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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. Synopsis Multi‐omics analysis allows for an in‐depth view of the molecular dynamics of a microbial ecosystem. The study evaluates if different omics data types show similar results or if each of them provides unique insights into the dynamics of a drug‐perturbed microbial ecosystem. The estimation of relative species abundance is highly consistent between omics methods that allow species resolution. For capturing functional changes, each omics data type shows slightly different results, showing the usefulness of studying a system on different molecular levels. The antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in a synthetic community of 32 gut microbiome species, leading to a decreased oligosaccharide uptake and induction of stress responses related to protein quality control. The synthetic community breaks down the antihelmintic drug niclosamide, likely protecting community members from the drug. Graphical Abstract Multi‐omics analysis allows for an in‐depth view of the molecular dynamics of a microbial ecosystem. The study evaluates if different omics data types show similar results or if each of them provides unique insights into the dynamics of a drug‐perturbed microbial ecosystem.

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.

ObjectiveChanges 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.DesignWe 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.ResultsWe 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.ConclusionTaken 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.

Pharmaceuticals can directly inhibit the growth of gut bacteria, but the degree such interactions manifest in complex community settings is an open question. Here we compared the effects of 30 drugs on a 32-species synthetic community with their effects on each community member in isolation. While most individual drug–species interactions remained the same in the community context, communal behaviors emerged in 26% of all tested cases. Cross-protection, during which drug-sensitive species became protected in community, was 6-times more frequent than cross-sensitization, the converse phenomenon. Cross-protection decreased and cross-sensitization increased at higher drug concentrations, suggesting that the resilience of microbial communities can collapse when perturbations get stronger. By metabolically profiling drug-treated communities, we showed that both drug biotransformation and bioaccumulation contribute mechanistically to communal protection. As a proof-of-principle, we molecularly dissected a prominent case: species expressing specific nitroreductases degraded niclosamide, thereby protecting both themselves and sensitive community members.

Multi-omics analyses are increasingly employed in microbiome studies to obtain a holistic view of molecular changes occurring within microbial communities exposed to different conditions. However, it is not always clear to what extent each omics data type contributes to our understanding of the community dynamics 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, namely 16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics, and metabolomics. Using this controlled setting, we find that all omics methods with species resolution in their readouts are highly consistent in estimating relative species abundances across conditions. Furthermore, different omics methods complement each other in their ability to capture functional changes in response to the drug perturbations. For example, while nearly all 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 and metabolomics revealed a decrease in polysaccharide uptake, likely caused by Bacteroidota depletion. Taken together, our study provides insights into how multi-omics datasets can be utilised to reveal complex molecular responses to external perturbations in microbial communities.Competing Interest StatementThe authors have declared no competing interest.

Background Chronic migraine (CM) is the most severe and burdensome subtype of migraine. Fremanezumab is a monoclonal antibody that targets the calcitonin gene-related peptide pathway as a migraine preventive therapy. This study aimed to conduct a cost-effectiveness analysis of fremanezumab from a societal perspective in the Netherlands, using a Markov cohort simulation model. Methods The base-case cost-effectiveness analysis adhered to the Netherlands Authority guidelines. Fremanezumab was compared with best supportive care (BSC; acute migraine treatment only) in patients with CM and an inadequate response to topiramate or valproate and onabotulinumtoxinA (Dutch patient group [DPG]). A supportive analysis was conducted in the broader group of CM patients with prior inadequate response to 2–4 different classes of migraine preventive treatments. One-way sensitivity, probabilistic sensitivity, and scenario analyses were conducted. Results Over a lifetime horizon, fremanezumab is cost saving compared with BSC in the DPG (saving of €2514 per patient) and led to an increase of 1.45 quality-adjusted life-years (QALYs). In the broader supportive analysis, fremanezumab was cost effective compared with BSC, with an incremental cost-effectiveness ratio of €2547/QALY gained. Fremanezumab remained cost effective in all sensitivity and scenario analyses. Conclusion In comparison to BSC, fremanezumab is cost saving in the DPG and cost effective in the broader population.