- Species and population specific gene expression in blood transcriptomes of marine turtles. - We observed strong species-specific expression of these genes, as well as distinct transcriptomic profiles between green turtle foraging aggregations that inhabit areas of greater or lesser anthropogenic disturbance.. - To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.. - 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 in a credit line to the data.. - In particular, RNA- sequencing can be used to characterize global gene expression and sequence diversity across functional components of the genome. - In this study, we assem- bled de novo blood transcriptomes and examined gene expression across four species of marine turtles to characterize and compare the transcriptomic diversity within and across species. - Finally, we used differential gene expression and func- tional gene enrichment analyses to explore potential drivers of responses to varying environmental conditions within green turtle foraging aggregations. - We functionally annotated the green turtle blood tran- scriptome using Blast2GO to investigate the functions of genes shared or differentially expressed between species or green turtle foraging aggregations [38]. - Biological processes represented in the green turtle blood transcriptome are shown in Figure S1 and Table S2.. - Of the annotated GO terms in the biological process category, the majority fell within biosynthetic processes. - Sequences in the green turtle blood transcriptome were involved with 140 KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways [39]. - However, we note that the higher annotation percentages here compared to the green turtle blood transcriptome were likely due to an additional filtering step applied in our computational streamlined methods using Transdecoder (i.e., smaller. - purine, glycine, pyrimidine, argin- ine), though these differed slightly in comparison to the green turtle annotation above. - The largest functional groups of genes in this core set based off the green turtle transcriptome annotation were biosynthetic processes (n = 1447 genes), cellular protein modification processes (n = 1348 genes), and signal transduction (n = 1269 genes. - Additionally, this ‘marine turtle core gene set’ contained 84.4% of the genes in the core set across reptilian blood transcriptomes previously identified by Waits et al. - Green turtle - blood. - The number of genes in each GO slim functional category a from green turtle blood transcriptome genes that belonged to orthogroups present in all four species ’ blood transcriptomes and b multi-tissue leatherback transcriptome genes that belonged to orthogroups present in all four leatherback tissues. - Differential gene expression among green turtle foraging aggregations. - Green turtle gene expression signatures in our MDS analysis clustered by foraging aggregation, but to a lesser degree than among species (Fig. - We found signifi- cant differential gene expression between all three pair- wise comparisons of green turtle foraging aggregations, with the most differentially expressed genes between Ha- wai’i and California green turtles (6649 genes, FDR <. - 0.05), and the least between Hawai’i and Commonwealth of the Northern Mariana Islands (CNMI) green turtles (600 genes, FDR <. - 4 Differential gene expression between green turtle foraging aggregations. - following McGaugh et al. - We tested multiple numbers of samples to use as input for our de novo transcriptomes (n = 34, n = 19, and n = 4) to determine the optimal threshold of individuals for maximizing transcriptome completeness while minimizing computa- tional demands, chimeric sequences, and false-splitting due to sequence divergence between populations [82], and concatenated reads from four Hawai’ian green turtle individuals to generate the green turtle transcriptome.. - Exploratory mapping of green turtle sequences to the green turtle reference genome [83] also determined that Hawai’ian green turtles expressed the highest sequence diversity, further supporting this sample selection for the de novo transcriptome assembly. - We used Blast2GO (v to functionally an- notate the de novo green turtle transcriptome, and linked annotations to the other species-specific tran- scriptomes via orthogroups identified as described above. - Gene expression analyses between species. - functional enrichment between green turtle foraging aggregations. - differential expression analyses between the three green turtle foraging aggregations using the R packages edgeR and limma [89, 100]. - We then performed functional enrichment analyses of pairwise comparisons between green turtle foraging aggregations with a Kolmogorov- Smirnov test (weight01 algorithm) implemented in the R package TopGO [101]. - Input GO terms derived from the green turtle transcriptome annotation were fil- tered to biological process GO terms ≤ level 5. - The online version contains supplementary material available at https://doi.. - org/10.1186/s . - Annotation and orthogroup information for green turtle blood transcriptome assembly transcripts. - KEGG pathways for the green turtle transcriptome and the leatherback multi- tissue assembly.. - Differential gene expression analyses results for comparisons between green turtle foraging aggregations. - Functional enrichment analysis results for contrasts between green turtle foraging aggregations.. - Green turtle GO slim plots. - Bar plots representing the number of genes in each Gene Ontology (GO) slim functional category from the green turtle blood transcriptome.. - https://doi.org/10.1111/mec.13055.. - https://doi.. - org/10.1002/ece3.2685.. - https://doi.org/10.1016/. - https://doi.org/10.1186/s . - https://doi.org/10.1016/j.margen . - https://doi.org/10.1371/journal.pone.0223525.. - https://doi.org . - https://doi.org X543010.. - https://doi.org/10.3389/fmars . - https://doi.org/1 0.3389/fmars . - https://www.iucnredlist.org. - https://doi.org/10.1016/j.. - org/10.1016/j.ympev . - doi.org/10.1371/journal.pone.0013925.. - Anatomy of the leatherback turtle. - https://doi.org/10.1016/j.ympev . - https://doi.org/10.1093/oxfordjournals.molbev.a040735.. - Insights into the role of differential gene expression on the ecological adaptation of the snail Littorina saxatilis. - Status review of the green turtle (Chelonia mydas) under the Endangered Species Act. - https://doi.org/10.1 093/bioinformatics/bti610.. - https://doi.org/10.1002/pro.3715.. - https://doi.org/10.1016/j.aquatox . - https://doi.org/10.1021/acs.est.8b06487.. - https://doi.org/10.1038/nprot.2013.084.. - https://doi.org/10.1111/mec.14087.. - Tissue-specific gene expression. - https://doi.org/10.1016/j.celrep . - https://doi.org/10.1 093/icb/icz071.. - transcriptome annotation of the polar bear (Ursus maritimus). - https://doi.org/10.3389/fgene . - https://doi.org/10.1038/. - org/10.15447//SFEWS.2020V18ISS3ART2.. - https://doi.org/10.101 6/j.ympev . - https://doi.org/10.1098/. - https://doi.org/10.1073/pnas . - Variation in tissue-specific gene expression among natural populations. - https://doi.org/1 0.1186/gb-2005-6-2-r13.. - https://doi.org/10.1016/j.envpol . - https://doi.org/10.101 6/j.envpol . - The evolutionary history and global phylogeography of the green turtle (Chelonia mydas). - https://doi.org/10.1111/jbi.13483.. - https://doi.org/1 0.1111/mec.12980.. - A blubber gene expression index for evaluating stress in marine mammals. - https://doi.org/10.1371/journal.pone.0138861.. - https://doi.org/1 0.1111/mec.14547.. - https://doi.org/10.1038/ng.2615.. - https://doi.org/1 0.1038/nbt.1883.. - https://doi.org/10.1101/gr . - org/10.1093/bioinformatics/bts565.. - https://doi.org/10.1038/nmeth.4197.. - edgeR: a bioconductor package for differential expression analysis of digital gene expression data.. - https://doi.org/10.1093/bioinformatics/. - https://doi.org/1 0.1093/bioinformatics/btv351.. - https://doi.org/1 0.3389/fgene . - https://doi.org/10.1038/nmeth.1923.. - https://doi.org/10.1093/bioinformatics/btx364.. - https://doi.org/10.1093/nar/gky1085.. - https://doi.org/10.1093/nar/gkv007.. - https://bioconductor.org/packages/release/bioc/vignettes/topGO/inst/doc/. - https://doi.org/10.1093/bioinformatics/btv300.
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