Sanchez-Taltavull, D. | Search bioRxiv

bioRxiv2018

Connexin-43 dependent ATP release mediates macrophage activation during peritonitis

Peritonitis is the consequence of bacterial spillage into a sterile environment by gastrointestinal hollow-organ perforation that may lead to fulminant sepsis. Outcome of peritonitis-induced sepsis critically depends on macrophage activation by extracellular ATP release and associated para- and autocrine signaling via purinergic receptors. Mechanisms that mediate and control ATP release, however, are poorly understood. Here we show that TLR-2 and -4 agonists trigger ATP release via Connexin-43 (CX43) hemichannels in peritoneal macrophages leading to poor survival during sepsis. In humans, CX43 expression was upregulated on macrophages isolated from the peritoneal cavity in patients with intraperitoneal infection but not in healthy controls. Using a murine caecal ligation and puncture (CLP) model, we identified increased CX43 expression in activated infiltrating peritoneal, hepatic and pulmonary macrophages. Conditional MAC-CX43 KO Lyz2cre/creCx43flox/flox mice were developed to specifically assess the CX43 impact in macrophages. Both macrophage-specific CX43 deletion (using Lyz2cre/creCx43flox/flox mice) or pharmacological CX43 blockade were associated with reduced cytokine secretion by macrophages in response to LPS and CLP. This was ultimately resulting in increased survival in Lyz2cre/creCx43flox/flox mice and after pharmacological blockade. Specific inhibition of the purinergic receptor P2RY1 abrogated CX43 elicited cytokine responses. In conclusion, inhibition of autocrine ATP signaling via CX43 on macrophages and P2RY1 improves sepsis outcome in experimental peritonitis.\n\nBrief SummaryConnexin-43-mediated ATP release from macrophages in response to TLR-4 and -2 agonists modulates autocrine activation of macrophages in a P2Y1-dependent manner, ultimately determining sepsis survival.

immunology↗

bioRxiv2017

Uncovering Robust Patterns of MicroRNA Co-Expression across Cancers using Bayesian Relevance Networks

Co-expression networks have long been used as a tool for investigating the molecular circuitry governing biological systems. However, most algorithms for constructing co-expression networks were developed in the microarray era, before high-throughput sequencing--with its unique statistical properties--became the norm for expression measurement. Here we develop Bayesian Relevance Networks, an algorithm that uses Bayesian reasoning about expression levels to account for the differing levels of uncertainty in expression measurements between highly- and lowly-expressed entities, and between samples with different sequencing depths. It combines data from groups of samples (e.g., replicates) to estimate group expression levels and confidence ranges. It then computes uncertainty-moderated estimates of cross-group correlations between entities, and uses permutation testing to assess their statistical significance. Using large scale miRNA data from The Cancer Genome Atlas, we show that our Bayesian update of the classical Relevance Networks algorithm provides improved reproducibility in co-expression estimates and lower false discovery rates in the resulting co-expression networks. Software is available at www.perkinslab.ca/Software.html.

bioinformatics↗