Biology subjects

Willis, A. D.

Publications and source records attributed to Willis, A. D..

17 recordsLinked to original sources

bioRxiv2026

Scalable fold-change estimation from microbial metagenomics

Microbial sequencing data are subject to sample-specific and species-specific distortions that complicate inference about true microbial abundances. Recent work established assumptions under which relative fold-differences in microbial abundances can be estimated from sequencing data, accompanied by an estimation and testing method called radEmu. While radEmu has excellent error rates, its computational cost can be prohibitive for large-scale analyses. Here we introduce fastEmu, a scalable approximation to radEmu's testing methodology. fastEmu uses a small, data-driven subset of taxa to approximate the computationally expensive test statistic. In a metagenomic analysis, we show a reduction in per-taxon runtime from 4-8 hours to 0.5-45 seconds, and simulations illustrate no adverse impact on estimation error or Type I error rate control. fastEmu enables testing differential abundance for thousands of microbial taxa while retaining clear parameter interpretation and error rate control.

bioinformatics↗

bioRxiv2023

Analyzing microbial evolution through gene and genome phylogenies

AO_SCPLOWBSTRACTC_SCPLOWMicrobiome scientists critically need modern tools to explore and analyze microbial evolution. Often this involves studying the evolution of microbial genomes as a whole. However, different genes in a single genome can be subject to different evolutionary pressures, which can result in distinct gene-level evolutionary histories. To address this challenge, we propose to treat estimated gene-level phylogenies as data objects, and present an interactive method for the analysis of a collection of gene phylogenies. We use a local linear approximation of phylogenetic tree space to visualize estimated gene trees as points in low-dimensional Euclidean space, and address important practical limitations of existing related approaches, allowing an intuitive visualization of complex data objects. We demonstrate the utility of our proposed approach through microbial data analyses, including by identifying outlying gene histories in strains of Prevotella, and by contrasting Streptococcus phylogenies estimated using different gene sets. Our method is available as an open-source R package, and assists with estimating, visualizing and interacting with a collection of bacterial gene phylogenies. dimension reduction, microbiome, non-Euclidean, statistical genetics, visualization

bioinformatics↗

bioRxiv2023

Microbes with higher metabolic independence are enriched in human gut microbiomes under stress

A wide variety of human diseases are associated with loss of microbial diversity in the human gut, inspiring a great interest in the diagnostic or therapeutic potential of the microbiota. However, the ecological forces that drive diversity reduction in disease states remain unclear, rendering it difficult to ascertain the role of the microbiota in disease emergence or severity. One hypothesis to explain this phenomenon is that microbial diversity is diminished as disease states select for microbial populations that are more fit to survive environmental stress caused by inflammation or other host factors. Here, we tested this hypothesis on a large scale, by developing a software framework to quantify the enrichment of microbial metabolisms in complex metagenomes as a function of microbial diversity. We applied this framework to over 400 gut metagenomes from individuals who are healthy or diagnosed with inflammatory bowel disease (IBD). We found that high metabolic independence (HMI) is a distinguishing characteristic of microbial communities associated with individuals diagnosed with IBD. A classifier we trained using the normalized copy numbers of 33 HMI-associated metabolic modules not only distinguished states of health versus IBD, but also tracked the recovery of the gut microbiome following antibiotic treatment, suggesting that HMI is a hallmark of microbial communities in stressed gut environments.

microbiology↗

bioRxiv2023

Differences in gut metagenomes between dairy workers and community controls: a cross-sectional study

BackgroundAs a nexus of routine antibiotic use and zoonotic pathogen presence, the live-stock farming environment is a potential hotspot for the emergence of zoonotic diseases and antibiotic resistant bacteria. Livestock can further facilitate disease transmission by serving as intermediary hosts for pathogens as they undergo evolution prior to a spillover event. In light of this, we are interested in characterizing the microbiome and resistome of dairy workers, whose exposure to the livestock farming environment places them at risk for facilitating community transmission of antibiotic resistant genes and emerging zoonotic diseases. ResultsUsing shotgun sequencing, we investigated differences in the taxonomy, diversity and gene presence of the human gut microbiome of 10 dairy farm workers and 6 community controls, supplementing these samples with additional publicly available gut metagenomes. We observed greater abundance of tetracycline resistance genes and prevalence of cephamycin resistance genes in dairy workers metagenomes, and lower average gene diversity. We also found evidence of commensal organism association with plasmid-mediated tetracycline resistance genes in both dairy workers and community controls (including Faecalibacterium prausnitzii, Ligilactobacillus animalis, and Simiaoa sunii). However, we did not find significant differences in the prevalence of resistance genes or virulence factors overall, nor differences in the taxonomic composition of dairy worker and community control metagenomes. ConclusionsThis study presents the first metagenomics analysis of United States dairy workers, providing insights into potential risks of exposure to antibiotics and pathogens in animal farming environments. Previous metagenomic studies of livestock workers in China and Europe have reported increased abundance and carriage of antibiotic resistance genes in livestock workers. While our investigation found no strong evidence for differences in the abundance or carriage of antibiotic resistance genes and virulence factors between dairy worker and community control gut metagenomes, we did observe patterns in the abundance of tetracycline resistance genes and the prevalence of cephamycin resistance genes that is consistent with previous work.

genomics↗

bioRxiv2023

A highly conserved and globally prevalent cryptic plasmid is among the most numerous mobile genetic elements in the human gut

Plasmids are extrachromosomal genetic elements that often encode fitness enhancing features. However, many bacteria carry cryptic plasmids that do not confer clear beneficial functions. We identified one such cryptic plasmid, pBI143, which is ubiquitous across industrialized gut microbiomes, and is 14 times as numerous as crAssphage, currently established as the most abundant genetic element in the human gut. The majority of mutations in pBI143 accumulate in specific positions across thousands of metagenomes, indicating strong purifying selection. pBI143 is monoclonal in most individuals, likely due to the priority effect of the version first acquired, often from ones mother. pBI143 can transfer between Bacteroidales and although it does not appear to impact bacterial host fitness in vivo, can transiently acquire additional genetic content. We identified important practical applications of pBI143, including its use in identifying human fecal contamination and its potential as an inexpensive alternative for detecting human colonic inflammatory states.

microbiology↗

bioRxiv2022

Implications of taxonomic bias for microbial differential-abundance analysis

Differential-abundance (DA) analyses enable microbiome researchers to assess how microbial species vary in relative or absolute abundance with specific host or environmental conditions, such as health status or pH. These analyses typically use sequencing-based community measurements that are taxonomically biased to measure some species more efficiently than others. Understanding the effects that taxonomic bias has on the results of a DA analysis is essential for achieving reliable and translatable findings; yet currently, these effects are unknown. Here, we characterized these effects for DA analyses of both relative and absolute abundances, using a combination of mathematical theory and data analysis of real and simulated case studies. We found that, for analyses based on species proportions, taxonomic bias can cause significant errors in DA results if the average measurement efficiency of the community is associated with the condition of interest. These errors can be avoided by using more robust DA methods (based on species ratios) or quantified and corrected using appropriate controls. Wide adoption of our recommendations can improve the reproducibility, interpretability, and translatability of microbiome DA studies. This manuscript was rendered from commit 7412a36 of https://github.com/mikemc/differential-abundance-theory. Supporting data analyses can be found in the accompanying computational research notebook. Please post comments or questions on GitHub. The manuscript is licensed under a CC BY 4.0 License. See the GitHub Releases or Zenodo record for earlier versions.

bioinformatics↗

bioRxiv2022

Meta-analysis of metagenomes via machine learning and assembly graphs reveals strain switches in Crohn's disease

Microbial strains have closely related genomes but may have different phenotypes in the same environment. Shotgun metagenomic sequencing can capture the genomes of all strains present in a community but strain-resolved analysis from shotgun sequencing alone remains difficult. We developed an approach to identify and interrogate strain-level differences in groups of metagenomes. We use this approach to perform a meta-analysis of stool microbiomes from individuals with and without inflammatory bowel disease (IBD; Crohns disease, ulcerative colitis; n = 605), a disease for which there are not specific microbial biomarkers but some evidence that microbial strain variation may stratify by disease state. We first developed a machine learning classifier based on compressed representations of complete metagenomes (FracMinHash sketches) and identified genomes that correlate with IBD subtype. To rescue variation that may not have been present in the genomes, we then used assembly graph genome queries to recover strain variation for correlated genomes. Lastly, we developed a novel differential abundance framework that works directly on the assembly graph to uncover all sequence variants correlated with IBD. We refer to this approach as dominating set differential abundance analysis and have implemented it in the spacegraphcats software package. Using this approach, we identified five bacterial strains that are associated with Crohns disease. Our method captures variation within the entire sequencing data set, allowing for discovery of previously hidden disease associations.

bioinformatics↗

bioRxiv2022

happi: a Hierarchical Approach to Pangenomics Inference

Recovering metagenome-assembled genomes (MAGs) from shotgun sequencing data is an increasingly common task in microbiome studies, as MAGs provide deeper insight into the functional potential of both culturable and non-culturable microorganisms. However, metagenome-assembled genomes vary in quality, and may contain omissions and contamination. These errors present challenges for detecting genes and comparing gene enrichment across sample types. To address this, we propose happi, an approach to testing hypotheses about gene enrichment that accounts for genome quality. We illustrate the advantages of happi over existing approaches using published Saccharibacteria MAGs and via simulation.

genomics↗

bioRxiv2022

Structure-informed microbial population genetics elucidate selective pressures that shape protein evolution

Comprehensive sampling of natural genetic diversity with metagenomics enables highly resolved insights into the interplay between ecology and evolution. However, intra-population genomic variation represents the outcome of both stochastic and selective forces, making it difficult to identify whether variants are maintained by adaptive, neutral, or purifying processes. This is partly due to the reliance on gene sequences to interpret variants, which disregards the physical properties of three-dimensional gene products that define the functional landscape on which selection acts. Here we describe an approach to analyze genetic variation in the context of predicted protein structures, and apply it to study a marine microbial population within the SAR11 subclade 1a.3.V, which dominates low-latitude surface oceans. Our analyses reveal a tight association between the patterns of nonsynonymous polymorphism, selective pressures, and structural properties of proteins such as per-site relative solvent accessibility and distance to ligands, which explain up to 59% of genetic variance in some genes. In glutamine synthetase, a central gene in nitrogen metabolism, we observe decreased occurrence of nonsynonymous variants from ligand binding sites as a function of nitrate concentrations in the environment, revealing genetic targets of distinct evolutionary pressures maintained by nutrient availability. Our data also reveals that rare codons are purified from ligand binding sites when genes are under high selection, demonstrating the utility of structure-aware analyses to study the variants that likely impact translational processes. Overall, our work yields insights into the governing principles of evolution that shape the genetic diversity landscape within a globally abundant population, and makes available a software framework for structure-aware investigations of microbial population genetics. SignificanceIncreasing availability of metagenomes offers new opportunities to study evolution, but the equal treatment of all variants limits insights into drivers of sequence diversity. By capitalizing on recent advances in protein structure prediction capabilities, our study examines subtle evolutionary dynamics of a microbial population that dominates surface oceans through the lens of structural biology. We demonstrate the utility of structure-informed metrics to understand the distribution of nonsynonymous polymorphism, establish insights into the impact of changing nutrient availability on protein evolution, and show that even synonymous variants are scrutinized strictly to maximize translational efficiency when selection is high. Overall, our work illustrates new opportunities for discovery at the intersection between metagenomics and structural bioinformatics, and offers an interactive and scalable software platform to visualize and analyze genetic variants in the context of predicted protein structures and ligand-binding sites.

microbiology↗

bioRxiv2021

Segment number threshold determines juvenile onset of germline cluster proliferation in Platynereis dumerilii

Many animals rely on sexual reproduction to propagate by using gametes (oocytes and sperm). Development of sexual characters and generation of gametes are tightly coupled with the growth of an organism. Platynereis dumerilii is a marine segmented worm which has been used to study germline development and gametogenesis. Platynereis has 4 Primordial Germ Cells (PGCs) that arise early in development and these cells are thought to give rise to germ cell clusters found across the body in the juvenile worms. The germ cell clusters eventually form the gametes. The stages of germline development and how the 4 PGCs become the numerous germ cell clusters are not well-documented in the juvenile stages. Platynereis, like other segmented worms, grows by adding new segments at its posterior end. The number of segments generally reflect the growth state of the worms and therefore is a useful and easily measurable growth state metric. To understand how growth correlates with development and gametogenesis, we investigated germline development across several developmental stages using germline/multipotency markers. We found that segment number predicted the state of germline development and the abundance of germline clusters. Additionally, we found that keeping worms short in segment number via changing external conditions or via amputations supported segment number threshold requirement for germline development. Finally, we asked if these clusters in Platynereis play a role in regeneration (as similar free-roaming cells are observed in Hydra and planarian regeneration) and found that the clusters were not required for regeneration in Platynereis, suggesting a strictly germline nature. Overall, these molecular analyses suggest a previously unidentified developmental transition dependent on the growth state in juvenile Platynereis when germline proliferation is substantially increased.

developmental biology↗

bioRxiv2020

Gene-level metagenomics identifies genome islands associated with immunotherapy response

Researchers must be able to generate experimentally testable hypotheses from sequencing-based observational microbiome experiments to discover the mechanisms underlying the influence of gut microbes on human health. We describe a novel bioinformatics tool for identifying testable hypotheses based on gene-level metagenomic analysis of WGS microbiome data (geneshot). By applying geneshot to two independent previously published cohorts, we identified microbial genomic islands consistently associated with response to immune checkpoint inhibitor (ICI)-based cancer treatment in culturable type strains. The identified genomic islands are within operons involved in type II secretion, TonB-dependent transport, and bacteriophage growth. These results, as well as the underlying methodology, inform further mechanistic studies and facilitate the development of microbiome-enhanced therapeutics.

bioinformatics↗

bioRxiv2020

Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome

Microbial residents of the human oral cavity have long been a major focus of microbiology due to their influence on host health and their intriguing patterns of site specificity amidst the lack of dispersal limitation. Yet, the determinants of niche partitioning in this habitat are yet to be fully understood, especially among the taxa that belong to recently discovered branches of microbial life. Here we assembled metagenomes from daily tongue and dental plaque samples from multiple individuals and reconstructed 790 non-redundant genomes, 43 of which resolved to TM7 that formed six monophyletic clades distinctly associated either with plaque or with tongue. Both pangenomic and phylogenomic analyses grouped tongue-specific TM7 clades with other host-associated TM7 genomes. In contrast, plaque-specific TM7 grouped together with environmental TM7 genomes. Besides offering deeper insights into the ecology, evolution, and the mobilome of the cryptic members of the oral microbiome, our study reveals an intriguing resemblance between dental plaque and non-host environments indicated by the TM7 evolution, suggesting that plaque may have served as a stepping stone for environmental microbes to adapt to host environments for some clades of human associated microbes. Additionally, we report that prophages are widespread amongst oral-associated TM7, while absent from environmental TM7, suggesting that prophages may have played a role in adaptation of TM7 to the host environment.

microbiology↗

bioRxiv2019

A multi-view model for relative and absolute microbial abundances

The absolute abundance of bacterial taxa in human host-associated environments play a critical role in reproductive and gastrointestinal health. However, obtaining the absolute abundance of many bacterial species is typically prohibitively expensive. In contrast, relative abundance data for many species is comparatively cheap and easy to collect (e.g., with universal primers for the 16S rRNA gene). In this paper, we propose a method to jointly model relative abundance data for many taxa and absolute abundance data for a subset of taxa. Our method provides point and interval estimates for the absolute abundance of all taxa. Crucially, our proposal accounts for differences in the efficiency of taxon detection in the relative and absolute abundance data. We show that modeling taxon-specific efficiencies substantially reduces the estimation error for absolute abundance, and controls the coverage of interval estimators. We demonstrate the performance of our proposed method via a simulation study, a sensitivity study where we jackknife the taxa with observed absolute abundances, and a study of women with bacterial vaginosis.

genomics↗

bioRxiv2019

Estimation of cell lineage trees by maximum-likelihood phylogenetics

CRISPR technology has enabled large-scale cell lineage tracing for complex multicellular organisms by mutating synthetic genomic barcodes during organismal development. However, these sophisticated biological tools currently use ad-hoc and outmoded computational methods to reconstruct the cell lineage tree from the mutated barcodes. Because these methods are agnostic to the biological mechanism, they are unable to take full advantage of the datas structure. We propose a statistical model for the mutation process and develop a procedure to estimate the tree topology, branch lengths, and mutation parameters by iteratively applying penalized maximum likelihood estimation. In contrast to existing techniques, our method estimates time along each branch, rather than number of mutation events, thus providing a detailed account of tissue-type differentiation. Via simulations, we demonstrate that our method is substantially more accurate than existing approaches. Our reconstructed trees also better recapitulate known aspects of zebrafish development and reproduce similar results across fish replicates.

bioinformatics↗

bioRxiv2019

Clustering co-abundant genes identifies components of the gut microbiome that are reproducibly associated with colorectal cancer and inflammatory bowel disease

BackgroundWhole-genome \"shotgun\" (WGS) metagenomic sequencing is an increasingly widely used tool for analyzing the metagenomic content of microbiome samples. While WGS data contains gene-level information, it can be challenging to analyze the millions of microbial genes which are typically found in microbiome experiments. To mitigate the ultrahigh dimensionality challenge of gene-level metagenomics, it has been proposed to cluster genes by co-abundance to form Co-Abundant Gene groups (CAGs). However, exhaustive co-abundance clustering of millions of microbial genes across thousands of biological samples has previously been intractable purely due to the computational challenge of performing trillions of pairwise comparisons.\n\nResultsHere we present a novel computational approach to the analysis of WGS datasets in which microbial gene groups are the fundamental unit of analysis. We use the Approximate Nearest Neighbor heuristic for near-exhaustive average linkage clustering to group millions of genes by co-abundance. This results in thousands of high-quality CAGs representing complete and partial microbial genomes. We applied this method to publicly available WGS microbiome surveys and found that the resulting microbial CAGs associated with inflammatory bowel disease (IBD) and colorectal cancer (CRC) were highly reproducible and could be validated independently using multiple independent cohorts.\n\nConclusionsThis powerful approach to gene-level metagenomics provides a powerful path forward for identifying the biological links between the microbiome and human health. By proposing a new computational approach for handling high dimensional metagenomics data, we identified specific microbial gene groups that are associated with disease that can be used to identify strains of interest for further preclinical and mechanistic experimentation.

bioinformatics↗

bioRxiv2019

Consistent and correctable bias in metagenomic sequencing measurements

Measurements of biological communities by marker-gene and metagenomic sequencing are biased: The measured relative abundances of taxa or their genes are systematically distorted from their true values because each step in the experimental workflow preferentially detects some taxa over others. Bias can lead to qualitatively incorrect conclusions and makes measurements from different protocols quantitatively incomparable. A rigorous understanding of bias is therefore essential. Here we propose, test, and apply a simple mathematical model of how bias distorts marker-gene and metagenomics measurements: Bias multiplies the true relative abundances within each sample by taxon-and protocol-specific factors that describe the different efficiencies with which taxa are detected by the workflow. Critically, these factors are consistent across samples with different compositions, allowing bias to be estimated and corrected. We validate this model in 16S rRNA gene and shotgun metagenomics data from bacterial communities with defined compositions. We use it to reason about the effects of bias on downstream statistical analyses, finding that analyses based on taxon ratios are less sensitive to bias than analyses based on taxon proportions. Finally, we demonstrate how this model can be used to quantify bias from samples of defined composition, partition bias into steps such as DNA extraction and PCR amplification, and to correct biased measurements. Our model improves on previous models by providing a better fit to experimental data and by providing a composition-independent approach to analyzing, measuring, and correcting bias.

bioinformatics↗

bioRxiv2018

DivNet: Estimating diversity in networked communities

Diversity is a marker of ecosystem health in ecology, microbiology and immunology, with implications for disease diagnosis and infection resistance. However, accurately comparing diversity across environmental gradients is challenging, especially when number of different taxonomic groups in the community is large. Furthermore, existing approaches to estimating diversity do not perform well when the taxonomic groups in the community interact via an ecological network, such as by competing within their niche, or with mutualistic relationships. To address this, we propose DivNet, a method for estimating within- and between-community diversity in ecosystems where taxa interact via an ecological network. In particular, accounting for network structure permits more accurate estimates of alpha- and beta-diversity, even in settings with a large number of taxa and a small number of samples. DivNet is fast, accurate, precise, performs well with large numbers of taxa, and is robust to both weakly and strongly networked communities. We show that the advantages of incorporating taxon interactions into diversity estimation are especially clear in analyzing microbiomes and other high-diversity, strongly networked ecosystems. Therefore, to illustrate the method, we analyze the microbiome of seafloor basalts based on a 16S amplicon sequencing dataset with 1490 taxa and 13 samples.

bioinformatics↗