Identifying the Sources of Variation in Host-Associated Microbiomes Using Caenorhabditis elegans Restricted; Files Only
Boddu, Satya Spandana (Fall 2024)
Abstract
Microbiome, often referred to as the “forgotten organ”, plays a significant role in the health and functionality of its hosts. These collectives of microbes show variation across different host species, among individuals in a population and even within individuals over time. This dissertation quantifies this variation in the gut bacterial load using Caenorhabditis elegans model system through experimental and mathematical approaches. The goal is to understand the dynamics and drivers of inter-individual variation in gut microbiome composition. First, I introduce a high-throughput protocol for accurate single-worm bacterial load measurement, revealing the hidden heterogeneity among worms exposed to the same bacterial inoculum. Then, I discuss the challenges in bacterial quantification using colony forming units (CFUs) by extending the Most Probable Number (MPN) method and providing a new formula for combining dilution counts improving accuracy. Finally, I investigate the inter-individual variation across hosts through mono-colonization of individual nematode worms by focusing on the C. elegans model. This research advances our understanding of the role of host-microbe interaction in variation in microbiome composition.
Table of Contents
1 Introduction 1
1.1 Significance of the Intestinal Microbiome . . . . . . . . . . . . . . . . 2
1.2 Structure and Patterns in Host-associated Microbial Communities . . 3
1.3 Inferring Microbiome Ecology From Data: Top -Down and Bottom-Up Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 C. elegans as a model host . . . . . . . . . . . . . . . . . . . . . . . . 7
1.5 Dissertation Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Using Single-Worm Data to Quantify Heterogeneity in Caenorhabditis elegans -Bacterial Interactions 13
2.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3.1 Preparation of synchronized sterile C. elegans . . . . . . . . . 16
2.3.2 Feeding worms on live bacteria in liquid culture . . . . . . . . 21
2.3.3 Mechanical disruption of individual worms in a 96 -well format 23
2.3.4 Cleaning silicon carbide grit for re-use . . . . . . . . . . . . . 31
2.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.4.1 Bleach sterilization of live worms . . . . . . . . . . . . . . . . 32
2.4.2 Variations on multi-sample mechanical disruption . . . . . . . 32
2.4.3 Heterogeneity in bacterial colonization in adult worms . . . . 35
2.4.4 Importance of individual heterogeneity for accurate comparison of groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.4.5 Effects of individual heterogeneity on microbial transmission . 38
2.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3 Maximum Likelihood Estimators For Colony Forming Units 44
3.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.3.1 A Brief History of Counts . . . . . . . . . . . . . . . . . . . . 49
3.3.2 Simplest “Good” Estimator: Poisson . . . . . . . . . . . . . . 51
3.3.3 Combining Data: Common Bad Estimators . . . . . . . . . . 53
3.3.4 Too Few and Too Many . . . . . . . . . . . . . . . . . . . . . 55
3.3.5 Better Estimators: Poisson With Cutoff, aka What’s Countable, Exactly? . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.3.6 Crowding and the Most Probable Number . . . . . . . . . . . 60
3.3.7 Utility of the Models . . . . . . . . . . . . . . . . . . . . . . . 63
3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4 Variance in C. elegans gut bacterial load suggests complex host-microbe dynamics 72
4.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.3.1 Large variation in total bacterial population size is observed in the host but not in vitro . . . . . . . . . . . . . . . . . . . . . 75
4.3.2 Demographic noise does not explain variation in bacterial load 77
4.3.3 Static host heterogeneity does not explain the variation in bacterial load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.3.4 Modeling multiple states in the worm-bacteria system . . . . 90
4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
5 Summary 97
Appendix A Chapter 3 Supplemental Information 100
A.1 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 100
A.1.1 Bacterial Strains . . . . . . . . . . . . . . . . . . . . . . . . . 100
A.1.2 Worm Maintenance . . . . . . . . . . . . . . . . . . . . . . . . 100
A.1.3 Single Species Colonization . . . . . . . . . . . . . . . . . . . 101
A.1.4 Single Worm Digests . . . . . . . . . . . . . . . . . . . . . . . 102
A.1.5 Biosorter - Green Fluorescence Experiments . . . . . . . . . . 103
A.1.6 Simulations and computational methods . . . . . . . . . . . . 104
A.2 Logistic Model Fits for single species colonization . . . . . . . . . . . 106
A.3 Demographic noise model . . . . . . . . . . . . . . . . . . . . . . . . 107
A.4 Estimation of Ingestion and Excretion Rates . . . . . . . . . . . . . . 108
A.5 Additional Conditions for Inhibition of Bacterial Growth in the Intestine113
A.6 Run to run variation . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
A.7 CFU to GFP Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . 115
A.7.1 Transformation of GFP measurements to estimates for bacterial load in individual worms . . . . . . . . . . . . . . . . . . 116
A.7.2 Modeling in the log(CFU) space . . . . . . . . . . . . . . . . . 117
A.8 Probability distributions in the state switching model . . . . . . . . . 119
A.9 Probability distributions in the potential model . . . . . . . . . . . . 122
A.10 State switching with other bacteria . . . . . . . . . . . . . . . . . . . 125
Bibliography 127
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