The Next Generation Is Here: A Review of Transcriptomic Approaches in Marine Ecology

Abstract

As we enter the United Nations Decade of Ocean Science for Sustainable Development, extensive efforts to reverse the decline of ocean health are taking place. Moreover, the need to utilize innovative and integrative approaches to aid in these efforts and address marine ecological questions are urgent. Transcriptomic technologies provide tools to further our understanding of an organism’s biology by allowing researchers to rapidly gain information on the genetic variation of populations and the regulation of cellular processes and pathways through gene presence, absence, and expression. Here, we review the application of transcriptomics in the field of marine ecology over the last decade, following a systematic literature review approach. We found 478 articles that fit our search criteria of using transcriptomic approaches to address ecological hypotheses, with 70% of these studies occurring within the last 5 years. Among the analysed articles, 51.7% involved a type of stressor, 16.6% used transcriptomics to study adaptation, and another 15.9% researched ecological interactions. Most articles investigated species from kingdom Animalia, with a high representation from both molluscs (19.5%) and chordates (13.3%), and only 22% of studies had a fieldwork component. Our review demonstrates how the use of transcriptomic techniques in the field of marine ecology is increasing and how they are being applied. Although there are still challenges researchers experience using such techniques, particularly when annotating genes in non-model species and those with no prior genomic resources, these innovative technologies are extremely valuable in investigating differential gene expression, molecular pathways, and generating genomic resources.

Microeukaryote metabolism across the western North Atlantic Ocean revealed through autonomous underwater profiling

“…3B). Fungi are important microeukaryotes driving biogeochemical cycling and are poorly understood in marine systems 40 . These results are consistent with 18S rDNA data that show that this group comprises important members of the global bathypelagic zone, along with rhizaria (foraminifera and radiolarians), stramenopiles, and alveolates (including dinoflagellates and ciliates) 41 .…”

Comparative Transcriptomics of Fat Bodies between Symbiotic and Quasi-Aposymbiotic Adult Females of Blattella germanica with Emphasis on the Metabolic Integration with Its Endosymbiont Blattabacterium and Its Immune System

“…A potential limitation of the study is that we have not used RT-qPCR to validate the results, but recent research supports the view that RNA-seq results are robust and do not always require independent verification [69,70]. Accordingly, the validation of RNA-seq data with RT-qPCR has been declining over the past few years [71]. RT-qPCR-based validation has historically been necessary with microarray analysis; however, RNA-seq technology does not present the same problems as microarrays, and the added value of this validation practice was questioned recently [69][70][71].…”

“…Accordingly, the validation of RNA-seq data with RT-qPCR has been declining over the past few years [71]. RT-qPCR-based validation has historically been necessary with microarray analysis; however, RNA-seq technology does not present the same problems as microarrays, and the added value of this validation practice was questioned recently [69][70][71]. RNA-seq is very reproducible and highly accurate for quantifying expression levels [72,73].…”

Differential responses of selectively bred mussels (Perna canaliculus) to heat stress—survival, immunology, gene expression and microbiome diversity

“…The molecular response of marine invertebrates to environmental stress has been well-studied (see reviews: Gleason, 2019 ; Veldhoen et al, 2012 ; Page and Lawley, 2022 ) but to date, genomic and transcriptomic resources have been limited for P. canaliculus (but see Baettig et al, 2023a ). High-throughput sequencing approaches (e.g., RNAseq) can measure the expression levels for all genes within a single sample, even for non-model organisms.…”