Aberrant cell signaling can cause cancer and other diseases and is a focal point of drug research. A common approach is to infer signaling activity of pathways from gene expression. However, mapping gene expression to pathway components disregards the effect of post-translational modifications, and downstream signatures represent very specific experimental conditions. Here we present PROGENy, a method that overcomes both limitations by leveraging a large compendium of publicly available perturbation experiments to yield a common core of Pathway RespOnsive GENes. Unlike pathway mapping methods, PROGENy can (i) recover the effect of known driver mutations, (ii) provide or improve strong markers for drug indications, and (iii) distinguish between oncogenic and tumor suppressor pathways for patient survival. Collectively, these results show that PROGENy accurately infers pathway activity from gene expression in a wide range of conditions.

Bacterial metabolism plays a fundamental role in gut microbiota ecology and host-microbiome interactions. Yet the metabolic capabilities of most gut bacteria have remained unknown. Here we report growth characteristics of 96 phylogenetically diverse gut bacterial strains across 4 rich and 15 defined media. The vast majority of strains (76) grow in at least one defined medium, enabling accurate assessment of their biosynthetic capabilities. These do not necessarily match phylogenetic similarity, thus indicating a complex evolution of nutritional preferences. We identify mucin utilizers and species inhibited by amino acids and short-chain fatty acids. Our analysis also uncovers media for in vitro studies wherein growth capacity correlates well with in vivo abundance. Further value of the underlying resource is demonstrated by correcting pathway gaps in available genome-scale metabolic models of gut microorganisms. Together, the media resource and the extracted knowledge on growth abilities widen experimental and computational access to the gut microbiota.

Bacteria in the gut can modulate the availability and efficacy of therapeutic drugs. Yet, the systematic mapping of the respective interactions has only started recently 1 and the main underlying mechanism proposed is chemical transformation of drugs by microbes (biotransformation). Here, we investigated the depletion of 15 structurally diverse drugs by 25 representative gut bacterial strains. This revealed 70 bacteria-drug interactions, 29 of which had not been reported before. Over half of the new interactions can be ascribed to bioaccumulation, that is bacteria storing the drug intracellularly without chemically modifying it, and in most cases without their growth being affected. As a case in point, we studied the molecular basis of bioaccumulation of the widely used antidepressant duloxetine by using clickchemistry, thermal proteome profiling and metabolomics. We find that duloxetine binds to several metabolic enzymes and changes metabolite secretion of the respective bacteria. When tested in a defined microbial community of accumulators and non-accumulators, duloxetine markedly altered the community composition through metabolic cross-feeding. We further validated our findings in an animal model, showing that bioaccumulating bacteria attenuate the behavioral response of Caenorhabditis elegans to duloxetine. Taken together, bioaccumulation by gut bacteria may be a common mechanism that alters drug availability and bacterial metabolism, with implications for microbiota composition, pharmacokinetics, side effects and drug responses, likely in an individual manner.Therapeutic drugs can have a strong impact on the gut microbiome and vice versa 2-5 . The underlying drug-bacteria interactions can reduce microbial fitness 6 or alter the drug availability through biotransformation 7-14 . The latter can have either a positive or a negative impact on drug activity and efficacy. While drugs like lovastatin and sulfasalazine are chemically transformed by gut bacteria into their active forms, bacterial metabolism can inactivate drugs such as digoxin 15,16 , or cause toxic effects as in the case of irinotecan 17 .Furthering the diversity of susceptible drugs, over one hundred molecules were recently reported to be chemically modified by gut bacteria 1 . Yet, the mechanistic view on these interactions is largely confined to drug biotransformation 12,13 . Drug accumulation without metabolizationTo expand the knowledge of bacterial effect on drug availability, we systematically profiled interactions between 15 human-targeted drugs and 25 representative human gut bacterial strains (21 species; with additional subspecies or conspecific strains of Bifidobacterium longum, Escherichia coli and Bacteroides uniformis) (Supplementary Table 1). The bacterial species were selected to cover a broad phylogenetic and metabolic diversity representative of the healthy microbiota 18 (Extended Data Fig. 1a, Supplementary Table 1). On the drug side, 12 orally administered small molecule drugs (MW<500 Da), amenable to UPLC-UV-based quantificat...

Extracting signalling pathway activities from transcriptome data is important to infer mechanistic origins of transcriptomic dysregulation, for example in disease. A popular method to do so is by enrichment analysis of signature genes in e.g. differentially regulated genes. Previously, we derived signatures for signalling pathways by integrating public perturbation transcriptome data and generated a signature database called SPEED (Signalling Pathway Enrichment using Experimental Datasets), for which we here present a substantial upgrade as SPEED2. This web server hosts consensus signatures for 16 signalling pathways that are derived from a large number of transcriptomic signalling perturbation experiments. When providing a gene list of e.g. differentially expressed genes, the web server allows to infer signalling pathways that likely caused these genes to be deregulated. In addition to signature lists, we derive ‘continuous’ gene signatures, in a transparent and automated fashion without any fine-tuning, and describe a new algorithm to score these signatures.

No abstract

Aberrant cell signaling is known to cause cancer and many other diseases, as well as a focus of treatment. A common approach is to infer its activity on the level of pathways using gene expression. However, mapping gene expression to pathway components disregards the effect of posttranslational modifications, and downstream signatures represent very specific experimental conditions. Here w e present PROGENy, a method that overcomes both limitations by leveraging a large compendium of publicly available perturbation experiments to yield a common core of Pathway RespOnsive GENes. Unlike existing methods, PROGENy can (i) recover the effect of known driver mutations, (ii) provide or improve strong markers for drug indications, and (iii) distinguish between oncogenic and tumor suppressor pathways for patient survival. Collectively, these results show that PROGENy more accurately infers pathway activity from gene expression than other methods.

The replacement of fishmeal by plant proteins in aquafeeds imposes the use of synthetic methionine (MET) sources to balance the amino acid composition of alternative diets and so to meet the metabolic needs of fish of agronomic interest such as rainbow trout (RT-Oncorhynchus mykiss). Nonetheless, debates still exist to determine if one MET source is more efficiently used than another by fish. To address this question, the use of fish cell lines appeared a convenient strategy, since it allowed to perfectly control cell growing conditions notably by fully depleting MET from the media and studying which MET source is capable to restore cell growth/proliferation and metabolism when supplemented back. Thus, results of cell proliferation assays, Western blots, RT-qPCR and liquid chromatography analyses from two RT liver-derived cell lines revealed a better absorption and metabolization of DL-MET than DL-Methionine Hydroxy Analog (MHA) with the activation of the mechanistic Target Of Rapamycin (mTOR) pathway for DL-MET and the activation of integrated stress response (ISR) pathway for MHA. Altogether, the results clearly allow to conclude that both synthetic MET sources are not biologically equivalent, suggesting similar in vivo effects in RT liver and, therefore, questioning the MHA efficiencies in other RT tissues.

The objective of this experiment was to investigate the effects of dietary crude protein (CP) content and crystalline amino acids (CAA) supplementation patterns on the bacteria and their metabolites in the intestine of weaned pigs raised under clean (CSC) or unclean sanitary conditions (USC). One hundred forty-four piglets (6.35 ± 0.63 kg BW) were assigned to 1 of 3 diets to give 8 replicates, each with 3 pigs, over a 21-days period. Diets consisted of a high CP (HCP; 21%) and two low CP (LCP; 18%) diets supplemented with all 10 essential AA as CAA or only 6 CAA (Lys, Met, Thr, Trp, Val, and Ile). The CSC room was washed weekly, whereas the USC room had sow manure spread in the pens and was not washed throughout the experiment. Digesta from jejunum and colon were analyzed for ammonia N, short-chain fatty acids, and biogenic amines, but only colonic digesta was analyzed for microbiome composition (16s rRNA sequencing on MiSeq). Data were analyzed using R software for 16S rRNA and the MIXED procedure of SAS for microbial metabolites. Sanitation, CP content, and CAA supplementation patterns did not affect colonic bacterial composition or diversity in weaned pigs. Pigs raised under USC had greater (P &lt; 0.05) jejunal ammonia N than those under CSC. Pigs fed LCP diets had reduced (P &lt; 0.05) jejunal ammonia N compared to those fed HCP diet. No difference was found in colonic ammonia N. Interactions between sanitation and dietary CP content were observed (P &lt; 0.05) for jejunal acetate and colonic spermidine and spermine. In conclusion, reducing dietary CP lowered ammonia N content regardless of the sanitation and increased microbial metabolites in weaned pigs raised under USC. However, colonic bacterial composition and diversity were not influenced by dietary CP, sanitary conditions, or CAA supplementation patterns.