Fferent compounds (van de Steeg et al, 2018; Javdan et al, 2020). This experimental setup has the benefit that microbial community members don’t have to become selected a priori and encompasses microbial interactions that will impact drug metabolism, as shown for sequential L-dopa metabolism by two unique species (Maini Rekdal et al, 2019). A challenge of this strategy could be the uneven strain distribution in isolated microbial communities, which may well mask and underestimate the metabolic prospective of microbes identified at low abundance ex vivo, but could quite well be active and relevant in vivo. Comparable towards the described systematic bottomup method to test drug activity on representative panels of bacteria in isolation (Maier et al, 2018), equivalent efforts have already been employed to deduce their metabolic activity against a big panel of drugs (Zimmermann et al, 2019b). Testing microbial communities or single bacterial strains, as much as 65 of the assayed drugs had been metabolized, suggesting that the microbial drug metabolism is really a much more popular phenomenon than the couple of anecdotal examples collected over the final handful of decades (reviewed in Wilson Nicholson, 2017). Gaining molecular insights into microbial drug metabolism Ex vivo drug transformation assays with fecal communities isolated from distinctive individuals have demonstrated vast interpersonal variations inside the communities’ drug-metabolizing FP Antagonist Molecular Weight capacity (Zimmermann et al, 2019b) (Fig two), which are corroborated by variations in the drug-metabolizing possible for unique bacterial species and strains (Lindenbaum et al, 1981; Haiser et al, 2013; Zimmermann et al, 2019b). These findings suggest that the molecular mechanisms of microbial drug transformation should be identified to predict the drug-metabolizing capacity of an individual’s microbiome. To determine microbial enzymes and pathways accountable for drug conversion, several systems approaches have already been applied. Determined by the assumption that metabolic pathways are typically transcriptionally induced by their substrates, transcriptional comparison in the presence and absence of a provided drug could be performed. This strategy was successfully applied to determine the enzymes of Eggerthella lenta (DSM 2243) and Escherichia coli (K12) that metabolize digoxin (Haiser et al, 2013) and 5-fluoruracil (preprint: Spanogiannopoulos et al, 2019), respectively. Gain-of-function and loss-offunction genetic screens have been combined with mass spectrometry-based analytics to systematically determine genes involved in microbial drug metabolism (Zimmermann et al, 2019a, 2019b) (Fig two). Drug-specific chemical probes have also been employed to probe enzyme activity and to pull down enzymes conveying a drug conversion of interest, as elegantly applied for the identification of beta-glucuronidases (Kainate Receptor Antagonist Species Jariwala et al, 2020). Ultimately, computational approaches according to metabolic reaction networks, comparative genomics of bacterial isolates, or microbiome composition have been employed to determine doable genetic variables responsible for drug metabolism (Kl nemann et al, 2014; u Mallory et al, 2018; Guthrie et al, 2019). As soon as identified, microbial genes involved in drug metabolism can serve as prospective biomarkers to quantitatively predict the drug metabolic capacity of a given microbial neighborhood (Zimmermann et al, 2019b) (Fig 3), opening new paths for understanding the impact ofmicrobial drug metabolism around the host and eventually its role in the interpersonal variability in drug.