Biology subjects

Kelley, S. T.

Publications and source records attributed to Kelley, S. T..

15 recordsLinked to original sources

bioRxiv2026

Co-occurrence network multicollinearity artefacts using centered log-ratio transformation

Next-generation sequencing data is compositional in nature and ignoring this fact can significantly impact downstream data analyses. This is especially true in co-occurrence (correlation) networks because compositional data is prone to many false negative correlations. The centered log-ration (clr) transformation is a commonly used compositional data analysis (CoDA) approach that converts compositionally constrained data into real number space. The clr transformed values can subsequently be used in machine learning and statistical analyses such as Spearman or Pearson correlations used to generate co-occurrence networks. Here we show that the necessary zero substitution performed prior to clr transformation can create numerous multicollinearity artefacts. We also show that a newer CoDA approach called the robust centered log-ratio (rclr) eliminates these artefacts in correlation-based network analysis.

bioinformatics↗

bioRxiv2026

ARGus: A Co-assembly workflow for MAG generation, ARG detection, and virulence analysis

The emergence of antibiotic resistance among pathogenic bacteria is a significant global health challenge with multidrug resistance becoming increasingly common. Moreover, since antibiotic resistance genes (ARGs) can be transferred horizontally more bacteria are rapidly evolving resistance. In addition, emerging bacterial pathogens continue to arise from a combination of urbanization, animal agriculture, global movements of people, and inadequate sewage infrastructure. Researchers have begun applying deep sequencing and shotgun metagenomics to detect known and unknown pathogenic organisms and ARGs directly from environmental samples. Here, we describe a bioinformatics workflow that uses a co-assembly approach to assemble contigs across metagenomes and bin them into high coverage metagenomic assembled genomes (MAGs), while segregating out unbinned contigs that includes mobile elements (e.g., plasmids). The workflow includes annotation of coding sequences and differential determination of ARGs and virulence factors (VF) within the sets of both MAG genome bins and unbinned contigs and allows quantification of MAG, ARG and VF abundances for ecological (alpha and beta diversity) and network analyses. Workflow analysis of metagenomic samples collected from the heavily polluted Tijuana River identified hundreds of MAGs, including many high-quality bins and many novel potential pathogens, and found the vast majority of ARG sequence matches in the unbinned contigs. A combined network analysis found strong correlations (r > 0.90) between ARGs and specific MAGs, indicating which bacterial species is likely to contain the ARG. This workflow provides a powerful approach for public health metagenomics studies of emerging pathogens and ARGs.

bioinformatics↗

bioRxiv2025

Moisture and material shape microbial communities in the built environment through disturbance-productivity relationships

The built environment houses diverse microbial communities whose diversity and composition differ among building materials and environmental conditions. Ecological theory makes predictions about how productivity and diversity shape communities, and experiments in the built environment provide an opportunity to test these. We manipulated moisture (constant or repeated wet-dry cycling) on three common building materials to test predictions about alpha and beta diversity. The most productive material (oriented strand board) supported the highest bacterial alpha and beta diversity, and these diversity levels were reduced by repeated drying disturbances. Diversity patterns for fungi were more variable, with the highest alpha diversity on low-moderate productivity material (gypsum wallboard). Fungal beta diversity was reduced by disturbance on high-productivity material, but increased on the other materials. These patterns were driven largely by members of Bacillaceae, Sphingomonadaceae, and Aspergillaceae that reached high abundances in some treatments. Differences between bacteria and fungi may be due to the scale-dependence of productivity-diversity relationships. Together, these results indicate that disturbances can interact with building materials, in some cases leading to variation in community composition that makes it difficult to predict the conditions under which microorganisms with potential importance to health and safety will occur. ImportanceThe built environment--the homes, workplaces, vehicles, and other spaces where people spend most of their time--contains an enormous diversity of microorganisms with significance for human wellbeing, yet we know little about the factors shaping these microbial communities. We found that patterns of wetting and drying that mimic indoor leaks on common building materials affects the diversity of bacteria and fungi growing on the materials. But the identity of these microorganisms differed from one building material to another, and this was especially variable with wetting and drying. This means that common disturbances that lead to microbial growth in homes and offices can make it difficult to predict which microbes, including those that represent health threats to people, will occur in the built environment.

microbiology↗

bioRxiv2024

To impute or not to impute in untargeted metabolomics - that is the compositional question

Untargeted metabolomics often produce large datasets with missing values, arising from biological or technical factors, which can undermine statistical analyses and lead to biased biological interpretations. Imputation methods, such as k-Nearest Neighbors (kNN) and Random Forest (RF) regression are commonly used but their effects vary depending on the type of missing data e.g. Missing Completely At Random (MCAR) and Missing Not At Random (MNAR). Here, we determined the impacts of degree and type of missing data on the accuracy of kNN and RF imputation using two datasets: a targeted metabolomic dataset with spiked-in standards and an untargeted metabolomic dataset. We also assessed the effect of compositional data approaches (CoDA), such as the centered log-ratio (CLR) transform, on data interpretation, since these methods are increasingly being used in metabolomics. Overall, we found that kNN and RF performed more accurately when the proportion of missing data across samples for a metabolic feature was low. However, these imputations could not handle MNAR data and generated wildly inflated values or imputed values where none should exist. Furthermore, we show that the proportion of missing values had a strong impact on the accuracy of imputation which affected the interpretation of the results. Our results suggest extreme caution should be used with imputation even with modestly levels of missing data or when the type of missingness is unknown.

bioinformatics↗

bioRxiv2024

PyBootNet: A Python Package for Bootstrapping and Network Construction

PyBootNet is a user-friendly Python package that integrates bootstrapping analysis and correlation network construction. The package offers functions for generating bootstrapped network metrics, statistically comparing network metrics among datasets, and visualizing bootstrapped networks. PyBootNet is designed to be accessible and efficient, with minimal dependencies and straightforward input requirements. To demonstrate its functionality, we applied PyBootNet functions to compare networks within two disparate microbial community datasets: a mouse gut microbiome study and a microbiome study of a built environment. The PyBootNet functions applied include data preprocessing, bootstrapping, correlation matrix calculation, network statistics computation, and network visualization. In both datasets, we show that PyBootNet can generate robust bootstrapped network metrics and identify significant differences in one or more network metrics between pairs of networks. We also show that PyBootNet can create bootstrapped network graphs and identify clusters of nodes that are highly interconnected. We also confirmed its computational efficiency and scalability, which allows it to handle large and complex datasets. PyBootNet provides a powerful and extendible Python bioinformatics solution for bootstrapping analysis and network construction that can be applied to microbial, gene, metabolite and other biological data appropriate for network correlation comparison and analysis.

bioinformatics↗

bioRxiv2024

AncFlow: An Ancestral Sequence Reconstruction Approach for Determining Novel Protein Structural

The rapid growth of sequence data from high-throughput sequencing technologies has unveiled a vast number of previously unknown proteins, presenting a significant challenge in their functional characterization. Ancestral sequence reconstruction (ASR) has emerged as a powerful tool to elucidate the evolutionary history of protein families and identify sequence determinants of protein function. Here, we present AncFlow, an automated software pipeline that integrates phylogenetic analysis, subfamily identification, and ASR to generate ancestral protein sequences for structural prediction using state-of-the-art tools like AlphaFold. AncFlow streamlines the process of ASR by combining multiple sequence alignment, phylogenetic tree inference, subfamily identification, and ancestral sequence reconstruction from unaligned protein sequences. The reconstructed ancestral sequences are then subjected to structural prediction using AlphaFold, enabling the investigation of the structural basis of functional divergence within protein families. We validated AncFlow using two well-characterized protein families: acyltransferases and dehydrogenases. The pipeline successfully reconstructed ancestral sequences for multiple internal nodes of the phylogenetic trees, and their predicted structures were compared with those of extant proteins. By analyzing the structural similarities and differences between ancestral and extant proteins, we gained insights into the evolutionary mechanisms underpinning the functional diversification within these families. AncFlow demonstrates the potential of integrating ASR and structural prediction to unravel the structural basis of functional divergence in protein families. The insights gained from this approach can guide protein engineering efforts, facilitating the design of proteins with desired functions. As the amount of sequence data continues to grow, AncFlow provides a valuable tool for exploring the evolutionary landscape of proteins and accelerating the discovery of novel protein functions.

bioinformatics↗

bioRxiv2024

Genetic hypogonadal (Gnrh1 hpg) mouse model uncovers influence of reproductive axis on maturation of the gut microbiome during puberty

The gut microbiome plays a key role in human health and gut dysbiosis is linked to many sex-specific diseases including autoimmune, metabolic, and neurological disorders. Activation of the hypothalamic-pituitary-gonadal (HPG) axis during puberty leads to sexual maturation and development of sex differences through the action of gonadal sex steroids. While the gut microbiome also undergoes sex differentiation, the mechanisms involved remain poorly understood. Using a genetic hypogonadal (hpg) mouse model, we sampled the fecal microbiome of male and female wild-type and hpg mutant mice before and after puberty to determine how microbial taxonomy and function are influenced by age, sex, and the HPG axis. We showed that HPG axis activation during puberty is required for sexual maturation of the gut microbiota composition, community structure, and metabolic functions. We also demonstrated that some sex differences in taxonomic composition and amine metabolism developed independently of the HPG axis, indicating that sex chromosomes are sufficient for certain sex differences in the gut microbiome. In addition, we showed that age, independent of HPG axis activation, led to some aspects of pubertal maturation of the gut microbiota community composition and putative functions. These results have implications for microbiome-based treatments, indicating that sex, hormonal status, and age should be considered when designing microbiome-based therapeutics.

microbiology↗

bioRxiv2023

Hypogonadal (Gnrh1hpg) mice reveal niche-specific influence of reproductive axis and sex on intestinal microbial communities

The gut microbiome has been linked to many diseases with sex bias including autoimmune, metabolic, neurological, and reproductive disorders. Numerous studies report sex differences in fecal microbial communities, but how this differentiation occurs remains unclear. Using a genetic hypogonadal mouse model that does not produce sex steroids or go through puberty, we investigated how sex and the reproductive axis impact bacterial diversity within the small and large intestine. Both sex and reproductive axis inactivation altered bacterial composition in an intestinal section and niche-specific manner. Our results also implicated factors independent of the reproductive axis (i.e., sex chromosomes) in shaping intestinal communities. Additionally, our detailed profile of intestinal communities showed that fecal samples do not reflect bacterial diversity in the small intestine. Our results have ramifications for studying the impact of sex differences on the gut microbiome, particularly in sex-biased diseases and factoring in sex and steroid levels in microbial-based therapies.

microbiology↗

bioRxiv2023

Data-Driven Mathematical Approach for Removing Rare Features in Zero-Inflated Datasets

Sparse feature tables, in which many features are present in very few samples, are common in big biological data (e.g., metagenomics, transcriptomics). Ignoring the problem of zero-inflation can result in biased statistical estimates and decrease power in downstream analyses. Zeros are also a particular issue for compositional data analysis using log-ratios since the log of zero is undefined. Researchers typically deal with zero-inflated data by removing low frequency features, but the thresholds for removal differ markedly between studies with little or no justification. Here, we present CurvCut, a data-driven mathematical approach to zero-inflated feature removal based on curvature analysis of a "ball rolling down a hill", where the hill is a histogram of feature distribution. These histograms typically contain a point of regime change, a discontinuity with a sharp change in the characteristics of the distribution, that can be used as a cutoff point for low frequency feature removal that considers the data-specific nature of the feature distribution. Our results show that CurvCut works well across a variety of biological data types, including ones with both right- and left-skewed feature distributions, and rapidly generates clear visual results allowing researchers to select data-appropriate cutoffs for feature removal.

bioinformatics↗

bioRxiv2022

AutoPhy: Automated phylogenetic identification of novel protein subfamilies

Phylogenetic analysis of protein sequences provides a powerful means of identifying novel protein functions and subfamilies, and for identifying and resolving annotation errors. However, automation of functional clustering based on phylogenetic trees has been challenging, and most of it is done manually. Clustering phylogenetic trees usually requires the delineation of tree-based thresholds (e.g., distances), leading to an ad hoc problem. We propose a new phylogenetic clustering approach that identifies clusters without using ad hoc distances or other pre-defined values. Our workflow combines uniform manifold approximation and projection (UMAP) with Gaussian mixture models as a k-means like procedure to automatically group sequences into clusters. We then apply a "second pass" clade identification algorithm to resolve non-monophyletic groups. We tested our approach with several well-curated protein families (outer membrane porins, acyltransferase, and dehydrogenases) and showed our automated methods recapitulated known subfamilies. We also applied our methods to a broad range of different protein families from multiple databases, including Pfam, PANTHER, and UNIPROT. Our results showed that AutoPhy rapidly generated monophyletic clusters (subfamilies) within phylogenetic trees evolving at very different rates both within and among phylogenies. The phylogenetic clusters generated by AutoPhy resolved misannotations, determined new protein functional groups, and detected novel viral strains.

bioinformatics↗

bioRxiv2020

Gut metabolites are more predictive of disease- and cohoused- states than gut bacterial features in a mouse model of polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) impacts [~]10% of reproductive-aged women worldwide. In addition to infertility, women with PCOS suffer from metabolic dysregulation which increases their risk of developing type 2 diabetes, cardiovascular disease and non-alcoholic fatty liver disease. Studies have shown differences in the gut microbiome of women with PCOS compared to controls, a pattern replicated in mouse models. Recently, using a letrozole-induced mouse model of PCOS, we demonstrated that cohousing was protective against development of metabolic and reproductive phenotypes and showed via 16S amplicon sequencing that this protection correlated with time-dependent shifts in gut bacteria. Here, we applied untargeted metabolomics and shotgun metagenomics approaches to further analyze the longitudinal samples from the cohousing experiment. Analysis of beta diversity found that untargeted metabolites had the strongest correlation to both disease and cohoused states and that shifts in metabolite diversity were detected prior to shifts in bacterial diversity. In addition, log2-fold analyses found numerous metabolite features, particularly bile acids (BA), to be highly differentiated between placebo (P) and letrozole (LET), as well as cohoused LET versus LET. Our results indicate that changes in gut metabolites, particularly BAs, are associated with a PCOS-like phenotype in the LET mouse model as well as the protective effect of cohousing. Our results also suggest that transfer of metabolites via coprophagy occurs rapidly and may precipitate changes in bacterial diversity. This study joins a growing body of research highlighting changes in primary and secondary bile acids that may provide a link between host metabolism and gut microbes relevant to the pathology of PCOS. IMPORTANCEUsing a combination of untargeted metabolomics and metagenomics, we performed a comparative longitudinal analysis of the feces collected in a cohousing study with a PCOS mouse model. Our results showed that gut metabolite composition experienced earlier and more pronounced differentiation in both the disease model and cohoused mice compared with the microbial composition. Notably, statistical and machine learning approaches identified shifts in the relative abundance of primary and secondary BA, which have been implicated as modifiers of gut microbial growth and diversity. Network correlation analysis showed strong associations between particular BA and bacterial species, particularly members of Lactobacillus, and that these correlations were time and treatment dependent. Our results provide novel insights into host/microbe relationships related to hyperandrogenism in females and indicate that focused research into small molecule control of gut microbial diversity and host physiology may provide new therapeutic options for the treatment of PCOS.

microbiology↗

bioRxiv2019

Age and sex-dependent patterns of gut microbial diversity in human adults

Gut microbial diversity changes throughout the human lifespan and is known to be affected by host sex. We investigated the association of age, sex and gut bacterial alpha diversity in three large cohorts of adults from four geographical regions: US and UK cohorts in the American Gut Project, and two independent cohorts of Colombians and Chinese. In three of the four cohorts, we observed a strong positive association between age and alpha diversity in young adults that plateaued after age 40. We also found pronounced sex-dependent differences in younger but not middle-aged adults, and women had higher alpha diversity than men. In contrast, no association of alpha diversity with age or sex was observed in the Chinese cohort. These associations were maintained after adjusting for cardiometabolic parameters in the Colombian cohort and antibiotic usage in the AGP cohort, suggesting that these factors do not affect the association of alpha diversity with age and sex. We also used a machine learning approach to predict individual age based on the gut microbiome. Consistent with our alpha diversity-based findings, women had significantly higher predicted age than men in the US and UK cohort, with a reduced difference above age 40. This was not observed in the Colombian cohort and only in the group of middle-age adults in the Chinese cohort. Together, our results provide new insights into the influence of age and sex on biodiversity of the human gut microbiota during adulthood while highlighting similarities and differences across diverse cohorts.

microbiology↗

bioRxiv2018

Microbial and Metabolic Succession on Common Building Materials Under High Humidity Conditions

Despite considerable efforts to characterize the ecology of bacteria and fungi in the built environment (BE), the metabolic mechanisms underpinning their colonization and successional dynamics remain unclear. Here, we applied bacterial/viral particle counting, qPCR, 16S and ITS rRNA amplicon sequencing, and metabolomics to longitudinally characterize the ecological dynamics of four commonly used building materials maintained at high humidity conditions (~94% RH). We varied the natural inoculum provided to each material by placing them in different occupied spaces, and we wet the surface of half of the samples of each material to simulate a flooding event. As expected, different materials showed different bacterial and viral particle abundance, with wet materials having higher growth rates and lower alpha diversity compared to non-wetted materials. Wetting described the majority of the variance in bacterial, fungal and metabolite structure, and material type only influenced bacterial and metabolic diversity, while location of inoculation was only weakly associated with bacterial and fungal beta diversity. Metabolites indicative of microbial activity were identified, as were those that were native to the surface material. Glucose-phosphate was abundant on all materials (except mold-free gypsum) and was correlated with Enterobacteriaceae, which could indicate a potential bacterial nutrient source. A compound consistent with scopoletin, a plant metabolite with antimicrobial activity, was significantly negatively correlated with Bacillus and positively correlated with Pseudomonas and enriched in medium density fiberboard (MDF) materials. In wet samples, the alkaloids nigragillin and fumigaclavine C, both with antimicrobial properties, were significantly positively correlated with the fungal phylum Ascomycota. Nigragillin, was also negatively correlated with Bacillus and Pseudomonas abundance. Thiabendazole and azoxystrobin (anti-fungal compounds) were highly abundant on mold-resistant gypsum wallboard and likely directly influenced the decreased fungal growth observed on this material. The mold-resistant gypsum material also showed a significant increase in bacterial alpha diversity, and bacterial and viral particle abundance, as well as a decrease in metabolite diversity, likely a result of reduced fungal growth. Penicillium taxa were positively correlated with thiabendazole, which suggested the persistence of resistant strains. Also, specific to the wet samples, Bacillus abundance was positively correlated with the azoxystrobin, suggesting bi-directional competitive adaptation, and positively correlated with metabolites known to interfere with Pseudomonas biofilm formation, which could explain the anti-correlation between these taxa. As expected, high moisture conditions enabled faster growth of inoculating microorganisms, whose composition, chemistry, and competition was shaped by surface material, suggesting that both fungal and bacterial growth need to be considered when determining the impact of dampness in built environments.

microbiology↗

bioRxiv2018

American Gut: an Open Platform for Citizen-Science Microbiome Research

Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the USA, UK, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes as compared to environmental samples, demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations, uncover new molecules and kinds of molecular communities in the human stool metabolome, and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness, and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education.\n\nImportanceWe show that a citizen-science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n=1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet which we confirm with untargeted metabolomics on hundreds of samples.

microbiology↗

bioRxiv2018

Metagenomic analysis uncovers strong relationship between periodontal pathogens and vascular dysfunction in American Indian population

Periodontal disease (PD) is a well-known risk factor for cardiovascular disease (CVD) but the casual relationship is unclear. American Indians/Alaskan Natives (AI/AN) have high rate of both PD and CVD and a better understanding of how PD might affect heart health would be particularly helpful in this population. In this study, we sequenced the bacterial biofilms of periodontal (gum) pockets and used metagenomic sequencing and vascular health measurements (immune cytokine profiles and vascular flow) to determine the relationship of microbial pathogens and CVD. Twelve subjects were sequenced before and after standard periodontal treatment. Other measures taken before and after treatment included a full dental screening; serum concentration of key immune cytokines from blood samples; lipid profiles from fasting venous blood; and plasma glucose concentrations. The non-invasive Laser Doppler Fluxmetry (LDF) procedure was conducted to measures the microvascular vasodilation. We found highly significant relationships between the total abundance of 4 periodontal pathogens, Porphyromonas gingivalis, Fusobacterium nucleatum, Tannerella forsythia and Treponema denticola, and the inflammatory cytokine interleukin 1 beta (IL-1{beta}) (r=0.63; p=0.009) as well as with vascular flow post sodium nitroprusside (SNP) treatment (r=p=0.006). Two bacterial species that correlated most with IL-1{beta} were F. nucleatum and P. gingivalis. IL-1{beta} has been strongly implicated as a causal factor in atherosclerosis and in periodontal bone loss. To our knowledge, this is the first direct link between abundance of specific periodontal pathogens and cardiovascular disease in humans, and suggests that these pathogens could be used as warning signs for cardiovascular risk.

microbiology↗