Human protein kinases play fundamental roles mediating the majority of signal transduction pathways in eukaryotic cells as well as a multitude of other processes involved in metabolism, cell-cycle regulation, cellular shape, motility, differentiation and apoptosis. The human protein kinome contains 518 members. Most studies that focus on the human kinome require, at some point, the visualization of large amounts of data. The visualization of such data within the framework of a phylogenetic tree may help identify key relationships between different protein kinases in view of their evolutionary distance and the information used to annotate the kinome tree. For example, studies that focus on the promiscuity of kinase inhibitors can benefit from the annotations to depict binding affinities across kinase groups. Images involving the mapping of information into the kinome tree are common. However, producing such figures manually can be a long arduous process prone to errors. To circumvent this issue, we have developed a web-based tool called Kinome Render (KR) that produces customized annotations on the human kinome tree. KR allows the creation and automatic overlay of customizable text or shape-based annotations of different sizes and colors on the human kinome tree. The web interface can be accessed at: . A stand-alone version is also available and can be run locally.

BackgroundClostridium difficile is the leading cause of hospital-borne infections occurring when the natural intestinal flora is depleted following antibiotic treatment. Current treatments for Clostridium difficile infections present high relapse rates and new hyper-virulent and multi-resistant strains are emerging, making the study of this nosocomial pathogen necessary to find novel therapeutic targets.ResultsWe present iMLTC806cdf, an extensively curated reconstructed metabolic network for the C. difficile pathogenic strain 630. iMLTC806cdf contains 806 genes, 703 metabolites and 769 metabolic, 117 exchange and 145 transport reactions. iMLTC806cdf is the most complete and accurate metabolic reconstruction of a gram-positive anaerobic bacteria to date. We validate the model with simulated growth assays in different media and carbon sources and use it to predict essential genes. We obtain 89.2% accuracy in the prediction of gene essentiality when compared to experimental data for B. subtilis homologs (the closest organism for which such data exists). We predict the existence of 76 essential genes and 39 essential gene pairs, a number of which are unique to C. difficile and have non-existing or predicted non-essential human homologs. For 29 of these potential therapeutic targets, we find 125 inhibitors of homologous proteins including approved drugs with the potential for drug repositioning, that when validated experimentally could serve as starting points in the development of new antibiotics.ConclusionsWe created a highly curated metabolic network model of C. difficile strain 630 and used it to predict essential genes as potential new therapeutic targets in the fight against Clostridium difficile infections.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-014-0117-z) contains supplementary material, which is available to authorized users.

Background: Colorectal cancer (CRC) is one of the prevailing causes of cancer mortality in the world. A common screening test for CRC is based on the human hemoglobin immunochemical based fecal occult blood test (iFOBT), which consists in the detection of blood in the patient's stool. In addition to iFOBT, recent studies support the use of the gut microbiome as a biomarker for CRC prediction. However, these studies did not take into account the effect of blood itself on the microbiome composition, independently of CRC. Therefore, we investigated the microbiome of patients undergoing the iFOBT screening in order to determine the effect of blood alone. Our cohort consisted of patients who had no blood in their stools (n = 265) or did have blood but no underlying precancerous or cancerous lesions (n = 235). We also identified bacterial taxa specifically associated with the presence of blood in stools. Results: We observed significant differences in the intestinal bacterial composition that could be solely caused by the presence of blood in stools. More precisely, we identified 12 bacterial species showing significant differences in abundance between both our study groups. These species, Bacteroides uniformis, Collinsella aerofaciens, Eggerthella lenta and Clostridium symbiosum demonstrated increased abundance in the presence of blood. In contrast, the species Prevotella copri, Coprococcus eutactus and catus, Faecalibacterium prausnitzii, Roseburia faecis, Blautia obeum, Gemmiger formicilis and Clostridium celatum showed decreased abundance in patients with blood in their stools. Notably, we found multiple taxa that were reported in previous studies linking microbiome composition and diseases. Conclusions: We show that, in the absence of disease, blood in the stools has a major influence on the composition of the microbiome. Our data suggest that blood itself should be taken into consideration when investigating the microbiome signatures of intestinal diseases.

The gut microbiota, which consists of all bacteria, viruses, fungus, and protozoa living in the intestine, and the immune system have co-evolved in a symbiotic relationship since the origin of the immune system. The bacterial community forming the microbiota plays an important role in the regulation of multiple aspects of the immune system. This regulation depends, among other things, on the production of a variety of metabolites by the microbiota. These metabolites range from small molecules to large macro-molecules. All types of immune cells from the host interact with these metabolites resulting in the activation of different pathways, which result in either positive or negative responses. The understanding of these pathways and their modulations will help establish the microbiota as a therapeutic target in the prevention and treatment of a variety of immune-related diseases.

BackgroundType 2 diabetes is one of the leading non-infectious diseases worldwide and closely relates to excess adipose tissue accumulation as seen in obesity. Specifically, hypertrophic expansion of adipose tissues is related to increased cardiometabolic risk leading to type 2 diabetes. Studying mechanisms underlying adipocyte hypertrophy could lead to the identification of potential targets for the treatment of these conditions.ResultsWe present iTC1390adip, a highly curated metabolic network of the human adipocyte presenting various improvements over the previously published iAdipocytes1809. iTC1390adip contains 1390 genes, 4519 reactions and 3664 metabolites. We validated the network obtaining 92.6% accuracy by comparing experimental gene essentiality in various cell lines to our predictions of biomass production. Using flux balance analysis under various test conditions, we predict the effect of gene deletion on both lipid droplet and biomass production, resulting in the identification of 27 genes that could reduce adipocyte hypertrophy. We also used expression data from visceral and subcutaneous adipose tissues to compare the effect of single gene deletions between adipocytes from each compartment.ConclusionsWe generated a highly curated metabolic network of the human adipose tissue and used it to identify potential targets for adipose tissue metabolic dysfunction leading to the development of type 2 diabetes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0438-9) contains supplementary material, which is available to authorized users.