Searching the Resource Information Network
Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
Timely and reliable distinction of sepsis from non-infectious systemic inflammatory response syndrome (SIRS) supports adequate antimicrobial therapy and saves lives but is clinically challenging. Blood transcriptional profiling promises to deliver insights into the pathomechanisms of SIRS and sepsis and to accelerate the discovery of urgently sought sepsis biomarkers. However, suitable reference genes for normalizing gene expression in these disease conditions are lacking. In addition, variability in blood leukocyte subtype composition complicates gene profile interpretation. Here, we aimed to identify potential reference genes in natural killer (NK) cells and granulocytes from patients with SIRS and sepsis on intensive care unit (ICU) admission. Discovery by a two-step probabilistic selection from microarray data followed by validation through branched DNA assays in independent patients revealed several candidate reference genes in NK cells including AKIRIN1, PPP6R3, TAX1BP1, and ADRBK1. Initially, no candidate genes could be validated in patient granulocytes. However, we determined highly similar AKIRIN1 expression also in SIRS and sepsis granulocytes and no change by in vitro LPS challenge in granulocytes from healthy donors. Inspection of external neutrophil transcriptome datasets further support unchanged AKIRIN1 expression in human systemic inflammation. As a potential new reference gene in NK cells and granulocytes in infectious and inflammatory diseases, AKIRIN1 may improve our pathomechanistic understanding of SIRS and sepsis and help identifying new sepsis biomarkers.
Pubmed ID: 31075840
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
Software package that generates microarray quality metrics reports for data in Bioconductor microarray data containers (ExpressionSet, NChannelSet, AffyBatch). Reports contain both general and platform-specific sections. Both one and two color array platforms are supported.
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
View all literature mentionsSoftware for single-cell flow cytometry analysis. Its functions include management, display, manipulation, analysis and publication of the data stream produced by flow and mass cytometers.
View all literature mentionsAn Organization portal, Antibody supplier, Service resource,
View all literature mentions