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      Dysbiosis of skin microbiome and gut microbiome in melanoma progression

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          Abstract

          Background

          The microbiome alterations are associated with cancer growth and may influence the immune system and response to therapy. Particularly, the gut microbiome has been recently shown to modulate response to melanoma immunotherapy. However, the role of the skin microbiome has not been well explored in the skin tumour microenvironment and the link between the gut microbiome and skin microbiome has not been investigated in melanoma progression. Therefore, the aim of the present study was to examine associations between dysbiosis in the skin and gut microbiome and the melanoma growth using MeLiM porcine model of melanoma progression and spontaneous regression.

          Results

          Parallel analysis of cutaneous microbiota and faecal microbiota of the same individuals was performed in 8 to 12 weeks old MeLiM piglets. The bacterial composition of samples was analysed by high throughput sequencing of the V4-V5 region of the 16S rRNA gene. A significant difference in microbiome diversity and richness between melanoma tissue and healthy skin and between the faecal microbiome of MeLiM piglets and control piglets were observed. Both Principal Coordinate Analysis and Non-metric multidimensional scaling revealed dissimilarities between different bacterial communities. Linear discriminant analysis effect size at the genus level determined different potential biomarkers in multiple bacterial communities. Lactobacillus, Clostridium sensu stricto 1 and Corynebacterium 1 were the most discriminately higher genera in the healthy skin microbiome, while Fusobacterium, Trueperella, Staphylococcus, Streptococcus and Bacteroides were discriminately abundant in melanoma tissue microbiome. Bacteroides, Fusobacterium and Escherichia-Shigella were associated with the faecal microbiota of MeLiM piglets. Potential functional pathways analysis based on the KEGG database indicated significant differences in the predicted profile metabolisms between the healthy skin microbiome and melanoma tissue microbiome. The faecal microbiome of MeLiM piglets was enriched by genes related to membrane transports pathways allowing for the increase of intestinal permeability and alteration of the intestinal mucosal barrier.

          Conclusion

          The associations between melanoma progression and dysbiosis in the skin microbiome as well as dysbiosis in the gut microbiome were identified. Results provide promising information for further studies on the local skin and gut microbiome involvement in melanoma progression and may support the development of new therapeutic approaches.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12866-022-02458-5.

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          Most cited references180

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

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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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                Author and article information

                Contributors
                mrazek@iapg.cas.cz
                Journal
                BMC Microbiol
                BMC Microbiol
                BMC Microbiology
                BioMed Central (London )
                1471-2180
                25 February 2022
                25 February 2022
                2022
                : 22
                : 63
                Affiliations
                [1 ]GRID grid.435109.a, ISNI 0000 0004 0639 4223, Laboratory of Anaerobic Microbiology, , Institute of Animal Physiology and Genetics of the Czech Academy of Sciences, ; Videnska 1083, 142 20 Prague, Czech Republic
                [2 ]GRID grid.435109.a, ISNI 0000 0004 0639 4223, Laboratory of Applied Proteome Analyses, , Institute of Animal Physiology and Genetics of the Czech Academy of Sciences, ; Rumburska 89, 277 21 Libechov, Czech Republic
                [3 ]GRID grid.413094.b, ISNI 0000 0001 1457 0707, Department of Radiobiology, Faculty of Military Health Sciences, , University of Defence, ; Trebesska 1575, 500 01 Hradec Kralove, Czech Republic
                [4 ]GRID grid.4491.8, ISNI 0000 0004 1937 116X, Department of Cell Biology, Faculty of Science, , Charles University, ; Vinicna 7, 128 00 Prague, Czech Republic
                Article
                2458
                10.1186/s12866-022-02458-5
                8881828
                35216552
                249c16c7-8f40-4e1e-8c7b-c9e64c5f1c25
                © The Author(s) 2022

                Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 9 November 2021
                : 29 January 2022
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2022

                Microbiology & Virology
                melanoma,skin cancer,tumour microenvironment,melim,pig,skin microbiome,gut microbiome,gut-skin axis,dysbiosis,metagenomic analysis,ngs

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