induces glycogen synthesis. (Tzivion et al., 2011). In addition, Akt mediates protein synthesis via inhibiting TSC or Tuberin, which is an important regulator of cell proliferation and tumor development. TSC consists of a complex made by TSC 1 and TSC2. TSC1 stabilizes TSC2 which acts a GTPase activating protein (GAP) for G protein Rheb (Ras homolog enriched in brain). GTP bound Rheb stimulates mTORC1 activation. TSC1-TSC2 negatively controls mTORC1 by stimulating conversion of GTP-Rheb to GDP-Rheb (Huang et al., 2008). Akt results in the downstream activation of mTORC1 via phosphorylating and inhibiting TSC2 (Bhaskar & Hay, 2007; Inoki et al., 2002). 10 1.2.2.1 Inflammation disrupts insulin signaling. TNF-α is an important molecular link between inflammation and insulin sensitivity. As depicted in Figure 1.2, IRS-1 activation at tyrosine residues is vital for Akt activation and further downstream effects of insulin to occur. Over nutrition and glucose intake can stimulate production of TNF-α which can inhibit IRS-1 by phosphorylating it at serine residues, thus, inhibiting signaling downstream of insulin receptor (G S Hotamisligil et al., 1996; Ishizuka et al., 2007; Jiang et al., 2014; Kanety et al., 1995). HFD fed mice have high expression of genes encoding cytokines IL-6, TNF- α and IL-1 (Patrice D Cani et al., 2007; van der Heijden et al., 2015). Intriguingly, liver cytokines shot up as early as two weeks of high fat feeding indicating that liver was likely to be the first organ affected by high LPS levels (Patrice D Cani et al., 2007). Another important cytokine associated with insulin resistance is IL-6. IL-6 can decrease tyrosine phosphorylation of IRS-1 and hepatic cells when treated with IL-6 experienced complete loss of Akt activation (Senn et al., 2002). This can be a rationale for higher plasma levels of IL-6 in obese and type-2 diabetics as compared to healthy individuals (Kern et al., 2001; E. J. Park et al., 2010). An additional link between inflammation and insulin resistance is increased iNOS expression during LPS induced inflammation via TLR-4 signaling (Kim et al., 2012; Song et al., 2006). iNOS promotes reactions between nitric oxide and cysteine residues to form S-nitrosothiol adducts, a phenomenon known as S-nitrosylation. Nitrosylation of molecules such as Akt, IRS-1 and IR, crucial to insulin signaling can induce insulin resistance in the liver, muscle and adipose tissue (Sugita et al., 2002; Yasukawa et al., 2005). iNOS inhibitors can improve hepatic insulin resistance in genetically obese mice demonstrating the important role iNOS plays 11 in impaired insulin signaling (Fujimoto et al., 2005). However, another study with high fat fed iNOS knockout mice concluded that iNOS blockage improved insulin sensitivity in the muscle but not in the liver (Zanotto et al., 2016). 1.3 Gut Microbiome modulation: ‘Probiotics’ The interaction between gut microbiota and the host metabolism, immunity and disease has prompted immense research into altering the microbiome using several strategies. One such strategy being probiotics have garnered huge attention. The World Health Organization defines probiotics as “live microorganisms which when administered in adequate amounts, confer a health benefit to the host” (World Health Organization & Food and Agriculture Organization of the United Nations, 2002). The concept of oral consumption of live bacterium for beneficial effects is not new and dates to times when the term ‘probiotics’ was not even coined. However, the original hypothesis of the positive effects of certain bacteria was first introduced by Elie Metchnikoff in the early 1900s, who suggested that it is possible to modify gut microbiome to replace harmful microbes with much more helpful communities via consuming fermented milk enriched with lactic acid bacteria (Metchmikoff, 1908). Nowadays, probiotics are not that simple and instead probiotics are commercially available in different forms such as yogurt, nutritional bars, drinks and lyophilized pills. 12 Figure 1.3 Proposed protective properties of probiotics Many researchers have claimed that probiotics assert protective properties such as immunomodulation (Azad et al., 2018), protection against pathogens (Panigrahi et al., 2017; Sharifi-Rad et al., 2020), , lowered risk of cardio vascular disease (Sun & Buys, 2016), enhanced barrier function (Ohland & Macnaughton, 2010) and mental well-being (Dinan et al., 2013). More recently, it has also been proposed that probiotics by changing the microbiome can be of benefit to reduce insulin resistance (P D Cani et al., 2007; Kocsis et al., 2020) and weight control (Q. Zhang et al., 2016). However, for these beneficial effects to occur, probiotics must effectively colonize and persist in the gut the evidence of which is limited (Suez et al., 2019). A study reported probiotics to be effective in reversing antibiotic-induced dysbiosis in mice (Ekmekciu et al., 2017). HFD-induced dysbiosis and blunted insulin sensitivity were reversed post probiotics supplementation by changes in the gut microbiota (Bagarolli et al., 2017). However, on the other hand, another 13 study concluded that gut microbiome reconstitution post dysbiosis was poor with probiotic supplementation (Suez et al., 2018). In context of insulin sensitivity, a review analyzing the effectiveness of probiotics reported that some studies showed probiotics improve insulin sensitivity but at the same time some showed no changes in weight, insulin sensitivity or endotoxin and pro-inflammatory cytokine levels (Kesika et al., 2019). A study involving HFD fed C57Bl6 mice revealed that probiotics improved insulin resistance and decreased pro-inflammatory cytokine production via conventional cells expressing both Natural Killer receptors (NK) and T cell receptors (NKT cell) (Xiong Ma et al., 2008). In contrast, a study conducted in 68 healthy individuals orally supplemented with probiotics for 4 weeks, did not show modulation of cytokine secretion or NK cell activity (Seifert et al., 2011). A randomized double-blind trial with diabetic individuals showed that probiotic intake did not alter levels of insulin, cholesterol and insulin resistance (Razmpoosh et al., 2019). Another study done in non-obese diabetic mice revealed that probiotics had no effect on the onset of diabetes. Poor colonization of the gut by the probiotics was the rationale used to explain the results (Brown et al., 2016). Regarding the effectiveness of probiotics in obesity management, two meta-analysis conducted showed opposing results. One implied that probiotic consumption resulted in significant reduction of body mass index (BMI), whereas the other study showed no such promising effects (Rondanelli et al., 2017). These conflicting results can be due to a variety of causes, the most important being inability to survive and colonize the gut. Some studies have reflected on shedding of probiotics in the stool ceasing post supplementation (Sierra et al., 2010) indicating the inefficiency of probiotics to adhere to the gut. At the same time, other studies have shown that probiotics can adhere to intestinal epithelium (Turroni et al., 2013). Thus, an all-14 inclusive study on the efficacy of probiotics is therefore the need of the hour (Rondanelli et al., 2017). 1.3.1 Challenges around probiotic supplementation The global probiotic market is constantly growing and expected to reach USD 75.90 Billion by 2026 (Zion Market Research, 2020). Given the increasing popularity of probiotics, the safety, efficacy and survivability in the gastrointestinal tract must be gut must be studied. Designing a probiotic can be extremely challenging given the degree of variation in exact same probiotic supplementation outcomes in different individuals. These different outcomes are observed because of differences in the initial conditions of the hosts due to differences in an individual’s gut microbiome composition, age, diet, pre-existing medical conditions, varying environmental exposures, antibiotic treatments and patterns of underlying medical conditions. All these differences account to variation in degree of gut colonization and post supplementation persistence of probiotics in the gut (Suez et al., 2019; Zmora et al., 2016, 2018). In addition to inter-personal differences in response to probiotic consumption, successful probiotic design and delivery need to overcome many other challenges. For a probiotic to have any positive effect, probiotics must survive in the gut (Suez et al., 2019). Host gut offers many resistances such as low pH, bile production, perennial peristalsis and competition from pre-existing bacterium; which make it very difficult for the probiotics to survive and effectively colonize the gut (Cordonnier et al., 2015; Desmond et al., 2001; Ruiz et al., 2013). A high fat diet makes these resistances even more severe by creating a highly inflamed and oxidized environment in the gut (Ghosh et al., 2013; Moreira et al., 2012). High PUFA, especially linoleic acid concentrations have shown to reduce adhesion to human intestinal mucus indicating the 15 addition challenges HFD offer to probiotic survival (Kankaanpää et al., 2001). Oxidative stress and inflammation are inter-dependent conditions. The latter can often lead to the former and in turn increase itself or vice versa. Oxidative stress is initiated when activated phagocytic cells produce ROS and RNS during inflammation (Fialkow et al., 2007). This is followed by production of pro-inflammatory cytokines (Wu et al., 2013). LPS can synergistically increase ROS production via increased inflammation (J. Li et al., 2015). In hosts that consume a high fat diet, all these factors contribute to lower efficacy and survivability of probiotics (Kankaanpää et al., 2001). For bacteria that do manage to survive in the gut, effective colonization of the gut mucosa is necessary to administer positive outcomes in the host. In vitro studies in tissue cultures do suggest that probiotics can bind to mucosal cells. However, in vivo studies have inconsistent results (Ouwehand & Salminen, 2003). Probiotics when given to germ free mice can colonize relatively easier but face strong competition from already established microbiome in normal guts (Grimm et al., 2015). In rodents probiotics face competition from the pre-existing microbiome, where as in humans, the resistances to probiotics are person, strain and region specific (Zmora et al., 2018). A common commercial multi-strain probiotic, VSL#3, did not improve mucus barrier and the strains could not be quantified in mice (Gaudier et al., 2005). In healthy adults supplemented with L. rhamnosus GG for 2 weeks, 30% of the participants had detectable probiotics after 3 weeks (Saxelin et al., 2010), where as in another study with the same probiotic was detected in only 10% of the subjects (Tannock et al., 2000). Another study with a different strain, Bifidobacterium longum, reported stable persistence in 30% of the individuals even after ceasing consumption (Maldonado-Gómez et al., 2016). 16 Thus, there is an inconsistency in reports of viability of probiotics and bioengineering probiotics to improve the survival of strains having probiotic potential is an attractive approach (Mazhar et al., 2020). 1.3.2 Designer Probiotics As described, potentially beneficial probiotics may encounter significant limitations rendering them to be physiologically friable. These limitations can be minimized with the advancements in biotechnology and bioengineering. Research involving introduction of new genetic factors to parent strains to genetically bioengineer novel probiotics is garnering attention. For example, to enhance survival in the gut and overcome obstruction due to various gastric juices, listerial betaine uptake system (BetL) was cloned into probiotic strain of Bifidobacterium breve UCC2003. This designer probiotic colonized and persisted the gut and reduced level of systemic infection as compared to control strain in murine models challenged with Listeria monocytogenes, denoting enhanced clinical efficacy of the bioengineered strain. (Sheehan et al., 2007). In another study, oral administration of a recombinant bacterium encompassing a Shiga toxin receptor mimic on its surface, to mice completed protected mice when exposed to a 100% fatal dose of Shiga toxigenic E.coli (Paton et al., 2000). A clinical trial with human patients of Crohn’s disease treated with genetically modified Lactococcus lactis revealed decrease in disease activity when compared to patients who received placebo treatments. The modified probiotic was synthesized by replacing thymidylate gene with a synthetic sequence coding for IL-10, which is known to inhibit pro-inflammatory cytokines such as IL-1, TNF- A, INF-G and IL-6. (Braat et al., 2006). A similar approach was taken with a 17 recombinant E.coli Nissle based probiotic which expressed IL-10 and had proved to be efficient in managing IBD in a mouse mode (Gardlik et al., 2012). In lieu of the confusion surrounding the host gut colonization and persistence of commercially available probiotics, Dr. Gibson and her research team designed two probiotic bacterial strains; BioPersist and Biocoloniz, aiming to improve persistence and colonization efficacy of probiotics respectively. Designer probiotics simply refer to addition of new genetic properties to a presently conserved strain to improve its efficiency. 1.3.2.1 BioPersist BioPersist consists of a modified E.coli Nissle 1917 (EcN) strain. This strain of E.coli is named after bacteriologist Alfred Nissle who discovered the protective nature of this strain against enteropathogens like Salmonella Shigella in 1917 (Sonnenborn & Schulze, 2009). In cell culture experiments with human embryonic intestinal cells, EcN is known to inhibit invasive bacteria such as Salmonella, Shigella, Listeria, Legionella and other pathogenic strains of E.coli (Altenhoefer et al., 2004; Rund et al., 2013; Schierack et al., 2011). In another experiment, EcN has been shown to have confer innate defense properties to intestinal epithelial cells (Wehkamp et al., 2004). In animal models, EcN has proven to inhibit Enterohaemorrhagic E.coli (EHEC) (Maltby et al., 2013). Another study aimed to study in vivo effects of probiotic EcN in germ free mice, revealed that colonization of EcN up regulated tight junction proteins such as ZO-1 (Ukena et al., 2007). However, when unmodified E.coli Nissle 1917 was administered to healthy adults, only about 45% of the subjects had EcN recovered from their feces and probiotic was completely undetectable 48 weeks after discontinuation of probiotic supplementation indicating its inefficiency to effectively survive in the gut (Joeres-Nguyen-Xuan et al., 2010). 18 Figure 1.4 Simplified representation of BioPersist design & development (Sandeep Kaur Gill, 2018) In order to benefit from the positive outcomes of EcN supplementation, enhancing its survival is necessary. Thus, BioPersist was developed to improve survival in a highly inflamed and oxidized environment, commonly associated with high fat diets. Salmonella Typhimurium is an invasive enteric pathogen known to outgrow other microbes and survive in host gut because of its ability to use tetrathionate respiration (Hensel et al., 1999; Winter et al., 2010). Colonic bacteria produce toxic substances such as Hydrogen Sulfide (H2S) and the mucosal cells protect themselves from H2S by converting it to thiosulfate (S2O3 2-). Reactive oxygen species and nitric oxide radicals produced during inflammation oxidize thiosulfate to tetrathionate (Furne et al., 2001). Salmonella Typhimurium utilizes tetrathionate as a terminal respiratory electron. It possesses genes encoding for tetrathionate reductase (Ttr), which is a membrane bound enzyme that catalyzes the reduction of tetrathionate to thiosulphate. Thiosulphate is cleaved to sulphite and sulphide by the enzyme thiosulphate reductase. All the enzymes involved in tetrathionate respiration are encoded by ttrSR ttrBCA gene cluster (Hensel et al., 1999). 19 Figure 1.5 Tetrathionate respiration model seen in Salmonella typhimurium. Ttr- Tetrathionate reductase, Phs- Thiosulphate reductase & Asr- Sulphite reductase EcN. was genetically modified to incorporate the tetrathionate respiratory operon, ttrSR ttrBCA of Salmonella typhimurium. The genetically modified EcN strain was named ‘BioPersist’ aims to enhance persistence in host gut. 1.3.2.2 BioColoniz Lactobacillus reuteri is the parent strain of BioColoniz. Beneficial properties of L. reuteri have been well established. Firstly, it is known to produce antimicrobial substances which can inhibit colonization of pathogenic microbes (Chen et al., 2019; Mukai et al., 2002) Secondly, immense research involving L. reuteri has revealed that it can suppress inflammation by reducing production of pro-inflammatory cytokines (Navarro et al., 2017). Thirdly, it can improve intestinal barrier function, thereby reducing LPS leakage into the blood circulation (S. Li et al., 2019). 20 L. reuteri has been shown to suppress TNF-α production in LPS activated monocytes and macrophages extracted from the blood of children with Crohn’s disease, a condition marked with a highly inflamed gut (Lin et al., 2008). In germ free mice model, L. reuteri when administered one day before EHEC invasion challenge, colonization of EHEC was suppressed and L. reuteri protected the mice from disease manifestations (Eaton et al., 2011). A study conducted with 167 infants divided into probiotic and placebo groups, involved administering oral L. reuteri daily to the former, revealed no particular benefit to probiotic group. Analysis of fecal samples explained this was because of low colonization rate of L. reuteri with only 45% of the probiotic group infants testing positive for L. reuteri one-month post intervention (Sung et al., 2014). Figure 1.6 Simplified representation of BioColoniz design & development (Sandeep Kaur Gill, 2018) As mentioned earlier, for long tern colonization of any bacterium, colonization mediated by strong attachment factors or features to enhance survival is essential. Many microorganisms secrete special proteins that facilitate their attachment and persistence in human guts (Tran et al., 2011). One such microorganism is Vibrio cholerae which secretes an attachment factor, N-acetyl-glucosamine (GlcNAc) binding protein A (GbpA). The intestinal mucus contains mucins which have a densely packed network of O-glyacns that also contains GlcNAc. GbpA has 4 domains and domain 1 is crucial for mucin binding and intestinal colonization (E. Wong et al., 2012). 21 The first domain of GbpA, vital for mucosal attachment was expressed in L. reuteri DSM 2016 to improve its ability to colonize the gut. The GbpAD1 was expressed at the end of a pre-existing mucus binding protein (MBP) domain of L. reuteri to supplement binding protein action. A peptide spacer was also placed between the two protein domains to allow independent folding. The resultant modified strain was named ‘BioColoniz’ and was developed to enhance colonization ability of the probiotic in host gut. 1.4 Research overview & hypothesis The gut microbiome is known to affect many other pathways, making it an independent ‘bio-organ’, modulation of which with probiotics can have therapeutic benefits. Given the multiple host and strain specific factors that affect probiotic colonization and persistence in the host gut, the Gibson lab designed two designer probiotics: BioPersist and BioColoniz. I hypothesized that Designer probiotics protect from the adverse effects of high fat consumption and improve glucose metabolism, inflammation and oxidative stress in mice. 22 Chapter 2: BioPersist decreases inflammatory markers, while simultaneously improving insulin sensitivity. 2.1 Overview Over the past few decades there has a been a shift in our dietary fat intake. The rise in chronic diseases including diabetes, obesity, cardiovascular diseases, arthritis mirrors this shift (Rivera et al., 2002). The root cause of most these diseases is enduring inflammation (Libby, 2008). Gut microbiome plays an important role in inflammation (Schirmer et al., 2016). There is substantial evidence to establish the role of diet in modulating the gut microbiota composition and activity, which in turn impacts host health (Conlon & Bird, 2015). High fat diets have been constantly recognized for increasing circulating levels of LPS as a consequence of increased intestinal permeability (Moreira et al., 2012). LPS induces inflammation which leads to oxidative stress and insulin resistance and a further boost in the pre-existing inflammatory state (Dandona et al., 2004). The impact of gut microbiome on host metabolism, immunity and health has encouraged research into strategies aiming to modify the gut microbiome. One such strategy, probiotics have garnered attention and contend to possess anti-inflammatory properties (Oelschlaeger, 2010), immunomodulatory potential (Isolauri et al., 2004) and improve intestinal barrier function (Kumar et al., 2016). However, for these protective functions to occur physical proximity between probiotics and intestinal epithelial cells is essential, the evidence of which is contradictory (Suez et al., 2019). Thus, BioPersist and BioColoniz were engineered to enhance persistence and colonization in the gut respectively. 23 We have previously showed the detrimental effects of high n-6 PUFA consumption (Ghosh et al., 2013; C. K. Wong et al., 2015). This thesis aims to demonstrate beneficial impacts of BioPersist and BioColoniz consumption which can combat the detrimental effects of high n-6 PUFA feeding in mice. We witnessed improved glucose metabolism in high fat fed mice that were administered BioPersist. Additional investigation revealed a possible improvement in insulin signaling with higher activation of AKT, the main mediator of downstream effects of insulin in livers of mice that were administered BioPersist. Furthermore, inflammatory biomarkers such as iNOS and TNF- α and oxidative stress biomarkers such as thioredoxin and nitrotyrosine were decreased in the BioPersist group. The data we obtained indicates that BioPersist does confer protective properties in vivo. 2.2 Materials & Methods 2.2.1 Probiotic Strains Both the designer probiotic strains were cultured in the Gibson lab. Biocoloniz was cultivated in De Man, Rogosa and Sharpe (MRS) media on 1.8% MRS agar whereas, BioPersist was cultivated in Luria-Bertain Miller (LB) media and on 1.8% LB agar. The media was constituted in deionized water and the pH was adjusted to be 6.5 or 7.5 for BioColoniz and BioPersist respectively. BioPersist was grown in agitation but BioColoniz was grown without any agitation in anaerobic jars containing growth media at 37°C. The media used for growing BioColoniz was supplemented with 5ug/mL erythromycin while the media for BioPersist was supplemented with 4ug/mL tetracycline. 24 Table 2.1 Composition of the media used to cultivate BioColoniz Ingredients Amount Peptone 10g Beef extract 10g Yeast extract 5g Dextrose 20g Sodium Acetate 5g Polysorbate 80 (Tween 80) 1g Dipotassium Phosphate 2g Ammonium Citrate 2g Magnesium Sulfate 0.1g Manganese Sulfate 0.05g 2.2.2 Murine model & diet Mus musculus, the house mouse is a commonly used model to study human disease and behavior because of their physiological similarities to humans and ease of housing and breeding (Perlman, 2016). Furthermore, mice models are ideal for studying nutrition overload, diabetes, insulin resistance and obesity (Rodrigues, 2016; Speakman, 2019; Tschöp & Heiman, 2001). One such mice model, C57Bl/6 is frequently used to study the consequences of high fat diet resulting in obesity, diabetes and other complications that may arise such as atherosclerosis and CVDs (Schreyer et al., 1998; Surwit et al., 1988). This strain becomes obese, insulin resistant and hyperglycemic making it a robust model to study impaired glucose tolerance (Winzell & Ahrén, 2004). 25 Thus, to study the effects of BioPersist and BioColoniz on high fat dietary intake, C57Bl/6 strains were used. All the animal experiments were approved by the UBC’s Animal Care Committee under the protocol A15-0240. Female C57Bl/6 mice aged 4-5 months were housed at 26°C on a 12 hour light/dark cycle. The mice were randomly assigned to either of the three groups; a) high fat non-probiotic (NP) b) high fat BioPersist (PE) c) high fat BioColoniz (PL). We made sure that the mice were equally distributed, and age matched among the three groups. The mice diet consisted of a basal mouse diet mix (BioServ AIN-76A, New Jersey, USA) combined with 20% w/w corn oil (Mazola, ACH Food Companies Inc.). The final diet contained 13.89 kJ/g of energy out of which about 40% energy was derived from fat. The diets were made weekly and stored at -20°C and the food was replaced with freshly prepared one every alternate day to reduce oxidation. Food and water were provided ad libitum according to 5g food per day. The weight of the mice was monitored, and the mice were weight twice a week. 26 Table 2.2 Basal diet composition Ingredient Composition (% by weight) Sucrose 50 Casein 20 Corn Starch 15 Solka Floc-40 5 AIN-76 Mineral Mix 3.5 AIN-76 Vitamin Mix 1 DL- Methionine 0.3 Choline Bitartrate 0.2 Corn oil 5 To the basal diet, additional 20% w/w corn oil was added (80g basal diet mixed with 20g of corm oil to obtain 100g of the final diet) Table 2.3 % Final diet composition of macronutrients in rodent diet Component Weight % Energy % Carbohydrate 44.7 40.1 Protein 21.2 19.0 Fat 20.2 40.8 To correlate the study with the day-to-day world where majority of North Americans have or have had a high n-6 PUFA diet, the mice were gavaged in between the study, at the end of the second and fourth week. Probiotic mice groups were administered probiotic dose of 3x1012CFU/mL and 2x1012CFU/mL of BioPersist and Biocoloniz respectively. The mice were gavaged with an animal feeding 22G x 1.5” 1.25 mm straight needle (Cadence Science; Cranston, Rhode Island). Mice were assessed immediately after gavage, followed by another 27 check for signs of stress, lethargy, hunch, difficulty breathing or bleeding. The mice were checked again after 24 hours. After 6 weeks of feeding, the mice were ethically euthanized using isoflurane, an inhalant anesthetic, followed by overdose of carbon dioxide (Adult Rodent Anesthesia SOP, n.d.). Blood and tissues such as heart, liver, adipose, aorta and muscle were collected. The tissues were flash frozen in liquid nitrogen and placed at -80°C for further storage. The blood was collected in 2 ml Eppendorf tubes containing pre-cooled protease inhibitor (Thermo Fisher Scientific, Pierce 88666, Waltham, MA, USA) solution and placed on ice. Post tissue collection, the blood was centrifuged at 1000 rpm for 10 minutes to obtain serum. 2.2.3 Intraperitoneal glucose tolerance test (IPGTT) Intra-peritoneal glucose tolerance test was done at the end of the fifth week to assess fasting glucose. Animals were weighed and moved to new clean cages for a 6 hour fast. The animals had access to water during the fasting period. After 6 hours of fasting, mice were poked in their tail and blood glucose was measured using glucose test strips and glucometer (One-touch Ultra Mini® meter and test strips). Mice were injected intraperitoneally with 1g/kg body weight glucose solution. Glucose solution was prepared by glucose diluted in sterile saline filtered through 0.2micron filter (VWR, 28145-501, AB, Canada). Blood was withdrawn and glucose was measured again 15, 30, 60- and 120-minutes post glucose injection. After glucose measurement at 120 min, food was returned to the mice. 28 2.2.4 Protein Extraction Small section of frozen liver tissue weighing about 20-30mg was cut and submerged in 500 ul of extraction buffer consisting of radioimmunoprecipitation assay buffer (RIPA buffer) and protease inhibitor cocktail (Thermo Fisher Scientific Pierce, 88666, Waltham, MA, USA) and phosphatase inhibitor (Alfa Aesar, J61022, Tewksbury, MA, USA). The tissues were homogenized at 30 Hz for 2 minutes using a homogenizer (Retsch Mixer Mill M400, Germany) three times with an interval of one minute in between homogenization. This was followed by centrifugation at 20,000 rpm for 15 minutes at 4°C after which the supernatant was separated and put into fresh tubes. The isolated supernatant containing extracted protein was stored at -20°C till further use. Bradford protein assay (BioRad, 500-0006, California, USA), being one of the most commonly employed methods, was used to measure the total protein concentration in the extracts while bovine serum albumin (BSA) used as protein standard. This colorimetric assay was quick and sensitive (Kruger, 2009; Schaffner & Weissmann, 1973). Extracted proteins were diluted and incubated with Bradford reagent for 5 minutes, followed by quantification at 595nm using a spectrophotometer (Offenburg, Germany). The quantified proteins were diluted in dd water to maintain constant concentration across different samples. The diluted samples were further added with 50 ul of sample digestion buffer (SDB buffer) to get them ready for western blots. Table 2.4 RIPA buffer recipe Ingredients Amount 150mM Sodium Chloride 17.5 g 1%Triton-X 2 mL 0.5% Sodium deoxycholate 1g 0.1%SDS 0.2 g 29 50 mM Tris base 1.2 g Table 2.5 5X SDB buffer recipe Ingredients Amount 0.5m Tris 4.8 mL 100% Glycerol 10 mL 10 % SDS 8 mL Mercaptoethanol 2 mL 0.1% Bromophenol blue 4 mL dd water 11.2 mL 30 2.2.5 Western blots Western blots are commonly used to identify and quantify specific proteins from cell or tissue extracts. Western blotting procedures utilize three basic steps in the following order: (1) size based separation via gel electrophoresis, (2) transfer to a membrane, (3) detection of protein via complementary antibody binding (Mahmood & Yang, 2012). For gel electrophoresis, protein samples were prepared by diluting with distilled water and sample loading buffer (recipe in table 2.4). The diluted proteins were denatured and about 35 µl was run on SDS-polyacrylamide gel electrophoresis, followed by transfer to nitrocellulose membranes. Next, the membranes were blocked with 5% skimmed milk in Tris-buffered saline containing 0.1% Tween-20 (TBST-T) for 15 minutes at room temperature with constant shaking. The membranes were then incubated overnight with 5ml of primary antibody solution composed of 5% BSA dissolved in 1X TB-T, at 4°C with constant revolutions at 2rpm/min. Post incubation, the membranes were washed in TBS-T for 5 minutes 3 times. The membranes were then incubated with 5ml of secondary antibody solution containing 5% skimmed milk dissolved in TBS-T for two hours at room temperature with constant agitation at 2rpm/min. Primary and secondary antibodies used are mention in table 2.5 & 2.6 along with the concentration the antibodies were diluted to. Finally, antibodies were detected on the membrane by using ECL western blotting substrate (Thermo Fisher Scientific Pierce, 32109, Waltham, MA, USA) and visualized using Image Studio Density version 3.1. Band intensity was quantified via ImageJ and expressed as a ration to β-actin signal. Multiple antibodies were probed on the same blot using stripping buffer (Alfa Aesar, J62541, Tewksbury, MA, USA). 31 Table 2.6 List of Primary Antibodies used 1°Antibody Source Isotype Cat. # Dilution Insulin Receptor β Cell Signaling Technology Rabbit 3025 1:1000 IGF-I Receptor β Cell Signaling Technology Rabbit 9750 1:1000 Phospho-IGF-I Receptor β/ Insulin Receptor β Cell Signaling Technology Rabbit 3021 1:1000 Phospho-Akt (Ser473) Cell Signaling Technology Rabbit 4060 1:1000 Phospho-Akt (Thr308) Cell Signaling Technology Rabbit 13038 1:1000 Phospho-GSK-3 β Cell Signaling Technology Rabbit 5558 1:1000 Phospho-FoxO1 (Thr24) Cell Signaling Technology Rabbit 599 1:1000 Phospho-mTOR (Ser2448) Cell Signaling Technology Rabbit 5536 1:1000 Phospho-Tuberin/TSC2 (Ser939) Cell Signaling Technology Rabbit 3615 1:1000 GPX-4 Elabscience Rabbit EAP0711 1:1000 Thioredoxin Cell Signaling Technology Rabbit 2298S 1:1000 Nitrotyrosine Santa Cruz Biotechnology Mouse Sc-32757 1:1000 TNF-α Santa Cruz Biotechnology Goat Sc-1351 1:1000 32 1°Antibody Source Isotype Cat. # Dilution i-NOS Santa Cruz Biotechnology Rabbit Sc-651 1:1000 GSK-3 β Novus Biologicals Mouse NBP1-47470 1:1000 β-actin ABM Mouse G043 1:1000 Table 2.7 List of Secondary Antibodies used 2°Antibody Source Cat. # Dilution Anti-Rabbit Cell Signaling Technology 7074 1:2000 Anti-mouse Cell Signaling Technology 7076S 1:2000 Anti-Goat Rockland antibodies 05-703-002 1:3000 2.2.6 Probiotic Strain detection DNA was extracted from liver tissues using QIAamp DNA Stool Mini Kit (Qiagen, 51504). Liver samples were homogenized twice at 30 Hz for 2 minutes (Retsch Mixer Mill M400, Germany) and then heated to 95°C for 5 mins in a water bath to lyse cells and denature nucleases. 1 InhibitEX tablet from the kit was added to the samples, followed by centrifuging at 160g for 3 minutes. Next, Proteinase K was added to digest proteins. Supernatant was removed and transferred to new tube, to which Buffer Al was added. The tubes were then heated at 70°C for 10 mins to activate Proteinase K. Post heating, ethanol was added to the sample and tubes were vortexed. QIAamp spin columns were placed in 2mL tubes and lysate was added to the center of the column to precipitate out nucleic acids. 500 ul of wash Buffers AW1 and AW2 were then added, followed by a final elution in Buffer AE. PCR was performed on the extracted DNA using the Bio-rad S1000 Therma Cycler (Bio-rad). PCR tubes had contents as listed in 33 table 2.7. The PCR protocol was: 95°C for 2 mins, 95°C for 30 secs, Tm (melting temperature) for 30 secs, 72°C for annealing time, repeated at cycles of 30 or more, and then 4°C for storage. The amplified DNA product was run on 1% agarose (Mandel Scientific Company; Guelph, Ontario) gel and at 90-100 V for 30-40 mins. Band separation was compared with a 50 bp DNA Ladder (VWR, Canada). Gels were imaged on the Herolab gel imager Doc (Herolab, Wiesloch, UVT-28 ME, Germany). Table 2.8 PCR reaction set up Ingredients Amount EconoTaq DNA polymerase (Lucigen; Middleton, Wisconsin) 12.5 uL 10 mM forward and reverse primers 2.5 uL nuclease free water 5.5 uL DNA extracted 2 uL Table 2.9 Primer sequences used to detect BioPersist & BioColoniz Probiotic/ Gene Detected Forward Primer (5’-3’) Reverse Primer (5’3’) E. coli ACAGTGCGGCGCAGGCGGCAATGC TAACAAACGCTGCGGCTGCGCC L. reuteri AGCAGTAGGGAATCTTCCA CACCGCTACACATGGAG Muc 2 TCCACATTATCACCTTGAC AGGGAATCGGTAGACATC 34 2.2.7 Hormonal analysis in blood serum Rodent blood was collected and centrifuged at 1,000 rpm for 10 minutes at 4°C. The serum separated from the red blood cells and formed the supernatant, which was collected and shipped to Eve Technologies, Calgary for hormonal analysis of amylin, GIP, GLP-1, Insulin, PYY, Glucagon, PP and C-peptide. Eve Technologies performed a ‘Mouse/Rat Metabolic Array’ on the serum samples. This is an efficient technique as it can measure all the required metabolic hormones simultaneously in a single microwell. The assay is designed for protein analysis in serum and utilizes a multiplex immunoassay analyzed with Bio-Plex 200. The multiplex immunoassay used Luminex magnetic beads to quantify the hormones. The Bio-Plex 200 systems are extremely sensitive and can distinguish between biomolecules, enabling users to reliability analysis multiple targets in a single sample. 2.2.8 Statistical Analysis GraphPad Prism 6.0 (GraphPad Software Inc., San Diego,CA,USA) was used to graph and analyze the data obtained through the various experiments. Results are expressed as graphs with mean ±standard error of mean (S.E.M.) Data was analyzed by either One-Way or Two-Way ANOVA. The level of significance was set as p>0.05. The n value for each experiment is mentioned in the results section along with their respective graphs. 35 2.3 Results 2.3.1 Least weight gain was observed in BioPersist administered mice. Most mice steadily gained weight over the six-week period of the high fat feeding. Mice weights were monitored every four days to ensure that each mouse was consuming experimental diets. At the end of six weeks, the final body weight was measured and used to determine the change in body mass represented in figure 2.1. We observed a decrease in the amount of weight gain in mice gavaged with BioPersist at the end of the second week, after which the mice weights were did not vary, indicating that mice did not gain much weight after the gavage. Data was visualized as mean ± standard error (SEM). 36 Figure 2.1 Least weight gain observed in mice administered BioPersist A) percentage change in body weight, determined by subtracting body weight recorded at the end of each week with the body weight in the beginning of the study. Data was analyzed using two-way ANOVA (P<0.05) B) Change in body mass determined by subtracting final body weight from initial body weight. Data was analyzed using one-way ANOVA (*P=0.0008, +P=0.0138). * & + indicate significant difference between Non-probiotic (NP) & BioPersist and BioPersist and BioColoniz respectively. Abbreviations: NP, non-probiotic, n= 14 A. B. 37 2.3.2 BioPersist improved glucose metabolism Intra-peritoneal glucose tolerance test (IPGTT) is frequently used to assess glucose metabolism (Jørgensen et al., 2017). IPGTT done at the end of five weeks of the study clearly revealed that mice gavaged with BioPersist had better glucose metabolism compared to non-gavaged mice. Figure 2.2 BioPersist improved high fat diet induced insulin resistance A) IPGTT curves with fasting blood glucose and subsequent measurement of blood glucose at 15, 30, 60,120 minutes post glucose injection. Data was analyzed using two-way ANOVA (****P<0.0001, +++P=0.0005, ++P<0.008. B) Area Under the Curve, obtained by calculating area under the curve for each mouse and analyzed by one-way ANOVA (NP-BioPersist P=0.0005, BioPersist-BioColoniz P=0.0071). * & + indicate significant difference between NP & BioPersist and BioPersist and BioColoniz respectively. Abbreviations: NP, non-probiotic, n= 13 A. B. 38 2.3.3 BioPersist enhanced insulin signaling with increased insulin receptor β expression and phosphorylation of Akt The major difference in the glucose metabolism in designer probiotic gavaged mice and the mice that were not gavaged with the probiotics, prompted us to look at the insulin signaling pathway. As described, insulin is one of the most important anabolic enzymes which regulates cell growth, blood glucose levels and many more vital functions. The blood glucose homeostasis is made by maintaining a balance between glucose absorption in the intestine and production by liver and uptake and metabolism by muscle (Alan R. Saltiel & Kahn, 2001). Given the vital role of liver, we decided to study insulin signaling in the liver. The insulin signaling cascade is initiated at the insulin receptor. Insulin receptor and IGF-I receptor belong the family of tyrosine kinases which are characterized to possess α and β subunits (Patti & Kahn, 1998). Insulin/IGF-I binding to the α subunit leads to phosphorylation of the β subunits, initiating the cascade of signaling events which eventually lead to phosphorylation of Akt at Thr-308 (A R Saltiel & Kahn, 2001). For complete activation of Akt, phosphorylation at Ser-473 via mTORC2 is essential. Akt being the major regulator of the downstream effects of insulin, is important for insulin action (Manning & Toker, 2017). We found that insulin receptor β expression was significantly improved in BioPersist gavaged mice. Along the same lines, phosphorylation of insulin/IGF-I receptor β was also increased in mice that were gavaged with either of the two designer probiotics. Phosphorylation of Akt at threonine residue, a downstream effect of phosphorylation of the receptor was enhanced in mice administered BioPersist. No significant difference was observed in IGF-I receptor β expression and phosphorylation of Akt at Serine residue (Fig 2.3) 39 A. B. C. D. 40 Figure 2.3 BioPersist enhanced insulin signaling via improved insulin receptor expression and phosphorylation. A) Insulin receptor β expression normalized to β-actin, *P=0.038. B) IGF-I receptor β expression normalized to β-actin. C) Phosphorylated insulin/IGF-I receptor β normalized to β-actin, *P=0.0498, #P= 0.036. D) Serine phosphorylation of Akt. normalized to β-actin. E) Threonine phosphorylation of Akt normalized to β-actin, *P=0.0329. Data was analyzed using one-way ANOVA* & # indicate significant difference between NP & BioPersist and NP and BioColoniz respectively. Abbreviations: NP, non-probiotic, IGF, insulin-like growth factor, IR, insulin receptor. Each bar represents n of 8. 2.3.4 Impact of BioPersist & BioColoniz, on downstream effects of insulin Phosphorylation of Akt, enables it to bring about all the major effects of insulin. Activated Akt phosphorylates FOXOs, inhibiting transcriptional functions of FOXOs and in turn inhibiting cell apoptosis and gluconeogenesis (X. Zhang et al., 2011). Akt phosphorylates and inhibits GSK-3 β to promote glycogen synthesis (Manning & Toker, 2017). Akt also phosphorylates TSC-2 which is an inhibitor of mTORC-1. Once TSC-2 is deactivated via Akt phosphorylation, mTORC-1 is activated. No significant difference was observed in phosphorylation of FOXO or TSC-2 between the groups. A significant decrease in GSK-3 β and E. 41 phosphorylated GSK-3 β was detected in mice administered with BioPersist whereas, an increase was seen in activated mTOR levels in mice gavaged with either of the two designer probiotics. A. B. C. D. 42 Figure 2.4 Impact of BioPersist & BioColoniz on downstream effects of insulin A) P-FOXO expression normalized to β-actin. B) P-mTOR expression normalized to β-actin *P=0.0151, #P=0.0323 C) P-GSK-3β expression normalized to β-actin *P= 0.0283. D) GSK-3β expression normalized to β-actin **P= 0.0084.E) P-TSC2 expression normalized to β-actin. Data was analyzed using one-way ANOVA* & # indicate significant difference between NP & BioPersist and NP and BioColoniz respectively. Abbreviations: NP, non-probiotic, FOXO, Forhead box, GSK-3 β, glycogen synthase kinase 3 beta, mTOR, mammalian target of rapamycin, TSC-2, tuberous sclerosis complex, n=8 2.3.5 BioPersist decreased inflammatory biomarkers in rodent liver Inflammation has often been held liable for insulin resistance (Ding et al., 2010). The significant variations in some nodes of the insulin signaling pathway promoted us to look at inflammatory biomarkers such as TNF-α and iNOS, both of which were significantly decreased in BioPersist administered mice. E. 43 Figure 2.5 BioPersist decreased inflammatory biomarkers A) iNOS expression normalized to β-actin *P= 0.0452. B) TNF- α expression normalized to β-actin. ***P=0.0005, ++P= 0.0015 Data was analyzed using one-way ANOVA* & + indicate significant difference between NP & BioPersist and BioPersist and BioColoniz respectively. Abbreviations: NP, non-probiotic, iNOS, inducible nitric oxide synthase, TNF- α, tumor necrosis factor- alpha, n=8 2.3.6 BioPersist & BioColoniz help curb oxidative stress The pathophysiological processes responsible for oxidative stress and inflammation work in close association and often one results in the other. Decrease is oxidative stress biomarkers akin to depletion of inflammatory biomarkers post designer probiotic supplementation was observed. In addition, GPX-4, an important anti-oxidant was significantly increased in BioPersist gavaged mice when compared to non-probiotic group and the mice that were gavaged with BioColoniz. A. B. 44 Figure 2.6 BioPersist & BioColoniz help curb oxidative stress A) Thioredoxin expression normalized to β-actin #P= 0.0041. B) Nitrotyrosine expression normalized to β-actin *P= 0.0002, #P=0.0409 C) GPX-4 expression normalized to β-actin *P=0.0041, P+=0.0197. Data was analyzed using one-way ANOVA*, # and + indicate significant difference between NP & BioPersist, NP and BioColoniz and BioPersist and BioColoniz respectively. Abbreviations: NP, non-probiotic, GPX-4, glutathione peroxidase-4, n=8 A. B. C. 45 2.3.7 BioPersist elevated appetite curbing hormones, PYY & amylin Gut hormones are primarily secreted by gut epithelial enteroendocrine cells (EECs) and play a vital role in metabolism. Recent evidence suggests that there exists crosstalk between microbiota communities in the gut and EECs which influences gut hormone release and host metabolism (Gribble & Reimann, 2019; Lund et al., 2018). Microbiome metabolites such as short chain fatty acids (SCFAs), secondary bile acids and other structural components modulate the EECs, which line the mucosal lining of the gut to release hormones including glucagon-like peptide (GLP-1), peptide YY (PYY), gastric inhibitory peptide (GIP) and serotonin vital for metabolic activities (Martin et al., 2019). Serum analysis revealed that mice administered BioPersist had significantly elevated insulin, amylin & PYY concentrations while BioColoniz, had increased GLP-1. 46 A. B. C. D. 47 Figure 2.7 BioPersist elevated appetite curbing hormones A) insulin concentration in mice serum, *P= 0.0205. B) amylin concentration, *P= 0.0457. C) GLP-1 concentration ,#P=0.0342. D) PYY concentration *P= 0. E) 0385 C-peptide concentration. F) glucagon concentration. G) GIP concentration. H) PP concentration. Data was analyzed using one-way ANOVA, * , # indicate significant difference between NP & BioPersist, NP & BioColoniz respectively Abbreviations: NP, non-probiotic, GIP, gastric inhibitory peptide, GLP-1, glucagon like peptide-1, PYY, peptide YY, PP, pancreatic polypeptide. Each bar represents n of 9-11. E. F. G. H. 48 2.3.8 Designer probiotics do not translocate from the gut. In order to see if all the beneficial effects in the liver seen through the western blots was because of the probiotics translocating to the liver or via probiotics exerting protective affects from a distance, strain detection in the liver tissues was performed. 4 samples from each of the three groups were taken and PCR was used to amplify and detect ttr operon and Lactobacillus spp. in mice administered with BioPersist and BioColoniz respectively. Samples from the PCR were run again on a second gel to verify that DNA was extracted and amplified correctly. Probiotics were not detected in the liver tissues, indicating no translocation to the liver, which further justifies that administration of the designer probiotics did not lead to any sepsis. 49 Figure 2.8 Designer probiotics do not translocate from the gut A. Gel visualized with amplified PCR product and compared with DNA positive controls extracted from BioPersist and BioColoniz bacterial cultures. A 50 bp ladder was used to locate the amplified DNA. B. Gel visualized with amplified PCR product to show presence of liver DNA. A 1000 bp ladder was used. Abbreviations: NP, non-probiotic, PL, probiotic BioColoniz (Lactobacillus), EP, probiotics BioPersist (E. coli), n= 4. A. B. 50 2.4 Discussions 2.4.1 Least weight gain was observed in BioPersist administered mice. Animal models are often used to study the effects of various diets. A positive correlation between the amount of fat in diet and body weight gain has been reported in rodents (Boozer et al., 1995). Obesity was first introduced by ad libitum feeding with diets rich in fat in weanling in the early 1950s (Ingle, 1949). This ‘dietary obesity model’ has since been in use till date to study many diseases including metabolic syndrome (Fraulob et al., 2010), various types of cancers (Hilakivi-Clarke et al., 1997; Z. Liu et al., 2001) and brain dysfunctions (Davidson et al., 2012; Pistell et al., 2010). Generally, high fat diets range between 30%-78% of total energy intake (Buettner et al., 2007). Previous studies from our lab have reported that 6 weeks of high fat dietary intervention was adequate to induce weight gain and fat accumulation (Beam et al., 2015; Sears & Ghosh, 2016; C. K. Wong et al., 2015). Thus, we designed an experiment utilizing a diet consisting of 40% energy intake from fat and feed the three subgroups for a period of 6 weeks with the same. As C57Bl/6 strain of mice is known to gain weight and become insulin resistant (Schreyer et al., 1998), we used this strain for our experiments. Sex based differences have been reported in obesity studies using rodents with males gaining weight steadily throughout their lives as compared to females, whose body weights were found to be more stable in early adulthood. The latter being similar to the growth patterns in humans. In addition, female animal models are better responders to high fat diets. These differences account for females being more suitable for an obesity study in adult rodents (Hariri & Thibault, 2010). With regards to HFD, fatty acid bioconversion has been reported to have pronounced sex based differences with females having enhanced fatty acid bioconversion through higher activity of key enzymes involved in conversion of PUFAs to long-chain PUFAs (Burdge & Wootton, 2002). In addition, 51 previous studies in human subjects as well as rodent models have shown distinct effects of HFD consumption on males and females, with the latter having significantly increased expression of lipid peroxidation and carcinogenic byproducts (Aiken et al., 2016; Fang et al., 2007; Hilakivi-Clarke et al., 1999; Nair et al., 1997; Shapira, 2012). Along these lines, HFDs consumption has been linked to higher incidences of breast cancer in rodents and women (Chajès et al., 2012; Goodstine et al., 2003). Another major difference between male and female rodents in the female estrous cycle which can affect food intake (Andersen et al., 1997) and insulin sensitivity (Riant et al., 2009). Many a times, researchers use male rats to study obesity, limiting our knowledge of obesity in females (Hariri & Thibault, 2010). Therefore, in this study, we restricted our animal models to females. For any diet study, it is fundamental to monitor any changes in the subject weight. We recorded the weights of all our rodents every four days and weight fluctuation can be seen in fig. 2.1. Mice that were administered with BioPersist, did gain weight but the reported weight gain in this group was significantly lower than the weight gains in the other two groups. An important observation the graph in fig 2.1 points to is that this group was gaining weight till it was gavaged with BioPersist at the end of week 2, post which the significant differences in the weight gain patterns emerge. Oral gavage, a stressful but effective procedure to directly administer substances into the rodent gut has not been associated with weight loss (Arantes-Rodrigues et al., 2012), however, even if that was the case, we would have noted a similar pattern in the group gavaged with the second designer probiotic, BioColoniz. Probiotics have often been studied for weight loss properties. A study conducted in healthy Japanese adults showed that consumption of Lactobacillus gasseri based probiotic aided in lowering abdominal adiposity, however, constant consumption was suggested necessary to 52 maintain the effects (Kadooka et al., 2013). Another study involving Lactobacillus based probiotic administration to patients that had undergone Roux-en-Y gastric bypass (RNYGB) surgery demonstrated that probiotic improved post-surgery weight loss (Woodard et al., 2009). Unfortunately, a meta-analysis aiming to summarize research hitherto representing probiotic treatment for weight loss failed to demonstrate promising results, indicating that probiotics had limited efficiency in stirring weight loss (S. Park & Bae, 2015). 2.4.2 BioPersist improved glucose metabolism Glucose tolerance tests (GTTs) are often employed to assess glucose disposal in both humans and rodents and involve fasting followed by glucose administration and measurement of blood glucose levels after subsequent time intervals (Bowe et al., 2014). Some factors which may regulate the results of a GTT include fasting time, route of administration and amount of glucose administered (Andrikopoulos et al., 2008). According to WHO, the standard diagnostic test for impaired glucose tolerance involves administering glucose orally in over-night fasted subjects (World Health Organization, 2006). However, in mice, overnight fasting incites a catabolic state, depleting liver glycogen reserves and reducing variability in baseline blood glucose levels (Ayala et al., 2010). In fact, during IPGTT, plasma glucose levels and AUC for HFD mice fasted for 6 hours was reported to be significantly higher than the HFD mice fasted over-night when compared to chow fed mice in both situations (Andrikopoulos et al., 2008). This indicates that a 6 hour fast is suitable for assessing insulin function and therefore, we fasted the HFD mice used in our study for a period of 6 hours prior to GTT. The next factor to consider during any GTT is the route of administration. The glucose profiles of OGTT and IPGTT are distinct with a peak observed after 15 minutes and 30 minutes of glucose ingestion and injection 53 respectively and lower AUC drafted in both HFD and chow mice in OGTT than IPGTT (Andrikopoulos et al., 2008). These differences could be accounted for by the incretin effect which is minimal in case of IPGTT (Muniyappa et al., 2008; Oh, 2016). The rate of gastric emptying also affects glucose response during an OGTT (Ayala et al., 2010). Thus, to circumvent these physiological processes, we opted to do an IPGTT. The third aspect that we had to consider before the IPGTT was the doseage of glucose to be administered. The standard dose in mice is 1-2g/kg (Heikkinen et al., 2007). In obese mice, there is irregular fat distribution as compared to lean mass which includes key sites such as brain, liver and muscle responsible for glucose homeostasis (Jørgensen et al., 2017). When glucose administered to obese mice based on body weight, there is room for error as these mice will receive more glucose for the same lean body mass as compared to non-obese mice. The larger the volume of glucose administered, larger the bias towards glucose intolerance in obese mice (Ayala et al., 2010; Jørgensen et al., 2017). Hence our experiment encompassed 6 hour fasted mice injected intraperitoneally with 1g/kg glucose solution post fasting blood glucose reading. Substantial studies indicate that HFD induces intestinal microbial dysbiosis (Ghosh et al., 2013) along with glucose intolerance in mice (Williams et al., 2014; C. K. Wong et al., 2015). Previous research suggests that short term high fat feeding in young healthy individuals increases fasting glucose levels because of increased hepatic glucose production (Brøns et al., 2009). Our study demonstrated that administration of designer probiotic BioPersist significantly improved HFD induced impaired glucose metabolism (fig.2.2). In fact, the AUC for glucose disposal mirrors the AUC depicting the weight gained by mice over the 6-week feeding period (fig.2.2). Many reports have made strong correlations between BMI and impaired glucose tolerance in obese and overweight (Mokdad et al., 2003). Interestingly, in healthy men with impaired glucose 54 tolerance, glucose disposal was improved following weight loss via diet modifications (Lindgärde et al., 2009). A previous study obtained similar results with the same mice model but a different probiotic. This study involved Bifidobacterium animalis ssp lactis 420 probiotic supplementation to HFD induced obese C57Bl/6 mice and concluded that the probiotic reduced fat mass as well as improved glucose tolerance (Stenman et al., 2014). Another study with the same probiotic supported the probiotic’s ability to amend glucose metabolism and attributed this effect to decreased inflammation and bacterial translocation in probiotic supplemented animals (Amar et al., 2011). Although we used different probiotics, we do believe that improvement in glucose clearance in BioPersist administered mice is because of the attenuation in their inflammatory status, as represented by fig.2.5. The lowered inflammatory molecules indicate that inflammation induced disruption of insulin signalling was minimized in this group. BioPersist as mentioned earlier is a genetically designed probiotic with E.coli Nissle as its main constituent. Our results are consistent with another in vitro study involving recombinant E. coli Nissle strain stimulating insulin secretion in epithelial cells in response to glucose challenge. In this study, E. coli Nissle was engineered to secrete GLP-1 or PDX-1 protein, the latter being produced under the control of a glucose-responsive promoter (Duan et al., 2008). In our study, E.coli Nissle was modified to enhance its survivability in the gut and the pre-existing probiotic properties of the bacteria were left untampered and yet we observed improved glucose metabolism, indicating that E.coli Nissle strain has a beneficial effect and enhancing its persistence in the gut amplified its protective properties. 55 2.4.3 BioPersist enhanced insulin signaling with increased insulin receptor β expression and phosphorylation of Akt IR/IGF-IR-β is the point of initiation of the effects of insulin stimulation and is very important in maintaining metabolic homeostasis. A complete knockout of insulin receptor in mice has been reported to cause growth impairments, skeletal muscle hypotrophy, diabetic ketoacidosis and even death (Accili et al., 1996; Lamothe et al., 1998). In addition, insulin receptor expression is lowered in insulin resistant rodents (Soll et al., 1975). Indeed, a significant decrease in insulin receptor sites per cell is reported in adipose obtained from obese individuals (Kolterman et al., 1979). HFD induced insulin resistance is also accompanied with decreased insulin receptor expression with unaffected receptor affinity in rats (Grundleger & Thenen, 1982). An increase in insulin receptor protein in BioPersist gavaged mice clearly indicates that this designer probiotic possesses the ability to enhance insulin receptor expression irrespective of the high fat diet. A similar increase in phosphorylation of IR/IGF-IR-β was not only seen in BioPersist but also Biocoloniz. Tyrosine autophosphorylation of insulin/IGF-I hybrid insulin-IGF-1 receptor is one of the first responses to insulin/IGF-I binding receptor activation (White et al., 1985). Insulin stimulates autophosphorylation of insulin receptor on three tyrosine residues 1146,1150,1151 (Tavare et al., 1988) where as IGF-I receptor autophophorylates at Y1131,1135 and 1136 (Baserga et al., 1997). The antibody we used detected Tyr1131-phosphorylated IGF-I receptor and Tyr1146-phosphorylated insulin receptor. Overall, the data suggests increased insulin/IGF-I receptor activation which denotes enhanced insulin signaling. This is confirmed with the increase in threonine phosphorylation of Akt, one of the principle regulators of the downstream effects of insulin, in BioPersist 56 administered mice. Akt phosphorylation at both serine and threonine residues was reported to be significantly reduced in hepatic insulin resistance hamsters (Taghibiglou et al., 2002). HFD fed rats have remarkably reduced Akt phosphorylation in the liver, skeletal and cardiac muscle and adipose tissues when compared with low fat fed rats (Nascimento et al., 2006; Tremblay et al., 2001). In fact, insulin injections failed to upregulate insulin stimulated phosphorylation of IRS and Akt in HFD fed mice (De Fante et al., 2016). We are convinced that HFD causes an imbalance in the gut microbiome composition, simultaneously weakening gut barrier function and enhancing endotoxemia (Patrice D Cani et al., 2007; Esteve et al., 2011) which further disrupts insulin signaling. Previous research involving modulating gut microbiome of HFD mice to improve insulin signaling with antibiotics (Carvalho et al., 2012) and probiotics (Bagarolli et al., 2017) has showed some success. There lies a notable analogy between our reasoning and the explanations that these researchers proposed for their results indicating that HFD repercussions can be reversed by amending gut microbiome profile. 2.4.4 Impact of BioPersist & BioColoniz on downstream effects of insulin High fat diets have been associated with number of anomalies in insulin signaling (Calvo-Ochoa et al., 2014). HFD induced obesity in rats resulted in upregulated FoxO3a transcription factor and mitochondrial damage (Relling et al., 2006). In fact, in HFD fed transgenic mice depletion of FoxO1/3/4 improved insulin sensitivity and minimized immune cell infiltration , protecting mice from HFD induced insulin resistance (Nwadozi et al., 2016). In the absence of insulin, FOXO1 prevents the transcription of peroxisome proliferator-activated receptor gamma (PPARG) which is required for initiating adipogenesis and promotes the expression of glucose-6-sphatase, a key enzyme in gluconeogenesis (Jun Nakae et al., 2001). Akt 57 phosphorylates FOXOs causes their translocation from the nucleus to the cytoplasm. This prevents them from being transcriptionally active. Inactivation of FOXO prevents gluconeogenesis and promotes adipogenesis. However, FOXO3a has been associated with antioxidants manganese superoxide dismutase (MnSOD) and catalase (CAT) encoding genes (Westfall et al., 2017). In addition, depleted FOXO expression is strongly corelated with mammalian chondrocyte cell death (Akasaki et al., 2014). Thus, a balance in FOXO activity is necessary for longevity. Probiotic bacteria, Bifidobacterium longum and Lactobacillus acidophilus with Sir2 gene family mediated overexpression of antioxidant genes in human cell lines via FOXO regulated mechanisms (Guo et al., 2017). FOXO phosphorylation is relevantly similar across the probiotics and non-probiotic groups. Akt also phosphorylates GSK-3 which has two isoforms, α and β and both the isoforms are inhibited by Akt by phosphorylation at Ser-21 and Ser-9 residues of each isoform respectively (Srivastava & Pandey, 1998). Over expression of GSK-3 has been shown to impair insulin signaling by downregulation of IRS-1 (Cohen & Goedert, 2004; Eldar-Finkelman & Krebs, 1997). GSK-3 activity was reported to be higher in fat of HFD C57Bl/6 mice when compared to chow fed mice (Eldar-Finkelman et al., 1999). In fact, over expression and over activity of GSK-3 has been seen obese and diabetics (Henriksen & Dokken, 2006; Nikoulina et al., 2002). Inhibitors of GSK-3 have thus been studied in insulin resistant animal models and showed some promising results including enhanced glucose clearance (Cline et al., 2002; Martinez et al., 2002). Nevertheless, other studies have revealed that GSK-3 maybe important in regulating other pathways. Hitherto, the research on involvement of GSK-3 in insulin sensitivity has always been inhibitor of glycogen synthase activity, however, GSK-3β knock-out mice suffered embryonic lethality due to severe liver apoptosis, revealing an additional role for GSK-58 3β. This transgenic mouse model had excessive TNF-α and lacked genes involve in activation of NFκB signaling which plays an important role in cell survival, immunity and inflammation (Hoeflich et al., 2000). In addition GSK-3α knockout mice develop heart ailments two months after birth (Nabben & Neumann, 2016). BioPersist did not completely inhibit GSK-3, indeed, BioPersist administered mice had reduced GSK-3β expression and phosphorylation when compared to non-probiotic administered mice (fig.2.4). In addition, this group had reduced TNF-α expression (fig.2.5) and glucose dispersal when presented with a glucose challenge which alludes that a GSK-3 partial reduction helps improve glucose metabolism. Lastly, insulin and/or IGF-I stimulated Akt inhibits tuberin by phosphorylating it at Ser-939, Ser-1130 and Thr-146 and in the absence of mitogenic stimuli, TSC2 blocks phosphorylation of Ser-2448 on mTORC1, inhibiting its activity (Bhaskar & Hay, 2007). Chronic mTOR activation has been associated with promoting insulin resistance via inhibiting IRS-1 by phosphorylating it at serine resides, leading to impaired PI3K-Akt signaling (Takano et al., 2001). Previous research shows that an increase in phosphorylation of mTORC1 along with increased inhibitory phosphorylation of IRS-1 on Ser 636/Ser 639 and attenuated Akt phosphorylation at Thr 308 in response to high fat feeding in the muscle and liver of rats (Khamzina et al., 2005; L. Ma et al., 2010). This clearly establishes that mTORC1 is activated via mechanisms other that Akt-TSC2 signaling. Interestingly, mTOR induced inhibition of IRS-1 is induced by TNF-α which is elevated post high fat consumption (Ozes et al., 2001; Yang et al., 2015) and mTOR activity is also sensitive to availability of amino acids (Manning, B.D. L.C., 2003). mTORC1 activity is upregulated after high fat feeding, however, there is also contradictory research about attenuated mTORC1 activity in livers of rats fed high fat. This study elucidated that an early response in the liver to HFD involves reduced amino acid induced 59 mTORC1 activation (Kimball et al., 2015). In fact, mTORC1 signaling plays an important role in glucose homeostasis by regulating pancreatic β-cell function and TSC-2 KO mice with hyperactivated mTORC1 had higher insulin levels but reduced β-cell mass (Saxton & Sabatini, 2017). Thus, it is not astounding that chronic inhibition of mTORC1 via rapamycin to improve glucose tolerance has an opposite effect (Cunningham et al., 2007). Inhibitory phosphorylation of TCS-2 is conserved among the three groups of HFD mice, although the Ser-2448 phosphorylation of mTORC1 is enhanced in both the probiotic groups. BioPersist gavaged mice have higher mTORC1 activation and at the same time this group also shows improved insulin/IGF-I receptor and Akt activation, indicating that increased mTORC1 did not hamper insulin signaling (Gökhan S. Hotamisligil et al., 1993). Another captivating erity here is that TNF- α is significantly reduced in this group of mice, implying that increased mTORC1 is not due to increased TNF- α and rather an additional stimulant is responsible for maintaining the key biphasic effects of mTORC1 signaling in these designer probiotics administered mice. 2.4.5 BioPersist decreased inflammatory biomarkers in rodent liver Chronic inflammation is one of the most advocated mechanisms common to many pathological diseases including IBD, T2D, NAFLD and obesity and involves increased generation of ROS and pro-inflammatory cytokines such as TNF- α, IL-1β, IL-17a etc (Gökhan S. Hotamisligil, 2006). One of the first connections between inflammation and obesity was discovered when TNF- α was over-expressed in the adipose of four different obese rodent models (Gökhan S. Hotamisligil et al., 1993). This observation led to more substantial research on the role of TNF- α and its involvement in impairing insulin signaling. As discussed earlier, high fat diet consumption leads to an increase in inflammatory biomarker levels including TNF- 60 α and iNOS (Patrice D Cani et al., 2007; Gao et al., 2015), both of which disrupt insulin signaling. Blocking inflammation via specific pharmacological approaches has been employed in treating ailments. Probiotic solutions often claim to help reduce inflammation. HFD fed male rats had significantly reduced TNF- α and iNOS expression in liver when treated with VSL#3 multistrain probiotic (Esposito et al., 2009). However, a study showed that administration of VSL#3 failed to correct diabetogenic microbiome and delay the onset of diabetes in NOD mice because of poor colonization of the gut (Brown et al., 2016). Another study revealed that effects of VSL#3 probiotic were intestinal segment specific and the probiotic did not inhibit the advancement of severe inflammation in the colon and ileum of mice colitis model. In fact, VSL#3 had has no effect on inflammation in heterozygous TNFΔARE mice, a well-known rodent model of CD with elevated TNF- α (Hörmannsperger et al., 2009). Apart from probiotics, anti-TNF- α agents such as infliximab have been approved to reduce inflammation in CD patients (Magro & Portela, 2010). However, while infliximab therapy reduced disease activity in CD patients, insulin resistance was not altered (Koutroubakis et al., 2009). Similarly, iNOS blockage resulted in unimproved hepatic insulin signaling in HFD fed iNOS KO mice (Zanotto et al., 2016). Given the multiple inflammatory biomarkers involved in diminishing insulin signaling, blocking a sole biomolecule may not improve insulin signaling, rather the need of the hour is a holistic approach which looks at the root cause and tries to find a common solution to reduce chronic inflammation, excessive oxidative stress as well as improves insulin signaling. As evident from fig.2.5, BioPersist administered mice had lessened TNF- and iNOS protein content, correlating with our other findings including improved glucose tolerance, insulin receptor expression and Akt activity in this group. An explanation to reduced inflammatory biomarkers in this group could be improved barrier function which the common 61 commercial VSL#3 probiotics fails to strengthen in vivo (Gaudier et al., 2005). An improved gut barrier prevents LPS elicited immune response. 2.4.6 BioPersist & BioColoniz help curb oxidative stress markers As earlier mentioned, inflammation and oxidative stress go hand in hand, and both lead to elevation of the each other along with activation of molecular mechanisms which disrupt insulin action (Evans et al., 2003). In fact, oxidative stress precedes the inception of insulin resistance (Matsuzawa-Nagata et al., 2008). High fat diets mediate increase in production of ROS and RNS which directly modify and damage cellular molecules (Ghosh et al., 2006). Due to the highly reactive and ephemeral nature of ROS and RNS, it is very difficult to directly measure these species. One of the methods applied to estimate the levels of these reactive species include measurement of their relatively stable byproducts (Griendling et al., 2016). We employed this strategy to quantify peroxynitrile (ONOO-), a destructive free radicle which contributes to cell damage, inflammation and lipid peroxidation. Nitrotyrosine is a product of tyrosine nitration mediated by peroxynitrite and it is a well-recognized indicator of oxidative stress (Szabó et al., 2007). Surplus levels of it are also detected in diabetics which compared to healthy individuals, signifying its plausible role in the disease and its complications (Ceriello et al., 2001). Long- term consumption of high fat diets may also lead to accumulation of Nitrotyrosine in various tissues in rats (Roberts et al., 2002). It is evident from fig.2.6, that both the designer probiotics lead to reduction in Nitrotyrosine levels. Another method to judge oxidative stress levels is by measuring biomolecules produced in response to it. An example being stress-induced, redox enzyme, thioredoxin (TRX). TRX plays an important role in multiple biological processes including protection against oxidative 62 stress TRX also possesses cytokine like activity (Nakamura et al., 1997). In addition, TRX also possesses co-cytokine activity and serves as an chemoattract for monoctyles, T-cells and neutrophils, suggesting its role in inflammation (Bertini et al., 1999). TRX levels have been recognized as clinically significant markers of oxidative stress with high levels observed in cases of type 2 diabetes (Kakisaka et al., 2002), hepatitis C (Sumida et al., 2000), HIV (Nakamura et al., 1996), rheumatoid arthritis (Yoshida et al., 1999), chronic liver disease (Sumida et al., 2001) and patients undergoing cardiac bypass (Nakamura et al., 1998). TRX levels are also elevated in livers of rats injected with LPS (Buetler, 1998). This could be used to elucidate the reduced expression of TRX in mice gavaged with the designer probiotics. Akin to inflammatory biomarkers, we believe that designer probiotics averted LPS from crossing the gut barrier. Besides reducing oxidative stress biomarkers, BioPersist also increased GPX-4, a selenoenzyme that can neutralize lipid hyperoxides such as cholesterol (Maiorino et al., 1991). Lowered values of GPX-4 have been associated with obesity (Rupérez et al., 2014), diabetes (Katunga et al., 2015), cardiovascular disease (Crosley et al., 2013). High fat feeding of C57Bl/6 mice resulted increased lipid peroxidation and decreased expression of GPX-4 (Long et al., 2013). In fact, blunted expressions of GPX -1.-4 and -7 have been associated with compromised insulin signaling. This supports the linkage between ROS production and accumulation in insulin resistance (Kobayashi et al., 2009). BioPersist administered mice had high elevated GPX-4 and ameliorated insulin signaling, suggesting the involvement of designer probiotics in improving not only one but multiple bio-mechanisms that hamper insulin activity. 63 2.4.7 BioPersist elevated appetite curbing hormones, PYY & amylin Gut microbiome plays an important role in modulating metabolic hormones. High fat diets have been known to alter the secretion and activity of gastrointestinal hormones including insulin, leptin, ghrelin, GLP-1, PYY and many more (Adams et al., n.d.; Chakraborty et al., 2016; Kreznar et al., 2017; H.-M. Lee et al., 2002; Spannagel et al., 1996). High fat fed female C57Bl6 mice, the mice used in this study is known to be a robust model for impaired glucose tolerance with circulating insulin levels increasing progressively but markedly compromised insulin response when challenged with glucose (Winzell & Ahrén, 2004). Here, we see that mice that were gavaged with the designer probiotics had higher serum insulin levels, however, from fig.2.2 & fig.2.3 it is evident that BioPersist gavaged had improved glucose metabolism and insulin signalling indicating that this group did not have a disrupted insulin action. Not only were insulin concentrations high, C-peptide, a precursor to mature insulin was also elevated in this group. In addition, concentration of amylin, the hormone co-secreted with insulin, mirrors the concentration of serum insulin in the three groups. Unlike high fat induced hyperinsulinemia and reduced insulin sensitivity, long term high fat feeding does not affect amylin sensitivity much (Boyle et al., 2011). Amylin is deficient in diabetics and its analog, pramlintide is being considered as an approach to manage diabetes (Schmitz et al., 2004). Amylin is also known to monitor satiety and amylin administration reduces meal consumption in ad lib fed rats (Lutz et al., 1995). GLP-1 and GIP are incretin hormones and promote postprandial glucose dependent release of insulin (Baggio & Drucker, 2007). Their action is impaired in type 2 diabetics which have reduced GIP and GLP-1 secretion. However, the reduction in GLP-1 is relatively mild and does not mirror the significant pancreatic β-cell function attenuation (Gribble & Reimann, 2019). Along 64 with amylin, GLP-1 as well as peptide hormones, PYY and PP also contribute towards regulating hunger. The peptide hormones were also elevated in BioPersist gavaged mice where as GLP-1 was elevated in BioColoniz gavaged mice. It has been reported that PYY injection into rats inhibits food intake and prevents weight gain (Batterham et al., 2002) Some studies have also reported that obese individuals have lower basal fasting levels of PYY (Batterham et al., 2003). Similarly, overexpression of PP in mice led to lowered body weight (Ueno et al., 1999). This can be used to explain the least weight gain in BioPersist administered mice despite being on the same high fat diet as the mice in the other groups. 2.4.8 Designer probiotics do not translocate from the gut. The Gibson lab had earlier shown that designer probiotics showed consistent detection patterns in stool and colon of Muc2 -/- mice but not in chow fed C57Bl/6 (Gibson, Deanna L. Godovannyi & Sandeep, 2018; Sandeep Kaur Gill, 2018). They reasoned that the latter mice did not have an inflamed gut environment, necessary for tetrathionate respiration and survival of BioPersist. However, the former group is known to be a colitis model which implies that the gut of these mice was inflamed, contributing to survival of BioPersist. As described earlier, high fat diets are associated with intestinal inflammation (Araújo et al., 2017; Ghosh et al., 2013; Kim et al., 2012) and this elucidates how the designer probiotics persisted in the gut and exerted all the protective properties. In order to further confirm if these protective effects were exerted in liver via probiotics translocating to the liver or from a distance in the gut, strain detection experiments were done. In the past, questions have been raised on the safety of probiotics (Snydman, 2008). Consumption of probiotic strains of lactobacilli caused bacteremia and sepsis in two individuals and DNA fingerprinting confirmed that strain isolated was identical to the probiotic (Land et al., 65 2005). In fact, a case of sepsis in a young woman undergoing aortic valve replacement surgery from preoperative consumption of probiotic L. rhamnosus has been reported (Kochan et al., 2011). Both, BioPersist and BioColoniz were not detected in the liver, indicating that the probiotic bacteria did not cross the gut barrier and could not possibly cause any sepsis. In addition to not causing sepsis, BioPersist also has a self-regulatory mechanism to prevent proliferating in an external environment as the bacteria needs oxidative stress and inflammatory environment to survive. 66 Chapter 3: Conclusion 3.1 Limitations of research Despite the growing research in gut microbiome manipulation with probiotics, human trials are limited and fairly difficult to organize. Thus, rodent models epitomize as an excellent mean to study host-microbiota interface and effects of microbiome modulation on the host. However, various factors can limit the use of rodents in microbiome research, some of which are cage effects, inter-individual variation, genetic background and maternal effects (Hildebrand et al., 2013). One way to reduce these differences is by cohousing isogenic mice from different litters at the time of weaning, but this could result in aggressive behavior between mice. Another way to minimize cage effects could be mixing treatment groups and maximizing the number of cages (Deloris Alexander et al., 2006). Nevertheless, the former solution cannot be accepted as mice are coprophagic and the latter is also hard to achieve due to financial, time and labor restraints. Regarding the experimental data design, quantification of food intake data is a critical component of studying high fat diet induced weight and glucose metabolism alterations (Ali & Kravitz, 2018) and thus, could have been measured. 3.2 Significance of research The prevalence of overconsumption of fat (Madden et al., 2009), with a parallel increase in overweight and obese individuals in Canada (Twells et al., 2014) is alarming and this thesis aims to draw attention to a potential solution; gut microbiome modulation via probiotics. However, for most of the probiotics to be protective, proximity between the host gut and probiotics is necessary; the evidence of which stands divided and controversial (Suez et al., 67 2019). The novel designer probiotics proposed in this study target these limitations and intent to enhance probiotic colonization and persistence in the gut, leading to improved protection from high fat diets and thus prevention of excessive weight gain, impairment of insulin signaling, glucose metabolism along with reduced inflammation and oxidative stress. 3.3 Main conclusions Based on the results described in Chapter 2 of this thesis, I accept my hypothesis that Designer probiotics can protect from the adverse effects of high fat feeding and if not reverse but improve glucose metabolism, insulin signaling and inflammation and oxidative stress biomarkers. The main conclusions of my work are: 1. BioPersist administered mice resisted high fat diet induced weight gain and demonstrated improved glucose metabolism. 2. BioPersist supplementation enhanced insulin signaling with mice having higher expression and activation of insulin/IGF-I receptor along with higher phosphorylation of Akt, the main regulator of downstream effects of insulin in BioPersist gavaged mice 3. BioPersist administration significantly reduced inflammatory biomarkers in the liver. 4. Designer probiotics, BioPersist & BioColoniz supplementation decreased oxidative stress biomarkers along with increase in the anti-oxidant, GPX-4 in BioPersist administer mice. 5. BioPersist administration enhanced appetite curbing hormones amylin & PYY along with increase in insulin levels. 6. Designer probiotics did not cross the gut barrier and translocate to the liver. 68 Bibliography . Accili, D., Drago, J., Lee, E. J., Johnson, M. D., Cool, M. H., Salvatore, P., Asico, L. D., Jose, P. A., Taylor, S. I., & Westphal, H. (1996). Early neonatal death in mice homozygous for a null allele of the insulin receptor gene. Nature Genetics, 12(1), 106–109. https://doi.org/10.1038/ng0196-106 Adams, S. H., Lei, C., Jodka, C. M., Nikoulina, S. E., Hoyt, J. A., Gedulin, B., Mack, C. M., Kendall, E. S., & Modeling, V. (n.d.). Ingestive Behavior and Neurosciences PYY[3-36] Administration Decreases the Respiratory Quotient and Reduces Adiposity in Diet-Induced Obese Mice 1. Retrieved July 21, 2020, from https://academic.oup.com/jn/article-abstract/136/1/195/4664111 Adult Rodent Anesthesia SOP. (n.d.). Retrieved January 22, 2021, from https://animalcare.ubc.ca/planning-your-research/sops-guidelines Afshin, A., Sur, P. J., Fay, K. A., Cornaby, L., Ferrara, G., Salama, J. S., Mullany, E. C., Abate, K. H., Abbafati, C., Abebe, Z., Afarideh, M., Aggarwal, A., Agrawal, S., Akinyemiju, T., Alahdab, F., Bacha, U., Bachman, V. F., Badali, H., Badawi, A., … Murray, C. J. L. (2019). Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet, 393(10184), 1958–1972. https://doi.org/10.1016/S0140-6736(19)30041-8 Aiken, C. E., Tarry-Adkins, J. L., Penfold, N. C., Dearden, L., & Ozanne, S. E. (2016). Decreased ovarian reserve, dysregulation of mitochondrial biogenesis, and increased lipid peroxidation in female mouse offspring exposed to an obesogenic maternal diet. FASEB Journal, 30(4), 1548–1556. https://doi.org/10.1096/fj.15-280800 69 Akasaki, Y., Alvarez-Garcia, O., Saito, M., Caramés, B., Iwamoto, Y., & Lotz, M. K. (2014). FoxO transcription factors support oxidative stress resistance in human chondrocytes. Arthritis and Rheumatology, 66(12), 3349–3358. https://doi.org/10.1002/art.38868 Alhazmi, A., Stojanovski, E., McEvoy, M., & Garg, M. L. (2014). Macronutrient intake and type 2 diabetes risk in middle-aged Australian women. Results from the australian longitudinal study on women’s health. Public Health Nutrition, 17(7), 1587–1594. https://doi.org/10.1017/S1368980013001870 Ali, M. A., & Kravitz, A. V. (2018). Challenges in quantifying food intake in rodents. In Brain Research (Vol. 1693, Issue Pt B, pp. 188–191). Elsevier B.V. https://doi.org/10.1016/j.brainres.2018.02.040 Altenhoefer, A., Oswald, S., Sonnenborn, U., Enders, C., Schulze, J., Hacker, J., & Oelschlaeger, T. A. (2004). The probiotic Escherichia coli strain Nissle 1917 interferes with invasion of human intestinal epithelial cells by different enteroinvasive bacterial pathogens. FEMS Immunology and Medical Microbiology, 40(3), 223–229. https://doi.org/10.1016/S0928-8244(03)00368-7 Amar, J., Chabo, C., Waget, A., Klopp, P., Vachoux, C., Bermúdez‐Humarán, L. G., Smirnova, N., Bergé, M., Sulpice, T., Lahtinen, S., Ouwehand, A., Langella, P., Rautonen, N., Sansonetti, P. J., & Burcelin, R. (2011). Intestinal mucosal adherence and translocation of commensal bacteria at the early onset of type 2 diabetes: molecular mechanisms and probiotic treatment. EMBO Molecular Medicine, 3(9), 559–572. https://doi.org/10.1002/emmm.201100159 Andersen, M. E., Clewell, H. J., Gearhart, J., Allen, B. C., & Barton, H. A. (1997). Pharmacodynamic model of the rat estrus cycle in relation to endocrine disruptors. Journal 70 of Toxicology and Environmental Health, 52(3), 189–209. https://doi.org/10.1080/00984109708984060 Anderson, E. J., Lustig, M. E., Boyle, K. E., Woodlief, T. L., Kane, D. A., Lin, C. Te, Price, J. W., Kang, L., Rabinovitch, P. S., Szeto, H. H., Houmard, J. A., Cortright, R. N., Wasserman, D. H., & Neufer, P. D. (2009). Mitochondrial H2O2 emission and cellular redox state link excess fat intake to insulin resistance in both rodents and humans. Journal of Clinical Investigation, 119(3), 573–581. https://doi.org/10.1172/JCI37048 André, P., Laugerette, F., & Féart, C. (2019). Metabolic endotoxemia: A potential underlying mechanism of the relationship between dietary fat intake and risk for cognitive impairments in humans? Nutrients, 11(8), 1887. https://doi.org/10.3390/nu11081887 Andrikopoulos, S., Blair, A. R., Deluca, N., Fam, B. C., & Proietto, J. (2008). Evaluating the glucose tolerance test in mice. American Journal of Physiology - Endocrinology and Metabolism, 295(6). https://doi.org/10.1152/ajpendo.90617.2008 Angoa-Pérez, M., Zagorac, B., Francescutti, D. M., Winters, A. D., Greenberg, J. M., Ahmad, M. M., Manning, S. D., Gulbransen, B. D., Theis, K. R., & Kuhn, D. M. (2020). Effects of a high fat diet on gut microbiome dysbiosis in a mouse model of Gulf War Illness. Scientific Reports, 10(1), 1–12. https://doi.org/10.1038/s41598-020-66833-w Arantes-Rodrigues, R., Henriques, A., Pinto-Leite, R., Faustino-Rocha, A., Pinho-Oliveira, J., Teixeira-Guedes, C., Seixas, F., Gama, A., Colaço, B., Colaço, A., & Oliveira, P. A. (2012). The effects of repeated oral gavage on the health of male CD-1 mice. Lab Animal, 41(5), 129–134. https://doi.org/10.1038/laban0512-129 Araújo, J. R., Tomas, J., Brenner, C., & Sansonetti, P. J. (2017). Impact of high-fat diet on the intestinal microbiota and small intestinal physiology before and after the onset of obesity. 71 Biochimie, 141, 97–106. https://doi.org/https://doi.org/10.1016/j.biochi.2017.05.019 Armoni, M., Harel, C., Karni, S., Chen, H., Bar-Yoseph, F., Ver, M. R., Quon, M. J., & Karnieli, E. (2006). FOXO1 represses peroxisome proliferator-activated receptor-gamma1 and -gamma2 gene promoters in primary adipocytes. A novel paradigm to increase insulin sensitivity. The Journal of Biological Chemistry, 281(29), 19881–19891. https://doi.org/10.1074/jbc.M600320200 Ayala, J. E., Samuel, V. T., Morton, G. J., Obici, S., Croniger, C. M., Shulman, G. I., Wasserman, D. H., & McGuinness, O. P. (2010). Standard operating procedures for describing and performing metabolic tests of glucose homeostasis in mice. In DMM Disease Models and Mechanisms (Vol. 3, Issues 9–10, pp. 525–534). Company of Biologists. https://doi.org/10.1242/dmm.006239 Azad, M. A. K., Sarker, M., & Wan, D. (2018). Immunomodulatory Effects of Probiotics on Cytokine Profiles. BioMed Research International, 2018. https://doi.org/10.1155/2018/8063647 Bagarolli, R. A., Tobar, N., Oliveira, A. G., Araújo, T. G., Carvalho, B. M., Rocha, G. Z., Vecina, J. F., Calisto, K., Guadagnini, D., Prada, P. O., Santos, A., Saad, S. T. O., & Saad, M. J. A. (2017). Probiotics modulate gut microbiota and improve insulin sensitivity in DIO mice. Journal of Nutritional Biochemistry, 50, 16–25. https://doi.org/10.1016/j.jnutbio.2017.08.006 Ballal, K., Wilson, C. R., Harmancey, R., & Taegtmeyer, H. (2010). Obesogenic high fat western diet induces oxidative stress and apoptosis in rat heart. Molecular and Cellular Biochemistry, 344(1–2), 221–230. https://doi.org/10.1007/s11010-010-0546-y Baserga, R., Hongo, A., Rubini, M., Prisco, M., & Valentinis, B. (1997). The IGF-I receptor in 72 cell growth, transformation and apoptosis. In Biochimica et Biophysica Acta - Reviews on Cancer (Vol. 1332, Issue 3, pp. F105–F126). Elsevier. https://doi.org/10.1016/S0304-419X(97)00007-3 Beam, J., Botta, A., Ye, J., Soliman, H., Matier, B. J., Forrest, M., MacLeod, K. M., & Ghosh, S. (2015). Excess linoleic acid increases collagen I/III ratio and “stiffens” the heart muscle following high fat diets. Journal of Biological Chemistry, 290(38), 23371–23384. https://doi.org/10.1074/jbc.M115.682195 Bertini, R., Howard, O. M. Z., Dong, H. F., Oppenheim, J. J., Bizzarri, C., Sergi, R., Caselli, G., Pagliei, S., Romines, B., Wilshire, J. A., Mengozzi, M., Nakamura, H., Yodoi, J., Pekkari, K., Gurunath, R., Holmgren, A., Herzenberg, L. A., Herzenberg, L. A., & Ghezzi, P. (1999). Thioredoxin, a redox enzyme released in infection and inflammation, is a unique chemoattractant for neutrophils, monocytes, and T cells. Journal of Experimental Medicine, 189(11), 1783–1789. https://doi.org/10.1084/jem.189.11.1783 Bhaskar, P. T., & Hay, N. (2007). The two TORCs and Akt. Developmental Cell, 12(4), 487–502. https://doi.org/10.1016/j.devcel.2007.03.020 Boozer, C. N., Schoenbach, G., & Atkinson, R. L. (1995). Dietary fat and adiposity: a dose-response relationship in adult male rats fed isocalorically. The American Journal of Physiology, 268(4 Pt 1), E546-50. https://doi.org/10.1152/ajpendo.1995.268.4.E546 Boucher, J., Kleinridders, A., & Kahn, C. R. (2014). Insulin receptor signaling in normal and insulin-resistant states. Cold Spring Harbor Perspectives in Biology, 6(1), a009191. https://doi.org/10.1101/cshperspect.a009191 Boutagy, N. E., McMillan, R. P., Frisard, M. I., & Hulver, M. W. (2016). Metabolic endotoxemia with obesity: Is it real and is it relevant? In Biochimie (Vol. 124, pp. 11–20). 73 Elsevier B.V. https://doi.org/10.1016/j.biochi.2015.06.020 Bowe, J. E., Franklin, Z. J., Hauge-Evans, A. C., King, A. J., Persaud, S. J., & Jones, P. M. (2014). Assessing glucose homeostasis in rodent models. Journal of Endocrinology, 222(3), 13–25. https://doi.org/10.1530/JOE-14-0182 Boyle, C. N., Rossier, M. M., & Lutz, T. A. (2011). Influence of high-fat feeding, diet-induced obesity, and hyperamylinemia on the sensitivity to acute amylin. Physiology and Behavior, 104(1), 20–28. https://doi.org/10.1016/j.physbeh.2011.04.044 Braat, H., Rottiers, P., Hommes, D. W., Huyghebaert, N., Remaut, E., Remon, J. P., van Deventer, S. J. H., Neirynck, S., Peppelenbosch, M. P., & Steidler, L. (2006). A Phase I Trial With Transgenic Bacteria Expressing Interleukin-10 in Crohn’s Disease. Clinical Gastroenterology and Hepatology, 4(6), 754–759. https://doi.org/10.1016/j.cgh.2006.03.028 Brøns, C., Jensen, C. B., Storgaard, H., Hiscock, N. J., White, A., Appel, J. S., Jacobsen, S., Nilsson, E., Larsen, C. M., Astrup, A., Quistorff, B., & Vaag, A. (2009). Impact of short-term high-fat feeding on glucose and insulin metabolism in young healthy men. The Journal of Physiology, 587(10), 2387–2397. https://doi.org/10.1113/jphysiol.2009.169078 Brown, K., Godovannyi, A., Ma, C., Zhang, Y., Ahmadi-Vand, Z., Dai, C., Gorzelak, M. A., Chan, Y., Chan, J. M., Lochner, A., Dutz, J. P., Vallance, B. A., & Gibson, D. L. (2016). Prolonged antibiotic treatment induces a diabetogenic intestinal microbiome that accelerates diabetes in NOD mice. ISME Journal, 10(2), 321–332. https://doi.org/10.1038/ismej.2015.114 Buetler, T. M. (1998). Identification of glutathione S-transferase isozymes and γ- glutamylcysteine synthetase as negative acute-phase proteins in rat liver. Hepatology, 28(6), 74 1551–1560. https://doi.org/10.1002/hep.510280615 Buettner, R., Schölmerich, J., & Bollheimer, L. C. (2007). High-fat diets: Modeling the metabolic disorders of human obesity in rodents. In Obesity (Vol. 15, Issue 4, pp. 798–808). https://doi.org/10.1038/oby.2007.608 Burdge, G. C., & Wootton, S. A. (2002). Conversion of α-linolenic acid to eicosapentaenoic, docosapentaenoic and docosahexaenoic acids in young women. British Journal of Nutrition, 88(4), 411–420. https://doi.org/10.1079/bjn2002689 Calvo-Ochoa, E., Hernández-Ortega, K., Ferrera, P., Morimoto, S., & Arias, C. (2014). Short-term high-fat-and-fructose feeding produces insulin signaling alterations accompanied by neurite and synaptic reduction and astroglial activation in the rat hippocampus. Journal of Cerebral Blood Flow and Metabolism, 34(6), 1001–1008. https://doi.org/10.1038/jcbfm.2014.48 Cani, P D, Neyrinck, A. M., Fava, F., Knauf, C., Burcelin, R. G., Tuohy, K. M., Gibson, G. R., & Delzenne, N. M. (2007). Selective increases of bifidobacteria in gut microflora improve high-fat-diet-induced diabetes in mice through a mechanism associated with endotoxaemia. Diabetologia, 50(11), 2374–2383. https://doi.org/10.1007/s00125-007-0791-0 Cani, Patrice D, Amar, J., Iglesias, M. A., Poggi, M., Knauf, C., Bastelica, D., Neyrinck, A. M., Fava, F., Tuohy, K. M., Chabo, C., Waget, A., Delmée, E., Cousin, B., Sulpice, T., Chamontin, B., Ferrières, J., Tanti, J.-F., Gibson, G. R., Casteilla, L., … Burcelin, R. (2007). Metabolic Endotoxemia Initiates Obesity and Insulin Resistance. Diabetes, 56(7), 1761 LP – 1772. https://doi.org/10.2337/db06-1491 Carvalho, B. M., Guadagnini, D., Tsukumo, D. M. L., Schenka, A. A., Latuf-Filho, P., Vassallo, J., Dias, J. C., Kubota, L. T., Carvalheira, J. B. C., & Saad, M. J. A. (2012). Modulation of 75 gut microbiota by antibiotics improves insulin signalling in high-fat fed mice. Diabetologia, 55(10), 2823–2834. https://doi.org/10.1007/s00125-012-2648-4 Castaner, O., Goday, A., Park, Y.-M., Lee, S.-H., Magkos, F., Shiow, S.-A. T. E., & Schröder, H. (2018). The Gut Microbiome Profile in Obesity: A Systematic Review. International Journal of Endocrinology, 2018, 4095789. https://doi.org/10.1155/2018/4095789 Ceriello, A., Mercuri, F., Quagliaro, L., Assaloni, R., Motz, E., Tonutti, L., & Taboga, C. (2001). Detection of nitrotyrosine in the diabetic plasma: Evidence of oxidative stress. Diabetologia, 44(7), 834–838. https://doi.org/10.1007/s001250100529 Chajès, V., Torres-Mejiá, G., Biessy, C., Ortega-Olvera, C., Angeles-Llerenas, A., Ferrari, P., Lazcano-Ponce, E., & Romieu, I. (2012). ω-3 and ω-6 polyunsaturated fatty acid intakes and the risk of breast cancer in Mexican women: Impact of obesity status. Cancer Epidemiology Biomarkers and Prevention, 21(2), 319–326. https://doi.org/10.1158/1055-9965.EPI-11-0896 Chakraborty, T. R., Donthireddy, L., Adhikary, D., & Chakraborty, S. (2016). Long-term high fat diet has a profound effect on body weight, hormone levels, and estrous cycle in mice. Medical Science Monitor, 22, 1601–1608. https://doi.org/10.12659/MSM.897628 Chen, C.-C., Lai, C.-C., Huang, H.-L., Huang, W.-Y., Toh, H.-S., Weng, T.-C., Chuang, Y.-C., Lu, Y.-C., & Tang, H.-J. (2019). Antimicrobial Activity of Lactobacillus Species Against Carbapenem-Resistant Enterobacteriaceae. Frontiers in Microbiology, 10(APR), 789. https://doi.org/10.3389/fmicb.2019.00789 Cline, G. W., Johnson, K., Regittnig, W., Perret, P., Tozzo, E., Xiao, L., Damico, C., & Shulman, G. I. (2002). Effects of a novel glycogen synthase kinase-3 inhibitor on insulin-stimulated glucose metabolism in Zucker diabetic fatty (fa/fa) rats. Diabetes, 51(10), 2903–76 2910. https://doi.org/10.2337/diabetes.51.10.2903 Cohen, P., & Goedert, M. (2004). GSK3 inhibitors: Development and therapeutic potential. In Nature Reviews Drug Discovery (Vol. 3, Issue 6, pp. 479–487). Nature Publishing Group. https://doi.org/10.1038/nrd1415 Conlon, M. A., & Bird, A. R. (2015). The impact of diet and lifestyle on gut microbiota and human health. Nutrients, 7(1), 17–44. https://doi.org/10.3390/nu7010017 Cordain, L., Eaton, S. B., Sebastian, A., Mann, N., Lindeberg, S., Watkins, B. A., O’Keefe, J. H., & Brand-Miller, J. (2005). Origins and evolution of the Western diet: health implications for the 21st century. The American Journal of Clinical Nutrition, 81(2), 341–354. https://doi.org/10.1093/ajcn.81.2.341 Cordonnier, C., Thévenot, J., Etienne-Mesmin, L., Denis, S., Alric, M., Livrelli, V., & Blanquet-Diot, S. (2015). Dynamic In Vitro Models of the Human Gastrointestinal Tract as Relevant Tools to Assess the Survival of Probiotic Strains and Their Interactions with Gut Microbiota. Microorganisms, 3(4), 725–745. https://doi.org/10.3390/microorganisms3040725 Costa, R. R. S., Villela, N. R., Souza, M. das G. C., Boa, B. C. S., Cyrino, F. Z. G. A., Silva, S. V., Lisboa, P. C., Moura, E. G., Barja-Fidalgo, T. C., & Bouskela, E. (2011). High fat diet induces central obesity, insulin resistance and microvascular dysfunction in hamsters. Microvascular Research, 82(3), 416–422. https://doi.org/10.1016/j.mvr.2011.08.007 Crosley, L. K., Bashir, S., Nicol, F., Arthur, J. R., Hesketh, J. E., & Sneddon, A. A. (2013). The single-nucleotide polymorphism (GPX4c718t) in the glutathione peroxidase 4 gene influences endothelial cell function: Interaction with selenium and fatty acids. Molecular Nutrition and Food Research, 57(12), 2185–2194. https://doi.org/10.1002/mnfr.201300216 77 Cunningham, J. T., Rodgers, J. T., Arlow, D. H., Vazquez, F., Mootha, V. K., & Puigserver, P. (2007). mTOR controls mitochondrial oxidative function through a YY1-PGC-1α transcriptional complex. Nature, 450(7170), 736–740. https://doi.org/10.1038/nature06322 Dandona, P., Aljada, A., & Bandyopadhyay, A. (2004). Inflammation: the link between insulin resistance, obesity and diabetes. Trends in Immunology, 25(1), 4–7. https://doi.org/10.1016/J.IT.2003.10.013 David, L. A., Maurice, C. F., Carmody, R. N., Gootenberg, D. B., Button, J. E., Wolfe, B. E., Ling, A. V., Devlin, A. S., Varma, Y., Fischbach, M. A., Biddinger, S. B., Dutton, R. J., & Turnbaugh, P. J. (2014a). Diet rapidly and reproducibly alters the human gut microbiome. Nature, 505(7484), 559–563. https://doi.org/10.1038/nature12820 David, L. A., Maurice, C. F., Carmody, R. N., Gootenberg, D. B., Button, J. E., Wolfe, B. E., Ling, A. V., Devlin, A. S., Varma, Y., Fischbach, M. A., Biddinger, S. B., Dutton, R. J., & Turnbaugh, P. J. (2014b). Diet rapidly and reproducibly alters the human gut microbiome. Nature, 505(7484), 559–563. https://doi.org/10.1038/nature12820 Davidson, T. L., Monnot, A., Neal, A. U., Martin, A. A., Horton, J. J., & Zheng, W. (2012). The effects of a high-energy diet on hippocampal-dependent discrimination performance and blood-brain barrier integrity differ for diet-induced obese and diet-resistant rats. Physiology and Behavior, 107(1), 26–33. https://doi.org/10.1016/j.physbeh.2012.05.015 De Fante, T., Simino, L. A., Reginato, A., Payolla, T. B., Vitoréli, D. C. G., De Souza, M., Torsoni, M. A., Milanski, M., & Torsoni, A. S. (2016). Diet-induced maternal obesity alters insulin signalling in male mice offspring rechallenged with a high-fat diet in adulthood. PLoS ONE, 11(8). https://doi.org/10.1371/journal.pone.0160184 de Lorgeril, M., & Salen, P. (2006). The Mediterranean-style diet for the prevention of 78 cardiovascular diseases. Public Health Nutrition, 9(1a), 118–123. https://doi.org/DOI: 10.1079/PHN2005933 De Palma, G., Lynch, M. D. J., Lu, J., Dang, V. T., Deng, Y., Jury, J., Umeh, G., Miranda, P. M., Pastor, M. P., Sidani, S., Pinto-Sanchez, M. I., Philip, V., McLean, P. G., Hagelsieb, M. G., Surette, M. G., Bergonzelli, G. E., Verdu, E. F., Britz-McKibbin, P., Neufeld, J. D., … Bercik, P. (2017). Transplantation of fecal microbiota from patients with irritable bowel syndrome alters gut function and behavior in recipient mice. Science Translational Medicine, 9(379). https://doi.org/10.1126/scitranslmed.aaf6397 Deloris Alexander, A., Orcutt, R. P., Henry, J. C., Baker, J., Bissahoyo, A. C., & Threadgill, D. W. (2006). Quantitative PCR assays for mouse enteric flora reveal strain-dependent differences in composition that are influenced by the microenvironment. Mammalian Genome, 17(11), 1093–1104. https://doi.org/10.1007/s00335-006-0063-1 Desmond, C., Stanton, C., Fitzgerald, G. F., Collins, K., & Paul Ross, R. (2001). Environmental adaptation of probiotic lactobacilli towards improvement of performance during spray drying. International Dairy Journal, 11(10), 801–808. https://doi.org/10.1016/S0958-6946(01)00121-2 Dinan, T. G., Stanton, C., & Cryan, J. F. (2013). Psychobiotics: A Novel Class of Psychotropic. Biological Psychiatry, 74(10), 720–726. https://doi.org/10.1016/J.BIOPSYCH.2013.05.001 Ding, S., Chi, M. M., Scull, B. P., Rigby, R., Schwerbrock, N. M. J., Magness, S., Jobin, C., & Lund, P. K. (2010). High-Fat Diet: Bacteria Interactions Promote Intestinal Inflammation Which Precedes and Correlates with Obesity and Insulin Resistance in Mouse. PLOS ONE, 5(8), e12191. https://doi.org/10.1371/journal.pone.0012191 Dinicolantonio, J. J., & O’Keefe, J. H. (2018). Omega-6 vegetable oils as a driver of coronary 79 heart disease: The oxidized linoleic acid hypothesis. In Open Heart (Vol. 5, Issue 2, p. e000898). BMJ Publishing Group. https://doi.org/10.1136/openhrt-2018-000898 Duan, F., Curtis, K. L., & March, J. C. (2008). Secretion of insulinotropic proteins by commensal bacteria: Rewiring the gut to treat diabetes. Applied and Environmental Microbiology, 74(23), 7437–7438. https://doi.org/10.1128/AEM.01019-08 Eaton, K. A., Honkala, A., Auchtung, T. A., & Britton, R. A. (2011). Probiotic lactobacillus reuteri ameliorates disease due to enterohemorrhagic escherichia coli in germfree mice. Infection and Immunity, 79(1), 185–191. https://doi.org/10.1128/IAI.00880-10 Eckburg, P. B., Bik, E. M., Bernstein, C. N., Purdom, E., Dethlefsen, L., Sargent, M., Gill, S. R., Nelson, K. E., & Relman, D. A. (2005). Diversity of the human intestinal microbial flora. Science (New York, N.Y.), 308(5728), 1635–1638. https://doi.org/10.1126/science.1110591 Eckel, R. H., Grundy, S. M., & Zimmet, P. Z. (2005). The metabolic syndrome. Lancet, 365(9468), 1415–1428. https://doi.org/10.1016/S0140-6736(05)66378-7 Ekmekciu, I., von Klitzing, E., Fiebiger, U., Neumann, C., Bacher, P., Scheffold, A., Bereswill, S., & Heimesaat, M. M. (2017). The probiotic compound VSL#3 modulates mucosal, peripheral, and systemic immunity following murine broad-spectrum antibiotic treatment. Frontiers in Cellular and Infection Microbiology, 7(MAY). https://doi.org/10.3389/fcimb.2017.00167 Eldar-Finkelman, H., & Krebs, E. G. (1997). Phosphorylation of insulin receptor substrate 1 by glycogen synthase kinase 3 impairs insulin action. Proceedings of the National Academy of Sciences of the United States of America, 94(18), 9660–9664. https://doi.org/10.1073/pnas.94.18.9660 Eldar-Finkelman, H., Schreyer, S. A., Shinohara, M. M., LeBoeuf, R. C., & Krebs, E. G. (1999). 80 Increased glycogen synthase kinase-3 activity in diabetes- and obesity- prone C57BL/6J mice. Diabetes, 48(8), 1662–1666. https://doi.org/10.2337/diabetes.48.8.1662 Erridge, C., Attina, T., Spickett, C. M., & Webb, D. J. (2007). A high-fat meal induces low-grade endotoxemia: evidence of a novel mechanism of postprandial inflammation. The American Journal of Clinical Nutrition, 86(5), 1286–1292. https://doi.org/10.1093/ajcn/86.5.1286 Esposito, E., Iacono, A., Bianco, G., Autore, G., Cuzzocrea, S., Vajro, P., Canani, R. B., Calignano, A., Raso, G. M., & Meli, R. (2009). Probiotics Reduce the Inflammatory Response Induced by a High-Fat Diet in the Liver of Young Rats. The Journal of Nutrition, 139(5), 905–911. https://doi.org/10.3945/jn.108.101808 Esteve, E., Ricart, W., & Fernández-Real, J. M. (2011). Gut microbiota interactions with obesity, insulin resistance and type 2 diabetes: Did gut microbiote co-evolve with insulin resistance? In Current Opinion in Clinical Nutrition and Metabolic Care (Vol. 14, Issue 5, pp. 483–490). https://doi.org/10.1097/MCO.0b013e328348c06d Evans, J. L., Goldfine, I. D., Maddux, B. A., & Grodsky, G. M. (2003). Are oxidative stress - Activated signaling pathways mediators of insulin resistance and β-cell dysfunction? In Diabetes (Vol. 52, Issue 1, pp. 1–8). American Diabetes Association. https://doi.org/10.2337/diabetes.52.1.1 Fang, Q., Nair, J., Sun, X., Hadjiolov, D., & Bartsch, H. (2007). Etheno-DNA adduct formation in rats gavaged with linoleic acid, oleic acid and coconut oil is organ- and gender specific. Mutation Research - Fundamental and Molecular Mechanisms of Mutagenesis, 624(1–2), 71–79. https://doi.org/10.1016/j.mrfmmm.2007.04.002 Fialkow, L., Wang, Y., & Downey, G. P. (2007). Reactive oxygen and nitrogen species as signaling molecules regulating neutrophil function. Free Radical Biology and Medicine, 81 42(2), 153–164. https://doi.org/10.1016/J.FREERADBIOMED.2006.09.030 Fraulob, J. C., Ogg-Diamantino, R., Fernandes-Santos, C., Aguila, M. B., & Mandarim-de-Lacerda, C. A. (2010). A Mouse Model of Metabolic Syndrome: Insulin Resistance, Fatty Liver and Non-Alcoholic Fatty Pancreas Disease (NAFPD) in C57BL/6 Mice Fed a High Fat Diet. Journal of Clinical Biochemistry and Nutrition, 46(3), 212–223. https://doi.org/10.3164/jcbn.09-83 Fujimoto, M., Shimizu, N., Kunii, K., Martyn, J. A. J., Ueki, K., & Kaneki, M. (2005). A Role for iNOS in Fasting Hyperglycemia and Impaired Insulin Signaling in the Liver of Obese Diabetic Mice. Diabetes, 54(5), 1340 LP – 1348. https://doi.org/10.2337/diabetes.54.5.1340 Furne, J., Springfield, J., Koenig, T., DeMaster, E., & Levitt, M. D. (2001). Oxidation of hydrogen sulfide and methanethiol to thiosulfate by rat tissues: a specialized function of the colonic mucosa. Biochemical Pharmacology, 62(2), 255–259. https://doi.org/10.1016/S0006-2952(01)00657-8 Gao, M., Ma, Y., & Liu, D. (2015). High-Fat Diet-Induced Adiposity, Adipose Inflammation, Hepatic Steatosis and Hyperinsulinemia in Outbred CD-1 Mice. PLOS ONE, 10(3), e0119784. https://doi.org/10.1371/journal.pone.0119784 Gardlik, R., Palffy, R., Celec, P., & Gardlik, R. (2012). Recombinant Probiotic Therapy in Experimental Colitis in Mice. In Folia Biologica (Praha) (Vol. 58). Gaudier, E., Michel, C., Segain, J.-P., Cherbut, C., & Hoebler, C. (2005). Biochemical and Molecular Actions of Nutrients The VSL# 3 Probiotic Mixture Modifies Microflora but Does Not Heal Chronic Dextran-Sodium Sulfate-Induced Colitis or Reinforce the Mucus Barrier in Mice 1,2. www.nutrition.org. Ghosh, S., Kewalramani, G., Yuen, G., Pulinilkunnil, T., An, D., Innis, S. M., Allard, M. F., 82 Wambolt, R. B., Qi, D., Abrahani, A., & Rodrigues, B. (2006). Induction of mitochondrial nitrative damage and cardiac dysfunction by chronic provision of dietary ω-6 polyunsaturated fatty acids. Free Radical Biology and Medicine, 41(9), 1413–1424. https://doi.org/10.1016/J.FREERADBIOMED.2006.07.021 Ghosh, S., Molcan, E., Decoffe, D., Dai, C., & Gibson, D. L. (2013). Diets rich in n-6 PUFA induce intestinal microbial dysbiosis in aged mice. British Journal of Nutrition, 110(3), 515–523. https://doi.org/10.1017/S0007114512005326 Gibson, Deanna L. Godovannyi, A., & Sandeep, G. (2018). WO 2018/148847 A1. Goodstine, S. L., Zheng, T., Holford, T. R., Ward, B. A., Carter, D., Owens, P. H., & Mayne, S. T. (2003). Nutritional Epidemiology Dietary (n-3)/(n-6) Fatty Acid Ratio: Possible Relationship to Premenopausal but Not Postmenopausal Breast Cancer Risk in U.S. Women 1. In J. Nutr (Vol. 133). https://academic.oup.com/jn/article-abstract/133/5/1409/4616757 Gribble, F. M., & Reimann, F. (2019). Function and mechanisms of enteroendocrine cells and gut hormones in metabolism. In Nature Reviews Endocrinology (Vol. 15, Issue 4, pp. 226–237). Nature Publishing Group. https://doi.org/10.1038/s41574-019-0168-8 Griendling, K. K., Touyz, R. M., Zweier, J. L., Dikalov, S., Chilian, W., Chen, Y. R., Harrison, D. G., & Bhatnagar, A. (2016). Measurement of Reactive Oxygen Species, Reactive Nitrogen Species, and Redox-Dependent Signaling in the Cardiovascular System: A Scientific Statement from the American Heart Association. In Circulation Research (Vol. 119, Issue 5, pp. e39–e75). Lippincott Williams and Wilkins. https://doi.org/10.1161/RES.0000000000000110 Grimm, V., Radulovic, K., & Riedel, C. U. (2015). Colonization of C57BL/6 mice by a potential probiotic Bifidobacterium bifidum strain under germ-free and specific pathogen-free 83 conditions and during experimental colitis. PLoS ONE, 10(10). https://doi.org/10.1371/journal.pone.0139935 Grundleger, M. L., & Thenen, S. W. (1982). Decreased Insulin Binding, Glucose Transport, and Glucose Metabolism in Soleus Muscle of Rats Fed a High Fat Diet. Grundy, S. M. (2008). Metabolic syndrome pandemic. Arteriosclerosis, Thrombosis, and Vascular Biology, 28(4), 629–636. https://doi.org/10.1161/ATVBAHA.107.151092 Guijarro-Muñoz, I., Compte, M., Álvarez-Cienfuegos, A., Álvarez-Vallina, L., & Sanz, L. (2014). Lipopolysaccharide activates toll-like receptor 4 (TLR4)-mediated NF-κB signaling pathway and proinflammatory response in human pericytes. Journal of Biological Chemistry, 289(4), 2457–2468. https://doi.org/10.1074/jbc.M113.521161 Guo, Q., Li, S., Xie, Y., Zhang, Q., Liu, M., Xu, Z., Sun, H., & Yang, Y. (2017). The NAD+-dependent deacetylase, Bifidobacterium longum Sir2 in response to oxidative stress by deacetylating FOXO3a and SigH (σH) in Bifidobacterium longum and HEK293T cells respectively. Free Radical Biology and Medicine, 108, 929–939. https://doi.org/10.1016/j.freeradbiomed.2017.05.012 Hall, K. D. (2018). Did the Food Environment Cause the Obesity Epidemic? Obesity (Silver Spring, Md.), 26(1), 11–13. https://doi.org/10.1002/oby.22073 Hariri, N., & Thibault, L. (2010). High-fat diet-induced obesity in animal models. Nutrition Research Reviews, 23(2), 270–299. https://doi.org/10.1017/S0954422410000168 Harris, W. S., Assaad, B., & Poston, W. C. (2006). Tissue Omega-6/Omega-3 Fatty Acid Ratio and Risk for Coronary Artery Disease. American Journal of Cardiology, 98(4), 19–26. https://doi.org/10.1016/j.amjcard.2005.12.023 Hasan, N., & Yang, H. (2019). Factors affecting the composition of the gut microbiota, and its 84 modulation. In PeerJ (Vol. 2019, Issue 8). PeerJ Inc. https://doi.org/10.7717/peerj.7502 Heikkinen, S., Argmann, C. A., Champy, M., & Auwerx, J. (2007). Evaluation of Glucose Homeostasis. Current Protocols in Molecular Biology, 77(1). https://doi.org/10.1002/0471142727.mb29b03s77 Henriksen, E. J., & Dokken, B. B. (2006). Role of Glycogen Synthase Kinase-3 in Insulin Resistance and Type 2 Diabetes. Current Drug Targets, 7(11), 1435–1441. Hensel, M., Hinsley, A. P., Nikolaus, T., Sawers, G., & Berks, B. C. (1999). The genetic basis of tetrathionate respiration in Salmonella typhimurium. Molecular Microbiology, 32(2), 275–287. https://doi.org/10.1046/j.1365-2958.1999.01345.x Hilakivi-Clarke, L., Clarke, R., & Lippman, M. (1999). The influence of maternal diet on breast cancer risk among female offspring. In Nutrition (Vol. 15, Issue 5, pp. 392–401). Elsevier. https://doi.org/10.1016/S0899-9007(99)00029-5 Hilakivi-Clarke, L., Clarke, R., Onojafe, I., Raygada, M., Cho, E., & Lippman, M. (1997). A maternal diet high in n - 6 polyunsaturated fats alters mammary gland development, puberty onset, and breast cancer risk among female rat offspring. Proceedings of the National Academy of Sciences of the United States of America, 94(17), 9372–9377. https://doi.org/10.1073/pnas.94.17.9372 Hildebrand, F., Nguyen, T. L. A., Brinkman, B., Yunta, R. G., Cauwe, B., Vandenabeele, P., Liston, A., & Raes, J. (2013). Inflammation-associated enterotypes, host genotype, cage and inter-individual effects drive gut microbiota variation in common laboratory mice. Genome Biology, 14(1), 1–15. https://doi.org/10.1186/gb-2013-14-1-r4 Hildebrandt, M. A., Hoffmann, C., Sherrill-Mix, S. A., Keilbaugh, S. A., Hamady, M., Chen, Y. Y., Knight, R., Ahima, R. S., Bushman, F., & Wu, G. D. (2009). High-Fat Diet Determines 85 the Composition of the Murine Gut Microbiome Independently of Obesity. Gastroenterology, 137(5), 1716-1724.e2. https://doi.org/10.1053/j.gastro.2009.08.042 Hoeflich, K. P., Luo, J., Rubie, E. A., Tsao, M. S., Jin, O., & Woodgett, J. R. (2000). Requirement for glycogen synthase kinase-3β in cell survival and NF-κB activation. Nature, 406(6791), 86–90. https://doi.org/10.1038/35017574 Hörmannsperger, G., Clavel, T., Hoffmann, M., Reiff, C., Kelly, D., Loh, G., Blaut, M., Hölzlwimmer, G., Laschinger, M., & Haller, D. (2009). Post-Translational Inhibition of IP-10 Secretion in IEC by Probiotic Bacteria: Impact on Chronic Inflammation. PLoS ONE, 4(2), e4365. https://doi.org/10.1371/journal.pone.0004365 Hotamisligil, G S, Peraldi, P., Budavari, A., Ellis, R., White, M. F., & Spiegelman, B. M. (1996). IRS-1-mediated inhibition of insulin receptor tyrosine kinase activity in TNF-alpha- and obesity-induced insulin resistance. Science (New York, N.Y.), 271(5249), 665–668. https://doi.org/10.1126/science.271.5249.665 Hotamisligil, Gökhan S. (2006). Inflammation and metabolic disorders. In Nature (Vol. 444, Issue 7121, pp. 860–867). Nature Publishing Group. https://doi.org/10.1038/nature05485 Hotamisligil, Gökhan S., Shargill, N. S., & Spiegelman, B. M. (1993). Adipose expression of tumor necrosis factor-α: Direct role in obesity-linked insulin resistance. Science, 259(5091), 87–91. https://doi.org/10.1126/science.7678183 Huang, J., Dibble, C. C., Matsuzaki, M., & Manning, B. D. (2008). The TSC1-TSC2 complex is required for proper activation of mTOR complex 2. Molecular and Cellular Biology, 28(12), 4104–4115. https://doi.org/10.1128/MCB.00289-08 Ingle, D. J. (1949). A Simple Means of Producing Obesity in the Rat. Proceedings of the Society for Experimental Biology and Medicine, 72(3), 604–605. https://doi.org/10.3181/00379727-86 72-17513 Inoki, K., Li, Y., Zhu, T., Wu, J., & Guan, K.-L. (2002). TSC2 is phosphorylated and inhibited by Akt and suppresses mTOR signalling. Nature Cell Biology, 4(9), 648–657. https://doi.org/10.1038/ncb839 Ishizuka, K., Usui, I., Kanatani, Y., Bukhari, A., He, J., Fujisaka, S., Yamazaki, Y., Suzuki, H., Hiratani, K., Ishiki, M., Iwata, M., Urakaze, M., Haruta, T., & Kobayashi, M. (2007). Chronic tumor necrosis factor-alpha treatment causes insulin resistance via insulin receptor substrate-1 serine phosphorylation and suppressor of cytokine signaling-3 induction in 3T3-L1 adipocytes. Endocrinology, 148(6), 2994–3003. https://doi.org/10.1210/en.2006-1702 Isolauri, E., Salminen, S., & Ouwehand, A. C. (2004). Probiotics. Best Practice & Research Clinical Gastroenterology, 18(2), 299–313. https://doi.org/10.1016/J.BPG.2003.10.006 Jiang, Y., Biswas, S. K., Steinle, J. J., Steinle, J. J., & Steinle, J. J. (2014). Serine 307 on insulin receptor substrate 1 is required for SOCS3 and TNF-α signaling in the rMC-1 cell line. Molecular Vision, 20, 1463–1470. http://www.molvis.org/molvis/v20/1463 Joeres-Nguyen-Xuan, T. H., Boehm, S. K., Joeres, L., Schulze, J., & Kruis, W. (2010). Survival of the probiotic Escherichia coli Nissle 1917 (EcN) in the gastrointestinal tract given in combination with oral mesalamine to healthy volunteers. Inflammatory Bowel Diseases, 16(2), 256–262. https://doi.org/10.1002/ibd.21042 Jørgensen, M. S., Tornqvist, K. S., & Hvid, H. (2017). Calculation of Glucose Dose for Intraperitoneal Glucose Tolerance Tests in Lean and Obese Mice. Journal of the American Association for Laboratory Animal Science : JAALAS, 56(1), 95. Kadooka, Y., Sato, M., Ogawa, A., Miyoshi, M., Uenishi, H., Ogawa, H., Ikuyama, K., Kagoshima, M., & Tsuchida, T. (2013). Effect of Lactobacillus gasseri SBT2055 in 87 fermented milk on abdominal adiposity in adults in a randomised controlled trial. The British Journal of Nutrition, 110(9), 1696–1703. https://doi.org/10.1017/S0007114513001037 Kakisaka, Y., Nakashima, T., Sumida, Y., Yoh, T., Nakamura, H., Yodoi, J., & Senmaru, H. (2002). Elevation of serum thioredoxin levels in patients with type 2 diabetes. Hormone and Metabolic Research, 34(3), 160–164. https://doi.org/10.1055/s-2002-23201 Kanety, H., Feinstein, R., Papa, M. Z., Hemi, R., & Karasik, A. (1995). Tumor necrosis factor alpha-induced phosphorylation of insulin receptor substrate-1 (IRS-1). Possible mechanism for suppression of insulin-stimulated tyrosine phosphorylation of IRS-1. The Journal of Biological Chemistry, 270(40), 23780–23784. https://doi.org/10.1074/jbc.270.40.23780 Kankaanpää, P. E., Salminen, S. J., Isolauri, E., & Lee, Y. K. (2001). The influence of polyunsaturated fatty acids on probiotic growth and adhesion. FEMS Microbiology Letters, 194(2), 149–153. https://doi.org/10.1111/j.1574-6968.2001.tb09460.x Kasai, C., Sugimoto, K., Moritani, I., Tanaka, J., Oya, Y., Inoue, H., Tameda, M., Shiraki, K., Ito, M., Takei, Y., & Takase, K. (2015). Comparison of the gut microbiota composition between obese and non-obese individuals in a Japanese population, as analyzed by terminal restriction fragment length polymorphism and next-generation sequencing. BMC Gastroenterology, 15(1), 100. https://doi.org/10.1186/s12876-015-0330-2 Katunga, L. A., Gudimella, P., Efird, J. T., Abernathy, S., Mattox, T. A., Beatty, C., Darden, T. M., Thayne, K. A., Alwair, H., Kypson, A. P., Virag, J. A., & Anderson, E. J. (2015). Obesity in a model of gpx4 haploinsufficiency uncovers a causal role for lipid-derived aldehydes in human metabolic disease and cardiomyopathy. Molecular Metabolism, 4(6), 493–506. https://doi.org/10.1016/j.molmet.2015.04.001 88 Kern, P. A., Ranganathan, S., Li, C., Wood, L., & Ranganathan, G. (2001). Adipose tissue tumor necrosis factor and interleukin-6 expression in human obesity and insulin resistance. American Journal of Physiology. Endocrinology and Metabolism, 280(5), E745–E751. https://doi.org/10.1152/ajpendo.2001.280.5.E745 Kesika, P., Sivamaruthi, B. S., & Chaiyasut, C. (2019). Do Probiotics Improve the Health Status of Individuals with Diabetes Mellitus? A Review on Outcomes of Clinical Trials. In BioMed Research International (Vol. 2019). Hindawi Limited. https://doi.org/10.1155/2019/1531567 Khamzina, L., Veilleux, A., Bergeron, S., & Marette, A. (2005). Increased Activation of the Mammalian Target of Rapamycin Pathway in Liver and Skeletal Muscle of Obese Rats: Possible Involvement in Obesity-Linked Insulin Resistance. Endocrinology, 146(3), 1473–1481. https://doi.org/10.1210/en.2004-0921 Kim, K.-A., Gu, W., Lee, I.-A., Joh, E.-H., & Kim, D.-H. (2012). High Fat Diet-Induced Gut Microbiota Exacerbates Inflammation and Obesity in Mice via the TLR4 Signaling Pathway. PLOS ONE, 7(10), e47713. https://doi.org/10.1371/journal.pone.0047713 Kimball, S. R., Ravi, S., Gordon, B. S., Dennis, M. D., & Jefferson, L. S. (2015). Amino Acid–Induced Activation of mTORC1 in Rat Liver Is Attenuated by Short-Term Consumption of a High-Fat Diet. The Journal of Nutrition, 145(11), 2496–2502. https://doi.org/10.3945/jn.115.215491 Kinsell, L. W., Walker, G., Michaels, G. D., Olson, F. E., Coelho, M., McBride, Y., Fukayama, G., & Conklin, J. (1959). Dietary Fats and the Diabetic Patient. New England Journal of Medicine, 261(9), 431–434. https://doi.org/10.1056/NEJM195908272610904 Kobayashi, H., Matsuda, M., Fukuhara, A., Komuro, R., & Shimomura, I. (2009). Dysregulated 89 glutathione metabolism links to impaired insulin action in adipocytes. American Journal of Physiology - Endocrinology and Metabolism, 296(6), E1326–E1334. https://doi.org/10.1152/ajpendo.90921.2008 Kochan, P., Chmielarczyk, A., Szymaniak, L., Brykczynski, M., Galant, K., Zych, A., Pakosz, K., Giedrys-Kalemba, S., Lenouvel, E., & Heczko, P. B. (2011). Lactobacillus rhamnosus administration causes sepsis in a cardiosurgical patient-is the time right to revise probiotic safety guidelines? Clinical Microbiology and Infection, 17(10), 1589–1592. https://doi.org/10.1111/j.1469-0691.2011.03614.x Kocsis, T., Molnár, B., Németh, D., Hegyi, P., Szakács, Z., Bálint, A., Garami, A., Soós, A., Márta, K., & Solymár, M. (2020). Probiotics have beneficial metabolic effects in patients with type 2 diabetes mellitus: a meta-analysis of randomized clinical trials. Scientific Reports, 10(1), 11787. https://doi.org/10.1038/s41598-020-68440-1 Koliada, A., Syzenko, G., Moseiko, V., Budovska, L., Puchkov, K., Perederiy, V., Gavalko, Y., Dorofeyev, A., Romanenko, M., Tkach, S., Sineok, L., Lushchak, O., & Vaiserman, A. (2017). Association between body mass index and Firmicutes/Bacteroidetes ratio in an adult Ukrainian population. BMC Microbiology, 17(1), 120. https://doi.org/10.1186/s12866-017-1027-1 KOLTERMAN, O. G., REAVEN, G. M., & OLEFSKY, J. M. (1979). Relationship between in Vivo Insulin Resistance and Decreased Insulin Receptors in Obese Man*. The Journal of Clinical Endocrinology & Metabolism, 48(3), 487–494. https://doi.org/10.1210/jcem-48-3-487 Kopp, W. (2019). How western diet and lifestyle drive the pandemic of obesity and civilization diseases. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 12, 2221–2236. 90 https://doi.org/10.2147/DMSO.S216791 Koutroubakis, I. E., Oustamanolakis, P., Malliaraki, N., Karmiris, K., Chalkiadakis, I., Ganotakis, E., Karkavitsas, N., & Kouroumalis, E. A. (2009). Effects of tumor necrosis factor alpha inhibition with infliximab on lipid levels and insulin resistance in patients with inflammatory bowel disease. European Journal of Gastroenterology & Hepatology, 21(3), 283–288. https://doi.org/10.1097/MEG.0b013e328325d42b Kreznar, J. H., Keller, M. P., Traeger, L. L., Rabaglia, M. E., Schueler, K. L., Stapleton, D. S., Zhao, W., Vivas, E. I., Yandell, B. S., Broman, A. T., Hagenbuch, B., Attie, A. D., & Rey, F. E. (2017). Host Genotype and Gut Microbiome Modulate Insulin Secretion and Diet-Induced Metabolic Phenotypes. Cell Reports, 18(7), 1739–1750. https://doi.org/10.1016/j.celrep.2017.01.062 Kruger, N. J. (2009). The Bradford Method For Protein Quantitation (pp. 17–24). Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-59745-198-7_4 Kumar, M., Kissoon-Singh, V., Coria, A. L., Moreau, F., & Chadee, K. (2016). Probiotic mixture VSL#3 reduces colonic inflammation and improves intestinal barrier function in Muc2 mucin-deficient mice. American Journal of Physiology-Gastrointestinal and Liver Physiology, 312(1), G34–G45. https://doi.org/10.1152/ajpgi.00298.2016 Lamothe, B., Baudry, A., Desbois, P., Lamotte, L., Bucchini, D., De Meyts, P., & Joshi, R. L. (1998). Genetic engineering in mice: Impact on insulin signalling and action. In Biochemical Journal (Vol. 335, Issue 2, pp. 193–204). Portland Press Ltd. https://doi.org/10.1042/bj3350193 Land, M. H., Rouster-Stevens, K., Woods, C. R., Cannon, M. L., Cnota, J., & Shetty, A. K. (2005). Lactobacillus sepsis associated with probiotic therapy. Pediatrics, 115(1), 178–181. 91 https://doi.org/10.1542/peds.2004-2137 Lasker, S., Rahman, M. M., Parvez, F., Zamila, M., Miah, P., Nahar, K., Kabir, F., Sharmin, S. B., Subhan, N., Ahsan, G. U., & Alam, M. A. (2019). High-fat diet-induced metabolic syndrome and oxidative stress in obese rats are ameliorated by yogurt supplementation. Scientific Reports, 9(1), 1–15. https://doi.org/10.1038/s41598-019-56538-0 Lee, H.-M., Wang, G., Englander, E. W., Kojima, M., & Greeley Jr., G. H. (2002). Ghrelin, A New Gastrointestinal Endocrine Peptide that Stimulates Insulin Secretion: Enteric Distribution, Ontogeny, Influence of Endocrine, and Dietary Manipulations. Endocrinology, 143(1), 185–190. https://doi.org/10.1210/endo.143.1.8602 Lee, S., Lee, J., Choi, J., Kim, Y. W., Ryu, K. W., Kim, Y., & Kim, J. (2018). Dietary n-3 and n-6 polyunsaturated fatty acids, the FADS gene, and the risk of gastric cancer in a Korean population. Scientific Reports, 8(1), 1–7. https://doi.org/10.1038/s41598-018-21960-3 Lee, Y. S., Li, P., Huh, J. Y., Hwang, I. J., Lu, M., Kim, J. I., Ham, M., Talukdar, S., Chen, A., Lu, W. J., Bandyopadhyay, G. K., Schwendener, R., Olefsky, J., & Kim, J. B. (2011). Inflammation Is Necessary for Long-Term but Not Short-Term High-Fat Diet–Induced Insulin Resistance. Diabetes, 60(10), 2474 LP – 2483. https://doi.org/10.2337/db11-0194 Ley, R. E., Turnbaugh, P. J., Klein, S., & Gordon, J. I. (2006). Microbial ecology: human gut microbes associated with obesity. Nature, 444(7122), 1022–1023. https://doi.org/10.1038/4441022a Li, J., Lan, T., Zhang, C., Zeng, C., Hou, J., Yang, Z., Zhang, M., Liu, J., & Liu, B. (2015). Reciprocal activation between IL-6/STAT3 and NOX4/Akt signalings promotes proliferation and survival of non-small cell lung cancer cells. Oncotarget, 6(2), 1031–1048. https://doi.org/10.18632/oncotarget.2671 92 Li, S., Qi, C., Zhu, H., Yu, R., Xie, C., Peng, Y., Yin, S. W., Fan, J., Zhao, S., & Sun, J. (2019). Lactobacillus reuteri improves gut barrier function and affects diurnal variation of the gut microbiota in mice fed a high-fat diet. Food and Function, 10(8), 4705–4715. https://doi.org/10.1039/c9fo00417c Li, X., Wei, X., Sun, Y., Du, J., Li, X., Xun, Z., & Li, Y. C. (2019). High-fat diet promotes experimental colitis by inducing oxidative stress in the colon. American Journal of Physiology-Gastrointestinal and Liver Physiology, 317(4), G453–G462. https://doi.org/10.1152/ajpgi.00103.2019 Libby, P. (2008). Inflammatory Mechanisms: The Molecular Basis of Inflammation and Disease. Nutrition Reviews, 65(SUPPL.3), S140–S146. https://doi.org/10.1111/j.1753-4887.2007.tb00352.x Lin, Y. P., Thibodeaux, C. H., Peña, J. A., Ferry, G. D., & Versalovic, J. (2008). Probiotic Lactobacillus reuteri suppress proinflammatory cytokines via c-Jun. Inflammatory Bowel Diseases, 14(8), 1068–1083. https://doi.org/10.1002/ibd.20448 Lindgärde, F., Eriksson, K.-F., Lithell, H., & Saltin, B. (2009). Coupling between Dietary Changes, Reduced Body Weight, Muscle Fibre Size and Improved Glucose Tolerance in Middle-Aged Men with Impaired Glucose Tolerance. Acta Medica Scandinavica, 212(3), 99–106. https://doi.org/10.1111/j.0954-6820.1982.tb03179.x Liu, M., Weiss, M. A., Arunagiri, A., Yong, J., Rege, N., Sun, J., Haataja, L., Kaufman, R. J., & Arvan, P. (2018). Biosynthesis, structure, and folding of the insulin precursor protein. In Diabetes, Obesity and Metabolism (Vol. 20, Issue Suppl 2, pp. 28–50). Blackwell Publishing Ltd. https://doi.org/10.1111/dom.13378 Liu, Z., Uesaka, T., Watanabe, H., & Kato, N. (2001). High fat diet enhances colonic cell 93 proliferation and carcinogenesis in rats by elevating serum leptin. International Journal of Oncology, 19(5), 1009–1014. https://doi.org/10.3892/ijo.19.5.1009 Liu, Zhigang, Patil, I. Y., Jiang, T., Sancheti, H., Walsh, J. P., Stiles, B. L., Yin, F., & Cadenas, E. (2015). High-Fat Diet Induces Hepatic Insulin Resistance and Impairment of Synaptic Plasticity. PLOS ONE, 10(5), e0128274. https://doi.org/10.1371/journal.pone.0128274 Long, E. K., Olson, D. M., & Bernlohr, D. A. (2013). High-fat diet induces changes in adipose tissue trans-4-oxo-2-nonenal and trans-4-hydroxy-2-nonenal levels in a depot-specific manner. Free Radical Biology and Medicine, 63, 390–398. https://doi.org/10.1016/j.freeradbiomed.2013.05.030 Lund, M. L., Egerod, K. L., Engelstoft, M. S., Dmytriyeva, O., Theodorsson, E., Patel, B. A., & Schwartz, T. W. (2018). Enterochromaffin 5-HT cells – A major target for GLP-1 and gut microbial metabolites. Molecular Metabolism, 11, 70–83. https://doi.org/10.1016/j.molmet.2018.03.004 Ma, L., Ma, S., He, H., Yang, D., Chen, X., Luo, Z., Liu, D., & Zhu, Z. (2010). Perivascular fat-mediated vascular dysfunction and remodeling through the AMPK/mTOR pathway in high-fat diet-induced obese rats. Hypertension Research, 33(5), 446–453. https://doi.org/10.1038/hr.2010.11 Ma, X, Torbenson, M., Hamad, A. R. A., Soloski, M. J., & Li, Z. (2008). High-fat diet modulates non-CD1d-restricted natural killer T cells and regulatory T cells in mouse colon and exacerbates experimental colitis. Clinical & Experimental Immunology, 151(1), 130–138. https://doi.org/10.1111/j.1365-2249.2007.03530.x Ma, Xiong, Hua, J., & Li, Z. (2008). Probiotics improve high fat diet-induced hepatic steatosis and insulin resistance by increasing hepatic NKT cells. Journal of Hepatology, 49(5), 821–94 830. https://doi.org/10.1016/j.jhep.2008.05.025 Madden, S. M. M., Garrioch, C. F., & Holub, B. J. (2009). Direct Diet Quantification Indicates Low Intakes of (n-3) Fatty Acids in Children 4 to 8 Years Old. The Journal of Nutrition, 139(3), 528–532. https://doi.org/10.3945/jn.108.100628 Magro, F., & Portela, F. (2010). Management of inflammatory bowel disease with infliximab and other anti-tumor necrosis factor alpha therapies. In BioDrugs (Vol. 24, Issue SUPPL. 1, pp. 3–14). Springer. https://doi.org/10.2165/11586290-000000000-00000 Mahmood, T., & Yang, P.-C. (2012). Western blot: technique, theory, and trouble shooting. North American Journal of Medical Sciences, 4(9), 429–434. https://doi.org/10.4103/1947-2714.100998 Maiorino, M., Thomas, J. P., Girotti, A. W., & Ursini, F. (1991). Reactivity of phospholipid hydroperoxide glutathione peroxidase with membrane and lipoprotein lipid hydroperoxides. Free Radical Research, 12(1), 131–135. https://doi.org/10.3109/10715769109145777 Maldonado-Gómez, M. X., Martínez, I., Bottacini, F., O’Callaghan, A., Ventura, M., van Sinderen, D., Hillmann, B., Vangay, P., Knights, D., Hutkins, R. W., & Walter, J. (2016). Stable Engraftment of Bifidobacterium longum AH1206 in the Human Gut Depends on Individualized Features of the Resident Microbiome. Cell Host and Microbe, 20(4), 515–526. https://doi.org/10.1016/j.chom.2016.09.001 Maltby, R., Leatham-Jensen, M. P., Gibson, T., Cohen, P. S., & Conway, T. (2013). Nutritional Basis for Colonization Resistance by Human Commensal Escherichia coli Strains HS and Nissle 1917 against E. coli O157:H7 in the Mouse Intestine. PLoS ONE, 8(1), e53957. https://doi.org/10.1371/journal.pone.0053957 Manco, M., Putignani, L., & Bottazzo, G. F. (2010). Gut microbiota, lipopolysaccharides, and 95 innate immunity in the pathogenesis of obesity and cardiovascular risk. In Endocrine Reviews (Vol. 31, Issue 6, pp. 817–844). Endocr Rev. https://doi.org/10.1210/er.2009-0030 Manning, B.D. L.C., C. (2003). TSC Genes – Novel Players in the Growth Regulation Network. February, 573–578. Manning, B. D., & Toker, A. (2017). AKT/PKB Signaling: Navigating the Network. In Cell (Vol. 169, Issue 3, pp. 381–405). Cell Press. https://doi.org/10.1016/j.cell.2017.04.001 Marshall, J. A., Hoag, S., Shetterly, S., & Hamman, R. F. (1994). Dietary fat predicts conversion from impaired glucose tolerance to NIDDM: The San Luis Valley Diabetes Study. Diabetes Care, 17(1), 50–55. https://doi.org/10.2337/diacare.17.1.50 Marshall, Julie A., Hamman, R. F., & Baxter, J. (1991). High-Fat, Low-Carbohydrate Diet and the Etiology of Non-Insulin-dependent Diabetes Mellitus: The San Luis Valley Diabetes Study. American Journal of Epidemiology, 134(6), 590–603. https://doi.org/10.1093/oxfordjournals.aje.a116132 Martin, A. M., Sun, E. W., Rogers, G. B., & Keating, D. J. (2019). The influence of the gut microbiome on host metabolism through the regulation of gut hormone release. Frontiers in Physiology, 10(MAR). https://doi.org/10.3389/fphys.2019.00428 Martinez, A., Castro, A., Dorronsoro, I., & Alonso, M. (2002). Glycogen synthase kinase 3 (GSK-3) inhibitors as new promising drugs for diabetes, neurodegeneration, cancer, and inflammation. In Medicinal Research Reviews (Vol. 22, Issue 4, pp. 373–384). John Wiley & Sons, Ltd. https://doi.org/10.1002/med.10011 Matsuzawa-Nagata, N., Takamura, T., Ando, H., Nakamura, S., Kurita, S., Misu, H., Ota, T., Yokoyama, M., Honda, M., Miyamoto, K., & Kaneko, S. (2008). Increased oxidative stress precedes the onset of high-fat diet–induced insulin resistance and obesity. 96 Metabolism - Clinical and Experimental, 57(8), 1071–1077. https://doi.org/10.1016/j.metabol.2008.03.010 Mazhar, S. F., Afzal, M., Almatroudi, A., Munir, S., Ashfaq, U. A., Rasool, M., Raza, H., Munir, H. M. W., Rajoka, M. S. R., & Khurshid, M. (2020). The Prospects for the Therapeutic Implications of Genetically Engineered Probiotics. Journal of Food Quality, 2020. https://doi.org/10.1155/2020/9676452 Metchmikoff, E. M. P. C. (1908). The Prolongation of Life; Optimistic Studies. New York & London : G.P. Putman’s Sons. https://archive.org/details/prolongationofli00metciala/page/n6/mode/2up Mokdad, A. H., Ford, E. S., Bowman, B. A., Dietz, W. H., Vinicor, F., Bales, V. S., & Marks, J. S. (2003). Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. Journal of the American Medical Association, 289(1), 76–79. https://doi.org/10.1001/jama.289.1.76 Moreira, A. P. B., Texeira, T. F. S., Ferreira, A. B., Do Carmo Gouveia Peluzio, M., & De Cássia Gonçalves Alfenas, R. (2012). Influence of a high-fat diet on gut microbiota, intestinal permeability and metabolic endotoxaemia. British Journal of Nutrition, 108(5), 801–809. https://doi.org/10.1017/S0007114512001213 Moylan, J. S., & Reid, M. B. (2007). Oxidative stress, chronic disease, and muscle wasting. Muscle & Nerve, 35(4), 411–429. https://doi.org/10.1002/mus.20743 Mukai, T., Asasaka, T., Sato, E., Mori, K., Matsumoto, M., & Ohori, H. (2002). Inhibition of binding of Helicobacter pylori to the glycolipid receptors by probiotic Lactobacillus reuteri . FEMS Immunology & Medical Microbiology, 32(2), 105–110. https://doi.org/10.1111/j.1574-695x.2002.tb00541.x 97 Muniyappa, R., Lee, S., Chen, H., & Quon, M. J. (2008). Current approaches for assessing insulin sensitivity and resistance in vivo: Advantages, limitations, and appropriate usage. In American Journal of Physiology - Endocrinology and Metabolism (Vol. 294, Issue 1). American Physiological Society. https://doi.org/10.1152/ajpendo.00645.2007 Murphy, E. A., Velazquez, K. T., & Herbert, K. M. (2015). Influence of high-fat diet on gut microbiota: a driving force for chronic disease risk. Current Opinion in Clinical Nutrition and Metabolic Care, 18(5), 515–520. https://doi.org/10.1097/MCO.0000000000000209 Nabben, M., & Neumann, D. (2016). GSK-3 Inhibitors: Anti-Diabetic Treatment Associated with Cardiac Risk?: Editorial to: “The Impact of Chronic Glycogen Synthase Kinase-3 Inhibition on Remodeling of Normal and Pre-Diabetic Rat Hearts.” by Barbara Huisamen et al. In Cardiovascular Drugs and Therapy (Vol. 30, Issue 3, pp. 233–235). Springer New York LLC. https://doi.org/10.1007/s10557-016-6669-y Nair, J., Vaca, C. E., Velic, I., Mutanen, M., Valsta, L. M., & Bartsch, H. (1997). High dietary omega-6 polyunsaturated fatty acids drastically increase the formation of etheno-DNA base adducts in white blood cells of female subjects. Cancer Epidemiology and Prevention Biomarkers, 6(8). Nakae, J, Kitamura, T., Silver, D. L., & Accili, D. (2001). The forkhead transcription factor Foxo1 (Fkhr) confers insulin sensitivity onto glucose-6-phosphatase expression. The Journal of Clinical Investigation, 108(9), 1359–1367. https://doi.org/10.1172/JCI12876 Nakae, Jun, Kitamura, T., Silver, D. L., & Accili, D. (2001). The forkhead transcription factor Foxo1 (Fkhr) confers insulin sensitivity onto glucose-6-phosphatase expression. Journal of Clinical Investigation, 108(9), 1359–1367. https://doi.org/10.1172/jci12876 Nakamura, H., De Rosa, S., Roederer, M., Anderson, M. T., Dubs, J. G., Yodoi, J., Holmgren, 98 A., Herzenberg, L. A., & Herzenberg, L. A. (1996). Elevation of plasma thioredoxin levels in HIV-infected individuals. International Immunology, 8(4), 603–611. https://doi.org/10.1093/intimm/8.4.603 Nakamura, H., Nakamura, K., & Yodoi, J. (1997). REDOX REGULATION OF CELLULAR ACTIVATION. Annual Review of Immunology, 15(1), 351–369. https://doi.org/10.1146/annurev.immunol.15.1.351 Nakamura, H., Vaage, J., Valen, G., Padilla, C. A., Björnstedt, M., & Holmgren, A. (1998). Measurements of plasma glutaredoxin and thioredoxin in healthy volunteers and during open-heart surgery. Free Radical Biology and Medicine, 24(7–8), 1176–1186. https://doi.org/10.1016/S0891-5849(97)00429-2 Narasimhan, S., Nagarajan, L., Vaidya, R., Gunasekaran, G., Rajagopal, G., Parthasarathy, V., Unnikrishnan, R., Anjana, R., Mohan, V., & Sudha, V. (2016). Dietary fat intake and its association with risk of selected components of the metabolic syndrome among rural South Indians. Indian Journal of Endocrinology and Metabolism, 20(1), 47–54. https://doi.org/10.4103/2230-8210.172248 Nascimento, E. B. M., Fodor, M., Van Der Zon, G. C. M., Jazet, I. M., Meinders, A. E., Voshol, P. J., Vlasblom, R., Baan, B., Eckel, J., Maassen, J. A., Diamant, M., & Ouwens, D. M. (2006). Insulin-mediated phosphorylation of the proline-rich Akt substrate PRAS40 is impaired in insulin target tissues of high-fat diet-fed rats. Diabetes, 55(12), 3221–3228. https://doi.org/10.2337/db05-1390 Nathan, C., & Ding, A. (2010). Nonresolving Inflammation. In Cell (Vol. 140, Issue 6, pp. 871–882). Cell Press. https://doi.org/10.1016/j.cell.2010.02.029 Navarro, J. B., Mashburn-Warren, L., Bakaletz, L. O., Bailey, M. T., & Goodman, S. D. (2017). 99 Enhanced Probiotic Potential of Lactobacillus reuteri When Delivered as a Biofilm on Dextranomer Microspheres That Contain Beneficial Cargo. Frontiers in Microbiology, 8(MAR), 489. https://doi.org/10.3389/fmicb.2017.00489 Nikoulina, S. E., Ciaraldi, T. P., Mudaliar, S., Carter, L., Johnson, K., & Henry, R. R. (2002). Inhibition of glycogen synthase kinase 3 improves insulin action and glucose metabolism in human skeletal muscle. Diabetes, 51(7), 2190–2198. https://doi.org/10.2337/diabetes.51.7.2190 Noeman, S. A., Hamooda, H. E., & Baalash, A. A. (2011). Biochemical Study of Oxidative Stress Markers in the Liver, Kidney and Heart of High Fat Diet Induced Obesity in Rats. Diabetology & Metabolic Syndrome, 3(1), 17. https://doi.org/10.1186/1758-5996-3-17 Nwadozi, E., Roudier, E., Rullman, E., Tharmalingam, S., Liu, H., Gustafsson, T., & Haas, T. L. (2016). Endothelial FoxO proteins impair insulin sensitivity and restrain muscle angiogenesis in response to a high‐fat diet. The FASEB Journal, 30(9), 3039–3052. https://doi.org/10.1096/fj.201600245R Oelschlaeger, T. A. (2010). Mechanisms of probiotic actions – A review. International Journal of Medical Microbiology, 300(1), 57–62. https://doi.org/10.1016/J.IJMM.2009.08.005 Oh, T. J. (2016). In vivo models for incretin research: From the intestine to the whole body. In Endocrinology and Metabolism (Vol. 31, Issue 1, pp. 45–51). Korean Endocrine Society. https://doi.org/10.3803/EnM.2016.31.1.45 Ohland, C. L., & Macnaughton, W. K. (2010). Probiotic bacteria and intestinal epithelial barrier function. American Journal of Physiology. Gastrointestinal and Liver Physiology, 298(6), G807-19. https://doi.org/10.1152/ajpgi.00243.2009 Ouwehand, A. C., & Salminen, S. (2003). In vitro adhesion assays for probiotics and their in 100 vivo relevance: a review. Microbial Ecology in Health and Disease, 15, 175–184. https://doi.org/10.1080/08910600310019886 Ozes, O. N., Akca, H., Mayo, L. D., Gustin, J. A., Maehama, T., Dixon, J. E., & Donner, D. B. (2001). A phosphatidylinositol 3-kinase/Akt/mTOR pathway mediates and PTEN antagonizes tumor necrosis factor inhibition of insulin signaling through insulin receptor substrate-1. Proceedings of the National Academy of Sciences of the United States of America, 98(8), 4640–4645. https://doi.org/10.1073/pnas.051042298 Panigrahi, P., Parida, S., Nanda, N. C., Satpathy, R., Pradhan, L., Chandel, D. S., Baccaglini, L., Mohapatra, A., Mohapatra, S. S., Misra, P. R., Chaudhry, R., Chen, H. H., Johnson, J. A., Morris, J. G., Paneth, N., & Gewolb, I. H. (2017). A randomized synbiotic trial to prevent sepsis among infants in rural India. Nature, 548(7668), 407–412. https://doi.org/10.1038/nature23480 Park, E. J., Lee, J. H., Yu, G.-Y., He, G., Ali, S. R., Holzer, R. G., Österreicher, C. H., Takahashi, H., & Karin, M. (2010). Dietary and Genetic Obesity Promote Liver Inflammation and Tumorigenesis by Enhancing IL-6 and TNF Expression. Cell, 140(2), 197–208. https://doi.org/https://doi.org/10.1016/j.cell.2009.12.052 Park, S., & Bae, J. H. (2015). Probiotics for weight loss: A systematic review and meta-analysis. Nutrition Research, 35(7), 566–575. https://doi.org/10.1016/j.nutres.2015.05.008 Pataky, M. W., Wang, H., Yu, C. S., Arias, E. B., Ploutz-Snyder, R. J., Zheng, X., & Cartee, G. D. (2017). High-Fat Diet-Induced Insulin Resistance in Single Skeletal Muscle Fibers is Fiber Type Selective. Scientific Reports, 7(1), 1–11. https://doi.org/10.1038/s41598-017-12682-z Paton, A. W., Morona, R., & Paton, J. C. (2000). A new biological agent for treatment of Shiga 101 toxigenic Escherichia coli infections and dysentery in humans. Nature Medicine, 6(3), 265–270. https://doi.org/10.1038/73111 Patterson, E., Wall, R., Fitzgerald, G. F., Ross, R. P., & Stanton, C. (2012). Health implications of high dietary omega-6 polyunsaturated fatty acids. In Journal of Nutrition and Metabolism (Vol. 2012, p. 16). Hindawi Limited. https://doi.org/10.1155/2012/539426 Patti, M. E., & Kahn, C. R. (1998). The insulin receptor--a critical link in glucose homeostasis and insulin action. Journal of Basic and Clinical Physiology and Pharmacology, 9(2–4), 89–109. https://doi.org/10.1515/jbcpp.1998.9.2-4.89 Perlman, R. L. (2016). Mouse models of human disease: An evolutionary perspective. Evolution, Medicine, and Public Health, 2016(1), 170–176. https://doi.org/10.1093/emph/eow014 Petersen, M. C., & Shulman, G. I. (2018). Mechanisms of insulin action and insulin resistance. In Physiological Reviews (Vol. 98, Issue 4, pp. 2133–2223). American Physiological Society. https://doi.org/10.1152/physrev.00063.2017 Pflughoeft, K. J., & Versalovic, J. (2012). Human Microbiome in Health and Disease. Annual Review of Pathology: Mechanisms of Disease, 7(1), 99–122. https://doi.org/10.1146/annurev-pathol-011811-132421 Pistell, P. J., Morrison, C. D., Gupta, S., Knight, A. G., Keller, J. N., Ingram, D. K., & Bruce-Keller, A. J. (2010). Cognitive impairment following high fat diet consumption is associated with brain inflammation. Journal of Neuroimmunology, 219(1–2), 25–32. https://doi.org/10.1016/j.jneuroim.2009.11.010 Pither, J., Botta, A., Maity, C., & Ghosh, S. (2017). Analysis using national databases reveals a positive association between dietary polyunsaturated fatty acids with TV watching and diabetes in European females. PLOS ONE, 12(3), e0173084. 102 https://doi.org/10.1371/journal.pone.0173084 Popkin B.M., Haines P.S., J. L. et al. (2004). No Title. . . Preventive Medicine, 32, 245–254. https://doi.org/10.1006/pmed.2000.0807 Pussinen, P. J., Havulinna, A. S., Lehto, M., Sundvall, J., & Salomaa, V. (2011). Endotoxemia is associated with an increased risk of incident diabetes. Diabetes Care, 34(2), 392–397. https://doi.org/10.2337/dc10-1676 Razmpoosh, E., Javadi, A., Ejtahed, H. S., Mirmiran, P., Javadi, M., & Yousefinejad, A. (2019). The effect of probiotic supplementation on glycemic control and lipid profile in patients with type 2 diabetes: A randomized placebo controlled trial. Diabetes and Metabolic Syndrome: Clinical Research and Reviews, 13(1), 175–182. https://doi.org/10.1016/j.dsx.2018.08.008 Relling, D. P., Esberg, L. B., Fang, C. X., Johnson, W. T., Murphy, E. J., Carlson, E. C., Saari, J. T., & Ren, J. (2006). High-fat diet-induced juvenile obesity leads to cardiomyocyte dysfunction and upregulation of Foxo3a transcription factor independent of lipotoxicity and apoptosis. Journal of Hypertension, 24(3), 549–561. https://doi.org/10.1097/01.hjh.0000203846.34314.94 Riant, E., Cogo, H., Arnal, J.-F. O., My Burcelin, R., & Team, P. G. (2009). Estrogens Protect against High-Fat Diet-Induced Insulin Resistance and Glucose Intolerance in Mice. https://doi.org/10.1210/en.2008-0971 Rivera, J. A., Barquera, S., Campirano, F., Campos, I., Safdie, M., & Tovar, V. (2002). Epidemiological and nutritional transition in Mexico: rapid increase of non-communicable chronic diseases and obesity. Public Health Nutrition, 5(1a), 113–122. https://doi.org/10.1079/phn2001282 103 Roberts, C. K., Vaziri, N. D., Ni, Z., & Barnard, R. J. (2002). Correction of long-term diet-induced hypertension and nitrotyrosine accumulation by diet modification. Atherosclerosis, 163(2), 321–327. https://doi.org/10.1016/S0021-9150(02)00010-2 Rodrigues, R. (2016). A Comprehensive Review: The Use of Animal Models in Diabetes Research. Journal of Analytical & Pharmaceutical Research, 3(6). https://doi.org/10.15406/japlr.2016.03.00071 Rohr, M. W., Narasimhulu, C. A., Rudeski-Rohr, T. A., & Parthasarathy, S. (2020). Negative Effects of a High-Fat Diet on Intestinal Permeability: A Review. In Advances in Nutrition (Vol. 11, Issue 1, pp. 77–91). Oxford University Press. https://doi.org/10.1093/advances/nmz061 Rondanelli, M., Faliva, M. A., Perna, S., Giacosa, A., Peroni, G., & Castellazzi, A. M. (2017a). Using probiotics in clinical practice: Where are we now? A review of existing meta-analyses. Gut Microbes, 8(6), 521–543. https://doi.org/10.1080/19490976.2017.1345414 Rondanelli, M., Faliva, M. A., Perna, S., Giacosa, A., Peroni, G., & Castellazzi, A. M. (2017b). Using probiotics in clinical practice: Where are we now? A review of existing meta-analyses. Gut Microbes, 8(6), 521–543. https://doi.org/10.1080/19490976.2017.1345414 Ruiz, L., Margolles, A., & Sánchez, B. (2013). Bile resistance mechanisms in Lactobacillus and Bifidobacterium. In Frontiers in Microbiology (Vol. 4, Issue DEC). Frontiers Research Foundation. https://doi.org/10.3389/fmicb.2013.00396 Rund, S. A., Rohde, H., Sonnenborn, U., & Oelschlaeger, T. A. (2013). Antagonistic effects of probiotic Escherichia coli Nissle 1917 on EHEC strains of serotype O104: H4 and O157: H7. International Journal of Medical Microbiology, 303(1), 1–8. https://doi.org/10.1016/j.ijmm.2012.11.006 104 Rupérez, A. I., Gil, A., & Aguilera, C. M. (2014). Genetics of oxidative stress in obesity. In International Journal of Molecular Sciences (Vol. 15, Issue 2, pp. 3118–3144). Molecular Diversity Preservation International. https://doi.org/10.3390/ijms15023118 Saltiel, A R, & Kahn, C. R. (2001). Insulin signalling and the regulation of glucose and lipid metabolism. Nature, 414(6865), 799–806. https://doi.org/10.1038/414799a Saltiel, Alan R., & Kahn, C. R. (2001). Insulin signalling and the regulation of glucose and lipid metabolism. In Nature (Vol. 414, Issue 6865, pp. 799–806). Nature Publishing Group. https://doi.org/10.1038/414799a Sandeep Kaur Gill, by. (2018). Designer probiotics as a novel therapeutic against inflammatory bowel disease [University of British Columbia]. https://doi.org/10.14288/1.0373455 Sarbassov, D. D., Guertin, D. A., Ali, S. M., & Sabatini, D. M. (2005). Phosphorylation and regulation of Akt/PKB by the rictor-mTOR complex. Science (New York, N.Y.), 307(5712), 1098–1101. https://doi.org/10.1126/science.1106148 Saxelin, M., Lassig, A., Karjalainen, H., Tynkkynen, S., Surakka, A., Vapaatalo, H., Järvenpää, S., Korpela, R., Mutanen, M., & Hatakka, K. (2010). Persistence of probiotic strains in the gastrointestinal tract when administered as capsules, yoghurt, or cheese. International Journal of Food Microbiology, 144(2), 293–300. https://doi.org/10.1016/j.ijfoodmicro.2010.10.009 Saxton, R. A., & Sabatini, D. M. (2017). mTOR Signaling in Growth, Metabolism, and Disease. In Cell (Vol. 168, Issue 6, pp. 960–976). Cell Press. https://doi.org/10.1016/j.cell.2017.02.004 Schaffner, W., & Weissmann, C. (1973). A rapid, sensitive, and specific method for the determination of protein in dilute solution. Analytical Biochemistry, 56(2), 502–514. 105 https://doi.org/10.1016/0003-2697(73)90217-0 Schierack, P., Kleta, S., Tedin, K., Babila, J. T., Oswald, S., Oelschlaeger, T. A., Hiemann, R., Paetzold, S., & Wieler, L. H. (2011). E. coli Nissle 1917 affects Salmonella adhesion to porcine intestinal epithelial cells. PLoS ONE, 6(2). https://doi.org/10.1371/journal.pone.0014712 Schirmer, M., Smeekens, S. P., Vlamakis, H., Jaeger, M., Oosting, M., Franzosa, E. A., Jansen, T., Jacobs, L., Bonder, M. J., Kurilshikov, A., Fu, J., Joosten, L. A. B., Zhernakova, A., Huttenhower, C., Wijmenga, C., Netea, M. G., & Xavier, R. J. (2016). Linking the Human Gut Microbiome to Inflammatory Cytokine Production Capacity. Cell, 167(4), 1125-1136.e8. https://doi.org/10.1016/j.cell.2016.10.020 Schreyer, S. A., Wilson, D. L., & LeBoeuf, R. C. (1998). C57BL/6 mice fed high fat diets as models for diabetes-accelerated atherosclerosis. Atherosclerosis, 136(1), 17–24. https://doi.org/10.1016/s0021-9150(97)00165-2 Sears, C., & Ghosh, S. (2016). Excess Omega-6 Polyunsaturated Fatty Acid Intake Is Associated with Negative Cardiovascular, Intestinal and Metabolic Outcomes in Mice. Canadian Journal of Diabetes, 40(4), 278–279. https://doi.org/10.1016/j.jcjd.2016.06.002 Seifert, S., Bub, A., Franz, C. M. A. P., & Watzl, B. (2011). Probiotic Lactobacillus casei Shirota Supplementation Does Not Modulate Immunity in Healthy Men with Reduced Natural Killer Cell Activity. The Journal of Nutrition, 141(5), 978–984. https://doi.org/10.3945/jn.110.136440 Sender, R., Fuchs, S., & Milo, R. (2016). Revised Estimates for the Number of Human and Bacteria Cells in the Body. PLoS Biology, 14(8). https://doi.org/10.1371/journal.pbio.1002533 106 Senn, J. J., Klover, P. J., Nowak, I. A., & Mooney, R. A. (2002). Interleukin-6 Induces Cellular Insulin Resistance in Hepatocytes. Diabetes, 51(12), 3391 LP – 3399. https://doi.org/10.2337/diabetes.51.12.3391 Sergi, D., Naumovski, N., Heilbronn, L. K., Abeywardena, M., O’Callaghan, N., Lionetti, L., & Luscombe-Marsh, N. (2019). Mitochondrial (dys)function and insulin resistance: From pathophysiological molecular mechanisms to the impact of diet. In Frontiers in Physiology (Vol. 10, p. 532). Frontiers Media S.A. https://doi.org/10.3389/fphys.2019.00532 Shapira, N. (2012). Women’s higher risk with N-6 PUFA vs. men’s relative advantage: An “N-6 gender nutrition paradox” hypothesis. Israel Medical Association Journal, 14(7), 435–441. Sharifi-Rad, J., Rodrigues, C. F., Stojanović-Radić, Z., Dimitrijević, M., Aleksić, A., Neffe-Skocińska, K., Zielińska, D., Kołożyn-Krajewska, D., Salehi, B., Prabu, S. M., Schutz, F., Docea, A. O., Martins, N., & Calina, D. (2020). Probiotics: Versatile bioactive components in promoting human health. In Medicina (Lithuania) (Vol. 56, Issue 9, pp. 1–30). MDPI AG. https://doi.org/10.3390/medicina56090433 Sheehan, V. M., Sleator, R. D., Hill, C., & Fitzgerald, G. F. (2007). Improving gastric transit, gastrointestinal persistence and therapeutic efficacy of the probiotic strain Bifidobacterium breve UCC2003. Microbiology, 153(10), 3563–3571. https://doi.org/10.1099/mic.0.2007/006510-0 Sierra, S., Lara-Villoslada, F., Sempere, L., Olivares, M., Boza, J., & Xaus, J. (2010). Intestinal and immunological effects of daily oral administration of Lactobacillus salivarius CECT5713 to healthy adults. Anaerobe, 16(3), 195–200. https://doi.org/10.1016/j.anaerobe.2010.02.001 Singh, R. K., Chang, H. W., Yan, D., Lee, K. M., Ucmak, D., Wong, K., Abrouk, M., Farahnik, 107 B., Nakamura, M., Zhu, T. H., Bhutani, T., & Liao, W. (2017). Influence of diet on the gut microbiome and implications for human health. In Journal of Translational Medicine (Vol. 15, Issue 1, pp. 1–17). BioMed Central Ltd. https://doi.org/10.1186/s12967-017-1175-y Snydman, D. R. (2008). The Safety of Probiotics. Clinical Infectious Diseases, 46(s2), S104–S111. https://doi.org/10.1086/523331 Soll, A. H., Kahn, C. R., & Neviile, D. M. (1975). Insulin Binding to Liver Plasma Membranes in the Obese Hyperglycemic (o b/o b) Mouse DEMONSTRATION OF A DECREASED NUMBER OF FUNCTIONALLY NORMAL RECEPTORS. In THE JOURNAL OF BIOLOGKAL CHEMISTRY (Vol. 250, Issue 12). http://www.jbc.org/ Song, M. J., Kim, K. H., Yoon, J. M., & Kim, J. B. (2006). Activation of Toll-like receptor 4 is associated with insulin resistance in adipocytes. Biochemical and Biophysical Research Communications, 346(3), 739–745. https://doi.org/10.1016/j.bbrc.2006.05.170 Sonnenborn, U., & Schulze, J. (2009). The non-pathogenic Escherichia coli strain Nissle 1917 – features of a versatile probiotic. Microbial Ecology in Health and Disease, 21(3–4), 122–158. https://doi.org/10.3109/08910600903444267 Spannagel, A. W., Nakano, I., Tawil, T., Chey, W. Y., Liddle, R. A., & Green, G. M. (1996). Adaptation to fat markedly increases pancreatic secretory response to intraduodenal fat in rats. American Journal of Physiology - Gastrointestinal and Liver Physiology, 270(1 33-1). https://doi.org/10.1152/ajpgi.1996.270.1.g128 Speakman, J. R. (2019). Use of high-fat diets to study rodent obesity as a model of human obesity. In International Journal of Obesity (Vol. 43, Issue 8, pp. 1491–1492). Nature Publishing Group. https://doi.org/10.1038/s41366-019-0363-7 Srinivasan, K., Viswanad, B., Asrat, L., Kaul, C. L., & Ramarao, P. (2005). Combination of 108 high-fat diet-fed and low-dose streptozotocin-treated rat: A model for type 2 diabetes and pharmacological screening. Pharmacological Research, 52(4), 313–320. https://doi.org/10.1016/j.phrs.2005.05.004 Srivastava, A. K., & Pandey, S. K. (1998). Potential mechanism(s) involved in the regulation of glycogen synthesis by insulin. Molecular and Cellular Biochemistry, 182(1–2), 135–141. https://doi.org/10.1023/A:1006857527588 Stenman, L. K., Waget, A., Garret, C., Klopp, P., Burcelin, R., & Lahtinen, S. (2014). Potential probiotic Bifidobacterium animalis ssp. lactis 420 prevents weight gain and glucose intolerance in diet-induced obese mice. Beneficial Microbes, 5(4), 437–445. https://doi.org/10.3920/BM2014.0014 Suez, J., Zmora, N., Segal, E., & Elinav, E. (2019). The pros, cons, and many unknowns of probiotics. Nature Medicine, 25(May). https://doi.org/10.1038/s41591-019-0439-x Suez, J., Zmora, N., Zilberman-Schapira, G., Mor, U., Dori-Bachash, M., Bashiardes, S., Zur, M., Regev-Lehavi, D., Ben-Zeev Brik, R., Federici, S., Horn, M., Cohen, Y., Moor, A. E., Zeevi, D., Korem, T., Kotler, E., Harmelin, A., Itzkovitz, S., Maharshak, N., … Elinav, E. (2018). Post-Antibiotic Gut Mucosal Microbiome Reconstitution Is Impaired by Probiotics and Improved by Autologous FMT. Cell, 174(6), 1406-1423.e16. https://doi.org/10.1016/j.cell.2018.08.047 Sugita, H., Kaneki, M., Tokunaga, E., Sugita, M., Koike, C., Yasuhara, S., Tompkins, R. G., & Martyn, J. A. J. (2002). Inducible nitric oxide synthase plays a role in LPS-induced hyperglycemia and insulin resistance. American Journal of Physiology. Endocrinology and Metabolism, 282(2), E386–E394. https://doi.org/10.1152/ajpendo.00087.2001 Sumida, Y., Nakashima, T., Yoh, T., Kakisaka, Y., Nakajima, Y., Ishikawa, H., Mitsuyoshi, H., 109 Okanoue, T., Nakamura, H., & Yodoi, J. (2001). Serum thioredoxin elucidates the significance of serum ferritin as a marker of oxidative stress in chronic liver diseases. Liver International, 21(5), 295–299. https://doi.org/10.1034/j.1600-0676.2001.210501.x Sumida, Y., Nakashima, T., Yoh, T., Nakajima, Y., Ishikawa, H., Mitsuyoshi, H., Sakamoto, Y., Okanoue, T., Kashima, K., Nakamura, H., & Yodoi, J. (2000). Serum thioredoxin levels as an indicator of oxidative stress in patients with hepatitis C virus infection. Journal of Hepatology, 33(4), 616–622. https://doi.org/10.1016/S0168-8278(00)80013-6 Sun, J., & Buys, N. J. (2016). Glucose- and glycaemic factor-lowering effects of probiotics on diabetes: a meta-analysis of randomised placebo-controlled trials. The British Journal of Nutrition, 115(7), 1167–1177. https://doi.org/10.1017/S0007114516000076 Sung, V., Hiscock, H., Tang, M. L. K., Mensah, F. K., Nation, M. L., Satzke, C., Heine, R. G., Stock, A., Barr, R. G., & Wake, M. (2014). Treating infant colic with the probiotic Lactobacillus reuteri: Double blind, placebo controlled randomised trial. BMJ (Online), 348. https://doi.org/10.1136/bmj.g2107 Surwit, R. S., Kuhn, C. M., Cochrane, C., McCubbin, J. A., & Feinglos, M. N. (1988). Diet-induced type II diabetes in C57BL/6J mice. Diabetes, 37(9), 1163–1167. https://doi.org/10.2337/diab.37.9.1163 Suzuki, T., & Hara, H. (2010). Dietary fat and bile juice, but not obesity, are responsible for the increase in small intestinal permeability induced through the suppression of tight junction protein expression in LETO and OLETF rats. Nutrition & Metabolism, 7(1), 19. https://doi.org/10.1186/1743-7075-7-19 Szabó, C., Ischiropoulos, H., & Radi, R. (2007). Peroxynitrite: Biochemistry, pathophysiology and development of therapeutics. In Nature Reviews Drug Discovery (Vol. 6, Issue 8, pp. 110 662–680). Nat Rev Drug Discov. https://doi.org/10.1038/nrd2222 Taghibiglou, C., Rashid-Kolvear, F., Van Iderstine, S. C., Le-Tien, H., George Fantus, I., Lewis, G. F., & Adeli, K. (2002). Hepatic very low density lipoprotein-ApoB overproduction is associated with attenuated hepatic insulin signaling and overexpression of protein-tyrosine phosphatase 1B in a fructose-fed hamster model of insulin resistance. Journal of Biological Chemistry, 277(1), 793–803. https://doi.org/10.1074/jbc.M106737200 Takano, A., Usui, I., Haruta, T., Kawahara, J., Uno, T., Iwata, M., & Kobayashi, M. (2001). Mammalian Target of Rapamycin Pathway Regulates Insulin Signaling via Subcellular Redistribution of Insulin Receptor Substrate 1 and Integrates Nutritional Signals and Metabolic Signals of Insulin. Molecular and Cellular Biology, 21(15), 5050–5062. https://doi.org/10.1128/mcb.21.15.5050-5062.2001 Tan, B. L., Norhaizan, M. E., & Liew, W. P. P. (2018a). Nutrients and oxidative stress: Friend or foe? In Oxidative Medicine and Cellular Longevity (Vol. 2018). Hindawi Limited. https://doi.org/10.1155/2018/9719584 Tan, B. L., Norhaizan, M. E., & Liew, W. P. P. (2018b). Nutrients and oxidative stress: Friend or foe? In Oxidative Medicine and Cellular Longevity (Vol. 2018). Hindawi Limited. https://doi.org/10.1155/2018/9719584 Tannock, G. W., Munro, K., Harmsen, H. J. M., Welling, G. W., Smart, J., & Gopal, P. K. (2000). Analysis of the fecal microflora of human subjects consuming a probiotic product containing Lactobacillus rhamnosus DR20. Applied and Environmental Microbiology, 66(6), 2578–2588. https://doi.org/10.1128/AEM.66.6.2578-2588.2000 Tavare, J. M., O’Brien, R. M., Siddle, K., & Denton, R. M. (1988). Analysis of insulin-receptor phosphorylation sites in intact cells by two-dimensional phosphopeptide mapping. 111 Biochemical Journal, 253(3), 783–788. https://doi.org/10.1042/bj2530783 Thevaranjan, N., Puchta, A., Schulz, C., Naidoo, A., Szamosi, J. C., Verschoor, C. P., Loukov, D., Schenck, L. P., Jury, J., Foley, K. P., Schertzer, J. D., Larché, M. J., Davidson, D. J., Verdú, E. F., Surette, M. G., & Bowdish, D. M. E. (2017). Age-Associated Microbial Dysbiosis Promotes Intestinal Permeability, Systemic Inflammation, and Macrophage Dysfunction. Cell Host & Microbe, 21(4), 455-466.e4. https://doi.org/10.1016/J.CHOM.2017.03.002 Tran, H. T., Barnich, N., & Mizoguchi, E. (2011). Potential role of chitinases and chitin-binding proteins in host-microbial interactions during the development of intestinal inflammation. In Histology and Histopathology (Vol. 26, Issue 11, pp. 1453–1464). NIH Public Access. https://doi.org/10.14670/HH-26.1453 Tremblay, F., Lavigne, C., Jacques, H., & Marette, A. (2001). Defective Insulin-Induced GLUT4 Translocation in Skeletal Muscle of High Fat-Fed Rats Is Associated with Alterations in Both Akt/Protein Kinase B and Atypical Protein Kinase C (ζ/λ) Activities. Diabetes, 50(8), 1901–1910. https://doi.org/10.2337/diabetes.50.8.1901 Tsai, W. C., Li, Y. H., Lin, C. C., Chao, T. H., & Chen, J. H. (2004). Effects of oxidative stress on endothelial function after a high-fat meal. Clinical Science, 106(3), 315–319. https://doi.org/10.1042/CS20030227 Tschöp, M., & Heiman, M. L. (2001). Rodent obesity models: An overview. In Experimental and Clinical Endocrinology and Diabetes (Vol. 109, Issue 6, pp. 307–319). https://doi.org/10.1055/s-2001-17297 Turroni, F., Serafini, F., Foroni, E., Duranti, S., Motherway, M. O., Taverniti, V., Mangifesta, M., Milani, C., Viappiani, A., Roversi, T., Sánchez, B., Santoni, A., Gioiosa, L., Ferrarini, 112 A., Delledonne, M., Margolles, A., Piazza, L., Palanza, P., Bolchi, A., … Ventura, M. (2013). Role of sortase-dependent pili of Bifidobacterium bifidum PRL2010 in modulating bacterium–host interactions. Proceedings of the National Academy of Sciences, 110(27), 11151–11156. https://doi.org/10.1073/PNAS.1303897110 Twells, L. K., Gregory, D. M., Reddigan, J., & Midodzi, W. K. (2014). Current and predicted prevalence of obesity in Canada: a trend analysis. CMAJ Open, 2(1), E18–E26. https://doi.org/10.9778/cmajo.20130016 Tzivion, G., Dobson, M., & Ramakrishnan, G. (2011). FoxO transcription factors; Regulation by AKT and 14-3-3 proteins. Biochimica et Biophysica Acta, 1813(11), 1938–1945. https://doi.org/10.1016/j.bbamcr.2011.06.002 Ukena, S. N., Singh, A., Dringenberg, U., Engelhardt, R., Seidler, U., Hansen, W., Bleich, A., Bruder, D., Franzke, A., Rogler, G., Suerbaum, S., Buer, J., Gunzer, F., & Westendorf, A. M. (2007). Probiotic Escherichia coli Nissle 1917 inhibits leaky gut by enhancing mucosal integrity. PLoS ONE, 2(12). https://doi.org/10.1371/journal.pone.0001308 van der Heijden, R. A., Sheedfar, F., Morrison, M. C., Hommelberg, P. P. H., Kor, D., Kloosterhuis, N. J., Gruben, N., Youssef, S. A., de Bruin, A., Hofker, M. H., Kleemann, R., Koonen, D. P. Y., & Heeringa, P. (2015). High-fat diet induced obesity primes inflammation in adipose tissue prior to liver in C57BL/6j mice. Aging, 7(4), 256–268. https://doi.org/10.18632/aging.100738 Verdugo-Meza, A., Ye, J., Dadlani, H., Ghosh, S., & Gibson, D. L. (2020). Connecting the Dots Between Inflammatory Bowel Disease and Metabolic Syndrome: A Focus on Gut-Derived Metabolites. Nutrients, 12(5), 1434. https://doi.org/10.3390/nu12051434 Wehkamp, J., Harder, J., Wehkamp, K., Wehkamp-Von Meissner, B., Schlee, M., Enders, C., 113 Sonnenborn, U., Nuding, S., Bengmark, S., Fellermann, K., Schröder, J. M., & Stange, E. F. (2004). NF-κB- and AP-1-mediated induction of human beta defensin-2 in intestinal epithelial cells by Escherichia coli Nissle 1917: A novel effect of a probiotic bacterium. Infection and Immunity, 72(10), 5750–5758. https://doi.org/10.1128/IAI.72.10.5750-5758.2004 Westfall, S., Lomis, N., Kahouli, I., Dia, S. Y., Singh, S. P., & Prakash, S. (2017). Microbiome, probiotics and neurodegenerative diseases: deciphering the gut brain axis. In Cellular and Molecular Life Sciences (Vol. 74, Issue 20, pp. 3769–3787). Birkhauser Verlag AG. https://doi.org/10.1007/s00018-017-2550-9 White, M. F., Takayama, S., & Kahn, C. R. (1985). THE JOURNAL OF BIOLOGICAL CHEMISTRY Differences in the Sites of Phosphorylation of the Insulin Receptor in Vivo and in Vitro* (Vol. 260, Issue 16). Wilcox, G. (2005). Insulin and Insulin. The Clinical Biochemist. Reviews, 26(May), 19–39. http://www.ncbi.nlm.nih.gov/pubmed/16278749 Wiley, N. C., Dinan, T. G., Ross, R. P., Stanton, C., Clarke, G., & Cryan, J. F. (2017). The microbiota-gut-brain axis as a key regulator of neural function and the stress response: Implications for human and animal health1,2. Journal of Animal Science, 95(7), 3225–3246. https://doi.org/10.2527/jas.2016.1256 Williams, L. M., Campbell, F. M., Drew, J. E., Koch, C., Hoggard, N., Rees, W. D., Kamolrat, T., Ngo, H. T., Steffensen, I. L., Gray, S. R., & Tups, A. (2014). The development of diet-induced obesity and glucose intolerance in C57Bl/6 mice on a high-fat diet consists of distinct phases. PLoS ONE, 9(8). https://doi.org/10.1371/journal.pone.0106159 Winter, S. E., Thiennimitr, P., Winter, M. G., Butler, B. P., Huseby, D. L., Crawford, R. W., 114 Russell, J. M., Bevins, C. L., Adams, L. G., Tsolis, R. M., Roth, J. R., & Bäumler, A. J. (2010). Gut inflammation provides a respiratory electron acceptor for Salmonella. Nature, 467(7314), 426–429. https://doi.org/10.1038/nature09415 Winzell, M. S., & Ahrén, B. (2004). The high-fat diet-fed mouse: A model for studying mechanisms and treatment of impaired glucose tolerance and type 2 diabetes. Diabetes, 53(SUPPL. 3), S215–S219. https://doi.org/10.2337/diabetes.53.suppl_3.S215 Wong, C. K., Botta, A., Pither, J., Dai, C., Gibson, W. T., & Ghosh, S. (2015). A high-fat diet rich in corn oil reduces spontaneous locomotor activity and induces insulin resistance in mice. Journal of Nutritional Biochemistry, 26(4), 319–326. https://doi.org/10.1016/j.jnutbio.2014.11.004 Wong, E., Vaaje-Kolstad, G., Ghosh, A., Hurtado-Guerrero, R., Konarev, P. V, Ibrahim, A. F. M., Svergun, D. I., Eijsink, V. G. H., Chatterjee, N. S., & van Aalten, D. M. F. (2012). The Vibrio cholerae Colonization Factor GbpA Possesses a Modular Structure that Governs Binding to Different Host Surfaces. PLOS Pathogens, 8(1), e1002373. https://doi.org/10.1371/journal.ppat.1002373 Woodard, G. A., Encarnacion, B., Downey, J. R., Peraza, J., Chong, K., Hernandez-Boussard, T., & Morton, J. M. (2009). Probiotics improve outcomes after roux-en-Y gastric bypass surgery: A prospective randomized trial. Journal of Gastrointestinal Surgery, 13(7), 1198–1204. https://doi.org/10.1007/s11605-009-0891-x World Health Organization. (2006). definition and diagnosis of diabetes mellitus and intermediate hyperglycemia RepoRt of a WHo/IDf ConsultatIon. Wu, Y., Lu, J., Antony, S., Juhasz, A., Liu, H., Jiang, G., Meitzler, J. L., Hollingshead, M., Haines, D. C., Butcher, D., Roy, K., & Doroshow, J. H. (2013). Activation of TLR4 Is 115 Required for the Synergistic Induction of Dual Oxidase 2 and Dual Oxidase A2 by IFN-γ and Lipopolysaccharide in Human Pancreatic Cancer Cell Lines. The Journal of Immunology, 190(4), 1859 LP – 1872. https://doi.org/10.4049/jimmunol.1201725 Xie, Q., Chen, J., & Yuan, Z. (2012). Post-translational regulation of FOXO. Acta Biochimica et Biophysica Sinica, 44(11), 897–901. https://doi.org/10.1093/abbs/gms067 Yamauchi, T., Tobe, K., Tamemoto, H., Ueki, K., Kaburagi, Y., Yamamoto-Honda, R., Takahashi, Y., Yoshizawa, F., Aizawa, S., Akanuma, Y., Sonenberg, N., Yazaki, Y., & Kadowaki, T. (1996). Insulin signalling and insulin actions in the muscles and livers of insulin-resistant, insulin receptor substrate 1-deficient mice. Molecular and Cellular Biology, 16(6), 3074–3084. https://doi.org/10.1128/mcb.16.6.3074 Yang, P., Zhao, Y., Zhao, L., Yuan, J., Chen, Y., Varghese, Z., Moorhead, J. F., Chen, Y., & Ruan, X. Z. (2015). Paradoxical effect of rapamycin on inflammatory stress-induced insulin resistance in vitro and in vivo. Scientific Reports, 5(1), 14959. https://doi.org/10.1038/srep14959 Yasukawa, T., Tokunaga, E., Ota, H., Sugita, H., Martyn, J. A. J., & Kaneki, M. (2005). S-nitrosylation-dependent inactivation of Akt/protein kinase B in insulin resistance. The Journal of Biological Chemistry, 280(9), 7511–7518. https://doi.org/10.1074/jbc.M411871200 Yoshida, S., Katoh, T., Tetsuka, T., Uno, K., Matsui, N., & Okamoto, T. (1999). Involvement of thioredoxin in rheumatoid arthritis: Its costimulatory roles in the TNF-α-induced production of IL-6 and IL-8 from cultured synovial fibroblasts. Journal of Immunology, 163(1), 351–358. Zanotto, T. M., Quaresma, P. G. F., Guadagnini, D., Weissmann, L., Santos, A. C., Vecina, J. F., 116 Calisto, K., Santos, A., Prada, P. O., & Saad, M. J. A. (2016). Blocking iNOS and endoplasmic reticulum stress synergistically improves insulin resistance in mice. Molecular Metabolism, 6(2), 206–218. https://doi.org/10.1016/j.molmet.2016.12.005 Zhang, Q., Wu, Y., & Fei, X. (2016). Effect of probiotics on body weight and body-mass index: a systematic review and meta-analysis of randomized, controlled trials. International Journal of Food Sciences and Nutrition, 67(5), 571–580. https://doi.org/10.1080/09637486.2016.1181156 Zhang, X., Tang, N., Hadden, T. J., & Rishi, A. K. (2011). Akt, FoxO and regulation of apoptosis. In Biochimica et Biophysica Acta - Molecular Cell Research (Vol. 1813, Issue 11, pp. 1978–1986). Elsevier B.V. https://doi.org/10.1016/j.bbamcr.2011.03.010 Zion Market Research. (2020). Probiotic Market : Size,share & trends analysis report. https://www.zionmarketresearch.com/report/probiotics-market Zmora, N., Zeevi, D., Korem, T., Segal, E., & Elinav, E. (2016). Taking it Personally: Personalized Utilization of the Human Microbiome in Health and Disease. Cell Host & Microbe, 19(1), 12–20. https://doi.org/10.1016/j.chom.2015.12.016 Zmora, N., Zilberman-Schapira, G., Suez, J., Mor, U., Dori-Bachash, M., Bashiardes, S., Kotler, E., Zur, M., Regev-Lehavi, D., Brik, R. B. Z., Federici, S., Cohen, Y., Linevsky, R., Rothschild, D., Moor, A. E., Ben-Moshe, S., Harmelin, A., Itzkovitz, S., Maharshak, N., … Elinav, E. (2018). Personalized Gut Mucosal Colonization Resistance to Empiric Probiotics Is Associated with Unique Host and Microbiome Features. Cell, 174(6), 1388-1405.e21. https://doi.org/10.1016/j.cell.2018.08.041 117 Appendix Table A.3.1 Nanodrop concentration of DNA extracted for Probiotic detection experiment Sample # Conc. 1 (ng/ul) Conc. 2 (ng/ul) Average CONP 7 235.9 245.7 240.8 CONP 8 309.6 398.7 354.15 CONP 9 200.4 287.6 244 CONP 10 243.9 198.9 221.4 COPL 7 190.1 156.8 173.45 COPL 8 339.4 320.8 330.1 COPL 9 213.0 215.0 214 COPL 10 223.8 268.9 246.35 COEP 7 748.9 674.2 711.55 COEP 8 205.1 187.9 196.5 COEP 9 169.8 170.8 170.3 COEP 10 218.7 220.9 219.8
Thesis/Dissertation
eng
Biochemistry and Molecular Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Designer probiotic supplementation outcomes on high fat diet
Text