The utility of reptile blood transcriptomes in molecular ecology.
Damien S WaitsDasia Y SimpsonAmanda M SparkmanAnne M BronikowskiTonia S SchwartzPublished in: Molecular ecology resources (2019)
Reptiles and other nonmammalian vertebrates have transcriptionally active nucleated red blood cells. If blood transcriptomes can provide quantitative data to address questions relevant to molecular ecology, this could circumvent the need to euthanize animals to assay tissues. This would allow longitudinal sampling of animals' responses to treatments, as well as sampling of protected taxa. We developed and annotated blood transcriptomes from six reptile species and found on average 25,000 proteins are being transcribed in the blood, and there is a CORE group of 9,282 orthogroups that are found in at least four of six species. In comparison to liver transcriptomes from the same taxa, approximately two-thirds of the orthogroups were found in both blood and liver; and a similar percentage of ecologically relevant gene groups (insulin and insulin-like signalling, electron transport chain, oxidative stress, glucocorticoid receptors) were found transcribed in both blood and liver. As a resource, we provide a user-friendly database of gene ids identified in each blood transcriptome. Although on average 37% of reads mapped to haemoglobin, importantly, the majority of nonhaemoglobin transcripts had sufficient depth (e.g., 97% at ≥10 reads) to be included in differential gene expression analysis. Thus, we demonstrate that RNAseq blood transcriptomes from a very small blood sample (<10 μl) is a minimally invasive option in nonmammalian vertebrates for quantifying expression of a large number of ecologically relevant genes that would allow longitudinal sampling and sampling of protected populations.
Keyphrases
- single cell
- oxidative stress
- genome wide
- minimally invasive
- type diabetes
- gene expression
- emergency department
- machine learning
- high throughput
- high resolution
- dna methylation
- poor prognosis
- electronic health record
- skeletal muscle
- rna seq
- cross sectional
- deep learning
- optical coherence tomography
- cord blood
- glycemic control
- insulin resistance
- genetic diversity