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      Effect of a bioconverted product of Lotus corniculatus seed on the axillary microbiome and body odor

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          Abstract

          The skin microbiome, especially the axillary microbiome, consists of odor-causing bacteria that decompose odorless sweat into malodor compounds, which contributes to the formation of body odor. Plant-derived products are a cheap source of bioactive compounds that are common ingredients in cosmetics. Microbial bioconversion of natural products is an ecofriendly and economical method for production of new or improved biologically active compounds. Therefore, in this study, we tested the potential of a Lactobacillus acidophilus KNU-02-mediated bioconverted product (BLC) of Lotus corniculatus seed to reduce axillary malodor and its effect on the associated axillary microbiota. A chemical profile analysis revealed that benzoic acid was the most abundant chemical compound in BLC, which increased following bioconversion. Moreover, BLC treatment was found to reduce the intensity of axillary malodor. We tested the axillary microbiome of 18 study participants, divided equally into BLC and placebo groups, and revealed through 16S rRNA gene sequencing that Staphylococcus, Corynebacterium, and Anaerococcus were the dominant taxa, and some of these taxa were significantly associated with axillary malodor. After one week of BLC treatment, the abundance of Corynebacterium and Anaerococcus, which are associated with well-known odor-related genes that produce volatile fatty acids, had significantly reduced. Likewise, the identified odor-related genes decreased after the application of BLC. BLC treatment enhanced the richness and network density of the axillary microbial community. The placebo group, on the other hand, showed no difference in the microbial richness, odor associated taxa, and predicted functional genes after a week. The results demonstrated that BLC has the potential to reduce the axillary malodor and the associated odor-causing bacteria, which makes BLC a viable deodorant material in cosmetic products.

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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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            Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences

            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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              Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB

              A 16S rRNA gene database ( http://greengenes.lbl.gov ) addresses limitations of public repositories by providing chimera screening, standard alignment, and taxonomic classification using multiple published taxonomies. It was found that there is incongruent taxonomic nomenclature among curators even at the phylum level. Putative chimeras were identified in 3% of environmental sequences and in 0.2% of records derived from isolates. Environmental sequences were classified into 100 phylum-level lineages in the Archaea and Bacteria .
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                Author and article information

                Contributors
                jhshin@knu.ac.kr
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                12 May 2021
                12 May 2021
                2021
                : 11
                : 10138
                Affiliations
                [1 ]GRID grid.258803.4, ISNI 0000 0001 0661 1556, Department of Applied Biosciences, , Kyungpook National University, ; Daegu, 41566 Republic of Korea
                [2 ]R&I Center, COSMAX BTI, Seongnam, 13486 Republic of Korea
                [3 ]GRID grid.497695.0, National Development Institute of Korean Medicine, ; 94, Hwarang-ro, Gyeongsan, Gyeongsangbuk-do 38540 Republic of Korea
                [4 ]Experiment Research Institute, National Agricultural Products Quality Management Service, Gimcheon, Gyeongsangbuk-do 39660 Republic of Korea
                Article
                89606
                10.1038/s41598-021-89606-5
                8115508
                33980951
                bbab334e-eab2-42ff-a6d9-d593cbc08f39
                © The Author(s) 2021

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 December 2020
                : 7 April 2021
                Funding
                Funded by: Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through Agricultural Microbiome R&D Program funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA)
                Award ID: 918010043SB010
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                microbiology,applied microbiology
                Uncategorized
                microbiology, applied microbiology

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