Towards a mechanistic understanding of reciprocal drug–microbiome interactions

Review18 March 2021Open Access Towards a mechanistic understanding of reciprocal drug–microbiome interactions Michael Zimmermann Corresponding Author Michael Zimmermann [email protected] orcid.org/0000-0002-5797-3589 Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Kiran Raosaheb Patil Kiran Raosaheb Patil orcid.org/0000-0002-6166-8640 Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany The Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK Search for more papers by this author Athanasios Typas Athanasios Typas orcid.org/0000-0002-0797-9018 Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Lisa Maier Corresponding Author Lisa Maier [email protected] orcid.org/0000-0002-6473-4762 Interfaculty Institute of Microbiology and Infection Medicine, University of Tübingen, Tübingen, Germany Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, Tübingen, Germany Search for more papers by this author Michael Zimmermann Corresponding Author Michael Zimmermann [email protected] orcid.org/0000-0002-5797-3589 Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Kiran Raosaheb Patil Kiran Raosaheb Patil orcid.org/0000-0002-6166-8640 Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany The Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK Search for more papers by this author Athanasios Typas Athanasios Typas orcid.org/0000-0002-0797-9018 Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Lisa Maier Corresponding Author Lisa Maier [email protected] orcid.org/0000-0002-6473-4762 Interfaculty Institute of Microbiology and Infection Medicine, University of Tübingen, Tübingen, Germany Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, Tübingen, Germany Search for more papers by this author Author Information Michael Zimmermann *,1, Kiran Raosaheb Patil1,2, Athanasios Typas1,3 and Lisa Maier *,4,5 1Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany 2The Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK 3Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany 4Interfaculty Institute of Microbiology and Infection Medicine, University of Tübingen, Tübingen, Germany 5Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, Tübingen, Germany *Corresponding author. Tel: +49 6221 387 8740; E-mail: [email protected] *Corresponding author. Tel: +49 7071 29 80187; E-mail: [email protected] Molecular Systems Biology (2021)17:e10116https://doi.org/10.15252/msb.202010116 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Broad-spectrum antibiotics target multiple gram-positive and gram-negative bacteria, and can collaterally damage the gut microbiota. Yet, our knowledge of the extent of damage, the antibiotic activity spectra, and the resistance mechanisms of gut microbes is sparse. This limits our ability to mitigate microbiome-facilitated spread of antibiotic resistance. In addition to antibiotics, non-antibiotic drugs affect the human microbiome, as shown by metagenomics as well as in vitro studies. Microbiome–drug interactions are bidirectional, as microbes can also modulate drugs. Chemical modifications of antibiotics mostly function as antimicrobial resistance mechanisms, while metabolism of non-antibiotics can also change the drugs’ pharmacodynamic, pharmacokinetic, and toxic properties. Recent studies have started to unravel the extensive capacity of gut microbes to metabolize drugs, the mechanisms, and the relevance of such events for drug treatment. These findings raise the question whether and to which degree these reciprocal drug–microbiome interactions will differ across individuals, and how to take them into account in drug discovery and precision medicine. This review describes recent developments in the field and discusses future study areas that will benefit from systems biology approaches to better understand the mechanistic role of the human gut microbiota in drug actions. Introduction Our understanding of how the human gut microbiota contributes to health and disease, and how it changes over time, life stages, different geographic regions, and in response to environmental factors has increased dramatically over the last decade (The Integrative HMP (iHMP) Research Network Consortium, 2019; Pasolli et al, 2019; Nayfach et al, 2019; Falony et al, 2016). The current consensus is that the gut microbiome has a highly individualized composition, especially at the bacterial strain level (Franzosa et al, 2015). Further, healthy individuals retain a largely stable microbiota composition for most of their adulthood (Sommer et al, 2017; Mehta et al, 2018). This composition is established in early stages of life (Bäckhed et al, 2015; Wampach et al, 2017) and is dependent more on the environment than on host genetics (Rothschild et al, 2018). Hence strong perturbations, such as dietary shifts and antibiotic consumption, can unbalance microbiome stability, with so far unpredictable recovery (Willing et al, 2011; Falony et al, 2016; Lynn et al, 2018). On the other hand, chemical modification of therapeutic compounds by intestinal bacteria can influence the therapeutic effect of drugs (Fig 1). We have only recently begun to explore these complex, bidirectional interactions between our resident microbes and medication. In this review, we provide an overview of the different systems-level approaches that can be employed to gain insights into the drug–microbiome–host triad (Fig 2). A better and more systematic understanding of these interactions and their underlying molecular constituents can be instrumental for diagnostic, prognostic, and ultimately, therapeutic applications. Figure 1. Overview on the drug–microbiome–host triad and their interactions Left: The intake of drugs can have a direct influence on individual members of the gut microbiome (classic example: antibiotics) but can also change the composition and functionality of the microbiome through indirect, host-mediated ways (example: proton-pump inhibitors, which might alter the microbiome composition by increasing the gastric pH). Right: Intestinal bacteria can modify and metabolise drugs. In addition, the microbiome can indirectly modulate host xenobiotic metabolism in the liver. Furthermore, there is crosstalk between all these interactions. Ultimately, these complex interactions can possibly have negative health consequences and cause interpersonal differences in treatment outcomes. Download figure Download PowerPoint Figure 2. Systems approaches to study drug–microbiome–host interactions Left: A wide variety of model systems can be used to study drug–microbiome–host interactions. On the microbial side, (possibly genetically modified) isolates in pure culture or synthetic or stool-derived microbial communities are applied. On the host side, simple cell culture systems, intestinal organoids but also different animal models can be employed. Right: Diverse technologies help to decipher drug–microbiome–host interactions. Approaches can be broadly divided into phenotypic characterization, OMICs approaches, and model-based predictions. Depending on the research question, appropriate model systems and suitable technologies can be combined. TPP: thermal proteome profiling, LiP-MS: limited proteolysis-coupled mass spectrometry. Download figure Download PowerPoint Therapeutic drugs alter the gut microbiome composition Evidence from metagenomic-based cohorts and clinical studies—the top-down approach Exploring the factors that explain inter-individual differences in the intestinal microbiome composition across large population cohorts have repeatedly identified medication as a main contributor (Falony et al, 2016; Ticinesi et al, 2017; Jackson et al, 2018; Vich Vila et al, 2020). Although such studies have been insightful and have revealed the cumulative and dramatic impact medication has on the gut microbiome composition, they are still underpowered for separating the effects of individual drug classes. To begin stratifying these effects, one can broadly separate drugs to antimicrobials, developed to target microbes, and to drugs designed to interact with human/host targets, here referred to as human-targeted drugs. Antimicrobial drugs comprise antibiotics, antifungals, antiprotozoals, antivirals, and anti-archaeals. These compounds target proteins that are typically absent in the host or are clearly distinguishable from their human homologues, yet they are often present in commensal microbes colonizing the human body. As a consequence, antimicrobials can “collaterally damage” the microbiome and thereby have mild to severe side effects to patients (Kuhn et al, 2016). This has been best studied for antibiotics, with clinical and animal studies illustrating changes in the gut microbiome composition and physiological host parameters, such as metabolic, cognitive, and immune functions (Cho et al, 2012; Cox et al, 2014; Hwang et al, 2015; Fröhlich et al, 2016; Hagan et al, 2019). Initial data indicate that the microbiota of healthy patients can partially rebound post-antibiotic treatment (Rashid et al, 2015; Palleja et al, 2018). However, it remains unclear whether this is true for a broader and/or more diverse population, and what are the links to antibiotic classes, initial microbiome composition and treatment duration. Similarly, our knowledge on the target spectra, mode of action, and resistance mechanisms of the different classes of antibiotics and their specific effect on gut commensal bacterial species is scarce (preprint: Maier et al, 2020). To gain mechanistic insights into these matters, assays, tools, and test systems from decades of antibiotic research on pathogens can be capitalized and adapted to study gut commensal species in pure culture, within microbial communities and within the host, especially at a systematic level (Fig 2) (Maier & Typas, 2017). Such detailed mechanistic knowledge can help design better and more precise strategies to prevent or revert antibiotics-caused "collateral damage," which at the moment are based on generic processes with limited success and/or adverse outcomes, such as fecal transplantation or administration of probiotics (Zmora et al, 2018; Suez et al, 2018; DeFilipp et al, 2019) (Box 2). For host-targeted drugs, increasing evidence suggests that they are associated with shifts in gut microbiome composition. Known examples span a broad range of therapeutic classes and include the antidiabetic metformin, proton-pump inhibitors, antipsychotics, non-steroidal anti-inflammatory drugs, paracetamol, opioids, selective serotonin reuptake inhibitors, laxatives, and statins (Le Bastard et al, 2018; Jackson et al, 2018; Kummen et al, 2020; MetaCardis Consortium et al, 2020). These shifts are not necessarily unfavorable for the host. In certain cases, host-targeted drugs can diversify the gut microbiome (MetaCardis Consortium et al, 2020)—a feature generally linked to a healthy microbiome. However, the functional implications of these taxonomic shifts, for example in terms of altered metabolic capacities and/or antibiotic resistance repertoires, need to be assessed separately for each compound (Vich Vila et al, 2020). Current clinical studies of the effects of medication on the gut microbiome have mostly been cross-sectional, while interventional or longitudinal approaches and comparisons to treatment-naïve but diseased control groups are often missing. As a result, it is difficult to differentiate between disease-mediated and drug-related effects. This issue is exemplified by the antidiabetic drug metformin. The drug shows limited oral bioavailability, resulting in high intestinal drug concentration. It was one of the first non-antibiotic drugs that was shown to influence gut microbiome composition (Napolitano et al, 2014) and revealed the need to stratify for treatment when interpreting microbiome signatures (Forslund et al, 2015). At the same time, this finding stimulated causal studies that directly linked compositional shifts to the improvement of metabolic dysfunction and hyperglycemia (Wu et al, 2017). One proposed mechanism involves metformin decreasing the relative abundance of Bacteroides fragilis and downregulating its associated bile salt hydrolase activity. This leads to an accumulation of glycoursodeoxycholic acid, which inhibits the intestinal farnesoid X receptor (FXT) signaling and thereby improves various metabolic outcomes in mice, including hyperglycemia (Sun et al, 2018). Other proposed mechanisms to explain the microbiome-mediated hypoglycemic effect of metformin include the microbial production of short-chain fatty acids, promotion of gut barrier integrity and increased secretion of gut hormones such as glucagon-like peptide 1 and peptide YY (PYY) (reviewed in Pryor et al, 2020). Remarkably, several model systems such as Caenorhabditis elegans (Cabreiro et al, 2013), mice (Shin et al, 2014), and rats (Bauer et al, 2018) were instrumental in elucidating these metformin–microbiome–host interactions, highlighting the translation of these phenomena between evolutionarily distant organisms and demonstrating the utility of different model organisms to study these interactions. In contrast to metformin, we are far from dissecting the interaction of the vast majority of host-targeted drugs with gut microbes. It remains unclear whether these drugs act directly on the microbes, what is their spectrum and underlying molecular interactions, and what is the impact on the microbiome as a whole, on the drug’s therapeutic action and on the host. To close this knowledge gap and optimize drug therapies, further well-designed clinical studies are needed, which must be seamlessly coordinated with bottom-up approaches (Fig 2). Ex vivo studies—accelerating mechanistic understanding of drug–microbiome interactions by reducing the complexity and increasing the throughput—the bottom-up approach While clinical studies provide an excellent global picture of drug effects on the microbiome, ex vivo approaches allow for a systematic, controlled, and question-specific dissection of these interactions at various scales ranging from molecules to inter-organismal interactions. Recent advances in high-throughput approaches for the cultivation of fastidious anaerobes (Box 1) allowed the first systematic studies of the effects of drugs on intestinal microbes. A large-scale in vitro screen of 1,200 marketed drugs showed direct impact on the growth of at least one of forty tested human gut commensal species for 78% of the antibacterial drugs, 53% of other antimicrobials, and 24% of the human-targeted drugs (Maier et al, 2018). Although drugs across all therapeutic classes had a direct impact on gut commensal species, the effect was most pronounced for antimetabolites, antipsychotics, and calcium-channel blockers. Some of these compounds, such as antimetabolites, target conserved enzymes and pathways in prokaryotes and eukaryotes and thus, likely have the same mode of action in gut commensals as in host cells. However, for the vast majority of human-targeted drugs with activity against gut bacteria, their bacterial targets remain obscure. Identifying microbial targets for these drugs will open new possibilities for repurposing them as antibacterials and/or for mitigating their collateral damage on gut bacteria. Intriguingly, human-targeted drugs impacting microbes in vitro resembled antibiotics with respect to their reported side effects in clinics, providing initial evidence that they also impact gut commensals in vivo. Moreover, antibiotic-resistant microbes were in general also more resistant to human-targeted drugs, suggesting that resistance mechanisms against antibiotics and non-antibiotics at least partially overlap. Initial profiling of these common resistance mechanisms revealed efflux pumps, transporters and detoxifications mechanisms. Other activities, such as cell envelope properties, stress responses and target modification are also likely involved. Precisely mapping this level of cross-resistance and collateral sensitivity (i.e., resistance to one drug providing sensitivity to another) is vital to mitigate the risks human-targeted drugs may entail for antibiotic resistance and to exploit collateral sensitivity opportunities to delay, prevent or revert antibiotic resistance (Pál et al, 2015; Baym et al, 2016). To this end, a number of established systems approaches can be specifically geared to deconvolute drug targets and reveal resistance mechanism, as demonstrated for chemical genetics (Cacace et al, 2017; Kintses et al, 2019), proteomics (thermal proteome profiling (Mateus et al, 2020), limited proteolysis-coupled mass spectrometry (Schopper et al, 2017), and metabolomics (Zampieri et al, 2018) (Fig 2). Box 1. Representative microbes and microbiomes A: Representative microbes The significance of systemic mapping of drug–microbiome interactions increases with the number of representative microbes tested. Consequently, comprehensive species and strain collections are essential. The benefit of such collections further increases, the better the isolates are characterized (e.g., genome sequence), and the more detailed metadata information is provided (e.g., health status of the host). Gut microbiome isolate collections The compilation of such collections usually follows certain selection criteria—such as being representative for the gut microbiome of healthy individuals—and focuses on type strains, which are obtained from publicly available strain collections such as DSMZ, ATCC/BEI Resources, etc. (www.dsmz.de, http://www.atcc.org, www.beiresources.org) (e.g., Tramontano et al, 2018). Further collections are needed that are representative for other body sites, certain diseases, age-groups, ethnicities, food preferences, etc.. While most concentrate on maximizing phylogenetic diversity of prevalent and abundant species, for a global picture it is also important to capture rare species and species diversity (i.e., strain-level variation). Strain-level variation Current studies only phenotype one or few strains per species, usually starting with type strains. For most tested species, it is unknown how representative they are. Although pangenomes can be estimated for many gut species (Zou et al, 2019), it is unclear how this translates into phenotypic variation. However, previous work suggests that drug metabolism and drug sensitivity are strain-specific traits (Koppel et al, 2018; preprint: Maier et al, 2020) and that functional strain differences can impact human health. Such observations underline the importance of sampling many strains per bacterial species. Several efforts have been recently made toward this aim by collecting hundreds of human gut bacterial isolates. In the future, such collections need to continue expanding to cover strain and species diversity—for example, many unknown species are predicted from metagenome-assembled genomes (Almeida et al, 2019; Pasolli et al, 2019; Nayfach et al, 2019). Recent examples for such libraries include: Broad Institute-OpenBiome Microbiome Library (Poyet et al, 2019). Culturable Genome Reference (CGR) Collection (Zou et al, 2019). Human Gastrointestinal Bacteria Culture Collection (HBC) (Forster et al, 2019). Global Microbiome Conservancy (http://microbiomeconservancy.org). Collection of coexisting isolates from the same host Instead of collecting and phenotyping strains from a large number of different individuals, strain collections can originate from a single person (Goodman et al, 2011; Coyne et al, 2014). As these co-resident strains are collected from the same human host, they capture the co-evolved and coexisting strain-level diversity within one individual. Personalized collections are of particular value for investigations of inter-individual differences in drug–microbiome interactions. B: Microbiomes The number of different community compositions to be examined scales almost infinitely. To tackle this challenge, two fundamentally different approaches can be pursued: synthetic communities can be assembled starting from axenic bacterial cultures (bottom-up approach) or natural, self-assembled communities, e.g., derived from human stool can be utilized (top-down approach). Synthetic communities Reductionist consortia of defined organisms are assembled in modular ways, either donor-specific or pooled. Individual community members are usually well-characterized and ideally genetically tractable. Systematic manipulations of the strain and genetic composition of synthetic communities enable the identification of causal links between the composition and observed community phenotypes (Shetty et al, 2019). Stoolbanks Stool samples provide a non-invasive starting point for studying the complex, self-assembled human microbiome (Bolan et al, 2016) and can be incubated with drugs ex vivo (Maurice et al, 2013; van de Steeg et al, 2018). Recently, so-called “stoolbanks” became more sophisticated in order to promote accessibility to fecal microbiota transplantation in clinical practice (Cammarota et al, 2019). But they can also be used for research purposes, especially if they are open-access and non-profit, such as OpenBiome. Subsequent microbiome preservation efforts aim for long-term storage: for example, the “The Microbiota Vault” (www.microbiotavault.org) is a project to conserve the microbial diversity associated with our bodies and environments for future generations. In both setups, key functional and compositional profiles of the gut microbiota need to be maintained, for example in continuous flow bioreactor systems or microfluidic gut models (Guzman-Rodriguez et al, 2018). As these technically laborious systems are challenging to adapt to high-throughput workflows, continuous dilution batch cultures in multi-well formats have been successfully applied to screen drug effects on microbial communities (Venturelli et al, 2018; Li et al, 2019). The numerous interactions observed between human-targeted drugs and gut microbes in vitro beg the question of whether they are relevant in vivo. For example, it is unclear whether microbes alone similarly respond to drugs as when part of a community, and how the spatially structured intestinal environments and drug concentration gradients inside the host affect drug response. One way to leverage drug–microbiome interactions to the community level is to test assembled (“synthetic”) communities (Box 1). Microbes can behave the same in communities as in an axenic culture (the drug being as effective against them) or can have communal emergent properties: be more protected (cross-protection) or sensitized (cross-sensitization) to the drug. It is currently unclear how often such emerging communal properties occur and/or what drives them. Drug chemical modification can lead to both cross-protection (Vega & Gore, 2014) and cross-sensitization (Roemhild et al, 2020), but also other less direct effects could elicit similar results: the change in physiological stage of the bacterial cells (e.g., stress responses and transporters induced at the community level), changes of environment (i.e., pH changes (Ratzke & Gore, 2018)), or the opening of niches in a competitive environment. To investigate such responses systematically, robust high-throughput ways are needed to grow communities (Box 1) and to follow species abundance, ideally at an absolute quantification level (e.g., by metaproteomics (Li et al, 2020), Fig 2). Understanding the frequency and molecular drivers of such interactions will be of paramount importance to exploit or mitigate microbiome-mediated drug effects in clinics (Fig 3). Figure 3. Applications of knowledge gain from studying drug–microbiome–host interactions Diagnostics and Prognostics: Microbiome-derived biomarkers (macromolecules, metabolites and compositions) can be used to diagnose diseases, but also for prognosis of the disease course or to predict treatment success. Protection and Prevention: Various measures can be applied to reduce undesired drug effects on the microbiome or to suppress chemical drug modifications by intestinal bacteria. With better understanding of the drug–microbiome–host triad, interventions of increased specificity can be employed (i.e., from fecal transplants to defined restoration therapeutics). Intervention and Modulation: There are both abiotic and biotic approaches to influence the microbiome, its functional output and consequently drug–microbiome–host interactions. For more detailed explanations, see Box 2. Download figure Download PowerPoint Microbiome effects on drugs Microbes alter the chemistry of drugs and drug metabolites Given the structural similarity between small molecule drugs and endogenous metabolites, the fact that many drugs are derived from natural products, and the large enzymatic potential of the microbiome, microbial drug metabolism is to be expected. Indeed, already in the early 20th century the drug prontosil was found to require bacterial conversion to unfold its antibiotic effects (Fuller, 1937). Since then, accumulating evidence suggests that microbial modification of drugs and drug metabolites seems to be the rule rather than the exception. Such microbial drug metabolism can result in the same or different chemical products as the human metabolic enzymes, leading to drug activation (e.g., sulfasalazine, Sousa et al, 2014), inactivation (e.g., L-dopa and digoxin (Lindenbaum et al, 1981; Haiser et al, 2013; Maini Rekdal et al, 2019)) or toxicity (e.g., sorivudine and brivudine, (Zimmermann et al, 2019a; Nakayama et al, 1997). In addition to drug molecules, drug metabolites are also subject to microbial metabolism. Phase II drug metabolites (produced by conjugation reactions) have been found to be deconjugated to their precursor molecules (i.e., phase I metabolites (Wallace et al, 2010) or original drug molecules (Taylor et al, 2019)) by microbes. More importantly, these types of microbial metabolism can impact pharmacokinetics, in particular the intestinal abundance of drug and drug metabolites, and thereby alter drug response and toxicity (Wallace et al, 2010; Taylor et al, 2019). Since differences in microbiome-encoded genetic contents far exceed genetic differences between human individuals, it is very likely that the microbiota composition may be behind a large fraction of person-to-person variation in drug response, especially in terms of drug side effects. In the following paragraphs, we will discuss various approaches to investigate microbiome drug metabolism, its impact on drug response and potential avenues to harness microbiome drug metabolism to improve therapeutic drug interventions. The latter would undoubtedly present an opportunity for the pharmaceutical industry and precision medicine applications in clinics. Systematic studies reveal extensive microbial drug metabolism A compound’s metabolism in the human body is a decisive factor for its success during preclinical and clinical drug development. To assess drug metabolism early in drug discovery pipelines, numerous in vitro and in silico protocols have been developed and standardized. New technologies, such as microfluidics screens and machine learning predictions have been recently incorporated in such pipelines (Kirchmair et al, 2015; Eribol et al, 2016). The use of cellular or cell-free enzyme preparation (e.g., cytosolic and microsome isolations) enables systematic ex vivo high-throughput screens for the metabolism of hundreds of compounds in parallel (Williamson et al, 2017; Underhill & Khetani, 2018). The results of such systematic assays, together with insights from in vivo drug metabolism, are the basis for rule-based and machine learning computational methods to predict xenobiotic metabolism (Djoumbou-Feunang et al, 2019; de Bruyn Kops et al, 2019). In contrast to human drug metabolism, comparable large-scale data sets for microbiome drug metabolism are mostly lacking, limiting the information available to build predictive models of microbial drug modifications. To circumvent this limitation, several research groups have used information on primary and secondary metabolism to infer potential drug modification reactions based on biochemical reactions and substrate structures (Klünemann et al, 2014; Guthrie et al, 2019). Although this approach is consistent with the chemical similarity between drugs and endogenous compounds, it suffers from the fact that the genes, biochemistry, and lifestyle of most gut microbiome members are poorly characterized (Almeida et al, 2019). This makes it also challenging to define a (standardized) set of microbiome-derived species/strains/enzymes to test their activity against drug molecules, as it exists for human drug-metabolizing enzymes. As a workaround, two recent studies have cultured complete human fecal communities to