Biology subjects

Bradley, P. H.

Publications and source records attributed to Bradley, P. H..

18 recordsLinked to original sources

bioRxiv2026

Phylogenize2: robust phylogenetic methods link genes to phenotypes across host-associated and environmental microbiomes

In microbiome studies, associations between microbial functions and the environment are often confounded by phylogeny. While some methods explicitly account for this confounder, they require information about genome content, limiting their use in biomes where few genomes have been available. To make these methods more universally accessible, we have developed Phylogenize2, a redesigned phylogeny-aware tool for linking microbial gene families to abundance phenotypes. Phylogenize2 integrates large metagenome-assembled genome collections, including both biome-specific collections from MGnify and a broadly sampled general purpose database, GlobDB, to substantially expand species coverage, allowing its application in environments like the mouse gut and ocean. In addition, by default, Phylogenize2 uses a new robust phylogenetic testing framework that has been optimized for microbial abundance data, while also allowing the use of other comparative methods such as POMS. In an experimental mouse study, Phylogenize2 identifies that Muribaculaceae with higher abundance on a high-fat diet are enriched for proteins in the thioredoxin family, with likely roles in oxidative stress. When we apply Phylogenize2 to a polar ocean study, we find that a molybdenum-dependent PaoABC/YagTSR-like aldehyde oxidoreductase system differentiates mesopelagic from surface-dwelling Flavobacteriaceae, suggesting that aldehyde detoxification may be important for organisms that degrade marine snow. Together, these results show that Phylogenize2 expands phylogeny-aware microbiome analysis beyond the human gut and can provide insight into the genetic basis of microbiome-encoded traits in diverse environments. ImportanceMicrobiome studies often set out to identify which microbes are more or less abundant across environments, but these patterns can be difficult to interpret. Phylogenize2 is an open-source software package that allows researchers to ask whether individual microbial gene families are associated with the environment across independent branches of the microbial tree of life. By incorporating large collections of genomes from uncultivated microbes, as well as modern statistical methods designed for microbial abundance data, Phylogenize2 makes this approach practical for microbiomes beyond the human gut, including in model organisms like lab mice and free-living environments like the ocean. We also provide a pipeline that allows the use of new genome collections. In two case studies, we demonstrate that Phylogenize2 effectively prioritizes specific genes and pathways from metagenomic data, thereby leading researchers from changes in microbial abundance to more biologically interpretable explanations.

bioinformatics↗

bioRxiv2026

A robust, sensitive phylogenetic method enables gene-level metagenomic analyses

A key goal in the microbiome field is to move from taxonomic associations towards mechanistic hypotheses about microbial gene function. However, most methods for linking microbiome changes to specific genes are biased towards finding marker genes, with weak evidence for functional relevance. Phylogenetic regression can address this issue and has been previously applied to changes in microbial prevalence, but many environments (such as the gut in health vs. disease) are characterized more by changes in abundance, which presents unique statistical challenges. We show that when applied to real differential abundances from metagenomes, phylogenetic regression has an anti-conservative bias, indicating inflated false positives. We develop an alternative non-parametric method called "robust permutration," designed specifically for differential abundance data, and evaluate its performance against phylogenetic regression as well as several other phylogenetic comparative methods in realistic simulations of metagenomic data. These results show that robust permutration is the most powerful method that appropriately controls the false positive rate. We further apply robust permutration to a human case-control study of liver cirrhosis, revealing that Lachnospiraceae abundance in disease is linked to a previously uncharacterized iron- sulfur transcription factor encoded near homologs of the butyryl-CoA oxygen oxidoreductase system, a recently discovered system for oxygen detoxification. This illustrates how robust, sensitive phylogenetic methods can enable the generation of new molecular hypotheses directly from metagenomic case-control data. ImportancePreviously, we showed that phylogenetic regression can effectively detect genes associated with microbial presence or absence while correcting for evolutionary relationships. Unexpectedly, however, we here observe that this method can lead to high false positive rates when applied to microbial abundance data. In realistic simulations, other methods we test either have similar problems with false positives, or display very low power. We outline a new statistical test that better accounts for measurement uncertainty, outliers, and model violations, achieving more balanced sensitivity and accuracy than competing methods. Applying this test to a cirrhosis study reveals an uncharacterized transcription factor enriched in disease, with an apparent role in oxidative stress based on its sequence and gene neighborhood. This suggests a functional explanation for the observed taxonomic shifts, and demonstrates how improved phylogenetic methods could help inform future microbiome-targeted treatments.

bioinformatics↗

bioRxiv2026

Anaerobic riboflavin degradation by human gut Lachnospiraceae

Vitamins mediate a web of cross-feeding interactions in the human gut. Many Gram-positive gut microbes, in particular, are predicted to be vitamin auxotrophs. Previous studies of these microbes, however, have tended to use rich media, precluding controlled perturbations of low abundance nutrients. We tested the ability of diverse Lachnospiraceae, the most common Gram-positives in the gut, to grow on a chemically defined medium. Even though this medium contained riboflavin, we found that predicted riboflavin auxotrophs grew poorly, including the bile metabolizer Clostridium scindens. High-dose riboflavin supplementation enhanced growth, but also revealed that surprisingly, C. scindens catabolizes riboflavin into lumichrome, making it the first reported anaerobe to do so. The only previously described catabolic pathway for riboflavin requires oxygen and has no homologs in C. scindens. In high-dose riboflavin, a single gene neighborhood with an aldolase, oxidoreductases, and a riboflavin kinase/adenylyltransferase was upregulated, suggesting an alternative anaerobic degradation or overflow pathway. Similar neighborhoods were detected in several other Lachnospiraceae, including Faecalicatena fissicatena, the only other anaerobe reported to degrade riboflavin. Reanalysis of published metabolomic data showed that in vivo, both riboflavin and lumichrome were more abundant in colonized (vs. germ-free) mouse ceca, and that in vitro, Lachnospiraceae isolates depleted riboflavin while certain Gram-negative isolates overproduced it. These results demonstrate that a member of the Lachnospiraceae can anaerobically convert an essential B vitamin into lumichrome, a molecule recently shown to have anti-inflammatory properties. Vitamin catabolism may both structure cross-feeding interactions in the gut and affect host health. ImportanceLachnospiraceae, the most prevalent Gram-positives in the human gut, produce many health-relevant metabolites, but are genetically intractable and are often grown in rich medium, complicating physiological studies. By performing a comparative study with chemically defined media, we identify for the first time a specific anaerobe that can break down riboflavin to lumichrome. Using transcriptomics, we also identify a specific gene neighborhood representing the first candidate pathway for anaerobic flavin degradation. This neighborhood is conserved in a handful of other Lachnospiraceae, including F. fissicatena, the only other anaerobe known to degrade riboflavin (to the related product hydroxyethylflavin). These results may explain decades-old observations implicating gut microbes in the formation of riboflavin degradation products. Furthermore, lumichrome and related metabolites have recently been shown to inhibit host mucosal-associated invariant T (MAIT) cell activation, suggesting an additional mechanism by which commensal Lachnospiraceae may dampen inflammation.

microbiology↗

bioRxiv2025

Optimizing Strain Selection for Association Studies Under Hard Cost Constraints

Quantitative genetics methods that link genotype to phenotype can be especially powerful tools in model organisms and non-human populations, where researchers can apply well-controlled perturbations to commercially-available collections of strains. However, purchasing and phenotyping large collections can be cost-prohibitive. We evaluate several approaches to select subsets of a panel of strains with the aim of maximizing power under fixed budgetary constraints. Some approaches focus solely on costs, others on genetic diversity, and some on both simultaneously. To assess how these results generalize, we simulate data across two biological and statistical settings: phylogenetic regression across bacterial isolates, and linear-mixed model regression across mouse strains. Surprisingly, we find that simply selecting the cheapest strains until the budget is exhausted (MinCost) is usually among the top-performing methods, and that algorithms that consider genetic diversity without weighting by cost tend to show much lower power overall. Methods that consider both cost and diversity are typically, though not universally, the top performers. A comparison of diversity-maximizing objectives reveals that the most commonly studied objective (MaxMin) tends to be the among the least effective at retaining power. Instead, two alternative objectives that have not been previously studied in this context -- maximizing the total minor allele frequency (MaxMAF) and maximizing the total sum of pairwise genetic distances (MaxSum) -- typically perform best. Finally, in addition to fully simulated data, we also test these approaches by subsampling real data on cardiovascular phenotypes from the Hybrid Mouse Diversity Panel (HMDP). The three best methods on these data are cost-aware (p-Median, MaxSum, and MinCost). Based on these results, we recommend investigators consider approaches that maximize total MAF or pairwise genetic distance weighted by strain cost, but we also conclude that a surprisingly robust approach is to simply pick the most strains one can afford.

genomics↗

bioRxiv2025

T-cell receptor specificity landscape revealed through de novo peptide design

T-cells play a key role in adaptive immunity by mounting specific responses against diverse pathogens. An effective binding between T-cell receptors (TCRs) and pathogen-derived peptides presented on Major Histocompatibility Complexes (MHCs) mediate an immune response. However, predicting these interactions remains challenging due to limited functional data on T-cell reactivities. Here, we introduce a computational approach to predict TCR interactions with peptides presented on MHC class I alleles, and to design novel immunogenic peptides for specified TCR-MHC complexes. Our method leverages HERMES, a structure-based, physics-guided machine learning model trained on the protein universe to predict amino acid preferences based on local structural environments. Despite no direct training on TCR-pMHC data, the implicit physical reasoning in HERMES enables us to make accurate predictions of both TCR-pMHC binding affinities and T-cell activities across diverse viral epitopes and cancer neoantigens, achieving up to 0.72 correlation with experimental data. Leveraging our TCR recognition model, we develop a computational protocol for de novo design of immunogenic peptides. Through experimental validation in three TCR-MHC systems targeting viral and cancer peptides, we demonstrate that our designs--with up to five substitutions from the native sequence--activate T-cells at success rates of up to 50%. Lastly, we use our generative framework to quantify the diversity of the peptide recognition landscape for various TCR-MHC complexes, offering key insights into T-cell specificity in both humans and mice. Our approach provides a platform for immunogenic peptide and neoantigen design, as well as for evaluating TCR specificity, offering a computational framework to inform design of engineered T-cell therapies and vaccines.

biophysics↗

bioRxiv2024

Human xenobiotic metabolism proteins have full-length and split homologs in the gut microbiome

Xenobiotics, including pharmaceutical drugs, can be metabolized by both host and microbiota, in some cases by homologous enzymes. We conducted a systematic search for all human proteins with gut microbial homologs. Because gene fusion and fission can obscure homology detection, we built a pipeline to identify not only full-length homologs, but also cases where microbial homologs were split across multiple adjacent genes in the same neighborhood or operon ("split homologs"). We found that human proteins with full-length gut microbial homologs disproportionately participate in xenobiotic metabolism. While this included many different enzyme classes, short-chain and aldo-keto reductases were the most frequently detected, especially in prevalent gut microbes, while cytochrome P450 homologs were largely restricted to lower-prevalence facultative anaerobes. In contrast, human proteins with split homologs tended to play roles in central metabolism, especially of nucleobase-containing compounds. We identify twelve specific drugs that gut microbial split homologs may metabolize; two of these, 6-mercaptopurine by xanthine dehydrogenase (XDH) and 5-fluorouracil by dihydropyrimidine dehydrogenase (DPYD), have been recently confirmed in mouse models. This work provides a comprehensive map of homology between the human and gut microbial proteomes, indicates which human xenobiotic enzyme classes are most likely to be shared by gut microorganisms, and finally demonstrates that split homology may be an underappreciated explanation for microbial contributions to drug metabolism. Article SummaryWe develop a pipeline to systematically find human proteins with gut microbial homologs, including those split across multiple microbial genes (e.g., operons). This reveals thousands of proteins with full-length gut homologs, especially reductases and hydrolases that metabolize xenobiotics. Nearly two dozen split homologs are also observed for central metabolic enzymes, many of which can transform substrate analogs; in two cases, previous studies verify that microbial split homologs enable the expected drug to be metabolized in vivo. These results, which we provide as a resource, map out homology and shed light on parallel drug metabolism between host and microbiome.

genomics↗

bioRxiv2024

Small amounts of misassembly can have disproportionate effects on pangenome-based metagenomic analyses

Individual genes from microbiomes can drive host-level phenotypes. To help identify such candidate genes, several recent tools estimate microbial gene copy numbers directly from metagenomes. These tools rely on alignments to pangenomes, which in turn are derived from the set of all individual genomes from one species. While large-scale metagenomic assembly efforts have made pangenome estimates more complete, mixed communities can also introduce contamination into assemblies, and it is unknown how robust pangenome-based metagenomic analyses are to these errors. To gain insight into this problem, we re-analyzed a case-control study of the gut microbiome in cirrhosis, focusing on commensal Clostridia previously implicated in this disease. We tested for differentially prevalent genes in the Lachnospiraceae, then investigated which were likely to be contaminants using sequence similarity searches. Out of 86 differentially prevalent genes, we found that 33 (38%) were probably contaminants originating in taxa such as Veillonella and Haemophilus, unrelated genera that were independently correlated with disease status. Our results demonstrate that even small amounts of contamination in metagenome assemblies, below typical quality thresholds, can threaten to overwhelm gene-level metagenomic analyses. However, we also show that such contaminants can be accurately identified using a method based on gene-to-species correlation. After removing these contaminants, we observe that several flagellar motility gene clusters in the Lachnospira eligens pangenome are associated with cirrhosis status. We have integrated our analyses into an analysis and visualization pipeline, PanSweep, that can automatically identify cases where pangenome contamination may bias the results of gene-resolved analyses. ImportanceMetagenome-assembled genomes, or MAGs, can be constructed without pure cultures of microbes. Large scale efforts to build MAGs have yielded more complete pangenomes (i.e., sets of all genes found in one species). Pangenomes allow us to measure strain variation in gene content, which can strongly affect phenotype. However, because MAGs come from mixed communities, they can contaminate pangenomes with unrelated DNA, and how much this impacts downstream analyses has not been studied. Using a metagenomic study of gut microbes in cirrhosis as our test case, we investigate how contamination affects analyses of microbial gene content. Surprisingly, even small, typical amounts of MAG contamination (<5%) result in disproportionately high levels of false positive associations (38%). Fortunately, we show that most contaminants can be automatically flagged, and provide a simple method for doing so. Furthermore, applying this method reveals a new association between cirrhosis and gut microbial motility.

bioinformatics↗

bioRxiv2024

Adaptive adjustment of significance thresholds produces large gains in microbial gene annotations and metabolic insights

Gene function annotations enable microbial ecologists to make inferences about metabolic potential from genomes and metagenomes. However, even tools that use the same database and general approach can differ markedly in the annotations they recover. We compare three popular methods for identifying KEGG Orthologs, applying them to genomes drawn from a range of bacterial families that occupy different host-associated and free-living biomes. Our results show that by adaptively tuning sequence similarity thresholds, sensitivity can be substantially improved while maintaining accuracy. We observe the largest improvements when few reference sequences exist for a given protein family, and when annotating genomes from non-model organisms (such as gut-dwelling Lachnospiraceae). Our results suggest that straightforward heuristic adjustments can broadly improve microbial metabolic predictions.

bioinformatics↗

bioRxiv2022

Structure-based prediction of T cell receptor:peptide-MHC interactions

The regulatory and effector functions of T cells are initiated by the binding of their cell-surface T cell receptor (TCR) to peptides presented by major histocompatibility complex (MHC) proteins on other cells. The specificity of TCR:peptide-MHC interactions thus underlies nearly all adaptive immune responses. Despite intense interest, generalizable predictive models of TCR:peptide-MHC specificity remain out of reach; two key barriers are the diversity of TCR recognition modes and the paucity of training data. Inspired by recent breakthroughs in protein structure prediction achieved by deep neural networks, we evaluated structural modeling as a potential avenue for prediction of TCR epitope specificity. We show that a specialized version of the neural network predictor AlphaFold can generate models of TCR:peptide-MHC interactions that can be used to discriminate correct from incorrect peptide epitopes with substantial accuracy. Although much work remains to be done for these predictions to have widespread practical utility, we are optimistic that deep learning-based structural modeling represents a path to generalizable prediction of TCR:peptide-MHC interaction specificity.

immunology↗

bioRxiv2022

Peptide binding specificity prediction using fine-tuned protein structure prediction networks

Peptide binding proteins play key roles in biology, and predicting their binding specificity is a long-standing challenge. While considerable protein structural information is available, the most successful current methods use sequence information alone, in part because it has been a challenge to model the subtle structural changes accompanying sequence substitutions. Protein structure prediction networks such as AlphaFold model sequence-structure relationships very accurately, and we reasoned that if it were possible to specifically train such networks on binding data, more generalizable models could be created. We show that placing a classifier on top of the AlphaFold network and fine-tuning the combined network parameters for both classification and structure prediction accuracy leads to a model with strong generalizable performance on a wide range of Class I and Class II peptide-MHC interactions that approaches the overall performance of the state-of-the-art NetMHCpan sequence-based method. The peptide-MHC optimized model shows excellent performance in distinguishing binding and non-binding peptides to SH3 and PDZ domains. This ability to generalize well beyond the training set far exceeds that of sequence only models, and should be particularly powerful for systems where less experimental data is available. Significance statementPeptide binding proteins carry out a variety of biological functions in cells and predicting their binding specificity could significantly improve our understanding of molecular pathways. Deep neural networks have achieved high structure prediction accuracy, but are not trained to predict binding specificity. Here we describe an approach to extending such networks to jointly predict protein structure and binding specificity. We incorporate AlphaFold into this approach, and fine-tune its parameters on peptide-MHC Class I and II structural and binding data. The fine-tuned model approaches state-of-the-art classification accuracy on peptide-MHC specificity prediction and generalizes to other peptide-binding systems such as the PDZ and SH3 domains.

bioinformatics↗

bioRxiv2021

Computational design of nanoscale rotational mechanics in de novo protein assemblies

Natural nanomachines like the F1/F0-ATPase contain protein components that undergo rotation relative to each other. Designing such mechanically constrained nanoscale protein architectures with internal degrees of freedom is an outstanding challenge for computational protein design. Here we explore the de novo construction of protein rotary machinery from designed axle and ring components. Using cryoelectron microscopy, we find that axle-ring systems assemble as designed and populate diverse rotational states depending on symmetry match or mismatch and the designed interface energy landscape. These mechanical systems with internal rotational degrees of freedom are a step towards the systematic design of genetically encodable nanomachines. One-Sentence SummaryComputationally designed self-assembling protein rotary machines sample internal degrees of freedom sculpted within the energy landscape.

synthetic biology↗

bioRxiv2021

Convergent clonal selection of donor- and recipient-derived CMV-specific T cells in hematopoietic stem cell transplant patients

Competition between antigen-specific T cells for peptide:MHC (p:MHC) complexes shapes the ensuing T cell response. Mouse model studies provided compelling evidence that competition is a highly effective mechanism controlling the activation of naive T cells. However, assessing the effect of T cell competition in the context of a human infection requires defined pathogen kinetics and trackable naive and memory T cell populations of defined specificity. A unique cohort of non-myeloablative hematopoietic stem cell transplant (nmHSCT) patients allowed us to assess T cell competition in response to CMV reactivation, which was documented with detailed virology data. In our cohort, HSCT donors and recipients were CMV-seronegative and -positive, respectively, thus providing genetically distinct memory and naive T cell populations. We used single-cell transcriptomics to track donor versus recipient-derived T cell clones over the course of 90 days. We found that donor-derived T cell clones proliferated and expanded substantially following CMV-reactivation. However, for immunodominant CMV epitopes, recipient-derived memory T cells remained the overall dominant population. This dominance was maintained despite more robust clonal expansion of donor-derived T cells in response to CMV reactivation. Interestingly, the donor-derived T cells that were recruited into these immunodominant memory populations shared strikingly similar TCR properties with the recipient-derived memory T cells. This selective recruitment of identical and nearly identical clones from the naive into the immunodominant memory T cell pool suggests that competition does not interfere with rejuvenating a memory T cell population, but results in selection of convergent clones to the memory T cell pool. SignificanceAn existing memory T cell population specific for a single epitope is sufficient to effectively curtail responses to any new antigens if the original epitope is present in a vaccination regimen or heterologous infections. We asked if T cell competition precludes recruitment of any new, naive T cells to an existing memory T cell pool in context of CMV-specific T cell responses in a cohort of transplant patients. Our data indicate that competition does not prevent recruitment of naive T cells into the memory T cell pool, but selects for T cells with nearly or fully congruent T cell receptor specificities. We discuss the implications of rejuvenating a memory T cell pool while preserving the T cell receptor repertoire.

immunology↗

bioRxiv2020

TCR meta-clonotypes for biomarker discovery with tcrdist3: quantification of public, HLA-restricted TCR biomarkers of SARS-CoV-2 infection

As the mechanistic basis of adaptive cellular antigen recognition, T cell receptors (TCRs) encode clinically valuable information that reflects prior antigen exposure and potential future response. However, despite advances in deep repertoire sequencing, enormous TCR diversity complicates the use of TCR clonotypes as clinical biomarkers. We propose a new framework that leverages antigen-enriched repertoires to form meta-clonotypes - groups of biochemically similar TCRs - that can be used to robustly identify and quantify functionally similar TCRs in bulk repertoires. We apply the framework to TCR data from COVID-19 patients, generating 1831 public TCR meta-clonotypes from the 17 SARS-CoV-2 antigen-enriched repertoires with the strongest evidence of HLA-restriction. Applied to independent cohorts, meta-clonotypes targeting these specific epitopes were more frequently detected in bulk repertoires compared to exact amino acid matches, and 59.7% (1093/1831) were more abundant among COVID-19 patients that expressed the putative restricting HLA allele (FDR < 0.01), demonstrating the potential utility of meta-clonotypes as antigen-specific features for biomarker development. To enable further applications, we developed an open-source software package, tcrdist3, that implements this framework and facilitates flexible workflows for distance-based TCR repertoire analysis.

immunology↗

bioRxiv2019

Drug resistant gut bacteria mimic a host mechanism for anticancer drug clearance

Pharmaceuticals are the top predictor of inter-individual variations in gut microbial community structure1, consistent with in vitro evidence that host-targeted drugs inhibit gut bacterial growth2 and are extensively metabolized by the gut microbiome3,4. In oncology, bacterial metabolism has been implicated in both drug efficacy5,6 and toxicity7,8; however, the degree to which bacterial drug sensitivity and metabolism can be driven by conserved pathways also found in mammalian cells remains poorly understood. Here, we show that anticancer fluoropyrimidine drugs inhibit the growth of diverse gut bacterial strains by disrupting pyrimidine metabolism, as in mammalian cells. Select bacteria metabolized 5-fluorouracil (5-FU) to its inactive metabolite dihydrofluorouracil (DHFU), mimicking the major host pathway for drug clearance. The preTA operon was necessary and sufficient for 5-FU inactivation in Escherichia coli, exhibited high catalytic efficiency for the reductive reaction, decreased the bioavailability and efficacy of oral fluoropyrimidine treatment in mice, and was prevalent in the gut microbiomes of colorectal cancer patients prior to and during treatment. The observed conservation of both the targets and pathways for metabolism of therapeutics across domains highlights the need to distinguish the relative contributions of human and microbial cells to drug disposition9, efficacy, and side effect profiles.

microbiology↗

bioRxiv2018

phylogenize: a web tool to identify microbial genes underlying environment associations

SummaryPhylogenetic comparative methods are powerful but presently under-utilized ways to identify microbial genes underlying differences in community composition. These methods help to identify functionally important genes because they test for associations beyond those expected when related microbes occupy similar environments. We present phylogenize, a pipeline with web, QIIME2, and R interfaces that allows researchers to perform phylogenetic regression on 16S amplicon and shotgun sequencing data and to visualize results. phylogenize applies broadly to both host-associated and environmental microbiomes. Using Human Microbiome Project and Earth Microbiome Project data, we show that phylogenize draws similar conclusions from 16S versus shotgun sequencing and reveals both known and candidate pathways associated with host colonization.\n\nAvailabilityphylogenize is available at https://phylogenize.org and https://bitbucket.org/pbradz/phylogenize.\n\nContactkpollard@gladstone.ucsf.edu

bioinformatics↗

bioRxiv2018

Minor isozymes tailor yeast metabolism to carbon availability

Isozymes are enzymes that differ in sequence but catalyze the same chemical reactions. Despite their apparent redundancy, isozymes are often retained over evolutionary time for reasons that can be unclear. We find that, in yeast, isozymes are strongly enriched in central carbon metabolism. Using a gene expression compendium, we find that many isozyme pairs show anticorrelated expression during the respirofermentative shift, suggesting roles in adapting to changing carbon availability. Building on this observation, we assign function to two minor central carbon isozymes, aconitase 2 (ACO2) and pyruvate kinase 2 (PYK2). ACO2 is expressed during fermentation and proves advantageous when glucose is limiting. PYK2 is expressed during respiration and proves advantageous for growth on three-carbon substrates. PYK2s deletion is rescued by expressing the major pyruvate kinase, but only if that enzyme carries mutations mirroring PYK2s allosteric regulation. Thus, central carbon isozymes enable more precise tailoring of metabolism to changing nutrient availability.\n\nImportanceGene duplication is one of the main evolutionary drivers of new protein function. However, some gene duplicates have nevertheless persisted long-term without apparent divergence in biochemical function. Further, under standard lab conditions, many isozymes have subtle or no knockout phenotypes. These factors make it hard to assess the unique contributions of individual isozymes to fitness. We therefore developed a method to identify experimental perturbations that could expose such contributions, and applied it to yeast gene expression data, revealing a potential role for a set of yeast isozymes in adapting to changing carbon sources. Our experimental confirmation of distinct roles for two \"minor\" yeast isozymes, including one with no previously described knockout phenotype, highlight that even apparently redundant paralogs can have important and unique functions, with implications for genome-scale metabolic modeling and systems-level studies of quantitative genetics.

systems biology↗

bioRxiv2018

Human T cell receptor occurrence patterns encode immune history, genetic background, and receptor specificity

The T cell receptor (TCR) repertoire encodes immune exposure history through the dynamic formation of immunological memory. Statistical analysis of repertoire sequencing data has the potential to decode disease associations from large cohorts with measured phenotypes. However, the repertoire perturbation induced by a given immunological challenge is conditioned on genetic background via major histocompatibility complex (MHC) polymorphism. We explore associations between MHC alleles, immune exposures, and shared TCRs in a large human cohort. Using a previously published repertoire sequencing dataset augmented with high-resolution MHC genotyping, our analysis reveals rich structure: striking imprints of common pathogens, clusters of co-occurring TCRs that may represent markers of shared immune exposures, and substantial variations in TCR-MHC association strength across MHC loci. Guided by atomic contacts in solved TCR:peptide-MHC structures, we identify sequence covariation between TCR and MHC. These insights and our analysis framework lay the groundwork for further explorations into TCR diversity.

immunology↗

bioRxiv2017

Phylogeny-corrected identification of microbial gene families relevant to human gut colonization

The mechanisms by which different microbes colonize the healthy human gut versus other body sites, the gut in disease states, or other environments remain largely unknown. Identifying microbial genes influencing fitness in the gut could lead to new ways to engineer probiotics or disrupt pathogenesis. We approach this problem by measuring the statistical association between having a species having a gene and the probability that the species is present in the gut microbiome. The challenge is that closely related species tend to be jointly present or absent in the microbiome and also share many genes, only a subset of which are involved in gut adaptation. We show that this phylogenetic correlation indeed leads to many false discoveries and propose phylogenetic linear regression as a powerful solution. To apply this method across the bacterial tree of life, where most species have not been experimentally phenotyped, we use metagenomes from hundreds of people to quantify each species prevalence in and specificity for the gut microbiome. This analysis reveals thousands of genes potentially involved in adaptation to the gut across species, including many novel candidates as well as processes known to contribute to fitness of gut bacteria, such as acid tolerance in Bacteroidetes and sporulation in Firmicutes. We also find microbial genes associated with a preference for the gut over other body sites, which are significantly enriched for genes linked to fitness in an in vivo competition experiment. Finally, we identify gene families associated with higher prevalence in patients with Crohns disease, including Proteobacterial genes involved in conjugation and fimbria regulation, processes previously linked to inflammation. These gene targets may represent new avenues for modulating host colonization and disease. Our strategy of combining metagenomics with phylogenetic modeling is general and can be used to identify genes associated with adaptation to any environment.\n\nAuthor SummaryWhy do certain microbes and not others colonize our gut, and why do they differ between healthy and sick people? One explanation is the genes in their genomes. If we can find microbial genes involved in gut adaptation, we may be able to keep out pathogens and encourage the growth of beneficial microbes. One could look for genes that were present more often in prevalent microbes, and less often in rare ones.\n\nHowever, this ignores that related species are more likely to share an environment and also share many unrelated phenotypes simply because of common ancestry. To solve this problem, we used a method from ecology that accounts for phylogenetic relatedness. We first calculated gut prevalence for thousands of species using a compendium of shotgun sequencing data, then tested for genes associated with prevalence, adjusting for phylogenetic relationships. We found genes that are associated with overall gut prevalence, with a preference for the gut over other body sites, and with the gut in Crohns disease versus health. Many of these findings have biological plausibility based on existing literature. We also showed agreement with the results of a previously published high-throughput screen of bacterial gene knockouts in mice. These results, and this type of analysis, may eventually lead to new strategies for maintaining gut health.

bioinformatics↗