Biology subjects

Ravi, J.

Publications and source records attributed to Ravi, J..

13 recordsLinked to original sources

bioRxiv2026

amR: an R package suite to predict antimicrobial resistance in bacterial pathogens

MotivationIdentifying bacterial antimicrobial resistance (AMR) is critical for diagnostics and treatment, but resistance is a complex trait arising from myriad mechanisms spanning multiple molecular scales. Existing computational approaches often function as black boxes and rarely explore cross-species or multi-drug patterns. We developed amR, an integrated R package suite that provides a complete framework from bacterial genome data curation to interpretable AMR predictions, enabling identification of resistance mechanisms across species and drugs. ResultsThe amR R package suite contains three modular packages. amRdata downloads genomes and paired antimicrobial susceptibility testing data from BV-BRC and processes them, constructs pangenomes, and extracts features at gene/protein cluster, protein domain, annotated Clusters of Orthologous Groups and ResFinder AMR-associated features, and structural variant scales; data are stored in memory-efficient formats (Parquet, DuckDB). amRml trains interpretable machine learning models per species-drug combination, calculates feature importance and performance metrics, and provides rich ground for hypothesis generation and mechanism discovery. amRviz provides an interactive Shiny dashboard to explore metadata distributions and model performance across species and drugs, visualize top predictive AMR features, and analyze cross-model patterns across geographic/temporal strata. We apply the suite to Shigella sonnei, achieving a median Matthews Correlation Coefficient of 0.89 across 23 drugs and drug classes. With thousands of genomes, multi-scale features, and interpretable models, amR provides an accessible, comprehensive framework for AMR research. The amR package suite is installable via GitHub (https://github.com/JRaviLab/amR; BSD-3-Clause license).

bioinformatics↗

bioRxiv2025

Genotype-phenotype modeling of light ecotypes in Prochlorococcusreveals genomic signatures of ecotypic divergence

Prochlorococcus species are the most abundant marine photosynthetic bacteria. Despite broadly shared phenotypic traits and marine habitats, they exhibit remarkable genomic diversity. We ask what genomic signatures underlie its ecotypic divergence into high- and low-light adapted lineages, and whether these signatures can still be recovered from incomplete assemblies. From [~]1,000 publicly available Prochlorococcus genomes, we focused on those with information on their light adaptation ecotype (high-light/low-light), phylogenetic clades, and depth of isolation. Across these divisions, we calculated average nucleotide identity and constructed pangenomes to assess cyanobacterial core genes vs. those that separate ecotypes. Despite scant conservation, we observe a sharp taxon separation by light ecotypes. Classical machine learning models trained to predict ecotype achieve near-perfect binary classification accuracy even when predicting on partial genomes (Matthews Correlation Coefficient = 0.86 - 1.00), while regression models trained to predict the depth of isolation performed poorly, with high root mean square error values (37.6 - 42.0m). For ecotype prediction, we analyzed top gene features across model runs and classes; these features included photosynthesis-associated genes and pathways, as well as many novel markers of unknown function. When separating ecotypes further by previously described phylogenetic clades, genomic content and composition show even clearer separation among clades, supporting the taxonomic breadth of the Prochlorococcus collective. These results emphasize the genomic specialization underlying ecotypic divergence and support the utility of ML approaches for cyanobacterial ecotype prediction from metagenomic data. Expanded sampling will yield novel clade-specific biology. All data, models, and results are available on GitHub: https://github.com/JRaviLab/cyano_adaptation. ImportanceProchlorococcus are common aquatic cyanobacteria that can derive energy from light. They can be classified into high-/low-light ecotypes depending on how they use light. Prochlorococcus have small genomes compared to other bacteria, but the gene sets they carry are also remarkably flexible, which may help them survive and adapt to their harsh oceanic environment. We studied hundreds of Prochlorococcus genomes from around the world in an effort to predict ecotypes from partial genome sequences. We used comparative genomics, machine learning, and other statistical methods to identify genomic features associated with ecotypes. These statistical approaches predicted ecotypes accurately, reliably, and according to large differences in gene content and genome structure. Our results support that Prochlorococcus can be divided into different species or genera based on clades, and provide many gene targets for further research to understand cyanobacterial circadian rhythms or improve their bioengineering potential as chassis organisms.

bioinformatics↗

bioRxiv2025

Mapping genetic and phenotypic diversity of Pseudomonas aeruginosa across clinical and environmental isolation sites

Pseudomonas aeruginosa is a clinically significant opportunistic pathogen adept at thriving in both host-associated and environmental settings. To define the extent to which P. aeruginosa isolates specialize across niches and identify genotype-phenotype correlates, we performed whole genome sequencing and comprehensive phenotypic characterization of 125 P. aeruginosa isolates from diverse clinical and environmental sites, evaluating virulence-associated traits, including motility, cytotoxicity, biofilm formation, pyocyanin production, and antimicrobial susceptibility. We identify that genomic diversity does not correlate with isolation source or most virulence phenotypes. Instead, we find that the two major P. aeruginosa clades (Groups A and B) delineate phylogeny and cytotoxicity, with Group B strains showing significantly higher cytotoxicity than Group A. Sequence analysis revealed previously uncharacterized alleles of genes encoding type III secretion effector proteins. We observed high variability amongst strains and isolation sources in all four assayed virulence phenotypes. Antimicrobial resistance (AMR) is exclusively observed in clinical isolates, not environmental, reflecting antibiotic exposure-driven selection. Bacterial GWAS revealed a statistically significant association between cytotoxicity and exoU presence, and we identified a novel exoU allelic variant with decreased cytotoxicity, demonstrating that functional diversity within well-characterized virulence factors may still influence pathogenic outcomes. In summary, our analyses of 125 diverse isolates suggest that the ability of P. aeruginosa to thrive across diverse niches is driven by broadly conserved genetic repertoire rather than niche-specific accessory genes. ImportancePseudomonas aeruginosa is a clinically significant opportunistic pathogen adept at thriving in both host-associated and environmental niches. A major gap in our understanding of this difficult-to-treat pathogen is whether niche specialization occurs in the context of human disease. Addressing this question is critical for guiding effective infection control strategies. Previous large-scale studies have focused solely on genotypic or phenotypic analyses; when paired, they have been limited to a single phenotypic assay or to a small number of isolates from one source, or relied on PCR-based methods targeting a restricted set of genes. To comprehensively uncover niche specialization and pathogenic versatility, we performed whole genome sequencing and phenotypic characterization of 5 virulence-associated traits including AST of 125 clinical and environmental P. aeruginosa isolates. Our systems-level findings challenge reductionist models of bacterial niche specialization, instead supporting an integrated view where conserved genomic systems enable opportunistic pathogenesis across diverse environments.

microbiology↗

bioRxiv2025

From sequence to signature: Uncovering multiscale AMR features across bacterial pathogens with supervised machine learning

Since the clinical introduction of antibiotics in the 1940s, antimicrobial resistance (AMR) has become an increasingly dire threat to global public health. Pathogens acquire AMR much faster than we discover new drugs (antibiotics), warranting innovative methods to better understand its molecular underpinnings. Traditional approaches for detecting AMR in novel bacterial strains are time-consuming and labor-intensive. However, advances in sequencing technology offer a plethora of bacterial genome data, and computational approaches like machine learning (ML) provide an optimistic scope for in silico AMR prediction. Here, we introduce a comprehensive multiscale ML approach to predict AMR phenotypes and identify AMR molecular features associated with a single drug or drug family, stratified by time and geographical locations. As a case study, we focus on a subset of the World Health Organizations Bacterial Priority Pathogens, the frequently drug-resistant and nosocomial ESKAPE pathogens: Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species. We started with sequenced genomes with lab-derived AMR phenotypes, constructed pangenomes, clustered gene and protein sequences, and extracted protein domains to generate pangenomic features across molecular scales. To uncover the molecular mechanisms behind drug-/drug class-specific resistance, we trained logistic regression ML models on our datasets. These yielded ranked lists of AMR-associated genes, proteins, and domains. In addition to recapitulating known AMR features, our models identified novel candidates for experimental validation. The models were performant across molecular scales, data types, and drugs while achieving a median normalized Matthews correlation coefficient of 0.89. Prediction performance showed resilience even when evaluated on geographical and temporal holdouts. We also evaluated model generalizability and cross-resistance across the drug-/drug class-specific models cross-tested on other available drug-/drug class genomes. Finally, we uncovered multiple drug class resistance features using multiclass and multilabel models. Our holistic approach promises reliable prediction of existing and developing resistance in newly sequenced pathogen genomes, while pinpointing the mechanistic molecular contributors of AMR. All our models and results are available at our interactive web app, https://jravilab.org/amr.

bioinformatics↗

bioRxiv2025

Integrating multiple transcriptome-based methods for drug repurposing in tuberculosis

Tuberculosis (TB) remains the leading cause of infectious disease mortality worldwide, killing over one million people annually. Rising antibiotic resistance has added urgency to the need for host-directed therapeutics (HDTs) that modulate host immune responses alongside directly targeting the pathogen. Repurposing FDA-approved drugs is particularly attractive for this purpose because their safety profiles are already well established, substantially reducing development time and cost. Transcriptomic methods have successfully identified repurposable therapeutics for TB based on connectivity mapping, which identifies drugs that reverse disease gene expression patterns. However, these applications are limited to a small subset of data belonging to a specific data platform and a few connectivity methods. Expanding beyond these constrained settings introduces substantial challenges, including dataset heterogeneity across transcriptomics platforms and biological conditions, uncertainty about optimal scoring methods, and the lack of systematic approaches to identify robust disease signatures. We developed a computational workflow that integrates 28 TB gene expression signatures and multiple connectivity scoring methods to capture dominant TB signals regardless of variation in microarray and RNAseq platforms, cell types, and infection conditions. We systematically identified 64 FDA-approved drugs as promising TB host-directed therapeutics. These high-confidence drug candidates include known HDTs such as statins (rosuvastatin, fluvastatin, lovastatin) and tamoxifen, recently validated in experimental TB models. Our prioritized candidate drugs reveal enrichment for therapeutically TB-relevant mechanisms, e.g., cholesterol metabolism inhibition and immune modulation pathways. Network analysis of disease-drug interactions identified 12 key bridging genes (including IL-8, CXCR2) that represent potential novel druggable targets for TB host-directed therapy. This work establishes transcriptome-based connectivity mapping as a viable approach for systematic HDT discovery in bacterial infections and provides a robust computational framework applicable to other infectious diseases. Our findings offer immediate opportunities for experimental validation of prioritized drug candidates and mechanistic investigation of identified druggable targets in TB pathogenesis.

bioinformatics↗

bioRxiv2023

Modeling the START transition in the budding yeast cell cycle

Budding yeast, Saccharomyces cerevisiae, is widely used as a model organism to study the genetics underlying eukaryotic cellular processes and growth critical to cancer development, such as cell division and cell cycle progression. The budding yeast cell cycle is also one of the best-studied dynamical systems owing to its thoroughly resolved genetics. However, the dynamics underlying the crucial cell cycle decision point called the START transition, at which the cell commits to a new round of DNA replication and cell division, are under-studied. The START machinery involves a central cyclin-dependent kinase; cyclins responsible for starting the transition, bud formation, and initiating DNA synthesis; and their transcriptional regulators. However, evidence has shown that the mechanism is more complicated than a simple irreversible transition switch. Activating a key transcription regulator SBF requires the phosphorylation of its inhibitor, Whi5, or an SBF/MBF monomeric component, Swi6, but not necessarily both. Also, the timing and mechanism of the inhibitor Whi5s nuclear export, while important, are not critical for the timing and execution of START. Therefore, there is a need for a consolidated model for the budding yeast START transition, reconciling all known regulatory and spatial dynamics. We built a detailed mathematical model (START-BYCC) for the START transition in the budding yeast cell cycle based on all established molecular interactions and experimental phenotypes. START-BYCC recapitulates the underlying dynamics and correctly emulates key phenotypic traits of [~]150 known START mutants, including regulation of size control, localization of inhibitor/transcription factor complexes, and the nutritional effects on size control. Such a detailed mechanistic understanding of the underlying dynamics gets us closer towards deconvoluting the aberrant cellular development in cancer. All wildtype and mutant simulations of our START-BYCC model are available at sbmlsimulator.org/simulator/by-start, and the supporting data is available on GitHub: github.com/jravilab/start-bycc.

systems biology↗

bioRxiv2022

MolEvolvR: a web-app for characterizing proteins using molecular evolution and phylogeny

Studying proteins through the lens of evolution can reveal features such as conserved domains, lineage-specific variants, and co-occurring domain architectures in phylogenetic context across all superkingdoms. MolEvolvR enables researchers to conduct such evolution-focused studies to generate testable hypotheses about protein function and evolution. MolEvolvR is a novel web-app allowing researchers to visualize the molecular evolution of their proteins of interest in a phylogenetic context across the tree of life. It accepts multiple input formats - protein/domain sequences, homologous proteins, or domain scans - and, using a general-purpose computational workflow, returns detailed homolog data and dynamic graphical summaries (e.g., phylogenetic trees, multiple sequence alignments, domain architectures, domain proximity networks, phyletic spreads, co-occurrence patterns across lineages). MolEvolvR performs domain-centric searches to capture remote homologs that are missed by full-length searches, integrates domain architecture evolution with phyletic distribution analyses, and provides evolutionary context visualizations that reveal lineage-specific adaptations versus those that are broadly conserved. Thus, MolEvolvR is a powerful, easy-to-use web interface for computational protein characterization. The web-app can be accessed here: https://jravilab.org/molevolvr.

genomics↗

bioRxiv2022

Feedback in the β-catenin destruction complex imparts bistability and cellular memory

Wnt ligands are considered classical morphogens, for which the strength of the cellular response is proportional to the concentration of the ligand. Herein, we show an emergent property of bistability arising from feedback among the Wnt destruction complex proteins that target the key transcriptional co-activator {beta}-catenin for degradation. Using biochemical reconstitution, we identified positive feedback between the scaffold protein Axin and the kinase GSK3. Theoretical modeling of this feedback between Axin and GSK3 predicted that the activity of the destruction complex exhibits bistable behavior. We experimentally confirmed these predictions by demonstrating that cellular cytoplasmic {beta}-catenin concentrations exhibit an "all-or-none" response with sustained memory (hysteresis) of the signaling input. This bistable behavior was transformed into a graded response and memory was lost through inhibition of GSK3. These findings provide a mechanism for establishing decisive, switch-like cellular response and memory upon Wnt pathway stimulation. One Sentence SummaryPositive feedback within the {beta}-catenin destruction complex gives rise to bistability and memory in response to Wnt stimulation, imparting signal transduction accuracy and insulation.

cell biology↗

bioRxiv2022

DciA helicase operators exhibit diversity across bacterial phyla

A fundamental requirement for life is the replication of an organisms DNA. Studies in Escherichia coli and Bacillus subtilis have set the paradigm for DNA replication in bacteria. During replication initiation in E. coli and B. subtilis, the replicative helicase is loaded onto the DNA at the origin of replication by an ATPase helicase loader. However, most bacteria do not encode homologs to the helicase loaders in E. coli and B. subtilis. Recent work has identified the DciA protein as a predicted helicase operator that may perform a function analogous to the helicase loaders in E. coli and B. subtilis. DciA proteins, which are defined by the presence of a DUF721 domain (termed the DciA domain herein), are conserved in most bacteria but have only been studied in mycobacteria and {gamma}-proteobacteria (Pseudomonas aeruginosa and Vibrio cholerae). Sequences outside of the DciA domain in Mycobacterium tuberculosis DciA are essential for protein function but are not conserved in the P. aeruginosa and V. cholerae homologs, raising questions regarding the conservation and evolution of DciA proteins across bacterial phyla. To comprehensively define the DciA protein family, we took a computational evolutionary approach and analyzed domain architectures and sequence properties of DciA-domain containing proteins across the tree of life. These analyses identified lineage-specific domain architectures amongst DciA homologs as well as broadly conserved sequence-structural motifs. The diversity of DciA proteins represents the evolution of helicase operation in bacterial DNA replication and highlights the need for phylum-specific analyses of this fundamental biological process. IMPORTANCEDespite the fundamental importance of DNA replication for life, this process remains understudied in bacteria outside of Escherichia coli and Bacillus subtilis. In particular, most bacteria do not encode the helicase loading proteins that are essential in E. coli and B. subtilis for DNA replication. Instead, most bacteria encode a DciA homolog that likely constitutes the predominant mechanism of helicase operation in bacteria. However, it is still unknown how DciA structure and function compare across diverse phyla that encode DciA proteins. In this study, we perform computational evolutionary analyses to uncover tremendous diversity amongst DciA homologs. These studies provide a significant advance in our understanding of an essential component of the bacterial DNA replication machinery.

microbiology↗

bioRxiv2022

Novel Internalin P homologs in Listeria

Listeria monocytogenes (Lm) is a bacterial pathogen that causes listeriosis in immunocompromised individuals, particularly pregnant women. Several virulence factors support the intracellular lifecycle of Lm and facilitate cell-to-cell spread, allowing it to occupy multiple niches within the host and cross protective barriers, including the placenta. One family of virulence factors, internalins, contributes to Lm pathogenicity by inducing specific uptake and conferring tissue tropism. Over 25 internalins have been identified thus far, but only a few have been extensively studied. Internalins contain leucine-rich repeat (LRR) domains which enable protein-protein interactions, allowing Lm to bind host proteins. Notably, other Listeria species express internalins but cannot colonize human hosts, prompting questions regarding the evolution of internalins within the genus Listeria. Internalin P (InlP) promotes placental colonization through interaction with the host protein afadin. Though prior studies of InlP have begun to elucidate its role in Lm pathogenesis, there remains a lack of information regarding homologs in other Listeria species. Here, we have used a computational evolutionary approach to identify InlP homologs in additional Listeria species. We found that L. ivanovii londoniensis (Liv) and L. seeligeri (Ls) encode InlP homologs. We also found InlP-like homologs in L. innocua and the recently identified species L. costaricensis. All newly identified homologs lack the full-length LRR6 and LRR7 domains found in Lms InlP. These findings inform on the evolution of one key Lm virulence factor, InlP, and serve as a springboard for future evolutionary studies of Lm pathogenesis as well as mechanistic studies of Listeria internalins. Impact StatementThe intracellular bacterial pathogen Listeria monocytogenes can breach protective barriers in the pregnant host, allowing for the colonization of the placenta in pregnant women and resulting in numerous adverse pregnancy outcomes. Previous studies aimed at delineating the mechanisms behind placental colonization of L. monocytogenes identified a key virulence factor, internalin P (InlP). The internalin family of proteins has been studied extensively due to their conservation in the Listeria genus and their contribution to virulence and pathogenicity in L. monocytogenes. Still, many questions remain regarding the evolution of internalins and their potential roles in non-pathogenic Listeria. Our work addresses this gap in knowledge by 1) identifying additional InlP homologs in Listeria, including L. ivanovii, L. seeligeri, L. innocua, and L. costaricensis, and 2) characterizing these homologs using computational evolutionary methods to compare their primary sequences, domain architectures, and structural models. Together, our findings contribute to the field by providing insights into the evolution of one key member of the internalin family as well as serving as a catalyst for future studies of InlP and its role in Listeria pathogenesis.

microbiology↗

bioRxiv2021

The glutathione import system satisfies the Staphylococcus aureus nutrient sulfur requirement and promotes interspecies competition.

Sulfur is an indispensable element for proliferation of bacterial pathogens. Prior studies indicated that the human pathogen, Staphylococcus aureus utilizes glutathione (GSH) as a source of nutrient sulfur; however, mechanisms of GSH acquisition are not defined. Here, we identify a previously uncharacterized five-gene locus comprising a putative ABC-transporter and {gamma}-glutamyl transpeptidase (ggt) that promotes S. aureus proliferation in medium supplemented with either reduced or oxidized GSH (GSSG) as the sole source of nutrient sulfur. Based on these phenotypes, we name this transporter the Glutathione import system (GisABCD). We confirm that Ggt is capable of cleaving GSH and GSSG {gamma}-bonds and that this process is required for their use as nutrient sulfur sources. Additionally, we find that the enzyme is cell associated. Bioinformatic analyses reveal that only Staphylococcus species closely related to S. aureus encode GisABCD-Ggt homologues. Homologues are not detected in Staphylococcus epidermidis. Consequently, we establish that GisABCD-Ggt provides a competitive advantage for S. aureus over S. epidermidis in a GSH-dependent manner. Overall, this study describes the discovery of a nutrient sulfur acquisition system in S. aureus that targets GSH and promotes competition against other staphylococci commonly associated with the human microbiota.

microbiology↗

bioRxiv2021

A Broadly Conserved Deoxycytidine Deaminase Protects Bacteria from Phage Infection

The El Tor biotype of Vibrio cholerae is responsible for perpetuating the longest cholera pandemic in recorded history (1961-current). The genomic islands VSP-1 and -2 are two understudied genetic features that distinguish El Tor from previous pandemics. To understand their utility, we calculated the co-occurrence of VSP genes across bacterial genomes. This analysis predicted the previously uncharacterized vc0175, herein renamed deoxycytidylate deaminase Vibrio (dcdV), is in a gene network with dncV, a cyclic GMP-AMP synthase involved in phage defense. DcdV consists of two domains, a P-loop kinase and a deoxycytidylate deaminase, that are required for the deamination of dCTP and dCMP, inhibiting phage predation by corrupting cellular nucleotide concentrations. Additionally, DcdV is post-translationally inhibited by a unique noncoding RNA encoded 5 of the dcdV locus. DcdV homologs are conserved in bacteria and eukaryotes and our results identify V. cholerae DcdV as the founding member of a previously undescribed bacterial phage defense system.

microbiology↗

bioRxiv2020

Phage-shock-protein (Psp) Envelope Stress Response: Evolutionary History and Discovery of Novel Players

Bacterial phage shock protein (PSP) systems stabilize the bacterial cell membrane and protect against envelope stress. These systems have been associated with virulence, but despite their critical roles, PSP components are not well-characterized outside proteobacteria. Using comparative genomics and protein sequence-structure-function analyses, we systematically identified and analyzed PSP homologs, phyletic patterns, domain architectures, and gene neighborhoods. This approach underscored the evolutionary significance of the system, revealing that the core PspA gene (Snf7 in ESCRT outside bacteria) was present in the Last Universal Common Ancestor (LUCA), and that this ancestral functionality has since diversified into multiple novel, distinct PSP systems across life. Several novel partners of the PSP system were identified: (i) the Toastrack domain, likely facilitating assembly of sub-membrane stress-sensing and signaling complexes, (ii) the newly-defined HAAS-PadR-like transcriptional regulator pair system, and (iii) multiple independent associations with ATPase, CesT/Tir-like chaperone, and Band-7 domains in proteins thought to mediate sub-membrane dynamics. Our work also uncovered links between the PSP components and other domains, such as novel variants of SHOCT-like domains, suggesting roles in assembling membrane-associated complexes of proteins with disparate biochemical functions. Results are available at https://jravilab.org/psp. ImportancePhage shock proteins (PSP) are virulence-associated, cell membrane stress-protective systems. They have mostly been characterized in proteobacteria and firmicutes. We now show that PSP systems were present in the Last Universal Common Ancestor, and that homologs have evolved and diversified into newly identified functional contexts. Recognizing the conservation and evolution of PSP systems across bacterial phyla contributes to our understanding of stress response mechanisms in prokaryotes. Moreover, the newly discovered PSP modularity will likely prompt new studies of lineage-specific cell-envelope structures, lifestyles, and adaptation mechanisms. Finally, our results validate use of domain architecture and genetic context for discovery in comparative genomics.

evolutionary biology↗