Role of Tumor Necrosis Factor-α in the Human Systemic Endotoxin-Induced Transcriptome

<div><p>TNFα has been implicated in the pathogenesis of various inflammatory diseases. Different strategies to inhibit TNFα in patients with sepsis and chronic inflammatory conditions have shown contrasting outcomes. Although TNFα inhibitors are widely used in clinical practice, the impact of TNFα antagonism on white blood cell gene expression profiles during acute inflammation in humans <i>in vivo</i> has not been assessed. We here leveraged the established model of human endotoxemia to examine the effect of the TNFα antagonist, etanercept, on the genome-wide transcriptional responses in circulating leukocytes induced by intravenous LPS administration in male subjects. Etanercept pre-treatment resulted in a markedly dampened transcriptional response to LPS. Gene co-expression network analysis revealed this LPS-induced transcriptome can be categorized as TNFα responsive and non-responsive modules. Highly significant TNFα responsive modules include NF-kB signaling, antiviral responses and T-cell mediated responses. Within these TNFα responsive modules we delineate fundamental genes involved in epigenetic modifications, transcriptional initiation and elongation. Thus, we provide comprehensive information about molecular pathways that might be targeted by therapeutic interventions that seek to inhibit TNFα activity during human inflammatory diseases.</p></div

Genomic analysis of the systemic LPS-induced transcriptional response and impact of TNFα inhibition.

<p><b>A</b>. Volcano plot analysis (integrating p-values and log2 foldchanges) for the LPS-induced response in subjects treated with placebo. <b>B</b>. Volcano plot analysis of the LPS-induced response in subjects treated with the TNFα antagonist etanercept. Red dots in panels A and B indicate probes that showed a fold-change ≥1.5 or ≤1.5. <b>C</b>. Unsupervised hierarchical clustering heatmap of the 4077 LPS-induced transcripts that were influenced by etanercept treatment as identified by ANOVA (q-value <0.05). Columns represent subject samples and rows represent transcripts. Red indicates increased gene expression, and blue indicates decreased gene expression.</p

TNFα responsive module hub (driver) genes and co-expression network visualization.

<p>Genes within transcriptional modules can be categorized as peripheral or hubs on the basis of how correlated a gene is with all other genes in the network, defined as the genes' connectivity measure, <b>k</b>. High intramodulr connectivities denote highly important module genes oftentimes possessing transcriptional factor activity. <b>A</b>. Unsupervised hierarchical clustering heatmap plot of the TNFα responsive module hub genes. Red denotes high expression; blue denotes low expression. The relative importance of each module within the co-expression network can be highlighted by unsupervised visualizations of each genes' weighted correlation coefficient. This was implemented in the Cytoscape® platform <b>B</b>. TNFα responsive co-expression modules were visualized by an organic layout considering weighted correlation coefficients >0.1 (equivalent to correlation coefficient >0.9).</p

Functional annotation and hub genes for the LPS-induced co-expression modules.

<p>LPS-induced transcriptome is organized into 38 co-expression network modules. Each module was analyzed for enrichment of biological pathways by IPA (Ingenuity® systems, <a href="http://www.ingenuity.com" target="_blank">www.ingenuity.com</a>).</p

LPS-induced TNFα responsive module genes linked to transcriptional initiation, elongation and epigenetic regulation.

<p>Genes within LPS-induced TNFα responsive co-expression modules possessing epigenetic regulation, transcriptional initiation and elongation properties. kTotal, total connectivity, k. kWithin, intra-modular connectivity. kOut, extra-modular connectivity. <i>log2</i> FC LPS, log2 transformed foldchange for the placebo-treated pre- and post-LPS challenged samples. <i>log2</i> FC LPS+Etan, log2 transformed foldchange for the etanercept-treated pre- and post-LPS challenged samples. Gene names marked in bold type denote module genes identified as top module hub genes.</p

A Transcriptomic Biomarker to Quantify Systemic Inflammation in Sepsis - A Prospective Multicenter Phase II Diagnostic Study.

International audienceDevelopment of a dysregulated immune response discriminates sepsis from uncomplicated infection. Currently used biomarkers fail to describe simultaneously occurring pro- and anti-inflammatory responses potentially amenable to therapy. Marker candidates were screened by microarray and, after transfer to a platform allowing point-of-care testing, validated in a confirmation set of 246 medical and surgical patients. We identified up-regulated pathways reflecting innate effector mechanisms, while down-regulated pathways related to adaptive lymphocyte functions. A panel of markers composed of three up- (Toll-like receptor 5; Protectin; Clusterin) and 4 down-regulated transcripts (Fibrinogen-like 2; Interleukin-7 receptor; Major histocompatibility complex class II, DP alpha1; Carboxypeptidase, vitellogenic-like) described the magnitude of immune alterations. The created gene expression score was significantly greater in patients with definite as well as with possible/probable infection than with no infection (median (Q25/Q75): 80 (60/101)) and 81 (58/97 vs. 49 (27/66), AUC-ROC=0.812 (95%-CI 0.755-0.869), p<0.0001). Down-regulated lymphocyte markers were associated with prognosis with good sensitivity but limited specificity. Quantifying systemic inflammation by assessment of both pro- and anti-inflammatory innate and adaptive immune responses provides a novel option to identify patients-at-risk and may facilitate immune interventions in sepsis