scholarly journals The Escherichia coli Transcriptome Mostly Consists of Independently Regulated Modules

2019 ◽  
Author(s):  
Anand V. Sastry ◽  
Ye Gao ◽  
Richard Szubin ◽  
Ying Hefner ◽  
Sibei Xu ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Transcriptional Regulatory Network ◽  
Transcriptional Regulators ◽  
Specific Gene ◽  
Gene Sets ◽  
Underlying Principle ◽  
Transcriptional Regulatory ◽  
Genetic Perturbations ◽  
Function Discovery ◽  
Multiple Strains

AbstractUnderlying 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.

scholarly journals The Escherichia coli transcriptome mostly consists of independently regulated modules

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Anand V. Sastry ◽  
Ye Gao ◽  
Richard Szubin ◽  
Ying Hefner ◽  
Sibei Xu ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Transcriptional Regulatory Network ◽  
Transcriptional Regulators ◽  
Specific Gene ◽  
E Coli ◽  
Gene Sets ◽  
Transcriptional Regulatory ◽  
Genetic Perturbations ◽  
Function Discovery ◽  
Multiple Strains

AbstractUnderlying 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 apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome.

Transcriptional regulatory network analysis of developing human erythroid progenitors reveals patterns of coregulation and potential transcriptional regulators

2006 ◽  
Vol 28 (1) ◽  
pp. 114-128 ◽  
Author(s):  
M. A. Keller ◽  
S. Addya ◽  
R. Vadigepalli ◽  
B. Banini ◽  
K. Delgrosso ◽  
...  
Keyword(s):  
Network Analysis ◽  
Blood Cell ◽  
Regulatory Network ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Expression Patterns ◽  
Transcriptional Regulators ◽  
Erythroid Progenitors ◽  
Hematopoietic Stem ◽  
Transcriptional Regulatory

Deciphering the molecular basis for human erythropoiesis should yield information benefiting studies of the hemoglobinopathies and other erythroid disorders. We used an in vitro erythroid differentiation system to study the developing red blood cell transcriptome derived from adult CD34+ hematopoietic progenitor cells. mRNA expression profiling was used to characterize developing erythroid cells at six time points during differentiation ( days 1, 3, 5, 7, 9, and 11). Eleven thousand seven hundred sixty-three genes (20,963 Affymetrix probe sets) were expressed on day 1, and 1,504 genes, represented by 1,953 probe sets, were differentially expressed (DE) with 537 upregulated and 969 downregulated. A subset of the DE genes was validated using real-time RT-PCR. The DE probe sets were subjected to a cluster metric and could be divided into two, three, four, five, or six clusters of genes with different expression patterns in each cluster. Genes in these clusters were examined for shared transcription factor binding sites (TFBS) in their promoters by comparing enrichment of each TFBS relative to a reference set using transcriptional regulatory network analysis. The sets of TFBS enriched in genes up- and downregulated during erythropoiesis were distinct. This analysis identified transcriptional regulators critical to erythroid development, factors recently found to play a role, as well as a new list of potential candidates, including Evi-1, a potential silencer of genes upregulated during erythropoiesis. Thus this transcriptional regulatory network analysis has yielded a focused set of factors and their target genes whose role in differentiation of the hematopoietic stem cell into distinct blood cell lineages can be elucidated.

scholarly journals Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Kevin Rychel ◽  
Anand V. Sastry ◽  
Bernhard O. Palsson
Keyword(s):  
Machine Learning ◽  
Bacillus Subtilis ◽  
Biofilm Formation ◽  
Transcriptional Regulatory Network ◽  
Population Level ◽  
Cellular Functions ◽  
Fundamental Interest ◽  
Gene Sets ◽  
Transcriptional Regulatory ◽  
Dependent Activity

AbstractThe 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 putative or recently discovered roles for at least five regulons, such as a relationship between histidine utilization and quorum sensing. The TRN also facilitates quantification of population-level sporulation states. 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.

scholarly journals Machine Learning Uncovers a Data-Driven Transcriptional Regulatory Network for the Crenarchaeal Thermoacidophile Sulfolobus acidocaldarius

2021 ◽  
Vol 12 ◽  
Author(s):  
Siddharth M. Chauhan ◽  
Saugat Poudel ◽  
Kevin Rychel ◽  
Cameron Lamoureux ◽  
Reo Yoo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stress Responses ◽  
Regulatory Network ◽  
Transcriptional Regulatory Network ◽  
Cellular Responses ◽  
Sulfolobus Acidocaldarius ◽  
Network Discovery ◽  
Gene Sets ◽  
Transcriptional Regulatory ◽  
Export System

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 five 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 five 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 extremophile’s 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 toward regulatory network discovery in archaea.

scholarly journals Machine Learning of Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators

2021 ◽  
Author(s):  
Akanksha Rajput ◽  
Hannah Tsunemoto ◽  
Anand V Sastry ◽  
Richard Szubin ◽  
Kevin Rychel ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Transcriptional Regulatory Network ◽  
Critical Role ◽  
Gene Clusters ◽  
Transcriptional Regulators ◽  
Critical Conditions ◽  
Future Research ◽  
Secretion Systems ◽  
Metabolism Regulation ◽  
Transcriptional Regulatory

The transcriptional regulatory network (TRN) of Pseudomonas aeruginosa plays a critical role in coordinating numerous cellular processes. We extracted and quality controlled all publicly available RNA-sequencing datasets for P. aeruginosa to find 281 high-quality transcriptomes. We produced 83 new RNAseq data sets under critical conditions to generate a comprehensive compendium of 364 transcriptomes. We used this compendium to reconstruct the TRN of P. aeruginosa using independent component analysis (ICA). We identified 104 independently modulated sets of genes (called iModulons), among which 81 (78%) reflect the effects of known transcriptional regulators. We show that iModulons: 1) play an important role in defining the genomic boundaries of biosynthetic gene clusters (BGCs); 2) show increased expression of the BGCs and associated secretion systems in conditions that emulate cystic fibrosis (CF); 3) show the presence of a novel BGC named RiPP (bacteriocin producer) which might have a role in worsening CF outcomes; 4) exhibit the interplay of amino acid metabolism regulation and central metabolism across carbon sources, and 5) clustered according to their activity changes to define iron and sulfur stimulons. Finally, we compare the iModulons of P. aeruginosa with those of E. coli to observe conserved regulons across two gram negative species. This comprehensive TRN framework covers almost every aspect of the transcriptional regulatory machinery in P. aeruginosa, and thus could prove foundational for future research of its physiological functions.

scholarly journals Multiple transcription factors co-regulate the Mycobacterium tuberculosis adaptation response to vitamin C

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Malobi Nandi ◽  
Kriti Sikri ◽  
Neha Chaudhary ◽  
Shekhar Chintamani Mande ◽  
Ravi Datta Sharma ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Mycobacterium Tuberculosis ◽  
Vitamin C ◽  
Large Scale ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Transcriptional Regulators ◽  
Transcriptional Regulatory ◽  
Modulation Of Gene Expression

Abstract Background Latent tuberculosis infection is attributed in part to the existence of Mycobacterium tuberculosis in a persistent non-replicating dormant state that is associated with tolerance to host defence mechanisms and antibiotics. We have recently reported that vitamin C treatment of M. tuberculosis triggers the rapid development of bacterial dormancy. Temporal genome-wide transcriptome analysis has revealed that vitamin C-induced dormancy is associated with a large-scale modulation of gene expression in M. tuberculosis. Results An updated transcriptional regulatory network of M.tuberculosis (Mtb-TRN) consisting of 178 regulators and 3432 target genes was constructed. The temporal transcriptome data generated in response to vitamin C was overlaid on the Mtb-TRN (vitamin C Mtb-TRN) to derive insights into the transcriptional regulatory features in vitamin C-adapted bacteria. Statistical analysis using Fisher’s exact test predicted that 56 regulators play a central role in modulating genes which are involved in growth, respiration, metabolism and repair functions. Rv0348, DevR, MprA and RegX3 participate in a core temporal regulatory response during 0.25 h to 8 h of vitamin C treatment. Temporal network analysis further revealed Rv0348 to be the most prominent hub regulator with maximum interactions in the vitamin C Mtb-TRN. Experimental analysis revealed that Rv0348 and DevR proteins interact with each other, and this interaction results in an enhanced binding of DevR to its target promoter. These findings, together with the enhanced expression of devR and Rv0348 transcriptional regulators, indicate a second-level regulation of target genes through transcription factor- transcription factor interactions. Conclusions Temporal regulatory analysis of the vitamin C Mtb-TRN revealed that there is involvement of multiple regulators during bacterial adaptation to dormancy. Our findings suggest that Rv0348 is a prominent hub regulator in the vitamin C model and large-scale modulation of gene expression is achieved through interactions of Rv0348 with other transcriptional regulators.

scholarly journals Coordination logic of the sensing machinery in the transcriptional regulatory network of Escherichia coli

2007 ◽  
Vol 35 (20) ◽  
pp. 6963-6972 ◽  
Author(s):  
Sarath Chandra Janga ◽  
Heladia Salgado ◽  
Agustino Martínez-Antonio ◽  
Julio Collado-Vides
Keyword(s):  
Escherichia Coli ◽  
Regulatory Network ◽  
Transcriptional Regulatory Network ◽  
Transcriptional Regulatory

scholarly journals PRECISE 2.0: an expanded high-quality RNA-seq compendium for Escherichia coli K-12 reveals high-resolution transcriptional regulatory structure

2021 ◽  
Author(s):  
Cameron R. Lamoureux ◽  
Katherine T. Decker ◽  
Anand V. Sastry ◽  
John Luke McConn ◽  
Ye Gao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Multiple Scales ◽  
Transcriptional Regulatory Network ◽  
Differential Expression Analysis ◽  
Rna Seq ◽  
High Quality ◽  
Standardized Protocol ◽  
Transcriptional Regulatory ◽  
K 12

Uncovering the structure of the transcriptional regulatory network (TRN) that modulates gene expression in prokaryotes remains an important challenge. Transcriptomics data is plentiful, necessitating the development of scalable methods for converting this data into useful knowledge about the TRN. Previously, we published the PRECISE dataset for Escherichia coli K-12 MG1655, containing 278 RNA-seq datasets created using a standardized protocol. Here, we present PRECISE 2.0, which is nearly three times the size of the original PRECISE dataset and also created using a standardized protocol. We analyze PRECISE 2.0 at multiple scales, demonstrating multiple analytical strategies for extracting knowledge from this dataset. Specifically, we: (1) highlight patterns in gene expression across the dataset; (2) utilize independent component analysis to extract 218 independently modulated groups of genes (iModulons) that describe the TRN at the systems level; (3) demonstrate the utility of iModulons over traditional differential expression analysis; and (4) uncover 6 new potential regulons. Thus, PRECISE 2.0 is a large-scale, high-quality transcriptomics dataset which may be analyzed at multiple scales to yield important biological insights.

scholarly journals Genetic program activity delineates risk, relapse, and therapy responsiveness in Multiple Myeloma

2020 ◽  
Author(s):  
Matthew A. Wall ◽  
Serdar Turkarslan ◽  
Wei-Ju Wu ◽  
Samuel A. Danziger ◽  
David J. Reiss ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Disease Progression ◽  
Biological Networks ◽  
Somatic Mutations ◽  
Chromosomal Abnormalities ◽  
Transcriptional Regulatory Network ◽  
Transcriptional Regulators ◽  
Activity Profile ◽  
Downstream Effects ◽  
Transcriptional Regulatory

AbstractDespite recent advancements in the treatment of multiple myeloma (MM), nearly all patients ultimately relapse and many become refractory to their previous therapies. Although many therapies exist with diverse mechanisms of action, it is not yet clear how the differences in MM biology across patients impacts the likelihood of success for existing therapies and those in the pipeline. Therefore, we not only need the ability to predict which patients are at high risk for disease progression, but also a means to understand the mechanisms underlying their risk. We hypothesized that knowledge of the biological networks that give rise to MM, specifically the transcriptional regulatory network (TRN) and the mechanisms by which mutations impact gene regulation, would enable improved predictions of disease progression and actionable insights for treatment. Here we present a method to infer TRNs from multi-omics data and apply it to the generation of a MM TRN that links chromosomal abnormalities and somatic mutations to downstream effects on gene expression via perturbation of transcriptional regulators. We find that 141 genetic programs underlie the disease and that the activity profile of these programs fall into one of 25 distinct transcriptional states. These transcriptional signatures prove to be more predictive of outcomes than do mutations and reveal plausible mechanisms for relapse, including the establishment of an immuno-suppressive microenvironment. Moreover, we observe subtype-specific vulnerabilities to interventions with existing drugs and motivate the development of new targeted therapies that appear especially promising for relapsed refractory MM.

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