Dataset Information

Ovary transcriptome profiling via artificial intelligence reveals a transcriptomic fingerprint predicting egg quality in striped bass, Morone saxatilis.

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.

SUBMITTER: Chapman RW

PROVIDER: S-EPMC4018430 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Ovary transcriptome profiling via artificial intelligence reveals a transcriptomic fingerprint predicting egg quality in striped bass, Morone saxatilis.

Chapman Robert W RW Reading Benjamin J BJ Sullivan Craig V CV

PloS one 20140512 5

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 ...[more]

PMID: 24820964

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Project description:BACKGROUND: The striped bass and its relatives (genus Morone) are important fisheries and aquaculture species native to estuaries and rivers of the Atlantic coast and Gulf of Mexico in North America. To open avenues of gene expression research on reproduction and breeding of striped bass, we generated a collection of expressed sequence tags (ESTs) from a complementary DNA (cDNA) library representative of their ovarian transcriptome. RESULTS: Sequences of a total of 230,151 ESTs (51,259,448 bp) were acquired by Roche 454 pyrosequencing of cDNA pooled from ovarian tissues obtained at all stages of oocyte growth, at ovulation (eggs), and during preovulatory atresia. Quality filtering of ESTs allowed assembly of 11,208 high-quality contigs ? 100 bp, including 2,984 contigs 500 bp or longer (average length 895 bp). Blastx comparisons revealed 5,482 gene orthologues (E-value < 10-3), of which 4,120 (36.7% of total contigs) were annotated with Gene Ontology terms (E-value < 10-6). There were 5,726 remaining unknown unique sequences (51.1% of total contigs). All of the high-quality EST sequences are available in the National Center for Biotechnology Information (NCBI) Short Read Archive (GenBank: SRX007394). Informative contigs were considered to be abundant if they were assembled from groups of ESTs comprising ? 0.15% of the total short read sequences (? 345 reads/contig). Approximately 52.5% of these abundant contigs were predicted to have predominant ovary expression through digital differential display in silico comparisons to zebrafish (Danio rerio) UniGene orthologues. Over 1,300 Gene Ontology terms from Biological Process classes of Reproduction, Reproductive process, and Developmental process were assigned to this collection of annotated contigs. CONCLUSIONS: This first large reference sequence database available for the ecologically and economically important temperate basses (genus Morone) provides a foundation for gene expression studies in these species. The predicted predominance of ovary gene expression and assignment of directly relevant Gene Ontology classes suggests a powerful utility of this dataset for analysis of ovarian gene expression related to fundamental questions of oogenesis. Additionally, a high definition Agilent 60-mer oligo ovary 'UniClone' microarray with 8 × 15,000 probe format has been designed based on this striped bass transcriptome (eArray Group: Striper Group, Design ID: 029004).

Dataset Information

Ovary transcriptome profiling via artificial intelligence reveals a transcriptomic fingerprint predicting egg quality in striped bass, Morone saxatilis.

Publications

Ovary transcriptome profiling via artificial intelligence reveals a transcriptomic fingerprint predicting egg quality in striped bass, Morone saxatilis.

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