Biology subjects

Rajaram, S. V.

Publications and source records attributed to Rajaram, S. V..

5 recordsLinked to original sources

bioRxiv2026

New paradigm of CRISPR spacerome for classification of global Escherichia coli lineages

Our research team has recently identified a proof-of-concept for a previously unrecognized spacerome signature in a subset of globally distributed E. coli. Motivated by our initial observation, we pursued a more in-depth CRISPR spacerome analysis, which revealed a novel insight in discrimination of global E. coli sequence type (ST) lineages. We systematically retrieved publicly available E. coli complete genomes, analyzed and compared CRISPR spacerome of globally distributed strains. We found that global strains harbored spacerome that has remained conserved spaceromes for decades across multiple continents. Strains with the conserved spacerome belong to specific ST lineages of clinical importance. In addition, most protospacers were assigned to Gammaproteobacteria and Caudoviricetes. Our findings reveal unexpected long-term conservation of CRISPR spaceromes and their potential as high-resolution markers for E. coli epidemiological tracking.

microbiology↗

bioRxiv2025

Sec and Tat mediated secretion safeguards Mycobacterium tuberculosis membrane homeostasis

Protein secretion drives Mycobacterium tuberculosis (Mtb) physiology and pathogenesis, yet a unified picture of the machinery and its role in cell membrane homeostasis is still lacking. By comprehensively curating published evidence, we assembled a systems-level map of Mtb secretion encompassing 92 components and 198 mechanistic reactions across Sec, Tat, and ESX pathways. The secretory components identified were integrated with high-throughput ChIP-Seq and transcriptome datasets to elucidate the regulation of the secretion system. Using CRISPRi, conditional depletion of SecA1 or TatA impaired growth in vitro and survival ex vivo. Quantitative secretome revealed decreased export of SecA1- and TatA-dependent substrates, with enrichment of cytosolic proteins in culture filtrates, indicating increased membrane permeability. Membrane proteomics showed depletion dependent increased metabolic/lipid-degrading proteins and decreased cell-wall/cell-process proteins, consistent with loss of membrane stability. Ultrastructural defects and increased ethidium bromide uptake confirmed impaired membrane integrity. Together, our multi-omics and functional genetics established SecA1 and TatA as essential guardians of Mtb membrane integrity which provided valuable datasets and a framework for secretion-dependent Mtb pathogenesis.

microbiology↗

bioRxiv2025

DeepBioSim: Efficient and Versatile Methods for Microbiome Data Simulation with Minimal Statistical Assumptions

BackgroundThe human microbiome profoundly influences health and disease. Robust computational and statistical tools for identifying causal microbe-disease links are therefore critical to uncovering the mechanistic basis of these associations. Yet benchmarking such tools remains difficult: microbiome datasets are sparse, high-dimensional, and contain complex dependencies, and no gold-standard reference set exists. Realistic simulated data with embedded ground truth are essential for fair evaluation of analytical tools. Current simulators often impose strong assumptions, require hard-to-obtain auxiliary information, or fail to scale to large, high-dimensional datasets. ResultsWe introduce DeepBioSim, a DEEP-learning framework for BIOlogical SIMulation of microbiome data. DeepBioSim uses variational autoencoders (VAEs) to generate realistic microbiome datasets by sampling directly from the latent distribution of metagenomic or metatranscriptomic count data. ConclusionsThe approach is fast, accurate, and scalable, generating highly realistic synthetic microbiome datasets without extensive hyper-parameter tuning or phylogenetic input. Tests on human RNA-seq data confirm versatility of DeepBioSim, showing it can also reliably simulate single-organism omics profiles.

bioinformatics↗

bioRxiv2025

Metatranscriptomic analysis reveals toxin-antitoxin system shifts in caries-associated oral microbiomes

BackgroundToxin-antitoxin (TA) systems are bacterial regulatory elements involved in persistence, dormancy, and biofilm stability under stress conditions such as limited nutrients, acid exposure, and antimicrobial treatment. Although their roles are well-characterized in isolated pathogens, TA systems remain largely unexamined in the context of the oral microbiome. Given their potential involvement in microbial adaptation during oral diseases and treatment, we aimed to identify transcriptionally active TA systems and evaluate their condition-specific expression patterns across oral health states using metatranscriptomic data. ResultsWe re-analyzed two publicly available supragingival metatranscriptomic datasets: a longitudinal dataset by Carda-Dieguez et.al. (Dieguez) capturing treatment transitions (fluoride (Fl), fluoride-arginine (Fl-Ar)) in caries-active and caries-free individuals, and a cross-sectional dataset by Ev et.al., (Ev) profiling caries-active lesions (CA), caries-inactive (CI), and caries-free (CF) states. After quality control, host sequences removal, rRNA removal, and HUMAnN3-based functional profiling, we identified 1,189 unique UniRef90 gene clusters with known TA associations. Differential analysis using ANCOM-BC revealed 197 TA-related gene clusters as significantly modulated in at least one condition. Of these, 22 gene clusters were differentially expressed in Dieguez, 38 in Ev, with 77 shared across both datasets. Distinct TA expression signatures were observed across healthy (CF, CI, baseline), lesion types (CAa, CAi, CAs), and treatment stages (Fl, Fl-Ar), suggesting condition-specific regulatory activity. Functional annotation of differentially expressed TA genes using the Toxin-antitoxin system database, eggNOG-mapper, and InterProScan, revealed 18 genes with strong evidence for their involvement in key bacterial stress-response pathways. These included members of the ParD-ParE, RelE/StbE-RelB/StbD, FitA, and YafQ families, which are annotated with mRNA catabolism, transcriptional repression, and prokaryotic defense-associated pathways. ConclusionThis is the first metatranscriptomic analysis profiling functional shifts in toxin-antitoxin systems across oral healthy, caries, and treatment states. TA systems showed dynamic and condition-specific expression, with pathway annotations suggesting their roles in microbial persistence, stress adaptation, and ecological remodeling during caries progression. These findings open new avenues for targeting microbial stress modules in precision microbiome therapeutics.

bioinformatics↗

bioRxiv2023

T9GPred: A Comprehensive Computational Tool for the Prediction of Type 9 Secretion System, Gliding Motility and the Associated Secreted Proteins

Type 9 secretion system (T9SS) is one of the least characterized secretion systems exclusively found in the Bacteroidetes phylum which comprise various environmental and economically relevant bacteria. While T9SS plays a central role in bacterial movement termed gliding motility, survival and pathogenicity, there is an unmet need for a comprehensive tool that predicts T9SS, gliding motility and proteins secreted via T9SS. In this study, we develop such a computational tool, Type 9 secretion system and Gliding motility Prediction (T9GPred). To build this tool, we manually curated published experimental evidence and identified mandatory components for T9SS and gliding motility prediction. We also compiled experimentally characterized proteins secreted via T9SS and determined the presence of three unique types of C-terminal domain signals, and these insights were leveraged to predict proteins secreted via T9SS. Notably, using recently published experimental evidence, we show that T9GPred has high predictive power. Thus, we used T9GPred to predict the presence of T9SS, gliding motility and associated secreted proteins across 693 completely sequenced Bacteroidetes strains. T9GPred predicted 402 strains to have T9SS, of which 327 strains are also predicted to exhibit gliding motility. Further, T9GPred also predicted putative secreted proteins for the 402 strains. In a nutshell, T9GPred is a novel computational tool for systems-level prediction of T9SS and streamlining future experimentation. The source code of the computational tool is available in our GitHub repository: https://github.com/asamallab/T9GPred. The tool and its predicted results are compiled in a web server available at: https://cb.imsc.res.in/t9gpred/.

bioinformatics↗