Medicine, Faculty of
Medicine, Department of
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Dong, Kang
Doctor of Philosophy - PhD
University of British Columbia
Pulmonary exacerbations (PEx) are common in cystic fibrosis (CF) and are significant risk factors for lung disease progression. PEx are often diagnosed when a patient presents to clinic with increased respiratory symptoms and decreased lung function. However, changes can sometimes be subtle or the patient can be a poor perceiver of their symptoms, potentially leading to missed or delayed diagnosis of PEx. Blood-based biomarkers reflective of the host immune response and inflammatory activity have the potential to predict PEx in CF. However, due to the poorly defined pathophysiology of PEx, individual candidate biomarkers are unlikely to capture the full spectrum of immune and inflammatory responses in CF. The central hypothesis of this thesis is that by profiling molecules (protein, transcripts, and modifier genes) in blood with unbiased high-throughput ‘omics’ technologies, we can identify blood-based biomarkers to predict PEx risk and PEx-related treatment outcomes in CF. First, I assessed changes in blood proteins in response to IV antibiotic treatment for PEx. Early changes in IGFR2 were associated with symptom improvement by the end of treatment, suggesting its potential to act as a predictive marker of symptomatic treatment response in CF PEx. I further identified blood biomarker candidates (genes and proteins) from CF individuals with paired stable and PEx samples. Based on these candidates, a 16-gene panel and a 9-protein panel were developed predictive of imminent PEx risk (AUCs of 0.88 and 0.83, respectively). I then evaluated blood proteins to predict azithromycin treatment response in CF individuals. Early changes in serum calprotectin were predictive of PEx risk by day 168, indicating that serum calprotectin represents a promising predictive biomarker to identify individuals who derive benefit from azithromycin treatment. Finally, I investigated genetic modifiers of PEx risk in pre-school children with CF and identified the SLC9A3 variant as significantly associated with the risk of PEx. In summary, I showed that blood-based biomarkers could reliably predict response to treatment and PEx risk in CF individuals, which allows for more personalized monitoring and earlier diagnosis and treatment of CF PEx.
PREDICTIVE BIOMARKERS OF PULMONARY EXACERBATIONS IN CYSTIC FIBROSIS by Kang Dong B.Sc., Shanghai Ocean University, 2014 M.Sc., Temple University, 2016 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Experimental Medicine) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) October 2022 © Kang Dong, 2022 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Predictive Biomarkers of Pulmonary Exacerbations in Cystic Fibrosis submitted by Kang Dong in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Experimental Medicine Examining Committee: Dr. Bradley Quon, Associate Professor, Department of Medicine, UBC Supervisor Dr. Scott Tebbutt, Professor, Department of Medicine, UBC Supervisory Committee Member Dr. Sharon Dell, Professor, Department of Pediatrics, UBC University Examiner Dr. Najib Ayas, Associate Professor, Department of Medicine, UBC University Examiner Additional Supervisory Committee Members: Dr. Kelly McNagny, Professor, Department of Medical Genetics, UBC Supervisory Committee Member Dr. Andrew Sandford, Professor, Department of Medicine, UBC Supervisory Committee Member iii Abstract Pulmonary exacerbations (PEx) are common in cystic fibrosis (CF) and are significant risk factors for lung disease progression. PEx are often diagnosed when a patient presents to clinic with increased respiratory symptoms and decreased lung function. However, changes can sometimes be subtle or the patient can be a poor perceiver of their symptoms, potentially leading to missed or delayed diagnosis of PEx. Blood-based biomarkers reflective of the host immune response and inflammatory activity have the potential to predict PEx in CF. However, due to the poorly defined pathophysiology of PEx, individual candidate biomarkers are unlikely to capture the full spectrum of immune and inflammatory responses in CF. The central hypothesis of this thesis is that by profiling molecules (protein, transcripts, and modifier genes) in blood with unbiased high-throughput ‘omics’ technologies, we can identify blood-based biomarkers to predict PEx risk and PEx-related treatment outcomes in CF. First, I assessed changes in blood proteins in response to IV antibiotic treatment for PEx. Early changes in IGFR2 were associated with symptom improvement by the end of treatment, suggesting its potential to act as a predictive marker of symptomatic treatment response in CF PEx. I further identified blood biomarker candidates (genes and proteins) from CF individuals with paired stable and PEx samples. Based on these candidates, a 16-gene panel and a 9-protein panel were developed predictive of imminent PEx risk (AUCs of 0.88 and 0.83, respectively). I then evaluated blood proteins to predict azithromycin treatment response in CF individuals. Early changes in serum calprotectin were predictive of PEx risk by day 168, indicating that serum calprotectin represents a promising predictive biomarker to identify individuals who derive benefit from azithromycin treatment. Finally, I investigated genetic modifiers of PEx risk in pre-school children with CF iv and identified the SLC9A3 variant as significantly associated with the risk of PEx. In summary, I showed that blood-based biomarkers could reliably predict response to treatment and PEx risk in CF individuals, which allows for more personalized monitoring and earlier diagnosis and treatment of CF PEx. v Lay Summary Individuals with cystic fibrosis (CF) experience recurrent episodes of worsening respiratory symptoms, termed pulmonary exacerbations (PEx). PEx account for over half of lung function loss over a lifetime and ultimately lead to an earlier death. Early identification of individuals at elevated risk of PEx can potentially improve their clinical outcomes and mitigate loss in lung function. In this thesis, I developed blood tests to predict PEx risk and evaluate response to anti-infective and anti-inflammatory treatments in CF individuals. I also evaluated the influence of genetic factors on the risk of PEx requiring hospitalization in young children with CF. Findings from this thesis demonstrate that blood tests can be used to a reliably predict PEx risk in CF individuals which may facilitate personalized medical monitoring and allow earlier diagnosis and treatment of CF PEx. vi Preface A version of Chapter 2 has been published. [Dong, K.], Moon, K. M., Chen, V., Ng, R., Foster, L. J., Tebbutt, S. J., & Quon, B. S. (2019). Identification of novel blood biomarkers of treatment response in cystic fibrosis pulmonary exacerbations by label-free quantitative proteomics. Sci Rep, 9(1), 17126. doi:10.1038/s41598-019-53759-1. Quon, B. S. conceived and designed the study. Quon, B. S., Ng, R., and Tebbutt, S. J. obtained funding and provided supervision for the study. Dong, K. and Moon, K. M. collected data. Dong, K. (90%) and Chen, V. (10%) performed statistical analyses. Dong, K. drafted the table and figures. Dong, K. drafted the manuscript. All authors reviewed the manuscript and provided critical input. This study was approved by the Clinical Research Ethics Board of the University of British Columbia (H12-00835). The reuse and reprint of the published article are with permission from the journal referenced. Section of Chapter 3 has been published in an abstract format for the North American Cystic Fibrosis Conference. [Dong, K.], Chen, V., Shannon, C., Tebbutt, S. J., Ng, R., & Quon, B. S. (2021). 533: Whole-blood transcriptome biomarkers of pulmonary exacerbations in cystic fibrosis. Journal of Cystic Fibrosis, 20, S251-S252. doi:10.1016/S1569-1993(21)01957-3. Quon, B.S. and Dong, K. conceived and designed the study. Quon, B.S. obtained funding and provided supervision for the study. Dong, K. collected data, performed statistical analyses, drafted the table and figures, and drafted the abstract. All authors reviewed the abstract and provided critical input. The rest section of Chapter 2 is based on unpublished data where Dong, K. performed the NanoString nCounter® experiments, protein quantification experiments, and data analysis. Jang, vii J. assisted in performing the experimental studies. This study was approved by the Clinical Research Ethics Board of the University of British Columbia (H12-00835). A version of Chapter 4 has been published. [Dong, K.], Singh, A., Ng, R. T., Sin, D. D., Tebbutt, S. J., Ratjen, F., & Quon, B. S. (2019). Proteomic Profiling to Identify Blood Biomarkers Predictive of Response to Azithromycin in Children and Adolescents with Cystic Fibrosis. Chest, 156(4), 667-673. doi:10.1016/j.chest.2019.05.017. Quon, B.S. conceived and designed the study. Quon, B.S. obtained funding and provided supervision for the study. Dong, K. and Ratjen, F. collected data. Dong, K. (70%) and Singh, A. (30%) performed statistical analyses. Dong, K. drafted tables and figures. Dong, K. drafted the manuscript. All authors reviewed the manuscript and provided critical input. The reuse and reprint of the published article are with permission from the journal referenced. Chapter 5 is based on unpublished data. Quon, B. S. and [Dong, K.] conceived and designed the study. Quon, B. S. and Dong, K. obtained data from the Canadian Cystic Fibrosis Registry and Canadian Cystic Fibrosis Gene Modifier study. Quon, B.S. and Strug, L.J. provided supervision for the study. Gong, J. extracted data from the Canadian Cystic Fibrosis Gene Modifier study database. Dong, K. performed statistical analyses and drafted tables and figures. viii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ....................................................................................................................... viii List of Tables .............................................................................................................................. xiv List of Figures ............................................................................................................................ xvii List of Abbreviations ...................................................................................................................xx Acknowledgements .................................................................................................................. xxiv Dedication ................................................................................................................................. xxvi Chapter 1: Introduction ................................................................................................................1 1.1.1 CFTR Mutations ..................................................................................................... 1 1.1.2 Role of CFTR in CF Lung Disease ......................................................................... 4 1.1.3 Pathogenesis of CF Lung Disease ........................................................................... 6 1.1.4 Treatment of CF Lung Disease ............................................................................... 9 1.2 Cystic Fibrosis Pulmonary Exacerbations .................................................................... 11 1.2.1 Prevalence and Clinical Significance .................................................................... 11 1.2.2 Definitions and Diagnosis ..................................................................................... 11 1.2.3 Pathophysiology of Pulmonary Exacerbations ..................................................... 12 1.3 Biomarkers in CF Pulmonary Exacerbations ................................................................ 13 1.3.1 Biomarkers are Needed to Predict Pulmonary Exacerbations .............................. 13 1.3.2 Why Sample Blood for Biomarker Discovery? .................................................... 13 ix 1.3.3 Literature Review of Blood Biomarkers in CF Pulmonary Exacerbations ........... 14 1.3.4 Genetic Modifiers of CF Lung Disease and Pulmonary Exacerbations ............... 17 1.4 Research Hypothesis and Specific Aims ...................................................................... 18 1.4.1 Hypothesis............................................................................................................. 18 1.4.2 Specific aims ......................................................................................................... 19 Chapter 2: Identification of novel blood biomarkers of treatment response in cystic fibrosis pulmonary exacerbations by label-free quantitative proteomics ............................................21 2.1 Introduction ................................................................................................................... 21 2.2 Patients and Methods .................................................................................................... 23 2.2.1 Study cohort .......................................................................................................... 23 2.2.2 Blood samples and clinical outcomes ................................................................... 23 2.2.3 Untargeted proteomics profiling of blood proteins ............................................... 24 2.2.4 Analysis of candidate blood proteins .................................................................... 25 2.2.5 Protein-protein interaction (PPI) network and pathway enrichment analysis ...... 26 2.2.6 Statistical analyses ................................................................................................ 26 2.3 Results ........................................................................................................................... 29 2.3.1 Clinical characteristics at hospital admission for PEx .......................................... 29 2.3.2 Clinical outcomes of PEx treatment ..................................................................... 31 2.3.3 Correlation between protein levels at hospital admission (V1) with clinical and demographic factors .............................................................................................................. 34 2.3.4 Longitudinal changes in candidate and LC-MS/MS blood proteins ..................... 35 2.3.5 Protein-protein interaction (PPI) network and pathway enrichment analysis by STRING algorithm ................................................................................................................ 37 x 2.3.6 Correlation between early change in blood protein levels from hospital admission (V1) to Day 5 (V2) with changes in clinical outcomes from hospital admission (V1) to IV antibiotic completion (V4) .................................................................................................... 41 2.3.7 Correlation between overall change in blood protein levels from hospital admission (V1) to IV antibiotic completion (V4) with changes in clinical outcomes from hospital admission (V1) to IV antibiotic completion (V4) ................................................... 42 2.4 Discussion ..................................................................................................................... 43 Chapter 3: Whole blood transcriptomic and inflammatory protein biomarkers for predicting imminent pulmonary exacerbations in cystic fibrosis ............................................46 3.1 Introduction ................................................................................................................... 46 3.2 Patients and Methods .................................................................................................... 48 3.2.1 Study design and patient cohort ............................................................................ 48 3.2.2 Blood samples and clinical outcomes ................................................................... 49 3.2.3 Total RNA extraction, library construction and RNA sequencing ....................... 50 3.2.4 Alignment, quantification, and pre-processing of RNA-seq data ......................... 51 3.2.5 Inflammatory protein analysis .............................................................................. 52 3.2.6 Differential expression testing and functional enrichment analysis ..................... 54 3.2.7 In silico immune cell type deconvolution ............................................................. 56 3.2.8 Development of biomarker panel to predict PEx within 4 months ....................... 56 3.2.9 Analytical method validation ................................................................................ 57 3.2.10 Assessment of biomarker expression on external cohort ...................................... 58 3.2.11 Statistical analysis ................................................................................................. 59 3.3 Results ........................................................................................................................... 59 xi 3.3.1 Clinical characteristics of the study cohort 1 ........................................................ 59 3.3.2 Changes in clinical outcomes during PEx ............................................................ 61 3.3.3 Sources of variation in gene expression ................................................................ 62 3.3.4 Exacerbation-related changes in gene expression ................................................. 64 3.3.5 Exacerbation-related changes in immune cell composition .................................. 69 3.3.6 Sources of variation in protein expression ............................................................ 72 3.3.7 Exacerbation-related changes in protein expression ............................................. 74 3.3.8 Identification of biomarker candidates for PEx .................................................... 76 3.3.9 Biomarker candidates to predict PEx within 4 months ......................................... 77 3.3.10 Assessment of biomarker gene expression with NanoString nCounter® ............. 80 3.3.11 Assessing levels of biomarker genes in external datasets ..................................... 87 3.4 Discussion ..................................................................................................................... 90 Chapter 4: Proteomic Profiling to Identify Blood Biomarkers Predictive of Treatment Response to Azithromycin in Children and Adolescents with Cystic Fibrosis ......................95 4.1 Introduction ................................................................................................................... 95 4.2 Patients and Methods .................................................................................................... 96 4.2.1 Study Cohort ......................................................................................................... 96 4.2.2 Proteomic analysis for blood proteins ................................................................... 97 4.2.3 Statistical analyses ................................................................................................ 98 4.3 Results ........................................................................................................................... 99 4.3.1 Clinical characteristics .......................................................................................... 99 4.3.2 Baseline protein levels to predict response to azithromycin treatment ............... 101 4.3.3 Blood protein changes from day 0 to day 28 of azithromycin treatment ............ 101 xii 4.3.4 Correlation between early change in blood protein level and change in FEV1 % predicted from day 0 to day 28 and day 0 to day 168 of azithromycin treatment .............. 105 4.3.5 Correlation between early change in blood protein level and change in weight from day 0 to day 28 and day 0 to day 168 of azithromycin treatment .............................. 106 4.3.6 Change in serum calprotectin levels following 28 days of azithromycin treatment predicts protocol defined PEx frequency risk by day 168 of treatment .............................. 106 4.4 Discussion ................................................................................................................... 110 Chapter 5: Genetic Susceptibility of Pulmonary Exacerbations in Preschoolers with Cystic Fibrosis ........................................................................................................................................114 5.1 Introduction ................................................................................................................. 114 5.2 Patients and Methods .................................................................................................. 116 5.2.1 Study cohort ........................................................................................................ 116 5.2.2 Clinical characteristics ........................................................................................ 116 5.2.3 Sample genotyping and quality control .............................................................. 117 5.2.4 List of candidate single-nucleotide polymorphisms (SNPs) ............................... 118 5.2.5 Statistical analyses .............................................................................................. 119 5.3 Results ......................................................................................................................... 121 5.3.1 Patient cohort ...................................................................................................... 121 5.3.2 Statistical power and sample size calculation ..................................................... 123 5.3.3 Testing candidate SNPs for association with severe PEx in the first 5 years of life 124 5.3.4 Testing SLC9A3 gene variant for association with cumulative PEx counts within the first five years of life ..................................................................................................... 127 xiii 5.3.5 Testing SLC9A3 gene variant for association with age of onset of first PEx ..... 128 5.3.6 Identifying biomarkers predictive of PEx risk in young CF children ................. 130 5.1 Discussion ................................................................................................................... 130 Chapter 6: Conclusions and Future Directions ......................................................................134 6.1 Overview ..................................................................................................................... 134 6.2 Limitations and Future Directions .............................................................................. 137 6.3 Conclusion .................................................................................................................. 140 References ...................................................................................................................................141 Appendices ..................................................................................................................................172 Appendix A ............................................................................................................................. 172 A.1 Supplemental tables ................................................................................................ 172 Appendix B ............................................................................................................................. 177 B.1 Supplemental tables ................................................................................................ 177 Appendix C ............................................................................................................................. 181 C.1 Supplemental tables ................................................................................................ 181 xiv List of Tables Table 1. Clinical characteristics at hospital admission (V1) ......................................................... 30 Table 2. Longitudinal changes in FEV1% predicted, CFRSD-CRISS, and candidate blood proteins following PEx treatment ................................................................................................. 31 Table 3. Correlation between blood protein levels at hospital admission (V1) with baseline FEV1% predicted .......................................................................................................................... 34 Table 4. Gene ontology (GO) biological process pathway enrichment analysis based on DE proteins from V1 to V2 ................................................................................................................. 37 Table 5. Reactome pathway enrichment analysis based on differentially expressed (DE) proteins from V1 to V2 ............................................................................................................................... 38 Table 6. Blood proteins with early change in levels from V1 to V2 and that correlate with changes in CFRSD-CRISS from V1 to V4 in comparison to candidate proteins measured with immunoassays ............................................................................................................................... 42 Table 7. List of inflammatory protein analytes ............................................................................. 53 Table 8. Characteristics of subjects at clinically stable visits in cohort 1 ..................................... 60 Table 9. Changes in clinical outcomes at PEx in cohort 1 ............................................................ 61 Table 10. Differentially expressed genes between clinically stable and PEx ............................... 66 Table 11. Differentially expressed inflammatory proteins between clinically stable and PEx .... 75 Table 12. Biomarker candidate genes and proteins for PEx ......................................................... 76 Table 13. Characteristics of subjects in cohort 2 .......................................................................... 77 Table 14. nCounter® CodeSet design .......................................................................................... 81 Table 15. Clinical characteristics of the study cohort 1&2 and external cohorts ......................... 87 xv Table 16. Baseline clinical characteristics of subjects included from the AZ0004 study .......... 100 Table 17. Blood proteins measured with MRM-MS, Olink ProSeek, and ELISA with significant (p-value < 0.05) fold-change from day 0 to day 28 of azithromycin treatment .......................... 104 Table 18. Associations between early change in biomarker levels and change in FEV1% predicted from day 0 to day 28 ................................................................................................... 105 Table 19. Early change in blood protein following 28 days of azithromycin treatment predicts protocol-defined PEx frequency risk by day 168 of treatment ................................................... 107 Table 20. Comparison of baseline (day 0) serum calprotectin levels measured with ELISA between protocol-defined PEx vs. non-PEx groups (from the discovery cohort) and between discovery vs. validation cohorts .................................................................................................. 112 Table 21. Calculation of CFTR variant severity score ............................................................... 117 Table 22. Candidate single-nucleotide polymorphisms (SNPs) ................................................. 118 Table 23. The demographic and clinical characteristics of the study cohort .............................. 123 Table 24. Association test results of candidate SNPs and early risk of PEx .............................. 125 Table 25. Association test results of candidate SNPs and risk of severe PEx in CF individuals diagnosed through newborn screening ........................................................................................ 126 Table 26. Number of CF individuals who had ≥ 1 PEx before the age of 5 years vs. rs57221529 genotypes .................................................................................................................................... 127 Table 27. Summarizations of performance in predicting PEx risk in young CF children using genetic and clinical variables ...................................................................................................... 130 Table 28. Blood protein levels at hospital admission (V1) significantly correlated with age .... 172 Table 29. Blood proteins with statistically significant fold-change in levels from V1 to V2 measured with LC-MS/MS ......................................................................................................... 174 xvi Table 30. Blood proteins with statistically significant fold-change in levels from V1 to V4 measured with LC-MS/MS ......................................................................................................... 176 Table 31. Differential expression gene test between clinically stable and PEx that adjusted for peripheral blood cell composition ............................................................................................... 177 Table 32. Pathways with significant differential gene expression between clinically stable and PEx samples adjusted for peripheral blood cell counts .............................................................. 180 Table 33. Clinical characteristics between received, enrolled, and subjects from AZ0004 study treatment arm .............................................................................................................................. 181 Table 34. MRM-MS protein panel abbreviation list ................................................................... 182 Table 35. Correlation between baseline blood protein level and change in FEV1% predicted from day 0 to day 28 and day 0 to day 168 of azithromycin treatment ............................................... 184 Table 36. Correlation between baseline blood protein level and change in weight from day 0 to day 28 and day 0 to day 168 of azithromycin treatment ............................................................. 191 Table 37. Correlation between change in blood protein level from day 0 to day 28 and change in FEV1% predicted from day 0 to day 28 and day 0 to day 168 of azithromycin treatment ......... 196 Table 38. Correlation between change in blood protein level from day 0 to day 28 and change in weight from day 0 to day 28 and day 0 to day 168 of azithromycin treatment .......................... 203 xvii List of Figures Figure 1. Classes of CFTR mutations ............................................................................................. 2 Figure 2. Airway surface liquid in (A) normal airway and (B) cystic fibrosis ............................... 5 Figure 3. Flow diagram of LC-MS/MS data pre-processing ........................................................ 27 Figure 4. Longitudinal changes in (A) FEV1% predicted, (B) CFRSD-CRISS, (C) IL-6, (D) IL-8, (E) TNF-α, (F) calprotectin following PEx treatment .................................................................. 33 Figure 5. Volcano plot demonstrating blood proteins measured with LC-MS/MS with statistically significant fold-change from (A) V1 to V2 and (B) V1 to V4 ..................................................... 36 Figure 6. Protein-protein interaction (PPI) network of DE proteins from V1 to V2 and color-coded proteins enriched for GO biology process pathway ........................................................... 39 Figure 7. Protein-protein interaction (PPI) network of DE proteins from V1 to V2 and color-coded proteins enriched for Reactome pathway ........................................................................... 40 Figure 8. Flow diagram of study design and cohort selection ...................................................... 49 Figure 9. Biomarker discovery workflow ..................................................................................... 57 Figure 10. Principal component analysis (PCA) of gene expression in cohort 1 (A) Scree plot that displays explained variance by each PC. (B) PCA indicated clusters corresponding to sample conditions. (C) PCA indicated clusters corresponding to sex. (D) PCA indicated clusters corresponding to CFTR genotype. (E) Spearman’s correlation between PCs and clinical variables and white blood cell proportions. .................................................................................................. 63 Figure 11. Volcano plot of differentially expressed genes between paired clinically stable and PEx visits ...................................................................................................................................... 65 Figure 12. Top 10 enriched (A) Canonical and (B) Gene Ontology Pathways ............................ 68 xviii Figure 13. Spearman’s correlation analysis between clinical blood count (%) and CIBERSORTx estimated blood counts (%) (A) Neutrophils, (B) Lymphocytes, (C) Monocytes ........................ 70 Figure 14. CIBERSORTx estimated immune cell counts (%) between clinically stable and PEx....................................................................................................................................................... 71 Figure 15. Principal component analysis (PCA) of protein expression in cohort 1 (A) Scree plot that displayed explained variance by each PC. (B) PCA indicated clusters corresponding to sample conditions. (C) PCA indicated clusters corresponding to sex. (D) PCA indicated clusters corresponding to CFTR genotype. (E) Spearman’s correlation between PCs and clinical variables and white blood cell proportions. .................................................................................................. 73 Figure 16. Volcano plot of differential expression protein between clinically stable and PEx .... 74 Figure 17. Forest plot of AUC for biomarker panels. ................................................................... 79 Figure 18. ROC curve of biomarker panels in testing data (A) 16-gene panel (AUC = 0.88). (B) 9-protein panel (AUC = 0.83). (C) Calprotectin (AUC = 0.75). (D) Clinical panel (female sex, FEV1% predicted, and the number of IV antibiotics in the preceding year; AUC = 0.71) ........... 80 Figure 19. Levels of 16 biomarker genes measured with RNA-seq in clinically stable samples . 82 Figure 20. Principal component analysis (PCA) of biomarker candidate genes measured with NanoString nCounter® technology .............................................................................................. 83 Figure 21. Levels of biomarker candidate genes vs. negative control. ......................................... 84 Figure 22. Correlation between biomarker candidate genes measured with RNA-seq vs. NanoString nCounter® ................................................................................................................. 85 Figure 23. ROC curve of nCounter® panel in test data (AUC = 0.83) ........................................ 86 Figure 24. Principal component analysis (PCA) of study cohorts 1&2 and external cohorts (A) Original data, (B) ComBat corrected data .................................................................................... 88 xix Figure 25. Levels of biomarker candidate genes between study cohorts and external datasets ... 89 Figure 26. Volcano plot demonstrating the statistical significance of fold-change in blood protein levels from day 0 to day 28 of azithromycin treatment measured with MRM-MS, Olink ProSeekⓇ Multiplex Inflammation I Panel, and ELISA ............................................................ 103 Figure 27. Change in serum calprotectin levels from day 0 to day 28 predicts protocol-defined PEx frequency following 168 days of azithromycin treatment .................................................. 107 Figure 28. ROC analysis: change in calprotectin from day 0 to day 28 predicts protocol-defined PEx frequency following 168 days of azithromycin treatment: A. discovery cohort; B. validation cohort .......................................................................................................................................... 109 Figure 29. Flow diagram of the study cohort selection .............................................................. 122 Figure 30. Sample size at 80% statistical power ......................................................................... 124 Figure 31. Number of CF individuals diagnosed through newborn screening from 2000 to 2015..................................................................................................................................................... 126 Figure 32. Cumulative PEx counts within the first five years of life ......................................... 128 Figure 33. Kapan-Meier survival curve of age distribution at first PEx ..................................... 129 xx List of Abbreviations ASL: Airway surface liquid AUC: Area under receiver operating characteristic curve B. cepacia complex: Burkholderia cepacia complex BAL: Bronchoalveolar lavage BMI: Body mass index CAMP: Cathelicidin antimicrobial peptide CF: Cystic Fibrosis CFRSD-CRISS: CF respiratory symptom diary and chronic respiratory infection symptom score CFTR: Cystic fibrosis transmembrane conductance regulator CGMS: Canadian CF Gene Modifier Study COPD: Chronic obstructive pulmonary disease CPM: Counts per million CRP: C-reactive protein CV: Coefficient of variation DE: Differentially expressed DNA: Deoxyribonucleic acid ELISA: Enzyme-linked immunosorbent assay ENaC: Epithelial sodium channel FDR: False discovery rate FEV1: Forced expiratory volume in one second G-CSF: Granulocyte colony-stimulating factor xxi GEO: Gene expression omnibus GM-CSF: Granulocyte-macrophage colony-stimulating factor GO: Gene Ontology GWAS: Genome-wide association study Hb: Hemoglobin HPLC: High-Performance Liquid Chromatography HR: Hazard ratio HRG: Histidine-rich glycoprotein Ig: Immunoglobulin IGFR2: Insulin-like growth factor 2 receptor IL: Interleukin IQR: Interquartile range IV: Intravenous KEGG: Kyoto Encyclopedia of Genes and Genomes LC-MS/MS: Liquid chromatography-tandem mass spectrometry LLOD: Lower limit of detection LY96: Myeloid differentiation protein-2 MPO: Myeloperoxidase MRM-MS: Multiple reaction monitoring mass spectrometry MRSA: Methicillin-resistant Staphylococcus aureus MSD: Membrane-spanning Domain MSSA: Methicillin-sensitive Staphylococcus aureus NBD: Nucleotide-binding domain xxii NF-κB: Nuclear factor kappa B P. aeruginosa: Pseudomonas aeruginosa PBMC : Peripheral blood mononuclear cell PCA: Principal component analysis PERMANOVA: Permutational multivariate analysis of variance PEx: Pulmonary exacerbations PIGF: Placental growth factor PPI: Protein-protein interaction QC: Quality control RIN: RNA integrity number RNA: Ribonucleic acids ROC: Receiver operating characteristic curve RSEM: RNA-Seq by Expectation Maximization S. aureus: Staphylococcus aureus S100: S-100 calcium-binding cytosolic protein SAA: Serum amyloid A SD: Standard deviation SLC6A14: Solute carrier family 6 member 14 SLC9A3: Solute carrier family 9 member A3 SNP: Single nucleotide polymorphism SPLUNC1: Short palate lung and nasal epithelial clone 1 STAGE: STop And Go Extraction STAR: Spliced Transcripts Alignment to a Reference xxiii STOP study: Standardized Treatment of Pulmonary Exacerbations Th: T helper TLR: Toll-like Receptor TNF: Tumor necrosis factor TPM: Transcript per million xxiv Acknowledgements I would like to convey my gratefulness to many individuals and institutions who assisted me in completing the projects described in this thesis in a timely and rigorous manner. More importantly, these individuals have facilitated my professional development as a scientist. First and foremost, I would like to thank my supervisor Dr. Bradley Quon for being a mentor, motivator, advocate, and supervisor of my Ph.D. journal. It’s a cliché to say, “I wouldn’t be here without my supervisor”, but it is 100% true. You have played a massive role in my development as a scientist and inspired me to be a better researcher. Additionally, I would like to extend similar thanks to my thesis committee, Dr. Scott Tebbutt, Dr. Andrew Sandford, and Dr. Kelly McNagny. I am incredibly grateful for all your patience, kindness, and support over the past several years. This Ph.D. journey has allowed me to collaborate with numerous brilliant scientists both locally and nationally. Not in any particular order, I would like to thank Mr. David Ngan, Ms. Sheena Tam, Ms. Beth Whalen, Ms. Basak Sahin, Dr. Gurpreet Singhera, Dr. Feng Xu, Dr. Yuan Chao (Tim) Xue, Ms. Haojun Huang, Dr. Janice Leung, and Dr. Don Sin from the Centre for Heart Lung Innovation; Dr. Zsuzsanna Hollander, Ms. Virginia Chen, Mr. Casey Shannon, Dr. Amrit Singh, and Dr. Raymond Ng from the PROOF Centre of excellence; Dr. Lisa Strug, Dr. Jiafen Gong, and Ms. Katherine Keenan from the Hospital for Sick Children; Ms. Stephanie Cheng and Ms. Teresa Le from Canadian Cystic Fibrosis Patient Registry. I would like to thank Ms. Jiah Jang, Ms. Naomi Potter, Mr. Sameer Desai, Mr. Miguel Prieto, Dr. Alessandro Franciosi, and Dr. Grace Lam, my current and former fellow lab mates. I xxv have been extremely fortunate to be surrounded by such wonderful people. Thank you so much for your friendship and assistance during this time. Most importantly, I would like to express my gratitude to my wife, Lu Xu, for her patience and unwavering support over the last five years. Throughout this journal, I have been lucky to have my parents' support and willingness to listen to my successes and failures. Hannah Dong, my sweet little angel, who wakes me up at midnight and tells me it's time to work on my thesis, receives a special award. Finally, I would like to thank the Mitacs Accelerate fellowship, Compute Canada, and UBC Advanced Research Computing Sockeye for providing funding and resources to help me finish this thesis. xxvi Dedication To the cystic fibrosis patients, clinical coordinators, and CF clinical team involved in this research. Thank you for dedicating your time and information to science. Your generosity will improve the quality of life for future generations suffering from cystic fibrosis pulmonary disease. 1 Chapter 1: Introduction Cystic Fibrosis (CF) is a life-limiting autosomal recessive disease affecting at least 100,000 people globally and 4,300 individuals in Canada (1, 2). It is a monogenic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. The lack of CFTR protein or impaired function leads to excessively viscous secretions in multiple organ systems and is associated with numerous diseases and comorbidities. Individuals with CF experience inflammation (3), infection (4), structural lung disease soon after birth (5), and progressive lung function decline over a lifetime (6). Nearly 85% of CF individuals are affected by pancreatic insufficiency in the first year of life (7). Meconium ileus is observed in almost 20% of CF newborns (8), as well as intestinal inflammation (9) and altered microbiome composition (10) in children with CF. A higher prevalence of CF-related diabetes (11), bone disease (12), and male infertility (13) are also detected in CF adults. Above all, lung disease is the leading cause of morbidity and mortality in people with CF and the primary determinant of patient quality of life and treatment burden. 1.1 Genetic Basis and Pathogenesis of CF Lung Disease 1.1.1 CFTR Mutations Since the discovery of the CFTR gene in 1989 by Drs. Lap-Chee Tsui and John Riordan at the Hospital for Sick Children in Toronto (14, 15) and Dr. Francis Collins at the University of Michigan (16), nearly 2,000 CFTR variants have been described and over 400 of them are currently known to be disease-causing (17). The CFTR encoded protein primarily functions as a chloride channel to transport ions across the apical membrane of epithelial cells and assists with 2 bicarbonate secretion (18). CFTR belongs to the large family of adenine nucleotide-binding cassette transporters and consists of two membrane-spanning domains (MSD1 and MSD2), two cytosolic nucleotide-binding domains (NBD1 and NBD2), and an exclusive regulatory domain (R domain) (18, 19). Figure 1. Classes of CFTR mutations CFTR mutations have been categorized into six classes based on their impact on CFTR protein expression and function. In Class I mutations, a premature termination codon is created in mRNAs due to nonsense, frameshift, aberrant splicing mutations, and large deletions and insertions in the CFTR gene. These mutations produce a truncated CFTR mRNA rapidly degraded by nonsense-mediated RNA decay. Class II mutations result in the synthesis of a misfolded protein and transport minimal partially functioning protein to the plasma membrane. Class III and IV mutations result in relatively preserved levels of CFTR but reduced ion transport due to inadequate channel regulation 3 and conductance, respectively. Class V mutations lead to reduced synthesis of CFTR due to splicing defects. Class VI mutations reduce CFTR stability at the cell membrane and increase protein turnover. Reprinted from The Lancet, Vol. 397(10290), Shteinberg, M., Haq, I. J., Polineni, D., & Davies, J. C, Cystic Fibrosis, 2195-2211, Copyright (2021), with permission from Elsevier. CFTR mutations have been categorized into six classes based on their impact on CFTR protein expression and function (Figure 1) (1). Class I and II mutations affect protein production and processing, resulting in little to no CFTR at the plasma membrane and this generally correlates with a severe CF phenotype. In Class I mutations, a premature termination codon is created in messenger ribonucleic acids (mRNAs) due to nonsense, frameshift, aberrant splicing mutations, and large deletions and insertions in the CFTR gene (20, 21). These mutations result in the production of a truncated CFTR mRNA that is rapidly degraded by nonsense-mediated RNA decay (22) (21). Class II mutations result in the synthesis of a misfolded protein and transport of minimal partially functioning protein to the plasma membrane (23). The most common mutation, F508del, is a Class II mutation, affects nearly 90% of CF patients in Canada (2). Class III and IV mutations result in relatively preserved levels of CFTR but reduced ion transport due to inadequate channel regulation and conductance, respectively. Class V mutations lead to reduced synthesis of CFTR due to splicing defects. Class VI mutations reduce CFTR stability at the cell membrane and increase protein turnover. This classification system is oversimplified but provides an overview of how genetic mutation classes correlate with functional protein availability, which has proven beneficial in discovering therapies for specific protein defects (24). In clinical practice, however, the value of this classification system is limited since people with the same CFTR genotype present with substantial diversity in clinical expression, particularly in CF lung disease (25). More than 4 half of the variability in lung function could be attributable to environmental factors and non-CFTR genetic modifiers (26). 1.1.2 Role of CFTR in CF Lung Disease CF lung disease is characterized by mucus obstruction, respiratory infection, and persistent inflammation. The exact mechanisms linking CFTR dysfunction to lung disease is unclear; nevertheless, CFTR dysfunction is thought to be associated with decreased airway surface liquid (ASL) volume and pH (27, 28) and thick viscous mucus production (29). 5 Figure 2. Airway surface liquid in (A) normal airway and (B) cystic fibrosis The apical surface of the airway epithelium is covered with a thin layer of fluid referred to as airway surface liquid (ASL). In the normal airway (A), inhaled particles are captured by gel-forming mucins and removed through mucociliary clearance. Chloride, bicarbonate, and fluid are transported into the airway by CFTR. The epithelial sodium channel (ENaC) absorbs sodium to maintain ASL hydration and mucociliary transport. Short palate lung and nasal epithelial clone 1 (SPLUNC1) is secreted into the airway and regulates ASL volume by inhibiting ENaC activity. Antimicrobial factors in ASL play a vital role in the innate host response. In the CF airway (B), defective CFTR disrupts the homeostasis of chloride secretion and sodium absorption, resulting in decreased ASL volume, pH, and consequently producing thick viscous mucus. SPLUNC1 fails to regulate ENaC, leading to sodium hyper-absorption and further ASL volume depletion. Hence, inhaled pathogens are trapped and cannot be eliminated by mucociliary clearance. Reprinted from Thorax, Vol. 71(3), Haq, I. J., Gray, M. A., Garnett, J. P., Ward, C., & Brodlie, M., Airway surface liquid homeostasis in cystic fibrosis: pathophysiology and therapeutic targets, 284-287, Copyright (2016), with permission from BMJ Publishing Group Ltd. The apical surface of the airway epithelium is covered with a thin layer of fluid referred to as airway surface liquid (ASL), which plays an essential role in airway homeostasis (30). ASL is composed of a mucus layer and a periciliary layer. In the healthy airway (Figure 2A), inhaled particles are captured by gel-forming mucins in the mucus layer and removed through mucociliary clearance. The periciliary layer surrounds the cilia and sits in the middle of the mucus layer and airway epithelial cells. High concentrations of membrane-bound mucins in the periciliary layer, tethering together with cilia and apical epithelial cells, form a macromolecular net to prevent mucus and inhaled particles from penetrating the periciliary layer and damaging the airway (27). In addition, CFTR and epithelial sodium channel (ENaC) play an essential role in maintaining the ASL hydration (31, 32). CFTR transports chloride, bicarbonate, and fluid into 6 the airway. At the same time, the ENaC absorbs sodium from the airway, resulting in optimum osmotic gradients to absorb excess fluid in the airway lumen and preventing excessive ASL dehydration (33). The secreted protein, short palate lung and nasal epithelial clone 1 (SPLUNC1), also regulates ASL volume by inhibiting ENaC activity (34). Antimicrobial factors are also found in ASL, and they play a vital role in the innate host response. In the CF airway (Figure 2B), loss of functional CFTR disrupts the homeostasis of chloride secretion and sodium absorption, resulting in decreased ASL volume and consequently producing thick viscous mucus (29, 35, 36). Defective CFTR-mediated bicarbonate transport also leads to an acidic airway environment, decreasing antimicrobial activity (28). This observation was confirmed with the CF pig model, in which anti-bacterial activities were restored by increasing the ASL pH (28). This acidic airway environment also influences SPLUNC1 function since it cannot inhibit ENaC activity and leads to worse airway dehydration (34). Consequently, inhaled foreign substances, including pathogens, are trapped in the thick viscous mucus and cannot be eliminated by mucociliary clearance (18). These primary airway abnormalities result in a vicious cycle of infection, inflammation, and structural airway damage. 1.1.3 Pathogenesis of CF Lung Disease Persistent neutrophilic inflammation and structural and functional lung defects are observed in CF individuals soon after birth, although the disease progresses typically in a “clinically silent” fashion (37). Significant levels of inflammatory markers (i.e., interleukin (IL)-8, neutrophil elastase, peroxidases, and their oxidants) have been observed in bronchoalveolar lavage (BAL) fluid in CF newborns, even with normal lung function and no bacterial colonization (38). Up to 30% of CF infants show detectable free neutrophil elastase activity in 7 the BAL fluid (39). Neutrophil elastase belongs to the serine proteinase family and is critical for killing engulfed bacteria; however, its accumulation in the airway can overwhelm anti-protease defence, impair ciliary function, damage airway connective tissue, and lead to bronchiectasis (40-43). Data collected by the Australian Respiratory Early Surveillance Team for Cystic Fibrosis program reported that free neutrophil elastase activity in BAL fluid at three months of age could predict computed tomography scan-diagnosed bronchiectasis by three years of age (44). By school age, 60% - 80% of CF children are affected by bronchiectasis, air trapping and lung function abnormalities (45-47). Respiratory infection is another major contributor to CF lung disease. Organisms commonly isolated from CF individuals include Staphylococcus aureus (both methicillin-sensitive and methicillin-resistant), Pseudomonas aeruginosa, Burkholderia cepacia complex, Haemophilus influenzae, Achromobacter species, and Stenotrophomonas maltophilia (2, 6). Nontuberculous mycobacteria infections have also been reported in recent years; however, these organisms are cultured less frequently than others (6). Staphylococcus aureus (S. aureus) is the most frequently isolated bacterial organism from young children with CF (6). Nearly 30% of CF infants culture positive for S. aureus during the first six months of life, and the prevalence of infection increases to 70% by adolescence (6, 39). When sampled from the lower respiratory tract, CF infants and young children infected with S. aureus have higher levels of pulmonary inflammation, as measured by neutrophil counts, neutrophil elastase, and IL-8 levels in BAL fluid (48). In a recent multi-centre observational study, a higher density of S. aureus cultured from throat swab was associated with accelerated lung function decline in CF teenagers (49). 8 Pseudomonas aeruginosa (P. aeruginosa) is the major opportunistic bacterial pathogen in CF and affects approximately 40% of CF individuals (2, 6). Patients with chronic P. aeruginosa infection experience increased pulmonary inflammation, accelerated lung function decline, and earlier death (50-52). Previous studies reported increased IL-8 released from airway epithelial cells after P. aeruginosa stimulation, leading to neutrophil recruitment and increased neutrophil elastase accumulation (53, 54). A study based on US CF Foundation Registry data indicated that treating chronic P. aeruginosa infection with inhaled tobramycin was associated with longer survival in CF individuals (55). Fortunately, therapies to eradicate initial P. aeruginosa infection are effective in up to 90% of cases. The prevalence of P. aeruginosa infection is decreasing, and many CF patients are now transitioning to adult CF care without a history of P. aeruginosa infection (6). Burkholderia cepacia complex (B. cepacia complex) infects approximately 3-5% of the North American CF population (2, 6). The most common isolated Burkholderia species are B. multivorans and B. cenocepacia. Although these organisms are frequently isolated from a moist environment, strong patient-to-patient transmission has been identified in CF individuals infected with B. cenocepacia, which necessitates strict infection control practices in CF care centres (56). Previous studies have reported that B. cepacia complex infection was associated with poor lung function, increased risk of death and worse clinical outcomes in CF individuals, especially in individuals post lung transplantation (57-59). The prevalence of Haemophilus influenzae, Achromobacter species, and Stenotrophomonas maltophilia infection is lower in CF individuals when compared to S. aureus and P. aeruginosa, ranging from 5% to 20% (6). Less robust data has been gathered in these individuals due to small sample sizes; however, individuals infected with these organisms are 9 reported to have worse clinical outcomes, including lower lung function and more frequent pulmonary exacerbations (PEx) (60-62). 1.1.4 Treatment of CF Lung Disease As an inherited chronic progressive disease, the treatment of CF is lifelong and generally starts at the time of diagnosis. Treatments targeting CF lung disease are focused on optimizing airway clearance, managing chronic infection, and reducing airway inflammation (63-65). Airway clearance therapy eliminates viscous secretions from the airway, reducing the risk of infection and improving gas exchange. It is recommended to perform airway clearance twice a day as maintenance therapy and more frequently during PEx (63). Various airway clearance techniques have been adopted in patients’ daily practice, such as the active cycle of breathing technique, autogenic drainage, chest physical therapy, high-frequency chest wall oscillating vest, positive expiratory device, and airway oscillating device (66). All these techniques involve huff coughing, which requires patients to take a slow deep breath, hold for two to three seconds, and then exhale forcefully but slowly (67). In addition, inhaled mucus altering agents are commonly used together with these airway clearance techniques, such as hypertonic saline and recombinant human DNase (i.e., dornase alfa, rhDNase). Hypertonic saline can aid expectoration by promoting water and mucus secretion into the airway. In CF adults and children older than six years of age, long-term usage of hypertonic saline has been associated with a decreased frequency of PEx (68). Dornase alfa, unlike hypertonic saline, is an enzyme that breaks down DNA polymers generated by neutrophils. In CF individuals, it has been found to improve lung function and reduce PEx (63). 10 Antibiotics, in addition to airway clearance therapies, are critical in the management of chronic respiratory infections in people with CF. Inhaled antibiotics, such as tobramycin and aztreonam, have been shown to be effective in treating both early and chronic P. aeruginosa infections, and they have been associated with improved lung function and a lower risk of hospitalization (63, 69-71). Unfortunately, CF patients are typically exposed to a variety of pathogens, primarily bacteria but also fungi (72) and viruses (73), limiting antibiotic usage and complicating the management of chronic respiratory infections. Another essential component of CF lung disease management is reducing airway inflammation. Ibuprofen and oral azithromycin are two anti-inflammatory medications that are recommended in the Cystic Fibrosis Pulmonary Guidelines (63). High-dose ibuprofen has been demonstrated to inhibit neutrophil migration in CF youth (74). Treatment with high-dose ibuprofen, on the other hand, may raise the risk of gastrointestinal bleeding; thus, it's not generally utilized in clinical practice. Chronic use of oral azithromycin is recommended (63) and has been demonstrated to improve lung function and decrease the frequency of PEx in individuals with and without evidence of chronic P. aeruginosa infection (75-77). Tremendous progress has been achieved in CF lung disease management in the past 30 years. Lung function, measured as forced expiratory volume in one second (FEV1) percent predicted, increased to > 90% in CF adolescents while was < 80% in the 1980s (2, 6). However, improvements in slowing lung disease progression remain unsatisfactory. The rate of lung function decline is nearly constant in CF adolescents and adults across successive birth cohorts (2, 6). These observations suggest that a more proactive approach in clinical practice is needed to identify and treat disease earlier, potentially reducing the risk of irreversible airway damage and changing the disease trajectory. 11 1.2 Cystic Fibrosis Pulmonary Exacerbations 1.2.1 Prevalence and Clinical Significance Pulmonary exacerbations (PEx) are common in individuals with CF and are significant risk factors for lung disease progression (78-80). These events are associated with lung function decline (81), reduced quality of life (82), hospitalizations (83), and decreased survival (79). In 2019, approximately 40% of adults and 23% of children/adolescents followed in the CF Foundation Registry required at least one course of intravenous (IV) antibiotics for the treatment of a PEx. By retrospectively analyzing longitudinal lung function measures from 851 CF adults and adolescents, repeated PEx accounted for 52% of the decline in FEV1 over a lifetime, ultimately leading to an earlier death (84). One in 4 CF adults diagnosed with a PEx failed to recover within 90% of their prior baseline lung function despite treatment with IV antibiotics (81). PEx are also significant events in pre-school children with CF. Frequent PEx before two years of age correlated with worse FEV1% predicted by the age of five (85). PEx treated with IV antibiotics in early life were associated with the presence of chest computed tomography diagnosed bronchiectasis and lower weight-for-age levels (85). 1.2.2 Definitions and Diagnosis Despite the CF Foundation Outcomes Group’s appeal for the development of a consensus diagnosis of PEx in 1994, there has yet to be a universally agreed-upon definition of a PEx (86). Most studies currently define CF PEx as acute worsening of respiratory symptoms and lung function requiring treatment with antibiotics (83, 87-89). In retrospective studies, the need for IV 12 antibiotics remains the most widely used definition of PEx as it is readily available in CF patient registries (6, 79). Although this definition tends to select for more severe PEx events (90, 91), it can still be helpful to study risk factors and biomarkers of PEx (92). In clinical practice, diagnosis of a PEx typically relies upon a combination of increased respiratory symptoms (i.e., increased cough, sputum production, hemoptysis, shortness of breath) and decreased lung function (i.e., drop in FEV1 percent predicted) (88, 93). However, changes in lung function can sometimes be subtle or the patient can be a poor perceiver of their symptoms, potentially leading to missed or delayed diagnosis of PEx (80). According to the Standardized Treatment of Pulmonary Exacerbations (STOP) study, 85% of CF patients treated with IV antibiotics in the hospital for a PEx had respiratory symptoms for more than seven days before admission, and one-third reported respiratory symptoms for longer than 21 days (94). However, less than half of these patients were given oral antibiotics before being admitted to the hospital, suggesting that some patients may have protracted symptoms consistent with PEx but are not treated because of delays in diagnosis. Considering the severity of PEx in the progression of CF lung disease, late- or missed-diagnosed PEx could lead to an accelerated loss in lung function and increased morbidity and mortality in CF individuals. 1.2.3 Pathophysiology of Pulmonary Exacerbations Despite extensive knowledge regarding the adverse impact of PEx on health outcomes, the pathophysiology of these events remains poorly understood. It is generally believed that homeostasis between host immunity and the microbiome within the airways is disrupted during a PEx, although the cause for this disruption can be variable (87). Triggers of PEx include respiratory viral infection (i.e., respiratory syncytial virus, influenza, and rhinovirus) (95-97), 13 acquisition of new bacteria (98), changes in the population structure of chronically colonizing bacteria (98), poor medication adherence, and sometimes unknown factors. Although both infectious and non-infectious factors can trigger PEx, these events are typically treated empirically with systemic antibiotics as it is often difficult to exclude a bacterial etiology. 1.3 Biomarkers in CF Pulmonary Exacerbations 1.3.1 Biomarkers are Needed to Predict Pulmonary Exacerbations A biomarker is defined as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” (99). As respiratory symptoms are typically triggered by uncontrolled infection and inflammation, molecular biomarkers (i.e., genes, transcripts, proteins, metabolites, and epigenetic markers) that aid in the detection of immune or inflammatory activity before the onset of overt symptoms may help stratify CF individuals according to their PEx risk and enable CF physicians to either increase the intensity of patient monitoring or initiate effective preventative therapies (i.e., inhaled antibiotics, mucolytics) in a timelier fashion to prevent PEx and improve clinical outcomes. 1.3.2 Why Sample Blood for Biomarker Discovery? There is tremendous interest in identifying biomarkers that reflect disease activity to diagnose and predict PEx in CF. Both sputum and blood from CF patients have been studied to search for biomarkers (100, 101). However, sampling sputum has significant drawbacks. Patients must produce sufficient sputum for laboratory analysis which can be difficult if the patient is 14 either clinically stable or early in the course of a PEx. Even though sputum can be induced with the use of hypertonic saline (102), this approach is prone to sampling variability. Moreover, sputum processing is laborious and cannot be feasibly performed in most clinical laboratories. In contrast, blood is highly desirable for developing biomarker tests. Blood can be readily sampled and is minimally invasive for most patients. Protocols for blood collection, processing, shipment, and storage have been standardized (103, 104). Factors that impact sample quality, such as platelet contamination in plasma and serum, have been evaluated in multicenter clinical trials, and the recommended processing time (30 minutes to less than 4 hours on ice) and centrifugation setting (2,500 – 3,000 g for 30 minutes) were reported to minimize the platelet contamination (105, 106). More importantly, blood is the pipeline of the immune system, and therefore circulating molecules involved in the host immune response to infection and inflammation might directly participate in the underlying pathogenesis of PEx (107). 1.3.3 Literature Review of Blood Biomarkers in CF Pulmonary Exacerbations Numerous markers of inflammation have been studied using blood from CF individuals. According to a systematic review of blood biomarkers in CF PEx, seventy-eight unique blood protein biomarkers have been studied in the context of diagnosing PEx, predicting PEx outcomes, and capturing the response to PEx treatment (100). Among these, serum C-reactive protein (CRP) and calprotectin have been the most extensively studied and offer the most significant potential for clinical use (108, 109). Substantial reductions in the levels of CRP and calprotectin have been observed in CF individuals from beginning to end of PEx treatment (110, 111). However, when used as a diagnostic marker of PEx, CRP has an overall poor track record 15 for disease activity (112) and high within-subject variability among repeat clinically stable samples (113). CRP is an acute-phase protein primarily produced by liver hepatocytes and it circulates in the blood. CRP activates complement component 1q protein to trigger the classical complement pathway of the innate immune system and elevates dramatically in response to infection and inflammation (114). Despite the correlation between CRP and disease activity, the level of CRP in clinically stable subjects tends to fluctuate and is significantly influenced by underlying lung disease severity (113) and genetic factors (115, 116), limiting its application in PEx diagnosis. Calprotectin is a heterodimeric complex formed by two S-100 calcium-binding cytosolic proteins, S100A8 and S100A9 (117). It represents almost two-thirds of a neutrophil’s soluble cytosolic protein content and is released extracellularly by activated or damaged cells (117). The existing data support calprotectin as a marker of ongoing neutrophilic inflammation (118) and plays a vital role in eliminating microbes by mediating neutrophil and macrophage migration to the site of infection (119). Calprotectin has been demonstrated to be predictive of time to next exacerbation and is associated with changes in lung function in clinically stable CF individuals (110, 120). High-throughput, large-scale analytical technologies are now widely used to assess gene expression, proteins, lipids, and metabolites from biospecimens (121). Blood leukocyte gene expression has been examined to identify biomarker candidates of PEx in CF individuals. Nick and colleagues applied microarray technology to study gene expression in peripheral blood mononuclear cells (PBMCs) isolated from 10 CF subjects before and after IV antibiotic treatment and identified a 10-gene signature that changes with the treatment (122). In a subsequent study, the 10-gene signature was measured from whole blood during PEx to identify 16 four molecular clusters predictive of mortality and disease progression in 5-years of follow-up (123). However, this 10-gene panel was derived from PBMCs and therefore excluded myeloid-derived granulocytes like neutrophils, the principal immune effector cells found in the blood during CF PEx (18, 87). In a more recent study, Jiang and colleagues evaluated circulating neutrophil gene expression using RNA-sequencing in 16 CF patients before and after PEx treatment (124). Six differentially expressed genes were identified to discriminate between PEx and post-treatment stable subjects (124). However, a significant limitation to this study is that isolating cells from whole blood can induce artefactual changes in gene expression and increase technical bias and variability. Discovering blood biomarkers from whole blood may provide a complementary picture of changes in the host immune response and inflammatory activity during CF PEx, resulting in a broader range of blood biomarker candidates. In addition to gene transcript signatures, our research group has applied multiple reaction monitoring mass spectrometry (MRM-MS), a targeted proteomics technology, to predict imminent PEx from clinically stable visit plasma samples (125). We identified a 6-protein biomarker panel that demonstrated better performance for predicting imminent PEx (Area under the Receiver Operating Characteristic Curve, AUC = 0.74) than currently used biomarkers, including FEV1% predicted (AUC = 0.55) and CRP (AUC = 0.61) (125). We have validated the performance of this protein biomarker panel in an independent pediatric cohort (126). However, mass spectrometry-based proteomics technologies have limitations in detecting low-abundance proteins, such as cytokines and chemokines. Cytokines and chemokines are integrally involved in the host immune response and inflammatory activity and may serve as potential biomarkers for PEx in CF individuals. 17 In brief, prior studies confirm that the expression of blood proteins and genes change during CF PEx and are reflective of the host immune response and inflammatory activity. These findings provide proof-of-concept that blood biomarkers can be used to predict PEx in CF individuals. 1.3.4 Genetic Modifiers of CF Lung Disease and Pulmonary Exacerbations In addition to blood biomarkers, genetic modifiers of CF lung disease have been examined and can potentially serve as biomarkers for PEx. According to a recent systematic review by Shanthikumar and colleagues, 124 candidate genes have been found to be associated with lung function or first-time growth/persistent infection with P. aeruginosa (127). However, results from most of the reviewed studies were inconsistent since many of them had small sample sizes and heterogeneous outcome measures. Benefiting from the formation of the International CF Gene Modifier Consortium, researchers can apply the systematic genome-wide approach to search for modifier genes of the CF lung disease (128, 129). Corvol and colleagues pooled the genome-wide association study (GWAS) results from North American and French cohorts to perform a meta-analysis on 6365 CF individuals and identified five single nucleotide polymorphisms (SNPs) associated with variation in the severity of lung disease, whereby lung disease severity was measured by a CF specific lung disease phenotype (128, 130). Many genes contained by or close to the identified SNPs, such as solute carrier family 9 member A3 (SLC9A3) (131, 132), solute carrier family 6 member A14 (SLC6A14) (132), and HLA Class II (133, 134), were confirmed to associate with lung function or risk of P. aeruginosa infections in multiple studies. SLC9A3 and SLC6A14 were also pleiotropic for the risk of meconium ileus in CF individuals (132). Genetic modifiers, combined with easily accessible clinical measures 18 obtained early in life, have demonstrated their potential as biomarkers to predict the onset of cystic fibrosis-related diabetes later in life (135). Genetic susceptibility to PEx has been studied in children with asthma but not in CF, even though PEx are important mediators of lung function loss in CF (136). 1.4 Research Hypothesis and Specific Aims 1.4.1 Hypothesis Blood-based biomarkers reflective of the host immune response and inflammatory activity have the potential to predict PEx in CF individuals. However, due to the poorly defined and complex pathophysiology of PEx, individual candidate biomarkers are unlikely to capture the full spectrum of an individual’s immune and inflammatory response. Examining the immune and inflammatory pathways associated with PEx events with high throughput “omics” technologies will allow us to elucidate the molecular underpinnings of PEx and enhance the diagnosis and prediction of these events (137-139). Moreover, as PEx are important mediators of lung function decline in CF individuals, genetic modifiers associated with lung function variation may also influence the frequency or severity of PEx. Therefore, I hypothesize that molecular candidates (proteins, transcripts, and modifier genes) detectable in peripheral blood can predict PEx risk in CF. This thesis aims to develop blood-based biomarkers to predict PEx and PEx-related treatment outcomes in CF patients using unbiased high throughput ‘omics’ technologies and data-driven computational approaches. Reliably predicting these events is critical since it allows for more personalized medical monitoring and earlier diagnosis and treatment of PEx to improve clinical outcomes. 19 1.4.2 Specific aims Aim 1: Determine changes in blood protein biomarkers from CF individuals treated with IV antibiotics for PEx. In Chapter 2, changes in previously reported blood protein biomarkers were evaluated in CF individuals treated with IV antibiotics for PEx. Label-free quantitative proteomics was used to identify novel blood proteins associated with early response to IV antibiotics. Correlations between early changes in blood proteins and clinical outcomes (i.e., lung function and symptoms) were studied. Aim 2: Develop biomarker panels using blood transcriptomics and inflammatory protein markers to predict imminent PEx in CF individuals. In Chapter 3, RNA sequencing and immunoassays were applied to profile whole blood gene expression and inflammatory protein markers, respectively. 1) Blood biomarker candidates (transcripts and proteins) were identified from CF patients who have paired samples from clinically stable vs. PEx. 2) Blood biomarker panels were developed based on prior identified biomarker candidates to predict imminent PEx. 3) The blood transcriptomic panel was transferred to a more clinically applicable NanoString nCounter® platform. Aim 3: Examine early changes in blood protein biomarkers following treatment with azithromycin to identify biomarkers predictive of subsequent PEx risk in CF children and adolescents. 20 In Chapter 4, blood proteins were profiled with two targeted proteomics methods at day 0 and day 28 of azithromycin treatment. Early changes in blood protein levels from day 0 to day 28 of treatment were examined in relation to changes in FEV1% predicted and weight by days 28 and 168, and to predict PEx risk by day 168. Aim 4: Identify genetic modifiers that can predict susceptibility to PEx in preschool children with CF. In Chapter 5, By leveraging the extensive genotyping data generated by the Canadian CF Gene Modifier Study and comprehensive demographic and clinical data collected in the Canadian CF Registry (CCFR), 1) I examined the effects of candidate genetic modifiers, which correlate with variation in lung function in CF, on the risk of severe PEx in CF children. 2) A biomarker panel was developed by incorporating genetic modifiers and clinical variables to predict severe PEx in CF children. 21 Chapter 2: Identification of novel blood biomarkers of treatment response in cystic fibrosis pulmonary exacerbations by label-free quantitative proteomics 2.1 Introduction Individuals with CF experience recurrent episodes of PEx that are characterized by intermittent worsening of respiratory signs and symptoms (87-89) and are associated with increased morbidity and irreversible loss of lung function (82, 140). Triggers of PEx include respiratory viral infection (i.e., respiratory syncytial virus, influenza, and rhinovirus) (95-97), acquisition of new bacteria (98), changes in the population structure of chronically colonizing bacteria (98), poor medication adherence, and sometimes unknown factors. However, these events are typically treated with antibiotics in conjunction with increased airway clearance therapies (i.e., hypertonic saline and dornase alfa) (87, 89). Based on the 2019 Cystic Fibrosis Foundation Patient Registry, over 40% of CF adults were diagnosed with at least one PEx that required treatment with IV antibiotics (6). Unfortunately, many individuals with CF fail to regain their baseline lung function despite IV antibiotic treatment (81). Suboptimal PEx outcomes may be due to delayed recognition and treatment, varied treatment decisions, and differences in approach to monitoring treatment response, including recovery in lung function and/or resolution in signs and symptoms (94, 141). A novel adjunctive strategy that can provide an additional dimension to the objective monitoring of PEx treatment response is desired as it has the potential to improve clinical outcomes for CF individuals. Biomarkers measured objectively and reproducibly have been studied to help guide therapeutic interventions in diseases such as chronic obstructive pulmonary disease (COPD) (99, 22 142, 143). In CF, blood-based biomarkers that reflect systemic inflammation, such as CRP and calprotectin, decrease significantly following PEx treatment (100, 108, 110, 120). However, for a biomarker to aid in clinical decision making, an early change is potentially more informative in assisting treatment decisions as it provides the opportunity for CF physicians to modify treatments earlier than if they had waited for the patient to respond or not clinically, which can take longer in some patients. Based on the results of a prior study performed by our group, admission but not early change in CRP was found to help predict treatment outcomes (144). Therefore, CRP does not appear to be a valuable marker of early response to PEx treatment in individuals with CF. In this study, we recruited adult CF individuals who were diagnosed with a PEx and required hospitalization for IV antibiotic treatment. We prospectively collected blood samples, CF respiratory symptom diary, and spirometry (i.e., FEV1) within 24 hours of admission, day 5, day 10, and IV antibiotic treatment completion. Blood samples from these CF subjects have previously been evaluated in a prior study using multiple-reaction monitoring mass spectrometry (MRM-MS), a targeted proteomics approach (145). The objective of this study is to apply label-free (untargeted) liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify novel blood protein biomarkers that are associated with early response to IV antibiotics (i.e., from hospital admission to day 5) and to determine if early changes correlate with clinical outcome by the end of IV antibiotic treatment, in terms of improvement in lung function and respiratory symptoms. 23 2.2 Patients and Methods 2.2.1 Study cohort Adult CF individuals were recruited prospectively when diagnosed with a PEx and admitted to St. Paul’s Hospital (Vancouver, BC) for IV antibiotic therapy between July 1, 2013 and June 30, 2015. The PEx events were defined according to changes in respiratory symptoms with a modified Fuchs PEx score of at least 4/12 and requiring hospitalization for IV antibiotic treatment (88). Subjects were excluded if they had a history of solid organ transplantation or were receiving chronic immunosuppressive treatment. Participating subjects received IV antibiotic treatment in conjunction with airway clearance therapies; however, the IV antibiotic regimen and duration of PEx treatment were left to the discretion of the most responsible CF physician. 2.2.2 Blood samples and clinical outcomes Blood samples and clinical outcomes were obtained from subjects within 24 hours of hospitalization for IV antibiotic therapy (V1), treatment day 5 (V2), treatment day 10 (V3), and IV antibiotic treatment completion (V4). Blood samples (serum, EDTA-treated) were collected and processed following standard operating procedures and then stored at -80℃ until thawing for batched analysis. Clinical characteristics including age, sex, baseline lung function (i.e., FEV1), PEx requiring IV antibiotic treatment in the prior year, and sputum microbiology were collected at hospital admission (V1). Baseline lung function was defined as the best FEV1% predicted in the 6 months prior to the index PEx event. CF Respiratory Symptom Diary-Chronic Respiratory 24 Infection Symptom Score (CFRSD-CRISS) and FEV1% predicted were recorded at V1 and each of the following time points (V2, V3, V4). CFRSD-CRISS is scaled from 0 to 100 with a higher score indicating more severe respiratory symptoms (146). Clinical outcomes of interest included absolute change in CFRSD-CRISS and relative change in FEV1 % predicted from admission (V1) to treatment completion (V4). The absolute change in FEV1 % predicted was not analyzed as it was primarily influenced by baseline lung function with more significant increases seen with higher baseline lung function (94, 141). 2.2.3 Untargeted proteomics profiling of blood proteins Four microliters blood plasma samples were analyzed with label-free LC-MS/MS at the University of British Columbia Proteomics Core Facility. In brief, to facilitate the analysis of less abundant plasma proteins, fourteen of the most abundant proteins were first immunodepleted using the Human 14 Multiple Affinity Removal Spin Cartridge (Agilent Technologies, Santa Clara, CA), which removes albumin, immunoglobulin (Ig) G, alpha 1-antitrypsin, IgA, transferrin, haptoglobin, fibrinogen, alpha 2-macroglobulin, alpha 1-acid glycoprotein, IgM, apolipoprotein AI, apolipoprotein AII, complement C3 and transthyretin (147). After immunodepletion, the remaining plasma samples were digested with trypsin overnight as previously described (147). Peptides generated by trypsin digestion were then desalted and purified with C-18 STop And Go Extraction (STAGE) Tips (148). Purified peptides were then fractionated using the Agilent 1100 High-Performance Liquid Chromatography (HPLC) system at 50 µL/min flow rate (149). The HPLC system was composed of an analytical column and a trap column. The analytical column was operated at 50°C using an in-house packed 75 µm C18 column heater. The trap column added onto the analytical column was a 2 cm-long fused silica 25 column with 100 µm-inner diameters and packed with 5 µm-diameter Aqua C-18 beads (Phenomenex, Torrance, CA). The analytical gradient was changed from 10% to 35% Buffer B (80% acetonitrile, 0.1% formic acid) for the first 60 minutes and then washed with 100% Buffer B for 15 minutes. The sample was initially fractionated into 96 wells (45 seconds per well) then pooled in a non-contiguous manner (every 6th well was pooled), which resulted in 6 fractions for further LC-MS/MS analysis (149). Isolated 6 fractions were then loaded into the Impact II Q-ToF mass spectrometer (Bruker, Germany) (150). The resulting peptides identified by LC-MS/MS were searched with MaxQuant software (version 1.5.3.30) with default label-free-quantitation setting and match-between runs options enabled (151). All plasma samples were evaluated in duplicate, and the mean values were used for subsequent analyses. The LC-MS/MS data were deposited in the PRoteomics IDEntifications (PRIDE) database under accession number PXD016089. 2.2.4 Analysis of candidate blood proteins Five candidate blood proteins that were not detected in LC-MS/MS due to low abundance were analyzed in blood serum samples with multiplex electrochemiluminescence immunoassays, including IL-1β, IL-6, IL-8, tumor necrosis factor (TNF)-α, and calprotectin (Meso Scale Discovery, Carlsbad, CA). 60 µL serum samples were used to analyze IL-1β, IL-6, IL-8, and TNF-α. Lower limit of detection (LLOD) of IL-1β, IL-6, IL-8, and TNF-α were 0.05 pg/mL, 0.06 pg/mL, 0.07 pg/mL, and 0.04 pg/mL, respectively. 10 µL serum sample was used to analyzed calprotectin and the LLOD was 32 pg/mL. Among them, IL-1β was below the detection limits for most of the samples and, therefore, was excluded from subsequent analyses. All assays 26 were performed in duplicate with a mean coefficient of variation (CV) < 5%. The mean values of the duplicate measurements were used for subsequent analyses. 2.2.5 Protein-protein interaction (PPI) network and pathway enrichment analysis The differentially expressed (DE) proteins identified by LC-MS/MS were used as inputs for the PPI network and pathway enrichment analysis. The STRING database (version 11.0) was utilized to assess the protein functional association (152). The active PPI were identified based on high-throughput lab experiments and curated databases, such as the Gene Ontology (GO) database, Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and Reactome Pathway database. The predicted PPI included genomic context predictions, co-expression analysis, and automated text mining (152). Enriched GO biological process term and Reactome pathways were reported, and proteins involved in the host immune and inflammation-associated pathways were highlighted in the constructed PPI network. 2.2.6 Statistical analyses Statistical analyses were performed using R (version 3.5.0, the R Foundation for Statistical Computing, Vienna, Austria) and Prism 8 (GraphPad, La Jolla, CA). Continuous variables were presented as mean ± SD. Categorical variables were reported as numbers, with proportions in brackets. Longitudinal changes in the levels of candidate blood proteins and clinical outcomes (i.e., FEV1% predicted and CFRSD-CRISS) were analyzed with the Kruskal-Wallis test followed by the post-hoc Dunn’s test to correct for multiple comparisons. 27 Figure 3. Flow diagram of LC-MS/MS data pre-processing LC-MS/MS quantified 790 proteins; however, 55 reverse and potential contaminant proteins were removed. In addition, 394 proteins were excluded due to being detected in < 25% of subjects. Therefore, 341 proteins were used in the following analyses. e-Figure 1. Flow diagram of LC-MS/MS data pre-processing. LCͲMSͬMS data output from MaǆQuant ϳϵϬ proteins ϳϯϱ proteins Remove reverse and potential contaminant proteins Eǆclude proteins that detected on ф Ϯϱй subjects ϯϰϭ proteins Imputation missing value Log transformed normaliǌation DE Protein analǇsis with Limma 28 The LC-MS/MS data were pre-processed as described in the flow diagram (Figure 3). Proteins were excluded if identified as reversed or contaminated during peptide searching by MaxQuant software or detected in less than 25% of blood samples. Three hundred and forty-one proteins passed the quality control matrix. Missing values from these 341 proteins were imputed with half of the minimum abundance of each protein across the analyzed samples. The levels of proteins were log2 transformed before subsequent analyses. Fold-changes of blood proteins from V1 to V2 and V1 to V4 were analyzed with the Limma R software package (version 3.48.3) and adjusted for baseline lung function, sex, age, and an interaction term between sex and age. The Benjamini-Hochberg procedure was used to control the false discovery rate (FDR) for each analysis, and FDR ≤ 0.10 were reported for DE proteins. To assess how blood protein levels at V1 might be confounded by disease severity and clinical characteristics at baseline, the correlation between blood protein levels at V1 and age, sex, and baseline lung function were evaluated with Spearman’s correlation analysis. Additionally, we examined the relations between early (V1 to V2) and overall (V1 to V4) changes in blood proteins with changes in clinical outcomes from V1 to V4 (i.e., the relative change in FEV1% predicted, absolute change in CFRSD-CRISS) with Spearman’s correlation analysis. Reported p-values were analyzed with a two-tailed test and a p-value < 0.05 was considered significant. To evaluate the robustness of our findings, we performed a post-hoc sensitivity analysis that randomly selected one PEx from three subjects who had repeated PEx events. 29 2.3 Results 2.3.1 Clinical characteristics at hospital admission for PEx Clinical characteristics of participating subjects at hospital admission (V1) were summarized in Table 1. In brief, 25 PEx events from 22 unique CF subjects were eligible for this study. The baseline lung function measured by FEV1% predicted was 63.4 ± 21.5%. Fifteen out of 22 (68%) CF subjects had moderate to severe airflow obstruction (FEV1% predicted < 70%). FEV1% predicted dropped to 53.2 ± 20.7% at V1 for PEx, with CFRSD-CRISS OF 50.5 ± 7.5, where 56% of PEx events were characterized with over 10% relative drop in lung function when compared to their baseline. The modified Fuchs score for these events ranged from 4 to 8, with a median score of 6. Furthermore, 52% of PEx were characterized as non-acute events, which patients described a change in respiratory symptoms for at least two weeks prior to V1, where 4 of these events received oral antibiotics before hospitalization. 30 Table 1. Clinical characteristics at hospital admission (V1) Number of PEx 25 Number of subjects 22 Age, mean (SD) 34.8 (12.9) Female, No. (%) 12 (48) Genotype, No. (%) ∆F508 Homozygous 11 (50) ∆F508 Heterozygous 7 (32) Other (non-∆F508) 4 (18) FEV1% predicted, mean (SD) 53.2 (20.7) >10% relative drop FEV1% predicted from baseline, No. (%) a 14 (56) BMI, mean (SD) 21.7 (3.5) CFRSD-CRISS, mean (SD) 50.5 (7.5) Modified Fuchs Score, median (range) 6 (4 to 8) Best FEV1% predicted in 6 months prior to PEx, mean (SD) 63.4 (21.5) Best FEV1% predicted in 6 months prior to PEx, No. (%) < 40 4 (16) 40-69 11 (44) 70-89 4 (16) ≥ 90 6 (24) Sputum Microbiology, No. (%) P. aeruginosa 14 (56) MSSA 12 (48) MRSA 4 (16) Burkholderia cepacia complex 3 (12) Symptom Onset, No. (%) > 2 weeks 13 (52) < 2 weeks 12 (48) Abbreviation: PEx, pulmonary exacerbations; FEV1, forced expiratory volume in 1 second; BMI, body mass index; CFRSD-CRISS, CF Respiratory Symptom Diary-Chronic Respiratory Infection Symptom Score; P. aeruginosa, Pseudomonas aeruginosa; MSSA, Methicillin-sensitive Staphylococcus aureus; MRSA, Methicillin-resistant Staphylococcus aureus. a Baseline lung function is defined as the best FEV1% predicted in the 6 months prior to the index PEx. 31 2.3.2 Clinical outcomes of PEx treatment Clinical outcomes of PEx treatment are summarized in Table 2. The duration of IV antibiotic treatment for PEx ranged from 13 to 24 days with a median of 14 days. An increase in FEV1% predicted was observed from V1 to each of the subsequent time points (V2, V3, and V4); however, these changes were not significant (Table 2; Figure 4A). Majority of events (n = 21, 84%) recovered to 90% of baseline lung function after treatment with IV antibiotics but fewer recovered to 100% of their baseline lung function (n = 9, 36%). Symptom questionnaires were completed in 20 (80%) PEx, where CFRSD-CRISS significantly decreased from V1 to each of the subsequent time points (V2, V3, V4; Table 2; Figure 4B), and 15 PEx had a greater than 11 point decrease in CFRSD-CRISS, which is considered the minimum clinically important difference (141, 146). Table 2. Longitudinal changes in FEV1% predicted, CFRSD-CRISS, and candidate blood proteins following PEx treatment Clinical Outcomes Hospital admission (V1, n = 25) Day 5 (V2, n = 21) Day 10 (V3, n = 20) Treatment completion (V4, n = 25) Days on IV antibiotics, median (range) -- -- -- 14 (13 to 24) FEV1% predicted No. (%) 25 (100) 20 (95) 20 (100) 25 (100) mean (SD) 53.2 (20.7) 58.3 (25.0) 59.3 (26.3) 61.9 (23.4) Relative change from V1, mean (SD) -- 10% (20%) 20% (20%) 18% (20%) Recovered ≥ 90% of baseline FEV1% predicted, No. (%) -- -- -- 21 (84) CFRSD-CRISS No. (%) 22 (88) 20 (95) 20 (100) 23 (92) mean (SD) 50.5 (7.5) 40.5 (7.8) 35.7 (8.2) 30.3 (10.2) Absolute change from V1, mean (SD) -- -11.1 (8.3) * -16.5 (9.4) *** -21.4 (12.7) *** 32 CFRSD-CRISS decrease > 11 points, No. (%) -- -- -- 15 (60) Candidate blood proteins No. (%) 25 (100) 21 (100) 20 (100) 25 (100) IL-6, pg/mL mean (SD) 4.6 (4.7) 1.4 (1.7) 1.4 (2.4) 1.2 (1.1) Absolute change from V1, mean (SD) -- -3.4 (4.0) ** -3.5 (3.4) *** -3.4 (4.2) *** IL-8, pg/mL mean (SD) 11.8 (8.0) 10.7 (6.3) 11.1 (3.7) 11.1 (4.5) Absolute change from V1, mean (SD) -- -1.7 (6.5) -1.4 (7.0) -0.7 (6.5) TNF-α, pg/mL mean (SD) 3.2 (1.7) 2.7 (0.8) 3.0 (1.0) 2.7 (0.7) Absolute change from V1, mean (SD) -- -0.6 (1.6) -0.4 (1.5) -0.5 (1.6) Calprotectin, ng/mL mean (SD) 14.7 (13.5) 10.9 (7.3) 8.6 (5.6) 7.3 (4.1) Absolute change from V1, mean (SD) -- -4.9 (9.8) -7.9 (12.1) -7.3 (12.2) * Abbreviation: PEx, pulmonary exacerbations; FEV1, forced expiratory volume in 1 second; CFRSD-CRISS, CF Respiratory Symptom Diary-Chronic Respiratory Infection Symptom Score; IL-6, Interleukin-6; IL-8, Interleukin-8; TNF-α, Tumor necrosis factor-α; SD, Standard deviation. Statistical significance: p-value < 0.05 (*), p-value < 0.01(**), p-value < 0.001(***). 33 Figure 4. Longitudinal changes in (A) FEV1% predicted, (B) CFRSD-CRISS, (C) IL-6, (D) IL-8, (E) TNF-α, (F) calprotectin following PEx treatment Abbreviation: FEV1, forced expiratory volume in 1 second; CFRSD-CRISS, CF Respiratory Symptom Diary-Chronic Respiratory Infection Symptom Score; IL-6, Interleukin-6; IL-8, Interleukin-8; TNF-α, Tumor necrosis factor-α; V1: Admission; V2: Treatment Day 5; V3: Treatment Day 10; V4: Treatment completion. Statistical significance: p-value < 0.05 (*), p-value < 0.01(**), p-value < 0.001(***). 0255075100FEV1 % PredictedV1 V2 V3 V405102025IL6 pg/ml********V1 V2 V3 V40246810TNF-α pg/mlV1 V2 V3 V4020406080100CFRSD-CRISS *******V1 V2 V3 V4010203040IL8 pg/mlV1 V2 V3 V4a bc de fV1 V2 V3 V40204060Calprotectin ng/ml*ACBDE F34 2.3.3 Correlation between protein levels at hospital admission (V1) with clinical and demographic factors Serum IL-6 levels at V1 measured with an immunoassay inversely correlated with baseline FEV1% predicted (r = -0.40, p-value = 0.046; Table 3). The levels of 16 plasma proteins measured with LC-MS/MS also significantly correlated with baseline FEV1% predicted at V1 (Table 3). Moreover, significant correlations were identified between age and many plasma protein abundances at V1, and the correlations varied by sex (Table 28 in Appendix A). Table 3. Correlation between blood protein levels at hospital admission (V1) with baseline FEV1% predicted Protein Symbol Correlation a p-value Immunoassays Interleukin-6 IL-6 -0.403 0.046 Tumor necrosis factor-a TNF-a -0.279 0.176 Interleukin-8 IL-8 0.050 0.814 Calprotectin S100A8/9 -0.041 0.846 LC-MS/MS Neural cell adhesion molecule L1-like protein CHL1 -0.625 0.001 Mannose-binding protein C MBL2 -0.609 0.001 Basement membrane-specific heparan sulfate proteoglycan core protein HSPG2 -0.592 0.002 Tyrosine-protein phosphatase non-receptor type 18 PTPN18 -0.545 0.005 Ig gamma-1 chain C region IGHG1 -0.532 0.006 Ig alpha-1 chain C region IGHA1 -0.504 0.010 Myosin-9 MYH9 0.502 0.011 Glucosidase 2 subunit beta PRKCSH 0.472 0.017 Cadherin-6 CDH6 -0.442 0.027 Platelet basic protein PPBP -0.438 0.028 Nucleoside diphosphate kinase NME1-NME2 -0.431 0.031 Fructose-bisphosphate aldolase A ALDOA -0.431 0.032 Corticosteroid-binding globulin SERPINA6 -0.418 0.038 C-reactive protein CRP -0.407 0.043 Glutathione peroxidase 3 GPX3 -0.401 0.047 Apolipoprotein C-I APOC1 0.399 0.048 35 a Spearman’s correlation analysis 2.3.4 Longitudinal changes in candidate and LC-MS/MS blood proteins Serum IL-6 levels significantly decreased from V1 to each of the subsequent time points (V2, V3, V4; Table 2; Figure 4C), whereas significant changes were not identified between subsequent time points, which is consistent with IL-6 changing early in response to IV antibiotic therapy. Serum calprotectin levels significantly decreased from V1 to treatment completion (V4) but not at earlier time points (V2, V3). Serum IL-8 and TNF-a levels were not significantly changed between V1 and the subsequent time points. Following adjustment for baseline lung function, sex, age, and the interaction between sex and age, 47 proteins changed significantly from V1 to V2 with FDR ≤ 0.10 (Table 29 in Appendix A; Figure 5A), and 6 proteins changed significantly from V1 and V4 with FDR ≤ 0.10 (Table 30 in Appendix A; Figure 5B). 36 Figure 5. Volcano plot demonstrating blood proteins measured with LC-MS/MS with statistically significant fold-change from (A) V1 (admission) to V2 (treatment day 5) and (B) V1 (admission) to V4 (treatment completion) 47 proteins changed significantly from V1 to V2, and 6 proteins changed significantly from V1 and V4 with FDR ≤ 0.10. Baseline lung function, sex, age, and the interaction between sex and age were adjusted in the differential expression analysis. Protein abbreviations can be found in Tables 29 and 30 in Appendix A. 37 2.3.5 Protein-protein interaction (PPI) network and pathway enrichment analysis by STRING algorithm The online STRING database (version 11.0) was applied to identify the PPI network using DE proteins and their most enriched molecular pathways following IV antibiotic treatment. After analyzing 47 proteins that changed significantly (FDR ≤ 0.10) between V1 and V2, the top 10 enriched GO biological process terms (Table 4) and Reactome pathways (Table 5) are presented. Twenty-two of the 47 DE proteins were involved in the following immune/inflammatory-related GO biological processes: regulation of complement activation, regulation of acute inflammatory response, regulation of inflammatory response, immune effector process, and complement activation/classical pathway (Table 4; Figure 6). Based on the Reactome database, 24 of the 47 DE proteins were involved in 5 immune-related pathways (Table 5; Figure 7). Similar analyses were not applied to DE proteins between V1 and V4 as only 6 proteins were identified. Table 4. Gene ontology (GO) biological process pathway enrichment analysis based on DE proteins from V1 to V2 GO term ID Term description Observed gene count Background gene count FDR GO:0002673 regulation of acute inflammatory response 11 92 2.51E-13 GO:0030449 regulation of complement activation 10 52 2.51E-13 GO:0070613 regulation of protein processing 12 116 2.51E-13 GO:2000257 regulation of protein activation cascade 10 54 2.51E-13 GO:0072376 protein activation cascade 10 74 1.28E-12 GO:0050727 regulation of inflammatory response 13 338 2.60E-10 GO:0002252 immune effector process 18 927 3.73E-10 GO:0006958 complement activation, classical pathway 7 34 9.93E-10 GO:0032101 regulation of response to external stimulus 16 732 1.28E-09 GO:0030162 regulation of proteolysis 16 742 1.43E-09 38 Table 5. Reactome pathway enrichment analysis based on differentially expressed (DE) proteins from V1 to V2 RCTM term ID Term description Observed gene count Background gene count FDR HSA-166658 Complement cascade 11 56 1.21E-15 HSA-977606 Regulation of Complement cascade 10 47 1.10E-14 HSA-168249 Innate Immune System 20 1012 4.56E-12 HSA-168256 Immune System 24 1925 7.71E-11 HSA-109582 Hemostasis 13 601 4.48E-08 HSA-114608 Platelet degranulation 8 125 4.48E-08 HSA-140877 Formation of Fibrin Clot (Clotting Cascade) 6 39 4.48E-08 HSA-76002 Platelet activation, signaling and aggregation 9 256 3.12E-07 HSA-166663 Initial triggering of complement 4 21 8.68E-06 HSA-140837 Intrinsic Pathway of Fibrin Clot Formation 4 22 9.31E-06 39 Figure 6. Protein-protein interaction (PPI) network of DE proteins from V1 to V2 and color-coded proteins enriched for GO biology process pathway Color-coded nodes: regulation of complement activation (red); regulation of acute inflammatory response (purple); regulation of inflammatory response (green); immune effector process (yellow); complement activation, classical pathway (pink). 40 Figure 7. Protein-protein interaction (PPI) network of DE proteins from V1 to V2 and color-coded proteins enriched for Reactome pathway Color-coded nodes: complement cascade (red); regulation of complement cascade (purple); innate immune system (green); immune system (yellow); initial triggering of complement (pink). 41 2.3.6 Correlation between early change in blood protein levels from hospital admission (V1) to Day 5 (V2) with changes in clinical outcomes from hospital admission (V1) to IV antibiotic completion (V4) Among 47 DE proteins identified and quantified with LC-MS/MS, early change (V1 to V2) in the levels of just one protein, insulin-like growth factor 2 receptor (IGF2R), inversely correlated with the overall change in CFRSD-CRISS from V1 to V4 (r = -0.48, p-value = 0.04; Table 6). No significant correlations were identified between early change in the levels of 47 DE proteins and relative change in FEV1% predicted from V1 to V4. Additionally, early change in the levels of candidate blood proteins did not correlate with the overall change in either FEV1% predicted or CFRSD-CRISS from V1 to V4. A post-hoc sensitivity analysis indicated that the correlation between early change in IGF2R with the overall change in CFRSD-CRISS from V1 to V4 was generally consistent when we randomly selected one PEx from each of the three subjects who had repeat PEx (Table 6). 42 Table 6. Blood proteins with early change in levels from V1 to V2 and that correlate with changes in CFRSD-CRISS from V1 to V4 in comparison to candidate proteins measured with immunoassays Protein Symbol Correlation a p-value LC-MS/MS Insulin-like growth factor 2 receptor IGF2R -0.48 0.04 post-hoc sensitivity analysis Insulin-like growth factor 2 receptor b, mean (SD) IGF2R -0.49 (0.05) 0.09 (0.08) Immunoassays Interleukin-6 IL-6 0.20 0.43 Tumor necrosis factor-a TNF-a -0.29 0.24 Interleukin-8 IL-8 0.02 0.94 Calprotectin S100A8/9 0.04 0.86 a Spearman’s correlation analysis b Randomly selected one PEx from three subjects who had repeat PEx. Mean (SD) was calculated based on 6 repeat exams 2.3.7 Correlation between overall change in blood protein levels from hospital admission (V1) to IV antibiotic completion (V4) with changes in clinical outcomes from hospital admission (V1) to IV antibiotic completion (V4) Six plasma proteins identified and measured with LC-MS/MS significantly changed from V1 to V4; however, no significant correlations with the relative change in FEV1% predicted or absolute change in CFRSD-CRISS from V1 to V4 were observed. Similarly, the overall change in the levels of candidate blood proteins did not correlate with the overall change in either FEV1% predicted or CFRSD-CRISS from V1 to V4. 43 2.4 Discussion This is the first study to apply untargeted LC-MS/MS quantitative proteomics to identify blood proteins that change in response to IV antibiotics during the treatment of CF PEx. In addition to confirming blood protein biomarkers previously reported to change following IV antibiotics, including serum IL-6, calprotectin, and plasma CRP, several novel plasma proteins were also identified with LC-MS/MS (110, 153). Interestingly, more proteins exhibited changes early during the treatment course (i.e., by day 5 of treatment) and relatively fewer towards the end of the treatment. Based on pathway enrichment analysis many of these proteins are involved in complement activation and regulation of the host immune and inflammatory response. Proteins that changed early and remained significant by the end of IV antibiotic treatment included serum IL-6 and plasma CRP. However, the early and overall change in serum IL-6 and plasma CRP did not correlate with changes in clinical outcomes, including FEV1% predicted and CFRSD-CRISS, by the end of treatment. Calprotectin is a candidate biomarker that also changed significantly by the end of treatment but did not correlate with changes in clinical outcomes. In contrast, early change (V1 to V2) in insulin-like growth factor 2 receptor (IGF2R), also known as the cation-independent mannose-6-phosphate receptor or CI-M6PR), significantly correlated with symptom response (i.e., CFRSD-CRISS) by the end of IV antibiotic therapy. IGF2R is a multi-functional binding protein capable of binding insulin growth factor 2 (IGF2), mannose-6-phosphate (M6P), and retinoic acid (154). Depending on the binding ligands, IGF2R modulates several biological pathways, including cell migration, wound healing, angiogenesis, apoptosis, and viral infection response. Although the relationship between early change in IGFR2 and the overall symptom response to IV antibiotic therapy has not been studied previously, the IGF system has previously been reported to be modified by chronic inflammation in CF (155, 156). 44 Studies have shown that proinflammatory cytokines could influence the metabolism of IGF binding proteins and consequently suppress IGF1 action (157). Overexpression of IL-6 in a mouse model early after birth resulted in a 50-70% size reduction, mainly due to the decrease in circulating IGF1 levels (158). Many of the proteins that were found to be downregulated by day 5 of treatment are involved in complement activation (i.e., complement proteins C1r, C1q, C4b, C4b-binding protein, C8, C9). The complement proteins are vital components of the innate immune system, promoting neutrophilic inflammation and defending against pathogens (159). Unregulated or persistent complement activation triggers a destructive inflammatory cascade, leading to lung tissue damage and causing progressive loss of lung function (159, 160). Elevated levels of pro-inflammatory complement proteins have been observed in the sputum of individuals with CF (160, 161). Despite the potential lung-protective effects of downregulating the complement system early during PEx treatment, we did not observe a more significant recovery of lung function in such individuals. However, the sample size of this study was small, and therefore, this warrants further investigation. This study has some important limitations that should be acknowledged. Only 25 PEx events were analyzed in this exploratory study and therefore, this study could have been underpowered to examine relationships between biomarkers and treatment outcomes. Furthermore, there was minimal decrease observed in FEV1% predicted at hospitalization compared to the stable baseline as only half of the PEx events were characterized by a >10% relative drop in FEV1% predicted from baseline. As a result, the improvements in FEV1% predicted in response to treatment were relatively modest and may have limited the potential to identify significant correlations with changes in blood proteins levels. As this was an untargeted 45 discovery study, a large number of proteins were identified with LC-MS/MS proteomics and found to change significantly following treatment; however, the multiple statistical comparisons performed could have inflated the type 1 error. As such, the Benjamini-Hochberg method was applied to adjust for multiple testing with a cut-off FDR of ≤ 0.10. However, this approach may have been too stringent and resulted in false negatives at this discovery stage. In conclusion, this study confirmed that the candidate blood inflammatory markers (i.e., CRP, IL-6, and calprotectin) changed in response to IV antibiotic treatment for PEx. Moreover, using label-free LC-MS/MS quantitative proteomics, we identified several blood proteins involved in complement activation and immune/inflammatory-related pathways that changed in response to IV antibiotic treatment. Early change in IGFR2 correlated with symptom improvement by the end of treatment and requires further validation as an early marker of symptomatic treatment response in individuals with CF. 46 Chapter 3: Whole blood transcriptomic and inflammatory protein biomarkers for predicting imminent pulmonary exacerbations in cystic fibrosis 3.1 Introduction Cystic fibrosis (CF) pulmonary exacerbations (PEx) are clinically significant episodes associated with lung damage and permanent loss of lung function (87) (162). Despite intravenous (IV) antibiotic therapy, 25% of CF patients diagnosed with a PEx failed to recover within 90% of their former baseline lung function, as discussed in Chapter 1 (162). The factors that lead to inferior PEx outcomes are still being investigated but delayed diagnosis and treatment of PEx are likely contributory (80, 94, 163). Based on a large observational study, nearly 30% of individuals with a ≥ 10% drop in FEV1 % predicted when assessed in clinic were not diagnosed with a PEx and thus were not treated with antibiotics (80). Unfortunately, patients who are not treated with antibiotics in such circumstances are less likely to recover their lung function compared to those who are treated (164). Furthermore, according to data from the Standardized Treatment of Pulmonary Exacerbations study, 85% of CF patients treated with IV antibiotics in the hospital for PEx experienced respiratory symptoms for more than one week before admission, and one-third had respiratory symptoms for more than 21 days, which might indicate a delayed diagnosis in clinical practice (94). Given the need to proactively diagnose PEx, predicting PEx remains a priority. The number of IV antibiotics treated PEx in the preceding year is a strong predictor of future PEx and was significantly correlated with the time to the next PEx (92). Individuals who experienced ≥ 4 47 PEx were at increased risk for a recurrent PEx within the next 60 days. In contrast, if the individual only experienced one PEx in the preceding year, the time until the next PEx increased to 230 days. As these historical events do not represent the current immune and inflammatory activity of the patient, they are not dynamic predictors of which patients will develop a PEx to aid in clinical monitoring. Previous studies have shown that mRNA and inflammatory proteins measured in blood informed the resolution of pulmonary inflammation during PEx and were associated with treatment outcomes (i.e., FEV1 % predicted) (110, 124, 165, 166). However, limited biomarker candidates were predictive of PEx in CF individuals (120), and none of them have been adopted into clinical practice to assist in PEx diagnosis or treatment. On the other hand, quarterly clinical follow-ups are recommended for CF individuals as part of their disease management strategies (167). These regular clinical visits provide an ideal opportunity to investigate predictive biomarkers to inform PEx risk of CF individuals; thus, CF clinicians can modify their clinical practices, such as increase the frequency of clinical follow-ups and start home monitoring of lung function and symptoms so that treatment can be initiated in a timely fashion to ultimately prevent PEx onset and improve clinical outcomes. Our research group previously applied multiple reaction monitoring mass spectrometry (MRM-MS), a targeted proteomics technology, and identified a 6-protein biomarker panel that demonstrated good predictive performance for short-term PEx risk (125, 126). However, many inflammatory proteins involved in CF lung inflammation were not analyzed due to the limited sensitivity of mass spectrometry technologies, including type 2 cytokines (168), IL-17 family cytokines (169-172), and IL-23 (173). These inflammatory proteins are engaged in the host immune response and inflammation of CF lung disease (168, 174, 175), and may serve as 48 biomarker candidates in predicting PEx risk. Additionally, transcriptome profiling of isolated leukocyte cells, such as peripheral blood mononuclear cells (PBMC) (165) and neutrophils (124), have been used to identify biomarker candidates to inform treatment response of PEx and disease severity. Although findings from these studies have been subsequently validated in whole blood samples (122-124), profiling gene expression from whole blood directly can provide a comprehensive perspective of systemic immune response and inflammation during CF PEx and potentially lead to discovering novel biomarker candidates for CF PEx. Therefore, by profiling the whole blood transcriptome and inflammatory proteins in this study, we aim to explore the host immune response and inflammation during CF PEx and develop blood-based biomarkers to predict imminent PEx in CF individuals. 3.2 Patients and Methods 3.2.1 Study design and patient cohort Figure 8 outlines the study design and cohort selection. We included subjects from an adult CF cohort recruited at St. Paul’s Hospital (Vancouver, BC) from July 10, 2013 to August 2, 2017. Blood samples were collected longitudinally from subjects during clinically stable and PEx visits. Clinically stable was defined as no substantial changes in respiratory symptoms, pulmonary function, and respiratory therapies four weeks before blood collection. PEx was defined as a decline of more than 10% in relative FEV1% predicted or an increase of more than 16 points in the CF Respiratory Symptom Diary and Chronic Respiratory Infection Symptom Score (CFRSD-CRISS) (146) from previous clinically stable visits and the need for oral or IV 49 antibiotic treatment. Lung transplant recipients and patients receiving immunosuppressive medications or systemic corticosteroids were excluded. Figure 8. Flow diagram of study design and cohort selection 3.2.2 Blood samples and clinical outcomes Blood samples and clinical outcomes were obtained from subjects during clinical visits or within 24 hours of hospitalization for IV antibiotic treatment for PEx. PAXgene Blood RNA, EDTA-treated plasma and serum were collected following standard operating procedures and then stored at -80℃ until thawing for batched analysis. Complete blood count and differential 50 were measured at blood collection with the Advia 2120i hematology analyzer (Siemens Healthineers). Clinical characteristics, including age, sex, baseline lung function (i.e., FEV1% predicted), the number of PEx requiring IV antibiotic treatment in the prior year, and sputum microbiology were collected at each clinical visit and hospitalization. Baseline lung function was defined as the best FEV1% predicted in six months before the index visit. CFRSD-CRISS was also completed at each visit. CFRSD-CRISS is scaled from 0 to 100, with a higher score indicating more severe respiratory symptoms (146). 3.2.3 Total RNA extraction, library construction and RNA sequencing PAXgene Blood RNA tubes were thawed at room temperature, and total RNA was extracted from 5 mL PAXgene blood samples using the PAXgene Blood RNA Kit (IVD) (PreAnalytiX-Qiagen, Germany) according to the manufacturer’s protocol. Total RNA was quantified using NanoDrop 8000 UV-Vis Spectrophotometer (Thermo Fisher Scientific, Wilmington, DE) and the median concentration and interquartile range was 150 [106, 204] ng/µL. RNA integrity was assessed by RNA integrity number (RIN), measured by the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA) and the median RIN and interquartile range was 7.8 [7.1, 8.4]. Poly(A) RNA-seq libraries were constructed and sequenced at the Genome Quebec Innovation Centre at McGill University. In brief, ribosomal RNA (rRNA) was first depleted from 250 ng of total RNA using QIAseq FastSelect (Human 96rxns). Then, complementary DNA (cDNA) synthesis was achieved with NEBNext RNA First-Strand synthesis and NEBNext Ultra Directional RNA Second-Strand Synthesis Modules (New England BioLabs, Canada). 51 After that, libraries were prepared using the NEBNext Ultra II DNA Library Prep Kit for Illumina (New England BioLabs, Canada). Adapters and PCR primers were purchased from New England BioLabs. Prepared libraries were quantified using the Quant-iT™ PicoGreen® dsDNA Assay Kit (Life Technologies, Carlsbad, CA) and the Kapa Illumina GA with Revised Primers-SYBR Fast Universal kit (Kapa Biosystems). The average size fragment was determined using a LabChip GX (PerkinElmer) instrument. Before sequencing, the libraries were normalized, pooled, and then denatured in 0.05N NaOH and neutralized using HT1 buffer. Pooled libraries were loaded at 225pM on an Illumina NovaSeq S4 land using Xp protocol as recommended by the manufacturer. The sequencing run was performed in the paired-end mode for 2 x 101 cycles. A phiX library was used as a control and mixed with libraries at a 1% level. The base calling was performed with RTA v3. Program bcl2fastq2 v2.20 was then used to demultiplex samples and generate fastq reads files. The number of paired reads per sample ranged from 34.1 to 297.4 million, with a mean of 69.8 million reads and a median of 64.1 million reads. 3.2.4 Alignment, quantification, and pre-processing of RNA-seq data The quality of raw reads was assessed by MultiQC software (version 1.12) (176). Paired-end reads were aligned to the human reference genome (GRCh38.p13) and annotated based on GENCODE comprehensive gene annotation file (release 32) with Spliced Transcripts Alignment to a Reference (STAR) software (version 2.7.1a) (177). RNA-Seq by Expectation Maximization (RSEM) software (version 1.3.0) (178) was applied to quantify the alignment reads at the gene level and summarized gene expression as raw counts and transcript per million (TPM). Hemoglobin (Hb) transcripts were bioinformatically removed, and genes with low expression 52 (TPM < 1) in over 75% of samples were removed; as such, a total of 11,786 genes were retained in the following analyses. 3.2.5 Inflammatory protein analysis The concentrations of 55 inflammatory proteins (i.e., acute phase proteins, cytokines, chemokines, and growth factors) were evaluated in plasma and serum samples using two immunoassays. The V-PLEX human biomarker 54-Plex kit (Meso Scale Discovery, Carlsbad, CA) was applied to measure 54 blood proteins in plasma. This kit contained seven multiplex electrochemiluminescence immunoassay panels, which included Proinflammatory Panel 1 (human), Cytokine Panel 1 (human), Cytokine Panel 2 (human), Chemokine Panel 1 (human), TH17 Panel 1 (human), Angiogenesis Panel 1 (human), and Vascular Injury Panel 2 (human). Additionally, serum calprotectin, a neutrophil inflammatory marker, was analyzed with enzyme-linked immunosorbent assay (ELISA) (Alpco, Salem, NH). The complete list of inflammatory proteins and their lower limit of detection (LLOD) is summarized in Table 7. Sample volume required by each panel was different. Proinflammatory Panel 1 (human), Cytokine Panel 1 (human), and Angiogenesis Panel 1 (human) used 50 µL plasma samples. Cytokine Panel 2 (human), Chemokine Panel 1 (human), and TH17 Panel 1 (human) used 25 µL plasma samples. Vascular Injury Panel 2 (human) used 10 µL plasma samples. All analytes were measured in duplicate. The mean coefficient of variation (CV) of duplicate measurements was < 20%, and their mean values were used in subsequent analyses. Values less than the assay's lowest limit of detection were replaced with half of the lowest limit of detection for that assay. 53 Table 7. List of inflammatory protein analytes Analytes LLOD (pg/mL) Panel Analytes LLOD (pg/mL) Panel bFGF 0.09 Angiogenesis Panel 1 (human) IL-17C 0.68 Cytokine Panel 2 (human) Flt-1 0.9 Angiogenesis Panel 1 (human) IL-17D 3.87 Cytokine Panel 2 (human) PIGF 0.21 Angiogenesis Panel 1 (human) IL-1RA 1.12 Cytokine Panel 2 (human) Tie-2 31.3 Angiogenesis Panel 1 (human) IL-3 2.37 Cytokine Panel 2 (human) VEGF-A 0.4 Angiogenesis Panel 1 (human) IL-9 0.31 Cytokine Panel 2 (human) VEGF-C 9.91 Angiogenesis Panel 1 (human) TSLP 0.06 Cytokine Panel 2 (human) VEGF-D 4.36 Angiogenesis Panel 1 (human) IFN-γ 0.37 Proinflammatory Panel 1 (human) Eotaxin 3.26 Chemokine Panel 1 (human) IL-10 0.04 Proinflammatory Panel 1 (human) Eotaxin-3 1.77 Chemokine Panel 1 (human) IL-13 0.24 Proinflammatory Panel 1 (human) IL-8 (high-sensitive) 95.6 Chemokine Panel 1 (human) IL-1β 0.05 Proinflammatory Panel 1 (human) IP-10 0.37 Chemokine Panel 1 (human) IL-2 0.09 Proinflammatory Panel 1 (human) MCP-1 0.09 Chemokine Panel 1 (human) IL-4 0.02 Proinflammatory Panel 1 (human) MCP-4 0.18 Chemokine Panel 1 (human) IL-6 0.06 Proinflammatory Panel 1 (human) MDC 1.22 Chemokine Panel 1 (human) IL-8 0.07 Proinflammatory Panel 1 (human) MIP-1ɑ 3.02 Chemokine Panel 1 (human) IL12-p70 0.11 Proinflammatory Panel 1 (human) MIP-1β 0.17 Chemokine Panel 1 (human) TNF-ɑ 0.04 Proinflammatory Panel 1 (human) TARC 0.22 Chemokine Panel 1 (human) IL-17A Gen.B 0.41 TH17 Panel 1 (human) GM-CSF 0.16 Cytokine Panel 1 (human) IL-21 0.19 TH17 Panel 1 (human) IL-12/IL-23P40 0.33 Cytokine Panel 1 (human) IL-22 0.27 TH17 Panel 1 (human) 54 IL-15 0.15 Cytokine Panel 1 (human) IL-23 0.27 TH17 Panel 1 (human) IL-16 2.83 Cytokine Panel 1 (human) IL-27 4.20 TH17 Panel 1 (human) IL-17A 0.31 Cytokine Panel 1 (human) IL-31 0.45 TH17 Panel 1 (human) IL-1ɑ 0.09 Cytokine Panel 1 (human) MIP-3a 0.05 TH17 Panel 1 (human) IL-5 0.14 Cytokine Panel 1 (human) CRP 1.33 Vascular Injury Panel 2 (human) IL-7 0.12 Cytokine Panel 1 (human) ICAM-1 1.94 Vascular Injury Panel 2 (human) TNF-β 0.08 Cytokine Panel 1 (human) SAA 10.90 Vascular Injury Panel 2 (human) VEGF-A 1.12 Cytokine Panel 1 (human) VCAM-1 6.00 Vascular Injury Panel 2 (human) IL-17A/F 0.93 Cytokine Panel 2 (human) Calprotectin 7.9 µg/g ELISA IL-17B 0.19 Cytokine Panel 2 (human) 3.2.6 Differential expression testing and functional enrichment analysis Before testing for differentially expressed (DE) genes and proteins, principal components (PCs) were calculated from gene and protein expression data and were used to evaluate for batch effects and other confounders. Log2 transformed normalized raw counts (counts per million, CPM) and protein measures were used in the principal component analysis (PCA). The variations captured by each PC were displayed by the scree plot. PCA plot was used to visualize clusters of samples and colour coated by the categorical variables (i.e., sample conditions, sex, CFTR genotype). The similarity between sample clusters was tested by permutational multivariate analysis of variance (PERMANOVA). Correlations between PCs and continuous clinical variables were assessed by Spearman’s correlation analysis, including age, body mass index, baseline FEV1% predicted, CFRSD-CRISS, and the percentage of neutrophils, lymphocytes, monocytes, eosinophils, and basophils. 55 DE genes were analyzed based on the raw count data using Limma R package (version 3.48.3), voom function (179). As multiple blood samples were collected from the same subjects, ‘duplicateCorrelation’ function from Limma R package was applied to treat subjects as a random effect. The Benjamini-Hochberg procedure was used to control the false discovery rate (FDR) for each analysis. Genes with significant differential expression were defined as absolute fold change (FC) > 1.5 and FDR < 0.1. In a sensitivity analysis, the differential gene expression analysis was adjusted for cell counts in peripheral blood. PCs were calculated based on five cell types measured by the complete blood count (percentage of neutrophils, lymphocytes, monocytes, eosinophils, and basophils) and were adjusted as covariates in Limma. DE proteins were analyzed based on log2 transformed protein measures using the Limma R package (version 3.48.3). The ‘duplicateCorrelation’ function and Benjamini-Hochberg procedure were also used as previously described. FDR < 0.25 was used to define proteins with significant differential expression. In DE protein analysis, a more relaxed FDR threshold was used to retain candidate proteins (i.e., CRP) and proteins with relatively larger fold changes (fold change > 1.5) in subsequent analyses. Gene set collections from Molecular Signatures Database (180, 181), including hallmark (H), canonical (CP), and gene ontology (GO), were used to analyze functional groups among the significant DE genes using clusterProfiler R package (version 4.0.5) (182) and enriched pathways were reported if their FDR < 0.1. 56 3.2.7 In silico immune cell type deconvolution Gene expression changes in whole blood samples can be influenced by changes in cellular composition and gene expression changes within the individual cell type. To examine the changes in cellular composition between clinically stable and PEx, we applied the CIBERSORTx to deconvolute the bulk RNA-seq data into 22 immune cell types (183). A validated leukocyte gene signature matrix (LM22) was used as the signature matrix (184). Correlations between CIBERSORTx estimated blood counts and complete blood counts measured by the hematology analyzer were examined with Spearman’s correlation analysis. However, since only five types of leukocytes (neutrophil, lymphocyte, monocyte, eosinophil, and basophil) were measured by the hematology analyzer, we inferred CIBERSORTx estimated lymphocyte counts as the sum of naïve B cells, memory B cells, CD8 T cells, naïve CD4 T cells, resting memory CD4 T cells, activated memory CD4 T cells and regulatory T cells; and inferred CIBERSORTx estimated monocyte counts as the sum of monocytes, M0 macrophages, M1 macrophages, and M2 macrophages. 3.2.8 Development of biomarker panel to predict PEx within 4 months The biomarker discovery workflow is described in Figure 9. Following DE testing, the Elastic Net model was used to identify biomarker candidates from DE genes and proteins in order to differentiate PEx from clinically stable visits (cohort 1). Hyperparameters (𝜶 and 𝝀) of the Elastic Net model were optimized with leave-one-person-out cross-validation. Receiver operating characteristic (ROC) curve analysis was applied to assess the classification performance. The biomarker panel with the highest area under the ROC curve (AUC) was reported. 57 The random forest classifier was built based on biomarker candidates (genes, proteins, clinical variables) to predict subjects who experienced PEx within 4-months follow-up. In cohort 2, 75% of the samples were assigned as training data, while the remaining were assigned as testing data. The outcome-based random splitting was repeated five times to obtain training and testing datasets with comparable clinical characteristics (i.e., age, sex, FEV1 % predicted, PEx required IV antibiotics in the previous year). The hyperparameter (mtry) of the random forest model was optimized with leave-one-person-out cross-validation based on the training data. The prediction performance of the random forest classifier was evaluated with ROC curve analysis and AUC was reported based on testing data. Figure 9. Biomarker discovery workflow 3.2.9 Analytical method validation The NanoString nCounter® technology was used to validate RNA-seq gene expression data and recalibrate the biomarker panel to predict PEx within 4 months. With the support of NanoString Technologies' bioinformatics team, oligonucleotide probes (100 base pairs) were designed for each biomarker candidate and three housekeeping genes (ARPC4, RHOA, and TMBIM6). Housekeeping genes were chosen as being consistently expressed under varied Cohort 1• Identification of Blood Biomarker Candidates for PExCohort 2• Biomarker Candidates to Predict PEx within 4 monthsDE testing•genes and proteinsFeature selection•Elastic net classification•Leave one subject out cross-validationTraining data (75%)•Random forest classification•Leave one subject out cross-validationTesting data (25%)•ROC curve analysis58 sampling conditions based on RNA-seq data. Probes were designed based on coverage plots and transcript isoform expression in RNA-seq data. Additionally, six positive control and six negative control probes were designed to bind engineered RNA sequences and used to evaluate the assay performance. RNA samples were analyzed following the manufacturing protocol of the Molecular Phenotyping Core Laboratory at the Centre for Heart Lung Innovation. The assay performance and data quality were evaluated with Quality Control (QC) metrics using nSolver software, including imaging QC, binding density QC, positive control linearity QC, and limit of detection QC. Afterwards, we performed the background correction on raw counts using the background thresholding method, where all counts that fell below 20 (the threshold value) were adjusted to match the threshold value. Then the background-corrected raw counts were normalized to the geometric mean of three housekeeping genes. Normalized count data were used in the subsequent analyses. The detectability of biomarker candidates was determined by over 50% of samples having the normalized count above the mean + 2 standard deviations (SD) of six negative control probes. The concordance of RNA-seq data and nCounter® data for each biomarker candidate was evaluated with Spearman’s correlation analysis. 3.2.10 Assessment of biomarker expression on external cohort Two external whole blood RNA-seq datasets containing clinically stable samples from CF individuals were identified from the Gene Expression Omnibus (GEO) database, GSE124548 (185) and GSE136371 (186). Raw data files (FASTQ) of RNA-seq were queried from the GEO Sequence Read Archive and processed as previously described. Log2 transformed TPM + 0.001 value was used in this analysis. The ComBat function from the sva R package (version 3.40.0) 59 (187) was applied to remove the batch effects due to different studies. PCA plot was generated to evaluate the effectiveness of ComBat batch correction. 3.2.11 Statistical analysis Statistical analyses were performed using R (version 4.1.1, the R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were presented as median ± interquartile range (IQR). Categorical variables were reported as numbers, with proportions in brackets. Differences in continuous variables were analyzed with Wilcoxon rank-sum test or Wilcoxon signed-rank test. Differences in categorical variables were analyzed with Fisher’s exact test. Reported p-values were analyzed with a two-tailed test, and a p-value < 0.05 was considered significant. 3.3 Results 3.3.1 Clinical characteristics of the study cohort 1 Table 8 summarizes the clinical characteristics of participating individuals in study cohort 1. Blood samples were collected from 14 CF individuals during paired clinically stable and PEx visits. The time interval between clinically stable and PEx visits ranged from 109 to 301 days with a median day of 241. Eight out of 14 PEx events were treated with IV antibiotics and the others were treated with oral antibiotics. 60 Table 8. Characteristics of subjects at clinically stable visits in cohort 1 Characteristics Cohort 1 No. of unique subjects / samples 14 / 28 Age (years), median (IQR) 31.5 (14.5) Female a, No. (%) 6 (43%) Genotype a, No. (%) ∆F508 Homozygous 6 (42%) ∆F508 Heterozygous 4 (29%) Others 4 (29%) Baseline FEV1 % predicted b, median (IQR) 69.0 (28.5) Baseline FEV1 % predicted b, No. (%) < 40 0 (0%) 40 – 69 7 (50%) 70 – 89 4 (29%) ≥ 90 3 (21%) CFRSD-CRISS, median (IQR) 31.5 (5.0) BMI (kg/m2), median (IQR) 22.6 (2.8) Sputum microbiology, NO. (%) MSSA 9 (64%) MRSA 4 (29%) Pseudomonas aeruginosa 4 (29%) Stenotrophomonas maltophilia 2 (14%) Burkholderia cepacia complex 2 (14%) CF-related diabetes a, No. (%) 0 (0%) Exacerbation requiring intravenous antibiotics in prior year, No. (%) 6 (43%) a Proportion was calculated based on the number of unique subjects. b Baseline lung function is defined as the best FEV1% predicted in the 6 months prior to the index clinical visit. Abbreviation: PEx, pulmonary exacerbations; FEV1, forced expiratory volume in 1 second; BMI, body mass index; CFRSD-CRISS, CF Respiratory Symptom Diary-Chronic Respiratory Infection Symptom Score; MSSA, Methicillin-sensitive Staphylococcus aureus; MRSA, Methicillin-resistant Staphylococcus aureus; IQR, interquartile range. 61 3.3.2 Changes in clinical outcomes during PEx Table 9 summarizes the clinical outcomes and changes in white blood cell count and differentials from clinically stable to PEx. FEV1% predicted decreased significantly from stable to PEx (p-value < 0.001), with a relative drop of 20.1%. All the CF individuals had an elevated CFRSD-CRISS at PEx, with a median increase of 20.5. Additionally, significant changes were observed in the proportion of neutrophils and lymphocytes during PEx. Table 9. Changes in clinical outcomes at PEx in cohort 1 Characteristics Stable PEx P-value a FEV1 % predicted, median (IQR) 69.0 (28.5) 50.5 (29.0) < 0.001 Absolute drop in FEV1 % predicted, median (IQR) -11.5 (12.8) -- Relative drop in FEV1% predicted, median (IQR) -20.1 (17.5) -- CFRSD-CRISS, median (IQR) 31.5 (5.0) 53 .0 (10.3) < 0.001 Absolute increase in CFRSD-CRISS, median (IQR) 20.5 (10.0) -- White blood cell count (109/L) b, median (IQR) Neutrophil 6.1 (4.2) 8.0 (7.2) 0.08 Lymphocyte 1.8 (0.7) 1.3 (1.0) 0.06 Monocyte 0.4 (0.2) 0.5 (0.2) 0.07 Eosinophil 0.1 (0.1) 0.2 (0.1) 1 Basophil 0.04 (0.03) 0.03 (0.03) 0.7 White blood cell differential (%) b, median (IQR) Neutrophil % 69 (14) 77 (8) 0.048 Lymphocyte % 24 (12) 15 (6) 0.003 Monocyte % 5 (3) 6 (2) 0.787 Eosinophil % 2 (1) 2 (2) 0.497 Basophil % 0.5 (0.3) 0.4 (0.3) 0.455 62 a p-value: Wilcoxon signed-rank test b median and IQR were calculated based on 13 CF individuals as 1 PEx white blood cell count and differential was missing Abbreviation: PEx, pulmonary exacerbations; FEV1, forced expiratory volume in 1 second; CFRSD-CRISS, CF Respiratory Symptom Diary-Chronic Respiratory Infection Symptom Score; IQR, interquartile range. 3.3.3 Sources of variation in gene expression PCA was applied to characterize the primary sources of variation in gene expression. The top five PCs for gene expression explained 88% of the variance (Figure 10A). Samples with the same clinical condition (Stable vs. PEx) were clustered together and significantly different from each other (p-value = 0.002; Figure 10B). Subjects with the same sex or CFTR genotype were not clustered according to PCs (Figure 10C, Figure 10D, respectively). The first PC explained 43% of the total variance and was significantly associated with the proportion of neutrophils and lymphocytes. Other clinical characteristics, including age, BMI, and baseline FEV1% predicted, were not significantly correlated with the top five PCs. Because all the samples were analyzed on the same flow cell, the sequencing batch was not adjusted for in the subsequent analyses. 63 Figure 10. Principal component analysis (PCA) of gene expression in cohort 1 (A) Scree plot that displays explained variance by each PC. (B) PCA indicated clusters corresponding to sample conditions. (C) PCA 4324107410203040PC1 PC2 PC3 PC4 PC5PCsExplained variances (%)PERMANOVA test: p−value = 0.002PCs vs. Condition−25 0 25−2002040PC1: 43% expl. varPC2: 24% expl. varCondition PEx StablePERMANOVA test: p−value = 0.999PCs vs. Sex−25 0 25−2002040PC1: 43% expl. varPC2: 24% expl. varSex Female MalePERMANOVA test: p−value = 1PCs vs. Genotype−25 0 25−2002040PC1: 43% expl. varPC2: 24% expl. varGenotype Heter F508del Homo F508del Others* *−1 −0.75 −0.5 −0.25 0 0.25 0.5 0.75 1AgeBMIFEV1% predictedCFRSD−CRISSNeutrophil (%)Lymphocyte (%)Monocyte (%)Eosinophil (%)Basophil (%)PC1PC2PC3PC4PC5A BC DE64 indicated clusters corresponding to sex. (D) PCA indicated clusters corresponding to CFTR genotype. (E) Spearman’s correlation between PCs and clinical variables and white blood cell proportions. 3.3.4 Exacerbation-related changes in gene expression We applied differential expression analysis to evaluate genes that changed from clinically stable to PEx. Thirty-three DE genes were identified with fold change > 1.5 and FDR < 0.1, whereby 23 were up-regulated and 10 were down-regulated at PEx (Table 10; Figure 11). According to the Human Protein Atlas Immune Cells Database (188), 12 of 23 up-regulated DE genes were primarily expressed by neutrophils and 2 of 10 down-regulated DE genes were expressed primarily by T cells. Six DE genes were expressed mainly by monocytes. Four DE genes were identified as being expressed by eosinophils, basophils, memory B cells, and plasmacytoid dendritic cells (DC). The remaining DE genes had low immune cell specificity, which included a gene of the mitochondrial genome (MT-ND5). 65 Figure 11. Volcano plot of differentially expressed genes between paired clinically stable and PEx visits Thirty-three DE genes were identified with fold change > 1.5 and FDR < 0.1, whereby 23 were up-regulated and 10 were down-regulated at PEx. 66 Table 10. Differentially expressed genes between clinically stable and PEx Ensembl Gene ID Gene Log2 fold changea P-value FDR Immune Cell Type Specificity b ENSG00000213714 FAM209B 0.742 < 0.001 0.057 Eosinophil ENSG00000116815 CD58 0.645 < 0.001 0.057 Low immune cell specificity ENSG00000129757 CDKN1C -0.950 < 0.001 0.057 Monocyte ENSG00000123358 NR4A1 -0.837 < 0.001 0.057 Monocyte ENSG00000143546 S100A8 1.044 < 0.001 0.057 Neutrophil, Monocyte ENSG00000154589 LY96 0.868 < 0.001 0.057 Neutrophil ENSG00000198786 MT-ND5 -0.689 < 0.001 0.057 Low immune cell specificity ENSG00000124107 SLPI 0.880 0.001 0.057 Neutrophil ENSG00000145088 EAF2 0.680 0.001 0.057 Memory B-cell ENSG00000131174 COX7B 0.588 0.001 0.057 Low immune cell specificity ENSG00000182578 CSF1R -0.620 0.001 0.057 Monocyte ENSG00000136810 TXN 0.662 0.001 0.057 Low immune cell specificity ENSG00000119013 NDUFB3 0.594 0.001 0.057 Low immune cell specificity ENSG00000164104 HMGB2 0.596 0.001 0.057 Basophil ENSG00000163220 S100A9 0.775 0.001 0.057 Neutrophil, Monocyte ENSG00000244115 DNAJC25-GNG10 0.760 0.001 0.057 Neutrophil ENSG00000183779 ZNF703 -0.589 0.001 0.057 Monocyte ENSG00000110852 CLEC2B 0.732 0.001 0.058 Neutrophil ENSG00000242616 GNG10 0.729 0.002 0.061 Neutrophil ENSG00000180644 PRF1 -0.724 0.002 0.062 T cell ENSG00000232112 TMA7 0.591 0.002 0.064 Low immune cell specificity ENSG00000089692 LAG3 -0.744 0.003 0.071 T cell ENSG00000162654 GBP4 -0.657 0.003 0.072 Low immune cell specificity ENSG00000140379 BCL2A1 0.805 0.003 0.072 Neutrophil, Monocyte, Eosinophil ENSG00000204397 CARD16 0.768 0.003 0.073 -- ENSG00000130300 PLVAP -0.611 0.003 0.073 Plasmacytoid DC ENSG00000272196 H2AC19 0.597 0.004 0.079 Neutrophil ENSG00000164047 CAMP 0.673 0.005 0.081 Neutrophil, Eosinophil ENSG00000197353 LYPD2 -1.604 0.005 0.082 Monocyte ENSG00000110079 MS4A4A 0.614 0.005 0.083 Monocyte ENSG00000115590 IL1R2 0.616 0.005 0.084 Neutrophil ENSG00000189337 KAZN 0.620 0.005 0.085 Neutrophil ENSG00000163221 S100A12 0.842 0.006 0.087 Neutrophil, Monocyte a Fold change > 1.5 in up-regulated genes and down-regulated genes is equivalent to Log2 fold change > 0.58 and Log2 fold change < -0.58, respectively. b Immune cell type specificity was extracted from the Human Protein Altas Immune Cells Database (188) 67 Following the identification of DE genes, we performed over-representation analysis (189) with hallmark (H) and canonical pathways (CP), as well as gene ontology (GO) categories collected by the Molecular Signatures Database (180, 181) to identify the biological functional categories of interest. The top 10 enriched canonical pathways and GO categories are shown in Figure 12. In brief, inflammatory and innate immune-related pathways dominated the observed changes, with significant up-regulation of toll-like receptor signaling, antimicrobial peptides, cellular response to bacteria and associated molecules, antimicrobial humoral response, and nuclear factor kappa B (NF-κB). On a cellular level, neutrophil degranulation accounted for a substantial fraction of the up-regulated genes. Down-regulated DE genes were also evaluated; however, significantly enriched pathways were not observed. Additionally, significant enriched hallmark pathways were not identified with either up-regulated or down-regulated DE genes. 68 Figure 12. Top 10 enriched (A) Canonical and (B) Gene Ontology Pathways Inflammatory and innate immune-related pathways dominated the observed changes, with significant up-regulation of toll-like receptor signaling, antimicrobial peptides, cellular response to bacteria and associated molecules, antimicrobial humoral response, and nuclear factor kappa B (NF-κB). Abbreviation: PID, Pathway Interaction Database; Reactome, Reactome Pathway Database; Wiki, WikiPathways Pathway Database; GO, Gene Ontology; BP, Biological Process; CC, Cellular Component; MF, Molecular Function. PIDReactomeWiki0.0 0.1 0.2 0.3Endogenous TLR signalingIRAK4 deficiency (TLR2/4)Regulation of TLR by endogenous ligandDiseases of immune systemAntimicrobial peptidesAntigen processing−Cross presentationToll−like receptor TLR1:TLR2 cascadeToll−like receptor cascadesNeutrophil degranulationVitamin D receptor pathwayGene ratioCategory Innate immune related Adaptive immune related OtherUp−regulated genes vs. MsigDB C2 CPABPCCMF0.0 0.1 0.2 0.3Antimicrobial humoral responseActivation of NF−kappaBCellular response to bacteria associated moleculeCellular response to biotic stimulusUpregulation of transcription factor activityUp regulation of response to external stimulusDefence response to bacteriaResponse to bacteria associated moleculeVesicle lumenRAGE receptor bindingGene ratioCategory Inflammation related OtherUp−regulated genes vs. MsigDB C5 GOB -regulated genes vs. GO Pathways -regulated genes vs. Canonical Pathways 69 In the sensitivity analysis, we adjusted the cell count in the differential gene expression analysis to assess for changes in gene expression independent of cellular composition. PCs were calculated based on five cell types measured by the complete blood count (percentage of neutrophils, lymphocytes, monocytes, eosinophils, and basophils). The first three PCs explained 92% of the variation in cellular composition (59%, 23%, and 10%, respectively) and were thus adjusted as covariates in the differential gene expression analysis. Eighty-one genes (62 up-regulated and 19 down-regulated during PEx) had p-values < 0.05, but the smallest FDR was 0.26 (Table 31 in Appendix B). Previously identified pathways and GO categories were not enriched by genes identified in the sensitivity analysis (p-value < 0.05); however, the IL-1 signaling pathway was significantly enriched by the up-regulated genes during CF PEx and this was verified by multiple gene set collections (Table 32 in Appendix B). 3.3.5 Exacerbation-related changes in immune cell composition Before examining the differences in CIBERSORTx deconvoluted cellular composition between clinically stable and PEx, we assessed the accuracy of CIBERSORTx estimated cell counts by comparing them to hematology analyzer-measured complete blood counts. Significant correlations were observed in neutrophils, lymphocytes, and monocytes (Figure 13). Neutrophils and lymphocytes had strong positive correlations (R = 0.77 and R = 0.73, respectively) between complete blood counts and CIBERSORTx estimated cell counts. Monocytes had a moderate positive correlation (R = 0.58) between two measurements. 70 Figure 13. Spearman’s correlation analysis between clinical blood count (%) and CIBERSORTx estimated blood counts (%) (A) Neutrophils, (B) Lymphocytes, (C) Monocytes Although gene expression data were used to estimate the levels of 22 immune cell types, 11 immune cell types were detected in at least 75% of the samples and were kept in the subsequent analysis. M2 macrophage levels were significantly lower during PEx (p-value = 0.017; Figure 14). Additionally, memory B cells, CD8 T cells, activated memory CD4 T cells, and resting NK cells decreased during PEx with marginally significant p-values (Figure 14). R = 0.77, p < 2.2e−160.40.50.60.70.80.90.3 0.4 0.5 0.6 0.7Complete Blood Count (%)NeutrophilsAR = 0.73, p < 2.2e−160.00.10.20.30.40.50.1 0.2 0.3Estimated Blood Count (%)LymphocytesBR = 0.58, p = 3.5e−070.0250.0500.0750.1000.1250.05 0.10 0.15 0.20MonocytesCStable PEx Imminent PEx71 Figure 14. CIBERSORTx estimated immune cell counts (%) between clinically stable and PEx M2 macrophage levels were significantly lower during PEx (p = 0.017) and memory B cells, CD8 T cells, activated memory CD4 T cells, and resting NK cells decreased during PEx with marginally significant p-values. 0.2680.8890.0780.7610.0910.1940.0170.0780.0910.4260.761activated memory CD4 T cells regulatory T cells resting NK cellsnaïve B cells memory B cells CD8 T cells naïve CD4T cellsNeutrophils Monocytes M2 Macrophages resting mast cells0.000.250.500.750.000.250.500.750.000.250.500.75Estimated Cell Count (%)Stable PEx72 3.3.6 Sources of variation in protein expression PCA was applied to characterize the primary sources of variation in protein expression. The top five PCs for gene expression explained 81% of the variance (Figure 15A). Samples with the same clinical condition (Stable vs. PEx) were clustered together and significantly different from each other (p-value = 0.001; Figure 15B). Subjects with the same sex or CFTR genotype were not clustered according to PCs (Figure 15C, Figure 15D, respectively). The first PC explained 29% of the variance and was significantly associated with CFRSD-CRISS and the proportion of lymphocytes. Other clinical characteristics, including age, BMI, and baseline FEV1% predicted, were not significantly correlated with the top five PCs (Figure 15E). Since all the samples were evaluated on the same MSD or ELISA plate, the analytical batch was not adjusted for in subsequent analyses. 73 Figure 15. Principal component analysis (PCA) of protein expression in cohort 1 (A) Scree plot that displayed explained variance by each PC. (B) PCA indicated clusters corresponding to sample conditions. (C) PCA indicated clusters corresponding to sex. (D) PCA indicated clusters corresponding to CFTR genotype. (E) Spearman’s correlation between PCs and clinical variables and white blood cell proportions. 2920141081015202530PC1 PC2 PC3 PC4 PC5PCsExplained variances (%)PERMANOVA test: p−value = 0.001PCs vs. Condition−5.0 −2.5 0.0 2.5 5.0−2.50.02.55.0PC1: 29% expl. varPC2: 20% expl. varCondition PEx StablePERMANOVA test: p−value = 0.999PCs vs. Sex−5.0 −2.5 0.0 2.5 5.0−2.50.02.55.0PC1: 29% expl. varPC2: 20% expl. varSex Female MalePERMANOVA test: p−value = 1PCs vs. Genotype−5.0 −2.5 0.0 2.5 5.0−2.50.02.55.0PC1: 29% expl. varPC2: 20% expl. varGenotype Heter F508del Homo F508del Others* *−1 −0.75 −0.5 −0.25 0 0.25 0.5 0.75 1AgeBMIFEV1% predictedCFRSD−CRISSNeutrophil (%)Lymphocyte (%)Monocyte (%)Eosinophil (%)Basophil (%)PC1PC2PC3PC4PC5A BC DE74 3.3.7 Exacerbation-related changes in protein expression We applied differential expression analysis to evaluate inflammatory proteins that changed from clinically stable to PEx (Table 11; Figure 16). Several systemic inflammatory markers, including calprotectin, IL-1RA, serum amyloid A (SAA), C-reactive protein (CRP), and IL-10, were significantly elevated during PEx. Calprotectin, a neutrophilic inflammatory marker, showed the most significant increase during PEx. Two IL-17 family cytokines (IL-17A and IL-17C) and an angiogenic factor (placental growth factor, PIGF) also increased during PEx. Furthermore, PEx resulted in substantial decreases in a T helper type (Th) 1 cytokine, IL-12 (p70), and a Th2 cytokine, IL-4. Figure 16. Volcano plot of differential expression protein between clinically stable and PEx PEx resulted in substantial increases in inflammatory markers and IL-17 family cytokines; however, substantial decreases were observed in IL-4 and IL-12. 75 Table 11. Differentially expressed inflammatory proteins between clinically stable and PEx Protein Description Log2 Fold Changea P-value FDR Calprotectin Calprotectin 0.954 <0.001 0.008 PIGF Placental growth factor 0.325 0.001 0.018 IL-1RA Interleukin-1 receptor antagonist 0.824 0.001 0.018 SAA Serum amyloid A 1.413 0.005 0.062 IL-17A Interleukin-17A 0.518 0.017 0.153 IL-4 Interleukin-4 -0.605 0.018 0.153 IL-17C Interleukin-17C 0.895 0.022 0.162 IL-12p70 Interleukin-12p70 -0.574 0.025 0.166 CRP C-reactive protein 0.900 0.029 0.169 IL-10 Interleukin-10 0.789 0.046 0.238 ICAM-1 Intercellular adhesion molecule 0.183 0.056 0.265 VEGF Vascular endothelial growth factor 0.376 0.076 0.328 IL-17A/F Interleukin-17A/F 0.685 0.096 0.374 IL-6 Interleukin-6 0.602 0.102 0.374 MIP-3a C-C motif chemokine 20 0.425 0.108 0.374 IL-15 Interleukin-15 0.178 0.138 0.448 IP-10 C-X-X motif chemokine 10 0.514 0.207 0.614 MIP-1a C-C motif chemokine 3 0.306 0.237 0.614 VCAM-1 Vascular cell adhesion protein 1 0.109 0.237 0.614 IL-7 Interleukin-7 0.238 0.240 0.614 IL-13 Interleukin-13 -0.116 0.250 0.614 TNF-a Tumor necrosis factor-a 0.159 0.265 0.614 IL-22 Interleukin-22 0.597 0.272 0.614 IL-17A Gen.B Interleukin-17A 0.641 0.308 0.641 VEGF-A Vascular endothelial growth factor A 0.217 0.308 0.641 VEGF-D Vascular endothelial growth factor D 0.097 0.338 0.675 VEGF-C Vascular endothelial growth factor C -0.182 0.392 0.730 IL-16 Interleukin-16 0.094 0.397 0.730 IL-27 Interleukin-27 0.193 0.407 0.730 MDC C-C motif chemokine 22 -0.120 0.424 0.734 IL-1a Interleukin-1a 0.199 0.487 0.767 IL-12/IL-23p40 Interleukin-12 0.163 0.500 0.767 MCP-1 C-C motif chemokine 2 0.120 0.500 0.767 MIP-1β C-C motif chemokine 4 0.117 0.509 0.767 IL-17B Interleukin-17B 0.279 0.516 0.767 Flt-1 Vascular endothelial growth factor receptor 1 0.069 0.561 0.811 Eotaxin Eotaxin 0.094 0.601 0.817 TSLP Thymic stromal lymphopoietin 0.187 0.607 0.817 bFGF Fibroblast growth factor 2 0.134 0.613 0.817 MCP-4 C-C motif chemokine 13 -0.072 0.642 0.832 IL-3 Interleukin-3 -0.176 0.674 0.832 76 IL-17D Interleukin-17D -0.135 0.684 0.832 IFN-γ Interferon-γ -0.218 0.688 0.832 IL-21 Interleukin-21 0.057 0.725 0.856 TNF-β Tumor necrosis factor-β -0.107 0.747 0.864 TARC C-C motif chemokine 17 -0.169 0.786 0.888 Eotaxin-3 C-C motif chemokine 26 0.042 0.840 0.916 GM-CSF Granulocyte-macrophage colony-stimulating factor 0.060 0.846 0.916 IL-8 Interleukin-8 -0.029 0.884 0.933 IL-5 Interleukin-5 0.030 0.908 0.933 IL-2 Interleukin-2 -0.035 0.915 0.933 Tie-2 Angiopoietin-1 receptor -0.005 0.957 0.957 a Fold change > 1.5 in up-regulated genes and down-regulated genes equaled to Log2 fold change > 0.58 and Log2 fold change < -0.58, respectively. 3.3.8 Identification of biomarker candidates for PEx Following the differential expression analysis, we used an elastic net model to identify biomarker candidate genes and proteins that differentiated PEx from clinically stable visits. Sixteen DE genes were identified as biomarker candidates capable of classifying clinically stable and PEx (𝛼 = 0.550, 𝛽 = 0.026, cross-validation AUC = 0.83 ± 0.28; Table 12). Additionally, nine DE proteins were identified as biomarker candidates with a strong performance in discriminating clinically stable and PEx (𝛼 = 0.003, 𝛽 = 5.173, cross-validation AUC = 0.92 ± 0.18; Table 12). Table 12. Biomarker candidate genes and proteins for PEx Biomarker panel Molecular features 16-gene panel SLPI, KAZN, FAM209B, CLEC2B, MS4A4A, EAF2, HMGB2, TXN, LYPD2, PLVAP, ZNF703, MT-ND5, CDKN1C, NR4A1, GBP4, CAMP 9-protein panel IL-1RA, IL-4, IL-12p70, IL-17A, IL-17C, CRP, Calprotectin, PIGF, SAA 77 3.3.9 Biomarker candidates to predict PEx within 4 months To develop a biomarker panel to identify subjects who would experience a PEx within 4 months from those who would not, study cohort 2 was randomly split into training and testing cohorts. Their clinical characteristics are summarized in Table 13. Despite the limited sample size in testing data (n = 10), there was a comparable proportion of subjects who experienced PEx within 4 months. Training and testing datasets were comparable in age and FEV1% predicted. CFRSD-CRISS was slightly higher in the testing cohort. Table 13. Characteristics of subjects in cohort 2 Characteristics Cohort 2 Training Testing No. of unique subjects / samples 39 / 44 30/34 9/10 Age (years), median (IQR) 31.0 (13.3) 29.0 (13.8) 31.5 (9.0) Female a, No. (%) 15 (38%) 12 (40%) 3 (33%) Genotype a, No. (%) ∆F508 Homozygous 13 (33%) 10 (33%) 3 (33%) ∆F508 Heterozygous 18 (46%) 15 (50%) 3 (33%) Others 8 (21%) 5 (17%) 3 (33%) Baseline FEV1 % predicted b, median (IQR) 78.5 (24.3) 78.5 (24.3) 77.0 (30.5) Baseline FEV1 % predicted b, No. (%) < 40 4 (9%) 3 (9%) 1(10%) 40 – 69 9 (20%) 7 (21%) 2 (20%) 70 – 89 18 (41%) 14 (41%) 4 (40%) ≥ 90 13 (30%) 10 (29%) 3 (30%) CFRSD-CRISS, median (IQR) 29.0 (14.0) 26.0 (13.3) 35.5 (11.0) BMI (kg/m2), median (IQR) 22.2 (4.2) 22.0 (5.4) 23.5 (3.0) Sputum microbiology, NO. (%) MSSA 20 (45%) 16 (47%) 4 (40%) MRSA 5 (11%) 5 (15%) 0 (0%) Pseudomonas aeruginosa 16 (36%) 11 (32%) 5 (50%) 78 Stenotrophomonas maltophilia 1 (2%) 1 (3%) 0 (0%) Burkholderia cepacia complex 4 (9%) 4 (12%) 0 (0%) CF-related diabetes a, No. (%) 9 (23%) 8 (27%) 1 (11%) Exacerbation requiring intravenous antibiotics in prior year, No. (%) 11 (25%) 9 (26%) 2 (20%) Time to next PEx, No (%) < 2 months 9 (20%) 5 (15%) 4 (40%) 2 – 3 months 3 (7%) 3 (9%) 0 (0) 3 – 4 months 14 (32%) 12 (35%) 2 (20%) > 4 months 18 (41%) 14 (41%) 4 (40%) a Proportion was calculated based on the number of unique subjects. b Baseline lung function is defined as the best FEV1% predicted in the 6 months prior to the index clinical visit. Abbreviation: PEx, pulmonary exacerbations; FEV1, forced expiratory volume in 1 second; BMI, body mass index; CFRSD-CRISS, CF Respiratory Symptom Diary-Chronic Respiratory Infection Symptom Score; MSSA, Methicillin-sensitive Staphylococcus aureus; MRSA, Methicillin-resistant Staphylococcus aureus; IQR, interquartile range. Two random forest classifiers were developed based on biomarker candidate genes and proteins, respectively. The ROC analysis resulted in an AUC of 0.88 for the 16-gene panel and an AUC of 0.83 for the 9-protein panel in testing data. Additionally, we assessed the predictive ability of calprotectin and candidate clinical features (female sex, FEV1% predicted, and the number of IV antibiotics in the preceding year) (92, 190), yielding AUCs of 0.75 and 0.71, respectively. Figure 17 summarizes the AUC (95% CI) for all examined biomarker panels. Figure 18 shows the ROC curves for individual biomarker panel. 79 Figure 17. Forest plot of AUC for biomarker panels. The error bar is the 95% confidence interval (CI) computed with 2000 stratified bootstrap replicates 0.750.830.880.71Clinial panelCalprotectin9−protein panel16−gene panel0.00 0.25 0.50 0.75 1.00AUC−ROCBiomarker panel80 Figure 18. ROC curve of biomarker panels in testing data (A) 16-gene panel (AUC = 0.88). (B) 9-protein panel (AUC = 0.83). (C) Calprotectin (AUC = 0.75). (D) Clinical panel (female sex, FEV1% predicted, and the number of IV antibiotics in the preceding year; AUC = 0.71) 3.3.10 Assessment of biomarker gene expression with NanoString nCounter® We validated the expression of biomarker candidate genes with NanoString nCounter® technology and recalibrated the random forest classifier with nCounter® data. The designed probes and their targeted isoforms for each candidate gene and housekeeping gene are listed in Table 14. The mitochondrial gene MT-ND5 was not included in the nCounter® panel. Because 16−gene panel (AUC = 0.88)0.000.250.500.751.000.00 0.25 0.50 0.75 1.001 − specificitysensitivityA9−protein panel (AUC = 0.83)0.000.250.500.751.000.00 0.25 0.50 0.75 1.001 − specificitysensitivityBCalprotectin (AUC = 0.75)0.000.250.500.751.000.00 0.25 0.50 0.75 1.001 − specificitysensitivityCClinical panel (AUC = 0.71)0.000.250.500.751.000.00 0.25 0.50 0.75 1.001 − specificitysensitivityD81 each nCounter® slide can capture up to 2,000,000 counts, mitochondrial gene levels were substantially greater than genes measured from the human genome. According to the RNA-seq data, the level of MT-ND5 was significantly higher than the level of other biomarker genes (Kruskal-Wallis test p-value < 0.001; Figure 19); hence, incorporating MT-ND5 might potentially lead to over-saturation of the nCounter® slide imaging. In addition, as each nCounter® cartridge can only measure 12 samples, our samples were analyzed across seven nCounter® cartridges. PCA plot revealed that batch effects were not introduced by the cartridges (Figure 20). Table 14. nCounter® CodeSet design Group Probe ID Gene # Hit Isoforms hit by probe Biomarker ENST00000296435.2:83 CAMP 1 ENST00000296435.2 Biomarker ENST00000440480.1:29 CDKN1C 4 ENST00000440480.1; ENST00000616138.1; ENST00000414822.1; ENST00000313407.1 Biomarker ENST00000228438.2:14 CLEC2B 1 ENST00000228438.2 Biomarker ENST00000273668.7:719 EAF2 2 ENST00000273668.7; ENST00000490434.1 Biomarker ENST00000371325.1:361 FAM209B 1 ENST00000371325.1 Biomarker ENST00000355754.7:1 GBP4 1 ENST00000355754.7 Biomarker ENST00000438704.1:664 HMGB2 1 ENST00000438704.1 Biomarker ENST00000361144.9:398 KAZN 1 ENST00000361144.9 Biomarker ENST00000359228.4:118 LYPD2 1 ENST00000359228.4 Biomarker ENST00000337908.5:1044 MS4A4A 2 ENST00000337908.5; ENST00000343968.1 Biomarker ENST00000394825.1:30 NR4A1 1 ENST00000394825.1 Biomarker ENST00000252590.9:479 PLVAP 1 ENST00000252590.9 Biomarker ENST00000338380.2:71 SLPI 1 ENST00000338380.2 Biomarker ENST00000374517.1:73 TXN 1 ENST00000374517.1 Biomarker ENST00000331569.6:686 ZNF703 1 ENST00000331569.6 Housekeeping ENST00000397261.1:1170 ARPC4 5 ENST00000397261.1; ENST00000440787.1; ENST00000417500.1; ENST00000498623.1; ENST00000433034.1 Housekeeping ENST00000418115.1:1337 RHOA 7 NM_001313947.2; NM_001313941.2; NM_001313946.2; NM_001664.4; NM_001313943.2; 82 NM_001313945.2; NM_001313944.2 Housekeeping ENST00000267115.10:215 TMBIM6 18 ENST00000267115.10; ENST00000550445.1; ENST00000552699.1; ENST00000549130.1; ENST00000547832.1; ENST00000423828.1; ENST00000552370.1; ENST00000553022.1; ENST00000547798.1; ENST00000552635.1; ENST00000548201.1; ENST00000549445.1; ENST00000547187.1; ENST00000549385.1; ENST00000546796.1; ENST00000395006.1; ENST00000549966.1; ENST00000546914.1 Figure 19. Levels of 16 biomarker genes measured with RNA-seq in clinically stable samples The level of MT-ND5 was significantly higher than the level of other biomarker genes (Kruskal-Wallis test p-value < 0.001) 0200400CAMPCDKN1CCLEC2BEAF2FAM209B GBP4HMGB2KAZNLYPD2MS4A4AMT−ND5NR4A1PLVAP SLPI TXNZNF703TPM + 0.00183 Figure 20. Principal component analysis (PCA) of biomarker candidate genes measured with NanoString nCounter® technology Samples were analyzed across seven nCounter® cartridges (A-G) The detectability of candidate genes was then assessed using the negative control threshold. Each sample had six negative control probes tested, and the negative control threshold was set as the mean + 2 standard deviation (SD) of measured negative control probes. We compared the expression levels of candidate genes from each participant to their negative control threshold. Four genes (CLEC2B, FAM209B, NR4A1, and ZNF703) were determined to be undetectable as more than half of the samples fell below the negative control threshold (100%, 86%, 99%, 68%, respectively; Figure 21). Following that, we examined the correlation between RNA-seq and NanoString nCounter® measurements for each candidate gene (Figure 22). With the exception of four undetectable genes, other candidate genes showed a moderate to strong positive correlation between RNA-seq and NanoString nCounter®. NR4A1 also positively −2024−2.5 0.0 2.5 5.0PC1, 35.77% variationPC2, 20.58% variationcartridgeABCDEFG84 correlated between two platforms; nevertheless, it was eliminated from subsequent analyses due to the failed detectability test. Figure 21. Levels of biomarker candidate genes vs. negative control. The Grey dot indicated the negative control threshold for each participant measured with mean + 2 standard deviation (SD) of six negative control probes NR4A1 PLVAP SLPI TXN ZNF703GBP4 HMGB2 KAZN LYPD2 MS4A4ACAMP CDKN1C CLEC2B EAF2 FAM209B010203040025507520406001002003004000306090050010001500200001020300204060030060090001002003000200040006000010020005010015020001002003004000102030Sample IDNormalized CountsStable PEx Imminent PEx85 Figure 22. Correlation between biomarker candidate genes measured with RNA-seq vs. NanoString nCounter® ρ = 0.71, p = 6.5e−13ρ = 0.46, p = 2.3e−05ρ = 0.35, p = 0.0019ρ = 0.82, p < 2.2e−16ρ = 0.6, p = 6.7e−09ρ = 0.7, p = 8.7e−13ρ = −0.15, p = 0.2ρ = 0.63, p = 5.7e−10ρ = 0.7, p < 2.2e−16ρ = 0.32, p = 0.0052ρ = 0.88, p < 2.2e−16ρ = 0.74, p < 2.2e−16ρ = 0.17, p = 0.15ρ = 0.58, p = 2.8e−08ρ = −0.24, p = 0.04NR4A1 PLVAP SLPI TXN ZNF703GBP4 HMGB2 KAZN LYPD2 MS4A4ACAMP CDKN1C CLEC2B EAF2 FAM209B0 10 20 30 40 0 100 200 300 600 900 500 100015002000 20 40 600 100 200 300 400 2000 4000 6000 0 20 40 60 0 30 60 90 0 25 50 750 50 100 150 200 0 100 200 300 1 2 3 4 5 100 200 300 400 0 10 20 30 400.00.51.01.52.02.5024601234051015202505100501001502000204002460306090120051015200100200300123402550750510152001234Normalized countTPM + 0.001condition Stable PEx Imminent PEx86 We recalibrated the random forest classifier based on NanoString nCounter® data since 11 biomarker candidate genes were detected and positively associated with RNA-seq results. The ROC analysis yielded an AUC of 0.83 (95% confidence interval: 0.5 to 1) in testing data (Figure 23), which was comparable to our original RNA-seq finding (AUC = 0.88; Delong’s test p-value = 0.82). Figure 23. ROC curve of nCounter® panel in test data (AUC = 0.83) nCounter® panel (AUC = 0.83)0.000.250.500.751.000.00 0.25 0.50 0.75 1.001 − specificitysensitivity87 3.3.11 Assessing levels of biomarker genes in external datasets Two external whole blood RNA-seq datasets from CF individuals were identified in the GEO database and were utilized to examine the generalizability of our biomarker findings (185, 186). Table 15 summarizes the clinical characteristics provided with the RNA-seq data files. According to the PCA plot, samples were segregated by studies (Figure 24A). Therefore, we applied ComBat function to correct the batch effects due to different studies. Following this, samples from both study cohorts and external datasets were clustered together (Figure 24B). Since only clinically stable samples were available in the external datasets, we evaluated the expression levels of biomarker candidate genes in clinically stable samples from our study cohorts and the two external datasets. Significant changes in levels of each biomarker candidate gene across study cohorts and external datasets were not identified (Figure 25). This finding suggests that despite differences in specific clinical variables (i.e., CFTR genotype and FEV1% predicted), selected biomarker candidate genes show similar expression levels in clinically stable CF individuals. Table 15. Clinical characteristics of the study cohort 1&2 and external cohorts Study cohort 1 & 2 External cohort Clinically stable PEx Imminent PEx GSE124548 Kopp et. al., (185) GSE136371 Pineau et. al., (186) Size (n) 32 14 26 20 33 Age (years), mean (SD) 35.1 (12.0) 35.7 (11.7) 31.3 (10.3) 21.6 (9.5) -- Age (years), median (IQR) 33.5 (16.8) 32.5 (14.5) 27.0 (12.0) -- 28 (10) FEV1% predicted, mean (SD) 74.2 (22.9) 56.2 (18.3) 77.5 (18.2) 74.2 (20.5) -- 88 FEV1% predicted, median (IQR) 75.5 (28.5) 50.5 (29) 77.5 (20.5) -- 48 (24) Genotype Homozygous F508del, No. (%) 12 (38%) 6 (42%) 7 (27%) 20 (100%) 33 (100%) Heterozygous F508del, No. (%) 10 (31%) 4 (29%) 16 (62%) -- -- Other, No. (%) 10 (31%) 4 (29%) 3 (11%) -- -- Figure 24. Principal component analysis (PCA) of study cohorts 1&2 and external cohorts (A) Original data, (B) ComBat corrected data Unadjusted data−100 0 100−1000100PC1: 51% expl. varPC2: 21% expl. varStudy cohort GSE124548 GSE136371Combat Adjusted−200 −100 0 100−60−40−2002040PC1: 53% expl. varPC2: 8% expl. varStudy cohort GSE124548 GSE136371A B89 Figure 25. Levels of biomarker candidate genes between study cohorts and external datasets Despite differences in specific clinical variables, candidate genes show similar expression levels in clinically stable CF subjects. PLVAP SLPI TXN ZNF703LYPD2 MS4A4A MT−ND5 NR4A1FAM209B GBP4 HMGB2 KAZNCAMP CDKN1C CLEC2B EAF2Stable PExStable PExStable PExStable PEx01234−3−2−1012−202−4−20234564567834567834567024024−101223456723456−3−2−10−8−40−2−1012log2(TPM + 0.001)Study cohort GSE124548 GSE13637190 3.4 Discussion In this study, we found that inflammatory and innate immune-related pathways were enriched during PEx by profiling whole blood transcriptome and inflammatory proteins at paired clinically stable and PEx visits. Genes and inflammatory proteins associated with neutrophilic inflammation, such as S100A family genes, serum calprotectin, and IL-17 family cytokines, were elevated during PEx. Additionally, we developed blood-based biomarker panels based on this molecular data capable of identifying CF individuals who are at imminent (< 4 months) risk of an exacerbation (16-gene panel: AUC = 0.88; 9-protein panel: AUC = 0.83). The gene expression panel discovered with RNA-Seq was successfully validated using a more targeted and clinically translatable RNA measurement platform. Biomarker candidate genes involved in inflammation and the innate immune response to bacterial pathogens were significantly increased during PEx, including the S100 gene family (A8, A9, A12), CAMP, LY96, and SLPI. S100A8, A9, and A12 encode calcium- and zinc-binding proteins that induce neutrophil chemotaxis and adhesion and play a predominant role in regulating immune and inflammatory processes (191-193). The S100A8/A9 protein complex (a.k.a. calprotectin) was significantly increased during CF PEx in our study and has previously been demonstrated to be predictive of time to next exacerbation in clinically stable CF individuals (120). Elevated S100A12 gene expression was also reported in a previous study examining blood neutrophil-derived RNA-seq data from CF individuals during PEx (124). Cathelicidin antimicrobial peptide (CAMP) and myeloid differentiation protein-2 (LY96) gene expression were significantly increased during PEx. CAMP protein binds to negatively charged bacterial membranes and can eliminate pathogenic microbes in many organisms (194). LY96 protein functions alongside Toll-like receptor (TLR) 4 to recognize bacterial lipopolysaccharides 91 and induces an inflammatory response (195). Although most molecules were involved in inducing inflammation during PEx, secretory leukocyte proteinase inhibitor (SLPI) and IL-1R2 gene expression were increased during PEx which encodes proteins that regulate the inflammatory response. SLPI protects epithelial tissues from proteolytic degradation (i.e., neutrophil-derived elastase) (196, 197). IL-1R2 acts as a decoy receptor that regulates the IL-1 signaling pathway. Interestingly, the other regulator of the IL-1 signaling pathway, IL-1RA, was measured in the cytokine panel and significantly increased during CF PEx. IL-1RA tends to increase together with IL-1 family cytokines and plays a role in terminating the inflammatory response (198). After adjusting for cell counts in the sensitivity analysis, the IL-1 signaling pathway was significantly enriched by up-regulated genes (fold change > 1.5), including IL-1R1, IL-1R2, IL-1RAP, and IL-18R1. Based on protein data, a considerable increase in IL-17A and IL-17C were identified during CF PEx which highlights the critical function of IL-17 cytokines in CF lung disease consistent with previous studies (199-201). IL-17A functions to recruit and activate neutrophils into sites of inflammation and induces the production of a number of other proinflammatory cytokines, such as TNF-a, IL-6, IL-8, and granulocyte-macrophage colony-stimulating factor (GM-CSF), to promote and sustain inflammation (170, 202-204). IL-17C is induced in epithelial cells by bacteria and inflammatory stimuli and stimulates the expression of proinflammatory cytokines, chemokines, and antimicrobial peptides (205). Additionally, IL-1 signaling has been shown to be important in Th17 cell development and cytokine secretion (206, 207), which may indicate the Th17-skewed inflammation during CF PEx. Elevations of other systemic inflammatory markers including SAA and CRP during PEx were also observed similar to 92 previous studies and their reductions have been shown associated with improvements in FEV1% predicted following antibiotic therapy (166). Blood transcriptomic and proteomic-based biomarker discovery have been implicated in CF previously to assess treatment response or predict PEx (122, 124, 125). In contrast to previous studies, we comprehensively profiled the whole blood transcriptome and inflammatory proteins during paired stable-PEx visits to identify biomarker candidates, which substantially reduced the variability due to heterogeneity of baseline clinical characteristics and environmental factors, and avoided technical variability introduced from the cell isolation process. We then developed biomarker panels to predict imminent PEx risk in apparently stable CF individuals based on these transcript and protein candidates, a setting whereby a reliable biomarker might improve clinical decision-making. Furthermore, we investigated the generalizability of our biomarker findings by evaluating our biomarkers on two external datasets. Because many of the original discoveries were based on a small sample size and may not represent the heterogeneity of the patient population, clinical translation of biomarker results was sometimes restricted by a lack of generalizability (208, 209). Therefore, validating findings in external datasets is critical in biomarker research. Only clinically stable (and not PEx) samples from CF individuals were available in two publicly accessible whole blood RNA-seq datasets (185, 186). The expression levels of the 16-gene panel were stable at clinically stable visits across the three different study cohorts. Additionally, we applied analytical validation to recalibrate the 16-gene panel to a targeted NanoSting nCounter® platform which can benefit future external validation work and enhance clinical translation. 11 out of 16 biomarker panel genes were detectable by NanoString nCounter® platform and positively correlated with RNA-seq results. Because of its quick turnaround time, automation, 93 and ability to directly quantify RNA targets, the NanoString nCounter® technology is an attractive choice for clinical translation (210). This study has some important limitations that should be acknowledged. Only 14 CF individuals were studied in order to identify PEx-related biomarker candidates, which may not capture the heterogeneity of CF PEx and may limit the ability to uncover further PEx biomarker candidates. Analyzing a complex tissue, such as whole blood, with bulk RNA-seq is challenging because gene expression changes may be driven by changes in cellular composition during PEx (i.e., increased neutrophil %) and gene expression variations within a single cell type. To disentangle these two effects, we performed a sensitivity analysis to adjust cell count in differential expression analysis to assess for changes in gene expression independent of cellular composition. Genes involved in the IL-1 signaling pathway, such as IL-1R1, IL-1R2, IL-1RAP, and IL-18R1, were increased with a fold change > 1.5 and p-value < 0.05. Also, we applied immune cell type deconvolution to evaluate the changes in cellular composition between clinically stable and PEx. Future work should consider exploring biomarkers with single-cell RNA-seq, which will be able to simultaneously assess changes in gene expression and cellular composition, as well as evaluate cellular activity of each immune cell type based on its own gene expression pattern. Furthermore, none of the participants in this study received CFTR modulator therapy. Even though the effects of CFTR modulator therapy on systemic inflammation are not fully understood, the rate of CF PEx events is dramatically decreased in patients treated with highly effective CFTR modulator therapy (211, 212). As a result, our biomarker panels will need to be re-evaluated in patients taking CFTR modulator therapies. In conclusion, we identified blood transcripts and proteins associated with neutrophilic inflammation, cytokine production, and innate immune response to bacterial pathogens during 94 PEx. Based on biomarker candidates, a 16-gene panel and a 9-protein panel were constructed and showed good performance in predicting imminent PEx risk in CF individuals. The biomarker findings from this study are a step towards personalized medical monitoring and earlier diagnosis of CF PEx. Validation of biomarker findings in larger CF patient cohorts and CF patients treated with CFTR modulator therapies is necessary and represent key directions for future investigation. 95 Chapter 4: Proteomic Profiling to Identify Blood Biomarkers Predictive of Treatment Response to Azithromycin in Children and Adolescents with Cystic Fibrosis 4.1 Introduction Azithromycin is an anti-inflammatory therapy recommended in the Cystic Fibrosis Pulmonary Guidelines as a chronic medication to maintain lung health (63). It has anti-bacterial and anti-inflammatory properties and has been shown to reduce the risk of PEx in children and adolescents with and without chronic Pseudomonas infection (75, 76, 213, 214). However, azithromycin does not benefit all patients as nearly 20% of CF subjects treated with azithromycin for 168-days in the clinical trial still experienced at least one PEx requiring antibiotics (76). Blood-based biomarkers reflective of systemic inflammation have the potential to assess the clinical response to therapeutic interventions in CF (100). The anti-inflammatory property of azithromycin therapy has been confirmed in CF children and adolescents uninfected with P. aeruginosa where significantly lower levels of neutrophil counts, calprotectin, and serum amyloid A (SAA) were observed in the azithromycin treatment arm compared to the placebo arm. However, in CF subjects treated with azithromycin therapy, each subject may respond differently as the magnitude of changes in inflammatory markers varied (215). Additionally, changes in serum high-sensitivity C reactive protein (hsCRP), calprotectin, and SAA following 28-days of oral azithromycin treatment significantly correlated with changes in FEV1 % predicted and weight at day 28 (215). However, for a biomarker to inform clinical care, early 96 changes should be predictive of longer-term outcomes, thus enabling modification of therapeutic decisions in a timelier manner. To our knowledge, no prior studies have evaluated blood biomarkers to predict therapeutic response to azithromycin in individuals with CF. Therefore, the objective of this study was to apply novel and complementary proteomics technologies to assess early changes in blood-based protein biomarkers to identify individuals who will derive clinical benefit in terms of lung function improvement, weight gain, and PEx risk reduction by day 168. 4.2 Patients and Methods 4.2.1 Study Cohort This study involved pediatric subjects with CF from the treatment arm of the AZ0004 trial (76). In brief, individuals 6 to 18 years of age with CF were enrolled if their FEV1% predicted was ≥ 50%, and they were P. aeruginosa culture-negative in the previous year. Oral azithromycin was administered three times per week for 168 days. Clinical evaluations were performed on days 0, 28, 84, and 168. Serum and plasma samples collected on days 0 and 28 from 52 subjects were obtained from the Cystic Fibrosis Foundation Therapeutics Biorepository as previously described (215). Clinical data (i.e., FEV1% predicted, weight, protocol-defined PEx) and biomarker data on five candidate serum inflammatory markers previously measured with enzyme-linked immunosorbent assay (ELISA) were also obtained from the Cystic Fibrosis Foundation Therapeutic Development Network: calprotectin, hsCRP, SAA, myeloperoxidase (MPO), and granulocyte colony-stimulating factor (G-CSF) (215). 97 4.2.2 Proteomic analysis for blood proteins Plasma samples (20 µL) were analyzed with multiple-reaction monitoring mass spectrometry (MRM-MS) at the University of Victoria Genome BC Proteomics Centre (Victoria, BC, Canada) as described in a previous study (125). A bio-library of 127 unique proteins (represented by 230 signature peptide fragments) was measured in all plasma samples, and 108 proteins passed the quality control metrics. The abbreviations of measured proteins were listed in Table 34 in Appendix C. Most of the evaluated proteins relate to host immunity, inflammation, coagulation, and the acute phase response. Serum samples were analyzed by Olink Proteomics, and 92 inflammation-related proteins were measured using the Olink ProSeek® multiplex inflammation I panel (216). In brief, 1 µL of serum sample was mixed with a probe solution containing 92 pairs of DNA-oligonucleotide-conjugated antibodies. When the paired oligonucleotide-conjugated antibodies bind to epitopes on the target protein in close proximity to each other, the accompanying oligonucleotides are capable of annealing together, thus providing a polymerase chain reaction target sequence. The newly formed target sequence can then be amplified and quantified with the microfluidic real-time quantitative polymerase chain reaction (Fluidigm Corporation). The quantification results are presented as Normalized Protein expression values, which is an arbitrary value on a log2 scale. Normalized Protein expression values were calculated by normalizing the quantification cycle from quantitative polymerase chain reaction to both intra-assay and inter-assay variation. A high Normalized Protein expression value represents a high protein concentration. Seventy-five serum proteins passed the limit of detection and were included in downstream analyses. Subjects were excluded if they received additional antibiotics in the first 28 days of azithromycin as this could confound the relation between early biomarker change due to 98 azithromycin and clinical outcomes. All the plasma and serum samples were measured in duplicate. 4.2.3 Statistical analyses All analyses were performed using R (version 3.5.0, the R Foundation for Statistical Computing, Vienna, Austria). Continuous variables are presented as mean ± standard deviation (SD). Categorical variables are reported as numbers, with proportions in brackets. Blood proteins with statistically significant fold change from day 0 to day 28 were identified. MRM-MS, Olink ProSeek, and ELISA data were log2 transformed before conducting the analyses described here. Parametric paired t-test was applied to blood protein data, and a non-parametric Mann-Whitney U test was used to compare clinical data. Spearman’s correlations for baseline (day 0) and early change in blood biomarkers (day 0 to 28) with changes in FEV1% predicted and weight from day 0 to day 28 and from day 0 to day 168 were examined. A Poisson regression model was used to examine the relations between early change in blood biomarkers and frequency of PEx from day 0 to day 168. Performance characteristics of the identified blood protein biomarkers were evaluated using the area under the receiver operating characteristics curves (AUC) with the 5×5-fold cross-validation (217). Biomarker score cutoffs were chosen to maximize Youden’s index (sensitivity and specificity). Reported P values were analyzed with a two-tailed test, and a cutoff of 0.05 was considered significant. The Benjamini-Hochberg procedure was used to control the false discovery rate (FDR) for each analysis and the FDR-adjusted p-values < 0.10 were reported. 99 4.3 Results 4.3.1 Clinical characteristics Of the 52 participants with blood samples available from the treatment arm of the AZ0004 study (76), twelve subjects with CF were excluded due to receiving antibiotics between day 0 and day 28 in addition to azithromycin. The characteristics of the remaining 40 subjects with CF were similar to the baseline characteristics of the treatment arm of the AZ0004 study (Table 16; Table 33 in Appendix C). The average age of the cohort was 9.8 ± 3.1 years, with a baseline FEV1% predicted of 98.4 ± 16.4% and weight of 35.2 ± 13.7 kg. Eight out of 40 subjects (20%) were diagnosed with at least one protocol-defined PEx following 168 days of azithromycin treatment. 100 Table 16. Baseline clinical characteristics of subjects included from the AZ0004 study Characteristics Overall (n = 40) Protocol-defined exacerbation (n = 8) Non-exacerbation (n = 32) Age, mean (SD), y 9.8 (3.1) 9 (3.4) 10 (2.9) Participants aged 6 – 12 y, No. (%) 31 (78) 7 (88) 24 (75) Participants aged 13 – 18 y, No. (%) 9 (23) 1 (13) 8 (25) Female, No. (%) 19 (48) 5 (63) 14 (44) Genotype, No. (%) F508 Homozygous 17 (43) 2 (25) 15 (47) F508 Heterozygous 19 (48) 5 (63) 14 (44) Other (non-F508) 4 (10) 1 (13) 3 (9) FEV1% predicted, No. (%) 50-80 5 (13) 1 (13) 4 (13) 81-110 25 (63) 6 (75) 19 (59) >110 10 (25) 1 (13) 9 (28) FEV1% predicted, mean (SD) 98.4 (16.4) 90.1 (14.2) a 100.5 (16.2) a Weight, mean (SD), kg 35.2 (13.7) 32.5 (16.2) 35.8 (13.0) Height, mean (SD), cm 138.3 (18.0) 135.6 (21.4) 139.0 (17.1) BMI, mean (SD) 17.7 (3.1) 16.8 (3.0) 17.9 (3.1) Dose received (by kg), No. (%) 250 mg (18-35.9) 26 (65) 6 (75) 20 (63) 500 mg (≥ 36) 14 (35) 2 (25) 12 (37) Chronic medication use, No. (%) Dornase alfa 27 (68) 8 (100) 19 (59) Inhaled tobramycin 6 (15) 1 (13) 5 (16) Ibuprofen 1 (3) 0 (0) 1 (3) Hypertonic saline 7 (18) 1 (13) 6 (19) a P-value < 0.05 (Mann-Whitney U test) for the comparison of protocol-defined exacerbation and non-exacerbation groups. Abbreviations: BMI: body mass index; FEV1: forced expiratory volume in one second. 101 4.3.2 Baseline protein levels to predict response to azithromycin treatment Baseline (day 0) serum CD40 levels measured with Olink ProSeek significantly correlated with changes in FEV1% predicted from day 0 to day 28 (FDR < 0.10) but not from day 0 to day 168 (Table 35 in Appendix C). No significant correlations were identified between baseline blood protein levels measured with MRM-MS or candidate proteins measured with ELISA and changes in FEV1% predicted at day 28 and 168 of azithromycin treatment. Baseline histidine-rich glycoprotein (HRG) level measured with MRM-MS significantly correlated with changes in weight from day 0 to day 168 (FDR < 0.10; Table 36 in Appendix C). No significant correlations were identified between baseline blood protein levels measured with Olink ProSeek or ELISA and changes in weight at day 28 or 168. 4.3.3 Blood protein changes from day 0 to day 28 of azithromycin treatment Many proteins demonstrated significant fold-change from day 0 to day 28 of azithromycin treatment; however, only two proteins, both measured with Olink ProSeek, met the FDR cutoff of < 0.10 (Figure 26, Table 17). Serum C-X3-C motif chemokine ligand 1 (CX3CL1; FC = 1.15; FDR < 0.10) and chemokine ligand 20 (CCL20; FC = 0.62; FDR < 0.10) changed significantly following 28 days of azithromycin treatment. Levels of candidate proteins G-CSF, MPO, and SAA measured with ELISA also decreased significantly by day 28 but with FDR > 0.10. 102 ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●CO9CRPSAA4p−value <0.05●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●IL6IL−17CIL−17AIL−10RBPD−L1MMP−10Flt3LCCL28CD40CX3CL1CCL20p−value <0.05●●●●●MPOGCSFSAAp−value <0.05MRM Olink ELISA−2 −1 0 1 2 −2 −1 0 1 2 −2 −1 0 1 2012340123401234log2(fold change)−log(p−value)● ●q−value < 0.1 q−value > 0.1FDR < 0.10 FDR ≥ 0.10103 Figure 26. Volcano plot demonstrating the statistical significance of fold-change in blood protein levels from day 0 to day 28 of azithromycin treatment measured with MRM-MS, Olink ProSeekⓇ Multiplex Inflammation I Panel, and ELISA Abbreviations: MRM-MS CRP: C-reactive protein; SAA4: Serum amyloid A4; CO9: CO-like protein; Olink ProSeek CCL20: Chemokine ligand 20; CX3CL1: C-X3-C motif chemokine ligand 1; Flt3L: Fms-related tyrosine kinase 3 ligand; PD-L1: Programmed cell death 1 ligand 1; MMP-10: Matrix metalloproteinase-10; IL-17A: Interleukin-17A; IL-17C: Interleukin-17C; IL-10RB: Interleukin-10 receptor subunit beta; CCL28: C-C motif chemokine 28; IL-6: Interleukin-6; CD40: CD40L receptor; ELISA SAA: Serum amyloid A; G-CSF: Serum granulocyte colony-stimulating factor; MPO: Myeloperoxidase 104 Table 17. Blood proteins measured with MRM-MS, Olink ProSeek, and ELISA with significant (p-value < 0.05) fold-change from day 0 to day 28 of azithromycin treatment Protein Fold Change (Day 28 relative to Day 0) P-value FDR MRM-MS C-reactive protein (CRP) 0.586 0.011 0.265 Serum amyloid A4 (SAA4) 0.843 0.023 0.371 CO-like protein (CO9) 0.807 0.040 0.477 Olink ProSeek Chemokine ligand 20 (CCL20) 0.618 < 0.001 0.031 C-X3-C motif chemokine ligand 1 (CX3CL1) 1.146 < 0.001 0.045 Fms-related tyrosine kinase 3 ligand (Flt3L) 1.106 0.005 0.227 Programmed cell death 1 ligand 1 (PD-L1) 1.079 0.005 0.227 Matrix metalloproteinase-10 (MMP-10) 0.839 0.006 0.227 Interleukin-17A (IL-17A) 0.884 0.009 0.246 Interleukin-17C (IL-17C) 0.850 0.025 0.371 Interleukin-10 receptor subunit beta (IL-10RB) 1.054 0.029 0.371 C-C motif chemokine 28 (CCL28) 0.933 0.029 0.371 Interleukin-6 (IL-6) 0.802 0.046 0.501 CD40L receptor (CD40) 1.075 0.049 0.501 ELISA Serum Amyloid A (SAA) 0.442 0.004 0.182 Granulocyte Colony-Stimulating Factor (G-CSF) 0.769 0.014 0.287 Myeloperoxidase (MPO) 0.837 0.029 0.371 High-sensitivity C-reactive protein (hsCRP) 0.633 0.050 0.501 Calprotectin 0.790 0.071 0.627 105 4.3.4 Correlation between early change in blood protein level and change in FEV1 % predicted from day 0 to day 28 and day 0 to day 168 of azithromycin treatment Early changes in the levels of 15 plasma proteins measured with MRM-MS significantly correlated with changes in FEV1 % predicted from day 0 to day 28 (FDR < 0.10) but not with changes in FEV1 % predicted from day 0 to day 168 (Table 18, Table 37 in Appendix C). There were no significant correlations between early change in protein levels measured with Olink ProSeek or candidate proteins measured with ELISA and changes in FEV1 % predicted at day 28 and 168 of azithromycin treatment. Table 18. Associations between early change in biomarker levels and change in FEV1% predicted from day 0 to day 28 Change in FEV1% predicted from Day 0 to Day 28 Change in FEV1% predicted from Day 0 to Day 168 Protein Correlation a P-value FDR Correlation a P-value FDR MRM-MS Insulin-like growth factor-binding protein 3 (IBP3) 0.507 0.001 0.091 0.356 0.024 0.474 Apolipoprotein C-II (APOC2) 0.467 0.002 0.099 -0.040 0.806 0.900 Paraoxonase 1 (PON1) 0.458 0.003 0.099 0.170 0.295 0.597 Apolipoprotein A-I (APOA1) 0.446 0.004 0.099 0.224 0.165 0.474 CYTC;NRP2 0.436 0.005 0.099 0.093 0.569 0.759 Serum albumin (ALBU) 0.423 0.007 0.099 0.235 0.145 0.474 Apolipoprotein C-III (APOC3) 0.422 0.007 0.099 0.073 0.655 0.804 Coagulation factor XIII A chain (F13A) 0.411 0.008 0.099 0.192 0.234 0.527 Alpha-2-macroglobulin (A2MG) 0.408 0.009 0.099 0.224 0.165 0.474 Plasma kallikrein (KLKB1) 0.396 0.011 0.099 0.221 0.170 0.474 Cadherin-5 (CADH5) 0.395 0.012 0.099 0.244 0.126 0.474 Inter-alpha-trypsin inhibitor heavy chain H2 (ITIH2) 0.393 0.012 0.099 0.248 0.123 0.474 Cytochrome c (CYTC) 0.390 0.013 0.099 0.085 0.600 0.722 Plasma serine protease inhibitor (IPSP) 0.388 0.013 0.099 0.324 0.042 0.474 Vitamin D-binding protein (VTDB) 0.386 0.014 0.099 0.152 0.349 0.662 106 a Spearman’s correlation analysis Abbreviations: MRM-MS: multiple-reaction monitoring mass spectrometry 4.3.5 Correlation between early change in blood protein level and change in weight from day 0 to day 28 and day 0 to day 168 of azithromycin treatment Early changes in SAA measured with ELISA significantly correlated with changes in weight from day 0 to day 28 (FDR < 0.10), but this correlation was not significant by day 168 (Table 38 in Appendix C). Early changes in blood protein levels measured with MRM-MS and Olink ProSeek did not significantly correlate with weight changes at day 28 and 168 of azithromycin treatment. 4.3.6 Change in serum calprotectin levels following 28 days of azithromycin treatment predicts protocol defined PEx frequency risk by day 168 of treatment Early change in serum calprotectin levels measured with ELISA was significantly associated with the frequency of protocol-defined pulmonary exacerbations by day 168 (FDR < 0.10; Figure 27). Three additional serum proteins (EN-RAGE, CCL23, and IL-17A) measured with Olink ProSeek were also associated with the frequency of protocol-defined pulmonary exacerbations by day 168; however, these proteins did not meet the FDR cutoff of < 0.10 (Table 19). 107 Figure 27. Change in serum calprotectin levels from day 0 to day 28 predicts protocol-defined PEx frequency following 168 days of azithromycin treatment Table 19. Early change in blood protein following 28 days of azithromycin treatment predicts protocol-defined PEx frequency risk by day 168 of treatment Protein p-value FDR ELISA Calprotectin 0.002 0.012 Olink ProSeek Protein S100-A12 (EN-RAGE) 0.007 0.523 C-C motif chemokine 23 (CCL23) 0.014 0.523 Interleukin-17A (IL-17A) 0.049 0.740 ●● ● ●●●●● ●● ● ● ●●● ●●●●● ●●●● ●●●● ●●●●●●●●p−value = 0.002 with q−value = 0.0120123−3 −2 −1 0 1 2 3Change in calprotectin from day 0 to day 28 (mcg/mL)Protocol−defined PEx number P-value = 0.002 with FDR = 0.012Change in Calprotectin from Day 0 to Day 28 (µg/mL)108 When analyzing PEx frequency between day 0 and day 168 as a binary variable (0 vs. ≥ 1), change in serum calprotectin from day 0 to day 28 demonstrated good performance in the prediction of PEx risk (AUC = 0.76, 95% CI: 0.57-0.95). The ROC analysis resulted in a 28-day calprotectin change cutoff of -0.26 (Youden index = 0.55, sensitivity = 0.88, specificity = 0.68). Six out of 15 (40%) subjects who had calprotectin change above the cutoff from day 0 to day 28 experienced at least one PEx. On the contrary, only 8% of subjects experienced PEx in those with a calprotectin change below the cutoff (Figure 28A). In a post-hoc analysis to validate the predictive value of early change in calprotectin to identify subjects with modified PEx risk by day 168, day 0 and 28 calprotectin data from 62 subjects who were enrolled in the treatment arm of the AZ0004 study (but not included in our discovery analysis as no samples were remaining) were obtained from the Cystic Fibrosis Foundation Therapeutic Development Network (Table 33). In the validation cohort, 25% of research participants who had a calprotectin change above the cutoff (≥ -0.26) still experienced at least one PEx while only 10% of subjects experienced at least one PEx when having calprotectin change below the cutoff (< -0.26) following 28 days of azithromycin treatment (Figure 28B). 109 Figure 28. ROC analysis: change in calprotectin from day 0 to day 28 predicts protocol-defined PEx frequency following 168 days of azithromycin treatment: A. discovery cohort; B. validation cohort 110 4.4 Discussion This study evaluated early changes in blood protein biomarker levels in order to predict both short- and longer-term therapeutic responses to azithromycin in children and adolescents with CF. A biomarker that changes early and that is predictive of longer-term clinical response may help identify individuals who would be expected to derive therapeutic benefit from azithromycin. Despite our more comprehensive approach to profiling the blood proteome compared to the Ratjen et al. study (215), which also utilized samples from the AZ0004 study (76), our study confirmed that early changes in blood protein levels following 28 days of azithromycin treatment correlated with short- but not longer-term changes in FEV1% predicted and weight. However, we found that early changes in serum calprotectin levels following the first 28 days of azithromycin treatment were predictive of PEx risk by day 168. Calprotectin is a neutrophilic protein found in the sputum and sera of individuals with CF. Recent studies have demonstrated that serum calprotectin is a predictive biomarker of PEx risk in CF and decreases in response to PEx treatment (110, 120, 218). Individuals with higher serum calprotectin levels during stable clinical measurement experience a shorter time to first PEx (120). Similarly, a higher serum calprotectin level following PEx treatment is predictive of time until next PEx (110). Therefore, the findings from our study add to prior literature supporting calprotectin as a robust biomarker of PEx risk in CF by demonstrating that serum calprotectin is responsive to azithromycin treatment and, more importantly, these changes in calprotectin associated with PEx risk. Individuals who had a decrease in serum calprotectin following azithromycin experienced lower PEx risk, whereas individuals who had an increase in calprotectin despite azithromycin experienced an increase in PEx risk. 111 Although we examined many proteins, the early change in very few blood proteins correlated with short-term (day 28) change in FEV1% predicted. However, it should be mentioned that there was no significant improvement in FEV1% predicted with azithromycin (vs. placebo) by day 168 of treatment in the AZ0004 trial (76). Of the 15 plasma proteins measured with MRM-MS that significantly correlated with short-term changes in FEV1% predicted at day 28, all proteins positively correlated with changes in FEV1% predicted, suggesting that they might represent negative acute phase reactants. While there was a negative correlation between early change in serum hsCRP, calprotectin, and SAA and early change in FEV1% predicted at day 28 similar to the findings by Ratjen et al. (215), the correlations were not statistically significant in this study, probably due to the smaller sample size (n=40 vs. n=122). An early decrease in serum CCL20 was observed following 28 days of azithromycin treatment. Interestingly, the magnitude and statistical significance of the reduction in CCL20 was comparable to candidate proteins (i.e., hsCRP, SAA) that have been extensively reported in the CF literature. Thus, CCL20 may represent a novel biomarker of systemic inflammation in CF or reflect the anti-inflammatory action of azithromycin. However, the significant decrease in CCL20 following azithromycin treatment did not correlate with either short- or longer-term changes in FEV1% predicted and weight, or exacerbation risk and therefore, similar to hsCRP and SAA, CCL20 decreases early but does not predict clinical response. This study has a number of important limitations. The sample size was small and limited by the number of remaining samples from the AZ0004 study, and our analysis may therefore have been underpowered to identify other promising biomarkers of treatment response. We performed a large number of correlation analyses and many of the significant p-values (< 0.05) did not meet the FDR < 0.10 following adjustment for multiple testing. While multiple-testing 112 adjustment reduces the likelihood of false discovery, this conservative approach may filter out potentially promising biomarkers at the discovery stage. As such, we have reported all profiled blood proteins with their p-value and FDR. Future studies will need to evaluate the performance of proteins with significant p-values in the larger patient cohorts. There were statistically significant differences in FEV1% predicted between exacerbation and non-exacerbation groups, and in age and height between discovery and validation cohorts. However, significant differences were not observed in the level of calprotectin at baseline (day 0) between exacerbation vs. non-exacerbation groups or discovery vs. validation cohorts and therefore we do not believe these differences in baseline clinical characteristics influenced our results or conclusions (Table 20). Additionally, although we focused on clinical outcomes including FEV1% predicted, weight, and PEx, symptom response may also be important. Unfortunately, symptom changes in response to azithromycin treatment were not collected in the clinical trial and thus could not be evaluated. Table 20. Comparison of baseline (day 0) serum calprotectin levels measured with ELISA between protocol-defined PEx vs. non-PEx groups (from the discovery cohort) and between discovery vs. validation cohorts Comparison Serum calprotectin levels Log2 transformed; mean (SD) P-value a Protocol-defined PEx vs. Non-PEx 1.03 (0.92) vs. 1.15 (0.87) 0.75 Discovery cohort vs. Validation cohort 1.12 (0.87) vs. 1.25 (1.26) 0.55 a Two-sample t-test In conclusion, we demonstrated that early changes in blood protein levels following 28 days of azithromycin treatment in children and adolescents with CF correlated with short-term but not long-term changes in FEV1% predicted. Change in serum calprotectin level within the 113 first 28 days of azithromycin therapy was predictive of PEx risk by day 168, thus representing a promising predictive biomarker to identify individuals who might be expected to derive longer-term benefits from azithromycin treatment in terms of PEx risk reduction. 114 Chapter 5: Genetic Susceptibility of Pulmonary Exacerbations in Preschoolers with Cystic Fibrosis 5.1 Introduction Cystic fibrosis (CF) is a monogenic disorder caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene (1). Nearly 2,000 CFTR gene variants have been described, and over 400 of them are disease-causing (17). The CFTR genotype was once believed to be the strongest predictor of lung disease severity in individuals with CF (219); however, this hypothesis was disapproved by the CF Twin-Sibling study, which demonstrated that over 50% of the variability in lung function could be attributed to environmental factors and non-CFTR genetic modifiers (26). According to a recent systematic review, 124 candidate genes have been identified as non-CFTR genetic modifiers and associated with lung function and first-time growth or chronically infected P. aeruginosa infection (127). However, the results of these studies have been inconsistent as most of them were based on small sample sizes and heterogeneous outcome measures. More recently, the systematic genome-wide approach was conducted to identify genetic modifiers of CF lung disease (128, 129, 220). The meta-analysis of genome-wide association study (GWAS) data on 6365 CF individuals from North America and French reported five loci associated with variation in lung function phenotypes (128). Genes annotated to these loci, including SLC9A3 (131, 132), SLC6A14 (132), and HLA Class II (133, 134), were confirmed in multiple studies to correlate with lung function or risk of P. aeruginosa infections. SLC9A3 and SLC6A14 were also pleiotropic for the risk of meconium ileus in CF individuals 115 (132). The effect sizes of these identified loci on lung disease severity were small; however, these findings revealed important mechanistic insights into CF lung disease and hold the potential to be studied as therapeutic targets (221, 222) to improve clinical outcomes. PEx are significant risk factors for lung disease progression in CF (79, 80, 84). Repeated PEx events accounted for over half of the decline in FEV1 over the lifetime of CF individuals (84). Infants and toddlers with CF are also vulnerable to PEx. CF children who experienced PEx before two years old tend to have a steeper decline in FEV1% predicted by the age of five years (85). Moreover, despite the absence of respiratory symptoms, increased pro-inflammatory markers (i.e., IL-8, neutrophil elastase, and neutrophil count) were observed in bronchoalveolar lavage (BAL) samples from young CF children under imminent risk of PEx (3). Diagnosis of a PEx in CF individuals typically relies upon a combination of increased respiratory symptoms and decreased lung function; however, diagnosis of a PEx in young CF children is challenging because of their “clinically silent” lung inflammation and inability to perform spirometry and expectorate sputum, which potentially leads to the under-diagnosis of PEx (45). Considering the clinical significance of PEx in young CF children and limited strategies to diagnose and predict these adverse events, novel approaches are needed to identify young CF children at an increased risk of PEx. Genetic factors, in combination with clinical characteristics obtained early in life, have demonstrated their potential as biomarkers to predict the onset of CF-related diabetes later in life (135). Also, although not in CF, genetic susceptibility to severe PEx has been studied in children with early childhood asthma (136). Motivated by these findings, we leveraged the extensive genotyping data generated by the Canadian CF Gene Modifier Study (CGMS) and comprehensive demographic and clinical data collected by the Canadian CF Registry (CCFR) in 116 this study and hypothesized that PEx risk in young children with CF can be predicted by using genetic factors and clinical characteristics obtained early in life. 5.2 Patients and Methods 5.2.1 Study cohort The CGMS cohort is a population-based study cohort which captures genetic data on about 70% of the Canadian CF population. Individuals enrolled in the CGMS cohort were included if they were born between 2000 and 2015 and diagnosed with CF before one year of age. The diagnosis of CF was based on the CCFR criteria, and the date of diagnosis was extracted from the CCFR database. Additionally, five-year clinical follow-up data for each individual were requested from the CCFR and were used to identify severe PEx events. Severe PEx was defined based on the need for hospitalization where the primary or secondary reason for hospitalization was a PEx. CF individuals were excluded if they died from non-PEx causes before five years old. Individuals who received CFTR modulator therapies before five years of age were recorded but were not excluded from the study. 5.2.2 Clinical characteristics The following clinical characteristics and tests were obtained from the CCFR: demographic variables (i.e., age, sex, race, height, weight, BMI percentiles, CFTR genotype, date of birth, and date of diagnosis with CF), lung function if applicable at the early ages, hospitalizations, CFTR modulator therapies, CF comorbidities (i.e., meconium ileus, pancreatic insufficient), microbiology, and date and causes of death. 117 Additionally, a CFTR variant severity score was calculated to evaluate the severity of CFTR mutations (135). A score ranges from 1 to 4, of which 1 means the least severe and 4 means the most severe, was assigned to each allele, then the final score is calculated depending on the two individual allele scores (Table 21). Table 21. Calculation of CFTR variant severity score CFTR allele score 1 1 2 3 4 CFTR allele score 2 1 1 1 1 1 2 1 2 2 2 3 1 2 3 4 4 1 2 4 5 5.2.3 Sample genotyping and quality control Genotype data of CGMS participants who met the inclusion criteria was requested from the Hospital for Sick Children. To avoid variability introduced by different data pre-processing criteria, quality control (QC) and imputation of genotype data were performed by the CGMS study group as reported previously (223). In brief, genotyping was performed on four illumina genome-wide platforms: the 610Quad, 660W, Omni5, and Omni2.5 BeadChip. 200 ng DNA were used per sample. Genotype calling was performed using Genome Studio V2011.1. Single nucleotide polymorphism (SNP) annotation was based on the Genome Reference Consortium 37. SNPs were removed if they were not annotated to chromosomes 1-22 or X, with a call rate of 118 90% or missing rs number. Duplicated SNPs and SNPs on chromosome X with a heterozygosity rate > 10% in the male sample were also removed (224). Each platform independently performed genotype QC. Beagle V4.1 was used for phasing and imputing missing genotype data (225). A hybrid reference panel comprised of 1000 Genomes reference Phase 3 data and whole-genome sequencing data from 101 Canadian CGMS participants were used in the imputation as this hybrid reference panel demonstrated improved imputation accuracy at disease-specific loci when compared to using public data sets, such as 1000 Genomes reference or Haplotype reference (226, 227). Whole-genome sequencing was performed by Complete Genomics at 30X coverage (226). 5.2.4 List of candidate single-nucleotide polymorphisms (SNPs) Nine SNPs were identified as candidate SNPs according to previous GWAS data, which were significantly associated with lung function or lung disease phenotypes (Table 22) (128, 129, 220). Table 22. Candidate single-nucleotide polymorphisms (SNPs) Gene Chromosome SNPs Minor allele Minor allele frequency Reported phenotype(s) Reference MUC4/MUC20 3 rs3103933 A 0.37 KNoRMA (128) SLC9A3 5 rs57221529 G 0.2 KNoRMA (128) HLA-DRA 6 rs116003090 C 0.31 KNoRMA (128) EHF/APIP 11 rs10742326 A 0.42 KNoRMA (128) AGTR2/SLC6A14 X rs5952223 T 0.28 KNoRMA (128) APIP/EHF 11 rs12793173 C 0.24 KNoRMA (129) IFRD1 7 rs7817 C 0.48 Lung function (220) IFRD1 7 rs6968084 T 0.14 Lung function (220) IFRD1 7 rs3807213 C 0.4 Lung function (220) 119 Abbreviation: KNoRMA: Kulich Normal Residual Mortality Adjusted lung disease phenotype (130); SNP: Single-nucleotide polymorphism. 5.2.5 Statistical analyses All analyses were performed using R (version 3.6.1, the R Foundation for Statistical Computing, Vienna, Austria). Median with interquartile range ([Q1, Q3]) was used to report summary statistics on continuous variables. Categorical variables were presented as numbers with proportions in brackets. To examine the difference in continuous variables, the Wilcoxon rank-sum test was used. The Chi-square test was used to examine the difference in categorical variables. A two-tailed test was used to analyze reported p-values, and a p-value < 0.05 was considered significant. Based on the number of CF individuals and the minor allele frequency of candidate SNPs, we calculated power and sample size using the genpwr R package (version 1.0.4). The additive model was tested for the power and sample size with a PEx rate of 0.3 and a type 1 error rate of 0.05. The effect sizes of candidate SNPs were unclear because these SNPs were discovered to associate with FEV1 as a continuous outcome; however, the outcome in our study was binary (PEx vs. non-PEx). Therefore, a series of hypothetical odds ratios, ranging from 1.5 to 3, were tested to estimate the statistical power and sample size. The associations between candidate SNPs and PEx risk were assessed using the GENESIS R package (version 2.22.2) (228). To mitigate the risk of confounding from population substructure (229), principal components (PCs) of genetic ancestry were calculated using the kinship matrix estimated based on genotype data by SNPRelate R package (version 1.26.0) (230) and PC-AiR function in GENESIS R package. The first 7 PCs were selected using 120 the Tracy-Widom test (AssocTests R package, version 1.0-1) to represent the population substructure and included as covariates in the association test (231). The genetic relationship matrix was included as a random effect in the model to estimate recent genetic relatedness based on the PCs calculated from PC-AiR using the pcrelate function in the GENESIS R package. In addition, adjustments were made for sex to account for variabilities in PEx risk which was a well-established demographic covariate (92). The Bonferroni correction was used to account for multiple comparisons, and SNPs with significant associations were defined as Bonferroni-corrected p-value (adjusted p-value) < 0.05. Subgroup analyses were used to assess the associations between candidate SNPs and PEx risk in individuals diagnosed with CF through newborn screening as over 73% of CF diagnoses were made through the newborn screening program based on the 2020 Canadian CF Registry Annual Data Report (232). Following the association test, we evaluated SNPs that were significantly associated with PEx risk in relation to two different clinical outcomes: cumulative PEx counts within the first five years of life and age at the first PEx event. The relationship between SNPs and cumulative PEx counts was assessed using Poisson regression. To conduct a time-to-event analysis, the survival R package (version 3.2-13) was used. The Kaplan-Meier survival curve was used to plot the age distribution at the first PEx event, and curves were compared using the log-rank test. The Cox proportional hazards regression model was used to estimate the hazard ratio between genotypes. Sex and first 7 PCs for genetic ancestry were included as covariates in Poisson and Cox proportional hazards regression analyses. The significant SNPs identified in prior association test and clinical characteristics were used to build a biomarker panel to predict PEx risk. Clinical characteristics evaluated in the biomarker analysis included CFTR variant severity score, sex, meconium ileus, and pancreatic 121 insufficiency. Performance characteristics of the SNPs and clinical variables were evaluated using the area under the receiver operating characteristics curves (AUC) with 10x10-fold cross-validation. 5.3 Results 5.3.1 Patient cohort The study cohort selection flow diagram is outlined in Figure 29. Three hundred and one CGMS participants met the inclusion criteria, and none were excluded due to non-PEx causes of death before the age of 5 years. Ninety-two CF individuals experienced a total of 161 episodes of PEx that required hospitalization in the first 5 years of life. Only 1 PEx event was defined based on the secondary reason for hospitalization as the primary hospitalization reason was missing and for the remainder of PEx events it was the prior reason for hospitalization. Home IV antibiotics treatment events were also investigated even though they were not part of the PEx definition in our study. Ten CF individuals received home IV antibiotics therapy during the study period with a median treatment duration of 10 [7 – 12.5] days. However, 9 out of 10 individuals were already included in the PEx group since they were also admitted to hospital for PEx treatment before the age of 5 years. 122 Figure 29. Flow diagram of the study cohort selection Table 23 summarizes the demographics and clinical characteristics of the study cohort. It was unsurprising that 82.4% of individuals with CF were of European ancestry. In total, 40.5% of CF individuals were diagnosed through newborn screening, whereas a lower percentage of individuals in the PEx cohort were diagnosed through newborn screening (PEx vs. Non-PEx: 23.1% vs. 48.3%; p-value < 0.001). Over 92% of individuals were diagnosed with pancreatic insufficiency at birth, and 81 (26.9%) individuals reported meconium ileus. CGMS Cohortn = 3,257Inclusion Criteria1. Born between January 1, 2000 - December 31, 20152. Diagnosed with CF before age of 1 year3. 5-year follow-up data Study Cohortn = 301n = 92≥ 1 PEx Before Age of 5 Yearsn = 209Yes NoExclusion Criteria1. Subjects died from non-PEx causes before the age of 5 years (n = 0)123 Table 23. The demographic and clinical characteristics of the study cohort Overall PEx Non-PEx Number of subjects, n 301 92 209 Female, n (%) 156 (51.8) 51 (55.4) 105 (50.2) Ethnic origin, n (%) European 248 (82.4) 73 (79.3) 175 (83.7) Asian 1 (0.3) 0 (0.0) 1 (0.5) African 3 (1.0) 0 (0.0) 3 (1.4) Other 44 (14.6) 16 (17.4) 28 (13.4) Mixed 5 (1.7) 3 (3.3) 2 (1.0) Newborn screening, n (%) 122 (40.5) 21 (23.1) 101 (48.3) Age at diagnosis in days, median [IQR] 23.0 [13.0, 87.0] 35.0 [11.5, 133.5] 22.5 [14.0, 57.3] Genotype, n (%) DF508 Homozygous 164 (54.5) 52 (56.5) 112 (53.6) DF508 Heterozygous 109 (36.2) 27 (29.3) 82 (39.2) Others 28 (9.3) 13 (14.1) 15 (7.2) CFTR variant severity score, n (%) a 1 34 (11.3) 7 (7.6) 27 (12.9) 2 34 (11.3) 11 (12.0) 23 (11.0) 3 168 (55.8) 53 (57.6) 115 (55.0) 4 42 (14.0) 11 (12.0) 31 (14.8) 5 15 (5.0) 7 (7.6) 8 (3.8) Pancreatic insufficiency, n (%) 279 (92.7) 90 (97.8) 189 (90.4) Meconium ileus, n (%) 81 (26.9) 25 (27.2) 56 (26.8) a CFTR variant severity score was missing in 6 subjects because the score could not be determined on one of their alleles. 5.3.2 Statistical power and sample size calculation The PEx rate of 30% was calculated by the percentage of individuals having PEx and was used to estimate statistical power and sample size. Figure 30 illustrates the relationship between estimated sample size and minor allele frequency at 80% power. With a type I error rate of 0.05 124 and a minor allele frequency of 0.2, 301 individuals could have more than 80% power to detect a 2-fold risk difference of PEx when having one minor allele. Figure 30. Sample size at 80% statistical power 5.3.3 Testing candidate SNPs for association with severe PEx in the first 5 years of life Nine candidate SNPs were reported as significantly associated with lung function in CF based on previous GWAS; however, the HLA-DRA variant (rs116003090) was an imputed SNP that was not genotyped in our study. First, we evaluated the early risk of PEx as a binary outcome that individuals either had PEx before the age of 5 years or not. The SLC9A3 variant, rs57221529, was significantly associated with early risk of PEx after adjusting for sex and the first 7 PCs for genetic ancestry (adjusted p-value = 0.04; Table 24). The odds of early PEx risk 1002003004005006007008009001000110012000.1 0.2 0.3 0.4Minor Allele FrequencySample size (n)Odds ratio1.51.7522.252.52.753Sample size calculation: additive model, alpha = 0.05, PEx rate = 0.3, power = 0.8125 increased by 1.95-fold (95% CI [1.22, 3.11]) when young CF individuals carried one G allele at rs57221529. Table 24. Association test results of candidate SNPs and early risk of PEx Gene Chr SNP Minor allele MAFa Odds ratiob 95% CIb P-value Adjusted p-valuec MUC4/MUC20 3 rs3103933 A 0.69 0.89 [0.59 - 1.33] 0.57 1.00 SLC9A3 5 rs57221529 G 0.19 1.95 [1.22 - 3.11] 0.004 0.04 IFRD1 7 rs6968084 C 0.15 1.50 [0.90 - 2.51] 0.12 0.98 IFRD1 7 rs3807213 T 0.57 1.15 [0.80 - 1.65] 0.44 1.00 IFRD1 7 rs7817 C 0.49 1.24 [0.88 - 1.76] 0.23 1.00 APIP/EHF 11 rs10742326 A 0.41 0.76 [0.53 - 1.09] 0.13 1.00 EHF/APIP 11 rs12793173 C 0.25 1.09 [0.72 - 1.64] 0.69 1.00 AGTR2/SLC6A14 X rs5952223 T 0.24 0.91 [0.63 - 1.31] 0.62 1.00 a Minor allele frequency (MAF) was estimated based on the study cohort (n = 301). b Odds ratio and 95% CI was calculated based on the exponentiation of coefficient in the generalized linear mixed model. c Bonferroni-corrected p-value. Additionally, we repeated the association test in CF individuals who were diagnosed through newborn screening because it captures most of the subjects since 2007 in our study (Figure 31) and is more generalizable to a contemporary CF population as over 73% of CF diagnoses were made through the newborn screening program according to the 2020 Canadian CF Registry Annual Data Report (232). Statistically significant associations were not identified in this subgroup analysis (Table 25), which might be due to the small sample size (n = 122) and lower event rate that likely limited the ability to detect a significant relationship (Figure 30). 126 Figure 31. Number of CF individuals diagnosed through newborn screening from 2000 to 2015 Table 25. Association test results of candidate SNPs and risk of severe PEx in CF individuals diagnosed through newborn screening Gene Chr SNP Minor allele MAFa Odds ratiob 95% CIb P-value Adjusted p-valuec MUC4/MUC20 3 rs3103933 A 0.69 1.49 [0.62 - 3.60] 0.37 1.00 SLC9A3 5 rs57221529 G 0.16 1.17 [0.43 - 3.22] 0.76 1.00 IFRD1 7 rs6968084 C 0.14 2.97 [0.96 - 9.20] 0.06 0.47 IFRD1 7 rs3807213 T 0.57 1.24 [0.57 - 2.70] 0.60 1.00 IFRD1 7 rs7817 C 0.49 1.09 [0.53 - 2.27] 0.81 1.00 APIP/EHF 11 rs10742326 A 0.41 0.92 [0.46 - 1.87] 0.83 1.00 EHF/APIP 11 rs12793173 C 0.28 0.90 [0.41 - 1.97] 0.79 1.00 AGTR2/SLC6A14 X rs5952223 T 0.23 1.01 [0.46 - 2.24] 0.98 1.00 ` a Minor allele frequency (MAF) was estimated based on the study cohort (n = 122). b Odds ratio was calculated based on the exponentiation of coefficient in the generalized linear mixed model. c Bonferroni-corrected p-value. 0102030402000200120022003200420052006200720082009201020112012201320142015Year of birthCountNewborn screening No Yes127 5.3.4 Testing SLC9A3 gene variant for association with cumulative PEx counts within the first five years of life When summarizing the percentage of CF individuals who had ≥ 1 PEx within the first 5 years of life in each rs57221529 genotype (AA vs. AG vs. GG), individuals with GG genotype was significantly higher than those with AA and AG genotypes (GG vs. AA vs. AG = 63.3% vs. 26.4% vs. 35.5%, p-value = 0.015; Table 26). Furthermore, after adjusting for sex and the first 7 PCs for genetic ancestry, Poisson regression revealed a significant correlation between rs57221529 and cumulative PEx frequency in the first five years of life (p-value = 0.001; Figure 32). CF individuals carried one more G allele at rs57221529 associated with an increase of 54% in PEx frequency. Table 26. Number of CF individuals who had ≥ 1 PEx before the age of 5 years vs. rs57221529 genotypes rs57221529 No. of CF subjects No. of subjects with PEx count ≥ 1, n (%) AA 197 52 (26.4%) AG 93 33 (35.5%) GG 11 7 (63.3%) 128 Figure 32. Cumulative PEx counts within the first five years of life CF subjects carried GG allele at rs57221529 associated with more frequent PEx than subjects with AA and AG genotype. 5.3.5 Testing SLC9A3 gene variant for association with age of onset of first PEx The Kaplan-Meier curve shows the age distribution at first PEx grouping by rs57221529 genotype (Figure 33). The median age of onset of the first PEx for the cohort was above 5 years for genotype AA (n = 197) and AG (n = 93), but the median age significantly decreased to 3 years for the GG genotype (n = 11; log-rank test p-value = 0.02). Moreover, we found that individuals with GG genotype at rs57221529 had a greater risk of PEx. This association was significant in Cox proportional hazard models that were adjusted for sex and the first 7 PCs for genetic ancestry (GG/AA hazard ratio [95% confidence interval]: 3.45 [1.54, 7.71], p-value = 0.003). Individuals with AG genotype had a similar risk of PEx when compared to AA genotype (hazard ratio [95% confidence interval]: 1.38 [0.89, 2.15], p-value = 0.15). 129 Figure 33. Kapan-Meier survival curve of age distribution at first PEx CF subjects with the GG allele at rs57221529 had a shorter time to first PEx than subjects with AA genotype (HR [95% CI]: 3.45 [1.54, 7.71]; p-value = 0.003). Abbreviation: HR: hazard ratio; CI: confidence interval +++HR [95% CI]:AG/AA: 1.38 [0.89, 2.15]; p = 0.15GG/AA: 3.45 [1.54, 7.71]; p = 0.0030.000.250.500.751.000 1 2 3 4 5Years since birthSurvival probabilityrs57221529 + + +AA AG GG197 181 163 157 152 15093 89 77 72 68 6611 9 8 6 5 5GGAGAA0 1 2 3 4 5Years since birthrs57221529Number at risk130 5.3.6 Identifying biomarkers predictive of PEx risk in young CF children Table 27 summarizes the performance of the single and the biomarker panel of genetic and clinical variables in predicting PEx risk in young CF children. The predictive performance is suboptimal for all tested variables (AUCs < 0.6). Table 27. Summarizations of performance in predicting PEx risk in young CF children using genetic and clinical variables AUC mean (SD) Sensitivity mean (SD) Specificity mean (SD) Single variant analysis CFTR variant severity score 0.52 (0.11) 0 (0) 1 (0) rs57221529 0.57 (0.08) 0.07 (0.08) 0.98 (0.03) Sex 0.48 (0.09) 0 (0) 1 (0) Pancreatic insufficiency 0.53 (0.04) 0 (0) 1 (0) Meconium ileus 0.43 (0.06) 0 (0) 1 (0) Multivariable analysis a Logistic regression 0.55 (0.10) 0.07 (0.08) 0.99 (0.02) a Rs57221529, CFTR variant severity score, sex, pancreatic insufficiency, and meconium ileus were used in the multivariant analysis. 5.1 Discussion This is the first study to examine the genetic susceptibility to PEx in young children with CF. We analyzed 8 candidate SNPs that were associated with lung function variation as PEx are important mediators of lung function decline in CF, and these SNPs may also influence the frequency or severity of PEx. We provide evidence for a significant association between the SLC9A3 variant, rs57221529, and early risk of PEx in young CF children. The PEx frequency in the first 5 years of life increased 1.54-fold when CF individuals carried one more minor allele (G) at rs57221529. Individuals with the GG genotype at rs57221529 were significantly younger at the time of first PEx than those who did not have a minor allele. Unfortunately, despite the 131 significant association between the SLC9A3 variant and PEx risk, this variant did not provide good prognostic performance in predicting PEx risk in young CF children, even when combined with clinical variables. SLC9A3 (a.k.a. NHE3) encodes a Na+ /H+ exchanger that is mainly expressed in the apical membrane and basolateral membrane of epithelial cells. Lack of Na+ /H+ exchanger can impair the acid-base balance and Na+ - fluid volume homeostasis. However, in the CF airway, loss of functional CFTR disrupts the homeostasis of chloride secretion and sodium absorption, resulting in decreased airway surface liquid volume and consequently producing thick viscous mucus (29, 35, 36). Defective CFTR-mediated bicarbonate transport also leads to an acidic airway environment, decreasing antimicrobial activity (28). Inhibition of SLC9A3 at the apical membrane or stimulating it at the basolateral membrane can prevent the secretion of H+ into the airway surface liquid and increase its pH (222, 233). Inhibiting SLC9A3 at the apical membrane can also reduce the Na+ reabsorption, which improves the height and fluidity of airway surface liquid (234). Both effects can enhance innate immune defences through improved airway hydration, bacterial killing, and antimicrobial peptide activities (233). Considering the effects of SLC9A3 on lung function (128, 132), risk of P. aeruginosa infection, and PEx, it has the potential to be a therapeutic target for slowing the progression of CF lung disease. Clinical characteristics that were easily obtained at birth were used in combination with rs57221529 variant to predict the PEx risk in young CF children. The diagnosis of meconium ileus and pancreatic insufficiency did not result in an accurate prediction of PEx risk in the first five years of life. The less than optimal predictive performance of the biomarker panel may indicate missing genetic factors and clinical variables or environmental/stochastic factors. For example, birth weight was an interesting clinical variable as low birth weight has been shown to 132 correlate with lung disease at ages 6 and 10 years in CF children (235). However, birth weight was missing in over 50% of our study cohort; therefore, it was not evaluated in the current study. Additionally, a single variant is often not informative for assessing disease risk in complex traits, such as CF lung disease and PEx (236). Future study may need to explore additional genetic variants that associate with infection susceptibility in predicting PEx. There are some important limitations should be acknowledged in this thesis chapter. We demonstrated the significant association between the SLC9A3 variant and the risk of PEx in young CF children; however, this finding has not yet been evaluated in a validation cohort. A future study has been proposed to validate the result of our study in other CF modifier consortium databases (i.e., US cohort or French cohort). In addition, only 301 CGMS participants met our inclusion criteria which may indicate the low generalizability of our study. However, more CF diagnosis has been made through the newborn screening program since 2007 in Canada, and the percentage has increased to over 73% in 2020 (232). Findings based on early diagnosed CF individuals could be more relevant to the current CF population. Furthermore, the use of the national patient registry limited our ability to identify less severe PEx events that do not require hospitalization; therefore, the genetic susceptibility of these less severe PEx events was not evaluated in the current study. In the future, a prospective observational study may be needed that uses a pre-defined definition of PEx events to study less severe PEx events. In conclusion, by leveraging genotype and clinical data from CGMS and CCFR, we identified a significant association between the SLC9A3 variant and PEx risk in young CF children. A biomarker panel including the SLC9A3 variant and clinical characteristics was developed to predict the risk of CF PEx in the first five years of life but the predictive 133 performance was suboptimal. A future study has been proposed to evaluate these findings in a larger validation cohort. 134 Chapter 6: Conclusions and Future Directions 6.1 Overview Pulmonary exacerbations (PEx) are common in individuals with cystic fibrosis (CF) and are significant risk factors for lung disease progression (79). These events are associated with permanent lung function loss (81), reduced quality of life (237, 238), hospitalizations (239), and decreased survival (8). A PEx is often diagnosed clinically by a combination of increased respiratory symptoms (i.e.., coughing, sputum production, hemoptysis, shortness of breath) and decreased lung function. However, changes in lung function can sometimes be subtle or the patient can be a poor perceiver of their symptoms, potentially leading to missed or delayed diagnosis of PEx (94). Because respiratory symptoms are typically triggered by uncontrolled infection and inflammation, many markers of inflammation have been studied in the context of diagnosing PEx, predicting PEx, and monitoring the response to PEx treatment using peripheral blood from CF individuals (100). However, studies focused on a single biomarker or isolated cells from whole blood may not provide a comprehensive picture of alterations in the host immune response and inflammation during CF PEx. The central hypothesis of this thesis is that by profiling molecular candidates (protein, transcripts, and modifier genes) in blood samples, we can explore the host immune and inflammatory response during CF PEx and identify blood-based biomarkers to predict PEx risk and PEx -related treatment outcomes in CF individuals. In this thesis, blood-based biomarkers were investigated to predict PEx events or PEx-related clinical outcomes, including predicting IV antibiotic treatment response for PEx (Chapter 2), predicting individuals at imminent risk of PEx (Chapter 3), identifying individuals who might 135 benefit from azithromycin treatment (Chapter 4). Additionally, in Chapter 5, we investigated the genetic modifiers of PEx risk in pre-school children with CF as previously identified biomarkers might be less applicable to young children with less systemic inflammation. In Chapter 2, we investigated the longitudinal changes in blood proteins in response to IV antibiotics treatment for CF PEx and the correlations between early changes in blood proteins and clinical outcomes. We first confirmed that previously reported inflammatory markers such as IL-6, calprotectin, and CRP decreased following IV antibiotics treatment (166). Following that, we detected additional blood proteins that changed in response to IV antibiotics treatment with untargeted LC-MS/MS quantitative proteomics. This is the first study to use untargeted LC-MS/MS quantitative proteomics to characterize blood protein changes during CF PEx treatment with IV antibiotics. Interestingly, more proteins changed early in the treatment course (i.e., by day 5 of treatment) and fewer towards the end of the treatment. Many of these proteins are involved in complement activation and regulation of the host immune and inflammatory response. Moreover, we identified that early changes in IGFR2 were associated with symptom improvement by the end of treatment, suggesting that it might be useful as a predictive marker of symptomatic treatment response in individuals with CF. In Chapter 3, we performed a retrospective analysis using samples from a longitudinal CF biomarker study to identify differentially expressed genes and proteins from CF individuals who have paired clinically stable and PEx samples (cohort 1). We then develop blood-based biomarker panels based on the candidate genes and proteins identified from the extreme phenotypes (cohort 1) to predict CF individuals who are at imminent (< 4 months) risk of a PEx (cohort 2). The whole blood transcriptome and 55 inflammatory proteins were profiled with RNA-Seq and electrochemiluminescence immunoassay, respectively. Genes and inflammatory 136 proteins associated with neutrophilic inflammation, such as S100A family genes, serum calprotectin, and IL-17 family cytokines, were elevated during PEx. Two biomarker panels, a 16-gene panel and a 9-protein panel, were developed and demonstrated good performance in predicting the imminent risk of a PEx with an AUC = 0.88 and AUC = 0.83, respectively. The gene expression panel discovered with RNA-Seq was successfully validated using a more targeted and clinically translatable RNA measurement platform. In Chapter 4, we involved pediatric individuals with CF from the treatment arm of the AZ0004 trial, in which oral azithromycin was administrated three times per week for 168 days. Two hundred and nineteen blood proteins were evaluated with targeted proteomic technologies. Five previously measured inflammatory markers were also re-analyzed. We identified that early changes in blood protein levels following 28 days of azithromycin treatment significantly correlated with short- but not longer-term changes in FEV1% predicted and weight. Furthermore, early changes in serum calprotectin following the first 28 days of azithromycin treatment were predictive of PEx risk by day 168. These findings indicated that serum calprotectin represented a promising predictive biomarker to identify individuals who might derive longer-term benefits from azithromycin treatment in terms of reduced PEx risk. In Chapter 5, by characterizing early diagnosed CF individuals from the Canadian CF Gene modifier study (CGMS) cohort, we investigated genetic modifiers of PEx risk in pre-school children with CF. After evaluating 8 genome-wide significant variants predictive of lung function (128, 129, 220), the SLC9A3 variant, rs57221529, was significantly associated with early risk of PEx. Additionally, cumulative PEx counts in the first 5 years of life increased 1.54-fold in individuals carried one minor allele (G) at rs57221529. Individuals with the GG genotype at rs57221529 had a significantly higher risk of PEx in early life than those who did not have a 137 minor allele. However, this association between SLC9A3 variant and PEx risk did not result in a good prediction of PEx risk in young CF children. This is the first study to examine genetic modifiers of PEx risk in young children with CF. The genetic modifier identified in this study has the potential to be a therapeutic target for slowing the progression of CF lung disease. 6.2 Limitations and Future Directions A natural step forward for this thesis is the validation of biomarker findings in individuals who have been treated with highly-effective CFTR modulator therapies. CFTR modulators are small molecule pharmacologic agents that target CFTR protein defects to address the underlying cause of the disease (24). According to a 5-year observational safety study using U.S. and U.K. CF registry data, ivacaftor, the first approved CFTR modulator, dramatically reduced PEx frequency when compared to the pre-treatment period and similar long-term results are anticipated with elexacaftor-tezacaftor-ivacaftor (ETI) (240). However, PEx remain a significant risk factor for hospitalizations in CF individuals despite their reduced frequency. Furthermore, the use of ivacaftor did not decrease the severity of each PEx event or increase the rate of lung function recovery (241). CF individuals who were treated with CFTR modulators were not included in the studies from this thesis as they initially represented a minority of our patients given their limited access in Canada during the study period. However, with the recent approval and reimbursement of ETI in Canada, nearly 90% of CF individuals could access and benefit from these game-changing therapies (1), which may affect the generalizability of the biomarker panels. Additionally, the effects of CFTR modulators on inflammation are still under-characterized (242). A recent study reported a reduction in serum IL-1β levels in CF patients after three months of treatment with ivacaftor/tezacaftor; however, similar effects on 138 inflammatory markers were not observed in patients treated with ivacaftor/lumacaftor (243). It is unclear whether the observed reduction in IL-1β levels results from improved host immune responses or the improvement of the airway environment. As biomarkers findings in this thesis are mainly involved in the host immune response and inflammation, evaluating the impact of CFTR modulators on biomarkers levels is necessary. Therefore, future studies need to address this limitation and validate the biomarker performance in patients on highly effective CFTR modulators. A major portion of this thesis focused on predicting PEx risk using blood-based biomarkers in CF individuals. However, we have not yet compared the clinical outcomes of PEx events detected using biomarkers to those diagnosed through standard care. Earlier detection of PEx has been achieved in CF using home spirometry and symptom monitoring; however, this approach only identified more frequent PEx events but did not result in a slower decline in lung function compared to standard care (244). The negative results from this trial raise an important question as to how PEx events predicted by biomarkers should be treated. Many early detected PEx events were preferentially treated with oral antibiotics as opposed to IV antibiotics which may have led to inferior outcomes (244). Prior studies have shown that more significant improvements in short-term lung function were observed in PEx events treated with IV antibiotics relative to those treated with oral or inhaled antibiotics (89). This heterogeneity in treatment decisions may contribute to the variation in clinical outcomes and mitigate the benefit of early prediction and diagnosis of PEx with biomarkers. Furthermore, there has been an increase in interest in PEx treated with oral antibiotics during standard care (245, 246), which has the potential to be used as a reference group when comparing clinical outcomes with PEx events identified by biomarkers. 139 Previous discovery-based biomarker studies have failed to validate their biomarker findings on a clinically translatable platform. In addition to blood-based biomarkers, proteins, mRNA, and metabolites measured from sputum, urine, and exhaled breath condensate have been studied to discover biomarker candidates for CF PEx (109, 247-249); however, specialized sample preparations are required, which limits their development as a clinical biomarker test. In this thesis, we transferred the 16-gene panel (Chapter 3) onto the NanoString nCounter® platform, a clinically translatable RNA measurement platform. A future analytical validation study will need to evaluate the accuracy, precision, and reproducibility of the biomarker test (250, 251). Moreover, the evidentiary linkage of biomarkers and clinical endpoints (a.k.a. clinical validation) and the clinical utility of the biomarker test must be demonstrated (250, 251). The feasibility of integrating molecular tests (protein and mRNA) into clinical practice as screening and risk-stratification tools to support clinician decision-making has been demonstrated in other diseases. Husain and colleagues reported using a 5-protein biomarker panel to identify the need for inpatient care in patients with respiratory symptoms (252). Sweeney and colleagues presented a point-of-care test to determine bacterial vs. viral infection in patients with sepsis (253). Confirming the clinical utility of blood-based biomarkers and ultimately integrating them into clinical practice will be significant milestones for future studies. With a more reliable prediction of imminent PEx events during quarterly clinical visits or earlier prediction of non-responders to PEx treatment during hospitalizations, CF clinicians can risk-stratify patients and direct clinical resources toward those with the highest risk. A final, major future direction of this work is to inform the underlying triggers of PEx events based on different inflammation patterns indicated by blood-based biomarkers. Our leading hypothesis is that inflammatory phenotypes reflective of underlying triggers of PEx will 140 respond differently to antibiotics treatment. The inflammatory phenotype identified upon clinical presentation may provide insights into how to treat a PEx event. Our current study shows that leukocytes genes and inflammatory proteins are involved in the innate immune response to bacterial infection during CF PEx; however, the inflammation patterns of other triggers, such as viral infection (254), medication non-adherence (255, 256), air pollution exposure (257, 258), asthma (259), and allergic bronchopulmonary aspergillosis (260), are poorly characterized in CF. An ongoing study led by our group aims to fill this gap by identifying clinically relevant inflammatory phenotypes of CF PEx by prospectively collecting clinical and demographic data, as well as biospecimens (i.e., blood, sputum, and nasopharyngeal swab) during CF PEx. 6.3 Conclusion The central objective of this thesis was to develop blood-based biomarkers to predict PEx and PEx-related treatment outcomes in CF individuals. Both single biomarkers and biomarker panels were identified to predict PEx events or PEx-related treatment outcomes. More reliable prediction made by these biomarkers is critical in CF individuals as it allows for more personalized medical monitoring as well as earlier diagnosis and treatment of PEx to improve clinical outcomes. Several future directions have also been proposed in this thesis to advance blood-based biomarkers in the clinical management of CF lung disease. 141 References 1. Shteinberg M, Haq IJ, Polineni D, Davies JC. Cystic fibrosis. 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Am J Respir Crit Care Med. 2004;169(7):816-21. 259. Balfour-Lynn IM, Elborn JS. "CF asthma": what is it and what do we do about it? Thorax. 2002;57(8):742-8. 260. Janahi IA, Rehman A, Al-Naimi AR. Allergic bronchopulmonary aspergillosis in patients with cystic fibrosis. Ann Thorac Med. 2017;12(2):74-82. 172 Appendices Appendix A A.1 Supplemental tables Table 28. Blood protein levels at hospital admission (V1) significantly correlated with age All Subjects Male Only Female Only Protein Symbol Rho p-value Rho p-value Rho p-value Voltage-dependent calcium channel subunit alpha-2/delta-1 CACNA2D1 -0.55 0.004 -0.63 0.021 -0.52 0.085 Plasminogen PLG 0.44 0.029 0.74 0.004 0.11 0.728 Beta-Ala-His dipeptidase CNDP1 0.44 0.027 0.31 0.297 0.46 0.133 Collectin-11 COLEC11 -0.44 0.028 -0.51 0.076 -0.36 0.248 Cartilage acidic protein 1 CRTAC1 0.41 0.039 0.44 0.135 0.61 0.034 Protein AMBP AMBP 0.27 0.190 0.58 0.039 0.08 0.795 Pantetheinase VNN1 0.38 0.063 0.75 0.003 0.31 0.328 Complement C1r subcomponent C1R 0.28 0.174 0.59 0.034 -0.08 0.812 Fibronectin FN1 -0.06 0.761 0.56 0.046 -0.48 0.117 Coagulation factor XI F11 0.26 0.205 0.66 0.014 0.09 0.778 C4b-binding protein alpha chain C4BPA 0.37 0.070 0.75 0.003 -0.05 0.871 Plasma serine protease inhibitor SERPINA5 0.32 0.119 0.67 0.012 0.12 0.712 Alpha-actinin-1 ACTN1 0.16 0.436 0.63 0.021 -0.18 0.570 Heat shock 70 kDa protein 6 HSPA6 0.17 0.428 0.56 0.047 -0.33 0.289 Tenascin-X TNXB -0.12 0.572 0.26 0.386 -0.60 0.041 Insulin-like growth factor-binding protein 3 IGFBP3 -0.21 0.313 0.21 0.493 -0.59 0.044 Alpha-2-macroglobulin A2M -0.38 0.062 0.01 0.964 -0.78 0.003 L-lactate dehydrogenase A chain LDHA -0.36 0.077 -0.18 0.547 -0.58 0.048 Ig gamma-1 chain C region IGHG1 -0.30 0.150 -0.01 0.972 -0.69 0.013 Ig alpha-1 chain C region IGHA1 -0.31 0.127 0.04 0.901 -0.58 0.048 Apolipoprotein(a) LPA 0.39 0.053 0.29 0.334 0.58 0.049 173 Gamma-enolase ENO2 -0.20 0.334 0.28 0.352 -0.70 0.012 Protein deglycase DJ-1 PARK7 -0.25 0.229 -0.02 0.936 -0.61 0.035 Calmodulin-like protein 5 CALML5 -0.37 0.072 -0.16 0.603 -0.67 0.016 174 Table 29. Blood proteins with statistically significant fold-change in levels from V1 to V2 measured with LC-MS/MS Protein Symbol Fold-Change mean Direction V1 to V2 P-value FDR Poliovirus receptor PVR 0.35 Down 0.001 0.048 Pyruvate kinase PKM PKM 0.35 Down 0.001 0.048 Plasma protease C1 inhibitor SERPING1 0.52 Down 0.001 0.048 Lipopolysaccharide-binding protein LBP 0.21 Down 0.001 0.048 Vitamin K-dependent protein S PROS1 0.56 Down 0.001 0.048 Proline-rich acidic protein 1 PRAP1 0.45 Down 0.001 0.049 Prothrombin F2 0.59 Down 0.001 0.049 Coagulation factor XIII A chain F13A1 0.49 Down 0.001 0.057 Glutathione peroxidase 3 GPX3 0.31 Down 0.002 0.059 Receptor-type tyrosine-protein phosphatase eta PTPRJ 0.36 Down 0.002 0.059 Cofilin-1 CFL1 0.44 Down 0.002 0.059 C-reactive protein CRP 0.26 Down 0.002 0.059 Serum amyloid P-component APCS 0.55 Down 0.003 0.059 Complement C1r subcomponent C1R 0.54 Down 0.003 0.059 Complement component C9 C9 0.52 Down 0.003 0.059 Alpha-actinin-1 ACTN1 0.25 Down 0.003 0.059 Superoxide dismutase [Cu-Zn] SOD1 0.49 Down 0.003 0.059 L-lactate dehydrogenase B chain LDHB 0.58 Down 0.003 0.059 Gelsolin GSN 0.71 Down 0.004 0.059 Complement component C8 alpha chain C8A 0.64 Down 0.004 0.059 Inter-alpha-trypsin inhibitor heavy chain H1 ITIH1 0.65 Down 0.004 0.059 Basement membrane-specific heparan sulfate proteoglycan core protein HSPG2 0.34 Down 0.004 0.059 Cholinesterase BCHE 0.49 Down 0.006 0.074 Protein Z-dependent protease inhibitor SERPINA10 0.54 Down 0.006 0.074 Heparin cofactor 2 SERPIND1 0.62 Down 0.006 0.074 Carboxypeptidase N catalytic chain CPN1 0.68 Down 0.006 0.074 Alpha-1-antichymotrypsin SERPINA3 0.56 Down 0.006 0.074 Tropomyosin alpha-3 chain TPM3 0.46 Down 0.006 0.074 Serum paraoxonase/arylesterase 1 PON1 0.46 Down 0.007 0.074 Insulin-like growth factor-binding protein 3 IGFBP3 0.61 Down 0.007 0.074 Lumican LUM 0.59 Down 0.007 0.077 Complement C1q subcomponent subunit B C1QB 0.62 Down 0.008 0.080 Actin, cytoplasmic 1 ACTB 0.54 Down 0.008 0.080 Coagulation factor XI F11 0.50 Down 0.009 0.080 Complement C4-B C4B 0.63 Down 0.009 0.080 175 Zyxin ZYX 0.43 Down 0.009 0.080 Desmoplakin DSP 0.14 Down 0.009 0.080 C4b-binding protein alpha chain C4BPA 0.35 Down 0.010 0.083 Talin-1 TLN1 0.44 Down 0.010 0.084 EGF-containing fibulin-like extracellular matrix protein 1 EFEMP1 0.37 Down 0.010 0.086 Cadherin-13 CDH13 0.62 Down 0.011 0.086 Alpha-2-antiplasmin SERPINF2 0.61 Down 0.011 0.086 Ceruloplasmin CP 0.63 Down 0.011 0.088 Insulin like growth factor 2 receptor IGF2R 0.47 Down 0.012 0.091 Tubulin beta-1 chain TUBB1 0.32 Down 0.013 0.098 Lysozyme C LYZ 0.52 Down 0.014 0.098 Tropomyosin alpha-4 chain TPM4 0.46 Down 0.014 0.100 176 Table 30. Blood proteins with statistically significant fold-change in levels from V1 to V4 measured with LC-MS/MS Protein Symbol Fold-change mean Direction V1 to V4 P-value FDR Fructose-bisphosphate aldolase B ALDOB 5.21 Up <0.001 < 0.001 Extracellular superoxide dismutase [Cu-Zn] SOD3 5.52 Up <0.001 < 0.001 Retinol-binding protein 4 RBP4 3.42 Up <0.001 0.002 C-reactive protein CRP 0.19 Down <0.001 0.011 Kininogen-1 KNG1 1.68 Up 0.001 0.044 Apolipoprotein E APOE 1.66 Up 0.002 0.091 177 Appendix B B.1 Supplemental tables Table 31. Differential expression gene test between clinically stable and PEx that adjusted for peripheral blood cell composition Ensembl Gene ID Gene Log2 fold change P-value FDR ENSG00000148516 ZEB1 0.608 0.001 0.261 ENSG00000112242 E2F3 0.791 0.002 0.261 ENSG00000111647 UHRF1BP1L 0.634 0.002 0.261 ENSG00000141298 SSH2 0.591 0.002 0.261 ENSG00000182853 VMO1 -1.756 0.002 0.261 ENSG00000096060 FKBP5 0.829 0.003 0.261 ENSG00000107954 NEURL1 -0.728 0.003 0.261 ENSG00000223865 HLA-DPB1 -0.619 0.003 0.261 ENSG00000008283 CYB561 -0.590 0.003 0.261 ENSG00000184515 BEX5 -0.731 0.004 0.261 ENSG00000197329 PELI1 0.760 0.004 0.261 ENSG00000090376 IRAK3 0.743 0.004 0.261 ENSG00000213402 PTPRCAP -0.592 0.005 0.261 ENSG00000171940 ZNF217 0.593 0.005 0.261 ENSG00000123066 MED13L 0.694 0.005 0.261 ENSG00000109466 KLHL2 0.913 0.005 0.261 ENSG00000138764 CCNG2 0.668 0.005 0.261 ENSG00000115594 IL1R1 0.742 0.006 0.261 ENSG00000215301 DDX3X 0.648 0.006 0.261 ENSG00000148950 IMMP1L -0.641 0.006 0.261 ENSG00000172594 SMPDL3A 0.660 0.007 0.261 ENSG00000151414 NEK7 0.598 0.007 0.261 ENSG00000168899 VAMP5 -0.602 0.007 0.261 ENSG00000140443 IGF1R 0.588 0.007 0.261 ENSG00000058091 CDK14 0.738 0.007 0.261 ENSG00000179921 GPBAR1 -0.586 0.007 0.261 178 ENSG00000164104 HMGB2 0.752 0.007 0.261 ENSG00000150681 RGS18 0.750 0.007 0.261 ENSG00000064886 CHI3L2 -0.727 0.007 0.261 ENSG00000132514 CLEC10A -0.700 0.008 0.261 ENSG00000100644 HIF1A 0.589 0.008 0.261 ENSG00000176542 USF3 0.594 0.008 0.261 ENSG00000133657 ATP13A3 0.652 0.008 0.261 ENSG00000163605 PPP4R2 0.646 0.008 0.261 ENSG00000169826 CSGALNACT2 0.663 0.008 0.261 ENSG00000165029 ABCA1 0.788 0.009 0.261 ENSG00000102218 RP2 0.650 0.009 0.261 ENSG00000115604 IL18R1 0.819 0.011 0.261 ENSG00000185950 IRS2 0.623 0.011 0.261 ENSG00000129757 CDKN1C -0.977 0.012 0.261 ENSG00000183779 ZNF703 -0.720 0.012 0.261 ENSG00000119408 NEK6 0.609 0.013 0.261 ENSG00000277443 MARCKS 0.644 0.014 0.261 ENSG00000137449 CPEB2 0.701 0.015 0.261 ENSG00000110422 HIPK3 0.604 0.015 0.261 ENSG00000105835 NAMPT 0.632 0.016 0.261 ENSG00000168769 TET2 0.686 0.016 0.261 ENSG00000065534 MYLK 0.673 0.016 0.261 ENSG00000198363 ASPH 0.666 0.016 0.261 ENSG00000168497 CAVIN2 0.734 0.019 0.262 ENSG00000164251 F2RL1 0.656 0.020 0.263 ENSG00000286140 DERPC -0.590 0.020 0.263 ENSG00000204020 LIPN 0.886 0.020 0.263 ENSG00000114770 ABCC5 0.603 0.020 0.263 ENSG00000116991 SIPA1L2 0.662 0.020 0.263 ENSG00000123358 NR4A1 -0.738 0.021 0.264 ENSG00000249992 TMEM158 0.896 0.021 0.264 ENSG00000168081 PNOC -0.618 0.021 0.264 ENSG00000151151 IPMK 0.692 0.022 0.264 ENSG00000134463 ECHDC3 0.709 0.023 0.264 ENSG00000073756 PTGS2 0.618 0.025 0.265 ENSG00000055609 KMT2C 0.590 0.026 0.265 ENSG00000189337 KAZN 0.657 0.026 0.265 ENSG00000026559 KCNG1 -0.854 0.026 0.265 179 ENSG00000177989 ODF3B -0.643 0.027 0.265 ENSG00000134755 DSC2 0.768 0.029 0.271 ENSG00000005249 PRKAR2B 0.664 0.031 0.274 ENSG00000132470 ITGB4 1.006 0.032 0.276 ENSG00000196083 IL1RAP 0.687 0.032 0.277 ENSG00000137801 THBS1 0.780 0.035 0.277 ENSG00000109906 ZBTB16 0.704 0.037 0.279 ENSG00000147408 CSGALNACT1 0.715 0.038 0.279 ENSG00000180316 PNPLA1 0.587 0.038 0.279 ENSG00000113916 BCL6 0.625 0.042 0.284 ENSG00000102908 NFAT5 0.609 0.043 0.285 ENSG00000179299 NSUN7 0.634 0.044 0.286 ENSG00000198695 MT-ND6 0.683 0.044 0.287 ENSG00000136514 RTP4 -0.651 0.045 0.287 ENSG00000104177 MYEF2 0.622 0.045 0.287 ENSG00000173930 SLCO4C1 0.617 0.046 0.288 ENSG00000115590 IL1R2 0.704 0.048 0.292 180 Table 32. Pathways with significant differential gene expression between clinically stable and PEx samples adjusted for peripheral blood cell counts Database Pathways P-value FDR Gene Reactome Signaling by interleukins 1.30E-06 0.001 IL18R1/PELI1/IRAK3/IL1R1/IL1R2/IL1RAP/BCL6/ IRS2/PTGS2/ZEB1/HIF1A Reactome Interleukin-1 family signaling 1.64E-05 0.003 IL18R1/PELI1/IRAK3/IL1R1/IL1R2/IL1RAP Reactome Interleukin-1 signaling 4.62E-05 0.006 PELI1/IRAK3/IL1R1/IL1R2/IL1RAP PID IL-1 pathway 8.72E-06 0.002 IRAK3/IL1R1/IL1R2/IL1RAP Wiki pathways Interleukin-1 pathway 6.95E-05 0.007 PELI1/IRAK3/IL1R1/IL1RAP Wiki pathways MECP2 and associated RETT syndrome 1.73E-04 0.015 FKBP5/TET2/BCL6/IGF1R KEGG Apoptosis 3.60E-04 0.020 IRAK3/IL1R1/IL1RAP/PRKAR2B Reactome Interleukin 4 and interleukin 13 signaling 9.03E-04 0.038 BCL6/PTGS2/ZEB1/HIF1A Biocarta IL-1R pathway 2.32E-04 0.017 IRAK3/IL1R1/IL1RAP Wiki pathways Signal transduction through IL-1R 3.35E-04 0.020 IRAK3/IL1R1/IL1RAP 181 Appendix C C.1 Supplemental tables Table 33. Clinical characteristics between received, enrolled, and subjects from AZ0004 study treatment arm Characteristics Discovery Cohort (n = 40) Validation Cohort (n = 62) AZ0004 study treatment arm (76) (n = 131) Age, mean (SD), y 9.8 (3.1) a 11.7 (3.1) a 10.7 (3.3) Participants aged 6 – 12 y, No. (%) 31 (78) 35 (56) 91 (69) Participants aged 13 – 18 y, No. (%) 9 (23) 27 (44) 40 (31) Female, No. (%) 19 (48) 23 (37) 54 (41) Genotype, No. (%) F508 Homozygous 17 (43) 30 (48) 57 (43) F508 Heterozygous 19 (48) 13 (21) 40 (30) Other (non-F508) 4 (10) 7 (11) 15 (12) FEV1% predicted, No. (%) 50-80 5 (13) 13 (21) 18 (14) 81-110 25 (63) 39 (63) 84 (64) >110 10 (25) 13 (21) 28 (21) FEV1% predicted, mean (SD) 98.4 (16.4) 96.1 (18.4) 97.7 (16.4) Weight, mean (SD), kg 35.2 (13.7) 40.6 (13.2) 37.4 (14.5) Height, mean (SD), cm 138.3 (18.0) a 148.3 (17.0) a 142.6 (17.9) BMI, mean (SD) 17.7 (3.1) 17.9 (2.5) 17.7 (3.0) Dose received (by kg), No. (%) 250 mg (18-35.9) 26 (65) 28 (45) 77 (59) 500 mg (≥ 36) 14 (35) 34 (55) 54 (41) Chronic medication use, No. (%) Dornase alfa 27 (68) 33 (53) 84 (64) Inhaled tobramycin 6 (15) 7 (11) 20 (15) Ibuprofen 1 (3) 3 (5) 9 (7) Hypertonic saline 7 (18) 7 (11) 24 (18) a p-value < 0.05 (two-sample t-test) for comparison of discovery and validation cohorts Abbreviations: BMI: body mass index; FEV1: forced expiratory volume in one second 182 Table 34. MRM-MS protein panel abbreviation list Protein ID Protein Name Protein ID Protein Name A1AT Alpha-1-antitrypsin FA10 Coagulation factor X A1BG Alpha-1B-glycoprotein FA12 Coagulation factor XII A2AP Alpha-2-antiplasmin FA5 Coagulation factor V A2MG Alpha-2-macroglobulin FA7 Coagulation factor VII AACT Alpha-1-antichymotrypsin FA9 Coagulation factor IX ACTC1 Actin, alpha cardiac muscle 1 FBLN1 Fibulin-1 AFAM Afamin FETUA Alpha-2-HS-glycoprotein ALBU Serum albumin FETUB Fetuin-B ALS Insulin-like growth factor-binding protein complex acid labile subunit FIBB Fibrinogen beta chain AMBP Protein AMBP FIBG Fibrinogen gamma chain ANGT Angiotensinogen FINC Fibronectin ANT3 Antithrombin-III GELS Gelsolin APOA1 Apolipoprotein A-I HBA Hemoglobin subunit alpha APOA2 Apolipoprotein A-II HEMO Hemopexin APOA4 Apolipoprotein A-IV HEP2 Heparin cofactor 2 APOB Apolipoprotein B-100 HPT Haptoglobin APOC2 Apolipoprotein C-II HRG Histidine-rich glycoprotein APOC3 Apolipoprotein C-III IBP3 Insulin-like growth factor-binding protein 3 APOH Beta-2-glycoprotein 1 IC1 Plasma protease C1 inhibitor APOL1 Apolipoprotein L1 IPSP Plasma serine protease inhibitor B2MG Beta-2-microglobulin ITIH2 Inter-alpha-trypsin inhibitor heavy chain H2 C1QA Complement C1q subcomponent subunit A ITIH4 Inter-alpha-trypsin inhibitor heavy chain H4 C1QC Complement C1q subcomponent subunit C KAIN Kallistatin C1R Complement C1r subcomponent KLKB1 Plasma kallikrein C1S Complement C1s subcomponent KNG1 Kininogen-1 C1S;MASP1 Complement C1s subcomponent;Mannan-binding lectin serine protease 1 LBP Lipopolysaccharide-binding protein C4BPA C4b-binding protein alpha chain LCAT Phosphatidylcholine-sterol acyltransferase CADH5 Cadherin-5 LUM Lumican CAH1 Carbonic anhydrase 1 MASP1 Mannan-binding lectin serine protease 1 CBG Corticosteroid-binding globulin MMP2 72 kDa type IV collagenase CBPN Carboxypeptidase N catalytic chain PEDF Pigment epithelium-derived factor CD14 Monocyte differentiation antigen CD14 PGRP2 N-acetylmuramoyl-L-alanine amidase CD5L CD5 antigen-like PLMN Plasminogen CERU Ceruloplasmin PLTP Phospholipid transfer protein CFAB Complement factor B PON1 Serum paraoxonase/arylesterase 1 CFAD Complement factor D PRDX2 Peroxiredoxin-2 CFAH Complement factor H PROC Vitamin K-dependent protein C 183 CFAI Complement factor I PROS Vitamin K-dependent protein S CLUS Clusterin PROZ Vitamin K-dependent protein Z CNDP1 Beta-Ala-His dipeptidase RET4 Retinol-binding protein 4 CO2 Complement C2 SAA4 Serum amyloid A-4 protein CO3 Complement C3 SHBG Sex hormone-binding globulin CO4A;CO4B Complement C4-A;Complement C4-B TETN Tetranectin CO6 Complement component C6 THBG Thyroxine-binding globulin CO7 Complement component C7 THRB Prothrombin CO8G Complement component C8 gamma chain TTHY Transthyretin CO9 Complement component C9 VASN Vasorin CRP C-reactive protein VCAM1 Vascular cell adhesion protein 1 CYTC Cystatin-C VTDB Vitamin D-binding protein CYTC;NRP2 Cystatin-C;Neuropilin-2 VTNC Vitronectin ECM1 Extracellular matrix protein 1 VWF von Willebrand factor F13A Coagulation factor XIII A chain ZA2G Zinc-alpha-2-glycoprotein F13B Coagulation factor XIII B chain ZAGL1;ZA2G Putative zinc-alpha-2-glycoprotein-like 1;Zinc-alpha-2-glycoprotein ZPI Protein Z-dependent protease inhibitor 184 Table 35. Correlation between baseline blood protein level and change in FEV1% predicted from day 0 to day 28 and day 0 to day 168 of azithromycin treatment a Spearman’s correlation analysis Change in FEV1% predicted from Day 0 to Day 28 Change in FEV1% predicted from Day 0 to Day 168 Panel Protein Correlation a P-value FDR Correlation a P-value FDR MRM-MS A1AT 0.07 0.66 0.98 -0.03 0.84 0.97 MRM-MS A1BG -0.18 0.27 0.88 -0.02 0.92 0.97 MRM-MS A2AP -0.06 0.72 0.98 -0.22 0.16 0.77 MRM-MS A2MG -0.07 0.67 0.98 -0.13 0.43 0.86 MRM-MS AACT -0.05 0.74 0.98 0.00 0.99 0.99 MRM-MS ACTC_ 0.01 0.96 0.98 -0.12 0.45 0.86 MRM-MS ACTC___ 0.06 0.71 0.98 -0.06 0.70 0.89 MRM-MS AFAM 0.06 0.69 0.98 -0.11 0.49 0.86 MRM-MS ALBU -0.03 0.86 0.98 -0.04 0.79 0.94 MRM-MS ALS -0.15 0.36 0.92 -0.29 0.07 0.54 MRM-MS AMBP -0.24 0.14 0.88 -0.20 0.22 0.82 MRM-MS ANGT -0.04 0.82 0.98 -0.09 0.56 0.86 MRM-MS ANT3 -0.04 0.83 0.98 -0.28 0.08 0.54 MRM-MS APOA1 -0.02 0.90 0.98 -0.13 0.43 0.86 MRM-MS APOA2 -0.11 0.52 0.92 -0.25 0.12 0.70 MRM-MS APOA4 -0.09 0.59 0.98 -0.12 0.46 0.86 MRM-MS APOB -0.21 0.20 0.88 -0.34 0.03 0.50 MRM-MS APOC2 -0.14 0.37 0.92 -0.13 0.42 0.86 MRM-MS APOC3 -0.11 0.48 0.92 -0.19 0.24 0.82 MRM-MS APOH -0.04 0.81 0.98 0.02 0.89 0.97 MRM-MS APOL1 0.24 0.13 0.88 -0.06 0.71 0.89 MRM-MS B2MG -0.03 0.87 0.98 -0.07 0.65 0.89 MRM-MS C1QA 0.00 0.99 0.99 -0.23 0.15 0.73 MRM-MS C1QC 0.06 0.72 0.98 -0.26 0.11 0.64 MRM-MS C1R -0.10 0.55 0.92 -0.10 0.56 0.86 MRM-MS C1S -0.12 0.45 0.92 -0.10 0.54 0.86 MRM-MS C1S;MASP1 -0.12 0.48 0.92 -0.10 0.53 0.86 MRM-MS C4BPA -0.21 0.18 0.88 -0.13 0.42 0.86 MRM-MS CADH5 -0.20 0.21 0.88 0.06 0.69 0.89 MRM-MS CAH1 0.31 0.05 0.88 0.31 0.05 0.50 MRM-MS CBG -0.18 0.27 0.88 -0.14 0.37 0.86 MRM-MS CBPN 0.08 0.61 0.98 0.13 0.43 0.86 MRM-MS CD14 -0.10 0.53 0.92 -0.22 0.18 0.79 MRM-MS CD5L -0.04 0.83 0.98 -0.13 0.41 0.86 185 MRM-MS CERU -0.12 0.46 0.92 0.03 0.84 0.97 MRM-MS CFAB -0.07 0.66 0.98 -0.14 0.38 0.86 MRM-MS CFAD -0.15 0.36 0.92 -0.20 0.23 0.82 MRM-MS CFAH -0.22 0.16 0.88 0.01 0.93 0.97 MRM-MS CFAI -0.05 0.76 0.98 -0.31 0.05 0.50 MRM-MS CLUS 0.01 0.95 0.98 -0.01 0.97 0.99 MRM-MS CNDP1 -0.01 0.94 0.98 -0.15 0.34 0.86 MRM-MS CO2 -0.15 0.37 0.92 -0.33 0.04 0.50 MRM-MS CO3 -0.25 0.13 0.88 -0.10 0.55 0.86 MRM-MS CO4A;CO4B -0.17 0.29 0.90 -0.11 0.51 0.86 MRM-MS CO6 -0.17 0.30 0.90 -0.05 0.74 0.91 MRM-MS CO7 -0.18 0.26 0.88 0.13 0.41 0.86 MRM-MS CO8G -0.04 0.79 0.98 0.13 0.44 0.86 MRM-MS CO9 -0.11 0.51 0.92 -0.11 0.49 0.86 MRM-MS CRP -0.08 0.63 0.98 -0.07 0.67 0.89 MRM-MS CYTC -0.12 0.46 0.92 -0.13 0.43 0.86 MRM-MS CYTC;NRP2 -0.20 0.21 0.88 -0.06 0.70 0.89 MRM-MS ECM1 -0.30 0.06 0.88 -0.11 0.48 0.86 MRM-MS F13A -0.21 0.20 0.88 0.05 0.77 0.93 MRM-MS F13B -0.23 0.16 0.88 0.21 0.19 0.81 MRM-MS FA10 0.02 0.90 0.98 -0.20 0.22 0.82 MRM-MS FA12 -0.35 0.03 0.88 -0.23 0.15 0.73 MRM-MS FA5 0.03 0.85 0.98 -0.15 0.35 0.86 MRM-MS FA7 -0.22 0.18 0.88 -0.15 0.34 0.86 MRM-MS FA9 0.03 0.88 0.98 -0.35 0.03 0.50 MRM-MS FBLN1 -0.13 0.43 0.92 -0.08 0.62 0.88 MRM-MS FETUA 0.03 0.86 0.98 -0.35 0.03 0.50 MRM-MS FETUB -0.14 0.41 0.92 0.12 0.47 0.86 MRM-MS FIBB -0.14 0.38 0.92 0.01 0.93 0.97 MRM-MS FIBG -0.14 0.37 0.92 -0.02 0.88 0.97 MRM-MS FINC -0.22 0.17 0.88 0.00 0.99 0.99 MRM-MS GELS 0.12 0.45 0.92 -0.02 0.90 0.97 MRM-MS HBA 0.29 0.06 0.88 0.30 0.06 0.54 MRM-MS HEMO -0.22 0.17 0.88 -0.07 0.66 0.89 MRM-MS HEP2 -0.10 0.54 0.92 -0.32 0.04 0.50 MRM-MS HPT -0.22 0.18 0.88 -0.34 0.03 0.50 MRM-MS HRG 0.03 0.85 0.98 0.03 0.84 0.97 MRM-MS IBP3 -0.29 0.07 0.88 -0.26 0.11 0.64 MRM-MS IC1 -0.01 0.96 0.98 -0.14 0.40 0.86 MRM-MS IPSP -0.12 0.46 0.92 -0.01 0.93 0.97 MRM-MS ITIH2 -0.08 0.63 0.98 0.02 0.89 0.97 186 MRM-MS ITIH4 -0.24 0.14 0.88 -0.08 0.62 0.88 MRM-MS KAIN 0.02 0.92 0.98 -0.19 0.23 0.82 MRM-MS KLKB1 -0.17 0.28 0.90 -0.35 0.03 0.50 MRM-MS KNG1 -0.03 0.88 0.98 -0.07 0.68 0.89 MRM-MS LBP -0.24 0.14 0.88 -0.29 0.07 0.54 MRM-MS LCAT -0.14 0.39 0.92 -0.16 0.31 0.86 MRM-MS LUM -0.01 0.96 0.98 0.01 0.96 0.98 MRM-MS MASP1 -0.04 0.79 0.98 -0.09 0.58 0.88 MRM-MS MMP2 0.07 0.67 0.98 -0.03 0.85 0.97 MRM-MS PEDF 0.03 0.85 0.98 0.09 0.57 0.86 MRM-MS PGRP2 -0.13 0.42 0.92 -0.10 0.54 0.86 MRM-MS PLMN -0.10 0.55 0.92 -0.13 0.43 0.86 MRM-MS PLTP -0.10 0.55 0.92 -0.18 0.28 0.86 MRM-MS PON1 -0.03 0.87 0.98 -0.08 0.63 0.88 MRM-MS PRDX2 0.26 0.11 0.88 0.34 0.03 0.50 MRM-MS PROC -0.18 0.26 0.88 -0.28 0.08 0.54 MRM-MS PROS -0.22 0.17 0.88 -0.23 0.15 0.73 MRM-MS PROZ -0.01 0.93 0.98 -0.13 0.41 0.86 MRM-MS RET4 -0.14 0.39 0.92 -0.14 0.40 0.86 MRM-MS SAA4 -0.18 0.26 0.88 -0.32 0.04 0.50 MRM-MS SHBG -0.19 0.24 0.88 -0.01 0.93 0.97 MRM-MS TETN 0.07 0.67 0.98 0.10 0.53 0.86 MRM-MS THBG 0.06 0.72 0.98 -0.19 0.25 0.84 MRM-MS THRB -0.04 0.81 0.98 -0.20 0.21 0.82 MRM-MS TTHY -0.05 0.78 0.98 -0.06 0.73 0.91 MRM-MS VASN -0.19 0.24 0.88 0.10 0.55 0.86 MRM-MS VCAM1 -0.26 0.11 0.88 -0.08 0.62 0.88 MRM-MS VTDB -0.27 0.09 0.88 -0.08 0.61 0.88 MRM-MS VTNC -0.16 0.32 0.92 0.01 0.93 0.97 MRM-MS VWF -0.30 0.06 0.88 0.07 0.68 0.89 MRM-MS ZA2G -0.11 0.52 0.92 -0.10 0.54 0.86 MRM-MS ZAGL1;ZA2G -0.13 0.42 0.92 -0.10 0.55 0.86 MRM-MS ZPI 0.00 0.99 0.99 -0.11 0.50 0.86 Olink ProSeek 4E-BP1 0.22 0.18 0.56 0.23 0.15 0.75 Olink ProSeek ADA 0.23 0.16 0.56 0.18 0.27 0.77 Olink ProSeek AXIN1 0.00 0.99 0.99 0.12 0.46 0.90 Olink ProSeek Beta-NGF 0.00 0.99 0.99 -0.04 0.82 0.95 Olink ProSeek CASP-8 0.09 0.59 0.82 0.26 0.11 0.75 187 Olink ProSeek CCL11 -0.02 0.92 0.99 -0.08 0.62 0.93 Olink ProSeek CCL19 -0.46 0.00 0.11 -0.25 0.12 0.75 Olink ProSeek CCL20 -0.17 0.30 0.70 0.04 0.79 0.95 Olink ProSeek CCL23 -0.13 0.42 0.76 0.09 0.59 0.92 Olink ProSeek CCL25 -0.12 0.45 0.77 -0.27 0.09 0.75 Olink ProSeek CCL28 0.09 0.57 0.82 0.17 0.28 0.77 Olink ProSeek CCL3 -0.20 0.21 0.61 0.06 0.74 0.95 Olink ProSeek CCL4 -0.22 0.18 0.56 0.09 0.56 0.92 Olink ProSeek CD244 -0.34 0.03 0.32 -0.13 0.42 0.90 Olink ProSeek CD40 -0.56 0.00 0.01 -0.30 0.06 0.75 Olink ProSeek CD5 -0.13 0.43 0.76 -0.01 0.94 0.96 Olink ProSeek CD6 -0.15 0.36 0.76 -0.19 0.25 0.77 Olink ProSeek CDCP1 -0.34 0.03 0.32 -0.06 0.71 0.95 Olink ProSeek CSF-1 0.11 0.51 0.80 -0.14 0.38 0.83 Olink ProSeek CST5 -0.30 0.06 0.43 -0.06 0.71 0.95 Olink ProSeek CX3CL1 -0.02 0.91 0.99 0.07 0.66 0.95 Olink ProSeek CXCL1 -0.25 0.12 0.52 -0.23 0.16 0.75 Olink ProSeek CXCL10 0.00 0.98 0.99 -0.11 0.49 0.91 Olink ProSeek CXCL11 -0.02 0.92 0.99 -0.21 0.20 0.75 Olink ProSeek CXCL5 -0.26 0.11 0.48 0.02 0.92 0.95 Olink ProSeek CXCL6 -0.03 0.86 0.99 0.04 0.82 0.95 Olink ProSeek CXCL9 0.07 0.68 0.85 -0.14 0.38 0.83 Olink ProSeek DNER -0.22 0.17 0.56 0.11 0.51 0.92 188 Olink ProSeek EN-RAGE -0.18 0.26 0.68 -0.16 0.32 0.77 Olink ProSeek FGF-19 0.09 0.57 0.82 -0.17 0.29 0.77 Olink ProSeek FGF-21 -0.04 0.79 0.97 -0.31 0.05 0.75 Olink ProSeek FGF-23 -0.16 0.32 0.70 0.09 0.60 0.92 Olink ProSeek FGF-5 0.03 0.88 0.99 0.08 0.60 0.92 Olink ProSeek Flt3L -0.34 0.03 0.32 0.00 0.98 0.98 Olink ProSeek GDNF -0.13 0.41 0.76 -0.09 0.59 0.92 Olink ProSeek HGF -0.17 0.29 0.70 -0.24 0.14 0.75 Olink ProSeek IL-10RA 0.33 0.04 0.32 0.20 0.21 0.75 Olink ProSeek IL-10RB -0.26 0.10 0.48 -0.11 0.52 0.92 Olink ProSeek IL-12B -0.10 0.54 0.82 0.21 0.19 0.75 Olink ProSeek IL-15RA 0.11 0.49 0.80 0.02 0.90 0.95 Olink ProSeek IL-17A 0.01 0.93 0.99 0.02 0.92 0.95 Olink ProSeek IL-17C 0.09 0.58 0.82 0.12 0.45 0.90 Olink ProSeek IL-18R1 -0.19 0.24 0.66 -0.06 0.71 0.95 Olink ProSeek IL10 -0.31 0.05 0.37 -0.22 0.18 0.75 Olink ProSeek IL18 0.34 0.03 0.32 0.23 0.15 0.75 Olink ProSeek IL5 -0.21 0.19 0.56 -0.10 0.53 0.92 Olink ProSeek IL6 0.14 0.37 0.76 -0.19 0.25 0.77 Olink ProSeek IL7 -0.17 0.30 0.70 -0.24 0.14 0.75 Olink ProSeek IL8 -0.39 0.01 0.26 -0.17 0.29 0.77 Olink ProSeek LAP TGF-beta-1 -0.13 0.44 0.76 0.03 0.86 0.95 Olink ProSeek LIF-R -0.17 0.31 0.70 0.06 0.70 0.95 189 Olink ProSeek MCP-1 -0.27 0.10 0.48 -0.02 0.91 0.95 Olink ProSeek MCP-2 -0.07 0.65 0.83 -0.04 0.82 0.95 Olink ProSeek MCP-3 -0.28 0.08 0.48 -0.09 0.60 0.92 Olink ProSeek MCP-4 -0.13 0.43 0.76 0.05 0.74 0.95 Olink ProSeek MMP-1 -0.04 0.82 0.99 -0.12 0.47 0.90 Olink ProSeek MMP-10 0.08 0.61 0.82 0.03 0.86 0.95 Olink ProSeek NT-3 -0.22 0.16 0.56 -0.05 0.78 0.95 Olink ProSeek OPG -0.27 0.09 0.48 -0.08 0.64 0.93 Olink ProSeek OSM -0.08 0.60 0.82 -0.26 0.11 0.75 Olink ProSeek PD-L1 0.01 0.96 0.99 -0.15 0.36 0.83 Olink ProSeek SCF 0.00 0.99 0.99 -0.21 0.19 0.75 Olink ProSeek SIRT2 0.07 0.65 0.83 0.19 0.25 0.77 Olink ProSeek SLAMF1 0.02 0.88 0.99 0.21 0.19 0.75 Olink ProSeek ST1A1 0.14 0.40 0.76 0.03 0.87 0.95 Olink ProSeek STAMPB 0.14 0.39 0.76 0.22 0.17 0.75 Olink ProSeek TGF-alpha -0.08 0.63 0.83 -0.25 0.12 0.75 Olink ProSeek TNFB -0.11 0.50 0.80 0.04 0.79 0.95 Olink ProSeek TNFRSF9 -0.40 0.01 0.25 -0.02 0.88 0.95 Olink ProSeek TNFSF14 -0.11 0.49 0.80 -0.13 0.43 0.90 Olink ProSeek TRAIL -0.17 0.28 0.70 -0.27 0.09 0.75 Olink ProSeek TRANCE -0.01 0.93 0.99 0.16 0.32 0.77 Olink ProSeek TWEAK -0.22 0.17 0.56 -0.16 0.31 0.77 Olink ProSeek uPA -0.27 0.09 0.48 -0.01 0.93 0.95 190 Olink ProSeek VEGFA -0.09 0.58 0.82 -0.23 0.15 0.75 ELISA Calprotectin -0.21 0.20 0.94 -0.21 0.20 0.66 ELISA G-CSF -0.02 0.89 0.94 -0.11 0.50 0.66 ELISA hsCRP -0.01 0.94 0.94 -0.11 0.49 0.66 ELISA MPO -0.02 0.88 0.94 0.00 1.00 1.00 ELISA SAA -0.14 0.39 0.94 -0.10 0.53 0.66 191 Table 36. Correlation between baseline blood protein level and change in weight from day 0 to day 28 and day 0 to day 168 of azithromycin treatment a Spearman’s correlation analysis Change in Weight from Day 0 to Day 28 Change in Weight from Day 0 to Day 168 Panel Protein Correlation a P-value FDR Correlation a P-value FDR MRM-MS A1AT 0.14 0.39 0.99 -0.03 0.86 0.96 MRM-MS A1BG 0.08 0.61 0.99 0.20 0.21 0.81 MRM-MS A2AP 0.10 0.54 0.99 -0.08 0.62 0.95 MRM-MS A2MG 0.03 0.86 0.99 0.27 0.09 0.80 MRM-MS AACT 0.00 0.98 0.99 -0.09 0.59 0.95 MRM-MS ACTC_ 0.16 0.32 0.99 0.39 0.01 0.38 MRM-MS ACTC___ 0.20 0.22 0.99 0.46 0.00 0.14 MRM-MS AFAM 0.02 0.91 0.99 0.10 0.55 0.95 MRM-MS ALBU 0.13 0.42 0.99 -0.01 0.93 0.97 MRM-MS ALS 0.18 0.26 0.99 0.11 0.51 0.95 MRM-MS AMBP 0.13 0.43 0.99 0.34 0.03 0.49 MRM-MS ANGT 0.10 0.53 0.99 0.16 0.32 0.90 MRM-MS ANT3 0.10 0.54 0.99 -0.01 0.96 0.97 MRM-MS APOA1 -0.03 0.87 0.99 -0.07 0.68 0.95 MRM-MS APOA2 -0.15 0.35 0.99 -0.21 0.19 0.81 MRM-MS APOA4 -0.13 0.44 0.99 0.03 0.83 0.96 MRM-MS APOB -0.05 0.76 0.99 -0.12 0.48 0.95 MRM-MS APOC2 -0.01 0.97 0.99 -0.17 0.29 0.90 MRM-MS APOC3 -0.04 0.81 0.99 -0.21 0.19 0.81 MRM-MS APOH 0.02 0.89 0.99 0.08 0.61 0.95 MRM-MS APOL1 0.09 0.58 0.99 -0.05 0.77 0.96 MRM-MS B2MG 0.11 0.48 0.99 0.16 0.32 0.90 MRM-MS C1QA 0.03 0.84 0.99 -0.20 0.21 0.81 MRM-MS C1QC 0.05 0.75 0.99 -0.03 0.84 0.96 MRM-MS C1R 0.11 0.51 0.99 0.08 0.62 0.95 MRM-MS C1S 0.13 0.43 0.99 0.27 0.10 0.80 MRM-MS C1S;MASP1 -0.06 0.70 0.99 0.07 0.65 0.95 MRM-MS C4BPA 0.07 0.67 0.99 0.10 0.54 0.95 MRM-MS CADH5 0.07 0.68 0.99 0.15 0.35 0.95 MRM-MS CAH1 0.05 0.77 0.99 0.01 0.96 0.97 MRM-MS CBG 0.06 0.73 0.99 -0.07 0.66 0.95 MRM-MS CBPN 0.10 0.56 0.99 0.21 0.18 0.81 MRM-MS CD14 -0.10 0.52 0.99 0.27 0.09 0.80 MRM-MS CD5L -0.09 0.57 0.99 0.01 0.96 0.97 MRM-MS CERU 0.03 0.84 0.99 0.10 0.53 0.95 192 MRM-MS CFAB 0.09 0.57 0.99 0.05 0.75 0.96 MRM-MS CFAD 0.11 0.51 0.99 0.08 0.61 0.95 MRM-MS CFAH 0.09 0.59 0.99 0.08 0.64 0.95 MRM-MS CFAI 0.13 0.41 0.99 -0.13 0.43 0.95 MRM-MS CLUS -0.06 0.69 0.99 -0.10 0.52 0.95 MRM-MS CNDP1 0.10 0.54 0.99 -0.03 0.84 0.96 MRM-MS CO2 -0.05 0.76 0.99 -0.07 0.67 0.95 MRM-MS CO3 0.02 0.91 0.99 0.01 0.96 0.97 MRM-MS CO4A;CO4B 0.14 0.40 0.99 -0.06 0.73 0.96 MRM-MS CO6 0.16 0.33 0.99 0.25 0.13 0.80 MRM-MS CO7 -0.12 0.45 0.99 -0.14 0.38 0.95 MRM-MS CO8G 0.07 0.65 0.99 0.07 0.68 0.95 MRM-MS CO9 0.24 0.14 0.99 0.14 0.40 0.95 MRM-MS CRP 0.22 0.17 0.99 0.25 0.12 0.80 MRM-MS CYTC 0.14 0.39 0.99 0.16 0.32 0.90 MRM-MS CYTC;NRP2 0.12 0.46 0.99 0.23 0.15 0.81 MRM-MS ECM1 -0.03 0.87 0.99 0.16 0.32 0.90 MRM-MS F13A -0.05 0.74 0.99 0.26 0.11 0.80 MRM-MS F13B 0.10 0.53 0.99 0.29 0.07 0.80 MRM-MS FA10 -0.07 0.65 0.99 0.05 0.75 0.96 MRM-MS FA12 -0.05 0.75 0.99 -0.05 0.77 0.96 MRM-MS FA5 0.12 0.47 0.99 0.05 0.78 0.96 MRM-MS FA7 0.00 0.98 0.99 0.25 0.12 0.80 MRM-MS FA9 0.10 0.56 0.99 -0.22 0.17 0.81 MRM-MS FBLN1 0.20 0.22 0.99 0.37 0.02 0.41 MRM-MS FETUA 0.14 0.39 0.99 -0.10 0.56 0.95 MRM-MS FETUB 0.01 0.93 0.99 0.06 0.72 0.96 MRM-MS FIBB 0.06 0.70 0.99 0.03 0.87 0.96 MRM-MS FIBG 0.07 0.67 0.99 0.00 0.99 0.99 MRM-MS FINC -0.08 0.63 0.99 0.10 0.55 0.95 MRM-MS GELS -0.03 0.88 0.99 -0.02 0.91 0.97 MRM-MS HBA 0.04 0.83 0.99 -0.04 0.82 0.96 MRM-MS HEMO -0.19 0.25 0.99 -0.10 0.52 0.95 MRM-MS HEP2 0.01 0.94 0.99 -0.17 0.31 0.90 MRM-MS HPT 0.01 0.95 0.99 0.06 0.73 0.96 MRM-MS HRG 0.12 0.47 0.99 0.51 0.00 0.09 MRM-MS IBP3 0.16 0.32 0.99 0.14 0.40 0.95 MRM-MS IC1 -0.10 0.54 0.99 -0.17 0.30 0.90 MRM-MS IPSP 0.01 0.95 0.99 -0.11 0.49 0.95 MRM-MS ITIH2 0.04 0.81 0.99 0.08 0.61 0.95 MRM-MS ITIH4 0.00 0.99 0.99 0.07 0.68 0.95 MRM-MS KAIN 0.01 0.95 0.99 -0.21 0.20 0.81 MRM-MS KLKB1 0.07 0.69 0.99 0.08 0.61 0.95 193 MRM-MS KNG1 0.03 0.85 0.99 -0.11 0.49 0.95 MRM-MS LBP 0.09 0.57 0.99 0.10 0.53 0.95 MRM-MS LCAT 0.03 0.86 0.99 -0.03 0.84 0.96 MRM-MS LUM -0.02 0.89 0.99 0.17 0.30 0.90 MRM-MS MASP1 -0.16 0.33 0.99 0.08 0.62 0.95 MRM-MS MMP2 0.03 0.87 0.99 0.32 0.04 0.57 MRM-MS PEDF 0.19 0.24 0.99 0.42 0.01 0.28 MRM-MS PGRP2 -0.10 0.55 0.99 -0.13 0.43 0.95 MRM-MS PLMN 0.17 0.29 0.99 0.24 0.14 0.81 MRM-MS PLTP -0.10 0.54 0.99 -0.14 0.40 0.95 MRM-MS PON1 0.10 0.56 0.99 0.13 0.43 0.95 MRM-MS PRDX2 0.04 0.79 0.99 -0.04 0.82 0.96 MRM-MS PROC 0.12 0.48 0.99 -0.02 0.92 0.97 MRM-MS PROS 0.07 0.67 0.99 0.13 0.42 0.95 MRM-MS PROZ 0.03 0.84 0.99 0.16 0.32 0.90 MRM-MS RET4 -0.07 0.65 0.99 -0.01 0.93 0.97 MRM-MS SAA4 -0.09 0.57 0.99 -0.02 0.89 0.97 MRM-MS SHBG -0.24 0.14 0.99 -0.18 0.25 0.90 MRM-MS TETN -0.06 0.72 0.99 0.19 0.24 0.90 MRM-MS THBG -0.01 0.93 0.99 0.04 0.81 0.96 MRM-MS THRB -0.09 0.60 0.99 -0.08 0.61 0.95 MRM-MS TTHY -0.07 0.69 0.99 -0.06 0.73 0.96 MRM-MS VASN 0.09 0.57 0.99 0.35 0.03 0.46 MRM-MS VCAM1 0.03 0.84 0.99 0.22 0.16 0.81 MRM-MS VTDB 0.01 0.93 0.99 0.02 0.89 0.97 MRM-MS VTNC 0.09 0.58 0.99 -0.05 0.78 0.96 MRM-MS VWF -0.01 0.96 0.99 0.08 0.64 0.95 MRM-MS ZA2G 0.01 0.97 0.99 0.23 0.16 0.81 MRM-MS ZAGL1;ZA2G 0.07 0.68 0.99 0.25 0.12 0.80 MRM-MS ZPI 0.12 0.47 0.99 0.13 0.43 0.95 Olink ProSeek 4E-BP1 0.14 0.40 0.86 0.10 0.55 0.93 Olink ProSeek ADA 0.29 0.07 0.86 0.38 0.02 0.59 Olink ProSeek AXIN1 0.15 0.34 0.86 0.02 0.92 0.98 Olink ProSeek Beta-NGF -0.26 0.11 0.86 -0.13 0.44 0.93 Olink ProSeek CASP-8 -0.04 0.81 0.89 -0.01 0.96 0.98 Olink ProSeek CCL11 0.11 0.51 0.86 0.08 0.64 0.93 Olink ProSeek CCL19 0.05 0.78 0.89 0.08 0.63 0.93 Olink ProSeek CCL20 -0.03 0.87 0.89 -0.05 0.74 0.93 Olink ProSeek CCL23 -0.01 0.97 0.99 -0.01 0.97 0.98 Olink ProSeek CCL25 -0.28 0.07 0.86 -0.24 0.13 0.78 Olink ProSeek CCL28 -0.11 0.50 0.86 -0.26 0.10 0.72 Olink ProSeek CCL3 0.13 0.42 0.86 0.28 0.08 0.72 Olink ProSeek CCL4 0.08 0.63 0.88 0.29 0.07 0.72 194 Olink ProSeek CD244 0.04 0.81 0.89 0.30 0.06 0.72 Olink ProSeek CD40 0.26 0.11 0.86 0.05 0.74 0.93 Olink ProSeek CD5 -0.30 0.06 0.86 -0.02 0.91 0.98 Olink ProSeek CD6 -0.12 0.46 0.86 0.07 0.67 0.93 Olink ProSeek CDCP1 -0.10 0.54 0.86 0.15 0.34 0.93 Olink ProSeek CSF-1 0.05 0.77 0.89 0.24 0.13 0.78 Olink ProSeek CST5 -0.11 0.52 0.86 0.04 0.81 0.93 Olink ProSeek CX3CL1 -0.18 0.27 0.86 0.04 0.79 0.93 Olink ProSeek CXCL1 0.08 0.63 0.88 -0.04 0.82 0.93 Olink ProSeek CXCL10 0.16 0.32 0.86 0.46 0.00 0.19 Olink ProSeek CXCL11 0.18 0.28 0.86 0.32 0.05 0.72 Olink ProSeek CXCL5 -0.09 0.56 0.86 -0.05 0.76 0.93 Olink ProSeek CXCL6 0.12 0.45 0.86 0.14 0.38 0.93 Olink ProSeek CXCL9 0.05 0.77 0.89 0.15 0.35 0.93 Olink ProSeek DNER -0.18 0.27 0.86 -0.11 0.52 0.93 Olink ProSeek EN-RAGE 0.14 0.38 0.86 -0.14 0.38 0.93 Olink ProSeek FGF-19 -0.25 0.13 0.86 0.09 0.58 0.93 Olink ProSeek FGF-21 0.03 0.84 0.89 0.18 0.26 0.88 Olink ProSeek FGF-23 0.10 0.56 0.86 0.27 0.10 0.72 Olink ProSeek FGF-5 -0.23 0.15 0.86 -0.13 0.43 0.93 Olink ProSeek Flt3L -0.33 0.04 0.86 -0.06 0.69 0.93 Olink ProSeek GDNF -0.12 0.46 0.86 -0.05 0.75 0.93 Olink ProSeek HGF -0.03 0.87 0.89 -0.08 0.63 0.93 Olink ProSeek IL-10RA 0.07 0.65 0.89 0.00 0.99 0.99 Olink ProSeek IL-10RB -0.04 0.81 0.89 0.20 0.21 0.88 Olink ProSeek IL-12B -0.08 0.63 0.88 0.19 0.25 0.88 Olink ProSeek IL-15RA -0.22 0.16 0.86 0.06 0.73 0.93 Olink ProSeek IL-17A -0.04 0.83 0.89 -0.09 0.58 0.93 Olink ProSeek IL-17C 0.04 0.81 0.89 0.04 0.80 0.93 Olink ProSeek IL-18R1 0.12 0.47 0.86 0.20 0.22 0.88 Olink ProSeek IL10 -0.20 0.23 0.86 -0.11 0.51 0.93 Olink ProSeek IL18 0.03 0.86 0.89 0.21 0.19 0.88 Olink ProSeek IL5 -0.07 0.69 0.89 -0.04 0.80 0.93 Olink ProSeek IL6 0.12 0.46 0.86 0.15 0.35 0.93 Olink ProSeek IL7 -0.14 0.39 0.86 0.10 0.54 0.93 Olink ProSeek IL8 0.18 0.27 0.86 0.18 0.25 0.88 Olink ProSeek LAP TGF-beta-1 -0.11 0.51 0.86 0.27 0.10 0.72 Olink ProSeek LIF-R -0.34 0.03 0.86 -0.14 0.39 0.93 Olink ProSeek MCP-1 -0.11 0.51 0.86 0.08 0.61 0.93 Olink ProSeek MCP-2 0.18 0.26 0.86 0.12 0.48 0.93 Olink ProSeek MCP-3 0.18 0.26 0.86 0.07 0.66 0.93 Olink ProSeek MCP-4 0.09 0.58 0.86 0.16 0.33 0.93 Olink ProSeek MMP-1 0.15 0.36 0.86 0.19 0.23 0.88 195 Olink ProSeek MMP-10 0.09 0.58 0.86 -0.01 0.93 0.98 Olink ProSeek NT-3 0.04 0.81 0.89 0.08 0.64 0.93 Olink ProSeek OPG 0.00 1.00 1.00 0.07 0.69 0.93 Olink ProSeek OSM 0.04 0.83 0.89 -0.05 0.75 0.93 Olink ProSeek PD-L1 -0.10 0.54 0.86 0.19 0.25 0.88 Olink ProSeek SCF -0.10 0.55 0.86 -0.08 0.62 0.93 Olink ProSeek SIRT2 0.19 0.25 0.86 0.13 0.44 0.93 Olink ProSeek SLAMF1 0.24 0.14 0.86 0.26 0.11 0.72 Olink ProSeek ST1A1 0.10 0.54 0.86 -0.04 0.80 0.93 Olink ProSeek STAMPB 0.13 0.42 0.86 0.10 0.55 0.93 Olink ProSeek TGF-alpha 0.07 0.68 0.89 0.02 0.89 0.98 Olink ProSeek TNFB -0.36 0.02 0.86 -0.09 0.58 0.93 Olink ProSeek TNFRSF9 -0.19 0.24 0.86 0.17 0.30 0.93 Olink ProSeek TNFSF14 0.04 0.79 0.89 -0.01 0.95 0.98 Olink ProSeek TRAIL 0.17 0.28 0.86 0.23 0.16 0.86 Olink ProSeek TRANCE -0.03 0.83 0.89 0.29 0.07 0.72 Olink ProSeek TWEAK -0.17 0.29 0.86 -0.03 0.83 0.93 Olink ProSeek uPA -0.24 0.14 0.86 0.11 0.50 0.93 Olink ProSeek VEGFA 0.09 0.57 0.86 0.10 0.55 0.93 ELISA Calprotectin 0.23 0.15 0.23 -0.05 0.78 0.78 ELISA G-CSF 0.09 0.60 0.72 -0.20 0.21 0.40 ELISA hsCRP 0.24 0.14 0.23 0.18 0.26 0.40 ELISA MPO -0.02 0.88 0.88 -0.08 0.63 0.76 ELISA SAA 0.36 0.02 0.13 0.20 0.22 0.40 196 Table 37. Correlation between change in blood protein level from day 0 to day 28 and change in FEV1% predicted from day 0 to day 28 and day 0 to day 168 of azithromycin treatment a Spearman’s correlation analysis Change in FEV1% predicted from Day 0 to Day 28 Change in FEV1% predicted from Day 0 to Day 168 Panel Protein Correlation a P-value FDR Correlation a P-value FDR MRM-MS A1AT 0.06 0.70 0.74 -0.04 0.82 0.90 MRM-MS A1BG 0.27 0.09 0.16 0.12 0.47 0.72 MRM-MS A2AP 0.36 0.02 0.12 0.23 0.15 0.47 MRM-MS A2MG 0.41 0.01 0.09 0.22 0.16 0.47 MRM-MS AACT 0.05 0.76 0.79 0.02 0.88 0.95 MRM-MS ACTC_ 0.25 0.13 0.19 0.25 0.11 0.47 MRM-MS ACTC___ 0.12 0.46 0.53 0.23 0.15 0.47 MRM-MS AFAM 0.32 0.04 0.14 0.22 0.17 0.47 MRM-MS ALBU 0.42 0.01 0.09 0.23 0.14 0.47 MRM-MS ALS 0.34 0.03 0.14 0.30 0.06 0.47 MRM-MS AMBP 0.23 0.16 0.23 0.40 0.01 0.31 MRM-MS ANGT 0.17 0.30 0.37 0.20 0.21 0.49 MRM-MS ANT3 0.31 0.05 0.14 0.25 0.12 0.47 MRM-MS APOA1 0.45 0.00 0.09 0.22 0.17 0.47 MRM-MS APOA2 0.22 0.17 0.24 0.13 0.44 0.72 MRM-MS APOA4 0.22 0.18 0.24 0.17 0.30 0.60 MRM-MS APOB -0.02 0.91 0.92 0.04 0.83 0.90 MRM-MS APOC2 0.47 0.00 0.09 -0.04 0.81 0.90 MRM-MS APOC3 0.42 0.01 0.09 0.07 0.66 0.80 MRM-MS APOH 0.36 0.02 0.12 0.13 0.41 0.72 MRM-MS APOL1 0.31 0.05 0.14 0.04 0.79 0.90 MRM-MS B2MG 0.28 0.08 0.15 0.04 0.80 0.90 MRM-MS C1QA 0.19 0.24 0.32 0.33 0.04 0.47 MRM-MS C1QC 0.26 0.10 0.17 0.25 0.12 0.47 MRM-MS C1R 0.34 0.03 0.14 0.12 0.46 0.72 MRM-MS C1S 0.29 0.06 0.15 0.21 0.19 0.47 MRM-MS C1S;MASP1 0.31 0.06 0.14 0.13 0.42 0.72 MRM-MS C4BPA 0.32 0.05 0.14 0.11 0.49 0.72 MRM-MS CADH5 0.39 0.01 0.09 0.24 0.13 0.47 MRM-MS CAH1 -0.04 0.78 0.81 -0.28 0.07 0.47 MRM-MS CBG 0.29 0.07 0.15 0.22 0.17 0.47 MRM-MS CBPN 0.27 0.10 0.17 0.09 0.59 0.77 MRM-MS CD14 0.02 0.92 0.92 0.11 0.50 0.72 MRM-MS CD5L 0.33 0.04 0.14 0.19 0.25 0.54 197 MRM-MS CERU 0.13 0.41 0.48 0.11 0.51 0.72 MRM-MS CFAB 0.19 0.25 0.32 0.06 0.74 0.87 MRM-MS CFAD 0.31 0.05 0.14 0.31 0.05 0.47 MRM-MS CFAH 0.31 0.05 0.14 0.09 0.59 0.77 MRM-MS CFAI 0.25 0.12 0.18 0.11 0.51 0.72 MRM-MS CLUS 0.33 0.04 0.14 0.07 0.69 0.83 MRM-MS CNDP1 0.28 0.08 0.15 0.21 0.19 0.47 MRM-MS CO2 0.26 0.11 0.18 0.41 0.01 0.31 MRM-MS CO3 0.36 0.02 0.12 0.10 0.52 0.72 MRM-MS CO4A;CO4B 0.27 0.09 0.16 0.08 0.61 0.78 MRM-MS CO6 0.26 0.11 0.18 0.00 0.98 0.98 MRM-MS CO7 0.28 0.08 0.15 0.17 0.29 0.60 MRM-MS CO8G 0.20 0.23 0.30 0.17 0.30 0.60 MRM-MS CO9 0.12 0.46 0.53 -0.01 0.97 0.98 MRM-MS CRP -0.14 0.40 0.47 -0.07 0.65 0.80 MRM-MS CYTC 0.39 0.01 0.09 0.09 0.60 0.77 MRM-MS CYTC;NRP2 0.44 0.00 0.09 0.09 0.57 0.76 MRM-MS ECM1 0.17 0.30 0.37 0.02 0.91 0.95 MRM-MS F13A 0.41 0.01 0.09 0.19 0.23 0.53 MRM-MS F13B 0.31 0.05 0.14 -0.21 0.19 0.47 MRM-MS FA10 0.15 0.36 0.43 0.30 0.06 0.47 MRM-MS FA12 0.37 0.02 0.11 0.41 0.01 0.31 MRM-MS FA5 0.29 0.07 0.15 0.12 0.44 0.72 MRM-MS FA7 0.32 0.04 0.14 -0.01 0.94 0.98 MRM-MS FA9 0.09 0.58 0.63 0.14 0.39 0.71 MRM-MS FBLN1 0.37 0.02 0.11 0.28 0.08 0.47 MRM-MS FETUA 0.24 0.14 0.21 0.42 0.01 0.31 MRM-MS FETUB 0.33 0.04 0.14 0.02 0.88 0.95 MRM-MS FIBB 0.28 0.08 0.15 -0.10 0.52 0.72 MRM-MS FIBG 0.32 0.05 0.14 -0.12 0.45 0.72 MRM-MS FINC 0.20 0.21 0.28 -0.20 0.21 0.49 MRM-MS GELS 0.28 0.08 0.15 0.21 0.19 0.47 MRM-MS HBA -0.06 0.73 0.76 -0.31 0.05 0.47 MRM-MS HEMO 0.29 0.07 0.15 0.12 0.48 0.72 MRM-MS HEP2 0.23 0.15 0.21 0.13 0.43 0.72 MRM-MS HPT 0.13 0.42 0.49 0.26 0.11 0.47 MRM-MS HRG 0.31 0.05 0.14 0.23 0.16 0.47 MRM-MS IBP3 0.51 0.00 0.09 0.36 0.02 0.47 MRM-MS IC1 0.26 0.10 0.17 0.11 0.50 0.72 MRM-MS IPSP 0.39 0.01 0.09 0.32 0.04 0.47 MRM-MS ITIH2 0.39 0.01 0.09 0.25 0.12 0.47 MRM-MS ITIH4 0.29 0.07 0.15 0.02 0.91 0.95 198 MRM-MS KAIN 0.25 0.12 0.19 0.18 0.26 0.56 MRM-MS KLKB1 0.40 0.01 0.09 0.22 0.17 0.47 MRM-MS KNG1 0.18 0.26 0.33 0.16 0.31 0.60 MRM-MS LBP 0.11 0.50 0.56 0.08 0.62 0.78 MRM-MS LCAT 0.38 0.02 0.11 0.13 0.43 0.72 MRM-MS LUM 0.29 0.07 0.15 0.23 0.16 0.47 MRM-MS MASP1 0.19 0.25 0.32 0.25 0.11 0.47 MRM-MS MMP2 -0.09 0.57 0.62 0.29 0.07 0.47 MRM-MS PEDF 0.23 0.15 0.22 0.15 0.36 0.68 MRM-MS PGRP2 0.35 0.03 0.13 0.24 0.14 0.47 MRM-MS PLMN 0.24 0.14 0.21 0.09 0.56 0.76 MRM-MS PLTP 0.31 0.05 0.14 0.27 0.09 0.47 MRM-MS PON1 0.46 0.00 0.09 0.17 0.30 0.60 MRM-MS PRDX2 -0.03 0.84 0.86 -0.27 0.09 0.47 MRM-MS PROC 0.16 0.33 0.40 0.01 0.96 0.98 MRM-MS PROS 0.30 0.06 0.15 0.23 0.16 0.47 MRM-MS PROZ 0.32 0.04 0.14 0.26 0.10 0.47 MRM-MS RET4 0.37 0.02 0.11 0.23 0.16 0.47 MRM-MS SAA4 0.11 0.52 0.58 -0.05 0.74 0.87 MRM-MS SHBG 0.10 0.55 0.61 0.10 0.52 0.72 MRM-MS TETN 0.30 0.06 0.15 0.10 0.53 0.72 MRM-MS THBG 0.25 0.11 0.18 0.17 0.29 0.60 MRM-MS THRB 0.18 0.26 0.32 0.06 0.73 0.87 MRM-MS TTHY 0.33 0.04 0.14 0.30 0.06 0.47 MRM-MS VASN 0.31 0.06 0.14 0.11 0.52 0.72 MRM-MS VCAM1 0.07 0.66 0.70 0.29 0.07 0.47 MRM-MS VTDB 0.39 0.01 0.09 0.15 0.35 0.66 MRM-MS VTNC 0.30 0.06 0.15 0.05 0.77 0.89 MRM-MS VWF 0.28 0.08 0.15 -0.21 0.19 0.47 MRM-MS ZA2G 0.30 0.06 0.14 0.04 0.81 0.90 MRM-MS ZAGL1;ZA2G 0.27 0.09 0.16 0.01 0.95 0.98 MRM-MS ZPI 0.19 0.24 0.32 0.20 0.21 0.49 Olink ProSeek 4E-BP1 -0.01 0.94 1.00 -0.26 0.11 0.70 Olink ProSeek ADA -0.04 0.79 1.00 -0.11 0.51 0.81 Olink ProSeek AXIN1 0.17 0.29 1.00 0.06 0.71 0.94 Olink ProSeek Beta-NGF -0.03 0.84 1.00 0.15 0.37 0.80 Olink ProSeek CASP-8 0.09 0.56 1.00 -0.07 0.65 0.93 199 Olink ProSeek CCL11 0.03 0.85 1.00 0.07 0.67 0.93 Olink ProSeek CCL19 0.05 0.75 1.00 0.19 0.25 0.72 Olink ProSeek CCL20 0.06 0.73 1.00 -0.01 0.93 0.99 Olink ProSeek CCL23 -0.30 0.06 1.00 -0.36 0.02 0.70 Olink ProSeek CCL25 0.25 0.12 1.00 0.11 0.50 0.81 Olink ProSeek CCL28 -0.03 0.83 1.00 -0.02 0.89 0.99 Olink ProSeek CCL3 0.13 0.41 1.00 0.14 0.38 0.80 Olink ProSeek CCL4 0.08 0.62 1.00 0.17 0.31 0.73 Olink ProSeek CD244 0.16 0.33 1.00 0.19 0.23 0.72 Olink ProSeek CD40 0.15 0.36 1.00 0.28 0.09 0.70 Olink ProSeek CD5 0.11 0.50 1.00 0.07 0.68 0.93 Olink ProSeek CD6 0.24 0.14 1.00 0.14 0.40 0.80 Olink ProSeek CDCP1 0.07 0.65 1.00 0.00 0.99 0.99 Olink ProSeek CSF-1 -0.08 0.63 1.00 -0.07 0.68 0.93 Olink ProSeek CST5 0.09 0.59 1.00 -0.08 0.62 0.91 Olink ProSeek CX3CL1 0.11 0.49 1.00 0.28 0.08 0.70 Olink ProSeek CXCL1 0.04 0.81 1.00 0.22 0.17 0.72 Olink ProSeek CXCL10 -0.01 0.94 1.00 0.01 0.96 0.99 Olink ProSeek CXCL11 -0.02 0.90 1.00 0.21 0.18 0.72 Olink ProSeek CXCL5 0.15 0.34 1.00 0.12 0.46 0.81 Olink ProSeek CXCL6 0.05 0.77 1.00 0.16 0.32 0.73 Olink ProSeek CXCL9 0.05 0.77 1.00 -0.06 0.72 0.94 200 Olink ProSeek DNER 0.26 0.10 1.00 0.20 0.22 0.72 Olink ProSeek EN-RAGE -0.04 0.82 1.00 0.14 0.40 0.80 Olink ProSeek FGF-19 -0.12 0.46 1.00 -0.20 0.21 0.72 Olink ProSeek FGF-21 0.22 0.18 1.00 0.40 0.01 0.70 Olink ProSeek FGF-23 -0.10 0.52 1.00 0.02 0.89 0.99 Olink ProSeek FGF-5 -0.03 0.87 1.00 0.02 0.88 0.99 Olink ProSeek Flt3L 0.20 0.22 1.00 0.14 0.41 0.80 Olink ProSeek GDNF 0.03 0.87 1.00 -0.16 0.32 0.73 Olink ProSeek HGF 0.02 0.89 1.00 0.19 0.23 0.72 Olink ProSeek IL-10RA -0.12 0.48 1.00 0.11 0.50 0.81 Olink ProSeek IL-10RB 0.07 0.67 1.00 -0.02 0.90 0.99 Olink ProSeek IL-12B 0.21 0.18 1.00 -0.02 0.92 0.99 Olink ProSeek IL-15RA -0.08 0.61 1.00 0.13 0.44 0.81 Olink ProSeek IL-17A 0.14 0.40 1.00 -0.06 0.73 0.94 Olink ProSeek IL-17C 0.12 0.45 1.00 -0.12 0.47 0.81 Olink ProSeek IL-18R1 -0.02 0.92 1.00 0.00 0.99 0.99 Olink ProSeek IL10 -0.09 0.59 1.00 0.08 0.61 0.91 Olink ProSeek IL18 -0.16 0.31 1.00 -0.09 0.57 0.90 Olink ProSeek IL5 0.10 0.55 1.00 0.01 0.98 0.99 Olink ProSeek IL6 -0.18 0.27 1.00 -0.34 0.03 0.70 Olink ProSeek IL7 0.04 0.80 1.00 0.20 0.21 0.72 Olink ProSeek IL8 0.18 0.27 1.00 0.27 0.09 0.70 201 Olink ProSeek LAP TGF-beta-1 0.02 0.91 1.00 0.11 0.51 0.81 Olink ProSeek LIF-R 0.01 0.97 1.00 0.02 0.89 0.99 Olink ProSeek MCP-1 0.06 0.71 1.00 0.19 0.24 0.72 Olink ProSeek MCP-2 0.01 0.96 1.00 0.03 0.83 0.99 Olink ProSeek MCP-3 0.13 0.42 1.00 0.18 0.26 0.72 Olink ProSeek MCP-4 0.04 0.83 1.00 0.19 0.24 0.72 Olink ProSeek MMP-1 -0.13 0.43 1.00 0.22 0.17 0.72 Olink ProSeek MMP-10 -0.28 0.08 1.00 -0.28 0.08 0.70 Olink ProSeek NT-3 0.16 0.32 1.00 0.18 0.28 0.73 Olink ProSeek OPG 0.00 1.00 1.00 -0.05 0.77 0.96 Olink ProSeek OSM -0.02 0.90 1.00 0.26 0.11 0.70 Olink ProSeek PD-L1 -0.04 0.81 1.00 0.20 0.23 0.72 Olink ProSeek SCF 0.32 0.04 1.00 -0.08 0.61 0.91 Olink ProSeek SIRT2 0.01 0.97 1.00 -0.12 0.44 0.81 Olink ProSeek SLAMF1 -0.12 0.44 1.00 -0.04 0.81 0.99 Olink ProSeek ST1A1 0.09 0.59 1.00 0.31 0.06 0.70 Olink ProSeek STAMPB 0.00 1.00 1.00 -0.16 0.31 0.73 Olink ProSeek TGF-alpha -0.03 0.84 1.00 0.23 0.15 0.72 Olink ProSeek TNFB 0.26 0.11 1.00 0.13 0.43 0.81 Olink ProSeek TNFRSF9 0.10 0.52 1.00 -0.02 0.91 0.99 Olink ProSeek TNFSF14 -0.03 0.87 1.00 0.32 0.05 0.70 Olink ProSeek TRAIL 0.13 0.42 1.00 0.01 0.96 0.99 202 Olink ProSeek TRANCE 0.33 0.04 1.00 0.24 0.14 0.72 Olink ProSeek TWEAK 0.22 0.18 1.00 0.17 0.31 0.73 Olink ProSeek uPA 0.28 0.08 1.00 0.05 0.74 0.95 Olink ProSeek VEGFA -0.07 0.69 1.00 0.26 0.11 0.70 ELISA Calprotectin -0.16 0.32 0.48 -0.10 0.54 0.56 ELISA G-CSF -0.19 0.25 0.48 -0.13 0.42 0.56 ELISA hsCRP -0.21 0.20 0.48 -0.10 0.56 0.56 ELISA MPO -0.14 0.40 0.48 -0.15 0.36 0.56 ELISA SAA -0.04 0.78 0.78 -0.14 0.40 0.56 203 Table 38. Correlation between change in blood protein level from day 0 to day 28 and change in weight from day 0 to day 28 and day 0 to day 168 of azithromycin treatment a Spearman’s correlation analysis Change in Weight from Day 0 to Day 28 Change in Weight from Day 0 to Day 168 Panel Protein Correlation a P-value FDR Correlation a P-value FDR MRM-MS A1AT -0.12 0.45 0.98 -0.04 0.80 1.00 MRM-MS A1BG -0.04 0.78 0.98 -0.18 0.26 1.00 MRM-MS A2AP 0.03 0.84 0.98 0.04 0.79 1.00 MRM-MS A2MG 0.10 0.52 0.98 0.06 0.72 1.00 MRM-MS AACT -0.21 0.19 0.98 -0.10 0.55 1.00 MRM-MS ACTC_ -0.18 0.27 0.98 -0.28 0.09 1.00 MRM-MS ACTC___ -0.12 0.47 0.98 -0.32 0.05 1.00 MRM-MS AFAM 0.23 0.15 0.98 -0.02 0.89 1.00 MRM-MS ALBU 0.11 0.49 0.98 -0.01 0.95 1.00 MRM-MS ALS 0.22 0.17 0.98 0.13 0.41 1.00 MRM-MS AMBP 0.15 0.35 0.98 0.00 1.00 1.00 MRM-MS ANGT 0.00 0.98 0.98 -0.13 0.44 1.00 MRM-MS ANT3 0.00 0.98 0.98 0.00 0.99 1.00 MRM-MS APOA1 0.13 0.42 0.98 0.07 0.65 1.00 MRM-MS APOA2 0.08 0.62 0.98 0.08 0.62 1.00 MRM-MS APOA4 0.17 0.30 0.98 -0.06 0.72 1.00 MRM-MS APOB -0.07 0.66 0.98 0.08 0.61 1.00 MRM-MS APOC2 0.15 0.37 0.98 0.09 0.58 1.00 MRM-MS APOC3 0.35 0.03 0.98 0.10 0.55 1.00 MRM-MS APOH 0.16 0.32 0.98 -0.16 0.34 1.00 MRM-MS APOL1 0.05 0.75 0.98 0.01 0.95 1.00 MRM-MS B2MG -0.18 0.27 0.98 -0.23 0.15 1.00 MRM-MS C1QA 0.00 0.98 0.98 0.14 0.37 1.00 MRM-MS C1QC -0.08 0.63 0.98 0.03 0.87 1.00 MRM-MS C1R -0.02 0.88 0.98 -0.11 0.49 1.00 MRM-MS C1S -0.13 0.43 0.98 -0.24 0.13 1.00 MRM-MS C1S;MASP1 -0.04 0.79 0.98 -0.06 0.70 1.00 MRM-MS C4BPA -0.15 0.35 0.98 -0.01 0.97 1.00 MRM-MS CADH5 0.09 0.60 0.98 -0.10 0.55 1.00 MRM-MS CAH1 -0.01 0.93 0.98 -0.04 0.81 1.00 MRM-MS CBG 0.07 0.68 0.98 0.03 0.85 1.00 MRM-MS CBPN 0.00 0.98 0.98 -0.10 0.52 1.00 MRM-MS CD14 -0.24 0.13 0.98 -0.22 0.17 1.00 MRM-MS CD5L 0.06 0.73 0.98 -0.29 0.07 1.00 MRM-MS CERU -0.17 0.30 0.98 -0.10 0.53 1.00 204 MRM-MS CFAB -0.04 0.83 0.98 0.03 0.86 1.00 MRM-MS CFAD 0.13 0.44 0.98 -0.15 0.35 1.00 MRM-MS CFAH -0.02 0.88 0.98 -0.02 0.89 1.00 MRM-MS CFAI -0.01 0.97 0.98 0.00 1.00 1.00 MRM-MS CLUS 0.10 0.55 0.98 0.08 0.62 1.00 MRM-MS CNDP1 0.16 0.33 0.98 0.15 0.37 1.00 MRM-MS CO2 0.08 0.61 0.98 0.15 0.36 1.00 MRM-MS CO3 0.15 0.37 0.98 0.08 0.62 1.00 MRM-MS CO4A;CO4B -0.08 0.64 0.98 0.04 0.81 1.00 MRM-MS CO6 0.02 0.89 0.98 -0.18 0.27 1.00 MRM-MS CO7 0.13 0.44 0.98 -0.05 0.76 1.00 MRM-MS CO8G -0.07 0.67 0.98 -0.13 0.43 1.00 MRM-MS CO9 -0.16 0.33 0.98 -0.10 0.56 1.00 MRM-MS CRP -0.32 0.04 0.98 -0.02 0.89 1.00 MRM-MS CYTC 0.01 0.94 0.98 -0.07 0.65 1.00 MRM-MS CYTC;NRP2 0.04 0.82 0.98 -0.20 0.21 1.00 MRM-MS ECM1 -0.05 0.77 0.98 -0.01 0.95 1.00 MRM-MS F13A -0.03 0.84 0.98 -0.23 0.16 1.00 MRM-MS F13B 0.04 0.81 0.98 -0.15 0.37 1.00 MRM-MS FA10 0.05 0.77 0.98 -0.11 0.48 1.00 MRM-MS FA12 0.08 0.61 0.98 -0.04 0.79 1.00 MRM-MS FA5 0.06 0.73 0.98 -0.06 0.69 1.00 MRM-MS FA7 0.03 0.83 0.98 -0.03 0.87 1.00 MRM-MS FA9 -0.05 0.78 0.98 -0.01 0.95 1.00 MRM-MS FBLN1 0.21 0.20 0.98 -0.16 0.31 1.00 MRM-MS FETUA -0.05 0.77 0.98 0.07 0.68 1.00 MRM-MS FETUB 0.12 0.45 0.98 0.06 0.69 1.00 MRM-MS FIBB -0.04 0.83 0.98 0.00 0.99 1.00 MRM-MS FIBG -0.03 0.87 0.98 0.08 0.62 1.00 MRM-MS FINC 0.19 0.25 0.98 0.23 0.16 1.00 MRM-MS GELS 0.27 0.09 0.98 0.01 0.94 1.00 MRM-MS HBA -0.03 0.85 0.98 -0.07 0.68 1.00 MRM-MS HEMO 0.10 0.54 0.98 0.01 0.97 1.00 MRM-MS HEP2 0.01 0.95 0.98 0.12 0.47 1.00 MRM-MS HPT -0.03 0.88 0.98 0.05 0.78 1.00 MRM-MS HRG 0.17 0.29 0.98 -0.12 0.48 1.00 MRM-MS IBP3 0.08 0.64 0.98 0.05 0.78 1.00 MRM-MS IC1 -0.05 0.74 0.98 0.13 0.43 1.00 MRM-MS IPSP 0.03 0.85 0.98 0.08 0.61 1.00 MRM-MS ITIH2 0.17 0.29 0.98 0.02 0.90 1.00 MRM-MS ITIH4 -0.06 0.73 0.98 -0.07 0.68 1.00 MRM-MS KAIN 0.13 0.43 0.98 0.16 0.32 1.00 MRM-MS KLKB1 0.09 0.57 0.98 -0.04 0.79 1.00 205 MRM-MS KNG1 0.06 0.73 0.98 0.04 0.80 1.00 MRM-MS LBP -0.09 0.59 0.98 -0.18 0.27 1.00 MRM-MS LCAT 0.11 0.50 0.98 0.12 0.48 1.00 MRM-MS LUM 0.26 0.11 0.98 -0.03 0.86 1.00 MRM-MS MASP1 0.04 0.81 0.98 0.06 0.71 1.00 MRM-MS MMP2 0.20 0.23 0.98 -0.04 0.81 1.00 MRM-MS PEDF 0.07 0.68 0.98 -0.26 0.11 1.00 MRM-MS PGRP2 0.14 0.37 0.98 0.00 0.99 1.00 MRM-MS PLMN 0.01 0.96 0.98 0.04 0.78 1.00 MRM-MS PLTP 0.16 0.32 0.98 0.10 0.54 1.00 MRM-MS PON1 0.12 0.46 0.98 0.00 0.98 1.00 MRM-MS PRDX2 0.00 0.98 0.98 -0.03 0.85 1.00 MRM-MS PROC -0.10 0.53 0.98 -0.13 0.42 1.00 MRM-MS PROS -0.01 0.96 0.98 -0.14 0.38 1.00 MRM-MS PROZ 0.13 0.41 0.98 -0.05 0.77 1.00 MRM-MS RET4 0.20 0.21 0.98 -0.08 0.63 1.00 MRM-MS SAA4 0.03 0.86 0.98 0.01 0.94 1.00 MRM-MS SHBG -0.02 0.89 0.98 -0.18 0.27 1.00 MRM-MS TETN 0.05 0.78 0.98 -0.13 0.41 1.00 MRM-MS THBG 0.00 0.98 0.98 0.02 0.89 1.00 MRM-MS THRB 0.12 0.45 0.98 0.06 0.69 1.00 MRM-MS TTHY 0.31 0.05 0.98 0.08 0.63 1.00 MRM-MS VASN 0.07 0.65 0.98 -0.19 0.24 1.00 MRM-MS VCAM1 0.17 0.29 0.98 -0.14 0.39 1.00 MRM-MS VTDB 0.06 0.72 0.98 -0.05 0.78 1.00 MRM-MS VTNC 0.14 0.39 0.98 0.05 0.75 1.00 MRM-MS VWF -0.03 0.84 0.98 0.01 0.96 1.00 MRM-MS ZA2G 0.16 0.31 0.98 -0.18 0.28 1.00 MRM-MS ZAGL1;ZA2G 0.20 0.22 0.98 -0.16 0.31 1.00 MRM-MS ZPI -0.11 0.51 0.98 -0.13 0.44 1.00 Olink ProSeek 4E-BP1 0.03 0.85 0.97 0.12 0.47 0.74 Olink ProSeek ADA -0.14 0.39 0.90 -0.09 0.60 0.77 Olink ProSeek AXIN1 -0.08 0.64 0.92 0.00 1.00 1.00 Olink ProSeek Beta-NGF 0.11 0.49 0.90 -0.10 0.53 0.77 Olink ProSeek CASP-8 -0.03 0.85 0.97 -0.10 0.53 0.77 Olink ProSeek CCL11 -0.09 0.56 0.90 -0.26 0.11 0.61 Olink ProSeek CCL19 -0.04 0.80 0.97 -0.26 0.10 0.61 Olink ProSeek CCL20 0.00 0.98 0.98 0.03 0.87 1.00 Olink ProSeek CCL23 0.06 0.71 0.94 -0.02 0.92 1.00 Olink ProSeek CCL25 -0.08 0.62 0.92 -0.10 0.54 0.77 Olink ProSeek CCL28 0.06 0.73 0.94 0.14 0.38 0.70 Olink ProSeek CCL3 -0.12 0.45 0.90 -0.16 0.34 0.70 Olink ProSeek CCL4 -0.13 0.41 0.90 -0.18 0.27 0.70 206 Olink ProSeek CD244 -0.10 0.54 0.90 -0.19 0.25 0.70 Olink ProSeek CD40 -0.10 0.53 0.90 -0.13 0.41 0.70 Olink ProSeek CD5 0.03 0.84 0.97 -0.09 0.58 0.77 Olink ProSeek CD6 -0.03 0.87 0.97 -0.15 0.37 0.70 Olink ProSeek CDCP1 -0.36 0.02 0.59 -0.26 0.10 0.61 Olink ProSeek CSF-1 -0.17 0.30 0.82 -0.23 0.16 0.70 Olink ProSeek CST5 0.12 0.46 0.90 -0.20 0.21 0.70 Olink ProSeek CX3CL1 0.10 0.54 0.90 -0.28 0.08 0.61 Olink ProSeek CXCL1 -0.09 0.56 0.90 0.01 0.94 1.00 Olink ProSeek CXCL10 -0.26 0.10 0.62 -0.33 0.04 0.61 Olink ProSeek CXCL11 -0.30 0.06 0.62 -0.28 0.07 0.61 Olink ProSeek CXCL5 0.08 0.64 0.92 -0.01 0.97 1.00 Olink ProSeek CXCL6 -0.09 0.58 0.91 -0.21 0.20 0.70 Olink ProSeek CXCL9 -0.26 0.10 0.62 -0.15 0.36 0.70 Olink ProSeek DNER -0.05 0.75 0.94 -0.09 0.56 0.77 Olink ProSeek EN-RAGE -0.25 0.13 0.62 -0.25 0.13 0.64 Olink ProSeek FGF-19 -0.12 0.44 0.90 -0.31 0.06 0.61 Olink ProSeek FGF-21 -0.23 0.15 0.62 -0.14 0.40 0.70 Olink ProSeek FGF-23 -0.16 0.31 0.82 -0.18 0.26 0.70 Olink ProSeek FGF-5 0.24 0.14 0.62 0.05 0.76 0.93 Olink ProSeek Flt3L 0.25 0.11 0.62 -0.13 0.42 0.70 Olink ProSeek GDNF -0.07 0.68 0.92 0.00 0.98 1.00 Olink ProSeek HGF -0.16 0.32 0.82 -0.13 0.42 0.70 Olink ProSeek IL-10RA 0.08 0.61 0.92 0.08 0.61 0.77 Olink ProSeek IL-10RB 0.05 0.77 0.95 -0.13 0.42 0.70 Olink ProSeek IL-12B -0.16 0.33 0.82 -0.30 0.06 0.61 Olink ProSeek IL-15RA -0.10 0.54 0.90 -0.11 0.50 0.77 Olink ProSeek IL-17A 0.02 0.89 0.97 0.20 0.22 0.70 Olink ProSeek IL-17C -0.10 0.52 0.90 0.01 0.95 1.00 Olink ProSeek IL-18R1 -0.25 0.11 0.62 -0.30 0.06 0.61 Olink ProSeek IL10 0.33 0.04 0.62 0.04 0.80 0.97 Olink ProSeek IL18 -0.36 0.02 0.59 -0.30 0.06 0.61 Olink ProSeek IL5 0.28 0.08 0.62 0.22 0.18 0.70 Olink ProSeek IL6 0.00 0.98 0.98 -0.04 0.82 0.98 Olink ProSeek IL7 0.02 0.89 0.97 0.00 0.98 1.00 Olink ProSeek IL8 -0.16 0.32 0.82 -0.32 0.04 0.61 Olink ProSeek LAP TGF-beta-1 -0.03 0.86 0.97 -0.15 0.34 0.70 Olink ProSeek LIF-R 0.13 0.42 0.90 -0.14 0.39 0.70 Olink ProSeek MCP-1 -0.02 0.92 0.97 -0.41 0.01 0.60 Olink ProSeek MCP-2 -0.16 0.31 0.82 -0.23 0.16 0.70 Olink ProSeek MCP-3 -0.21 0.19 0.65 -0.16 0.32 0.70 Olink ProSeek MCP-4 -0.10 0.56 0.90 -0.25 0.12 0.64 Olink ProSeek MMP-1 -0.19 0.24 0.80 -0.19 0.25 0.70 207 Olink ProSeek MMP-10 -0.05 0.74 0.94 0.00 0.99 1.00 Olink ProSeek NT-3 -0.12 0.48 0.90 -0.13 0.41 0.70 Olink ProSeek OPG 0.23 0.16 0.63 -0.12 0.45 0.72 Olink ProSeek OSM -0.27 0.09 0.62 -0.15 0.36 0.70 Olink ProSeek PD-L1 -0.07 0.66 0.92 -0.17 0.28 0.70 Olink ProSeek SCF 0.32 0.04 0.62 0.02 0.89 1.00 Olink ProSeek SIRT2 -0.05 0.75 0.94 0.00 0.98 1.00 Olink ProSeek SLAMF1 -0.22 0.17 0.64 -0.28 0.08 0.61 Olink ProSeek ST1A1 -0.21 0.19 0.65 -0.09 0.60 0.77 Olink ProSeek STAMPB 0.02 0.90 0.97 -0.01 0.94 1.00 Olink ProSeek TGF-alpha -0.23 0.15 0.62 -0.13 0.43 0.70 Olink ProSeek TNFB 0.12 0.48 0.90 -0.17 0.29 0.70 Olink ProSeek TNFRSF9 -0.01 0.97 0.98 -0.15 0.35 0.70 Olink ProSeek TNFSF14 -0.30 0.06 0.62 -0.21 0.19 0.70 Olink ProSeek TRAIL -0.25 0.13 0.62 -0.16 0.33 0.70 Olink ProSeek TRANCE -0.07 0.67 0.92 -0.06 0.70 0.87 Olink ProSeek TWEAK 0.01 0.97 0.98 -0.09 0.58 0.77 Olink ProSeek uPA 0.36 0.02 0.59 -0.09 0.57 0.77 Olink ProSeek VEGFA -0.16 0.31 0.82 -0.22 0.17 0.70 ELISA Calprotectin -0.30 0.06 0.12 -0.25 0.12 0.35 ELISA G-CSF -0.04 0.83 0.83 0.30 0.06 0.35 ELISA hsCRP -0.30 0.06 0.12 -0.11 0.51 0.77 ELISA MPO -0.20 0.22 0.33 -0.03 0.87 0.87 ELISA SAA -0.47 0.00 0.01 -0.16 0.32 0.64
Thesis/Dissertation
eng
Experimental Medicine
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Quon, Bradley S.
Predictive biomarkers of pulmonary exacerbations in cystic fibrosis
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