Abstract
Inherited gene transcripts deposited in oocytes direct early embryonic development in all vertebrates, but transcript profiles indicative of embryo developmental competence have not previously been identified. We employed artificial intelligence to model profiles of maternal ovary gene expression and their relationship to egg quality, evaluated as production of viable mid-blastula stage embryos, in the striped bass (Morone saxatilis), a farmed species with serious egg quality problems. In models developed using artificial neural networks (ANNs) and supervised machine learning, collective changes in the expression of a limited suite of genes (233) representing <2% of the queried ovary transcriptome explained >90% of the eventual variance in embryo survival. Egg quality related to minor changes in gene expression (<0.2-fold), with most individual transcripts making a small contribution (<1%) to the overall prediction of egg quality. These findings indicate that the predictive power of the transcriptome as regards egg quality resides not in levels of individual genes, but rather in the collective, coordinated expression of a suite of transcripts constituting a transcriptomic “fingerprint”. Correlation analyses of the corresponding candidate genes indicated that dysfunction of the ubiquitin-26S proteasome, COP9 signalosome, and subsequent control of the cell cycle engenders embryonic developmental incompetence. The affected gene networks are centrally involved in regulation of early development in all vertebrates, including humans. By assessing collective levels of the relevant ovarian transcripts via ANNs we were able, for the first time in any vertebrate, to accurately predict the subsequent embryo developmental potential of eggs from individual females. Our results show that the transcriptomic fingerprint evidencing developmental dysfunction is highly predictive of, and therefore likely to regulate, egg quality, a biologically complex trait crucial to reproductive fitness.
Highlights
Reproductive fitness is a key issue in evolutionary biology and one of the limiting components of reproduction is the formation of viable gametes
The mRNA extracted from ovary biopsies taken before the spawning season was subjected to microarray and results for the most informative 1469, 250 and 100 probe datasets were analyzed using artificial neural networks (ANNs) to model the relation of ovary transcriptome to egg quality
The mean CV R2 values for these models, a measure of the robustness of the ANN model based on its ability to predict egg quality, indicated that nearly 80% of variation in 4 h embryo survival and nearly 60% of variation in 24 h embryo survival could be predicted from gene expression measured using the top 250 probe dataset
Summary
Reproductive fitness is a key issue in evolutionary biology and one of the limiting components of reproduction is the formation of viable gametes. In wild and domestic animals, egg quality is affected by many factors and can be highly variable, with production of inviable eggs being common in many species, including humans. Poor egg quality, defined as the inability of developmentally incompetent eggs to produce viable embryos, is a serious problem faced in agriculture and human reproductive medicine that has persisted despite decades of attention. The earliest stages of vertebrate embryo development are characterized by rapid, synchronous cell divisions subdividing the zygote into a large population of blastomeres termed a ‘blastula’. During this time, the developmental competency and viability of the nascent embryo is governed by crucial maternal RNAs that are deposited in growing oocytes to direct early embryogenesis. In fish and other less derived vertebrates, the MZT involves a ‘midblastula transition’ (MBT) to longer, asynchronous, cell cycles that is accompanied by the activation of embryonic transcription [2,3,4]
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