Transcriptome of the dead: characterisation of immune genes and marker development from necropsy samples in a free-ranging marine mammal

Abstract

BackgroundTranscriptomes are powerful resources, providing a window on the expressed portion of the genome that can be generated rapidly and at low cost for virtually any organism. However, because many genes have tissue-specific expression patterns, developing a complete transcriptome usually requires a 'discovery pool' of individuals to be sacrificed in order to harvest mRNA from as many different types of tissue as possible. This hinders transcriptome development in large, charismatic and endangered species, many of which stand the most to gain from such approaches. To circumvent this problem in a model pinniped species, we 454 sequenced cDNA from testis, heart, spleen, intestine, kidney and lung tissues obtained from nine adult male Antarctic fur seals (Arctocephalus gazella) that died of natural causes at Bird Island, South Georgia.ResultsAfter applying stringent quality control criteria based on length and annotation, we obtained 12,397 contigs which, in combination with 454 data previously obtained from skin, gave a total of 23,096 unique contigs. Homology was found to 77.0% of dog (Canis lupus familiaris) transcripts, suggesting that the combined assembly represents a substantial proportion of this species' transcriptome. Moreover, only 0.5% of transcripts revealed sequence similarity to bacteria, implying minimal contamination, and the percentage of transcripts involved in cell death was low at 2.6%. Transcripts with immune-related annotations were almost five-fold enriched relative to skin and represented 13.2% of all spleen-specific contigs. By reference to the dog, we also identified transcripts revealing homology to five class I, ten class II and three class III genes of the Major Histocompatibility Complex and derived the putative genomic distribution of 17,121 contigs, 2,119 in silico mined microsatellites and 9,382 single nucleotide polymorphisms.ConclusionsOur findings suggest that transcriptome development based on samples collected post mortem may greatly facilitate genomic studies, not only of marine mammals but also more generally of species that are of conservation concern.

Brain transcriptome sequencing and assembly of three songbird model systems for the study of social behavior

“…Similarly, a recent RNA-sequencing study of pinnipeds successfully used post-mortem samples (Hoffman et al, 2013). Although clearly not an ideal strategy for studies aimed at quantifying gene expression, the use of recently killed samples is viable strategy for initial transcriptome description, and in our study gave access to a large portion of the transcriptome.…”

Cross-Amplification and Validation of SNPs Conserved over 44 Million Years between Seals and Dogs

“…The full set of SNPs (those that failed to amplify, the monomorphic and the polymorphic with respect to A. gazella ) with 120 bp flanking sequence were mapped to the dog genome (Broad Institute release 67) and the Arctocephalus transcriptome [24] using Blast [36]. When mapping against the genome, the most proximal gene to each SNP was selected and, through Swissprot [37], the Gene Ontology (GO) codes [38] for each gene were derived.…”

“…Homology to the dog ( Canis lupus familiaris ) genome was then exploited to map transcripts to specific chromosomes, allowing development of a genome-wide distributed panel of 104 polymorphic SNPs [23]. We have since expanded the original transcriptome to incorporate different types of tissue obtained at necropsy from animals that died of natural causes [24], allowing more than 9,300 SNPs to be identified. However, it would be desirable to develop additional SNPs, ideally also from non-coding regions of the genome.…”

Transcriptomic SNP discovery for custom genotyping arrays: impacts of sequence data, SNP calling method and genotyping technology on the probability of validation success

“…A transcriptome assembly based on Roche 454 sequencing is already available for this species, from which two SNP datasets were generated using NEWBLER and SWAP454 respectively [33, 34]. Here, we supplement this transcriptome with short read Illumina sequencing, allowing a comparison of SNP discovery methods tailored to different types of sequence data.…”