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MagMD: Database summarizing the metabolic action of gut microbiota to drugs

An increasing number of studies have reported that microbiome can affect drug response by altering pharmacokinetics and pharmacodynamics of formation of toxic metabolites. With the development of metagenomic sequencing, gut microbial composition as well as the metabolic function are drawing more and more attention for the patient stratification. The established microbiota databases provide useful information about the gut microbe-drug interactions. However, these databases generally lacked the detailed effects on substance and the metabolites, which are helpful in elucidating the mechanisms underlying drug biotransformation and personalized medicine. To address these issues, in this study, we developed Metabolic action of gut Microbiota to Drugs (MagMD), a database and a web-service covering 32, 678 records of interactions between 2,146 gut microbes, 36 enzymes and 219 substrates (mainly drugs). The detailed annotations for each entry, including the taxonomic level of microbes, the molecular form and PubChem ID of drugs from PubChem Compound Database, types of microbial secreted enzymes and the original reference links can also be accessed from the web service. Availability and implementation MagMD is a publicly available resource, constantly updated. It has an intuitive web interface and can be freely accessed at http://www.unimd.org/magmd.


Introduction
The drug-gut microbiota interactions are bidirectional, and the drug can reduce microbial fitness or alter drug availability through biotransformation. The microbes, in turn, can induce either a positive or a negative effect on drug activity and efficacy [2,25]. The gut microbiota could impact drug activity and efficacy through three main ways, including secreting microbial metabolites to interfere with drug metabolism, producing microbial enzymes to transform drug molecules and modifying drug metabolizing genes or enzymes in host liver or intestinal tissues [1].
So far it has been reported over 180 drugs are regarded as substrates for gut bacterial enzymes, and can be directly transformed by microbes in vivo [5,26]. Gut microbiota can directly metabolize drugs through enzymatic transformation or indirectly affect drugs through some special metabolites or pathways [4]. For example, Eggerthella lenta secretes cardiac glycoside reductase enzyme to reduce Lactone ring of Digoxin, forming an inactive metabolite (di-hydrodigoxin) leading to the bioavailability reduction of Digoxin's [5,6]. Clostridium scindens cleavages the sidechain of Dexamethasone, liberating an androgen metabolite, reducing drug concentration in the caecum, and increasing androgen metabolite concentration in the caecum and serum [3]. Enright et al. found that the activity of microbial enzyme affects the solubilisation capacity of bile salts to dissolve nine oral drugs including the antiepileptic phenytoin [7]. The microbiome can also change the epithelial permeability [8], gut motility [9] and other new mechanisms mediated drug absorption [10,11]. Hence, the mechanistic elucidation of gut microbiota in drug metabolism and toxicity will facilitate the rational drug design and provide important evidence for gut microbiota-target therapeutic.
Considering the importance of gut microbiota-drug interactions, several databases have been designed. PharmacoMicrobiomic contains 131 records about the gut microbiota and drug interactions, yet most of them only describe the effects of drugs on the gut microbiota [12]. Sun   microbiota interactions with specific species name, whereas only 7.12% of which have the detailed effects on substance and 11.02% of which have the records of metabolites [14]. Since these details are helpful in elucidating the mechanisms underlying drug biotransformation and personalized medicine. A typical example is that co-administration of the antiviral drug solivudine with the antitumor drug 5-fluorouracil (5-FU) can increase circulating 5-FU levels, resulting in 5-FU-related death, as the gut microbiota can transform solivudine into (E)-5-(2-bromovinyl)uracil (BVU) which is an inhibitor of hepatic dihydropyrimidine dehydrogenase and hepatic dihydropyrimidine dehydrogenase is responsible for the detoxification of 5-FU [15,16]. Moreover, gaining the product information of microbial metabolic substrates can aid in drug development. Gut microbiota enzymes, such as b-glucosidase and endo-b-glucosidase, can convert quercetin to aglycone, exhibiting antiplatelet activity. Hesperetin and naringenin belong to flavonoids like quercetin, which can also be converted into aglycones by gut microbiota enzymes [17,18]. According to the efficacy of quercetin, we can infer that hesperetin and naringenin may also have antiplatelet activity.
To better address demands mentioned above, in this study, we developed a new and regularly updated database, MagMD, to provide detailed information about 32,695 entries about the metabolic action between 2,146 gut microbes and 219 substrates. The details about the enzymes, metabolites and effects on substrates involved in the metabolic processes were also included. Our MagMD provides a user-friendly interface allowing easy access to the data. This platform provides a quick search of the database via its website at http://www.unimd.org/magmd.

Database sources and preprocessing
To collect the details involved in the drug-gut microbiota interactions, we first collected the publication using the PubMed ID recorded in the publicly available database (e.g. PharmacoMicrobiomic and MASI), and then search the related literatures from the PubMed [19] with the keywords ''gut microbiota", ''metabolism", ''gut microbiome", ''drug metabolism", ''gut microbiome" and ''drug pharmacokinetics". The literature-reported interaction entries were manually extracted from the collected publications and only the experimentally determined interactions were retained. After that 740 literature-reported records from 113 publications were included in the MagMAD, as well as the experimental details (e.g. Drug category, molecular formula and potential effects to the pharmaceutical efficacy) involved in the drug-gut microbiota interactions reported in the original publications were recorded if available. More details related to the drug metabolism were supplemented by integrating the information obtained from UniprotKB [20], ChemicalBook (https://www.chemicalbook.com/ProductIndex_EN.aspx), PubChem Compound [21] and Drugbank [22]. The potential effects to the pharmaceutical efficacy were further manually evaluated and grouped into 15 classes, such as toxicity increased, toxicity decreased, side effect, carcinogenic, activation, and activity decreased (more details can be found in the web server).
Moreover, as gut microbiota can affect drugs by secreting enzymes, we can build new interactions between the gut microbiota and drugs with the intermediary enzymes. We hypothesized a microbe can affect a drug if the microbe can secret the enzyme which was proved to metabolize the drug. Based on this hypothesis, we first collected information of the enzyme which can metabolize drugs and then the related protein sequences were retrieved from the UniProtKB database (https://www.uniprot.org). After that, we aligned the protein sequence of each enzyme against the Non-Redundant Protein Sequence Database (NR database) (https://www.ncbi.nlm.nih.gov/protein) using BLASTP. NR proteins with identity larger than 90% and coverage larger than 60% were regarded as matched. The taxonkit [23] was applied to assign the enzyme with microbes which were considered to be capable of secreting the enzyme. Hence, novel interactions between the microbes and the drugs were established using the enzymes as intermediary agent and 31,770 records were supplemented to our database.
To further expand our database, we tended to infer the association between the microbes and drugs with computational approach. With the detailed experimental results of 23 drugs metabolized by the gut microbiota of 20 healthy donors reported in Javadan et al.'s study [24], we retrieved the microbial composition (by relative abundance) of the 20 fecal samples as well as the normalized depletion of the 23 FDA approved drugs. For each drug, the key microbes involved in the drug metabolism were inferred using the stepwise linear regression method. Briefly, microbes with mean of relative abundance less than 5e-3 and variance less than 1e-4 across all the samples were filtered. The remained microbes were acted as variables and normalized depletion of the drugs were regarded as the response of the stepwise linear regression method. Microbes with a p value less than 0.05 were regarded as key microbes, and then 178 interactions were obtained.
Totally, 32,695 interactions between the microbes and drugs were established with these strategies. To facilitate users to distinguish predicted items, we included the source of each items in our database with the column named ''the ways to obtain records".

Results and conclusion
In the current release, our MagMD database holds over 32,695 records describing the interactions between 2,146 gut microbes and 219 substances (94.06% were drugs), involving 195 types of diseases. These records consist of 747 items derived from public databases and literatures, 31,770 items established using enzymes as intermediary agent, and 178 items predicted using the data in Javadan et al.'s study. To provide more details about the interactions, we also included the information of 36 enzymes related to 97.59% of these interactions. Moreover, to give an intuitive description about the effect of gut microbiota to the drug, we grouped the effects of the metabolic action of gut microbes to drugs into 15 types, such as activation, activity decreased, activity increased, inactivation, toxicity decreased, and toxicity increased, according to the original description in the related publications. More details about our database were shown in Fig. 1. The results showed that the most frequently presented microbe was Bacteroides uniformis, which are clinically important species and important in numerous metabolic activities. Similarly, the most frequently presented substance and enzyme were stevioside and b-glucosidase.
The MagMD also provided a user-friendly interface allowing easy access to the data and also provides a quick search of the database for details via its website at http://www.unimd.org/magmd (Fig. 2). The webpage consists of four interfaces, which are Home, Browse, Search, Download and About (Fig. 1). In the Search interface, users can search for metabolic actions by typing any one of the types of bacteria, enzyme substrate or effect. The users can also download all the records from the Download interface in xlsx format. The Browse interface gives the brief summary about the database.
Although, there are several databases related to gut microbiota and drugs [12,14] it still necessary to develop a more comprehensive database including the gut microbiota alteration of active substances, along with information on enzymes and effects of drug efficacy after metabolizing by gut microbiota. We believe that the proposed MagMD database will provide a broader knowledge for interaction between gut microbiota and pharmaceutical and better serve the related communities for both basic research and clinical application, such as personalized evaluation of therapeutic efficacy with the gut microbial composition. We also realize clearly some shortcomings of MagMD. As most of the interactions recorded were predicted and the confidence should be further evaluated. Besides that, more details about the interactions are required and the amount of data is not large enough.

Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.