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      Haem iron reshapes colonic luminal environment: impact on mucosal homeostasis and microbiome through aldehyde formation

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          Abstract

          Background

          The World Health Organization classified processed and red meat consumption as “carcinogenic” and “probably carcinogenic”, respectively, to humans. Haem iron from meat plays a role in the promotion of colorectal cancer in rodent models, in association with enhanced luminal lipoperoxidation and subsequent formation of aldehydes. Here, we investigated the short-term effects of this haem-induced lipoperoxidation on mucosal and luminal gut homeostasis including microbiome in F344 male rats fed with a haem-enriched diet (1.5 μmol/g) 14–21 days.

          Results

          Changes in permeability, inflammation, and genotoxicity observed in the mucosal colonic barrier correlated with luminal haem and lipoperoxidation markers. Trapping of luminal haem-induced aldehydes normalised cellular genotoxicity, permeability, and ROS formation on a colon epithelial cell line. Addition of calcium carbonate (2%) to the haem-enriched diet allowed the luminal haem to be trapped in vivo and counteracted these haem-induced physiological traits. Similar covariations of faecal metabolites and bacterial taxa according to haem-induced lipoperoxidation were identified.

          Conclusions

          This integrated approach provides an overview of haem-induced modulations of the main actors in the colonic barrier. All alterations were closely linked to haem-induced lipoperoxidation, which is associated with red meat-induced colorectal cancer risk.

          Electronic supplementary material

          The online version of this article (10.1186/s40168-019-0685-7) contains supplementary material, which is available to authorized users.

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          Most cited references45

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          FLASH: fast length adjustment of short reads to improve genome assemblies.

          Next-generation sequencing technologies generate very large numbers of short reads. Even with very deep genome coverage, short read lengths cause problems in de novo assemblies. The use of paired-end libraries with a fragment size shorter than twice the read length provides an opportunity to generate much longer reads by overlapping and merging read pairs before assembling a genome. We present FLASH, a fast computational tool to extend the length of short reads by overlapping paired-end reads from fragment libraries that are sufficiently short. We tested the correctness of the tool on one million simulated read pairs, and we then applied it as a pre-processor for genome assemblies of Illumina reads from the bacterium Staphylococcus aureus and human chromosome 14. FLASH correctly extended and merged reads >99% of the time on simulated reads with an error rate of <1%. With adequately set parameters, FLASH correctly merged reads over 90% of the time even when the reads contained up to 5% errors. When FLASH was used to extend reads prior to assembly, the resulting assemblies had substantially greater N50 lengths for both contigs and scaffolds. The FLASH system is implemented in C and is freely available as open-source code at http://www.cbcb.umd.edu/software/flash. t.magoc@gmail.com.
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            mixOmics: An R package for ‘omics feature selection and multiple data integration

            The advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of ‘omics data available from the package.
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              Swarm: robust and fast clustering method for amplicon-based studies

              Popular de novo amplicon clustering methods suffer from two fundamental flaws: arbitrary global clustering thresholds, and input-order dependency induced by centroid selection. Swarm was developed to address these issues by first clustering nearly identical amplicons iteratively using a local threshold, and then by using clusters’ internal structure and amplicon abundances to refine its results. This fast, scalable, and input-order independent approach reduces the influence of clustering parameters and produces robust operational taxonomic units.
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                Author and article information

                Contributors
                oceane.martin@u-bordeaux.fr
                + 33 5 82 06 64 57 , maiwenn.olier@inra.fr
                sandrine.ellerosimatos@inra.fr
                nathalie.naud@inra.fr
                Jacques.Dupuy@inra.fr
                Laurence.Huc@inra.fr
                stache@orange.fr
                vanessa.graillot@hotmail.fr
                Mathilde.Leveque@inra.fr
                valerie.bezirard@inra.fr
                cecile.helies@inra.fr
                florence.blas-y-estrada@inra.fr
                valerie.tondereau@inra.fr
                yannick.lippi@inra.fr
                claire.naylies@inra.fr
                peyriga@insa-toulouse.fr
                ccanlet@toulouse.inra.fr
                anne_marie.davila_gay@agroparistech.fr
                francois.blachier@agroparistech.fr
                laurent.ferrier@inra.fr
                elisa.boutet@inra.fr
                francoise.gueraud@inra.fr
                vassilia.theodorou@inra.fr
                + 33 5 82 06 63 70 , fabrice.pierre@inra.fr
                Journal
                Microbiome
                Microbiome
                Microbiome
                BioMed Central (London )
                2049-2618
                6 May 2019
                6 May 2019
                2019
                : 7
                : 72
                Affiliations
                [1 ]INRA, ToxAlim (Research Centre in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, Toulouse, France
                [2 ]ISNI 0000 0004 4910 6535, GRID grid.460789.4, INRA, UMR Physiologie de la Nutrition et du Comportement Alimentaire, AgroParisTech, INRA, , Université Paris-Saclay, ; Paris, France
                [3 ]ISNI 0000 0001 2286 8343, GRID grid.461574.5, INSA Toulouse LISBP MetaToul, ; Toulouse, France
                [4 ]ADIV, 10 Rue Jacqueline Auriol, 63039 Clermont-Ferrand, France
                Author information
                http://orcid.org/0000-0001-6106-8146
                Article
                685
                10.1186/s40168-019-0685-7
                6503375
                31060614
                f1cfd541-7262-4f7d-b164-3ec0da1c677a
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 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.

                History
                : 12 April 2018
                : 22 April 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001665, Agence Nationale de la Recherche;
                Award ID: ANR-10-ALIA-14
                Award ID: SecuriViande project
                Categories
                Research
                Custom metadata
                © The Author(s) 2019

                lipoperoxidation,barrier function,metabolites,dysbiosis,meat

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