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      Microbiome Big-Data Mining and Applications Using Single-Cell Technologies and Metagenomics Approaches Toward Precision Medicine

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          Abstract

          With the development of high-throughput sequencing technologies as well as various bioinformatics analytic tools, microbiome is not a “microbial dark matter” anymore. In this review, we first summarized the current analytical strategies used for big-data mining such as single-cell sequencing and metagenomics. We then provided insights into the integration of these strategies, showing significant advantages in fully describing microbiome from multiple aspects. Moreover, we discussed the correlation between gut microbiome with host organs and diseases, confirming the importance of big-data mining in clinical practices. We finally proposed new ideas about the trend of big-data mining in microbiome using multi-omics approaches and single-cell sequencing. The integration of multi-omics approaches and single-cell sequencing can provide full understanding of microbiome at both macroscopic level and microscopic level, thus contributing to precision medicine.

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          Most cited references31

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          Immunoglobulin A coating identifies colitogenic bacteria in inflammatory bowel disease.

          Specific members of the intestinal microbiota dramatically affect inflammatory bowel disease (IBD) in mice. In humans, however, identifying bacteria that preferentially affect disease susceptibility and severity remains a major challenge. Here, we used flow-cytometry-based bacterial cell sorting and 16S sequencing to characterize taxa-specific coating of the intestinal microbiota with immunoglobulin A (IgA-SEQ) and show that high IgA coating uniquely identifies colitogenic intestinal bacteria in a mouse model of microbiota-driven colitis. We then used IgA-SEQ and extensive anaerobic culturing of fecal bacteria from IBD patients to create personalized disease-associated gut microbiota culture collections with predefined levels of IgA coating. Using these collections, we found that intestinal bacteria selected on the basis of high coating with IgA conferred dramatic susceptibility to colitis in germ-free mice. Thus, our studies suggest that IgA coating identifies inflammatory commensals that preferentially drive intestinal disease. Targeted elimination of such bacteria may reduce, reverse, or even prevent disease development. Copyright © 2014 Elsevier Inc. All rights reserved.
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            Commensal host-bacterial relationships in the gut.

            One potential outcome of the adaptive coevolution of humans and bacteria is the development of commensal relationships, where neither partner is harmed, or symbiotic relationships, where unique metabolic traits or other benefits are provided. Our gastrointestinal tract is colonized by a vast community of symbionts and commensals that have important effects on immune function, nutrient processing, and a broad range of other host activities. The current genomic revolution offers an unprecedented opportunity to identify the molecular foundations of these relationships so that we can understand how they contribute to our normal physiology and how they can be exploited to develop new therapeutic strategies.
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              Unidentified curved bacilli in the stomach of patients with gastritis and peptic ulceration.

              Biopsy specimens were taken from intact areas of antral mucosa in 100 consecutive consenting patients presenting for gastroscopy. Spiral or curved bacilli were demonstrated in specimens from 58 patients. Bacilli cultured from 11 of these biopsies were gram-negative, flagellate, and microaerophilic and appeared to be a new species related to the genus Campylobacter. The bacteria were present in almost all patients with active chronic gastritis, duodenal ulcer, or gastric ulcer and thus may be an important factor in the aetiology of these diseases.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                09 October 2019
                2019
                : 10
                : 972
                Affiliations
                [1]Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular Imaging, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology , Wuhan, China
                Author notes

                Edited by: Jialiang Yang, Geneis (Beijing) Co. Ltd, China

                Reviewed by: Fengfeng Zhou, Jilin University, China; Xuefeng Cui, Tsinghua University, China; Minxian Wang, Broad Institute, United States

                *Correspondence: Kang Ning, ningkang@ 123456hust.edu.cn

                This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2019.00972
                6794611
                31649735
                c643bd4c-b25b-4ce4-ae9f-18f04360b012
                Copyright © 2019 Cheng, Cao and Ning

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 12 May 2019
                : 12 September 2019
                Page count
                Figures: 3, Tables: 1, Equations: 0, References: 81, Pages: 10, Words: 4389
                Categories
                Genetics
                Review

                Genetics
                big data,microbiome,metagenomics,single-cell sequencing,precision medicine
                Genetics
                big data, microbiome, metagenomics, single-cell sequencing, precision medicine

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