Biology subjects

Sastry, A. V.

Publications and source records attributed to Sastry, A. V..

28 records · Page 2Linked to original sources

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

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

PtrR (YneJ) is a novel E. coli transcription factor regulating the putrescine stress response and glutamate utilization

Although polyamines, such as putrescine (Ptr), induce envelope stress for bacteria, they are important as nitrogen and carbon sources. Ptr utilization in Escherichia coli involves protein glutamylation, and glutamate stands at a crossroads between catabolism and anabolism. This communication reports that the transcription factor YneJ, here renamed PtrR, is involved in the regulation of a small regulatory RNA gene, fnrS, and an operon, yneIHGF, encoding succinate-semialdehyde dehydrogenase, Sad (YneI), glutaminase, GlsB (YneH), and several other genes. The yneI promoter is activated during putrescine utilization under nitrogen/carbon starvation conditions, and we show that PtrR is important for the putrescine stress response. It is also a repressor of fnrS gene expression, involved in the cascade regulation of mRNA synthesis for the marA and sodB genes, involved in antibiotic responses. PtrR transcriptional regulation of fnrS leads to a regulatory cascade induced by this small RNA that affects mRNA levels of ompF and the multidrug resistance regulator, MarA. We propose that PtrR functions as a dual activator/repressor, and that its regulation is important for the responses to different stress conditions involving L-glutamine/L-glutamate and putrescine utilization. IMPORTANCEPutrescine is an important source of nitrogen for many organisms, but it also induces stress. Although its metabolism has been studied extensively, the regulatory mechanisms that control the stress response are still poorly understood. This study reveals that the HTH-type transcriptional regulator, YneJ in Escherichia coli, here re-named PtrR, is important for the putrescine stress response, in part because it plays a role in outer membrane porin regulation as a sensor in a regulatory cascade. Direct PtrR transcriptional regulation of the fnrS, yneI (sad), gltS and ptrR genes is documented and rationalized, and nine PtrR binding sites were identified using ChIP-Exo. A ptrR mutant exhibited altered resistance to a tetracycline group of antibiotics under microaerophilic conditions, suggesting that PtrR indirectly controls expression of porin genes such as ompF.

microbiology↗

bioRxiv2020

Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome

The transcriptional regulatory network (TRN) of Bacillus subtilis coordinates cellular functions of fundamental interest, including metabolism, biofilm formation, and sporulation. Here, we use unsupervised machine learning to modularize the transcriptome and quantitatively describe regulatory activity under diverse conditions, creating an unbiased summary of gene expression. We obtain 83 independently modulated gene sets that explain most of the variance in expression, and demonstrate that 76% of them represent the effects of known regulators. The TRN structure and its condition-dependent activity uncover novel or recently discovered roles for at least 5 regulons, such as a relationship between histidine utilization and quorum sensing. The TRN also facilitates quantification of population-level sporulation states, revealing a putative anaerobic metabolism role for SigG. As this TRN covers the majority of the transcriptome and concisely characterizes the global expression state, it could inform research on nearly every aspect of transcriptional regulation in B. subtilis.

systems biology↗

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

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↗

bioRxiv2019

Experimental evolution reveals the genetic basis and systems biology of superoxide stress tolerance

Bacterial response to oxidative stress is of fundamental importance. Oxidative stresses are endogenous, such as reactive oxidative species (ROS) production during respiration, or exogenous in industrial biotechnology, due to culture conditions or product toxicity. The immune system inflicts strong ROS stress on invading pathogens. In this study we make use of Adaptive Laboratory Evolution (ALE) to generate two independent lineages of Escherichia coli with increased tolerance to superoxide stress by up to 500% compared to wild type. We found: 1) that the use of ALE reveals the genetic basis for and systems biology of ROS tolerance, 2) that there are only 6 and 7 mutations, respectively, in each lineage, five of which reproducibly occurred in the same genes (iron-sulfur cluster regulator iscR, putative iron-sulfur repair protein ygfZ, pyruvate dehydrogenase subunit E aceE, succinate dehydrogenase sucA, and glutamine tRNA glnX), and 3) that the transcriptome of the strain lineages exhibits two different routes of tolerance: the direct mitigation and repair of ROS damage and the up-regulation of cell motility and swarming genes mediated through phosphate starvation, which has been linked to biofilm formation and aggregation. These two transcriptomic responses can be interpreted as flight and fight phenotypes.\n\nImportanceBacteria encounter oxidative stress from multiple sources. During pathogenic infections, our bodys immune system releases ROS as a form of antimicrobial defense whilst bacteria used in industrial biotechnology are frequently exposed to genetic modifications and culture conditions which induce oxidative stress. In order to get around the bodys defences, pathogens have developed various adaptations to tolerate high levels of ROS, and these adaptive mechanisms are not always well understood. At the same time, there is a need to improve oxidative stress tolerance for industrially relevant strains in order to increase robustness and productivity. In this study we generate two strains of superoxide tolerant Escherichia coli and identify several adaptive mechanisms. These findings can be directly applied to improve production strain fitness in an industrial setting. They also provide insight into potential virulence factors in other pathogens, highlighting potential targets for antimicrobial compounds.

systems biology↗

bioRxiv2019

Adaptive laboratory evolution of Escherichia coli under acid stress

The ability of Escherichia coli to tolerate acid stress is important for its survival and colonization in the human digestive tract. Here, we performed adaptive laboratory evolution of the laboratory strain E. coli K-12 MG1655 at pH 5.5 in glucose minimal medium. By 800 generations, six independent populations under evolution reached 18.0% higher growth rates than their starting strain at pH 5.5, while maintaining comparable growth rates to the starting strain at pH 7. We characterized the evolved strains to find that: (1) whole genome sequencing of isolated clones from each evolved population revealed mutations in rpoC appearing in 5 of 6 sequenced clones; (2) gene expression profiles revealed different strategies to mitigate acid stress, that are related to amino acid metabolism and energy production and conversion. Thus, a combination of adaptive laboratory evolution, genome resequencing, and expression profiling reveals, on a genome-scale, the strategies that E. coli deploys to mitigate acid stress.

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

Systematic discovery of uncharacterized transcription factors in Escherichia coli K-12 MG1655

Transcriptional regulation enables cells to respond to environmental changes. Yet, among the estimated 304 candidate transcription factors (TFs) in Escherichia coli K-12 MG1655, 185 have been experimentally identified and only a few tens of them have been fully characterized by ChIP methods. Understanding the remaining TFs is key to improving our knowledge of the E. coli transcriptional regulatory network (TRN). Here, we developed an integrated workflow for the computational prediction and comprehensive experimental validation of TFs using a suite of genome-wide experiments. We applied this workflow to: 1) identify 16 candidate TFs from over a hundred candidate uncharacterized genes; 2) capture a total of 255 DNA binding peaks for 10 candidate TFs resulting in six high-confidence binding motifs; 3) reconstruct the regulons of these 10 TFs by determining gene expression changes upon deletion of each TF; and 4) determine the regulatory roles of three TFs (YiaJ, YdcI, and YeiE) as regulators of L-ascorbate utilization, proton transfer and acetate metabolism, and iron homeostasis under iron limited condition, respectively. Together, these results demonstrate how this workflow can be used to discover, characterize, and elucidate regulatory functions of uncharacterized TFs in parallel.

microbiology↗