Explore the vast range of titles available.

Abdominal surgeries are lifesaving procedures but can be complicated by the formation of peritoneal adhesions, intra-abdominal scars that cause intestinal obstruction, pain, infertility, and significant health costs. Despite this burden, the mechanisms underlying adhesion formation remain unclear and no cure exists. Here, we show that contamination of gut microbes increases post-surgical adhesion formation. Using genetic lineage tracing we show that adhesion myofibroblasts arise from the mesothelium. This transformation is driven by epidermal growth factor receptor (EGFR) signaling. The EGFR ligands amphiregulin and heparin-binding epidermal growth factor, are sufficient to induce these changes. Correspondingly, EGFR inhibition leads to a significant reduction of adhesion formation in mice. Adhesions isolated from human patients are enriched in EGFR positive cells of mesothelial origin and human mesothelium shows an increase of mesothelial EGFR expression during bacterial peritonitis. In conclusion, bacterial contamination drives adhesion formation through mesothelial EGFR signaling. This mechanism may represent a therapeutic target for the prevention of adhesions after intra-abdominal surgery. Abdominal surgery can often lead to complications including the formation of peritoneal adhesions and the molecular mechanisms underlying this process are still unknown. Here, the authors suggest that bacterial contamination drives adhesion formation through mesothelial EGFR signalling.

Bacterial spillage into a sterile environment following intestinal hollow-organ perforation leads to peritonitis and fulminant sepsis. Outcome of sepsis critically depends on macrophage activation by extracellular ATP-release and associated autocrine signalling via purinergic receptors. ATP-release mechanisms, however, are poorly understood. Here, we show that TLR-2 and −4 agonists trigger ATP-release via Connexin-43 hemichannels in macrophages leading to poor sepsis survival. In humans, Connexin-43 was upregulated on macrophages isolated from the peritoneal cavity in patients with peritonitis but not in healthy controls. Using a murine peritonitis/sepsis model, we identified increased Connexin-43 expression in peritoneal and hepatic macrophages. Conditional Lyz2cre/creGja1flox/flox mice were developed to specifically assess Connexin-43 impact in macrophages. Both macrophage-specific Connexin-43 deletion and pharmacological Connexin-43 blockade were associated with reduced cytokine secretion by macrophages in response to LPS and CLP, ultimately resulting in increased survival. In conclusion, inhibition of autocrine Connexin-43-dependent ATP signalling on macrophages improves sepsis outcome.

Sepsis causes millions of deaths per year worldwide and is a current global health priority declared by the WHO. Sepsis-related deaths are a result of dysregulated inflammatory immune responses indicating the need to develop strategies to target inflammation. An important mediator of inflammation is extracellular adenosine triphosphate (ATP) that is released by inflamed host cells and tissues, and also by bacteria in a strain-specific and growth-dependent manner. Here, we investigated the mechanisms by which bacteria release ATP. Using genetic mutant strains of Escherichia coli ( E. coli ), we demonstrate that ATP release is dependent on ATP synthase within the inner bacterial membrane. In addition, impaired integrity of the outer bacterial membrane notably contributes to ATP release and is associated with bacterial death. In a mouse model of abdominal sepsis, local effects of bacterial ATP were analyzed using a transformed E. coli bearing an arabinose-inducible periplasmic apyrase hydrolyzing ATP to be released. Abrogating bacterial ATP release shows that bacterial ATP suppresses local immune responses, resulting in reduced neutrophil counts and impaired survival. In addition, bacterial ATP has systemic effects via its transport in outer membrane vesicles (OMV). ATP-loaded OMV are quickly distributed throughout the body and upregulated expression of genes activating degranulation in neutrophils, potentially contributing to the exacerbation of sepsis severity. This study reveals mechanisms of bacterial ATP release and its local and systemic roles in sepsis pathogenesis. Sepsis is a severe condition often caused by the body’s immune system overreacting to bacterial infections. This can lead to excessive inflammation which damages organs and requires urgent medical care. With sepsis claiming millions of lives each year, new and improved ways to treat this condition are urgently needed. One potential strategy for treating sepsis is to target the underlying mechanisms controlling inflammation. Inflamed and dying cells release molecules called ATP (the energy carrier of all living cells), which strongly influence the immune system, including during sepsis. In the early stages of an infection, ATP acts as a danger signal warning the body that something is wrong. However, over time, it can worsen infections by disturbing the immune response. Similar to human cells, bacteria release their own ATP, which can have different impacts depending on the type of bacteria and where they are located in the body. However, it is not well understood how bacterial ATP influences severe infections like sepsis. To investigate this question, Spari et al analysed how ATP is released from Escherichia coli , a type of bacteria that causes severe infections . This revealed that the bacteria secrete ATP directly in to their environment and via small membrane-bound structures called vesicles. Spari et al. then probed a mouse model of abdominal sepsis which had been infected with E. coli that release either normal or low levels of ATP. They found that the ATP released from E. coli impaired the mice’s survival and lowered the number of neutrophils (immune cells which are important for defending against bacteria) at the site of the infection. The ATP secreted via vesicles also altered the role of neutrophils but in more distant regions, and it is possible that these changes may be contributing to the severity of sepsis. These findings provide a better understanding of how ATP released from bacteria impacts the immune system during sepsis. While further investigation is needed, these findings may offer new therapeutic targets for treating sepsis.

Fibrosis is characterized by the excessive production of collagen and other extracellular matrix (ECM) components and represents a leading cause of morbidity and mortality worldwide. Previous studies of nonalcoholic steatohepatitis (NASH) with fibrosis were largely restricted to bulk transcriptome profiles. Thus, our understanding of this disease is limited by an incomplete characterization of liver cell types in general and hepatic stellate cells (HSCs) in particular, given that activated HSCs are the major hepatic fibrogenic cell population. To help fill this gap, we profiled 17,810 non-parenchymal cells derived from six healthy human livers. In conjunction with public single-cell data of fibrotic/cirrhotic human livers, these profiles enable the identification of potential intercellular signaling axes (e.g., ITGAV–LAMC1, TNFRSF11B–VWF and NOTCH2–DLL4) and master regulators (e.g., RUNX1 and CREB3L1 ) responsible for the activation of HSCs during fibrogenesis. Bulk RNA-seq data of NASH patient livers and rodent models for liver fibrosis of diverse etiologies allowed us to evaluate the translatability of candidate therapeutic targets for NASH-related fibrosis. We identified 61 liver fibrosis-associated genes (e.g., AEBP1, PRRX1 and LARP6 ) that may serve as a repertoire of translatable drug target candidates. Consistent with the above regulon results, gene regulatory network analysis allowed the identification of CREB3L1 as a master regulator of many of the 61 genes. Together, this study highlights potential cell–cell interactions and master regulators that underlie HSC activation and reveals genes that may represent prospective hallmark signatures for liver fibrosis.

Abstract Assessing similarity is highly important for bioinformatics algorithms to determine correlations between biological information. A common problem is that similarity can appear by chance, particularly for low expressed entities. This is especially relevant in single-cell RNA-seq (scRNA-seq) data because read counts are much lower compared to bulk RNA-seq. Recently, a Bayesian correlation scheme that assigns low similarity to genes that have low confidence expression estimates has been proposed to assess similarity for bulk RNA-seq. Our goal is to extend the properties of the Bayesian correlation in scRNA-seq data by considering three ways to compute similarity. First, we compute the similarity of pairs of genes over all cells. Second, we identify specific cell populations and compute the correlation in those populations. Third, we compute the similarity of pairs of genes over all clusters, by considering the total mRNA expression. We demonstrate that Bayesian correlations are more reproducible than Pearson correlations. Compared to Pearson correlations, Bayesian correlations have a smaller dependence on the number of input cells. We show that the Bayesian correlation algorithm assigns high similarity values to genes with a biological relevance in a specific population. We conclude that Bayesian correlation is a robust similarity measure in scRNA-seq data.

Evaluating the similarity of different measured variables is a fundamental task of statistics, and a key part of many bioinformatics algorithms. Here we propose a Bayesian scheme for estimating the correlation between different entities' measurements based on high-throughput sequencing data. These entities could be different genes or miRNAs whose expression is measured by RNA-seq, different transcription factors or histone marks whose expression is measured by ChIP-seq, or even combinations of different types of entities. Our Bayesian formulation accounts for both measured signal levels and uncertainty in those levels, due to varying sequencing depth in different experiments and to varying absolute levels of individual entities, both of which affect the precision of the measurements. In comparison with a traditional Pearson correlation analysis, we show that our Bayesian correlation analysis retains high correlations when measurement confidence is high, but suppresses correlations when measurement confidence is low-especially for entities with low signal levels. In addition, we consider the influence of priors on the Bayesian correlation estimate. Perhaps surprisingly, we show that naive, uniform priors on entities' signal levels can lead to highly biased correlation estimates, particularly when different experiments have widely varying sequencing depths. However, we propose two alternative priors that provably mitigate this problem. We also prove that, like traditional Pearson correlation, our Bayesian correlation calculation constitutes a kernel in the machine learning sense, and thus can be used as a similarity measure in any kernel-based machine learning algorithm. We demonstrate our approach on two RNA-seq datasets and one miRNA-seq dataset.

The toll-like receptor 5 (TLR5) agonist, CBLB502/Entolimod, is a peptide derived from bacterial flagellin and has been shown to protect against radiation-induced tissue damage in animal models. Here we investigated the protective mechanism of CBLB502 in the liver using models of ischemia-reperfusion injury and concanavalin A (ConA) induced immuno-hepatitis. We report that pretreatment of mice with CBLB502 provoked a concomitant activation of NF-κB and STAT3 signaling in the liver and reduced hepatic damage in both models. To understand the underlying mechanism, we screened for cytokines in the serum of CBLB502 treated animals and detected high levels of IL-22. There was no transcriptional upregulation of IL-22 in the liver, rather it was found in extrahepatic tissues, mainly the colon, mesenteric lymph nodes (MLN), and spleen. RNA-seq analysis on isolated hepatocytes demonstrated that the concomitant activation of NF-κB signaling by CBLB502 and STAT3 signaling by IL-22 produced a synergistic cytoprotective transcriptional signature. In IL-22 knockout mice, the loss of IL-22 resulted in a decrease of hepatic STAT3 activation, a reduction in the cytoprotective signature, and a loss of hepatoprotection following ischemia-reperfusion-induced liver injury. Taken together, these findings suggest that CBLB502 protects the liver by increasing hepatocyte resistance to acute liver injury through the cooperation of TLR5-NF-κB and IL-22-STAT3 signaling pathways.

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.

Understanding disease transmission in the workplace is essential for protecting workers. To model disease outbreaks, the small populations in many workplaces require that stochastic effects are considered, which results in higher uncertainty. The aim of this study was to quantify and interpret the uncertainty inherent in such circumstances. We assessed how uncertainty of an outbreak in workplaces depends on i) the infection dynamics in the community, ii) the workforce size, iii) spatial structure in the workplace, iv) heterogeneity in susceptibility of workers, and v) heterogeneity in infectiousness of workers. To address these questions, we developed a multiscale model: A deterministic model to predict community transmission, and a stochastic model to predict workplace transmission. We extended this basic workplace model to allow for spatial structure, and heterogeneity in susceptibility and infectiousness in workers. We found a non-monotonic relationship between the workplace transmission rate and the coefficient of variation (CV), which we use as a measure of uncertainty. Increasing community transmission, workforce size and heterogeneity in susceptibility decreased the CV. Conversely, increasing the level of spatial structure and heterogeneity in infectiousness increased the CV. However, when the model predicts bimodal distributions, for example when community transmission is low and workplace transmission is high, the CV fails to capture this uncertainty. Overall, our work informs modellers and policy makers on how model complexity impacts outbreak uncertainty. In particular: workforce size, community and workplace transmission, spatial structure and individual heterogeneity contribute in a specific and individual manner to the predicted workplace outbreak size distribution.

HIV-1 infected patients are effectively treated with highly active anti-retroviral therapy (HAART). Whilst HAART is successful in keeping the disease at bay with average levels of viral load well below the detection threshold of standard clinical assays, it fails to completely eradicate the infection, which persists due to the emergence of a latent reservoir with a half-life time of years and is immune to HAART. This implies that life-long administration of HAART is, at the moment, necessary for HIV-1-infected patients, which is prone to drug resistance and cumulative side effects as well as imposing a considerable financial burden on developing countries, those more afflicted by HIV, and public health systems. The development of therapies which specifically aim at the removal of this latent reservoir has become a focus of much research. A proposal for such therapy consists of elevating the rate of activation of the latently infected cells: by transferring cells from the latently infected reservoir to the active infected compartment, more cells are exposed to the anti-retroviral drugs thus increasing their effectiveness. In this paper, we present a stochastic model of the dynamics of the HIV-1 infection and study the effect of the rate of latently infected cell activation on the average extinction time of the infection. By analysing the model by means of an asymptotic approximation using the semi-classical quasi steady state approximation (QSS), we ascertain that this therapy reduces the average life-time of the infection by many orders of magnitudes. We test the accuracy of our asymptotic results by means of direct simulation of the stochastic process using a hybrid multi-scale Monte Carlo scheme.