Systematically investigating the impact of medication on the gut microbiome

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ScienceDirect Systematically investigating the impact of medication on the gut microbiome Lisa Maier1 and Athanasios Typas1,2 In the recent years, there is accumulating evidence for a strong impact of medication on the gut microbiota composition. This evidence comes from metagenomics-based associations and extends beyond classical antibacterials to a handful of humantargeted drugs. To answer whether such effects are direct and explore their consequences in human health, we need to develop experimental platforms that will allow for systematic profiling of drug–microbiota interactions. Here, we discuss approaches, considerations, experimental setups and strategies that can be used to tackle this need, but can be also readily transmitted to related questions in the microbiome field. A comprehensive understanding of how therapeutics interact with gut microbes will open up the path for further mechanistic dissection of such interactions, and ultimately improve not only our understanding of the gut microbiome, but also drug safety and efficacy. Addresses 1 European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany 2 European Molecular Biology Laboratory, Structural and Computational Biology Unit, Heidelberg, Germany Corresponding author: Typas, Athanasios ([email protected])

Current Opinion in Microbiology 2017, 39:128–135 This review comes from a themed issue on Bacterial systems biology Edited by Christoph Dehio and Dirk Bumann For a complete overview see the Issue and the Editorial https://doi.org/10.1016/j.mib.2017.11.001 1369-5274/ã 2017 Elsevier Ltd. All rights reserved.

Our understanding of the human microbiome has increased dramatically in the past decade. We now have a good understanding of the microbiota composition across different body locations, how it changes with time within or across individuals [1]. This vast improvement in chartographing the human microbiome has been fueled by advances in metagenomics and associated data analysis pipelines, allowing us to go from phylum/genus-level to strain-level views of the microbiome of thousands of individuals world-wide. At the same time, associations between microbiome shifts and lifestyle, diet and disease markers are increasingly being reported. As the field advances, studies are being more controlled for statistical coherence, technical biases and confounding factors [2,3]. Current Opinion in Microbiology 2017, 39:128–135

Medication, presumably confounding many earlier studies, has recently emerged as one of the most influential contributors in the gut microbiota composition [4,5]. This link goes beyond bona fide antibacterials, raising a number of interesting questions that need to be addressed in the future. Here, we give a short overview on the medication–microbiome relationship, and discuss approaches and concerns for investigating this matter systematically in the future.

Evidence that non-antibiotic drugs influence the gastrointestinal microbiome The collateral damage that antibacterials exert on our natural flora has been long known, though more appreciated in the recent past [6]. Despite the increased interest, even for antibiotics, we often have limited resolution of their effects on gut commensals, with for example, MIC breakpoints being reported for Gram-negative or Gram-positive anaerobes as collective groups in EUCAST [7]. For non-antibiotics, our knowledge is even scarcer. Generally, polypharmacy increases the rates of gastrointestinal disorders, and can mimic conditions such as inflammatory bowel disease (IBD) [8]. More specifically, a handful of drug classes have been probed in the past couple of years and associated with microbiome shifts (Table 1): proton pump inhibitors [9–11,12,13], non-steroidal anti-inflammatory drugs (NSAIDs) [14,15], antipsychotics [16,17], antidiabetics atypical [18,19,20] and chemotherapeutics [21,22]. With the increased attention in the role of medication on microbiota shifts [4,5], more drugs are bound to be tested in the future, likely in more controlled studies (more individuals, individual drugs). Although such data-driven associations have high clinical relevance, they fail to provide answers in two fundamental questions: (i) Are these effects direct and if so, what is the precise bacterial target of the drug? (ii) Is the drug effect on microbes (part of) its primary pharmacological mode of action (MoA) or an undesired side effect? Direct effects on gut microbes are conceivable for many non-antibiotic drugs. For example, various antipsychotics and antidepressants are known for their antibacterial activities. The first marketed antidepressant, the monoamine oxidase inhibitor iproniazid, actually is a repurposed tuberculostatic [23]. The largest class of antipsychotics (and with several derivatives as antihistamines), the phenothiazines were first identified for www.sciencedirect.com

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Table 1 Evidence that non-antibiotics impact gut microbiome composition. Therapeutic class

Drugs tested

Medication in general

Diverse drugs Polypharmacy Diverse proton pump inhibitors Omeprazole Metformin Acarbose Atypical antipsychotics Risperidone Olanzapine Diverse NSAIDs Indomethacin Diverse drugs Ezetimibe, simvastatin Diverse drugs General effect Carbendazim (fungicide) Trichloroacetamide (disinfectant)

Antiulcer/antireflux Antidiabetic Antipsychotic

Nonsteroidal anti-inflammatory drugs Chemotherapeutics Statins Corticosteroids Environmental chemicals

a b

Study reference Human

Rodent

[4,5] [58] [9,11,12,13] [10] [19,20] [18] [17] [16] – [14,15] – [21,22] – – – – –

– – [59] – [60] – – [61] [62] – [63] [64] [65] [66] [67] [68] [69]

b

a

a a

a b a a b a a

Mouse model Rat model.

their anti-infective properties [24]. Perhaps not-surprisingly, phenothiazines such as thioridazine, are now being considered for repurposing as anti-tuberculosis agents [25]. Newer antidepressant classes, such as selective serotonin reuptake inhibitors (SSRI), have also been reported to inhibit bacterial efflux pumps [23,26]. Many more human-targeting drugs or non-antibacterial antiinfectives are known to inhibit growth of particular microbes, but their mechanistic basis or what is the full extent of such effects, whether they are relevant for gut microbes and whether they can occur in vivo in the gut (drug concentration, microbial communities) are all elusive matters that need to be systematically addressed in the future. More challenging to experimentally address is whether reported microbiome effects are part of the drug’s primary MoA or just a side effect. For example, many psychotropic drugs induce weight changes [27] and a possible contribution to this adverse effect by the gut microbiome has been recently proposed [28]. More generally, gastrointestinal side effects are common for many drugs and could be partially due to the drug impact on the gut microbiome. By contrast, microbiome shifts being part of the MoA of the drug is less expected and has never been a consideration in drug discovery until very recently. Evidence for this comes from metformin, an antidiabetic drug inducing strong microbiome shifts in type II diabetes (T2D) patients [19]. Following this observation, it was shown that fecal transfer from metformin-exposed individuals into germ-free mice improved glucose tolerance [20], and that late-release metformin in the colon (thus not active in the liver) increased drug efficacy [29,30]. Together these findings underline a gut www.sciencedirect.com

microbiome-mediated mechanism behind metformin’s antihyperglymic MoA. Overall, the role of medication on our gut microbiome composition is likely to be much larger than previously anticipated. This has considerable ramifications both for human health and for drug development, especially when taking into account the increasing consumption of pharmaceuticals worldwide. It also constitutes a tremendous challenge in the coming years to comprehensively characterize this drug–microbiome interface.

Considerations when systematically probing the drug–microbiome–host interface Although statistical associations from metagenomics studies in clinical cohorts indicate physiological relevance of findings, they need to be further validated as they can be indirect or biased by confounders. Metformin provides a perfect example of the latter. Although earlier studies reported specific gut microbiome signatures for T2D patients [31,32], the signal turned out later to be due to metformin, the leading drug against T2D, and not the disease itself [19]. Indeed, in a subsequent interventional study, metformin treatment of naı¨ve T2D patients significantly altered the relative abundance of >80 bacterial strains [20]. Therefore, to systematically study the drug–microbiome interface in controlled manner, single drugs have to be first monitored and other contributing factors (other medication, health status, geographic/age/gender biases, microbiome diversity of cohort) need to be taken into account. This is hard to be done for all drugs on the market (>1400 obtained FDA-approved by the end of 2013 [33] and several drug candidates in the pipeline), let Current Opinion in Microbiology 2017, 39:128–135

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alone combinations of them. Similarly, it is hard to currently estimate the influence of gut microbiome diversity in such results. Even if such obstacles were to overcome, association studies cannot infer causality. Therefore, we need to develop efficient in vitro test systems to systematically prescreen for drug candidates with large impact on representative human commensals. Such high-throughput endeavors cannot be performed in mammalian model systems due to costs, scalability, phenotypic complexity and ethical concerns. Any meaningful ex vivo testing of drug–microbiome interaction requires knowledge of drug concentrations at the body sites of interest during treatment. With few exceptions (IBD, diarrhea or colorectal cancer), drugs are not intended to target the gut, and therefore their concentrations in the small intestine or colon are largely unknown. Standard drug parameters such as dose, plasma concentration, bioavailability, half-life and excretion route could be used to deduce broad approximations of small or large intestine concentrations, but often such information is imprecise due to lack of knowledge about overall drug metabolism and drug activity in the gut. In general, for orally taken drugs, intestinal concentrations are expected to be higher than reported plasma concentrations, as both the entire non-absorbed drug and the biliary excreted fraction of the absorbed drug eventually accumulate in the large intestine before excretion. There, exposure time is prolonged and large intestinal water retention locally increases drug concentrations even further. As we become increasingly aware of the effect of medication on gut microbes, it will be important to obtain precise bioactive drug concentrations along the intestine, and possibly information on drug stability and metabolism therein by host or microbial enzymes. From a microbial perspective, drug susceptibility tests are usually restricted to pathogens, which have low abundance, if not being absent from guts of healthy individuals, and tend to be more resistant to drugs. Obviously such knowledge is of limited value for assessing drug effects on abundant and prevalent commensals. Such organisms have been studied less in microbiology labs, with requirement for anaerobic growth conditions and sometimes fastidious growth hampering functional characterization and large-scale screening approaches [34]. However, to understand drug–microbiome interactions, it is inevitable to expand drug screening pipelines to those poorly studied branches of the phylogenetic tree.

In vitro set-ups for systematically probing host–drug–microbiome interactions Pairwise testing of drug collections against representative sets of prevalent and abundant gut microbes is the simplest imaginable approach to systematically assess microbiome–drug interactions. This requires microbiome Current Opinion in Microbiology 2017, 39:128–135

panel strain collections and adequate technical set-ups to perform high-throughput screens of arrayed small molecule compound libraries used in drug discovery screening under anaerobic conditions. Bacterial collections comprising cultivable and representative reference strains are publicly available for mouse intestinal bacteria (miBC) [35] and a similar resource will be soon available for the human microbiome ([36], in revision). Strain-level expansions of these collections or personalized arrayed libraries from individual donors [37] can provide further options. In such assays, different growth media compositions can be used to mimic specific (patho-)physiological gut environments (inflammation, bile concentration) or diet limitations. However, one-by-one screening approaches disregard the community influence, which may mask or exacerbate drug effects. Therefore, drug–microbe screening platforms will benefit from testing drug effects on communities. These can come directly from human stool dilution or by reconstitution from individual strains from microbiome strain collections. Comparisons between behaviors of species in isolation and in communities will delineate paths of cross-protection and cross-sensitization. Until now continuous bioreactor-type of setups (i.e. chemostats) have been mostly used to grow microbiome bacterial communities [38]. Simpler setups using microtiter plates or microfluidic devices are more amenable to high-throughput approaches and can maximize the number of drugs and communities probed. Continuous dilution of growing bacterial communities in such setups can substitute for chemostat-like conditions. In order to integrate mucosal surfaces and host responses to gut–microbiome interactions, bacterial cultures/communities can be exposed to human cultured cells via Transwell systems, micro-carrier beads, gut-on-a-chip models or gut organoids. Although such setups have been traditionally used to probe aerobes, recent innovative microfluidics-based models aim at overcoming this limitation and allow for separated yet proximal co-culture of human and (obligate) anaerobic microorganisms [39]. For all in vitro assays described here (Figure 1), the simplest and most direct readout for measuring the effect of the drug on the microbe is growth: optical density/ fluorescence of individual members or relative abundances of community members. However, at a second tier, drug responses can be characterized in more detail, for example, by integrating single-cell readouts, enzymatic/reporter assays or ‘omics’ techniques [40]. In addition, assays can be expanded in other dimensions: the spectrum of xenobiotics tested (e.g. host-compounds or dietary compounds) and the microbes probed (e.g. archaea and fungi).

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Figure 1

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Expansion to - food additives - host molecules - microbial compounds - drug combinations

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Drug Library e.g. antibiotics, human-targeted

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Strain Collections e.g. mouse/human derived

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Mammalian models

mixed communities presence of host cells

pure culture

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drug

- customized assays - single-cell analysis (microscopy, FACS,...) - ‘omics’ techniques (RNA-seq, MS,...)

t control drug Current Opinion in Microbiology

In vitro systems to comprehensively probe the drug–microbiome–host interface.

In vivo model systems for systematically probing host–drug–microbiome interactions Bridging in vitro simplified setups for microbiome communities with animal models for studying drug– microbiome–host interaction is possible without drastically compromising on throughput using model organisms. In the case of the nematode Caenorhabditis elegans and its bacterial diet (E. coli, Comamonas), both partners are amenable to high-throughput genetic screening and have been used to systematically study and mechanistically disentangle drug–microbiome–host interactions [41,42,43,44]. In a similar way, further common nonmammalian animal models can be employed for highthroughput drug–microbiome–host studies, such as the fruit fly Drosophila melanogaster [45], zebrafish and wax worm Galleria mellonella [46,47]. Unfortunately, these simpler model systems do not mimic the anaerobic or www.sciencedirect.com

microaerophilic environments of the human gut and therefore, many representatives of human microbiome cannot be studied with them. The most prominent and widely accepted model organism in the field is the lab mouse, due to availability of inbred and outbred strains, genetic tractability, the option for germ-free or gnotobiotic husbandry techniques, similarity to human microbiome composition at least at a phylum/family level, and broad availability of disease models developed over the past decades. So far, the impact of medication on the murine microbiota has been assessed only for a handful of drugs across few therapeutic classes (Table 1). As many bacterial genera of the human gut are absent of the mouse gut and vice versa [48], human–microbiota associated mice [49,50] or animal systems with more human-like microbiome compositions are Current Opinion in Microbiology 2017, 39:128–135

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often used in microbiome studies (e.g. pigs [51]). However, such arrangements and models further restrict the throughput. As the microbiome field is heavily data-driven, we often first identify drug effects from metagenome-association studies. In such a scenario, drug–microbiome interactions can be readily and comprehensively characterized in vitro, as clinical relevance has been already shown. The train of thought that animal testing has to follow in vitro observations comes from classical drug discovery pipelines, where in vitro hits are validated in mice models. The same is not necessarily applicable to the microbiome field, where prior clinical knowledge can circumvent the need

for animal models. However, animal models will still be valuable for testing intervention strategies that aim at improving therapy or for investigating physiological manifestations of microbiome–drug interactions in the host. Such findings will have to be ultimately verified in interventional clinical trials in humans (Figure 2).

Conclusions and future perspectives In summary, a systematic mapping of drug–microbiome interactions is eminent. This will be expedited by combining metagenomic-based association studies with in vitro and in vivo high-throughput screening approaches. Such knowledge will set the foundations for further mechanistic dissection of these interactions but also for

Figure 2

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Comparison of drug discovery pipeline to approaches to mapping drug effects on the microbiome. Current Opinion in Microbiology 2017, 39:128–135

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a better understanding of the contribution of microbiome to the MoA or to side effects of specific drugs. The ultimate gain will be avenues for optimizing treatment strategies and drug development. The interface of drugs and the microbiome goes beyond drug-inhibitory effects. Drugs can be also modified by gut microbes, thereby being activated, inactivated or metabolized to toxic products [52]. Currently at least 60 drugs are known to be converted by the microbiome [53], with most well understood examples including beta-glucuronidases re-activating the toxicity of the chemotherapeutic irinotecan in the gut [54] and a specific cytochrome oxidase of Eggerthella lenta reducing the potency of the cardiac drug digoxin [55]. Furthermore, microbiomes also heavily produce bioactive compounds such as secondary metabolites, G-protein coupled receptor agonists [56], and many more small molecules [57], which modulate microbial communities and host responses. Similar approaches and concepts as those described here can be applied to systematically assess these other facets of the drug–microbiome–host interface. In conclusion, the drug–microbiome–host interactions are manifold and multifaceted. Systematically mapping this interface and mechanistically dissecting its underlying principles will revolutionize our current view on pharmacology and will deepen our understanding of our microbiome ecosystem and its link to health.

Conflict of interest statement The authors declare no competing financial interests.

Acknowledgements LM is supported by the EMBL Interdisciplinary Postdoctoral program (EIPOD).

References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as:  of special interest  of outstanding interest 1.

2.

3.

Lloyd-Price J, Mahurkar A, Rahnavard G, Crabtree J, Orvis J, Hall AB, Brady A, Creasy HH, McCracken C, Giglio MG et al.: Strains, functions and dynamics in the expanded Human Microbiome Project. Nature 2017, 550:61. Kim D, Hofstaedter CE, Zhao C, Mattei L, Tanes C, Clarke E, Lauder A, Sherrill-Mix S, Chehoud C, Kelsen J et al.: Optimizing methods and dodging pitfalls in microbiome research. Microbiome 2017, 5:52. Costea PI, Zeller G, Sunagawa S, Pelletier E, Alberti A, Levenez F, Tramontano M, Driessen M, Hercog R, Jung F-E et al.: Towards standards for human fecal sample processing in metagenomic studies. Nat Biotechnol 2017 http://dx.doi.org/ 10.1038/nbt.3960.

4. 

Falony G, Joossens M, Vieira-Silva S, Wang J, Darzi Y, Faust K, Kurilshikov A, Bonder MJ, Valles-Colomer M, Vandeputte D et al.: Population-level analysis of gut microbiome variation. Science 2016, 352:560-564. In a large healthy population, medication is shown to explain the largest total variance in microbiota composition. 13 drugs (antibiotics, osmotic

www.sciencedirect.com

laxatives, IBD medication, female hormones, psycho(ana)leptics and an antihistamine) are reported as microbiome covariates, stressing the potential of drugs as confounding factors in clinical studies. 5.

Zhernakova A, Kurilshikov A, Bonder MJ, Tigchelaar EF, Schirmer M, Vatanen T, Mujagic Z, Vila AV, Falony G, Vieira-Silva S et al.: Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 2016, 352:565-569.

6.

Becattini S, Taur Y, Pamer EG: Antibiotic-induced changes in the intestinal microbiota and disease. Trends Mol Med 2016, 22:458-478.

7.

The European Committee on Antimicrobial Susceptibility Testing. Breakpoint tables for interpretation of MICs and zone diameters, version 7.1, 2017, http://www.eucast.org/fileadmin/src/media/ PDFs/EUCAST_files/Breakpoint_tables/v_7. 1_Breakpoint_Tables.pdfNo. [date unknown].

8.

Philpott HL, Nandurkar S, Lubel J, Gibson PR: Drug-induced gastrointestinal disorders. Frontline Gastroenterol 2014, 5:4957.

9.

Freedberg DE, Toussaint NC, Chen SP, Ratner AJ, Whittier S, Wang TC, Wang HH, Abrams JA: Proton pump inhibitors alter specific taxa in the human gastrointestinal microbiome: a crossover trial. Gastroenterology 2015, 149 883–885.e9.

10. Bajaj JS, Cox IJ, Betrapally NS, Heuman DM, Schubert ML, Ratneswaran M, Hylemon PB, White MB, Daita K, Noble NA et al.: Systems biology analysis of omeprazole therapy in cirrhosis demonstrates significant shifts in gut microbiota composition and function. Am J Physiol Gastrointest Liver Physiol 2014, 307: G951-G957. 11. Clooney AG, Bernstein CN, Leslie WD, Vagianos K, Sargent M, Laserna-Mendieta EJ, Claesson MJ, Targownik LE: A comparison of the gut microbiome between long-term users and nonusers of proton pump inhibitors. Aliment Pharmacol Ther 2016, 43:974-984. 12. Jackson MA, Goodrich JK, Maxan M-E, Freedberg DE, Abrams JA, Poole AC, Sutter JL, Welter D, Ley RE, Bell JT et al.:  Proton pump inhibitors alter the composition of the gut microbiota. Gut 2016, 65:749-756. In this study and [13], proton pump inhibitor (PPI) usage is associated with a shift in gut microbiota composition. Oral and upper GI tract microbes increase in people using PPIs. 13. Imhann F, Bonder MJ, Vich Vila A, Fu J, Mujagic Z, Vork L,  Tigchelaar EF, Jankipersadsing SA, Cenit MC, Harmsen HJM et al.: Proton pump inhibitors affect the gut microbiome. Gut 2016, 65:740-748. See annotation to Ref. [12]. 14. Ma¨kivuokko H, Tiihonen K, Tynkkynen S, Paulin L, Rautonen N: The effect of age and non-steroidal anti-inflammatory drugs on human intestinal microbiota composition. Br J Nutr 2010, 103:227-234. 15. Rogers MAM, Aronoff DM: The influence of non-steroidal antiinflammatory drugs on the gut microbiome. Clin Microbiol Infect 2016, 22 178.e1–178.e9. 16. Bahr SM, Tyler BC, Wooldridge N, Butcher BD, Burns TL, Teesch LM, Oltman CL, Azcarate-Peril MA, Kirby JR, Calarge CA: Use of the second-generation antipsychotic, risperidone, and secondary weight gain are associated with an altered gut microbiota in children. Transl Psychiatry 2015, 5:e652. 17. Flowers SA, Evans SJ, Ward KM, McInnis MG, Ellingrod VL: Interaction between atypical antipsychotics and the gut microbiome in a bipolar disease cohort. Pharmacotherapy 2017, 37:261-267. 18. Su B, Liu H, Li J, Sunli Y, Liu B, Liu D, Zhang P, Meng X: Acarbose treatment affects the serum levels of inflammatory cytokines and the gut content of bifidobacteria in Chinese patients with type 2 diabetes mellitus. J Diabetes 2015, 7:729-739. 19. Forslund K, Hildebrand F, Nielsen T, Falony G, Le Chatelier E,  Sunagawa S, Prifti E, Vieira-Silva S, Gudmundsdottir V, Krogh Pedersen H et al.: Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 2015, 528:262-266. Current Opinion in Microbiology 2017, 39:128–135

134 Bacterial systems biology

This study reveals that shifts in microbial signature of type 2 diabetic patients are mainly due to the most commonly prescribed antidiabetic drug, metformin, and are not associated with the disease status as previously assumed. This led to the realization that microbiome association studies can be confounded by non-antibiotic medication. 20. Wu H, Esteve E, Tremaroli V, Khan MT, Caesar R, Mannera˚s Holm L, Sta˚hlman M, Olsson LM, Serino M, Planas-Fe`lix M et al.: Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug. Nat Med 2017, 23:850-858. Combining a double-blinded randomized study, fecal transfer to germfree mice and in vitro drug–microbiota interactions, the findings of this study support the idea that the microbiota is part of the antihyperglycemic action of metformin. 21. Stringer AM, Al-Dasooqi N, Bowen JM, Tan TH, Radzuan M, Logan RM, Mayo B, Keefe DMK, Gibson RJ: Biomarkers of chemotherapy-induced diarrhoea: a clinical study of intestinal microbiome alterations, inflammation and circulating matrix metalloproteinases. Support Care Cancer 2013, 21:1843-1852. 22. Montassier E, Batard E, Massart S, Gastinne T, Carton T, Caillon J, Le Fresne S, Caroff N, Hardouin JB, Moreau P et al.: 16S rRNA gene pyrosequencing reveals shift in patient faecal microbiota during high-dose chemotherapy as conditioning regimen for bone marrow transplantation. Microb Ecol 2014, 67:690-699. 23. Macedo D, Filho AJMC, Soares de Sousa CN, Quevedo J, Barichello T, Ju´nior HVN, Freitas de Lucena D: Antidepressants, antimicrobials or both? Gut microbiota dysbiosis in depression and possible implications of the antimicrobial effects of antidepressant drugs for antidepressant effectiveness. J Affect Disord 2017, 208:22-32. 24. Ohlow MJ, Moosmann B: Phenothiazine: the seven lives of pharmacology’s first lead structure. Drug Discov Today 2011, 16:119-131. 25. Amaral L, Viveiros M: Thioridazine: a non-antibiotic drug highly effective, in combination with first line anti-tuberculosis drugs, against any form of antibiotic resistance of Mycobacterium tuberculosis due to its multi-mechanisms of action. Antibiotics 2017, 6:3. 26. Bohnert JA, Szymaniak-Vits M, Schuster S, Kern WV: Efflux inhibition by selective serotonin reuptake inhibitors in Escherichia coli. J Antimicrob Chemother 2011, 66:2057-2060. 27. Dent R, Blackmore A, Peterson J, Habib R, Kay GP, Gervais A, Taylor V, Wells G: Changes in body weight and psychotropic drugs: a systematic synthesis of the literature. PLoS One 2012, 7:e36889. 28. Kanji S, Fonseka TM, Marshe VS, Sriretnakumar V, Hahn MK, Mu¨ller DJ: The microbiome–gut–brain axis: implications for schizophrenia and antipsychotic induced weight gain. Eur Arch Psychiatry Clin Neurosci 2017 http://dx.doi.org/10.1007/ s00406-017-0820-z. 29. Buse JB, DeFronzo RA, Rosenstock J, Kim T, Burns C, Skare S,  Baron A, Fineman M: The primary glucose-lowering effect of metformin resides in the gut, not the circulation: results from short-term pharmacokinetic and 12-week dose-ranging studies. Diabetes Care 2016, 39:198-205. These two studies (see also Ref. [30]) demonstrate that colon-targeted delivery of metformin provides evidence that the drug predominately acts via gut-based mechanisms. 30. DeFronzo RA, Buse JB, Kim T, Burns C, Skare S, Baron A,  Fineman M: Once-daily delayed-release metformin lowers plasma glucose and enhances fasting and postprandial GLP-1 and PYY: results from two randomised trials. Diabetologia 2016, 59:1645-1654. See Buse et al. [29]. 31. Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, Liang S, Zhang W, Guan Y, Shen D et al.: A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 2012, 490:55-60. 32. Karlsson FH, Tremaroli V, Nookaew I, Bergstro¨m G, Behre CJ, Fagerberg B, Nielsen J, Ba¨ckhed F: Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 2013, 498:99-103. Current Opinion in Microbiology 2017, 39:128–135

33. Kinch MS, Haynesworth A, Kinch SL, Hoyer D: An overview of FDA-approved new molecular entities: 1827–2013. Drug Discov Today 2014, 19:1033-1039. 34. Browne HP, Forster SC, Anonye BO, Kumar N, Neville BA, Stares MD, Goulding D, Lawley TD: Culturing of “unculturable” human microbiota reveals novel taxa and extensive sporulation. Nature 2016, 533:543-546. 35. Lagkouvardos I, Pukall R, Abt B, Foesel BU, Meier-Kolthoff JP,  Kumar N, Bresciani A, Martı´nez I, Just S, Ziegler C et al.: The Mouse Intestinal Bacterial Collection (miBC) provides hostspecific insight into cultured diversity and functional potential of the gut microbiota. Nat Microbiol 2016, 1:16131. Mouse microbiome resource panel. 36. Tramontano M, Andrejev S, Pruteanu M, Klu¨nemann M, Tramontano M, Andrejev S, Pruteanu M, Klu¨nemann M:  Nutritional preferences of human gut bacteria reveal their metabolic idiosyncrasies. (in revision). Human microbiome resource panel. 37. Goodman AL, Kallstrom G, Faith JJ, Reyes A, Moore A, Dantas G, Gordon JI: Extensive personal human gut microbiota culture collections characterized and manipulated in gnotobiotic mice. Proc Natl Acad Sci 2011, 108:6252-6257. 38. Williams CF, Walton GE, Jiang L, Plummer S, Garaiova I, Gibson GR: Comparative analysis of intestinal tract models. Annu Rev Food Sci Technol 2015, 6:329-350. 39. Shah P, Fritz JV, Glaab E, Desai MS, Greenhalgh K, Frachet A,  Niegowska M, Estes M, Ja¨ger C, Seguin-Devaux C et al.: A microfluidics-based in vitro model of the gastrointestinal human–microbe interface. Nat Commun 2016, 7:11535. The authors introduce a microfluidic device for co-culturing human and microbial cells under gut-like conditions to study human–microbial crosstalk in vitro. 40. Maurice CF, Haiser HJ, Turnbaugh PJ: Xenobiotics shape the physiology and gene expression of the active human gut  microbiome. Cell 2013, 152:39-50. The authors combine flow cytometry, metatranscriptomics and metagenomics to investigate responses to xenobiotics in fresh fecal samples, revealing the unintentional effect of drugs on gut commensals. 41. Garcı´a-Gonza´lez AP, Ritter AD, Shrestha S, Andersen EC, Yilmaz LS, Walhout AJM: Bacterial metabolism affects the C.  elegans response to cancer chemotherapeutics. Cell 2017, 169 431–441.e8. Using high-throughput screening in an C. elegans model system, the authors of this study and [42] systematically assess the role of microbes in fluoropyrimidine-based chemotherapy. 42. Scott TA, Quintaneiro LM, Norvaisas P, Lui PP, Wilson MP, Leung KY, Herrera-Dominguez L, Sudiwala S, Pessia A,  Clayton PT et al.: Host–microbe co-metabolism dictates cancer drug efficacy in C. elegans. Cell 2017, 169 442–456.e18. See Garcia-Gonzales et al. [41]. 43. Cabreiro F, Au C, Leung K-Y, Vergara-Irigaray N, Cocheme´ HM, Noori T, Weinkove D, Schuster E, Greene NDE, Gems D: Metformin retards aging in C. elegans by altering microbial folate and methionine metabolism. Cell 2013, 153:228-239. 44. Zhang J, Holdorf AD, Walhout AJ: C. elegans and its bacterial diet as a model for systems-level understanding of host– microbiota interactions. Curr Opin Biotechnol 2017, 46:74-80. 45. Buchon N, Broderick NA, Lemaitre B: Gut homeostasis in a microbial world: insights from Drosophila melanogaster. Nat Rev Microbiol 2013, 11:615-626. 46. Kostic AD, Howitt MR, Garrett WS: Exploring host–microbiota interactions in animal models and humans. Genes Dev 2013, 27:701-718. 47. Mukherjee K, Raju R, Fischer R, Vilcinskas A: Galleria mellonella as a model host to study gut microbe homeostasis and brain infection by the human pathogen Listeria monocytogenes. Advances in Biochemical Engineering/Biotechnology. 2013:27-39. 48. Ley RE, Backhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI: Obesity alters gut microbial ecology. Proc Natl Acad Sci 2005, 102:11070-11075. www.sciencedirect.com

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49. Arrieta MC, Walter J, Finlay BB: Human microbiota-associated mice: a model with challenges. Cell Host Microbe 2016, 19:575578. 50. Nguyen TLA, Vieira-Silva S, Liston A, Raes J: How informative is the mouse for human gut microbiota research? Dis Model Mech 2015, 8:1-16. 51. Looft T, Allen HK, Cantarel BL, Levine UY, Bayles DO, Alt DP, Henrissat B, Stanton TB: Bacteria, phages and pigs: the effects of in-feed antibiotics on the microbiome at different gut locations. ISME J 2014, 8:1566-1576. 52. Spanogiannopoulos P, Bess EN, Carmody RN, Turnbaugh PJ: The  microbial pharmacists within us: a metagenomic view of xenobiotic metabolism. Nat Rev Microbiol 2016, 14:273-287. Comprehensive review on the role of the gut microbiota in xenobiotic metabolism. 53. Kuntz TM, Gilbert JA: Introducing the microbiome into precision medicine. Trends Pharmacol Sci 2017, 38:81-91. 54. Wallace BD, Wang H, Lane KT, Scott JE, Orans J, Koo JS, Venkatesh M, Jobin C, Yeh L-A, Mani S et al.: Alleviating cancer drug toxicity by inhibiting a bacterial enzyme. Science 2010, 330:831-835. 55. Haiser HJ, Gootenberg DB, Chatman K, Sirasani G, Balskus EP, Turnbaugh PJ: Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta. Science 2013, 341:295-298. 56. Cohen LJ, Esterhazy D, Kim S-H, Lemetre C, Aguilar RR, Gordon EA, Pickard AJ, Cross JR, Emiliano AB, Han SM et al.:  Commensal bacteria make GPCR ligands that mimic human signalling molecules. Nature 2017, 549:48-53. Certain microbial metabolites, that is, the N-acyl amides, can manipulate mammalian physiology by chemically mimicking eukaryotic signalling molecules and by interacting with GPCRs. This finding provides a conceivable explanation of small-molecule mediated host–microbiome interactions. 57. Donia MS, Fischbach MA: Small molecules from the human microbiota. Science 2015, 349 1254766-1254766. 58. Ticinesi A, Milani C, Lauretani F, Nouvenne A, Mancabelli L, Lugli GA, Turroni F, Duranti S, Mangifesta M, Viappiani A et al.: Gut microbiota composition is associated with polypharmacy in elderly hospitalized patients. Sci Rep 2017, 7:11102. 59. Shin CM, Kim N, Kim YS, Nam RH, Park JH, Lee DH, Seok Y-J, Kim Y-R, Kim J-H, Kim JM et al.: Impact of long-term proton pump inhibitor therapy on gut microbiota in F344 rats: pilot study. Gut Liver 2016, 10:896-901.

www.sciencedirect.com

60. Shin N-R, Lee J-C, Lee H-Y, Kim M-S, Whon TW, Lee M-S, Bae JW: An increase in the Akkermansia spp. population induced by metformin treatment improves glucose homeostasis in dietinduced obese mice. Gut 2014, 63:727-735. 61. Bahra SM, Weidemann BJ, Castro AN, Walsh JW, deLeon O, Burnett CML, Pearson NA, Murry DJ, Grobe JL, Kirby JR: Risperidone-induced weight gain is mediated through shifts in the gut microbiome and suppression of energy expenditure. EBioMedicine 2015, 2:1725-1734. 62. Morgan AP, Crowley JJ, Nonneman RJ, Quackenbush CR, Miller CN, Ryan AK, Bogue MA, Paredes SH, Yourstone S, Carroll IM et al.: The antipsychotic olanzapine interacts with the gut microbiome to cause weight gain in mouse. PLoS One 2014, 9:e115225. 63. Liang X, Bittinger K, Li X, Abernethy DR, Bushman FD, FitzGerald GA: Bidirectional interactions between indomethacin and the murine intestinal microbiota. Elife 2015, 4:e08973. 64. Forsga˚rd RA, Marrachelli VG, Korpela K, Frias R, Collado MC, Korpela R, Monleon D, Spillmann T, O¨sterlund P: Chemotherapyinduced gastrointestinal toxicity is associated with changes in serum and urine metabolome and fecal microbiota in male Sprague–Dawley rats. Cancer Chemother Pharmacol 2017, 80:317-332. 65. Catry E, Pachikian BD, Salazar N, Neyrinck AM, Cani PD, Delzenne NM: Ezetimibe and simvastatin modulate gut microbiota and expression of genes related to cholesterol metabolism. Life Sci 2015, 132:77-84. 66. Huang EY, Inoue T, Leone VA, Dalal S, Touw K, Wang Y, Musch MW, Theriault B, Higuchi K, Donovan S et al.: Using corticosteroids to reshape the gut microbiome. Inflamm Bowel Dis 2015, 21:963-972. 67. Hu J, Raikhel V, Gopalakrishnan K, Fernandez-Hernandez H, Lambertini L, Manservisi F, Falcioni L, Bua L, Belpoggi F, L Teitelbaum S: Effect of postnatal low-dose exposure to environmental chemicals on the gut microbiome in a rodent model. Microbiome 2016, 4:26. 68. Jin Y, Zeng Z, Wu Y, Zhang S, Fu Z: Oral exposure of mice to carbendazim induces hepatic lipid metabolism disorder and gut microbiota dysbiosis. Toxicol Sci 2015, 147:116-126. 69. Zhang Y, Zhao F, Deng Y, Zhao Y, Ren H: Metagenomic and metabolomic analysis of the toxic effects of trichloroacetamide-induced gut microbiome and urine metabolome perturbations in mice. J Proteome Res 2015, 14:1752-1761.

Current Opinion in Microbiology 2017, 39:128–135