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  • 1
    Online Resource
    Online Resource
    Pan-Genome Analysis of Transcriptional Regulation in Six Salmonella enterica Serovar Typhimurium Strains Reveals Their Different Regulatory Structures
    American Society for Microbiology ; 2022
    In:  mSystems Vol. 7, No. 6 ( 2022-12-20)
    Details
    In: mSystems, American Society for Microbiology, Vol. 7, No. 6 ( 2022-12-20)
    Abstract: Establishing transcriptional regulatory networks (TRNs) in bacteria has been limited to well-characterized model strains. Using machine learning methods, we established the transcriptional regulatory networks of six Salmonella enterica serovar Typhimurium strains from their transcriptomes. By decomposing a compendia of RNA sequencing (RNA-seq) data with independent component analysis, we obtained 400 independently modulated sets of genes, called iModulons. We (i) performed pan-genome analysis of the phylogroup structure of S . Typhimurium and analyzed the iModulons against this background, (ii) revealed different genetic signatures in pathogenicity islands that explained phenotypes, (iii) discovered three transport iModulons linked to antibiotic resistance, (iv) described concerted responses to cationic antimicrobial peptides, and (v) uncovered new regulons. Thus, by combining pan-genome and transcriptomic analytics, we revealed variations in TRNs across six strains of serovar Typhimurium. IMPORTANCE Salmonella enterica serovar Typhimurium is a pathogen involved in human nontyphoidal infections. Treating S . Typhimurium infections is difficult due to the species’s dynamic adaptation to its environment, which is dictated by a complex transcriptional regulatory network (TRN) that is different across strains. In this study, we describe the use of independent component analysis to characterize the differential TRNs across the S . Typhimurium pan-genome using a compendium of high-quality RNA-seq data. This approach provided unprecedented insights into the differences between regulation of key cellular functions and pathogenicity in the different strains. The study provides an impetus to initiate a large-scale effort to reveal the TRN differences between the major phylogroups of the pathogenic bacteria, which could fundamentally impact personalizing treatments of bacterial pathogens.
    Type of Medium: Online Resource
    ISSN: 2379-5077
    URL: Article
    DOI: 10.1128/msystems.00467-22
    Language: English
    Publisher: American Society for Microbiology
    Publication Date: 2022
    detail.hit.zdb_id: 2844333-0
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  • 2
    Online Resource
    Online Resource
    Data_Sheet_7.PDF
    Frontiers Media SA ; 2021
    In:  Frontiers in Microbiology Vol. 12 ( 2021-10-27)
    Details
    In: Frontiers in Microbiology, Frontiers Media SA, Vol. 12 ( 2021-10-27)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1664-302X
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fmicb.2021.753521
    DOI: 10.3389/fmicb.2021.753521.s001
    DOI: 10.3389/fmicb.2021.753521.s002
    DOI: 10.3389/fmicb.2021.753521.s003
    DOI: 10.3389/fmicb.2021.753521.s004
    DOI: 10.3389/fmicb.2021.753521.s005
    DOI: 10.3389/fmicb.2021.753521.s006
    DOI: 10.3389/fmicb.2021.753521.s007
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2587354-4
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  • 3
    Online Resource
    Online Resource
    proChIPdb: a chromatin immunoprecipitation database for prokaryotic organisms
    Oxford University Press (OUP) ; 2022
    In:  Nucleic Acids Research Vol. 50, No. D1 ( 2022-01-07), p. D1077-D1084
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 50, No. D1 ( 2022-01-07), p. D1077-D1084
    Abstract: The transcriptional regulatory network in prokaryotes controls global gene expression mostly through transcription factors (TFs), which are DNA-binding proteins. Chromatin immunoprecipitation (ChIP) with DNA sequencing methods can identify TF binding sites across the genome, providing a bottom-up, mechanistic understanding of how gene expression is regulated. ChIP provides indispensable evidence toward the goal of acquiring a comprehensive understanding of cellular adaptation and regulation, including condition-specificity. ChIP-derived data's importance and labor-intensiveness motivate its broad dissemination and reuse, which is currently an unmet need in the prokaryotic domain. To fill this gap, we present proChIPdb (prochipdb.org), an information-rich, interactive web database. This website collects public ChIP-seq/-exo data across several prokaryotes and presents them in dashboards that include curated binding sites, nucleotide-resolution genome viewers, and summary plots such as motif enrichment sequence logos. Users can search for TFs of interest or their target genes, download all data, dashboards, and visuals, and follow external links to understand regulons through biological databases and the literature. This initial release of proChIPdb covers diverse organisms, including most major TFs of Escherichia coli, and can be expanded to support regulon discovery across the prokaryotic domain.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkab1043
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 4
    Online Resource
    Online Resource
    Machine Learning of All Mycobacterium tuberculosis H37Rv RNA-seq Data Reveals a Structured Interplay between Metabolism, Stress Response, and Infection
    American Society for Microbiology ; 2022
    In:  mSphere Vol. 7, No. 2 ( 2022-04-27)
    Details
    In: mSphere, American Society for Microbiology, Vol. 7, No. 2 ( 2022-04-27)
    Abstract: 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, and some studies have performed system-level analysis, few have been able to provide a network-based model of the TRN that also provides the relative shifts in transcriptional regulator activity triggered by changing environments. Here, we compiled a compendium of nearly 650 publicly available, high quality M. tuberculosis RNA-sequencing data sets and applied an unsupervised machine learning method to obtain a quantitative, top-down TRN. It consists of 80 independently modulated gene sets known as “iModulons,” 41 of which correspond to known regulons. These iModulons explain 61% of the variance in the organism’s transcriptional response. We show that iModulons (i) reveal the function of poorly characterized regulons, (ii) describe the transcriptional shifts that occur during environmental changes such as shifting carbon sources, oxidative stress, and infection events, and (iii) identify intrinsic clusters of 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. IMPORTANCE Mycobacterium tuberculosis H37Rv is one of the world's most impactful pathogens, and a large part of the success of the organism relies on the differential expression of its genes to adapt to its environment. The expression of the organism's genes is driven primarily by its transcriptional regulatory network, and most research on the TRN focuses on identifying and quantifying clusters of coregulated genes known as regulons. While previous studies have relied on molecular measurements, in the manuscript we utilized an alternative technique that performs machine learning to a large data set of transcriptomic data. This approach is less reliant on hypotheses about the role of specific regulatory systems and allows for the discovery of new biological findings for already collected data. A better understanding of the structure of the M. tuberculosis TRN will have important implications in the design of improved therapeutic approaches.
    Type of Medium: Online Resource
    ISSN: 2379-5042
    URL: Article
    DOI: 10.1128/msphere.00033-22
    Language: English
    Publisher: American Society for Microbiology
    Publication Date: 2022
    detail.hit.zdb_id: 2844248-9
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