Consistency across multi-omics layers in a drug-perturbed gut microbial community.

Multi-omics analyses are used in microbiome studies to understand molecular changes in microbial communities exposed to different conditions. However, it is not always clear how much each omics data type contributes to our understanding and whether they are concordant with each other. Here, we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers (16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics and metabolomics). We find that all the omics methods with species resolution are highly consistent in estimating relative species abundances. Furthermore, different omics methods complement each other for capturing functional changes. For example, while nearly all the omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control. Metabolomics revealed a decrease in oligosaccharide uptake, likely caused by Bacteroidota depletion. Our study highlights how multi-omics datasets can be utilized to reveal complex molecular responses to external perturbations in microbial communities.

Identification of proteome markers for drug-induced liver injury in zebrafish embryos.

The zebrafish embryo (ZFE) is a promising alternative non-rodent model in toxicology, and initial studies suggested its applicability in detecting hepatic responses related to drug-induced liver injury (DILI). Here, we hypothesize that detailed analysis of underlying mechanisms of hepatotoxicity in ZFE contributes to the improved identification of hepatotoxic properties of compounds and to the reduction of rodents used for hepatotoxicity assessment. ZFEs were exposed to nine reference hepatotoxicants, targeted at induction of steatosis, cholestasis, and necrosis, and effects compared with negative controls. Protein profiles of the individual compounds were generated using LC-MS/MS. We identified differentially expressed proteins and pathways, but as these showed considerable overlap, phenotype-specific responses could not be distinguished. This led us to identify a set of common hepatotoxicity marker proteins. At the pathway level, these were mainly associated with cellular adaptive stress-responses, whereas single proteins could be linked to common hepatotoxicity-associated processes. Applying several stringency criteria to our proteomics data as well as information from other data sources resulted in a set of potential robust protein markers, notably Igf2bp1, Cox5ba, Ahnak, Itih3b.2, Psma6b, Srsf3a, Ces2b, Ces2a, Tdo2b, and Anxa1c, for the detection of adverse responses.

Metagenomic analysis of gut microbial communities from a Central Asian population.

OBJECTIVE: Changes in the gut microbiota are increasingly recognised to be involved in many diseases. This ecosystem is known to be shaped by many factors, including climate, geography, host nutrition, lifestyle and medication. Thus, knowledge of varying populations with different habits is important for a better understanding of the microbiome. DESIGN: We therefore conducted a metagenomic analysis of intestinal microbiota from Kazakh donors, recruiting 84 subjects, including male and female healthy subjects and metabolic syndrome (MetS) patients aged 25-75 years, from the Kazakh administrative centre, Astana. We characterise and describe these microbiomes, the first deep-sequencing cohort from Central Asia, in comparison with a global dataset (832 individuals from five countries on three continents), and explore correlations between microbiota, clinical and laboratory parameters as well as with nutritional data from Food Frequency Questionnaires. RESULTS: We observe that Kazakh microbiomes are relatively different from both European and East Asian counterparts, though similar to other Central Asian microbiomes, with the most striking difference being significantly more samples falling within the Prevotella-rich enterotype, potentially reflecting regional diet and lifestyle. We show that this enterotype designation remains stable within an individual over time in 82% of cases. We further observe gut microbiome features that distinguish MetS patients from controls (eg, significantly reduced Firmicutes to Bacteroidetes ratio, Bifidobacteria and Subdoligranulum, alongside increased Prevotella), though these overlap little with previously published reports and thus may reflect idiosyncrasies of the present cohort. CONCLUSION: Taken together, this exploratory study describes gut microbiome data from an understudied population, providing a starting point for further comparative work on biogeography and research on widespread diseases. TRIAL REGISTRATION NUMBER: ISRCTN37346212; Post-results.

Towards standards for human fecal sample processing in metagenomic studies.

Technical variation in metagenomic analysis must be minimized to confidently assess the contributions of microbiota to human health. Here we tested 21 representative DNA extraction protocols on the same fecal samples and quantified differences in observed microbial community composition. We compared them with differences due to library preparation and sample storage, which we contrasted with observed biological variation within the same specimen or within an individual over time. We found that DNA extraction had the largest effect on the outcome of metagenomic analysis. To rank DNA extraction protocols, we considered resulting DNA quantity and quality, and we ascertained biases in estimates of community diversity and the ratio between Gram-positive and Gram-negative bacteria. We recommend a standardized DNA extraction method for human fecal samples, for which transferability across labs was established and which was further benchmarked using a mock community of known composition. Its adoption will improve comparability of human gut microbiome studies and facilitate meta-analyses.

CART-a chemical annotation retrieval toolkit.

MOTIVATION: Data on bioactivities of drug-like chemicals are rapidly accumulating in public repositories, creating new opportunities for research in computational systems pharmacology. However, integrative analysis of these data sets is difficult due to prevailing ambiguity between chemical names and identifiers and a lack of cross-references between databases. RESULTS: To address this challenge, we have developed CART, a Chemical Annotation Retrieval Toolkit. As a key functionality, it matches an input list of chemical names into a comprehensive reference space to assign unambiguous chemical identifiers. In this unified space, bioactivity annotations can be easily retrieved from databases covering a wide variety of chemical effects on biological systems. Subsequently, CART can determine annotations enriched in the input set of chemicals and display these in tabular format and interactive network visualizations, thereby facilitating integrative analysis of chemical bioactivity data. AVAILABILITY AND IMPLEMENTATION: CART is available as a Galaxy web service (cart.embl.de). Source code and an easy-to-install command line tool can also be obtained from the web site. CONTACT: bork@embl.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOCAT2: a metagenomic assembly, annotation and profiling framework.

UNLABELLED: MOCAT2 is a software pipeline for metagenomic sequence assembly and gene prediction with novel features for taxonomic and functional abundance profiling. The automated generation and efficient annotation of non-redundant reference catalogs by propagating pre-computed assignments from 18 databases covering various functional categories allows for fast and comprehensive functional characterization of metagenomes. AVAILABILITY AND IMPLEMENTATION: MOCAT2 is implemented in Perl 5 and Python 2.7, designed for 64-bit UNIX systems and offers support for high-performance computer usage via LSF, PBS or SGE queuing systems; source code is freely available under the GPL3 license at http://mocat.embl.de CONTACT: : bork@embl.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A transcriptomics-based hepatotoxicity comparison between the zebrafish embryo and established human and rodent in vitro and in vivo models using cyclosporine A, amiodarone and acetaminophen.

The zebrafish embryo (ZFE) is a promising alternative, non-rodent model in toxicology, which has an advantage over the traditionally used models as it contains complete biological complexity and provides a medium to high-throughput setting. Here, we assess how the ZFE compares to the traditionally used models for liver toxicity testing, i.e., in vivo mouse and rat liver, in vitro mouse and rat hepatocytes, and primary human hepatocytes. For this comparison, we analyzed gene expression changes induced by three model compounds for cholestasis, steatosis, and necrosis. The three compounds, cyclosporine A, amiodarone, and acetaminophen, were chosen because of their relevance to human toxicity and these compounds displayed hepatotoxic-specific changes in the mouse in vivo data. Compound induced expression changes in the ZFE model shared similarity with both in vivo and in vitro. Comparison on single gene level revealed the presence of model specific changes and no clear concordance across models. However, concordance was identified on the pathway level. Specifically, the pathway "regulation of metabolism - bile acids regulation of glucose and lipid metabolism via FXR" was affected across all models and compounds. In conclusion, our study with three hepatotoxic model compounds shows that the ZFE model is at least as comparable to traditional models in identifying hepatotoxic activity and has the potential for use as a pre-screen to determine the hepatotoxic potential of compounds.

Gene expression markers in the zebrafish embryo reflect a hepatotoxic response in animal models and humans.

The zebrafish embryo (ZFE) is a promising non-rodent model in toxicology, and initial studies suggested its applicability in detecting hepatotoxic responses. Here, we hypothesize that the detailed analysis of underlying mechanisms of hepatotoxicity in ZFE contributes to the improved identification of hepatotoxic properties of new compounds and to the reduction of rodents used for screening. ZFEs were exposed to nine reference hepatotoxicants, targeted at induction of cholestasis, steatosis and necrosis, and two non-hepatotoxic controls. Histopathology revealed various specific morphological changes in the ZFE hepatocytes indicative of cell injury. Gene expression profiles of the individual compounds were generated using microarrays. Regulation of single genes and of pathways could be linked to hepatotoxic responses in general, but phenotype-specific responses could not be distinguished. Hepatotoxicity-associated pathways included xenobiotic metabolism and oxidoreduction related pathways. Overall analysis of gene expression identified a limited set of potential biomarkers specific for a common hepatotoxicity response. This set included several cytochrome P450 genes (cyp2k19, cyp4v7, cyp2aa3), genes related to liver development (pklr) and genes important in oxidoreduction processes (zgc:163022, zgc:158614, zgc:101858 and sqrdl). In conclusion, the ZFE model allows for identification of hepatotoxicants, without discrimination into specific phenotypes.

Exploring the zebrafish embryo as an alternative model for the evaluation of liver toxicity by histopathology and expression profiling.

The whole zebrafish embryo model (ZFE) has proven its applicability in developmental toxicity testing. Since functional hepatocytes are already present from 36 h post fertilization onwards, whole ZFE have been proposed as an attractive alternative to mammalian in vivo models in hepatotoxicity testing. The goal of the present study is to further underpin the applicability of whole ZFE for hepatotoxicity testing by combining histopathology and next-generation sequencing-based gene expression profiling. To this aim, whole ZFE and adult zebrafish were exposed to a set of hepatotoxic reference compounds. Histopathology revealed compound and life-stage-specific effects indicative of toxic injury in livers of whole ZFE and adult zebrafish. Next-generation sequencing (NGS) was used to compare transcript profiles in pooled individual RNA samples of whole ZFE and livers of adult zebrafish. This revealed that hepatotoxicity-associated expression can be detected beyond the overall transcription noise in the whole embryo. In situ hybridization verified liver specificity of selected highly expressed markers in whole ZFE. Finally, cyclosporine A (CsA) was used as an illustrative case to support applicability of ZFE in hepatotoxicity testing by comparing CsA-induced gene expression between ZFE, in vivo mouse liver and HepaRG cells on the levels of single genes, pathways and transcription factors. While there was no clear overlap on single gene level between the whole ZFE and in vivo mouse liver, strong similarities were observed between whole ZFE and in vivo mouse liver in regulated pathways related to hepatotoxicity, as well as in relevant overrepresented transcription factors. In conclusion, both the use of NGS of pooled RNA extracts analysis combined with histopathology and traditional microarray in single case showed the potential to detect liver-related genes and processes within the transcriptome of a whole zebrafish embryo. This supports the applicability of the whole ZFE model for compound-induced hepatotoxicity screening.

Application of toxicogenomics in hepatic systems toxicology for risk assessment: acetaminophen as a case study.

Hepatic systems toxicology is the integrative analysis of toxicogenomic technologies, e.g., transcriptomics, proteomics, and metabolomics, in combination with traditional toxicology measures to improve the understanding of mechanisms of hepatotoxic action. Hepatic toxicology studies that have employed toxicogenomic technologies to date have already provided a proof of principle for the value of hepatic systems toxicology in hazard identification. In the present review, acetaminophen is used as a model compound to discuss the application of toxicogenomics in hepatic systems toxicology for its potential role in the risk assessment process, to progress from hazard identification towards hazard characterization. The toxicogenomics-based parallelogram is used to identify current achievements and limitations of acetaminophen toxicogenomic in vivo and in vitro studies for in vitro-to-in vivo and interspecies comparisons, with the ultimate aim to extrapolate animal studies to humans in vivo. This article provides a model for comparison of more species and more in vitro models enhancing the robustness of common toxicogenomic responses and their relevance to human risk assessment. To progress to quantitative dose-response analysis needed for hazard characterization, in hepatic systems toxicology studies, generation of toxicogenomic data of multiple doses/concentrations and time points is required. Newly developed bioinformatics tools for quantitative analysis of toxicogenomic data can aid in the elucidation of dose-responsive effects. The challenge herein is to assess which toxicogenomic responses are relevant for induction of the apical effect and whether perturbations are sufficient for the induction of downstream events, eventually causing toxicity.