Biology subjects

Choudhary, K. S.

Publications and source records attributed to Choudhary, K. S..

7 recordsLinked to original sources

bioRxiv2022

A CRISPR/Cas9-based enhancement of high-throughput single-cell transcriptomics

Single-cell transcriptomics suffers from lapses in coverage of the full transcriptome, providing an incomplete gene expression profile of the cell. Here, we introduce single-cell CRISPRclean (scCLEAN), an in vitro molecular method that can be universally inserted into any single-cell RNA-seq workflow to improve the sensitivity of the assay. Utilizing CRISPR/Cas9, scCLEAN works to selectively remove highly abundant uninformative molecules, redistributing ~50% of reads to enrich for lowly expressed transcripts. Utilizing immune cells, we describe a validation of scCLEAN showing a 2.1-fold enrichment in library complexity with negligible off-target effects. Subsequently, applying scCLEAN to single-cell iso-seq samples results in a 4.6-fold improvement in unique isoform detection. Thus, demonstrating a benefit in short and long read sequencing applications. Finally, we illustrate the ability of scCLEAN to elucidate biological insights by applying it to two participant cohorts of cardiovascular samples, bringing to light novel molecular characteristics including inflammatory signatures.

genetics↗

bioRxiv2020

MetENP/MetENPWeb: An R package and web application for metabolomics enrichment and pathway analysis in Metabolomics Workbench

With the advent of high throughput mass spectrometric methods, metabolomics has emerged as an essential area of research in biomedicine with the potential to provide deep biological insights into normal and diseased functions in physiology. However, to achieve the potential offered by metabolomics measures, there is a need for biologist-friendly integrative analysis tools that can transform data into mechanisms that relate to phenotypes. Here, we describe MetENP, an R package, and a user-friendly web application deployed at the Metabolomics Workbench site extending the metabolomics enrichment analysis to include species-specific pathway analysis, pathway enrichment scores, gene-enzyme information, and enzymatic activities of the significantly altered metabolites. MetENP provides a highly customizable workflow through various user-specified options and includes support for all metabolite species with available KEGG pathways. MetENPweb is a web application for calculating metabolite and pathway enrichment analysis. Availability and ImplementationThe MetENP package is freely available from Metabolomics Workbench GitHub: (https://github.com/metabolomicsworkbench/MetENP), the web application, is freely available at (https://www.metabolomicsworkbench.org/data/analyze.php)

bioinformatics↗

bioRxiv2020

Elucidation of regulatory modes for five two-component systems in Escherichia coli reveals novel relationships

Escherichia coli uses two-component systems (TCSs) to respond to environmental signals. TCSs affect gene expression and are parts of E. coli’s global transcriptional regulatory network (TRN). Here, we identified the regulons of five TCSs in E. coli MG1655: BaeSR and CpxAR, which were stimulated by ethanol stress; KdpDE and PhoRB, induced by limiting potassium and phosphate, respectively; and ZraSR, stimulated by zinc. We analyzed RNA-seq data using independent component analysis (ICA). ChIP-exo data was used to validate condition-specific target gene binding sites. Based on this data we (1) identify the target genes for each TCS; (2) show how the target genes are transcribed in response to stimulus; and (3) reveal novel relationships between TCSs, which indicate non-cognate inducers for various response regulators, such as BaeR to iron starvation, CpxR to phosphate limitation, and PhoB and ZraR to cell envelope stress. Our understanding of the TRN in E. coli is thus notably expanded.Importance E. coli is a common commensal microbe found in human gut microenvironment; however, some strains cause diseases like diarrhea, urinary tract infections and meningitis. E. coli’s two-component system (TCS) modulates target gene expression, specially related to virulence, pathogenesis and anti-microbial peptides, in response to environmental stimuli. Thus, it is of utmost importance to understand the transcriptional regulation of the TCSs to infer its environmental adaptation and disease pathogenicity. Utilizing a combinatorial approach integrating RNAseq, independent component analysis, ChIP-exo and data mining, we show that TCSs have five different modes of transcriptional regulation. Our data further highlights non-cognate inducers of TCSs emphasizing cross-regulatory nature of TCSs in E. coli and suggests that TCSs may have a role beyond their cognate functionalities. In summary, these results when further incorporated with genome scale metabolic models can lead to understanding of metabolic capabilities of bacteria and correctly predict complex phenotype under diverse conditions.View Full Text

systems biology↗

bioRxiv2020

Reconstruction of Fur pan-regulon uncovers the complexity and diversity of transcriptional regulation in E. coli

Regulons for many transcription factors have been elucidated in model strains leading to an understanding of their role in producing physiological states. Comparative analysis of a regulon and its target genes between different strains of the same species is lacking. Ferric uptake regulator (Fur), involved in iron homeostasis, is one of the most conserved TFs, and is present in a wide range of bacteria. Using ChIP-exo experiments, we performed a comprehensive study of Fur binding sites in nine Escherichia coli strains with different lifestyles. 79 of the 431 target genes (18%) found belong to Fur core regulon, comprising genes involved in ion transport and metabolism, energy production and conversion, and amino acid metabolism and transport. 179 of the target genes (42%) comprise the accessory regulon, most of which were related to cell wall structure and biogenesis, and virulence factor pathways. The remaining target genes (173 or 40%) were in the unique regulon, with gene functions that were largely unknown. Furthermore, deletion of the fur gene led to distinct phenotypes in growth, motility, antibiotic resistance, and the change of siderophore production. These results provide a more complete understanding of how Fur regulates a set of target genes with surprising variation in closely related bacteria.

systems biology↗

bioRxiv2020

Pangenome analytics reveal two-component systems as conserved targets in ESKAPEE pathogens

Bacteria sense and respond to environmental stimuli through two-component systems (TCSs), that are composed of histidine kinase sensing and response regulator elements. TCSs are ubiquitous and participate in numerous cellular functions. TCSs across the ESKAPEE pathogens, representing the leading causes of nosocomial infections, were characterized using pangenome analytics, including annotation, mapping, pangenomic status, gene orientation, sequence variation, and structure. Our findings fall into two categories. 1) phylogenetic distribution of TCSs: (i) the number and types of TCSs varies between species of the ESKAPEE pathogens; (ii) TCSs are group-specific, i.e., Gram-positive and Gram-negative, except for KdpDE; (iii) most TCSs are conserved among genomes of an ESKAPEE, except in Pseudomonas aeruginosa. 2) sequence variation: (i) at the operon level, the genomic architecture of a TCS operon stratifies into a few discrete classes; and (ii) at the gene sequence level, histidine kinases, responsible for signal sensing, show sequence and structural variability as compared to response regulators that show a high degree of conservation. Taken together, this first comprehensive pangenomic assessment of TCSs reveals a range of strategies deployed by the ESKAPEE pathogens to manifest pathogenicity and antibiotic resistance. It further suggests that the conserved features of TCSs makes them an attractive group of potential targets with which to address antibiotic resistance.

microbiology↗

bioRxiv2019

The Escherichia coli Transcriptome Consists of Independently Regulated Modules

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we applied unsupervised learning to a compendium of high-quality Escherichia coli RNA-seq datasets to identify 70 statistically independent signals that modulate the expression of specific gene sets. We show that 50 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals was validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provided: (1) a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations, (2) a guide to gene and regulator function discovery, and (3) a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation forms an underlying principle that describes the composition of a model prokaryotic transcriptome.

systems biology↗

bioRxiv2018

The Staphylococcus aureus Two-Component System AgrAC Displays Four Distinct Genomic Arrangements That Delineate Genomic Virulence Factor Signatures

Two-component systems (TCSs) consist of a histidine kinase and a response regulator. Here, we evaluated the conservation of the AgrAC TCS among 149 completely sequenced S. aureus strains. It is composed of four genes: agrBDCA. We found that: i) AgrAC system (agr) was found in all but one of the 149 strains; ii) The agr positive strains were further classified into four agr types based on AgrD protein sequences, iii) the four agr types not only specified the chromosomal arrangement of the agr genes but also the sequence divergence of AgrC histidine kinase protein, which confers signal specificity, iv) the sequence divergence was reflected in distinct structural properties especially in the transmembrane region and second extracellular binding domain, and v) there was a strong correlation between the agr type and the virulence genomic profile of the organism. Taken together, these results demonstrate that bioinformatic analysis of the agr locus leads to a classification system that correlates with the presence of virulence factors and protein structural properties.

microbiology↗