2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Gut Microbiota Signature of Obese Adults Across Different Classifications

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Purpose

          Obesity is currently a major global public health issue. It has been shown by many that gut microbiota and microbial factors regulate the pathogenesis of obesity and metabolic abnormalities, but little is known about their roles in the different degrees of obesity. Here, we sought to investigate the microbial signatures of obesity of various severities.

          Patients and Methods

          We did this by characterizing the intestinal microbiome signature in a Chinese cohort of obese patients and healthy controls using 16S rRNA gene sequencing. To this end, obesity was sub-divided into four subgroups, including “Overweight”, Class I, Class II, and Class III obesity, based on body mass index (BMI).

          Results

          Microbial diversity decreased in obese subjects, and the reduction trend was correlated with the severity of obesity. We detected an expansion of Escherichia shigella in obese patients compared to healthy controls. The family Eubacterium coprostanoligenes and Tannerellaceae, the genera Eubacterium coprostanoligenes, Lachnospiraceae NK4A136, Parabacteroides, and Akkermansia, and the species Prevotella copri were microbial biomarkers of healthy people. Gammaproteobacteria and Enterobacterales were biomarkers of being “Overweight”. Erysipelatoclostridiaceae was a biomarker of Class I obesity. The class Bacilli and the order Lactobacillales were both biomarkers of Class II obesity. Negativicutes was a biomarker of Class III obesity. We further established relationships between this microbiome data and other biochemical data, including albumin, low-density lipoprotein (LDL), high-density lipoprotein (HDL), vitamin folic acid (FA) and vitamin B12 (VB12), and Interleukin-6 (IL-6) levels. Function prediction results showed a marked energy metabolism dysbiosis in obesity, especially in patients with Class III obesity.

          Conclusion

          These results suggested that people with different levels of obesity had distinct gut microbial signatures. Decreased microbial diversity, depletion of some specific taxa, and deviation in potential functions mirrored the severity of obesity in this cohort.

          Related collections

          Most cited references51

          • Record: found
          • Abstract: found
          • Article: not found

          DADA2: High resolution sample inference from Illumina amplicon data

          We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

              Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
                Bookmark

                Author and article information

                Journal
                Diabetes Metab Syndr Obes
                Diabetes Metab Syndr Obes
                dmso
                Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy
                Dove
                1178-7007
                29 December 2022
                2022
                : 15
                : 3933-3947
                Affiliations
                [1 ]Center of Gastrointestinal and Minimally Invasive Surgery, Department of General Surgery, the Third People’s Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University , Chengdu, People’s Republic of China
                [2 ]Medical Research Center, the Third People’s Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University , Chengdu, People’s Republic of China
                [3 ]Department of Dermatology, Xiangya Hospital, Central South University , Changsha, People’s Republic of China
                [4 ]College of Medicine, Southwest Jiaotong University , Chengdu, People’s Republic of China
                [5 ]Department of General Surgery, the Third People’s Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University , Chengdu, People’s Republic of China
                Author notes
                Correspondence: Tongtong Zhang, Center of Gastrointestinal and Minimally Invasive Surgery, Department of General Surgery, & Medical Research Center, the Third People’s Hospital of Chengdu, Chengdu, People’s Republic of China, Email 163zttong@163.com; Yanjun Liu, Center of Gastrointestinal and Minimally Invasive Surgery, Department of General Surgery, the Third People’s Hospital of Chengdu, No. 82, Qinglong Street, Qingyang District, Chengdu, 610031, People’s Republic of China, Emai lliuyanjun_001@163.com
                [*]

                These authors contributed equally to this work

                Author information
                http://orcid.org/0000-0003-0582-057X
                http://orcid.org/0000-0001-5667-1047
                Article
                387523
                10.2147/DMSO.S387523
                9807070
                36601354
                3c855add-57e8-4d6c-a36a-3a61aee6032a
                © 2022 Hu et al.

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                History
                : 25 August 2022
                : 28 October 2022
                Page count
                Figures: 6, Tables: 1, References: 51, Pages: 15
                Funding
                Funded by: the National Natural Science Foundation of China;
                Funded by: Chengdu High-level Key Clinical Specialty Construction Project;
                Funded by: the Science and Technology Project of The Health Planning Committee of Sichuan Municipality;
                Funds to support this work were provided by the National Natural Science Foundation of China [82170887 to LYJ]; Chengdu High-level Key Clinical Specialty Construction Project [LYJ]; and the Science and Technology Project of The Health Planning Committee of Sichuan Municipality [20PJ211 to YQ].
                Categories
                Original Research

                Endocrinology & Diabetes
                body mass index,degree of obesity,fecal microbiota,16s rrna sequencing

                Comments

                Comment on this article