Unknown,Transcriptomics,Genomics,Proteomics

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Competence classification of cumulus and granulosa cell transcriptome in embryos matched by morphology and female age

ABSTRACT: Mural granulosa cells (MGC) and cumulus cells (CC) were isolated immediately after oocyte retrieval during IVF treatment from the 16 competent and non competent follicles. mRNA was extracted resulting in 19 MGC and 27 CC samples of sufficient quality to be included in the study and expression profiles were generated on the Human Gene 1.0 ST Affymetrix array . Prediction of live birth after embryo transfer was performed using machine learning algorithms (support vector machines) with performance estimation by leave-one-out cross validation and independent validation on an external data set. We defined a signature of 30 genes expressed in CC predictive of live birth. This live birth prediction model had an accuracy of 81%, a sensitivity of 0.83, a specificity of 0.80, a positive predictive value of 0.77, and a negative predictive value of 0.86. Receiver operating characteristic analysis found an area under the curve of 0.86, significantly greater than random chance. When applied on 3 external data sets with the end-point outcome measure of blastocyst formation, the signature resulted in 62%, 75% and 88% accuracy, respectively.

ORGANISM(S): Homo sapiens

SUBMITTER: Rehannah Borup Helweg-Larsen

PROVIDER: E-MTAB-4012 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Adverse outcome pathways (AOP) contain summarizing sequences of key events (KE) relating toxicant exposures to specific adverse outcomes (AO) observed in exposed populations. Quantitative AOPs (qAOP) provide frameworks to assess AO risks based on KE quantification and the modeling of their relationships. This work aimed to develop the quantitative component of AOP 411 <Oxidative stress leading to decreased lung function>. To examine the pertinence of the resulting qAOP-based risk assessment approach, we applied it to predict the medium-term lung function evolution after switching from combustible cigarettes (CC) to heated tobacco products (HTP). We used publicly available data for oxidative stress (in vitro), ciliary beating frequency (in vitro), mucociliary clearance (human), and lung function (human). Following the course of smoking-related lung function decline (Fletcher and Peto, 1977, PMID 871704), we assumed a non-monotonic time evolution where the risk of the AO <decreased lung function> begins to decline after quitting smoking CC or switching to HTP use. Consequently, we designed our qAOP to evaluate the relative reduction (RR) of AO risk for HTP switching compared to the two scenarios of continuing or quitting CC consumption. We developed both data- and physiology-based KE relationship (KER) models. Despite sparse data with respect to dose- and time-response relationships, we successfully connected the measured KEs through in silico KER models. We then used the resulting qAOP to evaluate the exposure effects of switching from CC smoking to HTP use along AOP 411. This approach enabled in silico prediction of the AO RR for which in vivo data were not yet available. In summary, our qAOP provides a multiscale mechanistic approach to evaluate medium-term lung function evolution for the use case of switching from CC smoking to HTP use. The presented approach demonstrates how 21st Century Toxicology principles such as in vitro testing and in silico predictions can be integrated to achieve a meaningful risk assessment approach. It also represents a starting point for integrating additional AOP into an extended AOP network that includes other mechanisms leading to decreased lung function.

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Competence classification of cumulus and granulosa cell transcriptome in embryos matched by morphology and female age

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