Prediction of human DILI for CS1 compounds
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PROVIDER: S-TOXR1052 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:We used microarrays to detail the global programme of gene expression underlying CS1-regulated biological processes including increased cell adhesion and cell proliferation.
2009-02-04 | GSE14680 | GEO
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| S-TOXR903 | biostudies-other
Project description:The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.
| S-EPMC4568441 | biostudies-literature
Project description:Brevundimonas sp. CS1 Genome sequencing and assembly
| PRJNA703419 | ENA
Project description:The relevance of phenolic compounds in the human diet has increased in recent years, particularly due to their role as natural antioxidants and chemopreventive agents in different diseases. In the human body, phenolic compounds are mainly metabolized by the gut microbiota; however, their metabolism is not well represented in public databases and existing reconstructions. In a previous work, using different sources of knowledge, bioinformatic and modelling tools, we developed AGREDA, an extended metabolic network more amenable to analyze the interaction of the human gut microbiota with diet. Despite the substantial improvement achieved by AGREDA, it was not sufficient to represent the diverse metabolic space of phenolic compounds. In this article, we make use of an enzyme promiscuity approach to complete further the metabolism of phenolic compounds in the human gut microbiota. In particular, we apply RetroPath RL, a previously developed approach based on Monte Carlo Tree Search strategy reinforcement learning, in order to predict the degradation pathways of compounds present in Phenol-Explorer, the largest database of phenolic compounds in the literature. Reactions predicted by RetroPath RL were integrated with AGREDA, leading to a more complete version of the human gut microbiota metabolic network. We assess the impact of our improvements in the metabolic processing of various foods, finding previously undetected connections with output microbial metabolites. By means of untargeted metabolomics data, we present in vitro experimental validation for output microbial metabolites released in the fermentation of lentils with feces of children representing different clinical conditions.
| S-EPMC9279433 | biostudies-literature
Project description:The evaluation of possible interactions between chemical compounds and antitarget proteins is an important task of the research and development process. Here, we describe the development and validation of QSAR models for the prediction of antitarget end-points, created on the basis of multilevel and quantitative neighborhoods of atom descriptors and self-consistent regression. Data on 4000 chemical compounds interacting with 18 antitarget proteins (13 receptors, 2 enzymes, and 3 transporters) were used to model 32 sets of end-points (IC(50), K(i), and K(act)). Each set was randomly divided into training and test sets in a ratio of 80% to 20%, respectively. The test sets were used for external validation of QSAR models created on the basis of the training sets. The coverage of prediction for all test sets exceeded 95%, and for half of the test sets, it was 100%. The accuracy of prediction for 29 of the end-points, based on the external test sets, was typically in the range of R(2)(test) = 0.6-0.9; three tests sets had lower R(2)(test) values, specifically 0.55-0.6. The proposed approach showed a reasonable accuracy of prediction for 91% of the antitarget end-points and high coverage for all external test sets. On the basis of the created models, we have developed a freely available online service for in silico prediction of 32 antitarget end-points: http://www.pharmaexpert.ru/GUSAR/antitargets.html.
| S-EPMC3534763 | biostudies-other
Project description:Development of low-clearance (CL) compounds that are slowly metabolized is a major goal in the pharmaceutical industry. However, the pursuit of low intrinsic CL (CLint ) often leads to significant challenges in evaluating the pharmacokinetics of such compounds. Although in vitro-in vivo extrapolation is widely used to predict human CL, its application has been limited for low-CLint compounds because of the low turnover of parent compounds in metabolic stability assays. To address this issue, we focused on chimeric mice with humanized livers (PXB-mice), which have been increasingly reported to accurately predict human CL in recent years. The predictive accuracy for nine low-CLint compounds with no significant turnover in a human hepatocyte assay was investigated using PXB-mouse methods, such as single-species allometric scaling (PXB-SSS) approach and a novel physiologically based scaling (PXB-PBS) approach that assumes that the CLint per hepatocyte is equal between humans and PXB-mice. The percentages of compounds with predicted CL within 2- and 3-fold ranges of the observed CL for low-CLint compounds were 89% and 100%, respectively, for both PXB-SSS and PXB-PBS approaches. Moreover, the predicted CL was mostly consistent among the methods. Conversely, the percentages of compounds with predicted CL within 2- and 3-fold ranges of the observed CL for low-CLint compounds were 50% and 63%, respectively, for multispecies allometric (MA) scaling. Overall, these PXB-mouse methods were much more accurate than conventional MA scaling approaches, suggesting that PXB-mice are useful tools for predicting the human CL of low-CLint compounds that are slowly metabolized.
| S-EPMC8742647 | biostudies-literature
Project description:CS1 is one of a limited number of serologically distinct pili found in enterotoxigenic Escherichia coli (ETEC) strains associated with disease in people. The genes for the CS1 pilus are on a large plasmid, pCoo. We show that pCoo is not self-transmissible, although our sequence determination for part of pCoo shows regions almost identical to those in the conjugative drug resistance plasmid R64. When we introduced R64 into a strain containing pCoo, we found that pCoo was transferred to a recipient strain in mating. Most of the transconjugant pCoo plasmids result from recombination with R64, leading to acquisition of functional copies of all of the R64 transfer genes. Temporary coresidence of the drug resistance plasmid R64 with pCoo leads to a permanent change in pCoo so that it is now self-transmissible. We conclude that when R64-like plasmids are transmitted to an ETEC strain containing pCoo, their recombination may allow for spread of the pCoo plasmid to other enteric bacteria.
| S-EPMC400639 | biostudies-literature