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      Host-diet-gut microbiome interactions influence human energy balance: a randomized clinical trial

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          Abstract

          The gut microbiome is emerging as a key modulator of human energy balance. Prior studies in humans lacked the environmental and dietary controls and precision required to quantitatively evaluate the contributions of the gut microbiome. Using a Microbiome Enhancer Diet (MBD) designed to deliver more dietary substrates to the colon and therefore modulate the gut microbiome, we quantified microbial and host contributions to human energy balance in a controlled feeding study with a randomized crossover design in young, healthy, weight stable males and females (NCT02939703). In a metabolic ward where the environment was strictly controlled, we measured energy intake, energy expenditure, and energy output (fecal and urinary). The primary endpoint was the within-participant difference in host metabolizable energy between experimental conditions [Control, Western Diet (WD) vs. MBD]. The secondary endpoints were enteroendocrine hormones, hunger/satiety, and food intake. Here we show that, compared to the WD, the MBD leads to an additional 116 ± 56 kcals (P < 0.0001) lost in feces daily and thus, lower metabolizable energy for the host (89.5 ± 0.73%; range 84.2-96.1% on the MBD vs. 95.4 ± 0.21%; range 94.1-97.0% on the WD; P < 0.0001) without changes in energy expenditure, hunger/satiety or food intake (P > 0.05). Microbial 16S rRNA gene copy number (a surrogate of biomass) increases (P < 0.0001), beta-diversity changes (whole genome shotgun sequencing; P = 0.02), and fermentation products increase (P < 0.01) on an MBD as compared to a WD along with significant changes in the host enteroendocrine system (P < 0.0001). The substantial interindividual variability in metabolizable energy on the MBD is explained in part by fecal SCFAs and biomass. Our results reveal the complex host-diet-microbiome interplay that modulates energy balance.

          Abstract

          The gut microbiome is causally linked to body weight in preclinical models. Here, in a controlled feeding study, the authors show that greater delivery of gut-microbiome fermentable dietary substrates to the colon leads to a net negative energy balance that is accompanied by robust microbial and host responses.

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data

            Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
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              Complex heatmaps reveal patterns and correlations in multidimensional genomic data.

              Parallel heatmaps with carefully designed annotation graphics are powerful for efficient visualization of patterns and relationships among high dimensional genomic data. Here we present the ComplexHeatmap package that provides rich functionalities for customizing heatmaps, arranging multiple parallel heatmaps and including user-defined annotation graphics. We demonstrate the power of ComplexHeatmap to easily reveal patterns and correlations among multiple sources of information with four real-world datasets.
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                Author and article information

                Contributors
                Dr.Rosy@asu.edu
                Steven.R.Smith@AdventHealth.com
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                31 May 2023
                31 May 2023
                2023
                : 14
                : 3161
                Affiliations
                [1 ]GRID grid.489332.7, AdventHealth Translational Research Institute, ; Orlando, FL USA
                [2 ]GRID grid.215654.1, ISNI 0000 0001 2151 2636, Biodesign Center for Health through Microbiomes, , Arizona State University, ; Tempe, AZ USA
                [3 ]GRID grid.215654.1, ISNI 0000 0001 2151 2636, Biodesign Swette Center for Environmental Biotechnology, , Arizona State University, ; Tempe, AZ USA
                [4 ]Skyology Inc, San Francisco, CA USA
                [5 ]GRID grid.215654.1, ISNI 0000 0001 2151 2636, School of Sustainable Engineering and the Built Environment, , Arizona State University, ; Tempe, AZ USA
                Author information
                http://orcid.org/0000-0003-2065-0003
                http://orcid.org/0000-0003-0442-4217
                http://orcid.org/0000-0003-4060-5306
                http://orcid.org/0000-0001-5209-4625
                http://orcid.org/0000-0002-3678-149X
                http://orcid.org/0000-0001-6064-3524
                Article
                38778
                10.1038/s41467-023-38778-x
                10232526
                37258525
                f2ad276f-db9b-4bd8-8c83-8ebd65c52723
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 January 2023
                : 12 May 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000062, U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases);
                Award ID: R01DK105829
                Award ID: R01DK105829
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases)
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                Uncategorized
                microbiome,translational research
                Uncategorized
                microbiome, translational research

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