scholarly journals Ranking and selection of MII oocytes in human ICSI cycles using gene expression levels from associated cumulus cells

2013 ◽  
Vol 28 (11) ◽  
pp. 2930-2942 ◽  
Author(s):  
J. Ekart ◽  
K. McNatty ◽  
J. Hutton ◽  
J. Pitman
Keyword(s):  
Gene Expression ◽  
Cumulus Cells ◽  
Ranking And Selection ◽  
Expression Levels ◽  
Gene Expression Levels ◽  
Selection Of

scholarly journals Targeted reduction of highly abundant transcripts with pseudo-random primers

2015 ◽  
Author(s):  
Ophélie Arnaud ◽  
Sachi Kato ◽  
Stéphane Poulain ◽  
Charles Plessy
Keyword(s):  
Gene Expression ◽  
Reverse Transcription ◽  
Flexible Tool ◽  
Expression Levels ◽  
Random Primers ◽  
Sequencing Cost ◽  
Transcriptome Diversity ◽  
Gene Expression Levels ◽  
Selection Of

Transcriptome studies based on quantitative sequencing estimate gene expression levels by measuring the abundance of target RNAs in libraries of sequence reads. The sequencing cost is proportional to the total number of sequenced reads. Therefore, in order to cover rare RNAs, considerable quantities of abundant and identical reads have to be sequenced. This major limitation can be lifted by strategies used to deplete the library from some of the most abundant sequences. However, these strategies involve either an extra handling of the input RNA sample, or the use of a large number of reverse-transcription primers (termed "not-so-random primers"), which are costly to synthetize and customize. Here, we demonstrate that with a precise selection of only 40 "pseudo-random" reverse-transcription primers, it is possible to decrease the rate of undesirable abundant sequences within a library without affecting the transcriptome diversity. "Pseudo-random" primers are simple to design, and therefore are a flexible tool for enriching transcriptome libraries in rare transcripts sequences.

scholarly journals Genes for anti-Müllerian hormone and androgen receptor are underexpressed in human cumulus cells surrounding morphologically highly graded oocytes

2019 ◽  
Vol 7 ◽  
pp. 205031211986513 ◽  
Author(s):  
Sanja Dević Pavlić ◽  
Tamara Tramišak Milaković ◽  
Linda Panić Horvat ◽  
Kristina Čavlović ◽  
Hrvoje Vlašić ◽  
...  
Keyword(s):  
Gene Expression ◽  
Androgen Receptor ◽  
Follicular Fluid ◽  
Intracytoplasmic Sperm Injection ◽  
Cumulus Cells ◽  
Reproductive Technology ◽  
Expression Levels ◽  
Gene Expression Levels ◽  
Injection Procedure

Objectives: The aim of this study was to investigate the expression of genes crucial for the quality of the oocyte and whether expression levels of these genes in cumulus cells can be biological markers for the quality of the oocyte, zygote or embryo, or even for achievement of pregnancy after the assisted reproductive technology procedure. We examined the expression profile of the anti-Müllerian hormone (AMH) gene and its respective receptors: anti-Müllerian hormone receptor type 2 (AMHR2), follicle-stimulating hormone receptor (FSHR) and androgen receptor (AR) in cumulus cells (CCs) surrounding the oocyte, as well as AMH concentrations in follicular fluid of the associated follicle. The obtained gene expression levels were correlated with the morphological quality of the associated oocyte, zygote and embryo as well as with assisted reproductive technology outcome following the intracytoplasmic sperm injection procedure. Methods: This study involved 129 cumulus cells and 35 follicular fluid samples, taken from 58 patients undergoing the intracytoplasmic sperm injection procedure. Oocytes, zygotes and embryos were assessed for morphological quality. The relative gene expression of AMH, AMHR2, FSHR and AR was calculated using the delta–delta Ct method. Anti-Müllerian hormone concentrations in follicular fluids were measured by enzyme-linked immunosorbent assay. Results: The results yielded suggest a relationship between AMH, AR and oocyte morphology: AMH and AR gene expression levels in CCs surrounding morphologically optimal oocytes were significantly lower than in CCs surrounding oocytes with suboptimal morphology (p = 0.011 and p = 0.008, respectively). Statistically significant positive correlation was found between mRNA expression levels of AMH and FSHR (p < 0.001), AMH and AR (p = 0.001), AMHR2 and FSHR (p < 0.001), AMHR2 and AR (p < 0.001), as well as between FSHR and AR (p < 0.001). Conclusion: Assessed results point to AMH and AR relation with oocyte maturity, but not with its fertilization potential, or with embryo quality.

CALM2 gene expression levels in the cumulus cells as a marker for chromosomal abnormalities in the embryo in in vitro fertilization programs

2016 ◽  
Vol 10_2016 ◽  
pp. 64-72
Author(s):  
Safronova N.A. Safronova ◽  
Kalinina E.A. Kalinina ◽  
Donnikov A.E. Donnikov ◽  
Burmenskaya O.V. Burmenskaya ◽  
Makarova N.P. Makarova ◽  
...  
Keyword(s):  
Gene Expression ◽  
In Vitro Fertilization ◽  
Chromosomal Abnormalities ◽  
Cumulus Cells ◽  
Vitro Fertilization ◽  
Expression Levels ◽  
Gene Expression Levels

scholarly journals A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Yishu Wang ◽  
Lingyun Xu ◽  
Dongmei Ai
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Regression Model ◽  
The Cancer Genome Atlas ◽  
Low Rank ◽  
Expression Levels ◽  
Rank Regression ◽  
Prediction Of Prognosis ◽  
Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

scholarly journals High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Weitong Cui ◽  
Huaru Xue ◽  
Lei Wei ◽  
Jinghua Jin ◽  
Xuewen Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Small Sample ◽  
Differentially Expressed ◽  
Cancer Type ◽  
Rna Seq ◽  
Sample Sizes ◽  
Large Sample ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

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