Biology subjects

Poudel, S.

Publications and source records attributed to Poudel, S..

34 records · Page 2Linked to original sources

bioRxiv2022

Meta-omics-aided isolation of an elusive anaerobic arsenic-methylating soil bacterium

Soil microbiomes harbor unparalleled functional and phylogenetic diversity and are sources of novel metabolisms. However, extracting isolates with a targeted function from complex microbiomes is not straightforward, particularly if the associated phenotype does not lend itself to high-throughput screening. Here, we tackle the methylation of arsenic (As) in anoxic soils. By analogy to mercury, As methylation was proposed to be catalyzed by sulfate-reducing bacteria. However, to date, there are no anaerobic isolates capable of As methylation, whether sulfate-reducing or otherwise. The isolation of such a microorganism has been thwarted by the fact that the anaerobic bacteria harboring a functional arsenite S-adenosylmethionine methyltransferase (ArsM) tested to date did not methylate As in pure culture. Additionally, fortuitous As methylation can result from the release of non-specific methyltransferases upon lysis. Thus, we combined metagenomics, metatranscriptomics, and metaproteomics to identify the microorganisms actively methylating As in anoxic soil-derived microbial cultures. Based on the metagenome-assembled genomes of microorganisms expressing ArsM, we isolated Paraclostridium sp. strain EML, which was confirmed to actively methylate As anaerobically. This work is an example of the application of meta-omics to the isolation of elusive microorganisms.

microbiology↗

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↗

bioRxiv2021

Machine learning of all Mycobacterium Tuberculosis H37Rv RNA-seq data reveals complex interplay between metabolism, stress response, and infection

Mycobacterium tuberculosis is one of the most consequential human bacterial pathogens, posing a serious challenge to 21st century medicine. A key feature of its pathogenicity is its ability to adapt its transcriptional response to environmental stresses through its transcriptional regulatory network (TRN). While many studies have sought to characterize specific portions of the M. tuberculosis TRN, a systems level characterization and analysis of interactions among the controlling transcription factors remains to be achieved. Here, we applied an unsupervised machine learning method to modularize the M. tuberculosis transcriptome and describe the role of transcription factors (TFs) in the TRN. By applying Independent Component Analysis (ICA) to over 650 transcriptomic samples, we obtained 80 independently modulated gene sets known as "iModulons," many of which correspond to known regulons. These iModulons explain 61% of the variance in the organisms transcriptional response. We show that iModulons: 1) reveal the function of previously unknown regulons, 2) describe the transcriptional shifts that occur during environmental changes such as shifting carbon sources, oxidative stress, and virulence events, and 3) identify intrinsic clusters of transcriptional regulons that link several important metabolic systems, including lipid, cholesterol, and sulfur metabolism. This transcriptome-wide analysis of the M. tuberculosis TRN informs future research on effective ways to study and manipulate its transcriptional regulation, and presents a knowledge-enhanced database of all published high-quality RNA-seq data for this organism to date.

systems biology↗

bioRxiv2021

Mining all publicly available expression data to compute dynamic microbial transcriptional regulatory networks

We are firmly in the era of biological big data. Millions of omics datasets are publicly accessible and can be employed to support scientific research or build a holistic view of an organism. Here, we introduce a workflow that converts all public gene expression data for a microbe into a dynamic representation of the organisms transcriptional regulatory network. This five-step process walks researchers through the mining, processing, curation, analysis, and characterization of all available expression data, using Bacillus subtilis as an example. The resulting reconstruction of the B. subtilis regulatory network can be leveraged to predict new regulons and analyze datasets in the context of all published data. The results are hosted at https://imodulondb.org/, and additional analyses can be performed using the PyModulon Python package. As the number of publicly available datasets increases, this pipeline will be applicable to a wide range of microbial pathogens and cell factories.

bioinformatics↗

bioRxiv2021

Optimal dimensionality selection for independent component analysis of transcriptomic data

Independent Component Analysis (ICA) is an unsupervised machine learning algorithm that separates a set of mixed signals into a set of statistically independent source signals. Applied to high-quality gene expression datasets, ICA effectively reveals the source signals of the transcriptome as groups of co-regulated genes and their corresponding activities across diverse growth conditions. Two major variables that affect the output of ICA are the diversity and scope of the underlying data, and the user-defined number of independent components, or dimensionality, to compute. Availability of high-quality transcriptomic datasets has grown exponentially as high-throughput technologies have advanced; however, optimal dimensionality selection remains an open question. Here, we introduce a new method, called OptICA, for effectively finding the optimal dimensionality that consistently maximizes the number of biologically relevant components revealed while minimizing the potential for over-decomposition. We show that OptICA outperforms two previously proposed methods for selecting the number of independent components across four transcriptomic databases of varying sizes. OptICA avoids both over-decomposition and under-decomposition of transcriptomic datasets resulting in the best representation of the organisms underlying transcriptional regulatory network.

bioinformatics↗

bioRxiv2021

Elucidating the CodY regulon in Staphylococcus aureus USA300 substrains

CodY is a conserved broad acting transcription factor that regulates the expression of genes related to amino acid metabolism and virulence in methicillin-resistant Staphylococcus aureus (MRSA). CodY target genes have been studied by using in vitro DNA affinity purification and deep sequencing (IDAP-Seq). Here we performed the first in vivo determination of CodY target genes using a novel CodY monoclonal antibody in established ChIP-exo protocols. Our results showed, 1) the same 135 CodY promoter binding sites regulating 165 target genes identified in two closely related virulent S. aureus USA300 TCH1516 and LAC strains; 2) The differential binding intensity for the same target genes under the same conditions was due to sequence differences in the same CodY binding site in the two strains; 3) Based on transcriptomic data, a CodY regulon comprising 72 target genes that are differentially regulated relative to a CodY deletion strain, representing genes that are mainly involved in amino acid transport and metabolism, inorganic ion transport and metabolism, transcription and translation, and virulence; and 4) CodY systematically regulated central metabolic flux to generate branched-chain amino acids (BCAAs) by mapping the CodY regulon onto a genome-scale metabolic model of S. aureus. Our study performed the first system-level analysis of CodY in two closely related USA300 TCH1516 and LAC strains giving new insights into the similarities and differences of CodY regulatory roles between the closely related strains. ImportanceWith the increasing availability of whole genome sequences for many strains within the same pathogenic species, a comparative analysis of key regulators is needed to understand how the different strains uniquely coordinate metabolism and expression of virulence. To successfully infect the human host, Staphylococcus aureus USA300 relies on the transcription factor CodY to reorganize metabolism and express virulence factors. While CodY is a known key transcription factor, its target genes are not characterized on a genome-wide basis. We performed a comparative analysis to describe the transcriptional regulation of CodY between two dominant USA300 strains. This study motivates the characterization of common pathogenic strains and an evaluation of the possibility of developing specialized treatments for major strains circulating in the population.

microbiology↗

bioRxiv2020

Alternating handedness motifs in proteins classify structure and cofactor binding

Cofactor binding sites in proteins often are composed of favorable interactions of specific cofactors with the sidechains and/or backbone protein fold motifs. In many cases these motifs contain left-handed conformations which enable tight turns of the backbone that present backbone amide protons in direct interactions with cofactors termed cationic nests. Here, we defined alternating handedness of secondary structure as a search constraint within the PDB to systematically identify these cofactor binding nests. We identify unique alternating handedness structural motifs which are specific to the cofactors they bind. These motifs can guide the design of engineered folds that utilize specific cofactors and also enable us to gain a deeper insight into the evolution of the structure of cofactor binding sites.

bioinformatics↗

bioRxiv2020

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning

Independent component analysis (ICA) of bacterial transcriptomes has emerged as a powerful tool for obtaining co-regulated, independently-modulated gene sets (iModulons), inferring their activities across a range of conditions, and enabling their association to known genetic regulators. By grouping and analyzing genes based on observations from big data alone, iModulons can provide a novel perspective into how the composition of the transcriptome adapts to environmental conditions. Here, we present iModulonDB (imodulondb.org), a knowledgebase of prokaryotic transcriptional regulation computed from high-quality transcriptomic datasets using ICA. Users select an organism from the home page and then search or browse the curated iModulons that make up its transcriptome. Each iModulon and gene has its own interactive dashboard, featuring plots and tables with clickable, hoverable, and downloadable features. This site enhances research by presenting scientists of all backgrounds with co-expressed gene sets and their activity levels, which lead to improved understanding of regulator-gene relationships, discovery of transcription factors, and the elucidation of unexpected relationships between conditions and genetic regulatory activity. The current release of iModulonDB covers three organisms (E. coli, S. aureus, and B. subtilis) with 204 iModulons, and can be expanded to cover many additional organisms.

systems biology↗

bioRxiv2020

Characterizing the response of Acinetobacter baumannii ATCC 17978 to azithromycin in multiple in vitro growth conditions

Multi-drug resistant (MDR) Acinetobacter baumannii is one of the most concerning pathogens in hospital infections. A. baumannii is categorized as an "Urgent Threat" by the U.S. Centers for Disease Control and the highest priority pathogen by the World Health Organization due to its propensity for broad antibiotic resistance and its associated high mortality rates. New treatment options are urgently needed for MDR A. baumannii infections. Our prior studies have demonstrated an unappreciated utility of the macrolide azithromycin (AZM) against MDR A. baumannii in tissue-culture medium. This finding is all the more surprising since AZM has no appreciable activity against A. baumannii in standard bacteriological media. The basis for this media-dependent activity of AZM against A. baumannii is not fully defined. In this study, we utilize a variety of techniques (growth dynamics, bacterial cytological profiling, RNA sequencing, and LC/MS) to profile the response of MDR A. baumannii to AZM in both standard bacteriological and more physiological relevant mammalian tissue-culture medium. [Table 1]

microbiology↗

bioRxiv2020

Identifying the effect of vancomycin on HA-MRSA strains using bacteriological and physiological media

Healthcare-associated methicillin-resistant Staphylococcus aureus (HA-MRSA) USA100 strains are of major concern due to their evolving antibiotic resistant. They are resistant to a broad class of antibiotics like macrolides, aminoglycosides, fluoroquinolones, and many more. The selection of appropriate antibiotic susceptibility examination media is very important. Thus, we use bacteriological (CA-MHB) as well as physiological (R10LB) media to determine the effect of vancomycin on USA100 strains. The study includes the profiling behaviour of HA-MRSA USA100 D592 and D712 strains in the presence of vancomycin through various high-throughput assays. The US100 D592 and D712 strains were characterized at sub-inhibitory concentrations through growth curves, RNA sequencing, bacterial cytological profiling, and exo-metabolomics high throughput experiments. The study reveals the vancomycin resistance behavior of USA100 strains in dual media conditions using wide-ranging experiments.

microbiology↗

bioRxiv2020

Decomposition of transcriptional responses provides insights into differential antibiotic susceptibility

Responses of bacteria to antibiotic treatments depend on their environments. Differences between in vitro testing conditions and the physiological environments inside patients have resulted in poor antibiotic susceptibility predictions, contributing to treatment failures in the clinic. Here, we investigate how media composition affects antibiotic susceptibility in the laboratory strain E. coli K-12 MG1655, and contextualize these changes through machine learning of transcriptomics data. We show that complex transcriptional changes induced by different media or antibiotic treatment can be traced back to a few key regulators. Integration of results from machine learning with biochemical knowledge reveals fundamental shifts in respiration and iron availability that may explain media-dependent differential susceptibility to antibiotics. The data generation and analytical workflow used here can interrogate the regulatory state of a pathogen under any condition, and can be extended to additional strains and organisms for which data is available.

microbiology↗

bioRxiv2020

Profiling the effect of nafcillin on HA-MRSA D592 using bacteriological and physiological media

Staphylococcus aureus is a leading human pathogen associated with both hospital-acquired and community-acquired infections. The bacterium has steadily gained resistance to {beta}-lactams and other important first-line antibiotics culminating in its categorization as an urgent threat by the U.S. Centers for Disease Control and Prevention. Observations of a varying response to antimicrobial exposure as a function of media type has revealed that clinical susceptibility testing performed in standard bacteriological media might not adequately represent pharmacological responses in the patient. Such observations have encouraged research designed to identify media types that more closely mimic the in vivo environment. In this study, we examine the response of a hospital-acquired USA100 lineage methicillin-resistant, vancomycin-intermediate S. aureus (MRSA/VISA) strain (D592) to nafcillin in a bacteriological compared to a more physiological tissue culture-based medium. We performed multi-dimensional analysis including growth and bacterial cytological profiling, RNA sequencing, and exo-metabolomics measurements (both HPLC and LC/MS) to shed light on the media-dependent activity of the commonly prescribed {beta}-lactam antibiotic nafcillin. O_TBL View this table: org.highwire.dtl.DTLVardef@85b95borg.highwire.dtl.DTLVardef@14c1504org.highwire.dtl.DTLVardef@1f7e976org.highwire.dtl.DTLVardef@100598dorg.highwire.dtl.DTLVardef@1f770e9_HPS_FORMAT_FIGEXP M_TBL C_TBL

microbiology↗

bioRxiv2020

Matrix factorization recovers consistent regulatory signals from disparate datasets

The availability of gene expression data has dramatically increased in recent years. This data deluge could result in detailed inference of underlying regulatory networks, but the diversity of experimental platforms and protocols introduces critical biases that could hinder scalable analysis of existing data. Here, we show that the underlying structure of the E. coli transcriptome, as determined by Independent Component Analysis (ICA), is conserved across multiple independent datasets, including both RNA-seq and microarray datasets. We also show that echoes of this structure remain in the proteome, accelerating biological discovery through multi-omics analysis. We subsequently combined five transcriptomics datasets into a large compendium containing over 800 expression profiles and discovered that its underlying ICA-based structure was still comparable to that of the individual datasets. ICA thus enables deep analysis of disparate data to uncover new insights that were not visible in the individual datasets.

bioinformatics↗

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↗

bioRxiv2020

Revealing 29 sets of independently modulated genes in Staphylococcus aureus, their regulators and role in key physiological responses

The ability of Staphylococcus aureus to infect many different tissue sites is enabled, in part, by its Transcriptional Regulatory Network (TRN) that coordinates its gene expression to respond to different environments. We elucidated the organization and activity of this TRN by applying Independent Component Analysis (ICA) to a compendium of 108 RNAseq expression profiles from two S. aureus clinical strains (TCH1516 and LAC). ICA decomposed the S. aureus transcriptome into 29 independently modulated sets of genes (i-modulons) that revealed (1) high confidence associations between 21 i-modulons and known regulators; (2) an association between an i-modulon and {sigma}S, whose regulatory role was previously undefined; (3) the regulatory organization of 65 virulence factors in the form of three i-modulons associated with AgrR, SaeR and Vim-3, (4) the roles of three key transcription factors (codY, Fur and ccpA) in coordinating the metabolic and regulatory networks; and (5) a low dimensional representation, involving the function of few transcription factors, of changes in gene expression between two laboratory media (RPMI, CAMHB) and two physiological media (blood and serum). This representation of the TRN covers 842 genes representing 76% of the variance in gene expression that provides a quantitative reconstruction of transcriptional modules in S. aureus, and a platform enabling its full elucidation. Significance StatementStaphylococcus aureus infections impose an immense burden on the healthcare system. To establish a successful infection in a hostile host environment, S. aureus must coordinate its gene expression to respond to a wide array of challenges. This balancing act is largely orchestrated by the Transcriptional Regulatory Network (TRN). Here, we present a model of 29 independently modulated sets of genes that form the basis for a segment of the TRN in clinical USA300 strains of S. aureus. Using this model, we demonstrate the concerted role of various cellular systems (e.g. metabolism, virulence and stress response) underlying key physiological responses, including response during blood infection.

microbiology↗