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      Lactobacillus plantarum HNU082 alleviates dextran sulfate sodium-induced ulcerative colitis in mice through regulating gut microbiome

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          Abstract

          Probiotic Lp082 treats ulcerative colitis by optimizing the gut microbiota and the intestinal mucosal barrier (biological, chemical, mechanical and immune barriers), regulating inflammatory and disease pathways and reducing neutrophil infiltration.

          Abstract

          Probiotics have shown good efficacy in the prevention of ulcerative colitis (UC), but the specific mechanism remains unclear. Therefore, shotgun metagenomic and transcriptome analyses were performed to explore the preventive effect of a potential probiotic Lactobacillus plantarum HNU082 (Lp082) on UC and its specific mechanism. The results showed that Lp082 intervention ameliorated dextran sulfate sodium (DSS)-induced UC in mice, which was manifested in the increase in body weight, water intake, food intake, and colon length and the decrease in the DAI index, immune organ index, inflammatory factors and histopathological scores after Lp082 intake. The mechanism is deeply studied and it is discovered that Lp082 improves the intestinal mucosal barrier by co-optimizing biological barriers, chemical barriers, mechanical barriers, and immune barriers. Specifically, Lp082 improved the biological barrier by increasing the diversity, optimizing the species composition and the structure of the gut microbiota, increasing bacteria producing short chain fatty acids (SCFAs), and activating microbial metabolic pathways producing SCFAs so as to enhance the content of SCFAs. Lp082 optimized the chemical barrier by decreasing the mRNA expression of ICAM-1 and VCAM and by increasing the content of goblet cells and the mRNA expression and immunofluorescent protein content of mucin2. Lp082 ameliorated the mechanical barrier by decreasing the mRNA expression of claudin-1 and claudin-2, and by increasing the mRNA expression of ZO-1 and ZO-2 and the immunofluorescent protein content of ZO-1. Lp082 also optimized the immune barrier by increasing the mRNA expression of IL-10, TGF-β1, and TGF-β2 and by decreasing the mRNA expression and protein contents of IL-6, tumour necrosis factor-alpha (TNF-α) and myeloperoxidase (MPO). In addition, Lp082 can also regulate the metabolic pathways of inflammation and disease in mice, and notably, Lp082 inhibits the NF-κB signaling pathway by inhibiting NF-κB signaling molecules to alleviate UC. In conclusion, improving gut microbiota dysbiosis, protecting the intestinal mucosal barrier, regulating inflammatory and disease pathways, and affecting neutrophil infiltration are the potential mechanisms of probiotic Lp082 in alleviating UC. Our study enriches the mechanism and provides a new prospect for Lactobacillus plantarum HNU082 in the prevention of colitis, provides support for the development of probiotic-based microbial products as an alternative prevention strategy for UC, and provides guidance for the future probiotic prevention of human colitis.

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          Most cited references51

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          Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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            Correlation Coefficients

            Correlation in the broadest sense is a measure of an association between variables. In correlated data, the change in the magnitude of 1 variable is associated with a change in the magnitude of another variable, either in the same (positive correlation) or in the opposite (negative correlation) direction. Most often, the term correlation is used in the context of a linear relationship between 2 continuous variables and expressed as Pearson product-moment correlation. The Pearson correlation coefficient is typically used for jointly normally distributed data (data that follow a bivariate normal distribution). For nonnormally distributed continuous data, for ordinal data, or for data with relevant outliers, a Spearman rank correlation can be used as a measure of a monotonic association. Both correlation coefficients are scaled such that they range from -1 to +1, where 0 indicates that there is no linear or monotonic association, and the relationship gets stronger and ultimately approaches a straight line (Pearson correlation) or a constantly increasing or decreasing curve (Spearman correlation) as the coefficient approaches an absolute value of 1. Hypothesis tests and confidence intervals can be used to address the statistical significance of the results and to estimate the strength of the relationship in the population from which the data were sampled. The aim of this tutorial is to guide researchers and clinicians in the appropriate use and interpretation of correlation coefficients.
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              Best practices for analysing microbiomes

              Complex microbial communities shape the dynamics of various environments, ranging from the mammalian gastrointestinal tract to the soil. Advances in DNA sequencing technologies and data analysis have provided drastic improvements in microbiome analyses, for example, in taxonomic resolution, false discovery rate control and other properties, over earlier methods. In this Review, we discuss the best practices for performing a microbiome study, including experimental design, choice of molecular analysis technology, methods for data analysis and the integration of multiple omics data sets. We focus on recent findings that suggest that operational taxonomic unit-based analyses should be replaced with new methods that are based on exact sequence variants, methods for integrating metagenomic and metabolomic data, and issues surrounding compositional data analysis, where advances have been particularly rapid. We note that although some of these approaches are new, it is important to keep sight of the classic issues that arise during experimental design and relate to research reproducibility. We describe how keeping these issues in mind allows researchers to obtain more insight from their microbiome data sets.
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                Author and article information

                Contributors
                Journal
                FFOUAI
                Food & Function
                Food Funct.
                Royal Society of Chemistry (RSC)
                2042-6496
                2042-650X
                October 03 2022
                2022
                : 13
                : 19
                : 10171-10185
                Affiliations
                [1 ]Key Laboratory of Food Nutrition and Functional Food of Hainan Province, College of Food Science and Engineering, Hainan University, Haikou 570228, China
                [2 ]School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, China
                [3 ]One Health Institute, Hainan University, Haikou, Hainan 570228, China
                Article
                10.1039/D2FO02303B
                36111438
                5e1b3de9-d679-4151-98f7-87a3da1b0792
                © 2022

                http://rsc.li/journals-terms-of-use

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