Differential gene expression of Australian Cricotopus draysoni (Diptera: Chironomidae) populations reveals seasonal association in detoxification gene regulation

Abstract

Understanding the molecular mechanisms of organismal response to human-derived ecosystem change is recognised as a critical tool in monitoring and managing impacts, especially in freshwater systems. Fundamental to this approach is to determine the genes involved in responding to ecosystem change and detect modifications to their expression and activity in natural populations. Potential targets for this approach include well-known detoxification genes that are upregulated in response to stress. Here, we tested whether expression of such genes varied in association with differences in ecosystem health and could be detected in the field. We sampled populations of the freshwater midge, Cricotopus draysoni, from two geographically proximate sites in southeast Queensland, Australia, which differed in their ecosystem health, at multiple time points. We assessed transcriptome-level differential gene expression and predicted greatest differential expression between sites, associated with organismal responses to local physico-chemical factors. In contrast, we observed a clear and dramatic difference in gene expression – including of known detoxification genes – between time points, specifically between periods at the start and end of the austral summer rainfall when in-stream water levels are most different. These data suggest that these waterways experience greatest pollution load when water levels are high following rainfall events.

Response of Diamesa spp. (Diptera: Chironomidae) from Alpine streams to newly emergent contaminants and pesticides

“…The unknown magnitude of top-down effects and trophic cascades should be considered in future experiments. Assuming that toxicants can damage or influence DNA in a variety of ways, other approaches (e.g., gene expression analyses of genes involved in responding to chemical stress in natural populations) might be very useful to understand the molecular mechanisms of organismal response to human-derived ecosystem change (Bernabò et al, 2017;Krosch et al, 2017). Threats posed by contamination by CECs even at high altitude make it important to expand knowledge concerning heat and chemical tolerance in stenothermal insect species from freshwaters already threatened by temperature changes.…”

Cyanobacteria and Macroinvertebrate Relationships in Freshwater Benthic Communities beyond Cytotoxicity

“…Although the tolerance and detoxification capacity of benthic macroinvertebrates are still not completely elucidated, several studies into invertebrates and vertebrates tend to relate it to the enzymatic response against cellular oxidative stress or the activating immunity system [ 32 , 102 , 105 , 129 , 131 ]. The role of glutathione (GSH) in MC detoxification has long been known [ 132 ], but the effect of antioxidants as blockers of cyanotoxin accumulation and the metabolic pathways involved in detoxification processes have become particularly interesting in the last decade, regardless of antioxidants being produced naturally by the organism or obtained through diet, such as astaxanthins [ 31 , 32 , 133 ].…”

WEGO 2.0: a web tool for analyzing and plotting GO annotations, 2018 update

“…More datasets uploaded, there will be more column serials in the histogram. (D) Graph B: log ( 10 ) of P -values obtained from all datasets of selected GO terms in descending order, indicating the data differences, especially significant differences.…”

“…Gene Ontology (GO) was started by the GO Consortium in 1998 to focus on studies of the genome of three model organisms: Drosophila Melanogaster (fruit fly), Mus musculus (mouse) and Saccharomyces cerevisiae (brewer’s or baker’s yeast) ( 1–9 ). As a result of its unified and well-structured vocabulary, GO was quickly adopted across an array of genome projects ( 10 ), transcriptome projects ( 11–14 ), proteome projects ( 15 ) and more. GO consists of three sub-ontologies: biological process, cellular component and molecular function.…”

“…With the rapid development of high-throughput sequencing, the use of genome ( 10 ), transcriptome ( 11–14 ) and proteome ( 15 ) big data has become a major factor in downstream annotation and data analyses. In following this trend and answering user feedback, WEGO was updated to version 2.0 in 2018.…”