Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles.
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ABSTRACT:
SUBMITTER: Faith JJ
PROVIDER: S-EPMC1764438 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Machine learning approaches offer the potential to systematically identify transcriptional regulatory interactions from a compendium of microarray expression profiles. However, experimental validation of the performance of these methods at the genome scale has remained elusive. Here we assess the global performance of four existing classes of inference algorithms using 445 Escherichia coli Affymetrix arrays and 3,216 known E. coli regulatory interactions from RegulonDB. We also developed and applied the context likelihood of relatedness (CLR) algorithm, a novel extension of the relevance networks class of algorithms. CLR demonstrates an average precision gain of 36% relative to the next-best performing algorithm. At a 60% true positive rate, CLR identifies 1,079 regulatory interactions, of which 338 were in the previously known network and 741 were novel predictions. We tested the predicted interactions for three transcription factors with chromatin immunoprecipitation, confirming 21 novel interactions and verifying our RegulonDB-based performance estimates. CLR also identified a regulatory link providing central metabolic control of iron transport, which we confirmed with real-time quantitative PCR. The compendium of expression data compiled in this study, coupled with RegulonDB, provides a valuable model system for further improvement of network inference algorithms using experimental data. Keywords: mapping transcriptional regulation
2007-01-22 | GSE6836 | GEO
Project description:In the face of changes in their environment, bacteria adjust gene expression levels and produce appropriate responses. The individual layers of this process have been widely studied: the transcriptional regulatory network describes the regulatory interactions that produce changes in the metabolic network, both of which are coordinated by the signaling network, but the interplay between them has never been described in a systematic fashion. Here, we formalize the process of detection and processing of environmental information mediated by individual transcription factors (TFs), utilizing a concept termed genetic sensory response units (GENSOR units), which are composed of four components: (1) a signal, (2) signal transduction, (3) genetic switch, and (4) a response. We used experimentally validated data sets from two databases to assemble a GENSOR unit for each of the 189 local TFs of Escherichia coli K-12 contained in the RegulonDB database. Further analysis suggested that feedback is a common occurrence in signal processing, and there is a gradient of functional complexity in the response mediated by each TF, as opposed to a one regulator/one pathway rule. Finally, we provide examples of other GENSOR unit applications, such as hypothesis generation, detailed description of cellular decision making, and elucidation of indirect regulatory mechanisms.
| S-EPMC5540944 | biostudies-literature
Project description:BackgroundThe exploitation of the CRISPR/Cas9 machinery coupled to lambda (λ) recombinase-mediated homologous recombination (recombineering) is becoming the method of choice for genome editing in E. coli. First proposed by Jiang and co-workers, the strategy has been subsequently fine-tuned by several authors who demonstrated, by using few selected loci, that the efficiency of mutagenesis (number of mutant colonies over total number of colonies analyzed) can be extremely high (up to 100%). However, from published data it is difficult to appreciate the robustness of the technology, defined as the number of successfully mutated loci over the total number of targeted loci. This information is particularly relevant in high-throughput genome editing, where repetition of experiments to rescue missing mutants would be impractical. This work describes a "brute force" validation activity, which culminated in the definition of a robust, simple and rapid protocol for single or multiple gene deletions.ResultsWe first set up our own version of the CRISPR/Cas9 protocol and then we evaluated the mutagenesis efficiency by changing different parameters including sequence of guide RNAs, length and concentration of donor DNAs, and use of single stranded and double stranded donor DNAs. We then validated the optimized conditions targeting 78 "dispensable" genes. This work led to the definition of a protocol, featuring the use of double stranded synthetic donor DNAs, which guarantees mutagenesis efficiencies consistently higher than 10% and a robustness of 100%. The procedure can be applied also for simultaneous gene deletions.ConclusionsThis work defines for the first time the robustness of a CRISPR/Cas9-based protocol based on a large sample size. Since the technical solutions here proposed can be applied to other similar procedures, the data could be of general interest for the scientific community working on bacterial genome editing and, in particular, for those involved in synthetic biology projects requiring high throughput procedures.
| S-EPMC5404680 | biostudies-literature
Project description:Despite our increasing topological knowledge on regulation networks in model bacteria, it is largely unknown which of the many co-occurring regulatory events actually control metabolic function and the distribution of intracellular fluxes. Here, we unravel condition-dependent transcriptional control of Escherichia coli metabolism by large-scale (13)C-flux analysis in 91 transcriptional regulator mutants on glucose and galactose. In contrast to the canonical respiro-fermentative glucose metabolism, fully respiratory galactose metabolism depends exclusively on the phosphoenol-pyruvate (PEP)-glyoxylate cycle. While 2/3 of the regulators directly or indirectly affected absolute flux rates, the partitioning between different pathways remained largely stable with transcriptional control focusing primarily on the acetyl-CoA branch point. Flux distribution control was achieved by nine transcription factors on glucose, including ArcA, Fur, PdhR, IHF A and IHF B, but was exclusively mediated by the cAMP-dependent Crp regulation of the PEP-glyoxylate cycle flux on galactose. Five further transcription factors affected this flux only indirectly through cAMP and Crp by increasing the galactose uptake rate. Thus, E. coli actively limits its galactose catabolism at the expense of otherwise possible faster growth.
| S-EPMC3094070 | biostudies-literature
Project description:The biophysical properties of membrane phospholipids are controlled by the composition of their constituent fatty acids and are tightly regulated in Escherichia coli. The FabR (fatty acid biosynthesis repressor) transcriptional repressor controls the proportion of unsaturated fatty acids in the membrane by regulating the expression of the fabB (beta-ketoacyl-ACP synthase I) and fabA (beta-hydroxydecanoyl-ACP dehydratase/isomerase) genes. FabR binding to a DNA palindrome located within the promoters of the fabB and fabA genes required the presence of an unsaturated acyl-acyl carrier protein (ACP) or acyl-CoA and was antagonized by saturated acyl-ACP or acyl-CoA. The FabR-dependent repression of fabB and fabA by exogenous unsaturated fatty acids confirmed the role for FabR in responding to the acyl-CoA pool composition, and the perturbation of the unsaturated:saturated acyl-ACP ratio using a specific inhibitor of lipid A formation verified FabR-dependent regulation of fabB by the acyl-ACP composition in vivo. Thus, FabR plays a key role in controlling the membrane biophysical properties by regulating gene expression in response to the composition of the long-chain acyl-thioester pool. This mechanism ensures that a balanced composition of fatty acids is available for incorporation into the membrane via the PlsB/PlsC acyltransferases.
| S-EPMC2787350 | biostudies-literature
Project description:Human gene expression is regulated by more than 2,000 transcription factors and chromatin regulators1,2. Effector domains within these proteins can activate or repress transcription. However, for many of these regulators we do not know what type of effector domains they contain, their location in the protein, their activation and repression strengths, and the sequences that are necessary for their functions. Here, we systematically measure the effector activity of more than 100,000 protein fragments tiling across most chromatin regulators and transcription factors in human cells (2,047 proteins). By testing the effect they have when recruited at reporter genes, we annotate 374 activation domains and 715 repression domains, roughly 80% of which are new and have not been previously annotated3-5. Rational mutagenesis and deletion scans across all the effector domains reveal aromatic and/or leucine residues interspersed with acidic, proline, serine and/or glutamine residues are necessary for activation domain activity. Furthermore, most repression domain sequences contain sites for small ubiquitin-like modifier (SUMO)ylation, short interaction motifs for recruiting corepressors or are structured binding domains for recruiting other repressive proteins. We discover bifunctional domains that can both activate and repress, some of which dynamically split a cell population into high- and low-expression subpopulations. Our systematic annotation and characterization of effector domains provide a rich resource for understanding the function of human transcription factors and chromatin regulators, engineering compact tools for controlling gene expression and refining predictive models of effector domain function.
| S-EPMC10484233 | biostudies-literature
Project description:Large-scale bacterial genome sequencing efforts to date have provided limited information on the most prevalent category of disease: sporadically acquired infections caused by common pathogenic bacteria. Here, we performed whole-genome sequencing and de novo assembly of 312 blood- or urine-derived isolates of extraintestinal pathogenic (ExPEC) Escherichia coli, a common agent of sepsis and community-acquired urinary tract infections, obtained during the course of routine clinical care at a single institution. We find that ExPEC E. coli are highly genomically heterogeneous, consistent with pan-genome analyses encompassing the larger species. Investigation of differential virulence factor content and antibiotic resistance phenotypes reveals markedly different profiles among lineages and among strains infecting different body sites. We use high-resolution molecular epidemiology to explore the dynamics of infections at the level of individual patients, including identification of possible person-to-person transmission. Notably, a limited number of discrete lineages caused the majority of bloodstream infections, including one subclone (ST131-H30) responsible for 28% of bacteremic E. coli infections over a 3-yr period. We additionally use a microbial genome-wide-association study (GWAS) approach to identify individual genes responsible for antibiotic resistance, successfully recovering known genes but notably not identifying any novel factors. We anticipate that in the near future, whole-genome sequencing of microorganisms associated with clinical disease will become routine. Our study reveals what kind of information can be obtained from sequencing clinical isolates on a large scale, even well-characterized organisms such as E. coli, and provides insight into how this information might be utilized in a healthcare setting.
| S-EPMC4317167 | biostudies-literature
Project description:Enterotoxigenic Escherichia coli (ETEC) is an important pathogenic variant (pathovar) of E. coli in developing countries from a human health perspective, causing significant morbidity and mortality. Previous studies have examined specific regulatory networks in ETEC, although little is known about the global effects of inter- and intrakingdom signaling on the expression of virulence and colonization factors in ETEC. In this study, an E. coli/Shigella pan-genome microarray, combined with quantitative reverse transcriptase PCR (qRT-PCR) and RNA sequencing (RNA-seq), was used to quantify the expression of ETEC virulence and colonization factors. Biologically relevant chemical signals were combined with ETEC isolate E24377A during growth in either Luria broth (LB) or Dulbecco's modified Eagle medium (DMEM), and transcription was examined during different phases of the growth cycle; chemical signals examined included glucose, bile salts, and preconditioned media from E. coli/Shigella isolates. The results demonstrate that the presence of bile salts, which are found in the intestine and thought to be bactericidal, upregulates the expression of many ETEC virulence factors, including heat-stable (estA) and heat-labile (eltA) enterotoxin genes. In contrast, the ETEC colonization factors CS1 and CS3 were downregulated in the presence of bile, consistent with findings in studies of other enteric pathogens. RNA-seq analysis demonstrated that one of the most differentially expressed genes in the presence of bile is a unique plasmid-encoded AraC-like transcriptional regulator (peaR); other previously unknown genetic elements were found as well. These results provide transcriptional targets and putative mechanisms that should help improve understanding of the global regulatory networks and virulence expression in this important human pathogen.
| S-EPMC3294641 | biostudies-literature
Project description:A transcriptional regulatory network (TRN) constitutes the collection of regulatory rules that link environmental cues to the transcription state of a cell's genome. We recently proposed a matrix formalism that quantitatively represents a system of such rules (a transcriptional regulatory system [TRS]) and allows systemic characterization of TRS properties. The matrix formalism not only allows the computation of the transcription state of the genome but also the fundamental characterization of the input-output mapping that it represents. Furthermore, a key advantage of this "pseudo-stoichiometric" matrix formalism is its ability to easily integrate with existing stoichiometric matrix representations of signaling and metabolic networks. Here we demonstrate for the first time how this matrix formalism is extendable to large-scale systems by applying it to the genome-scale Escherichia coli TRS. We analyze the fundamental subspaces of the regulatory network matrix (R) to describe intrinsic properties of the TRS. We further use Monte Carlo sampling to evaluate the E. coli transcription state across a subset of all possible environments, comparing our results to published gene expression data as validation. Finally, we present novel in silico findings for the E. coli TRS, including (1) a gene expression correlation matrix delineating functional motifs; (2) sets of gene ontologies for which regulatory rules governing gene transcription are poorly understood and which may direct further experimental characterization; and (3) the appearance of a distributed TRN structure, which is in stark contrast to the more hierarchical organization of metabolic networks.
| S-EPMC2685017 | biostudies-literature
Project description:BackgroundThe positioning of genes in the genome is an important evolutionary degree of freedom for organizing gene regulation. Statistical properties of these distributions have been studied particularly in relation to the transcriptional regulatory network. The systematics of gene-gene distances then become important sources of information on the control, which different biological mechanisms exert on gene expression.ResultsHere we study a set of categories, which has to our knowledge not been analyzed before. We distinguish between genes that do not participate in the transcriptional regulatory network (i.e. that are according to current knowledge not producing transcription factors and do not possess binding sites for transcription factors in their regulatory region), and genes that via transcription factors either are regulated by or regulate other genes. We find that the two types of genes ("isolated" and "regulatory" genes) show a clear statistical repulsion and have different ranges of correlations. In particular we find that isolated genes have a preference for shorter intergenic distances.ConclusionsThese findings support previous evidence from gene expression patterns for two distinct logical types of control, namely digital control (i.e. network-based control mediated by dedicated transcription factors) and analog control (i.e. control based on genome structure and mediated by neighborhood on the genome).
| S-EPMC2804738 | biostudies-literature