MDIPID: Microbiota‐drug interaction and disease phenotype interrelation database DOI Creative Commons
Jiayi Yin, Hui Ma, Yuting Qi

et al.

iMeta, Journal Year: 2025, Volume and Issue: unknown

Published: March 21, 2025

The intricate bidirectional relationships among microbiota, microbial proteins, drugs, and diseases are essential for advancing precision medicine minimizing adverse drug reactions. However, there currently no data resources that comprehensively describe these valuable interactions. Therefore, the Microbiota-Drug Interaction Disease Phenotype Interrelation Database (MDIPID) database was developed in this study. MDIPID is distinctive its ability to elucidate complex interactions drugs/substances, disease phenotypes, thereby providing a comprehensive interconnected network facilitates identification of therapy targets advances personalized medicine. This resource expected become popular repository researchers aiming identify therapeutic targets, predict efficacy, develop new therapies, facilitating advancement can be accessed free without any login requirement at: https://idrblab.org/mdipid/. Accumulating evidence has demonstrated human microbiota plays vital role development individualized responses [1]. Indeed, (and their proteins), bidirectional. An in-depth thorough understanding increasingly regarded as key factor reducing reactions, attracting considerable attention [2]. There three major types interactions: (i) related proteins modulation on response (MMDR), which crucial drug-drug resistance [3]; (ii) or other exogenous substances impact (DEIM), fundamental pathophysiology improving efficacy [4]; (iii) microbiota-disease associations (MBDA), identifying predictive biomarkers helping stratify patients [5]. Since jointly determine development, accumulating relevant information will significantly enhance clinical predictions provide insights into therapies. Moreover, because response, formed by garnered significant interest emerged promising research direction [6]. For instance, colorectal cancer often experience diarrhea during irinotecan chemotherapy occurs metabolized toxic compound SN-38 action β-glucuronidase enzymes (GUSs) produced gut such Escherichia coli [7]. Consequently, several strategies have been proposed mitigate These leverage interaction networks could safely modulate taxa associated [3]. One approach reduce abundance pathogenic administering antibiotics, alleviating symptoms [8]. Another supplement with probiotics use GUSs inhibitors activity, thus mitigating occurrence [9]. addition played studies, based further help facilitate potential Currently, databases available offer between diseases, most remain freely accessible actively maintained. Some databases, microbe-drug association (MDAD) [10] gutMDisorder [11], microbiota-drug but do not capture drugs; some others, Peryton [12] Disbiome [13], approximately 1000 300 (a more list shown Supporting Information). Microbiota-active substance (MASI) [14] only describes interacting 806 microbiotas. best our knowledge, presents while also including underlying mechanisms, well Given importance an urgent need captures support rational delivery drugs. In study, therefore introduced systematically collect mechanisms (Figure 1). Specifically, (a) 6669 MMDR records detailing 881 drugs 628 species presented, involving 592 (MBPS) from 282 15 modulation, metabolic modification, sequestration, activation; (b) 11,760 DEIM describing variations induced 1066 drugs/exogenous provided, covering 43 groups substances, prebiotics, diet components, environmental toxicants; (c) 15,146 MBDA illustrating 2209 482 offered, encompassing 10 variation, decrease, increase, enrichment. Furthermore, categories collected collectively comprise includes 1818 2708 species, proteins. Overall, described emerge widely used empowers gain ultimately leading advancements treatment options. Microbiota gaining insight individual coordinate pharmacokinetics pharmacodynamics through various direct biotransformation, deactivation, bioaccumulation drug, indirectly affecting host's enzymes, transporters, immune system. Also, increasing highlights MBPS response. deactivate chemotherapeutic specific transporters uptake diminishing distribution throughout body concentration at target sites [15]. Thus, how developing novel strategies. Due scattered regarding effects literature, organized using methodology "METHODS" section. gathered included total belonging 3 kingdoms 28 phyla, (approved, trials, preclinical/investigative), 63 functional protein families (e.g., glutamate GABA antiporter family oxidoreductase), colon, small intestine, brain), experimental models Sprague-Dawley rats, MCF-7 breast carcinoma cells, BALB/c germ-free mice), studied phenotypes Parkinson disease, diabetes, colon cancer), methods 16S rDNA sequencing technology metagenomic sequencing), detailed mechanism 2). To exploration, offers variety searching "Home" page "Microbiota" menu (as illustrated Data access retrieval section). enable within database. Drug rapidly growing field holds [4]. Particularly, (such foods, natural products, toxicants) interact ways greatly influence composition function, reported syndromes, gastrointestinal cancers, neurodegenerative conditions [16]. Understanding different may they contribute processes. mentioned above, knowledge drug/substance likely interventions diet, fecal transplantation-based interventions) improve host therapy, minimize effects, immunotherapy, chemotherapy, radiotherapy. collecting targeted included: classes toxicants), 921 4 kingdoms, 48 function variation decrease abundance, promote growth, inhibit activity microbiota), oral, jejunum, intestine), C57BL/6 mice, CD-1 zebrafish), Alzheimer hyperlipidemia, technology), drugs/substances S1). were MDIPID, provided "Drug" section) corresponding refers fact when balance system disrupted, becomes unstable, diverse, pathogenic, undergoes colony remodeling, turn process [17]. number studies diseases. example, growth Canidia Albicans intestine schizophrenia, COVID-19, [18]; Malassezia restricta identified exacerbate inflammation inflammatory bowel [19]; anti-Saccharomyces cerevisiae antibodies (antibodies against Saccharomyces cerevisiae) increased Crohn's [20] compared healthy individuals clinically serological markers diagnosis disease. obtaining connection dysbiosis pathogenesis innovative captured encompasses classified under 333 International Classification Diseases 11th revision (ICD-11) standardized classes, 86 tract, kidney, vaginal), enrichment), methods/samples, comparative samples, microbe-disease S2). supplied, retrieved "Disease" menus complexity necessity constructing encompass simulate multifaceted dynamic exploring affect toxicity Additionally, simulating framework approaches, discovery, therapeutics, improved patient outcomes interventions. comprises network, efficient database, search presentation formats available. Researchers select suitable needs preferences. Detailed descriptions options below. Directly menu, pinpoint pertinent keyword searches (for name, etc.) deployment dropdown (categorized Genus Species Name, Status, Class Name) streamline process. On pages 2), greeted array information, general about atlas charting connections taxa, contains elements, across incorporates outlined regulatory Navigable via conduct utilizing keywords (like employing (organized Indication Genus, Name). drug-specific S1) (A) names, synonyms, Ro5 violations, current status, structural details, indications; (B) proteins; (C) analyses drug's listing affected tools, behind effects; (D) behavior, involved, mechanisms. Accessible find etc.), selecting appropriate (classified Status disease-specific S2) following sections: (including ICD-11 code, class); visualizing multidimensional entity, drug; variants impacted materials, elaborate mechanisms; drug(s) treat indicating type, status. From either "Protein" (sorted Protein Family along Displayed protein-specific S3) (name, gene, origins, family, tissue distribution, etc.); sheds light protein, expansive influences name affinity data, play. Recently, next-generation revolutionized progression breakthrough unveiled approaches rationally modulating prognosis deciphering issues requires address need, developed. provides view forming manipulation treatment. With rapid adoption artificial intelligence biomedical research, tremendous potential. It enables exploration accurately community. paves way poised seeking understand By leveraging insights, it drive medical advancements, effective treatments better outcomes. Comprehensive procedures collection preprocessing, standardization, website architecture implementation, found Supplementary Information. Jiayi Yin: Writing—original draft; curation. Hui Ma: Yuting Qi: Qingwei Zhao: curation; writing—review editing. Su Zeng: conceptualization. Fengcheng Li: visualization; Feng Zhu: conceptualization; study supported National Natural Science Foundation China (82373790, 32400516, 62402416); Zhejiang Provincial (LQN25C060001); Key R&D Program (2022YFC3400501); Leading Talent "Ten Thousand Plan" High-Level Talents Special Support Plan China; Double Top-Class Universities (181201*194232101). authors declare conflicts interest. No animals humans involved findings author upon reasonable request. materials (materials, methods, figures) online DOI iMeta http://www.imeta.science/. Figure S1. A typical illustrative diagram abundant MDIPID. S2. showing rich phenotype S3. Please note: publisher responsible content functionality supporting supplied authors. Any queries (other than missing content) should directed article.

Language: Английский

MDIPID: Microbiota‐drug interaction and disease phenotype interrelation database DOI Creative Commons
Jiayi Yin, Hui Ma, Yuting Qi

et al.

iMeta, Journal Year: 2025, Volume and Issue: unknown

Published: March 21, 2025

The intricate bidirectional relationships among microbiota, microbial proteins, drugs, and diseases are essential for advancing precision medicine minimizing adverse drug reactions. However, there currently no data resources that comprehensively describe these valuable interactions. Therefore, the Microbiota-Drug Interaction Disease Phenotype Interrelation Database (MDIPID) database was developed in this study. MDIPID is distinctive its ability to elucidate complex interactions drugs/substances, disease phenotypes, thereby providing a comprehensive interconnected network facilitates identification of therapy targets advances personalized medicine. This resource expected become popular repository researchers aiming identify therapeutic targets, predict efficacy, develop new therapies, facilitating advancement can be accessed free without any login requirement at: https://idrblab.org/mdipid/. Accumulating evidence has demonstrated human microbiota plays vital role development individualized responses [1]. Indeed, (and their proteins), bidirectional. An in-depth thorough understanding increasingly regarded as key factor reducing reactions, attracting considerable attention [2]. There three major types interactions: (i) related proteins modulation on response (MMDR), which crucial drug-drug resistance [3]; (ii) or other exogenous substances impact (DEIM), fundamental pathophysiology improving efficacy [4]; (iii) microbiota-disease associations (MBDA), identifying predictive biomarkers helping stratify patients [5]. Since jointly determine development, accumulating relevant information will significantly enhance clinical predictions provide insights into therapies. Moreover, because response, formed by garnered significant interest emerged promising research direction [6]. For instance, colorectal cancer often experience diarrhea during irinotecan chemotherapy occurs metabolized toxic compound SN-38 action β-glucuronidase enzymes (GUSs) produced gut such Escherichia coli [7]. Consequently, several strategies have been proposed mitigate These leverage interaction networks could safely modulate taxa associated [3]. One approach reduce abundance pathogenic administering antibiotics, alleviating symptoms [8]. Another supplement with probiotics use GUSs inhibitors activity, thus mitigating occurrence [9]. addition played studies, based further help facilitate potential Currently, databases available offer between diseases, most remain freely accessible actively maintained. Some databases, microbe-drug association (MDAD) [10] gutMDisorder [11], microbiota-drug but do not capture drugs; some others, Peryton [12] Disbiome [13], approximately 1000 300 (a more list shown Supporting Information). Microbiota-active substance (MASI) [14] only describes interacting 806 microbiotas. best our knowledge, presents while also including underlying mechanisms, well Given importance an urgent need captures support rational delivery drugs. In study, therefore introduced systematically collect mechanisms (Figure 1). Specifically, (a) 6669 MMDR records detailing 881 drugs 628 species presented, involving 592 (MBPS) from 282 15 modulation, metabolic modification, sequestration, activation; (b) 11,760 DEIM describing variations induced 1066 drugs/exogenous provided, covering 43 groups substances, prebiotics, diet components, environmental toxicants; (c) 15,146 MBDA illustrating 2209 482 offered, encompassing 10 variation, decrease, increase, enrichment. Furthermore, categories collected collectively comprise includes 1818 2708 species, proteins. Overall, described emerge widely used empowers gain ultimately leading advancements treatment options. Microbiota gaining insight individual coordinate pharmacokinetics pharmacodynamics through various direct biotransformation, deactivation, bioaccumulation drug, indirectly affecting host's enzymes, transporters, immune system. Also, increasing highlights MBPS response. deactivate chemotherapeutic specific transporters uptake diminishing distribution throughout body concentration at target sites [15]. Thus, how developing novel strategies. Due scattered regarding effects literature, organized using methodology "METHODS" section. gathered included total belonging 3 kingdoms 28 phyla, (approved, trials, preclinical/investigative), 63 functional protein families (e.g., glutamate GABA antiporter family oxidoreductase), colon, small intestine, brain), experimental models Sprague-Dawley rats, MCF-7 breast carcinoma cells, BALB/c germ-free mice), studied phenotypes Parkinson disease, diabetes, colon cancer), methods 16S rDNA sequencing technology metagenomic sequencing), detailed mechanism 2). To exploration, offers variety searching "Home" page "Microbiota" menu (as illustrated Data access retrieval section). enable within database. Drug rapidly growing field holds [4]. Particularly, (such foods, natural products, toxicants) interact ways greatly influence composition function, reported syndromes, gastrointestinal cancers, neurodegenerative conditions [16]. Understanding different may they contribute processes. mentioned above, knowledge drug/substance likely interventions diet, fecal transplantation-based interventions) improve host therapy, minimize effects, immunotherapy, chemotherapy, radiotherapy. collecting targeted included: classes toxicants), 921 4 kingdoms, 48 function variation decrease abundance, promote growth, inhibit activity microbiota), oral, jejunum, intestine), C57BL/6 mice, CD-1 zebrafish), Alzheimer hyperlipidemia, technology), drugs/substances S1). were MDIPID, provided "Drug" section) corresponding refers fact when balance system disrupted, becomes unstable, diverse, pathogenic, undergoes colony remodeling, turn process [17]. number studies diseases. example, growth Canidia Albicans intestine schizophrenia, COVID-19, [18]; Malassezia restricta identified exacerbate inflammation inflammatory bowel [19]; anti-Saccharomyces cerevisiae antibodies (antibodies against Saccharomyces cerevisiae) increased Crohn's [20] compared healthy individuals clinically serological markers diagnosis disease. obtaining connection dysbiosis pathogenesis innovative captured encompasses classified under 333 International Classification Diseases 11th revision (ICD-11) standardized classes, 86 tract, kidney, vaginal), enrichment), methods/samples, comparative samples, microbe-disease S2). supplied, retrieved "Disease" menus complexity necessity constructing encompass simulate multifaceted dynamic exploring affect toxicity Additionally, simulating framework approaches, discovery, therapeutics, improved patient outcomes interventions. comprises network, efficient database, search presentation formats available. Researchers select suitable needs preferences. Detailed descriptions options below. Directly menu, pinpoint pertinent keyword searches (for name, etc.) deployment dropdown (categorized Genus Species Name, Status, Class Name) streamline process. On pages 2), greeted array information, general about atlas charting connections taxa, contains elements, across incorporates outlined regulatory Navigable via conduct utilizing keywords (like employing (organized Indication Genus, Name). drug-specific S1) (A) names, synonyms, Ro5 violations, current status, structural details, indications; (B) proteins; (C) analyses drug's listing affected tools, behind effects; (D) behavior, involved, mechanisms. Accessible find etc.), selecting appropriate (classified Status disease-specific S2) following sections: (including ICD-11 code, class); visualizing multidimensional entity, drug; variants impacted materials, elaborate mechanisms; drug(s) treat indicating type, status. From either "Protein" (sorted Protein Family along Displayed protein-specific S3) (name, gene, origins, family, tissue distribution, etc.); sheds light protein, expansive influences name affinity data, play. Recently, next-generation revolutionized progression breakthrough unveiled approaches rationally modulating prognosis deciphering issues requires address need, developed. provides view forming manipulation treatment. With rapid adoption artificial intelligence biomedical research, tremendous potential. It enables exploration accurately community. paves way poised seeking understand By leveraging insights, it drive medical advancements, effective treatments better outcomes. Comprehensive procedures collection preprocessing, standardization, website architecture implementation, found Supplementary Information. Jiayi Yin: Writing—original draft; curation. Hui Ma: Yuting Qi: Qingwei Zhao: curation; writing—review editing. Su Zeng: conceptualization. Fengcheng Li: visualization; Feng Zhu: conceptualization; study supported National Natural Science Foundation China (82373790, 32400516, 62402416); Zhejiang Provincial (LQN25C060001); Key R&D Program (2022YFC3400501); Leading Talent "Ten Thousand Plan" High-Level Talents Special Support Plan China; Double Top-Class Universities (181201*194232101). authors declare conflicts interest. No animals humans involved findings author upon reasonable request. materials (materials, methods, figures) online DOI iMeta http://www.imeta.science/. Figure S1. A typical illustrative diagram abundant MDIPID. S2. showing rich phenotype S3. Please note: publisher responsible content functionality supporting supplied authors. Any queries (other than missing content) should directed article.

Language: Английский

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