Browse
Submit Data
Databases
API
Help

Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Ovary transcriptome profiling via application of artificial intelligence predicts egg quality in striped bass

ABSTRACT: We modeled profiles of ovary gene expression and their relationship to egg quality, evaluated as production of viable mid-blastula stage embryos, in striped bass (Morone saxatilis) using artificial neural networks and supervised machine learning. Collective changes in expression of a limited suite of genes (233) representing only 2% of the queried ovary transcriptome explained >90% of the eventual variance in embryo survival. Egg quality related to minor changes in expression (M-bM-^IM-$0.2-fold), with most gene transcripts making minor contribution (<1%) to the overall prediction of egg quality. Correlation analyses of this suite of candidate genes indicated that collective dysfunction of the ubiquitin-26S proteasome, COP9 signalosome, and subsequent control of the cell cycle engenders embryonic developmental incompetence in striped bass. Our results show that the transcriptomic signature evidencing this dysfunction is of, and therefore likely to influence, egg quality, a biologically complex trait that is crucial to reproductive fitness. Female striped bass were sorted into groups (N=8 each) producing M-bM-^@M-^Xhigh qualityM-bM-^@M-^Y or M-bM-^@M-^Xlow qualityM-bM-^@M-^Y eggs (spawns) based upon the percentage of eggs bearing viable 4 h embryos. Spawns with >50% of eggs producing 4 h embryos were considered to be of high quality and spawns with <30% of eggs producing 4 h embryos were considered to be of low quality.

ORGANISM(S): Morone saxatilis

SUBMITTER: Marine Genomics Core

PROVIDER: E-GEOD-42804 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Json Xml

Similar Datasets

Ovary transcriptome profiling via application of artificial intelligence predicts egg quality in striped bass

Project description:We modeled profiles of ovary gene expression and their relationship to egg quality, evaluated as production of viable mid-blastula stage embryos, in striped bass (Morone saxatilis) using artificial neural networks and supervised machine learning. Collective changes in expression of a limited suite of genes (233) representing only 2% of the queried ovary transcriptome explained >90% of the eventual variance in embryo survival. Egg quality related to minor changes in expression (≤0.2-fold), with most gene transcripts making minor contribution (<1%) to the overall prediction of egg quality. Correlation analyses of this suite of candidate genes indicated that collective dysfunction of the ubiquitin-26S proteasome, COP9 signalosome, and subsequent control of the cell cycle engenders embryonic developmental incompetence in striped bass. Our results show that the transcriptomic signature evidencing this dysfunction is of, and therefore likely to influence, egg quality, a biologically complex trait that is crucial to reproductive fitness.

2012-12-10 | GSE42804 | GEO

UCSF Center for Reproductive Health (CRH) Research Bank

Project description:<p>The purpose of this study is to provide a reference profile of small extracellular RNAs in body fluids. These samples were originally obtained in a study that had a different purpose. The purpose of the original study was to add excess biological materials during the course of IVF treatment to a tissue bank. These biological materials included follicular fluid (the liquid that comes out of the ovary with the egg), granulosa cells (cells that surround the egg and help it grow), and other biological materials that help eggs, sperm, and embryos stay healthy and grow outside the body. These banked specimens would allow researchers to learn more about infertility and how to treat it, including studying how to identify the healthiest eggs, sperm, and embryos and how to help them stay healthy and grow.</p>

| phs001695 | dbGaP

Morone saxatilis

Project description:Ovary transcriptome profiling via application of artificial intelligence predicts egg quality in striped bass

| PRJNA183347 | ENA

Proteomic portraits of egg quality in zebrafish (Danio rerio)

Project description:Humans and animals have problems producing eggs with high embryo developmental competence, but the causes of poor egg quality are usually unknown. This study delivered the first proteomic portraits of egg quality in zebrafish, a leading model for vertebrate development. Egg batches of good and poor quality, evidenced by embryo survival for 24 h, were used to create pooled or replicated sample sets subjected to different levels of fractionation before LC-MS/MS. Obtained spectra were searched against a custom zebrafish proteome database and detected proteins were annotated, categorized and quantified based on their normalized spectral counts. Manual and automated enrichment analyses were highly confirmative, showing that good and poor quality eggs have disparate proteomes. Proteins involved in protein synthesis, energy metabolism, and lipid metabolism, and certain vitellogenin products were strikingly underrepresented in poor quality eggs. Poor quality eggs also had significantly higher representation of proteins related to immune system and endosome/lysosome functioning, oncogenes, and apoptosis, as well as lectins and egg envelope proteins. Quantitative comparisons of highly abundant proteins revealed 9 candidate egg quality markers warranting further study. In conclusion, the zebrafish egg proteome appears to be linked to embryo developmental potential, a phenomenon that begs further investigation.

2017-11-27 | PXD005137 | Pride

Proteomic portraits of egg quality in zebrafish (Danio rerio)

2017-11-27 | PXD005129 | Pride

Egg transcriptome reveals molecular signature to predict developmental success and novel maternal-effect genes

Project description:Good quality or developmentally competent eggs result in high survival of progeny. Previous research has shed light on factors that determine egg quality, however, large gaps remain. Initial development of the embryo relies on maternally-inherited molecules, such as transcripts, deposited in the egg, thus, they would likely reflect egg quality. We performed microarray analysis on zebrafish fertilized eggs of different quality from unrelated, wildtype couples to obtain a global portrait of the egg transcriptome to determine its association with developmental competence and to identify new candidate maternal-effect genes. Fifteen of the most differentially expressed genes (DEGs) were validated by quantitative real-time PCR. Gene ontology analysis showed that enriched terms included ribosomes and translation. In addition, statistical modeling using partial least squares regression and genetics algorithm also demonstrated that gene signatures from the transcriptomic data can be used to predict reproductive success. Among the validated DEGs, otulina and slc29a1a were found to be increased in good quality eggs and to be predominantly localized in the ovaries. CRISPR/Cas9 knockout mutants of each gene revealed remarkable subfertility whereby the majority of their embryos were unfertilizable.Our novel findings suggested that even in varying quality of eggs due to heterogeneous causes from unrelated wildtype couples, gene signatures exist in the egg transcriptome, which can be used to predict developmental competence. Further, transcriptomic profiling revealed two new potential maternal-effect genes that have essential roles in vertebrate reproduction.

2019-02-01 | GSE109073 | GEO

Gene expression profiling of white bass when fed fish meal or fish meal replacement diets

Project description:White bass (Morone chrysops) are a popular sportfish throughout the southern United States, and one parent of the commercially successful hybrid striped bass (M. chrysops x M. saxatilis). Currently, white bass are cultured using diets formulated for other carnivorous fish, such as largemouth bass (Micropterus salmoides) or hybrid striped bass and contain a significant percentage of marine fish meal. Since there are no studies regarding the utilization of alternative proteins in this species, we evaluated global gene expression of white bass fed diets in which fish meal was partially or totally replaced by various combinations of soybean meal, poultry by-product meal, canola meal, soy protein concentrate, wheat gluten, or a commercial protein blend (Pro-Cision). Significant differential expressed genes and gene ontology of pairwise comparisons between control diet and each test diet are presented and discussed.

2024-02-14 | GSE220235 | GEO

Transcriptomes of the developing larval ovaries of Drosophila melanogaster during terminal filament formation

Project description:Insect ovaries are made up of tubular egg producing subunits called ovarioles, whose number largely determines the number of eggs that can be potentially laid. Ovariole number is directly determined by the number of cellular structures called terminal filaments, which are stacks of cells that assemble in the larval ovary. Elucidating the developmental and regulatory mechanisms of terminal filament formation is thus key to understanding the regulation of insect reproduction through ovariole number regulation. We systematically measured mRNA expression of all cells in the Drosophila larval ovary at the beginning, middle and end of terminal filament formation using RNA-seq. We also separated somatic and germ line cells during these stages and assessed their tissue-specific gene expression during larval ovary development.

2021-10-18 | GSE172015 | GEO

Mechanism of Hyperproteinemia-Induced Damage to Female Reproduction in a Genetic Silkworm Model

Project description:Hyperproteinemia is a metabolic disorder characterized by abnormally elevated plasma protein concentrations (PPC) in humans and animals. Here, a genetic silkworm model with high PPC was employed to investigate the effect of elevated PPC on female reproduction. Transcriptomic analysis revealed that high PPC induces downregulation of the ovarian development-related genes and disrupts ovarian sugar metabolism. Biochemical and endocrinal analyses revealed that high PPC increases trehalose and glucose levels in hemolymph and glycogen content in the fat body through activation of the gluconeogenic pathway and inhibition of the IIS-AKT pathway, thus disrupting characteristic metabolic homeostasis of sugar in the ovary. These resulted in ovarian developmental delay as well as reduced number and poor quality of eggs. Insulin supplementation effectively increased egg numbers by lowering blood sugar. These collective results provide new insights into the mechanisms by which high PPC negatively affects female reproduction and support the potential therapeutic effects of insulin.

2023-09-06 | GSE242203 | GEO

Honeybee egg size project-egg proteomics

Project description:Using the honey bee (Apis mellifera) as a model, we confirmed that honey bee queens actively adjust egg size in response to the number of workers: Queens lay smaller eggs in colonies with more worker bees than when in colonies with few workers. Reduced colony size also causes decreasing ovary weight but upregulation of energy production and protein processing in the ovaries. Complementary metabolomic and proteomic analyses of the produced eggs indicate that amino acid metabolism, cell development, and cell maturation processes are elevated in bigger eggs. Our study thus provides the first mechanistic insights into the adjustment of egg size in the honey bee, a model for complex social systems that demand integration of individual and collective life-history optimization.

2023-11-23 | PXD047190 |

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data