Biology subjects

Chauhan, S. M.

Publications and source records attributed to Chauhan, S. M..

8 recordsLinked to original sources

bioRxiv2026

Pangenomic decomposition reveals lineage-specific immune-evasion and virulence architectures in group A Streptococcus

Streptococcus pyogenes (group A Streptococcus, GAS) causes over 700 million infections annually and has resurged globally as a cause of invasive disease. However, the genomic basis by which distinct GAS lineages produce diverse clinical phenotypes remains incompletely understood. Here, we analyzed 399 quality-controlled complete genomes spanning 103 emm types and 27 countries and applied non-negative matrix factorization (NMF) to decompose the GAS pangenome into co-inherited gene modules capturing both clonal lineages and mobile elements. This framework revealed that the deepest division in the accessory genome is not defined by emm type, but by a 27-gene Sda-1-encoding prophage linked to the hypervirulent M1T1 pandemic clone. This NET-degrading module unites emm1, emm12, and emm77 lineages, including fixation in emm77/ST63, extending its distribution beyond classically invasive lineages. Across lineages, virulence determinants segregate into distinct combinations, indicating that invasive disease arises through convergent but mechanistically distinct programs. Consistent with this, highly invasive ST52-emm28 lacks Sda1 and deploys an alternative repertoire. The decomposition also resolves a gene module corresponding to the emm-pattern D regulon associated with skin tropism, linking accessory genome structure to host niche adaptation. We further identify three sequence-divergent speC paralogs on independent prophages. These findings define modular GAS virulence architectures and establish NMF-based pangenome analysis as a framework for genomic surveillance beyond single-locus typing. ImportanceGroup A Streptococcus causes diseases ranging from strep throat to life-threatening flesh-eating infections, with severe cases increasing in recent years and no vaccine currently available. The species is conventionally classified by its M protein gene, but this single-locus system does not capture the extensive genomic diversity driven by prophages that transfer toxin and immune-evasion genes between bacterial lineages. Here, we applied unsupervised machine learning to decompose the pangenome of nearly 400 complete genomes into modules of co-inherited genes. We find that the deepest division among lineages is determined by a prophage carrying a gene that enables evasion of neutrophil extracellular traps, and identify this module at fixation in a lineage not previously known to carry it. We also show that a key toxin gene exists as three independently evolved variants on distinct prophages, and that invasive lineages deploy distinct combinations of virulence factors. This framework enables genomic surveillance based on virulence architecture rather than single-gene typing.

microbiology↗

bioRxiv2024

The Pangenome of Escherichia coli

Thousands of complete genome sequences for strains of a species that are now available enable the advancement of pangenome analytics to a new level of sophistication. We collected 2,377 publicly-available complete genomes of Escherichia coli for detailed pangenome analysis. The core genome and accessory genomes consisted of 2,398 and 5,182 genes, respectively. We developed a machine learning approach to define the accessory genes characterizing the major phylogroups of E. coli plus Shigella: A, B1, B2, C, D, E, F, G, and Shigella. The analysis resulted in a detailed structure of the genetic basis of the phylogroups differential traits. This pangenome structure was largely consistent with a housekeeping-gene-based MLST distribution, sequence-based Mash distance, and the Clermont quadruplex classification. The rare genome consisted of 163,619 genes, about 79% of which represented variations of 315 underlying transposon elements. This analysis generated a mathematical definition of the genetic basis for a species.

genomics↗

bioRxiv2023

Genomic insights into Lactobacillaceae: Analyzing the Alleleome of core pangenomes for enhanced understanding of strain diversity and revealing Phylogroup-specific unique variants

The Lactobacillaceae familys significance in food and health, combined with available strain-specific genomes, enables genome assessment through pangenome analysis. The Alleleome of the core pangenomes of the Lactobacillaceae family, which identifies natural sequence variations, was reconstructed from the amino acid and nucleotide sequences of the core genes across 2,447 strains of 26 species. It comprised 3.71 million amino acid variants in 29,448 core genes across the family. The alleleome analysis of the Lactobacillaceae family revealed key findings: 1) In the core pangenome, amino acid substitutions prevailed over rare insertions and deletions, 2) Purifying negative selection primarily influenced core gene variations in the family, with diversifying selection noted in L. helveticus. L. plantarums core alleleome was investigated due to its industrial importance. In L. plantarum, the defining characteristics of its core alleleome included: 1) It is highly conserved; 2) Among 235 isolation sources, the primary categories displaying variant prevalence were fermented food, feces, and unidentified sources; 3) It is predominantly characterized by conservative and moderately conservative mutations; and 4) Phylogroup-specific core variant gene analysis identified unique variants (DltX, FabZ1, Pts23B, CspP) in phylogroups I and B which could be used as identifier or validation markers of strain or phylogroup.

genomics↗

bioRxiv2023

Using the E. coli Alleleome in Strain Design

Leveraging observed variants in strain design is a promising technique for creating strains with specific properties. Adaptive laboratory evolution (ALE) experiments generate variants that enhance fitness under specific conditions and can contribute to application-specific strain designs. Further, the wild-type (WT) coding alleleome of an organism, the complete set of its genes WT alleles, can provide an additional amount and diversity of variants not yet accessible from the aggregation of ALE experiment results. This study used both an ALE mutation database (3093 genomes) and a large set of WT genomes (12,661 genomes) to explore the sequence solution space of genes involved in tolerance to 10 conditions of industrial importance. To accomplish this, ALE variants for 22 genes previously identified as potentially important for industrial chemical tolerance were collected and supplemented with all available variants from the WT coding alleleome. A total of 4879 variants were reintroduced and used in 10 selection experiments. Both ALE and WT contributed highly enriched variants, where the enrichment and benefits depended on the conditions, genes, and gene product regions. The results also revealed that variants not originating from the initial experiment could potentially confer substantially greater benefits. Additionally, ALE and WT variants rarely overlapped on AA positions, but their clustering did coincide with where highly enriched variants were ultimately located. For genes primarily hosting potential gain-of-function variations, substitutions predicted to have a conservative impact frequently outperformed more radical substitutions. Case studies demonstrated that maximizing the amount of variants enabled easier identification of variant trends, which in turn can be used to better understand areas and characteristics of genes that can be feasibly varied, representing what could be thought of as a genome design variable. The combination of ALE and WT variants is a promising approach for use in future projects to better constrain and ultimately achieve practical coverage in the exploration of feasible sequence solution space. Visual Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=64 SRC="FIGDIR/small/558058v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1a8e843org.highwire.dtl.DTLVardef@28a134org.highwire.dtl.DTLVardef@16f6849org.highwire.dtl.DTLVardef@127b43c_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2023

Pangenome analysis reveals the genetic basis for taxonomic classification of the Lactobacillaceae family

Lactobacillaceae represent a large family of important microbes that are foundational to the food industry. Many genome sequences of Lactobacillaceae strains are now available, enabling us to conduct a comprehensive pangenome analysis of this family. We collected 3,591 high-quality genomes from public sources and found that: 1) they contained enough genomes for 26 species to perform a pangenomic analysis, 2) the normalized Heaps coefficient {lambda} (a measure of pangenome openness) was found to have an average value of 0.27 (ranging from 0.07-0.37), 3) the pangenome openness was correlated with the abundance and genomic location of transposons and mobilomes, 4) the pangenome for each species was divided into core, accessory, and rare genomes, that highlight the species-specific properties (such as motility and restriction-modification systems), 5) the pangenome of Lactiplantibacillus plantarum (which contained the highest number of genomes found amongst the 26 species studied) contained nine distinct phylogroups, and 6) genome mining revealed a richness of detected biosynthetic gene clusters, with functions ranging from antimicrobial and probiotic to food preservation, but [~]93% were of unknown function. This study provides the first in-depth comparative pangenomics analysis of the Lactobacillaceae family.

systems biology↗

bioRxiv2022

Advanced transcriptomic analysis reveals the role of efflux pumps and media composition in antibiotic responses of Pseudomonas aeruginosa

Pseudomonas aeruginosa is an opportunistic pathogen and major cause of hospital acquired infections. The pathogenicity and virulence of P. aeruginosa is largely determined by its transcriptional regulatory network (TRN). We used 411 transcription profiles of P. aeruginosa from diverse growth conditions to construct a quantitative TRN by identifying independently modulated sets of genes (called iModulons) and their condition-specific activity levels. The current study focused on the use of iModulons to analyze pathogenicity and antibiotic resistance of P. aeruginosa. Our analysis revealed: 1) 116 iModulons, 81 of which show strong association with known regulators; 2) novel roles of two-component systems in regulating antibiotics efflux pumps; 3) substrate-efflux pump associations; 4) differential iModulon activity in response to beta-lactam antibiotics in bacteriological and physiological media; 5) differential activation of Cell Division iModulon resulting from exposure to different beta-lactam antibiotics; and 6) a role of the PprB iModulon in the stress-induced transition from planktonic to biofilm lifestyle. In light of these results, the construction of an iModulon-based TRN provides a transcriptional regulatory basis for key aspects of P. aeruginosa infection, such as antibiotic stress responses and biofilm formation. Taken together, our results offer a novel mechanistic understanding of P. aeruginosa pathogenicity. SignificanceLarge data sets and machine learning are impacting a growing number of areas of research in the life sciences. Once the compendia of bacterial transcriptomes reached a critical size, we could use source signal extraction algorithms to find lists of co-regulated genes (called iModulons) associated with a transcription factor (TF) to them. The gene composition of iModulons and their condition-dependent activity levels constitute a quantitative description of the composition of bacterial transcriptomes. This study shows how this approach can be used to reveal the responses of P. aeruginosa to antibiotics and thus yield a deep regulatory understanding of pathogenicity properties. This study motivates the execution of similar studies for the other ESKAPEEs to yield a broad understanding of the role of TRNs in antibiotic responses to these urgent threat bacterial pathogens.

systems biology↗

bioRxiv2022

Pan-genomic analysis of transcriptional modules across Salmonella Typhimurium reveals the regulatory landscape of different strains

Salmonella enterica Typhimurium is a serious pathogen that is involved in human nontyphoidal infections. Tackling Typhimurium infections is difficult due to the species dynamic adaptation to its environment, which is dictated by a complex transcriptional regulatory network (TRN). While traditional biomolecular methods provide characterizations of specific regulators, it is laborious to construct the global TRN structure from this bottom-up approach. Here, we used a machine learning technique to understand the transcriptional signatures of S. enterica Typhimurium from the top down, as a whole and in individual strains. Furthermore, we conducted cross-strain comparison of 6 strains in serovar Typhimurium to investigate similarities and differences in their TRNs with pan-genomic analysis. By decomposing all the publicly available RNA-Seq data of Typhimurium with independent component analysis (ICA), we obtained over 400 independently modulated sets of genes, called iModulons. Through analysis of these iModulons, we 1) discover three transport iModulons linked to antibiotic resistance, 2) describe concerted responses to cationic antimicrobial peptides (CAMPs), 3) uncover evidence towards new regulons, and 4) identify two iModulons linked to bile responses in strain ST4/74. We extend this analysis across the pan-genome to show that strain-specific iModulons 5) reveal different genetic signatures in pathogenicity islands that explain phenotypes and 6) capture the activity of different phages in the studied strains. Using all high-quality publicly-available RNA-Seq data to date, we present a comprehensive, data-driven Typhimurium TRN. It is conceivable that with more high-quality datasets from more strains, the approach used in this study will continue to guide our investigation in understanding the pan-transcriptome of Typhimurium. Interactive dashboards for all gene modules in this project are available at https://imodulondb.org/ under the "Salmonella Typhimurium" page to enable browsing for interested researchers.

systems biology↗

bioRxiv2021

Machine learning uncovers a data-driven transcriptional regulatory network for the Crenarchaeal thermoacidophile Sulfolobus acidocaldarius

Dynamic cellular responses to environmental constraints are coordinated by the transcriptional regulatory network (TRN), which modulates gene expression. This network controls most fundamental cellular responses, including metabolism, motility, and stress responses. Here, we apply independent component analysis, an unsupervised machine learning approach, to 95 high-quality Sulfolobus acidocaldarius RNA-seq datasets and extract 45 independently modulated gene sets, or iModulons. Together, these iModulons contain 755 genes (32% of the genes identified on the genome) and explain over 70% of the variance in the expression compendium. We show that 5 modules represent the effects of known transcriptional regulators, and hypothesize that most of the remaining modules represent the effects of uncharacterized regulators. Further analysis of these gene sets results in: (1) the prediction of a DNA export system composed of 5 uncharacterized genes, (2) expansion of the LysM regulon, and (3) evidence for an as-yet-undiscovered global regulon. Our approach allows for a mechanistic, systems-level elucidation of an extremophiles responses to biological perturbations, which could inform research on gene-regulator interactions and facilitate regulator discovery in S. acidocaldarius. We also provide the first global TRN for S. acidocaldarius. Collectively, these results provide a roadmap towards regulatory network discovery in archaea.

systems biology↗