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      Multi-omics data reveals aberrant gut microbiota-host glycerophospholipid metabolism in association with neuroinflammation in APP/PS1 mice

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          ABSTRACT

          Numerous studies have described the notable impact of gut microbiota on the brain in Alzheimer’s disease (AD) via the gut – brain axis. However, the molecular mechanisms underlying the involvement of gut microbiota in the development of AD are limited. This study aimed to explore the potential mechanisms of gut microbiota in AD by integrating multi-omics data. In this study, APP/PS1 and WT mice at nine months of age were used as study mouse model. Cognitive function was assessed using the Morris water maze test. The levels of Aβ plaque and neuroinflammation in the brain were detected using immunofluorescence and PET/CT. In addition, we not only used 16S rRNA gene sequencing and metabolomics to explore the variation characteristics of gut microbiota and serum metabolism abundance, but also combined spatial metabolomics and transcriptomics to explore the change in the brain and identify their potential correlation. APP/PS1 mice showed significant cognitive impairment and amyloid-β deposits in the brain. The abundance of gut microbiota was significantly changed in APP/PS1 mice, including decreased Desulfoviobrio, Enterococcus, Turicibacter, and Ruminococcus and increased Pseudomonas. The integration of serum untargeted metabolomics and brain spatial metabolomics showed that glycerophospholipid metabolism was a common alteration pathway in APP/PS1 mice. Significant proliferation and activation of astrocyte and microglia were observed in APP/PS1 mice, accompanied by alterations in immune pathways. Integration analysis and fecal microbiota transplantation (FMT) intervention revealed potential association of gut microbiota, host glycerophospholipid metabolism, and neuroinflammation levels in APP/PS1 mice.

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          Most cited references59

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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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            UPARSE: highly accurate OTU sequences from microbial amplicon reads.

            Amplified marker-gene sequences can be used to understand microbial community structure, but they suffer from a high level of sequencing and amplification artifacts. The UPARSE pipeline reports operational taxonomic unit (OTU) sequences with ≤1% incorrect bases in artificial microbial community tests, compared with >3% incorrect bases commonly reported by other methods. The improved accuracy results in far fewer OTUs, consistently closer to the expected number of species in a community.
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              Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

              The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (> or = 95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.
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                Author and article information

                Journal
                Gut Microbes
                Gut Microbes
                Gut Microbes
                Taylor & Francis
                1949-0976
                1949-0984
                22 November 2023
                2023
                22 November 2023
                : 15
                : 2
                : 2282790
                Affiliations
                [a ]Department of Geriatrics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine; , Shanghai, China
                [b ]Medical Center on Aging of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine; , Shanghai, China
                [c ]Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine; , Shanghai, China
                [d ]Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine; , Shanghai, China
                Author notes
                CONTACT Miao Zhang zm11648@ 123456rjh.com.cn
                Xufeng Jiang jxf10885@ 123456rjh.com.cn Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine; , Shanghai, China
                Huidong Tang thd10495@ 123456rjh.com.cn Department of Geriatrics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine; , Shanghai, China
                [#]

                These authors have contributed equally to this work.

                Author information
                https://orcid.org/0000-0001-6772-3025
                Article
                2282790
                10.1080/19490976.2023.2282790
                10730179
                37992400
                1cb52711-abaa-4e2f-acbe-acd01c6eb522
                © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

                History
                Page count
                Figures: 8, References: 59, Pages: 1
                Categories
                Research Article
                Research Paper

                Microbiology & Virology
                neuroinflammation,alzheimer’s disease,gut microbiota,multi-omics,glycerophospholipid metabolism

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