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Medical progress is reflected in the advance from broad clinical syndromes to mechanistically coherent diagnoses. By this metric, research in sepsis is far behind other areas of medicine—the word itself conflates multiple different disease mechanisms, whilst excluding noninfectious syndromes (e.g., trauma, pancreatitis) with similar pathogenesis. New technologies, both for deep phenotyping and data analysis, offer the capability to define biological states with extreme depth. Progress is limited by a fundamental problem: observed groupings of patients lacking shared causal mechanisms are very poor predictors of response to treatment. Here, we discuss concrete steps to identify groups of patients reflecting archetypes of disease with shared underlying mechanisms of pathogenesis. Recent evidence demonstrates the role of causal inference from host genetics and randomised clinical trials to inform stratification analyses. Genetic studies can directly illuminate drug targets, but in addition they create a reservoir of statistical power that can be divided many times among potential patient subgroups to test for mechanistic coherence, accelerating discovery of modifiable mechanisms for testing in trials. Novel approaches, such as subgroup identification in-flight in clinical trials, will improve efficiency. Within the next decade, we expect ongoing large-scale collaborative projects to discover and test therapeutically relevant sepsis archetypes. [ABSTRACT FROM AUTHOR]

In-silico identification of potential target genes for disease is an essential aspect of drug target discovery. Recent studies suggest that successful targets can be found through by leveraging genetic, genomic and protein interaction information. Here, we systematically tested the ability of 12 varied algorithms, based on network propagation, to identify genes that have been targeted by any drug, on gene-disease data from 22 common non-cancerous diseases in OpenTargets. We considered two biological networks, six performance metrics and compared two types of input gene-disease association scores. The impact of the design factors in performance was quantified through additive explanatory models. Standard cross-validation led to over-optimistic performance estimates due to the presence of protein complexes. In order to obtain realistic estimates, we introduced two novel protein complex-aware cross-validation schemes. When seeding biological networks with known drug targets, machine learning and diffusion-based methods found around 2-4 true targets within the top 20 suggestions. Seeding the networks with genes associated to disease by genetics decreased performance below 1 true hit on average. The use of a larger network, although noisier, improved overall performance. We conclude that diffusion-based prioritisers and machine learning applied to diffusion-based features are suited for drug discovery in practice and improve over simpler neighbour-voting methods. We also demonstrate the large impact of choosing an adequate validation strategy and the definition of seed disease genes. [ABSTRACT FROM AUTHOR]

Background and Purpose: TREK two-pore-domain potassium (K2P ) channels play a critical role in regulating the excitability of somatosensory nociceptive neurons and are important mediators of pain perception. An understanding of the roles of TREK channels in pain perception and, indeed, in other pathophysiological conditions, has been severely hampered by the lack of potent and/or selective activators and inhibitors. In this study, we describe a new, selective opener of TREK channels, GI-530159.Experimental Approach: The effect of GI-530159 on TREK channels was demonstrated using 86 Rb efflux assays, whole-cell and single-channel patch-clamp recordings from recombinant TREK channels. The expression of K2P 2.1 (TREK1), K2P 10.1 (TREK2) and K2P 4.1 (TRAAK) channels was determined using transcriptome analysis from single dorsal root ganglion (DRG) cells. Current-clamp recordings from cultured rat DRG neurons were used to measure the effect of GI-530159 on neuronal excitability.Key Results: For recombinant human TREK1 channels, GI-530159 had similar low EC50 values in Rb efflux experiments and electrophysiological recordings. It activated TREK2 channels, but it had no detectable action on TRAAK channels nor any significant effect on other K channels tested. Current-clamp recordings from cultured rat DRG neurones showed that application of GI-530159 at 1 μM resulted in a significant reduction in firing frequency and a small hyperpolarization of resting membrane potential.Conclusions and Implications: This study provides pharmacological evidence for the presence of mechanosensitive TREK K2P channels in sensory neurones and suggests that development of selective K2P channel openers like GI-530159 could aid in the development of novel analgesic agents.Linked Articles: This article is part of a themed section on Recent Advances in Targeting Ion Channels to Treat Chronic Pain. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.12/issuetoc. [ABSTRACT FROM AUTHOR]

Background: Inference of active regulatory cascades under specific molecular and environmental perturbations is a recurring task in transcriptional data analysis. Commercial tools based on large, manually curated networks of causal relationships offering such functionality have been used in thousands of articles in the biomedical literature. The adoption and extension of such methods in the academic community has been hampered by the lack of freely available, efficient algorithms and an accompanying demonstration of their applicability using current public networks. Results: In this article, we propose a new statistical method that will infer likely upstream regulators based on observed patterns of up- and down-regulated transcripts. The method is suitable for use with public interaction networks with a mix of signed and unsigned causal edges. It subsumes and extends two previously published approaches and we provide a novel algorithmic method for efficient statistical inference. Notably, we demonstrate the feasibility of using the approach to generate biological insights given current public networks in the context of controlled in-vitro overexpression experiments, stem-cell differentiation data and animal disease models. We also provide an efficient implementation of our method in the R package QuaternaryProd available to download from Bioconductor. Conclusions: In this work, we have closed an important gap in utilizing causal networks to analyze differentially expressed genes. Our proposed Quaternary test statistic incorporates all available evidence on the potential relevance of an upstream regulator. The new approach broadens the use of these types of statistics for highly curated signed networks in which ambiguities arise but also enables the use of networks with unsigned edges. We design and implement a novel computational method that can efficiently estimate p-values for upstream regulators in current biological settings. We demonstrate the ready applicability of the implemented method to analyze differentially expressed genes using the publicly available networks. [ABSTRACT FROM AUTHOR]

The article discusses the treatment of five patients with inherited erythromelalgia (IEM) with a selective compound that blocked the Nav1.7 sodium channel resulting in a decrease in heat-induced pain in most of the patients. A comparison of the severity of the clinical phenotype with the hyperexcitability of the induced pluripotent stem cell (iPSC)-derived sensory neurons is tackled. The sensitivity of the in vitro IEM phenotype to Nav1.7 blockers is also explored.

Human genetic studies show that the voltage gated sodium channel 1.7 (Nav1.7) is a key molecular determinant of pain sensation. However, defining the Nav1.7 contribution to nociceptive signalling has been hampered by a lack of selective inhibitors. Here we report two potent and selective arylsulfonamide Nav1.7 inhibitors; PF-05198007 and PF-05089771, which we have used to directly interrogate Nav1.7’s role in nociceptor physiology. We report that Nav1.7 is the predominant functional TTX-sensitive Nav in mouse and human nociceptors and contributes to the initiation and the upstroke phase of the nociceptor action potential. Moreover, we confirm a role for Nav1.7 in influencing synaptic transmission in the dorsal horn of the spinal cord as well as peripheral neuropeptide release in the skin. These findings demonstrate multiple contributions of Nav1.7 to nociceptor signalling and shed new light on the relative functional contribution of this channel to peripheral and central noxious signal transmission. [ABSTRACT FROM AUTHOR]

A variety of diseases lead to degeneration of retinal ganglion cells (RGCs) and their axons within the optic nerve resulting in loss of visual function. Although current therapies may delay RGC loss, they do not restore visual function or completely halt disease progression. Regenerative medicine has recently focused on stem cell therapy for both neuroprotective and regenerative purposes. However, significant problems remain to be addressed, such as the long-term impact of reactive gliosis occurring in the host retina in response to transplanted stem cells. The aim of this work was to investigate retinal glial responses to intravitreally transplanted bone marrow mesenchymal stem cells (BM-MSCs) to help identify factors able to modulate graft-induced reactive gliosis. We found in vivo that intravitreal BM-MSC transplantation is associated with gliosis-mediated retinal folding, upregulation of intermediate filaments, and recruitment of macrophages. These responses were accompanied by significant JAK/STAT3 and MAPK (ERK1/2 and JNK) cascade activation in retinal Muller glia. Lipocalin-2 ( Lcn-2) was identified as a potential new indicator of graft-induced reactive gliosis. Pharmacological inhibition of STAT3 in BM-MSC cocultured retinal explants successfully reduced glial fibrillary acidic protein expression in retinal Muller glia and increased BM-MSC retinal engraftment. Inhibition of stem cell-induced reactive gliosis is critical for successful transplantation-based strategies for neuroprotection, replacement, and regeneration of the optic nerve. S tem C ells 2015;33:3006-3016 [ABSTRACT FROM AUTHOR]

The integrity of the epithelium is maintained by a complex but regulated interplay of processes that allow conversion of a proliferative state into a stably differentiated state. In this study, using human embryonic stem cell (hESC) derived Retinal Pigment Epithelium (RPE) cells as a model; we have investigated the molecular mechanisms that affect attainment of the epithelial phenotype. We demonstrate that RPE undergo a Mesenchymal–Epithelial Transition in culture before acquiring an epithelial phenotype in a FOXM1 dependent manner. We show that FOXM1 directly regulates proliferation of RPE through transcriptional control of cell cycle associated genes. Additionally, FOXM1 modulates expression of the signaling ligands BMP7 and Wnt5B which act reciprocally to enable epithelialization. This data uncovers a novel effect of FOXM1 dependent activities in contributing towards epithelial fate acquisition and furthers our understanding of the molecular regulators of a cell type that is currently being evaluated as a cell therapy. [ABSTRACT FROM AUTHOR]

Motivation: The abundance of many transcripts changes significantly in response to a variety of molecular and environmental perturbations. A key question in this setting is as follows: what intermediate molecular perturbations gave rise to the observed transcriptional changes? Regulatory programs are not exclusively governed by transcriptional changes but also by protein abundance and post-translational modifications making direct causal inference from data difficult. However, biomedical research over the last decades has uncovered a plethora of causal signaling cascades that can be used to identify good candidates explaining a specific set of transcriptional changes.Methods: We take a Bayesian approach to integrate gene expression profiling with a causal graph of molecular interactions constructed from prior biological knowledge. In addition, we define the biological context of a specific interaction by the corresponding Medical Subject Headings terms. The Bayesian network can be queried to suggest upstream regulators that can be causally linked to the altered expression profile.Results: Our approach will treat candidate regulators in the right biological context preferentially, enables hierarchical exploration of resulting hypotheses and takes the complete network of causal relationships into account to arrive at the best set of upstream regulators. We demonstrate the power of our method on distinct biological datasets, namely response to dexamethasone treatment, stem cell differentiation and a neuropathic pain model. In all cases relevant biological insights could be validated.Availability and implementation: Source code for the method is available upon request.Contact: daniel.ziemek@pfizer.comSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

We have used a previously unavailable model of pancreatic development, derived in vitro from human embryonic stem cells, to capture a time-course of gene, miRNA and histone modification levels in pancreatic endocrine cells. We investigated whether it is possible to better understand, and hence control, the biological pathways leading to pancreatic endocrine formation by analysing this information and combining it with the available scientific literature to generate models using a casual reasoning approach. We show that the embryonic stem cell differentiation protocol is highly reproducible in producing endocrine precursor cells and generates cells that recapitulate many aspects of human embryonic pancreas development, including maturation into functional endocrine cells when transplanted into recipient animals. The availability of whole genome gene and miRNA expression data from the early stages of human pancreatic development will be of great benefit to those in the fields of developmental biology and diabetes research. Our causal reasoning algorithm suggested the involvement of novel gene networks, such as NEUROG3/E2F1/KDM5B and SOCS3/STAT3/IL-6, in endocrine cell development We experimentally investigated the role of the top-ranked prediction by showing that addition of exogenous IL-6 could affect the expression of the endocrine progenitor genes NEUROG3 and NKX2.2. [ABSTRACT FROM AUTHOR]

Based on knowledge of early embryo development, where anterior neural ectoderm (ANE) development is regulated by native inhibitors of bone morphogenic protein (BMP) and Nodal/Activin signaling, most published protocols of human embryonic stem cell differentiation to ANE have demonstrated a crucial role for Smad signaling in neural induction. The drawbacks of such protocols include the use of an embryoid body culture step and use of polypeptide secreted factors that are both expensive and, when considering clinical applications, have significant challenges in terms of good manufacturing practices compliancy. The use of small molecules to direct differentiation of pluripotent stem cells toward a specified lineage represents a powerful approach to generate specific cell types for further understanding of biological function, for understanding disease processes, for use in drug discovery, and finally for use in regenerative medicine. We therefore aimed to find controlled and reproducible animal-component-free differentiation conditions that would use only small molecules. Here, we demonstrate that pluripotent stem cells can be reproducibly and efficiently differentiated to PAX6+ (a marker of neuroectoderm) and OCT4− (a marker of pluripotent stem cells) cells with the use of potent small inhibitors of the BMP and Activin/Nodal pathways, and in animal-component-free conditions, replacing the frequently used Noggin and SB431542. We also show by transcript analysis, both at the population level and for the first time at the single-cell level, that differentiated cells express genes characteristic for the development of ANE, in particular for the development of the future forebrain. S tem C ells 2012;30:1875-1884 [ABSTRACT FROM AUTHOR]

Background: The exacting nutritional requirements and complicated life cycles of parasites mean that they are not always amenable to high-throughput drug screening using automated procedures. Therefore, we have engineered the yeast Saccharomyces cerevisiae to act as a surrogate for expressing anti-parasitic targets from a range of biomedically important pathogens, to facilitate the rapid identification of new therapeutic agents. Methodology/Principal Findings: Using pyrimethamine/dihydrofolate reductase (DHFR) as a model parasite drug/drug target system, we explore the potential of engineered yeast strains (expressing DHFR enzymes from Plasmodium falciparum, P. vivax, Homo sapiens, Schistosoma mansoni, Leishmania major, Trypanosoma brucei and T. cruzi) to exhibit appropriate differential sensitivity to pyrimethamine. Here, we demonstrate that yeast strains (lacking the major drug efflux pump, Pdr5p) expressing yeast (ScDFR1), human (HsDHFR), Schistosoma (SmDHFR), and Trypanosoma (TbDHFR and TcDHFR) DHFRs are insensitive to pyrimethamine treatment, whereas yeast strains producing Plasmodium (PfDHFR and PvDHFR) DHFRs are hypersensitive. Reassuringly, yeast strains expressing field-verified, drug-resistant mutants of P. falciparum DHFR (Pfdhfr51I,59R,108N) are completely insensitive to pyrimethamine, further validating our approach to drug screening. We further show the versatility of the approach by replacing yeast essential genes with other potential drug targets, namely phosphoglycerate kinases (PGKs) and N-myristoyl transferases (NMTs). Conclusions/Significance: We have generated a number of yeast strains that can be successfully harnessed for the rapid and selective identification of urgently needed anti-parasitic agents. Author Summary: Parasites kill millions of people every year and leave countless others with chronic debilitating disease. These diseases, which include malaria and sleeping sickness, mainly affect people in developing countries. For this reason, few drugs have been developed to treat them. To make matters worse, many parasites are developing resistance to the drugs that are available. Thus, there is an urgent need to develop new drugs, but this is hampered by the fact that most parasites are difficult or impossible to grow in the laboratory. To address this, we have engineered baker's yeast to be dependent on the function of enzymes from either parasites or humans. In all, our engineered yeast constructs encompass six parasites (causing malaria, schistosomiasis, leishmaniasis, sleeping sickness, and Chagas disease) and three different enzymes that are known or potential drug targets. Further, we have increased yeast's sensitivity to drugs by deleting the gene for its major drug efflux pump. Because yeast is robust and easy to grow in the laboratory, we can use a robot to screen for drugs that will kill yeast dependent on a parasite enzyme, but not touch yeast dependent on the equivalent human enzyme. [ABSTRACT FROM AUTHOR]

Background: The exacting nutritional requirements and complicated life cycles of parasites mean that they are not always amenable to high-throughput drug screening using automated procedures. Therefore, we have engineered the yeast Saccharomyces cerevisiae to act as a surrogate for expressing anti-parasitic targets from a range of biomedically important pathogens, to facilitate the rapid identification of new therapeutic agents. Methodology/Principal Findings: Using pyrimethamine/dihydrofolate reductase (DHFR) as a model parasite drug/drug target system, we explore the potential of engineered yeast strains (expressing DHFR enzymes from Plasmodium falciparum, P. vivax, Homo sapiens, Schistosoma mansoni, Leishmania major, Trypanosoma brucei and T. cruzi) to exhibit appropriate differential sensitivity to pyrimethamine. Here, we demonstrate that yeast strains (lacking the major drug efflux pump, Pdr5p) expressing yeast (ScDFR1), human (HsDHFR), Schistosoma (SmDHFR), and Trypanosoma (TbDHFR and TcDHFR) DHFRs are insensitive to pyrimethamine treatment, whereas yeast strains producing Plasmodium (PfDHFR and PvDHFR) DHFRs are hypersensitive. Reassuringly, yeast strains expressing field-verified, drug-resistant mutants of P. falciparum DHFR (Pfdhfr51I,59R,108N) are completely insensitive to pyrimethamine, further validating our approach to drug screening. We further show the versatility of the approach by replacing yeast essential genes with other potential drug targets, namely phosphoglycerate kinases (PGKs) and N-myristoyl transferases (NMTs). Conclusions/Significance: We have generated a number of yeast strains that can be successfully harnessed for the rapid and selective identification of urgently needed anti-parasitic agents. [ABSTRACT FROM AUTHOR]

Background: To elucidate the biological processes affected by changes in growth rate and nutrient availability, we have performed a comprehensive analysis of the transcriptome, proteome and metabolome responses of chemostat cultures of the yeast, Saccharomyces cerevisiae, growing at a range of growth rates and in four different nutrient-limiting conditions. Results: We find significant changes in expression for many genes in each of the four nutrient-limited conditions tested. We also observe several processes that respond differently to changes in growth rate and are specific to each nutrient-limiting condition. These include carbohydrate storage, mitochondrial function, ribosome synthesis, and phosphate transport. Integrating transcriptome data with proteome measurements allows us to identify previously unrecognized examples of post-transcriptional regulation in response to both nutrient and growth-rate signals. Conclusions: Our results emphasize the unique properties of carbon metabolism and the carbon substrate, the limitation of which induces significant changes in gene regulation at the transcriptional and post-transcriptional level, as well as altering how many genes respond to growth rate. By comparison, the responses to growth limitation by other nutrients involve a smaller set of genes that participate in specific pathways. See associated commentary http://www.biomedcentral.com/1741-7007/8/62 [ABSTRACT FROM AUTHOR]

Because of the extreme impact of genome sequencing projects, protein sequences without accompanying experimental data now dominate public databases. Homology searches, by providing an opportunity to transfer functional information between related proteins, have become the de facto way to address this. Although a single, well annotated, close relationship will often facilitate sufficient annotation, this situation is not always the case, particularly if mutations are present in important functional residues. When only distant relationships are available, the transfer of function information is more tenuous, and the likelihood of encountering several well annotated proteins with different functions is increased. The consequence for a researcher is a range of candidate functions with little way of knowing which, if any, are correct. Here, we address the problem directly by introducing a computational approach to accurately identify and segregate related proteins into those with a functional similarity and those where function differs. This approach should find a wide range of applications, including the interpretation of genomics/proteomics data and the prioritization of targets for high-throughput structure determination. The method is generic, but here we concentrate on enzymes and apply high-quality catalytic site data. In addition to providing a series of comprehensive benchmarks to show the overall performance of our approach, we illustrate its utility with specific examples that include the correct identification of haptoglobin as a nonenzymatic relative of trypsin, discrimination of acid-D-amino acid ligases from a much larger ligase pool, and the successful annotation of BioH, a structural genomics target. [ABSTRACT FROM AUTHOR]

The role of conformational change in substrate binding, catalysis and product release is reviewed for 11 enzymes, for which crystal structures are available for the apo, substrate- and product-bound states. The extent of global conformational changes is measured, and the movements of the functional regions involved in catalysis and ligand binding are compared to the rest of the structure. We find that most of these enzymes undergo relatively small amounts of conformational change and particularly small changes in catalytic residue geometry, usually less than 1 Å. In some enzymes there is significant movement of the binding residues, usually on surface loops. [Copyright &y& Elsevier]

Genetic evidence of disease association has often been used as a basis for selecting of drug targets for complex common diseases. Likewise, the propagation of genetic evidence through gene or protein interaction networks has been shown to accurately infer novel disease associations at genes for which no direct genetic evidence can be observed. However, an empirical test of the utility of combining these approaches for drug discovery has been lacking. In this study, we examine genetic associations arising from an analysis of 648 UK Biobank GWAS and evaluate whether targets identified as proxies of direct genetic hits are enriched for successful drug targets, as measured by historical clinical trial data. We find that protein networks formed from specific functional linkages such as protein complexes and ligand–receptor pairs are suitable for even naïve guilt-by-association network propagation approaches. In addition, more sophisticated approaches applied to global protein–protein interaction networks and pathway databases, also successfully retrieve targets enriched for clinically successful drug targets. We conclude that network propagation of genetic evidence can be used for drug target identification. [ABSTRACT FROM AUTHOR]

The authors discuss aspects of a precompetitive activity for addressing the biological data needs of discovering new drugs. They mention the engagement of biologists in the target discovery, validation and evaluation of medications as well as the complexity on biological knowledge and anticipations. They also note the significance of the data sets for depicting the disease relevance, biological role and safety of drugs.