Metabolomic Analysis of Microbial Small Molecules in Non-Alcoholic Fatty Liver Disease Public

Gawey, Brent (Spring 2022)

Permanent URL: https://etd.library.emory.edu/concern/etds/tb09j700z?locale=fr
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Abstract

Introduction: High-resolution metabolomics (HRM) is an innovative platform that can quantify products of microbial metabolism in systemic circulation. δ-Valerobetaine (VB) and 5-methoxyindole-acetic acid (5-MIAA) are two recently discovered microbial metabolites with known systemic bioactivity. VB has been implicated in carnitine and fatty acid metabolism and 5-MIAA has been shown to activate hepatic detoxification pathways (Nrf2/ARE). These pathways are highly expressed in the liver and play a role in hepatic inflammatory diseases, such as non-alcoholic fatty liver disease (NAFLD). Our study aimed to investigate the associations of these novel metabolites with validated measures of liver fibrosis, hypothesizing that each metabolite would be associated with the underlying degree of liver fibrosis. We also performed metabolome wide association studies (MWAS) and pathway analysis to further characterize these metabolites. 

Methods: This cross-sectional study included 139 participants with pre-existing liver fibrosis (mean age 51.08 ± 12.44 years, 66% female). Fasting plasma was analyzed using HRM methodology. Liver fibrosis was quantified using liver stiffness measurement (LSM) scores. Regression analyses were used to investigate associations between level of VB and 5-MIAA with LSM. An MWAS was performed to identify metabolites significantly associated with VB and 5-MIAA. These results were further analyzed using pathway analysis. 

Results: In regression analysis, there were no significant associations between plasma level of VB and 5-MIAA with LSM. MWAS for VB identified a variety of lipids and an NAD precursor to be most significantly associated. Pathway analysis for VB revealed significant associations with limonene degradation, lysine and glutathione metabolism, and vitamin B1 metabolism. MWAS for 5-MIAA identified a variety of dicarboxylic acids and fatty acids demonstrating the most significant associations. Pathway analysis for 5-MIAA revealed significant associations with lipid metabolism, chondroitin sulfate degradation, and biopterin metabolism. 

Conclusions: While regression analysis did not show any significant associations between plasma level of VB and 5-MIAA with LSM in patients with NAFLD, network and pathway analysis found a variety of metabolites and metabolic pathways with known biological importance in liver fibrosis significantly associated with VB and 5-MIAA. These associations offer future direction for analysis of the gut microbiome’s role in host inflammatory responses and human disease.

Table of Contents

Table of Contents

1. Introduction                                                                              1

2. Methods

2.1 Study Design, Setting, Participants                                                   4

2.2 Variables                                                                                 4 

2.3 Plasma High-Resolution Metabolomics (HRM)                               5 

2.4 Data Analysis                                                                          6 

3. Results

3.1 Descriptive Data                                                                     8

3.2 Main Results - Metabolite Identification and Quantification            10

3.3 Main Results – δ-Valerobetaine Targeted Analysis                                   11

3.4 Main Results – δ-Valerobetaine Untargeted Analysis                       14

3.5 Main Results – 5-methoxyindole-acetic acid Targeted Analysis            17

3.6 Main Results – 5-methoxyindole-Acetic Acid Untargeted Analysis       20

4. Discussion

4.1 Key results (Targeted and untargeted analysis of VB)                       24

4.2 Key results (Targeted and untargeted analysis of 5-MIAA)                     25

4.3 Limitations                                                                              27

4.4 Interpretation                                                                          27

4.5 Generalizability                                                                       28

5. Appendix                                                                                             29

6. References                                                                                          30      

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