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      Strain-specific alterations in gut microbiome and host immune responses elicited by tolerogenic Bifidobacterium pseudolongum

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          Abstract

          The beneficial effects attributed to Bifidobacterium are largely attributed to their immunomodulatory capabilities, which are likely to be species- and even strain-specific. However, their strain-specificity in direct and indirect immune modulation remain largely uncharacterized. We have shown that B. pseudolongum UMB-MBP-01, a murine isolate strain, is capable of suppressing inflammation and reducing fibrosis in vivo. To ascertain the mechanism driving this activity and to determine if it is specific to UMB-MBP-01, we compared it to a porcine tropic strain B. pseudolongum ATCC25526 using a combination of cell culture and in vivo experimentation and comparative genomics approaches. Despite many shared features, we demonstrate that these two strains possess distinct genetic repertoires in carbohydrate assimilation, differential activation signatures and cytokine responses signatures in innate immune cells, and differential effects on lymph node morphology with unique local and systemic leukocyte distribution. Importantly, the administration of each B. pseudolongum strain resulted in major divergence in the structure, composition, and function of gut microbiota. This was accompanied by markedly different changes in intestinal transcriptional activities, suggesting strain-specific modulation of the endogenous gut microbiota as a key to immune modulatory host responses. Our study demonstrated a single probiotic strain can influence local, regional, and systemic immunity through both innate and adaptive pathways in a strain-specific manner. It highlights the importance to investigate both the endogenous gut microbiome and the intestinal responses in response to probiotic supplementation, which underpins the mechanisms through which the probiotic strains drive the strain-specific effect to impact health outcomes.

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          MUSCLE: multiple sequence alignment with high accuracy and high throughput.

          We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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            HISAT: a fast spliced aligner with low memory requirements.

            HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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              fastp: an ultra-fast all-in-one FASTQ preprocessor

              Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.
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                Author and article information

                Contributors
                bma@som.umaryland.edu
                JBromberg@som.umaryland.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                19 January 2023
                19 January 2023
                2023
                : 13
                : 1023
                Affiliations
                [1 ]GRID grid.411024.2, ISNI 0000 0001 2175 4264, Institute of Genome Sciences, , University of Maryland School of Medicine, ; Baltimore, MD 21201 USA
                [2 ]GRID grid.411024.2, ISNI 0000 0001 2175 4264, Department of Microbiology and Immunology, , University of Maryland School of Medicine, ; Baltimore, MD 21201 USA
                [3 ]GRID grid.413036.3, ISNI 0000 0004 0434 0002, Department of Surgery, , University of Maryland Medical Center, ; Baltimore, MD 21201 USA
                [4 ]GRID grid.411024.2, ISNI 0000 0001 2175 4264, Center for Vascular and Inflammatory Diseases, , University of Maryland School of Medicine, ; Baltimore, MD 21201 USA
                [5 ]GRID grid.411024.2, ISNI 0000 0001 2175 4264, Institute of Human Virology, , University of Maryland School of Medicine, ; Baltimore, MD 21201 USA
                [6 ]GRID grid.279885.9, ISNI 0000 0001 2293 4638, Present Address: Division of Lung Diseases, , National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), ; Bethesda, MD USA
                Article
                27706
                10.1038/s41598-023-27706-0
                9852428
                36658194
                0e732b08-35b3-4541-a5e1-69c46c028f55
                © The Author(s) 2023

                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
                : 29 June 2022
                : 6 January 2023
                Funding
                Funded by: National Institute of Health (NIH) National Heart, Lung and Blood Institute
                Award ID: R01HL148672
                Award ID: R01HL148672
                Award ID: R01HL148672
                Award ID: R01HL148672
                Award ID: R01HL148672
                Award ID: R01HL148672
                Award ID: R01HL148672
                Award ID: R01HL148672
                Award ID: R01HL148672
                Award ID: R01HL148672
                Award ID: R01HL148672
                Award ID: R01HL148672
                Award ID: R01HL148672
                Award ID: R01HL148672
                Award ID: R01HL148672
                Award Recipient :
                Funded by: NIH National Institute of Allergy and Infectious Diseases
                Award ID: U01AI170050
                Award ID: T32AI95190-10
                Award ID: U01AI170050
                Award ID: U01AI170050
                Award ID: U01AI170050
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2023

                Uncategorized
                immunology,transplant immunology,microbiology,microbial communities,microbial ecology,microbiome

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