Sequencing‐based gene network analysis provides a core set of gene resource for understanding thermal adaptation in Zhikong scallop Chlamys farreri

Abstract

Marine organisms are commonly exposed to variable environmental conditions, and many of them are under threat from increased sea temperatures caused by global climate change. Generating transcriptomic resources under different stress conditions are crucial for understanding molecular mechanisms underlying thermal adaptation. In this study, we conducted transcriptome-wide gene expression profiling of the scallop Chlamys farreri challenged by acute and chronic heat stress. Of the 13 953 unique tags, more than 850 were significantly differentially expressed at each time point after acute heat stress, which was more than the number of tags differentially expressed (320-350) under chronic heat stress. To obtain a systemic view of gene expression alterations during thermal stress, a weighted gene coexpression network was constructed. Six modules were identified as acute heat stress-responsive modules. Among them, four modules involved in apoptosis regulation, mRNA binding, mitochondrial envelope formation and oxidation reduction were downregulated. The remaining two modules were upregulated. One was enriched with chaperone and the other with microsatellite sequences, whose coexpression may originate from a transcription factor binding site. These results indicated that C. farreri triggered several cellular processes to acclimate to elevated temperature. No modules responded to chronic heat stress, suggesting that the scallops might have acclimated to elevated temperature within 3 days. This study represents the first sequencing-based gene network analysis in a nonmodel aquatic species and provides valuable gene resources for the study of thermal adaptation, which should assist in the development of heat-tolerant scallop lines for aquaculture.

Considerations for the use of transcriptomics in identifying the ‘genes that matter’ for environmental adaptation

“…In the scallop, temperature increase transcriptionally activates six modules of co-expressed genes, with each module targeting specific cellular pathways important to coping with environmental change, including protein folding, apoptosis and metabolism. Each module contains between 107 and 1640 genes, and is regulated by very few, highly connected hub genes (Fu et al, 2014). Populations of killifish that differ in their capacity to acclimate to changes in environmental salinity possess distinct gene networks.…”

“…For example, Costanzo et al (2010) used only 30% of the S. cerevisiae genome as query genes, but recovered genetic interactions for approximately 75% of the genome. Nonetheless, incorporating gene expression data does increase the accuracy of resulting networks because genes exhibiting similar expression profiles can be connected into modules of co-expressed genes (Fu et al, 2014). Knowledge of network topology can also facilitate predictive power.…”

Aquatic hyphomycetes in a changing environment

“…There is currently no straight-forward connection between a fungal genome and temperature preferences and tolerances of the corresponding taxon. In other taxa, some progress has been achieved: in the scallop Chlamys farreri gene network analysis has identified a core set of heat-stress responsive modules (Fu et al 2014), and transcriptomic signatures in Antarctic fish paralleled exposure to ambient temperature (Windisch et al 2014).…”

Transcriptomic responses to environmental change in fishes: Insights from RNA sequencing

“…Although underutilized in ecological genomics, gene network construction offers a promising alternative for functional analysis based on the notion that genes with a higher degree of connectedness have a greater impact on fitness through involvement in multiple cellular pathways (i.e., hub genes; Costanzo et al 2010). Although incorporation of differential expression data improves these networks by taking co-expression into account (Fu et al 2014), it is not necessary because they are built using databases of known gene interactions (Evans 2015). If specific (i.e., candidate) genes are the unit of interest, validation of gene expression using additional samples (either more biological replicates from the same experiment or, ideally, samples from a different population or incidence of environmental exposure) would be an asset to reduce the potential for false positives.…”