Dataset Information

Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters.

ABSTRACT: OBJECTIVES:To find and validate generalizable sepsis subtypes using data-driven clustering. DESIGN:We used advanced informatics techniques to pool data from 14 bacterial sepsis transcriptomic datasets from eight different countries (n = 700). SETTING:Retrospective analysis. SUBJECTS:Persons admitted to the hospital with bacterial sepsis. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:A unified clustering analysis across 14 discovery datasets revealed three subtypes, which, based on functional analysis, we termed "Inflammopathic, Adaptive, and Coagulopathic." We then validated these subtypes in nine independent datasets from five different countries (n = 600). In both discovery and validation data, the Adaptive subtype is associated with a lower clinical severity and lower mortality rate, and the Coagulopathic subtype is associated with higher mortality and clinical coagulopathy. Further, these clusters are statistically associated with clusters derived by others in independent single sepsis cohorts. CONCLUSIONS:The three sepsis subtypes may represent a unifying framework for understanding the molecular heterogeneity of the sepsis syndrome. Further study could potentially enable a precision medicine approach of matching novel immunomodulatory therapies with septic patients most likely to benefit.

SUBMITTER: Sweeney TE

PROVIDER: S-EPMC5953807 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters.

Sweeney Timothy E TE Azad Tej D TD Donato Michele M Haynes Winston A WA Perumal Thanneer M TM Henao Ricardo R Bermejo-Martin Jesús F JF Almansa Raquel R Tamayo Eduardo E Howrylak Judith A JA Choi Augustine A Parnell Grant P GP Tang Benjamin B Nichols Marshall M Woods Christopher W CW Ginsburg Geoffrey S GS Kingsmore Stephen F SF Omberg Larsson L Mangravite Lara M LM Wong Hector R HR Tsalik Ephraim L EL Langley Raymond J RJ Khatri Purvesh P

Critical care medicine 20180601 6

<h4>Objectives</h4>To find and validate generalizable sepsis subtypes using data-driven clustering.<h4>Design</h4>We used advanced informatics techniques to pool data from 14 bacterial sepsis transcriptomic datasets from eight different countries (n = 700).<h4>Setting</h4>Retrospective analysis.<h4>Subjects</h4>Persons admitted to the hospital with bacterial sepsis.<h4>Interventions</h4>None.<h4>Measurements and main results</h4>A unified clustering analysis across 14 discovery datasets revealed ...[more]

PMID: 29537985

Similar Datasets

Project description:Recent reviews have reinforced sponge-associated bacteria as a valuable source of structurally diverse secondary metabolites with potent biological properties, which makes these microbial communities promising sources of new drug candidates. However, the overall diversity of secondary metabolite biosynthetic potential present in bacteria is difficult to access due to the fact that the majority of bacteria are not readily cultured in the laboratory. Thus, use of cultivation-independent approaches may allow accessing "silent" and "cryptic" secondary metabolite biosynthetic gene clusters present in bacteria that cannot yet be cultured. In the present study, we investigated the diversity of secondary metabolite biosynthetic gene clusters (BGCs) in metagenomes of bacterial communities associated with three sponge species: Clathria reinwardti, Rhabdastrella globostellata, and Spheciospongia sp. The results reveal that the three metagenomes contain a high number of predicted BGCs, ranging from 282 to 463 BGCs per metagenome. The types of BGCs were diverse and represented 12 different cluster types. Clusters predicted to encode fatty acid synthases and polyketide synthases (PKS) were the most dominant BGC types, followed by clusters encoding synthesis of terpenes and bacteriocins. Based on BGC sequence similarity analysis, 363 gene cluster families (GCFs) were identified. Interestingly, no GCFs were assigned to pathways responsible for the production of known compounds, implying that the clusters detected might be responsible for production of several novel compounds. The KS gene sequences from PKS clusters were used to predict the taxonomic origin of the clusters involved. The KS sequences were related to 12 bacterial phyla with Actinobacteria, Proteobacteria, and Firmicutes as the most predominant. At the genus level, the KSs were most related to those found in the genera Mycolicibacterium, Mycobacterium, Burkholderia, and Streptomyces. Phylogenetic analysis of KS sequences resulted in detection of two known 'sponge-specific' BGCs, i.e., SupA and SwfA, as well as a new 'sponge-specific' cluster related to fatty acid synthesis in the phylum Candidatus Poribacteria and composed only by KS sequences of the three sponge-associated bacterial communities assessed here.

Project description:The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants of effective drugs. Disease-causing gene-mediated proteins are usually used as receptors to explore effective drug molecules. In this study, we analyzed two different RNA-Seq and one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted gene co-expression network and robust rank aggregation approaches, which revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 and IL6 as the host genomic biomarkers. Gene Ontology and pathway enrichment analyses of HubGs significantly enriched some crucial biological processes, molecular functions, cellular components and signaling pathways that are associated with the mechanisms of SARS-CoV-2 infections. Regulatory network analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 and MYC) and 5 miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p and hsa-miR-20a-5p) as the key transcriptional and post-transcriptional regulators of HubGs. Then, we conducted a molecular docking analysis to determine potential drug candidates that could interact with HubGs-mediated receptors. This analysis resulted in the identification of top-ranked ten drug agents, including Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole and Danoprevir. Finally, we investigated the binding stability of the top-ranked three drug molecules Nilotinib, Tegobuvir and Proscillaridin with the three top-ranked proposed receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore, the findings of this study might be useful resources for diagnosis and therapies of SARS-CoV-2 infections.

Dataset Information

Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters.

Publications

Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets