Data from: Post-ejaculation thermal stress causes changes to the RNA profile of sperm in an external fertiliser
Lymbery, Rowan; Evans, Jonathan; Kennington, Jason (2020), Data from: Post-ejaculation thermal stress causes changes to the RNA profile of sperm in an external fertiliser, Dryad, Dataset, https://doi.org/10.5061/dryad.8pk0p2nkh
Sperm cells experience considerable post-ejaculation environmental variation. However, little is known about whether this affects their molecular composition, likely due to the assumption that sperm are transcriptionally quiescent. Nevertheless, recent evidence shows sperm have distinct RNA profiles that affect fertilisation and embryo viability. Moreover, RNAs are expected to be highly sensitive to extracellular changes. One such group of RNAs are heat shock protein (hsp) transcripts, which function in stress responses and are enriched in sperm. Here, we exploit the tractability of the mussel Mytilus galloprovincialis to exposed paired samples of ejaculated sperm to ambient (19 °C) and increased (25 °C) temperatures, then measure (a) sperm motility phenotypes, and (b) mRNA levels of two target genes (hsp70 and hsp90) and several putative reference genes. We find no phenotypic changes in motility, but reduced mRNA levels for hsp90 and the putative reference gene gapdh at 25 °C. This could reflect either decay of specific RNAs, or changes in translation and degradation rates of transcripts to maintain sperm function under stress. These findings represent the first evidence for changes in sperm RNA profiles due to post-ejaculation environments, and suggest that sperm may be more vulnerable to stress from rising temperatures than currently thought.
This dataset contains data collected from the sperm of mussels, Mytilus galloprovincialis, during a controlled, split-ejaculate experiment. Sperm from each male were split into two aliquots; one was kept at ambient temperature (18C) and the other was treated to a high temperature (25C) for 10 minutes. For a subset of males, we measured motility traits of sperm in each treatment using computer-assisted sperm analysis (CASA). For another subset of males, we measured the abundance of RNA transcripts in sperm from four genes (hsp70, hsp90, gapdh and actin) using reverse-transription quantitative polymerase chain reaction (qPCR). Note that for logistical reasons, some males had both data types collected (CASA and qPCR), while other males had one or the other. The cycle threshold outputs from qPCR reactions were converted into molecule counts for analysis using the R package 'MCMC.qpcr' (https://cran.r-project.org/web/packages/MCMC.qpcr/index.html), based on amplification efficiencies for each gene calculated from standard curves.
The dataset contains three spreadsheets. The sheet titled "CASA" contains sperm motility data, with rows for sperm samples and columns for block (experimental day of collection), unique male ID, temperature treatment (A=ambient, H=high), sample ID, and the following CASA measurements: amplitude of lateral head displacement (ALH), beat cross frequency (BCF), curvilinear velocity (VCL), average path velocity (VAP), straight-line velocity (VSL), linearity (LIN), straightness (STR), number of motile sperm, number of immotile sperm, and total number of sperm cells tracked.
The sheet titled ""qPCR_standard_curves" contains results from qPCR standard cDNA curves of five-fold serial dilutions (10, 2, 0.4, 0.08, and 0.016 ng µL-1; each dilution was prepared in triplicate) for each gene, prepared using a non-experimental mussel sperm sample at ambient temperature. Columns denote cDNA concentration (dna), cycle threshold output (cq) and gene ID (gene).
The sheet titled "qPCR_gene_assays" contains the results from qPCR assays for each gene on experimental sperm samples. Each sperm sample (i.e. each male by treatment combination) had reactions prepared in triplicate for each gene. Columns denote sample ID, block (experimental day of collection), unique male ID, temperature treatment (A=ambient, H=high), and the cycle threshold output for each gene (hsp70, hsp90, gapdh and actin).
Australian Research Council, Award: DP170103290