A genome-wide systems analysis reveals strong link between colorectal cancer and trimethylamine N-oxide (TMAO), a gut microbial metabolite of dietary meat and fat

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

doi:10.1186/1471-2164-16-s7-s4

Cited by 159 publications

(116 citation statements)

References 22 publications

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“…The markers enriched in CRC oral samples are more unexpected. Besides Porphyromonas spp., Prevotella maculosa , we found a Lachnospiraceae SGB (potentially TMA-producing and consistent with gut results 10, 48–50 ), Capnocytophaga leadbetteri, Cardiobacterium hominis, etc. (Fig.…”

Section: Resultssupporting

confidence: 88%

Over 50000 metagenomically assembled draft genomes for the human oral microbiome reveal new taxa

Zhu

Liang

Chen

et al. 2019

Preprint

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21The oral cavity of each person is home for hundreds of bacterial species. While 22 taxa for oral diseases have been well studied using culture-based as well as 23 amplicon sequencing methods, metagenomic and genomic information remain 24 scarce compared to the fecal microbiome. Here we provide metagenomic shotgun 25 data for 3346 oral metagenomics samples, and together with 808 published 26 samples, assemble 56,213 metagenome-assembled genomes (MAGs). 64% of the 27 3,589 species-level genome bins contained no publicly available genomes, others 28 with only a handful. The resulting genome collection is representative of samples 29 around the world and across physiological conditions, contained many genomes 30 from Candidate phyla radiation (CPR) which lack monoculture, and enabled 31 discovery of new taxa such as a family within the Acholeplasmataceae order. 32 New biomarkers were identified for rheumatoid arthritis or colorectal cancer, 33 which would be more convenient than fecal samples. The large number of 34 metagenomic samples also allowed assembly of many strains from important 35 oral taxa such as Porphyromonas and Neisseria. Predicted functions enrich in 36 drug metabolism and small molecule synthesis. Thus, these data lay down a 37 genomic framework for future inquiries of the human oral microbiome. 38 39The human microbiome has been implicated in a growing number of diseases. 40The majority of microbial cells is believed to reside in the large intestine 1 and cohorts 41 with fecal metagenomic data contain over 1000 individuals 2, 3 . For the oral 42 microbiome, hundreds of metagenomic shotgun-sequenced samples have been 43 available from the Human Microbiome Project (HMP) and for rheumatoid arthritis 4-6 . 44A number of other diseases studied by Metagenome-wide association studies (MWAS) 45 using gut microbiome data also indicated potential contribution from the oral 46 microbiome in disease etiology 7-12 . Although the MWAS on rheumatoid arthritis was 47 the oral microbiome is believed to be well covered by culturing 13 , and analyses by 54 16S rRNA gene amplicon sequencing or polymerase chain reaction (PCR) are 55 common. Recently published large-scale metagenomic assembly efforts mostly 56 included fecal metagenomic data [14][15][16] . It is not clear how much is really missing for 57 the oral microbiome. The saliva, in particular, seems to have more bacterial species 58 per individual than the fecal microbiome 17 . 59After getting contigs using assembly algorithms suitable for metagenomic 60 data 18 , a central idea used by metagenomic binning algorithms is that genes or contigs 61 that co-vary in abundance among many samples belong to the same microbial 62 genome 8,[19][20][21] . Large cohorts are therefore prerequisites for high-quality assembly. 63Here we present 3346 new oral metagenomic samples, and 56,213 64 metagenome-assembled genomes (MAGs) which represent 3,589 species-level clades, 65revealing new taxa as well as substantially complementing the genomic content of 66 known species. This ...

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Section: Resultssupporting

confidence: 88%

Over 50000 metagenomically assembled draft genomes for the human oral microbiome reveal new taxa

Zhu

Liang

Chen

et al. 2019

Preprint

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“…In another study, 4-PBA significantly decreased ER stress, inflammatory cytokines, dendritic cell infiltration, airway inflammation, and AHR in the mouse model of ovalbumin-induced eosinophil-dominant asthma (33). However, 4-PBA and TMAO have not been used in clinical trials due to their toxicity and/or off-target effects (12,19,38,41,45,50,55,57,62). On the other hand, as we stated earlier, TUDCA is currently being used in clinical trials (NCT no.…”

Section: Discussionmentioning

confidence: 99%

Effect of a chemical chaperone, tauroursodeoxycholic acid, on HDM-induced allergic airway disease

Siddesha

Nakada

Mihavics

et al. 2016

American Journal of Physiology-Lung Cellular and Molecular Phys

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Endoplasmic reticulum (ER) stress-induced unfolded protein response plays a critical role in inflammatory diseases, including allergic airway disease. However, the benefits of inhibiting ER stress in the treatment of allergic airway disease are not well known. Herein, we tested the therapeutic potential of a chemical chaperone, tauroursodeoxycholic acid (TUDCA), in combating allergic asthma, using a mouse model of house dust mite (HDM)-induced allergic airway disease. TUDCA was administered during the HDM-challenge phase (preventive regimen), after the HDM-challenge phase (therapeutic regimen), or therapeutically during a subsequent HDM rechallenge (rechallenge regimen). In the preventive regimen, TUDCA significantly decreased HDM-induced inflammation, markers of ER stress, airway hyperresponsiveness (AHR), and fibrosis. Similarly, in the therapeutic regimen, TUDCA administration efficiently decreased HDM-induced airway inflammation, mucus metaplasia, ER stress markers, and AHR, but not airway remodeling. Interestingly, TUDCA administered therapeutically in the HDM rechallenge regimen markedly attenuated HDM-induced airway inflammation, mucus metaplasia, ER stress markers, methacholine-induced AHR, and airway fibrotic remodeling. These results indicate that the inhibition of ER stress in the lungs through the administration of chemical chaperones could be a valuable strategy in the treatment of allergic airway diseases.

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“…We applied the network-based ranking algorithms that we recently developed [8–10, 12–14] to prioritize diseases that are genetically related to each of the 171 metabolites. The output of this network-based ranking algorithm is a list of ranked diseases (AD and other diseases) for each of the 171 microbial metabolites (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Computational approaches have been widely used in biomedical fields, including drug discovery [6–10] and disease genetics prediction [11–13]. In one of our recent studies, we developed a hypothesis-driven genome-wide systems approach to reveal the strong mechanistic links between colorectal cancer and trimethylamine N-oxide (TMAO), a gut microbial metabolite of dietary meat and fat [14]. To date, however, computational approaches to systematically characterizing and understanding the complex host genome-microbiome metabolism interactions in AD have not been undertaken.…”

Section: Introductionmentioning

confidence: 99%

Towards understanding brain-gut-microbiome connections in Alzheimer’s disease

Wang²

2016

BMC Syst Biol

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144

100

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BackgroundAlzheimer’s disease (AD) is complex, with genetic, epigenetic, and environmental factors contributing to disease susceptibility and progression. While significant progress has been made in understanding genetic, molecular, behavioral, and neurological aspects of AD, relatively little is known about which environmental factors are important in AD etiology and how they interact with genetic factors in the development of AD. Here, we propose a data-driven, hypotheses-free computational approach to characterize which and how human gut microbial metabolites, an important modifiable environmental factor, may contribute to various aspects of AD.Materials and methodsWe integrated vast amounts of complex and heterogeneous biomedical data, including disease genetics, chemical genetics, human microbial metabolites, protein-protein interactions, and genetic pathways. We developed a novel network-based approach to model the genetic interactions between all human microbial metabolites and genetic diseases. We identified metabolites that share significant genetic commonality with AD in humans. We developed signal prioritization algorithms to identify the co-regulated genetic pathways underlying the identified AD-metabolite (brain-gut) connections.ResultsWe validated our algorithms using known microbial metabolite-AD associations, namely AD-3,4-dihydroxybenzeneacetic acid, AD-mannitol, and AD-succinic acid. Our study provides supporting evidence that human gut microbial metabolites may be an important mechanistic link between environmental exposure and various aspects of AD. We identified metabolites that are significantly associated with various aspects in AD, including AD susceptibility, cognitive decline, biomarkers, age of onset, and the onset of AD. We identified common genetic pathways underlying AD biomarkers and its top one ranked metabolite trimethylamine N-oxide (TMAO), a gut microbial metabolite of dietary meat and fat. These coregulated pathways between TMAO-AD may provide insights into the mechanisms of how dietary meat and fat contribute to AD.ConclusionsEmploying an integrated computational approach, we provide intriguing and supporting evidence for a role of microbial metabolites, an important modifiable environmental factor, in AD etiology. Our study provides the foundations for subsequent hypothesis-driven biological and clinical studies of brain-gut-environment interactions in AD.

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A genome-wide systems analysis reveals strong link between colorectal cancer and trimethylamine N-oxide (TMAO), a gut microbial metabolite of dietary meat and fat

Cited by 159 publications

References 22 publications

Over 50000 metagenomically assembled draft genomes for the human oral microbiome reveal new taxa

Over 50000 metagenomically assembled draft genomes for the human oral microbiome reveal new taxa

Effect of a chemical chaperone, tauroursodeoxycholic acid, on HDM-induced allergic airway disease

Towards understanding brain-gut-microbiome connections in Alzheimer’s disease

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