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      Understanding the evolution of immune genes in jawed vertebrates

      review-article
      1 , , 2 , 1 , 3 , 4 , 5 , 2 , 6 , 7 , 8 , 9 , 10 , 11 , 1 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 1 , 1 , 19 , 9 , 3
      Journal of Evolutionary Biology
      John Wiley and Sons Inc.
      adaptation, adaptive immunity, evolutionary immunology, genomics, host‐parasite interactions, immunogenetics, innate immunity, MHC, molecular evolution, vertebrates

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          Abstract

          Driven by co‐evolution with pathogens, host immunity continuously adapts to optimize defence against pathogens within a given environment. Recent advances in genetics, genomics and transcriptomics have enabled a more detailed investigation into how immunogenetic variation shapes the diversity of immune responses seen across domestic and wild animal species. However, a deeper understanding of the diverse molecular mechanisms that shape immunity within and among species is still needed to gain insight into—and generate evolutionary hypotheses on—the ultimate drivers of immunological differences. Here, we discuss current advances in our understanding of molecular evolution underpinning jawed vertebrate immunity. First, we introduce the immunome concept, a framework for characterizing genes involved in immune defence from a comparative perspective, then we outline how immune genes of interest can be identified. Second, we focus on how different selection modes are observed acting across groups of immune genes and propose hypotheses to explain these differences. We then provide an overview of the approaches used so far to study the evolutionary heterogeneity of immune genes on macro and microevolutionary scales. Finally, we discuss some of the current evidence as to how specific pathogens affect the evolution of different groups of immune genes. This review results from the collective discussion on the current key challenges in evolutionary immunology conducted at the ESEB 2021 Online Satellite Symposium: Molecular evolution of the vertebrate immune system, from the lab to natural populations.

          Abstract

          Reviewing current advances in our understanding of molecular evolution underpinning vertebrate immunity, we propose hypotheses to explain differences in selection modes across immune genes and discuss supporting evidence.

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          Most cited references363

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Is Open Access

            Highly accurate protein structure prediction with AlphaFold

            Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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              KEGG: kyoto encyclopedia of genes and genomes.

              M Kanehisa (2000)
              KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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                Author and article information

                Contributors
                michal.vinkler@natur.cuni.cz
                Journal
                J Evol Biol
                J Evol Biol
                10.1111/(ISSN)1420-9101
                JEB
                Journal of Evolutionary Biology
                John Wiley and Sons Inc. (Hoboken )
                1010-061X
                1420-9101
                31 May 2023
                June 2023
                : 36
                : 6 ( doiID: 10.1111/jeb.v36.6 )
                : 847-873
                Affiliations
                [ 1 ] Department of Zoology Faculty of Science Charles University Prague Czech Republic
                [ 2 ] Department of Biology University of Oxford Oxford UK
                [ 3 ] Department of Biology Lund University Lund Sweden
                [ 4 ] Department of Biology University of Central Florida Florida Orlando USA
                [ 5 ] Research Unit for Evolutionary Immunogenomics Department of Biology University of Hamburg Hamburg Germany
                [ 6 ] Institute for Immunology and Infection Research University of Edinburgh Edinburgh UK
                [ 7 ] Department of Veterinary Medicine University of Cambridge Cambridge UK
                [ 8 ] Department of Ecology and Evolutionary Biology University of Connecticut Storrs Connecticut USA
                [ 9 ] School of Biological Sciences University of East Anglia Norwich UK
                [ 10 ] Department of Botany and Zoology Masaryk University Brno Czech Republic
                [ 11 ] Menzies Institute for Medical Research University of Tasmania Hobart Tasmania Australia
                [ 12 ] Department of Parasitology University of Granada Granada Spain
                [ 13 ] Department of Biological Sciences University of Memphis Memphis Tennessee USA
                [ 14 ] Department of Genetics and Breeding Institute of Animal Science Prague Uhříněves Czech Republic
                [ 15 ] Faculty of Biology Institute of Environmental Sciences Jagiellonian University Kraków Poland
                [ 16 ] Department of Science Engineering and Build Environment Deakin University Victoria Waurn Ponds Australia
                [ 17 ] Department of Genetics and Microbiology Faculty of Science Charles University Prague Czech Republic
                [ 18 ] Department of Biology California State University Fresno California USA
                [ 19 ] Department of Ecology and Genetics Uppsala Universitet Uppsala Sweden
                Author notes
                [*] [* ] Correspondence

                Michal Vinkler, Department of Zoology, Faculty of Science, Charles University, Viničná 7, 128 43 Prague, Czech Republic.

                Email: michal.vinkler@ 123456natur.cuni.cz

                Author information
                https://orcid.org/0000-0003-3572-9494
                https://orcid.org/0000-0001-8222-7687
                https://orcid.org/0000-0001-8097-5331
                https://orcid.org/0000-0001-8702-773X
                https://orcid.org/0000-0002-4917-8358
                https://orcid.org/0000-0002-7203-0044
                https://orcid.org/0000-0002-7657-6191
                https://orcid.org/0000-0002-7216-8422
                https://orcid.org/0000-0003-3148-6296
                https://orcid.org/0000-0001-9030-7820
                https://orcid.org/0000-0002-8674-6203
                https://orcid.org/0000-0002-4550-1859
                https://orcid.org/0000-0003-4801-528X
                https://orcid.org/0000-0002-6288-5575
                https://orcid.org/0000-0002-9366-3764
                https://orcid.org/0000-0002-0659-8766
                https://orcid.org/0000-0001-8039-1264
                https://orcid.org/0009-0008-0972-6373
                https://orcid.org/0000-0002-4587-5033
                https://orcid.org/0000-0002-1202-4902
                https://orcid.org/0000-0001-6029-6171
                https://orcid.org/0000-0002-5840-779X
                https://orcid.org/0000-0001-7226-9074
                https://orcid.org/0000-0001-7167-9805
                Article
                JEB14181 JEB-2022-00312.R1
                10.1111/jeb.14181
                10247546
                37255207
                a32cf87d-5d7f-4ab1-89cb-0488cd324feb
                © 2023 The Authors. Journal of Evolutionary Biology published by John Wiley & Sons Ltd on behalf of European Society for Evolutionary Biology.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 April 2023
                : 23 September 2022
                : 26 April 2023
                Page count
                Figures: 3, Tables: 0, Pages: 27, Words: 25720
                Funding
                Funded by: Biotechnology and Biological Sciences Research Council , doi 10.13039/501100000268;
                Award ID: BB/K004468/1
                Award ID: BB/M011224/1
                Award ID: BB/N023803/1
                Award ID: BB/V000756/1
                Funded by: Department for Environment, Food and Rural Affairs, UK Government , doi 10.13039/501100000277;
                Award ID: OD0221
                Funded by: Deutsche Forschungsgemeinschaft , doi 10.13039/501100001659;
                Award ID: 437857095
                Funded by: Grantová Agentura České Republiky , doi 10.13039/501100001824;
                Award ID: 19‐20152Y
                Funded by: Grantová Agentura, Univerzita Karlova , doi 10.13039/100007543;
                Award ID: 646119
                Funded by: H2020 European Research Council , doi 10.13039/100010663;
                Award ID: ERC‐2019‐StG‐853272‐PALAEOFARM
                Funded by: John Fell Fund, University of Oxford , doi 10.13039/501100004789;
                Award ID: 0005172
                Funded by: Ministerstvo Školství, Mládeže a Tělovýchovy , doi 10.13039/501100001823;
                Award ID: SVV 260684/2023
                Funded by: Ministerstvo Zemědělství , doi 10.13039/501100006533;
                Award ID: MZE‐RO0723
                Funded by: National Institutes of Health , doi 10.13039/100000002;
                Award ID: 1R01AI123659‐01A1
                Funded by: Univerzita Karlova v Praze , doi 10.13039/100007397;
                Award ID: START/SCI/113 with reg. no. CZ.02.2.69/0.0/0.0/19_
                Funded by: Vetenskapsrådet , doi 10.13039/501100004359;
                Award ID: 2020‐04285
                Categories
                Review
                Review
                Custom metadata
                2.0
                June 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.9 mode:remove_FC converted:18.03.2024

                Evolutionary Biology
                adaptation,adaptive immunity,evolutionary immunology,genomics,host‐parasite interactions,immunogenetics,innate immunity,mhc,molecular evolution,vertebrates

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