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      Shrimp microbiome and immune development in the early life stages

      , ,
      Developmental & Comparative Immunology
      Elsevier BV

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          Most cited references106

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          Toll-Like Receptor Signaling Pathways

          Toll-like receptors (TLRs) play crucial roles in the innate immune system by recognizing pathogen-associated molecular patterns derived from various microbes. TLRs signal through the recruitment of specific adaptor molecules, leading to activation of the transcription factors NF-κB and IRFs, which dictate the outcome of innate immune responses. During the past decade, the precise mechanisms underlying TLR signaling have been clarified by various approaches involving genetic, biochemical, structural, cell biological, and bioinformatics studies. TLR signaling appears to be divergent and to play important roles in many aspects of the innate immune responses to given pathogens. In this review, we describe recent progress in our understanding of TLR signaling regulation and its contributions to host defense.
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            The gut microbiota shapes intestinal immune responses during health and disease.

            Immunological dysregulation is the cause of many non-infectious human diseases such as autoimmunity, allergy and cancer. The gastrointestinal tract is the primary site of interaction between the host immune system and microorganisms, both symbiotic and pathogenic. In this Review we discuss findings indicating that developmental aspects of the adaptive immune system are influenced by bacterial colonization of the gut. We also highlight the molecular pathways that mediate host-symbiont interactions that regulate proper immune function. Finally, we present recent evidence to support that disturbances in the bacterial microbiota result in dysregulation of adaptive immune cells, and this may underlie disorders such as inflammatory bowel disease. This raises the possibility that the mammalian immune system, which seems to be designed to control microorganisms, is in fact controlled by microorganisms.
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              Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data

              Motivation: The characterization of phylogenetic and functional diversity is a key element in the analysis of microbial communities. Amplicon-based sequencing of marker genes, such as 16S rRNA, is a powerful tool for assessing and comparing the structure of microbial communities at a high phylogenetic resolution. Because 16S rRNA sequencing is more cost-effective than whole metagenome shotgun sequencing, marker gene analysis is frequently used for broad studies that involve a large number of different samples. However, in comparison to shotgun sequencing approaches, insights into the functional capabilities of the community get lost when restricting the analysis to taxonomic assignment of 16S rRNA data. Results: Tax4Fun is a software package that predicts the functional capabilities of microbial communities based on 16S rRNA datasets. We evaluated Tax4Fun on a range of paired metagenome/16S rRNA datasets to assess its performance. Our results indicate that Tax4Fun provides a good approximation to functional profiles obtained from metagenomic shotgun sequencing approaches. Availability and implementation: Tax4Fun is an open-source R package and applicable to output as obtained from the SILVAngs web server or the application of QIIME with a SILVA database extension. Tax4Fun is freely available for download at http://tax4fun.gobics.de/. Contact: kasshau@gwdg.de Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Developmental & Comparative Immunology
                Developmental & Comparative Immunology
                Elsevier BV
                0145305X
                October 2023
                October 2023
                : 147
                : 104765
                Article
                10.1016/j.dci.2023.104765
                1f1f8c57-6e9f-440f-acc5-c10ad06c48be
                © 2023

                https://www.elsevier.com/tdm/userlicense/1.0/

                https://doi.org/10.15223/policy-017

                https://doi.org/10.15223/policy-037

                https://doi.org/10.15223/policy-012

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-004

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