The genomics revolution
is coming has already come. The past decade has seen countless advances in genomic techniques – many of which are now commonly found in any molecular ecologist’s toolbox. For example, instead of measuring gene expression in one or a few genes using RT qPCR, we can now measure genome-wide transcriptional activity using microarrays and RNA-sequencing (‘RNA-seq’). The amount of data being generated using these techniques has been growing exponentially over the past few years. So, Mariano Alvarez and colleagues decided that it was as good time as any to take stock of the past decade of transcriptomics studies in the wild.
The review nicely details the progression of studies that have measured gene expression in non-model organisms and “non-traditional” organisms in wild settings. The earliest studies were largely descriptive and characterized gene expression variation within and among populations. Next, the studies started to examine how environmental variation altered gene expression. Most recently, studies have begun to explore how gene expression is linked to phenotype (note: VERY few studies test the directional relationship between gene expression and phenotype).
As molecular ecology shifts from describing correlation to identifying causation, ecological transcriptomics will help elucidate the role of genomic elements that precede, regulate, and follow transcriptional modulation.
Other ‘omics techniques, such as epigenomics and proteomics, will ultimately help to illuminate the pathways by which gene expression affects phenotype.
RNA-seq vs. Microarrays
On thing of note in this review is that just over half (55%) of the studies used microarrays, and that the vast majority of these microarray studies were conducted in the earlier part of the decade of research. This is unsurprising given the precipitous drop in sequencing costs over the past few years. For most purposes, it is now cheaper to measure gene expression using RNA-sequencing than using a microarray. Furthermore, RNA-seq has multiple advantages when compared to microarrays, which Alvarez et al touch on in “Box 1” of their review (also detailed here).
So, as long as you have the wet-lab skills and computational & statistical savvy, you can easily measure gene expression variation in whatever organism and population you fancy (as long as you have a good question!). With RNA-seq, the transcriptomics world is your oyster.
Alvarez M, Schrey AW & Richards CL (2014) Ten years of transcriptomics in wild populations: what have we learned about their ecology and evolution? Molecular Ecology, DOI: 10.1111/mec.13055