MetabolitePredict: A de novo human metabolomics prediction system and its applications in rheumatoid arthritis

Wang, Quan Qiu; Xu, Rong

doi:10.1016/j.jbi.2017.06.002

Cited by 9 publications

(11 citation statements)

References 27 publications

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“…The 172 classified microbial metabolites captured in HMDB are currently the best-known list of human metabolites that are originated in microbes. We previously developed data-driven computational approaches to understand how microbial metabolites are mechanistically involved in various common complex diseases using this list of 172 microbial metabolites [13][14][15][16][17][18] . Standard measures of precision (fraction of recognized entities as positive that are truly positive), recall (true positive rate) and F1 (harmonic average of the precision and recall) were calculated and compared.…”

Section: Methodsmentioning

confidence: 99%

Automatic extraction, prioritization and analysis of gut microbial metabolites from biomedical literature

Wang

2020

Sci Rep

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Many diseases are driven by gene-environment interactions. one important environmental factor is the metabolic output of human gut microbiota. A comprehensive catalog of human metabolites originated in microbes is critical for data-driven approaches to understand how microbial metabolism contributes to human health and diseases. Here we present a novel integrated approach to automatically extract and analyze microbial metabolites from 28 million published biomedical records. First, we classified 28,851,232 MEDLINE records into microbial metabolism-related or not. Second, candidate microbial metabolites were extracted from the classified texts. Third, we developed signal prioritization algorithms to further differentiate microbial metabolites from metabolites originated from other resources. finally, we systematically analyzed the interactions between extracted microbial metabolites and human genes. A total of 11,846 metabolites were extracted from 28 million MEDLINE articles. The combined text classification and signal prioritization significantly enriched true positives among top: manual curation of top 100 metabolites showed a true precision of 0.55, representing a significant 38.3-fold enrichment as compared to the precision of 0.014 for baseline extraction. More importantly, 29% extracted microbial metabolites have not been captured by existing databases. We performed data-driven analysis of the interactions between the extracted microbial metabolite and human genetics. This study represents the first effort towards automatically extracting and prioritizing microbial metabolites from published biomedical literature, which can set a foundation for future tasks of microbial metabolite relationship extraction from literature and facilitate data-driven studies of how microbial metabolism contributes to human diseases. Genetic, epigenetic, and environmental factors contribute to the susceptibility, progression and outcomes of many common complex diseases 1-3. While significant progress has been made in understanding genetic, molecular, cellular aspects of human diseases, there is limited understanding how modifiable environmental factors such as food, nutrition, lifestyle, and physical activity are involved in human diseases and health. Human gut microbiota, an important modifiable intermediator between external environmental exposure and host genetics, exist in symbiotic relation with human hosts 4-8. Accumulating biomedical evidence indicates that gut microbiota and their metabolites strongly influence disease susceptibility and progression in humans 9-12. However, the underlying mechanisms remain largely unknown. We have recently developed data-driven computational approaches to understand how microbial metabolites are mechanistically involved in various common complex diseases including colorectal cancer 13,14 , Alzheimer's disease 15 , psoriasis 16 , and rheumatoid arthritis 17,18. For example, we developed network-based systems approaches to examine genetic interactions between microbial metabolites and huma...

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Section: Methodsmentioning

confidence: 99%

Automatic extraction, prioritization and analysis of gut microbial metabolites from biomedical literature

Wang

2020

Sci Rep

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“…The inferred de novo associations can be cross-checked against patient symptoms or other data sources that capture specific pathway-host and pathway-microbe information. Indeed, there exist a large number of human-specific and microbe-specific databases that can be explored for integration into a unified and minimally cross-compatible KBs that capture information at the DNA, RNA, transcriptome, protein, metabolic, and/or structural levels [6, 104, 105].…”

Section: Integration Of Knowledge Base and Experimental Datamentioning

confidence: 99%

Challenges in the construction of knowledge bases for human microbiome-disease associations

et al. 2019

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The last few years have seen tremendous growth in human microbiome research, with a particular focus on the links to both mental and physical health and disease. Medical and experimental settings provide initial sources of information about these links, but individual studies produce disconnected pieces of knowledge bounded in context by the perspective of expert researchers reading full-text publications. Building a knowledge base (KB) consolidating these disconnected pieces is an essential first step to democratize and accelerate the process of accessing the collective discoveries of human disease connections to the human microbiome. In this article, we survey the existing tools and development efforts that have been produced to capture portions of the information needed to construct a KB of all known human microbiome-disease associations and highlight the need for additional innovations in natural language processing (NLP), text mining, taxonomic representations, and field-wide vocabulary standardization in human microbiome research. Addressing these challenges will enable the construction of KBs that help identify new insights amenable to experimental validation and potentially clinical decision support. Electronic supplementary material The online version of this article (10.1186/s40168-019-0742-2) contains supplementary material, which is available to authorized users.

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“…We previously demonstrated that data-driven computational approaches have potential in uncovering mechanistic links between microbial metabolites and human diseases (colorectal cancer and Alzheimer’s disease) [24–26]. Specific for RA, we previously developed a mechanism-based prediction system, mMetabolitePredict, for human metabolome biomarker discovery and applied it to identify and prioritize metabolomic biomarkers for RA [27]. We found that among 259,170 prioritized chemicals/metabolites in human body, the subset of metabolites originated from human gut microbiota ranked highly [27].…”

Section: Introductionmentioning

confidence: 99%

“…Specific for RA, we previously developed a mechanism-based prediction system, mMetabolitePredict, for human metabolome biomarker discovery and applied it to identify and prioritize metabolomic biomarkers for RA [27]. We found that among 259,170 prioritized chemicals/metabolites in human body, the subset of metabolites originated from human gut microbiota ranked highly [27]. This finding motivated our current study (funded by Pfizer ASPIRE Rheumatology and Dermatology Research Award) to perform data-driven systematic analysis of which and how human gut microbial metabolites are involved in the immune-joint axis of the RA etiology at the genetic, functional and phenotypic levels.…”

Section: Introductionmentioning

confidence: 99%

Data-driven multiple-level analysis of gut-microbiome-immune-joint interactions in rheumatoid arthritis

Wang

2019

BMC Genomics

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BackgroundRheumatoid arthritis (RA) is the most common autoimmune disease and affects about 1% of the population. The cause of RA remains largely unknown and could result from a complex interaction between genes and environment factors. Recent studies suggested that gut microbiota and their collective metabolic outputs exert profound effects on the host immune system and are implicated in RA. However, which and how gut microbial metabolites interact with host genetics in contributing to RA pathogenesis remains unknown. In this study, we present a data-driven study to understand how gut microbial metabolites contribute to RA at the genetic, functional and phenotypic levels.ResultsWe used publicly available disease genetics, chemical genetics, human metabolome, genetic signaling pathways, mouse genome-wide mutation phenotypes, and mouse phenotype ontology data. We identified RA-associated microbial metabolites and prioritized them based on their genetic, functional and phenotypic relevance to RA. We evaluated the prioritization methods using short-chain fatty acids (SCFAs), which were previously shown to be involved in RA etiology. We validate the algorithms by showing that SCFAs are highly associated with RA at genetic, functional and phenotypic levels: SCFAs ranked at top 3.52% based on shared genes with RA, top 5.69% based on shared genetic pathways, and top 16.94% based on shared phenotypes. Based on the genetic-level analysis, human gut microbial metabolites directly interact with many RA-associated genes (as many as 18.1% of all 166 RA genes). Based on the functional-level analysis, human gut microbial metabolites participate in many RA-associated genetic pathways (as many as 71.4% of 311 genetic pathways significantly enriched for RA), including immune system pathways. Based on the phenotypic-level analysis, gut microbial metabolites affect many RA-related phenotypes (as many as 51.3% of 978 phenotypes significantly enriched for RA), including many immune system phenotypes.ConclusionsOur study demonstrates strong gut-microbiome-immune-joint interactions in RA, which converged on both genetic, functional and phenotypic levels.

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MetabolitePredict: A de novo human metabolomics prediction system and its applications in rheumatoid arthritis

Cited by 9 publications

References 27 publications

Automatic extraction, prioritization and analysis of gut microbial metabolites from biomedical literature

Automatic extraction, prioritization and analysis of gut microbial metabolites from biomedical literature

Challenges in the construction of knowledge bases for human microbiome-disease associations

Data-driven multiple-level analysis of gut-microbiome-immune-joint interactions in rheumatoid arthritis

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