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      The East Asian gut microbiome is distinct from colocalized White subjects and connected to metabolic health

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          Abstract

          East Asians (EAs) experience worse metabolic health outcomes compared to other ethnic groups at lower body mass indices; however, the potential role of the gut microbiota in contributing to these health disparities remains unknown. We conducted a multi-omic study of 46 lean and obese East Asian and White participants living in the San Francisco Bay Area, revealing marked differences between ethnic groups in bacterial richness and community structure. White individuals were enriched for the mucin-degrading Akkermansia muciniphila. East Asian subjects had increased levels of multiple bacterial phyla, fermentative pathways detected by metagenomics, and the short-chain fatty acid end-products acetate, propionate, and isobutyrate. Differences in the gut microbiota between the East Asian and White subjects could not be explained by dietary intake, were more pronounced in lean individuals, and were associated with current geographical location. Microbiome transplantations into germ-free mice demonstrated stable diet- and host genotype-independent differences between the gut microbiotas of East Asian and White individuals that differentially impact host body composition. Taken together, our findings add to the growing body of literature describing microbiome variations between ethnicities and provide a starting point for defining the mechanisms through which the microbiome may shape disparate health outcomes in East Asians.

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          The community of microbes living in the human gut varies based on where a person lives, in part because of differences in diets but also due to factors still incompletely understood. In turn, this ‘microbiome’ may have wide-ranging effects on health and diseases such as obesity and diabetes.

          Many scientists want to understand how differences in the microbiome emerge between people, and whether this may explain why certain diseases are more common in specific populations. Self-identified race or ethnicity can be a useful tool in that effort, as it can serve as a proxy for cultural habits (such as diets) or genetic information.

          In the United States, self-identified East Asian Americans often have worse ‘metabolic health’ (e.g. levels of sugar or certain fat molecules in the blood) at a lower weight than those identifying as White. Ang, Alba, Upadhyay et al. investigated whether this health disparity was linked to variation in the gut microbiome. Samples were collected from 46 lean and obese individuals living in the San Francisco Bay Area who identified as White or East Asian.

          The analyses showed that while the gut microbiome of White participants changed in association with obesity, the microbiomes of East Asian participants were distinct from their White counterparts even at normal weight, with features mirroring what was seen in White individuals in the context of obesity. Although these differences were connected to people’s current address, they were not attributable to dietary differences.

          Ang, Alba, Upadhyay et al. then transplanted the microbiome of the participants into genetically identical mice with microbe-free guts. The differences between the gut microbiomes of White and East Asian participants persisted in recipient animals. When fed the same diet, the mice also gained different amounts of weight depending on the ethnic identity of the microbial donor.

          These results show that self-identified ethnicity may be an important variable to consider in microbiome studies, alongside other factors such as geography. Ultimately, this research may help to design better, more personalized treatments for an array of conditions.

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          Most cited references99

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            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.
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              fastp: an ultra-fast all-in-one FASTQ preprocessor

              Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                07 October 2021
                2021
                : 10
                : e70349
                Affiliations
                [1 ] Department of Microbiology and Immunology, G.W. Hooper Research Foundation San Francisco United States
                [2 ] Diabetes Center, University of California San Francisco San Francisco United States
                [3 ] Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, University of California San Francisco San Francisco United States
                [4 ] Center for Molecular Toxicology and Carcinogenesis, Department of Veterinary & Biomedical Sciences, Pennsylvania State University College Park United States
                University of Bern Switzerland
                National Institute of Child Health and Human Development United States
                University of Bern Switzerland
                Instituto Gulbenkian de Ciencia Portugal
                Author notes

                These authors contributed equally.

                Author information
                https://orcid.org/0000-0002-9788-1283
                https://orcid.org/0000-0002-1398-9861
                https://orcid.org/0000-0002-7652-1043
                https://orcid.org/0000-0002-8649-1706
                https://orcid.org/0000-0002-0888-2875
                Article
                70349
                10.7554/eLife.70349
                8612731
                34617511
                56e341f1-2083-498b-ac6b-12fbd6ba2b99
                © 2021, Ang et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 14 May 2021
                : 06 October 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000062, National Institute of Diabetes and Digestive and Kidney Diseases;
                Award ID: R01DK114034
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000062, National Institute of Diabetes and Digestive and Kidney Diseases;
                Award ID: R01DK11230401
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01HL122593
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01AR074500
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01DK11230403S1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: P30DK098722
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000041, American Diabetes Association;
                Award ID: 1-18-PMF-003
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: T32HL007185
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Microbiology and Infectious Disease
                Custom metadata
                The gut microbiota of East Asians is distinct, dissociated from body mass, and self-sustains compared to White subjects from the same region, and therein may underpin a key health disparity.

                Life sciences
                human gut microbiome,ethnicity,multi-omics,metabolic syndrome,obesity,biogeography,human,mouse
                Life sciences
                human gut microbiome, ethnicity, multi-omics, metabolic syndrome, obesity, biogeography, human, mouse

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