A systems biology approach to predict and characterize human gut microbial metabolites in colorectal cancer

Wang, Quan Qiu; Li, Li; Xu, Rong

doi:10.1038/s41598-018-24315-0

Cited by 14 publications

(17 citation statements)

References 35 publications

(34 reference statements)

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“…Most of the data on the correlation between a microbiota and cancer tumors is in the gastroenterology [26][27][28][29][30][31]. Such intestinal microorganisms as Fusobacterium nucleatum, Streptococcus gallolyticus, Bacteroides fragilis, Escherichia coli, and Enterococcus faecalis are most often mentioned as potential participants of the process.…”

Section: Microbial Metabolites In Oncologymentioning

confidence: 99%

“…A treatment of large amounts of information on substances of bacterial origin potentially capable of being included in human metabolism allows us to distinguish six groups of compounds, based on the profile of which early diagnosis of colorectal cancer can be built [29]. There are short-chain fatty acids, bile acids, indoles, cresols, phenolic (phenyl-containing fatty) acids, and polyamines.…”

Section: Microbial Metabolites In Oncologymentioning

confidence: 99%

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Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology

Белобородова¹,

Grechko²,

Olenin³

2020

Metabolomics - New Insights Into Biology and Medicine

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In the twenty-first century, metabolomics allowed evaluating the profile of metabolites of various classes of compounds in the human body. The most important achievement of the metabolic approach is to obtain evidence of the intersection of human biochemical pathways and its microbiota. The effect of certain microbial metabolites on the work of key enzymes involved in the biotransformation of amino acids and other substances becomes more important in patients at risk of developing neurological and mental disorders and also contributes to the development of life-threatening conditions up to multiple organ failure after operations, injuries, and serious diseases. The authors of this chapter call the microbiota an "invisible organ," emphasizing its functional significance, and not just taxonomy, as previously thought. This chapter will discuss the mutually beneficial integration of the metabolome/microbiome in the body of healthy people and will focus on the effects of microbiota dysfunction.

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Section: Microbial Metabolites In Oncologymentioning

confidence: 99%

Section: Microbial Metabolites In Oncologymentioning

confidence: 99%

Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology

Белобородова¹,

Grechko²,

Olenin³

2020

Metabolomics - New Insights Into Biology and Medicine

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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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“…These microorganisms are greatly influenced by the acquired diet, living habits, living, and working environment [25]. Micro ecological environment composed of microorganisms and microbial related metabolites is related to the occurrence and development of CRC [26][27][28][29]. Many gut microbes, such as Fusobacteria, Streptococcus and Clostridium [25,[30][31][32], and microbial metabolites, such as hydrolytic, reductive enzymes [33], O(6)-methyl guanine [34], short chain fatty acids and secondary bile acids, are involved in the occurrence of CRCs [26,35,36].…”

Section: Introductionmentioning

confidence: 99%

Analysis of prognosis, genome, microbiome, and microbial metabolome in different sites of colorectal cancer

Pan

et al. 2019

J Transl Med

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Background: The colorectum includes ascending colon, transverse colon, descending colon, sigmoid colon, and rectum. Different sites of colorectal cancer (CRC) are different in many aspects, including clinical symptoms, biological behaviour, and prognosis. Purpose: This study aimed to analyse prognosis, genes, bacteria, fungi, and microbial metabolome in different sites of CRC. Methods: The Surveillance, Epidemiology, and End Results (SEER) database and STAT were used to statistically describe and analyse the prognosis in different sites of CRC. RNA sequences of CRC from Broad Institute's GDAC Firehose were re-annotated and reanalysed based on different sites using weighted gene co-expression network analysis (WGCNA). The Kaplan-Meier method was used to analyse the prognosis and Cytoscape was used to construct a drugtarget network based on DGIdb databases. Bacterial 16S V3-V4 and fungal ITS V3-V4 ribosomal RNA genes of stool samples were sequenced. Gas chromatography/mass spectrometry (GS/MS) was performed to detect the microbial metabolites in stool samples. Bioinformatics analysis was performed to compare distinct gut microorganisms and microbial metabolites between rectal and sigmoid cancers. Results: The prognosis in CRC with different sites is significantly different. The closer to the anus predicted longer survival time. The difference between genes and co-expression pairs in CRC with different sites were constructed. The relative abundance of 112 mRNAs and 26 lncRNAs correlated with the sites of CRC were listed. Nine differentially expressed genes at different sites of CRC were correlated with prognosis. A drug-gene interaction network contained 227 drug-gene pairs were built. The relative abundance of gut bacteria and gut fungus, and the content of microberelated metabolites were statistically different between rectal and sigmoid cancers. Conclusions: There are many differences in prognosis, genome, drug targets, gut microbiome, and microbial metabolome in different colorectal cancer sites. These findings may improve our understanding of the role of the CRC sites in personalized and precision medicine.

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A systems biology approach to predict and characterize human gut microbial metabolites in colorectal cancer

Cited by 14 publications

References 35 publications

Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology

Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology

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

Analysis of prognosis, genome, microbiome, and microbial metabolome in different sites of colorectal cancer

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