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Journal ArticleDOI: 10.1093/BFGP/ELAA029

A comprehensive review for gut microbes: technologies, interventions, metabolites and diseases.

02 Mar 2021-Briefings in Functional Genomics (Oxford Academic)-Vol. 20, Iss: 1, pp 42-60
Abstract: Gut microbes have attracted much more attentions in the recent decade since their essential roles in the development of metabolic diseases, cancer and neurological diseases Considerable evidence indicates that the metabolism of gut microbes exert influences on intestinal homeostasis and human diseases Here, we first reviewed two mainstream sequencing technologies involving 16s rRNA sequencing and metagenomic sequencing for gut microbes, and data analysis methods assessing alpha and beta diversity Next, we introduced some observational studies reflecting that many factors, such as lifestyle and intake of diets, drugs, contribute to gut microbes' quantity and diversity Then, metabolites produced by gut microbes were presented to understand that gut microbes exert on host homeostasis in the intestinal epithelium and immune system Finally, we focused on the molecular mechanism of gut microbes on the occurrence and development of several common diseases In-depth knowledge of the relationship among interventions, gut microbes and diseases might provide new insights in to disease prevention and treatment

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Journal ArticleDOI: 10.1016/J.YMETH.2021.05.016
Chunyan Ao1, Chunyan Ao2, Quan Zou2, Liang Yu1Institutions (2)
24 May 2021-Methods
Abstract: N2-methylguanosine is a post-transcriptional modification of RNA that is found in eukaryotes and archaea. The biological function of m2G modification discovered so far is to control and stabilize the three-dimensional structure of tRNA and the dynamic barrier of reverse transcription. To discover additional biological functions of m2G, it is necessary to develop time-saving and labor-saving calculation tools to identify m2G. In this paper, based on hybrid features and a random forest, a novel predictor, RFhy-m2G, was developed to identify the m2G modification sites for three species. The hybrid feature used by the predictor is used to fuse the three features of ENAC, PseDNC, and NPPS. These three features include primary sequence derivation properties, physicochemical properties, and position-specific properties. Since there are redundant features in hybrid features, MRMD2.0 is used for optimal feature selection. Through feature analysis, it is found that the optimal hybrid features obtained still contain three kinds of properties, and the hybrid features can more accurately identify m2G modification sites and improve prediction performance. Based on five-fold cross-validation and independent testing to evaluate the prediction model, the accuracies obtained were 0.9982 and 0.9417, respectively. The robustness of the predictor is demonstrated by comparisons with other predictors.

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1 Citations


Open accessJournal ArticleDOI: 10.1093/NAR/GKAB786
Liang Cheng1, Changlu Qi1, Haixiu Yang1, Minke Lu1  +6 moreInstitutions (2)
Abstract: gutMGene (http://bio-annotation.cn/gutmgene), a manually curated database, aims at providing a comprehensive resource of target genes of gut microbes and microbial metabolites in humans and mice. Metagenomic sequencing of fecal samples has identified 3.3 × 106 non-redundant microbial genes from up to 1500 different species. One of the contributions of gut microbiota to host biology is the circulating pool of bacterially derived small-molecule metabolites. It has been estimated that 10% of metabolites found in mammalian blood are derived from the gut microbiota, where they can produce systemic effects on the host through activating or inhibiting gene expression. The current version of gutMGene documents 1331 curated relationships between 332 gut microbes, 207 microbial metabolites and 223 genes in humans, and 2349 curated relationships between 209 gut microbes, 149 microbial metabolites and 544 genes in mice. Each entry in the gutMGene contains detailed information on a relationship between gut microbe, microbial metabolite and target gene, a brief description of the relationship, experiment technology and platform, literature reference and so on. gutMGene provides a user-friendly interface to browse and retrieve each entry using gut microbes, disorders and intervention measures. It also offers the option to download all the entries and submit new experimentally validated associations.

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Topics: Metagenomics (52%), Gut flora (51%)

1 Citations


Journal ArticleDOI: 10.1016/J.YMETH.2021.07.011
Hasan Zulfiqar1, Zi-Jie Sun1, Qin-Lai Huang1, Shi-Shi Yuan1  +4 moreInstitutions (2)
02 Aug 2021-Methods
Abstract: N4-methylcytosine (4mC) is a type of DNA modification which could regulate several biological progressions such as transcription regulation, replication and gene expressions. Precisely recognizing 4mC sites in genomic sequences can provide specific knowledge about their genetic roles. This study aimed to develop a deep learning-based model to predict 4mC sites in the Escherichia coli. In the model, DNA sequences were encoded by word embedding technique 'word2vec'. The obtained features were inputted into 1-D convolutional neural network (CNN) to discriminate 4mC sites from non-4mC sites in Escherichia coli genome. The examination on independent dataset showed that our model could yield the overall accuracy of 0.861, which was about 4.3% higher than the existing model. To provide convenience to scholars, we provided the data and source code of the model which can be freely download from https://github.com/linDing-groups/Deep-4mCW2V.

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Open accessJournal ArticleDOI: 10.3389/FMICB.2021.680101
Ying Han1, Zhaowei Gong1, Guizhi Sun1, Jing Xu1  +5 moreInstitutions (1)
Abstract: Acute myocardial infarction (AMI) continues as the main cause of morbidity and mortality worldwide. Interestingly, emerging evidence highlights the role of gut microbiota in regulating the pathogenesis of coronary heart disease, but few studies have systematically assessed the alterations and influence of gut microbiota in AMI patients. As one approach to address this deficiency, in this study the composition of fecal microflora was determined from Chinese AMI patients and links between gut microflora and clinical features and functional pathways of AMI were assessed. Fecal samples from 30 AMI patients and 30 healthy controls were collected to identify the gut microbiota composition and the alterations using bacterial 16S rRNA gene sequencing. We found that gut microflora in AMI patients contained a lower abundance of the phylum Firmicutes and a slightly higher abundance of the phylum Bacteroidetes compared to the healthy controls. Chao1 (P = 0.0472) and PD-whole-tree (P = 0.0426) indices were significantly lower in the AMI versus control group. The AMI group was characterized by higher levels of the genera Megasphaera, Butyricimonas, Acidaminococcus, and Desulfovibrio, and lower levels of Tyzzerella 3, Dialister, [Eubacterium] ventriosum group, Pseudobutyrivibrio, and Lachnospiraceae ND3007 group as compared to that in the healthy controls (P < 0.05). The common metabolites of these genera are mostly short-chain fatty acids, which reveals that the gut flora is most likely to affect the occurrence and development of AMI through the short-chain fatty acid pathway. In addition, our results provide the first evidence revealing remarkable differences in fecal microflora among subgroups of AMI patients, including the STEMI vs. NSTEMI, IRA-LAD vs. IRA-Non-LAD and Multiple (≥2 coronary stenosis) vs. Single coronary stenosis groups. Several gut microflora were also correlated with clinically significant characteristics of AMI patients, including LVEDD, LVEF, serum TnI and NT-proBNP, Syntax score, counts of leukocytes, neutrophils and monocytes, and fasting serum glucose levels. Taken together, the data generated enables the prediction of several functional pathways as based on the fecal microfloral composition of AMI patients. Such information may enhance our comprehension of AMI pathogenesis.

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Topics: Gut flora (53%), Dysbiosis (52%), Lachnospiraceae (52%)

Open accessJournal ArticleDOI: 10.3389/FMICB.2021.685549
Haixiu Yang1, Fan Tong2, Changlu Qi1, Ping Wang1  +2 moreInstitutions (2)
Abstract: Many microbes are parasitic within the human body, engaging in various physiological processes and playing an important role in human diseases. The discovery of new microbe-disease associations aids our understanding of disease pathogenesis. Computational methods can be applied in such investigations, thereby avoiding the time-consuming and laborious nature of experimental methods. In this study, we constructed a comprehensive microbe-disease network by integrating known microbe-disease associations from three large-scale databases (Peryton, Disbiome, and gutMDisorder), and extended the random walk with restart to the network for prioritizing unknown microbe-disease associations. The area under the curve values of the leave-one-out cross-validation and the fivefold cross-validation exceeded 0.9370 and 0.9366, respectively, indicating the high performance of this method. Despite being widely studied diseases, in case studies of inflammatory bowel disease, asthma, and obesity, some prioritized disease-related microbes were validated by recent literature. This suggested that our method is effective at prioritizing novel disease-related microbes and may offer further insight into disease pathogenesis.

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Topics: Disease (50%)
References
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240 results found


Open accessJournal ArticleDOI: 10.1126/SCIENCE.1110591
10 Jun 2005-Science
Abstract: The human endogenous intestinal microflora is an essential “organ” in providing nourishment, regulating epithelial development, and instructing innate immunity; yet, surprisingly, basic features remain poorly described. We examined 13,355 prokaryotic ribosomal RNA gene sequences from multiple colonic mucosal sites and feces of healthy subjects to improve our understanding of gut microbial diversity. A majority of the bacterial sequences corresponded to uncultivated species and novel microorganisms. We discovered significant intersubject variability and differences between stool and mucosa community composition. Characterization of this immensely diverse ecosystem is the first step in elucidating its role in health and disease.

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Topics: Intestinal mucosa (60%), Flora (microbiology) (50%)

6,268 Citations


Open accessJournal ArticleDOI: 10.1186/GB-2011-12-6-R60
Nicola Segata1, Jacques Izard2, Jacques Izard1, Levi Waldron1  +4 moreInstitutions (3)
24 Jun 2011-Genome Biology
Abstract: This study describes and validates a new method for metagenomic biomarker discovery by way of class comparison, tests of biological consistency and effect size estimation. This addresses the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities, which is a central problem to the study of metagenomics. We extensively validate our method on several microbiomes and a convenient online interface for the method is provided at http://huttenhower.sph.harvard.edu/lefse/.

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Topics: Biomarker discovery (51%), Metagenomics (51%)

6,049 Citations


Open accessJournal ArticleDOI: 10.1038/NATURE12820
23 Jan 2014-Nature
Abstract: Long-term dietary intake influences the structure and activity of the trillions of microorganisms residing in the human gut, but it remains unclear how rapidly and reproducibly the human gut microbiome responds to short-term macronutrient change. Here we show that the short-term consumption of diets composed entirely of animal or plant products alters microbial community structure and overwhelms inter-individual differences in microbial gene expression. The animal-based diet increased the abundance of bile-tolerant microorganisms (Alistipes, Bilophila and Bacteroides) and decreased the levels of Firmicutes that metabolize dietary plant polysaccharides (Roseburia, Eubacterium rectale and Ruminococcus bromii). Microbial activity mirrored differences between herbivorous and carnivorous mammals, reflecting trade-offs between carbohydrate and protein fermentation. Foodborne microbes from both diets transiently colonized the gut, including bacteria, fungi and even viruses. Finally, increases in the abundance and activity of Bilophila wadsworthia on the animal-based diet support a link between dietary fat, bile acids and the outgrowth of microorganisms capable of triggering inflammatory bowel disease. In concert, these results demonstrate that the gut microbiome can rapidly respond to altered diet, potentially facilitating the diversity of human dietary lifestyles.

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Topics: Microbiome (58%), Enterotype (55%), Gastrointestinal Microbiome (53%) ... show more

5,438 Citations


Open accessJournal ArticleDOI: 10.1038/NBT.2676
Abstract: Profiling phylogenetic marker genes, such as the 16S rRNA gene, is a key tool for studies of microbial communities but does not provide direct evidence of a community's functional capabilities. Here we describe PICRUSt (phylogenetic investigation of communities by reconstruction of unobserved states), a computational approach to predict the functional composition of a metagenome using marker gene data and a database of reference genomes. PICRUSt uses an extended ancestral-state reconstruction algorithm to predict which gene families are present and then combines gene families to estimate the composite metagenome. Using 16S information, PICRUSt recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host-associated and environmental communities, with quantifiable uncertainty. Our results demonstrate that phylogeny and function are sufficiently linked that this 'predictive metagenomic' approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available.

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Topics: Metagenomics (54%), Marker gene (52%), Human Microbiome Project (50%)

5,291 Citations


Open accessJournal ArticleDOI: 10.1038/NATURE06244
17 Oct 2007-Nature
Abstract: A strategy to understand the microbial components of the human genetic and metabolic landscape and how they contribute to normal physiology and predisposition to disease.

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Topics: Human Microbiome Project (66%), Microbiome (60%), Human microbiome (56%) ... show more

3,977 Citations


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