An integrated transcriptome and expressed variant analysis of sepsis survival and death

BackgroundSepsis, a leading cause of morbidity and mortality, is not a homogeneous disease but rather a syndrome encompassing many heterogeneous pathophysiologies. Patient factors including genetics predispose to poor outcomes, though current clinical characterizations fail to identify those at greatest risk of progression and mortality.MethodsThe Community Acquired Pneumonia and Sepsis Outcome Diagnostic study enrolled 1,152 subjects with suspected sepsis. We sequenced peripheral blood RNA of 129 representative subjects with systemic inflammatory response syndrome (SIRS) or sepsis (SIRS due to infection), including 78 sepsis survivors and 28 sepsis non-survivors who had previously undergone plasma proteomic and metabolomic profiling. Gene expression differences were identified between sepsis survivors, sepsis non-survivors, and SIRS followed by gene enrichment pathway analysis. Expressed sequence variants were identified followed by testing for association with sepsis outcomes.ResultsThe expression of 338 genes differed between subjects with SIRS and those with sepsis, primarily reflecting immune activation in sepsis. Expression of 1,238 genes differed with sepsis outcome: non-survivors had lower expression of many immune function-related genes. Functional genetic variants associated with sepsis mortality were sought based on a common disease-rare variant hypothesis. VPS9D1, whose expression was increased in sepsis survivors, had a higher burden of missense variants in sepsis survivors. The presence of variants was associated with altered expression of 3,799 genes, primarily reflecting Golgi and endosome biology.ConclusionsThe activation of immune response-related genes seen in sepsis survivors was muted in sepsis non-survivors. The association of sepsis survival with a robust immune response and the presence of missense variants in VPS9D1 warrants replication and further functional studies.Trial registrationClinicalTrials.gov NCT00258869. Registered on 23 November 2005.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-014-0111-5) contains supplementary material, which is available to authorized users

Deciphering the Developmental Dynamics of the Mouse Liver Transcriptome.

During development, liver undergoes a rapid transition from a hematopoietic organ to a major organ for drug metabolism and nutrient homeostasis. However, little is known on a transcriptome level of the genes and RNA-splicing variants that are differentially regulated with age, and which up-stream regulators orchestrate age-specific biological functions in liver. We used RNA-Seq to interrogate the developmental dynamics of the liver transcriptome in mice at 12 ages from late embryonic stage (2-days before birth) to maturity (60-days after birth). Among 21,889 unique NCBI RefSeq-annotated genes, 9,641 were significantly expressed in at least one age, 7,289 were differently regulated with age, and 859 had multiple (> = 2) RNA splicing-variants. Factor analysis showed that the dynamics of hepatic genes fall into six distinct groups based on their temporal expression. The average expression of cytokines, ion channels, kinases, phosphatases, transcription regulators and translation regulators decreased with age, whereas the average expression of peptidases, enzymes and transmembrane receptors increased with age. The average expression of growth factors peak between Day-3 and Day-10, and decrease thereafter. We identified critical biological functions, upstream regulators, and putative transcription modules that seem to govern age-specific gene expression. We also observed differential ontogenic expression of known splicing variants of certain genes, and 1,455 novel splicing isoform candidates. In conclusion, the hepatic ontogeny of the transcriptome ontogeny has unveiled critical networks and up-stream regulators that orchestrate age-specific biological functions in liver, and suggest that age contributes to the complexity of the alternative splicing landscape of the hepatic transcriptome

Functional analysis of gene clusters.

<p>The activation or suppression status of different biological functions associated with genes in (A) Perinatal and Neonatal, (B) Prenatal and Adult, (C) Neonatal, (D) Neonatal and Adolescent, (E) Adolescent and Adult, and (F) Adult groups respectively. Red represents activation and blue represents repression of a biological function. The individual biological functions represented in the labeled sub-clusters are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141220#pone.0141220.s013" target="_blank">S7 to</a><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141220#pone.0141220.s017" target="_blank">S11</a><b>Tables</b>.</p

Upstream regulator analysis.

<p>(A) Heatmap representing the number of genes targeted by each of the upstream regulators in the six temporal groups. Upstream regulators (rows) are ordered according to the hierarchical clustering (distance measure: correlation, linkage function: average) of the hit count matrix of the number of target genes. The red intensity is proportional to the number of targets. (B) Heatmap showing the temporal expression patterns of the hierarchically clustered upstream regulators (distance measure: Euclidean, linkage function: Ward). (C) Bar graph showing the hypergeometric <i>p-value</i> of the significance of association of upstream regulators in each sub-cluster in B with genes in each of the six temporal groups.</p