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      Characterization of the peripheral blood transcriptome and adaptive evolution of the MHC I and TLR gene families in the wolf ( Canis lupus)

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          Abstract

          Background

          The wolf ( Canis lupus) is one of the most widely distributed terrestrial mammals, because it is well adapted to various ecological niches and their corresponding pathogen environments. Immunological competence is a crucial factor involved in adapting to a changing environment and fighting pathogen infection in animals. In this study, the peripheral blood transcriptome of wolves was generated via RNA-seq to advance understanding of the wolf immunome, with a special focus on the major histocompatibility complex class I (MHC I) and toll-like receptor (TLR) gene families, which are involved in pathogen recognition and defense.

          Results

          The blood transcriptomic libraries of eight wolves originating from Tibet and Inner Mongolia were sequenced, and approximately 383 million reads were generated. Using a genome-guided assembly strategy, we obtained 123,851 unigenes, with a mean length of 845 bp and an N50 length of 1121 bp. On the basis of BLAST searches against the NCBI non-redundant protein database (Nr), a total of 36,192 (29.22%) unigenes were annotated. For functional classification, 24,663 unigenes were assigned to 13,016 Gene Ontology (GO) terms belonging to 51 sub-categories of the three main GO categories. Additionally, 7682 unigenes were classified into 6 Kyoto Encyclopedia of Genes and Genomes (KEGG) categories, in which the most represented functional sub-categories were signal transduction and the immune system, and 16,238 unigenes were functionally classified into 25 Eukaryotic Orthologous Groups (KOG) categories. We observed an overall higher ω ( d N/ d S) value at antigen-binding sites (ABSs) than at non-ABS regions as well as clear evidence of intergenic/intragenic recombination events at wolf MHC I loci. Additionally, our analysis revealed that carnivorous TLRs were dominated by purifying selection, with mean ω values at each TLR locus ranging from 0.173 to 0.527. However, we also found significant instances of positive selection that acted on several codons in pathogen recognition domains and were linked to species-specific differences in pathogen recognition.

          Conclusions

          This study represents the first attempt to characterize the blood transcriptome of the wolf and to highlight the value of investigating the immune system. Balancing selection and recombination have contributed to the historical evolution of wolf MHC I genes. Moreover, TLRs in carnivores have undergone adaptive evolution against the background of purifying selection, and a high level of adaptive evolution was detected in the wolf TLR system.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12864-017-3983-0) contains supplementary material, which is available to authorized users.

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          Most cited references57

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          Analyzing the mosaic structure of genes.

          Some genes in prokaryotes consist of a mosaic of regions derived from different ancestors by horizontal gene transfer. A method is described for demonstrating the statistical significance of such mosaic structure and for locating the crossover points separating different regions.
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            A modified bootscan algorithm for automated identification of recombinant sequences and recombination breakpoints.

            We have developed a modified BOOTSCAN algorithm that may be used to screen nucleotide sequence alignments for evidence of recombination without prior identification of nonrecombinant reference sequences. The algorithm is fast and includes a Bonferroni corrected statistical test of recombination to circumvent the multiple testing problems encountered when using the BOOTSCAN method to explore alignments for evidence of recombination. Using both simulated and real datasets we demonstrate that the modified algorithm is more powerful than other phylogenetic recombination detection methods and performs almost as well as one of the best substitution distribution recombination detection methods.
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              An exact nonparametric method for inferring mosaic structure in sequence triplets.

              Statistical tests for detecting mosaic structure or recombination among nucleotide sequences usually rely on identifying a pattern or a signal that would be unlikely to appear under clonal reproduction. Dozens of such tests have been described, but many are hampered by long running times, confounding of selection and recombination, and/or inability to isolate the mosaic-producing event. We introduce a test that is exact, nonparametric, rapidly computable, free of the infinite-sites assumption, able to distinguish between recombination and variation in mutation/fixation rates, and able to identify the breakpoints and sequences involved in the mosaic-producing event. Our test considers three sequences at a time: two parent sequences that may have recombined, with one or two breakpoints, to form the third sequence (the child sequence). Excess similarity of the child sequence to a candidate recombinant of the parents is a sign of recombination; we take the maximum value of this excess similarity as our test statistic Delta(m,n,b). We present a method for rapidly calculating the distribution of Delta(m,n,b) and demonstrate that it has comparable power to and a much improved running time over previous methods, especially in detecting recombination in large data sets.
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                Author and article information

                Contributors
                liuguangshuai917@163.com
                zhanghonghai67@126.com
                sunguolei1989@163.com
                zc37130@126.com
                shangshuai8983@126.com
                gao-xiaodong@163.com
                qfxiatian1993@163.com
                yangxf9066@163.com
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                7 August 2017
                7 August 2017
                2017
                : 18
                : 584
                Affiliations
                ISNI 0000 0001 0227 8151, GRID grid.412638.a, , Qufu Normal University, ; Jingxuan Street No. 57, Qufu, Shandong province China
                Article
                3983
                10.1186/s12864-017-3983-0
                5545864
                28784091
                eb9e0254-d77e-42e0-9107-86f7e240a336
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 16 February 2017
                : 1 August 2017
                Funding
                Funded by: Special Fund for Forest Scientific Research in the Public Welfare
                Award ID: 201404420
                Award Recipient :
                Funded by: National Natural Science Fund of China
                Award ID: 31372220
                Award ID: 31672313
                Award Recipient :
                Funded by: Science and Technology research Program of Shandong Province of China
                Award ID: 2013GSF11707
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                Genetics
                wolf,blood transcriptome,rna-seq,immune system,mhc,tlr
                Genetics
                wolf, blood transcriptome, rna-seq, immune system, mhc, tlr

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