scholarly journals Progesterone Promotes Mitochondrial Respiration at the Biochemical and Molecular Level in Germinating Maize Seeds

Plants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1326
Author(s):  
Hulya Turk
Keyword(s):  
Gene Expression ◽  
Mitochondrial Respiration ◽  
Citrate Synthase ◽  
Isocitrate Lyase ◽  
Malate Synthase ◽  
Total Soluble Protein ◽  
Total Carbohydrate ◽  
Expression Levels ◽  
Maize Seeds ◽  
Gene Expression Levels

This research aimed to investigate the effects of progesterone, a mammalian steroid sex hormone, on the mitochondrial respiration in germinating maize seeds. For this purpose, maize seeds were divided into four different groups (control, 10−6, 10−8, and 10−10 mol·L−1 progesterone) and were grown in a germination cabinet in the dark at 24.5 ± 0.5 °C for 4 d. The changes in gene expression levels of citrate synthase (CS), cytochrome oxidase (COX19), pyruvate dehydrogenase (Pdh1), and ATP synthase (ATP6), which is involved in mitochondrial respiration, were studied in root and cotyledon tissues. Significant increases were recorded in the gene expression levels of all studied enzymes. In addition, progesterone applications stimulated activities of malate synthase (MS), isocitrate lyase (ICL), and alpha-amylase, which are important enzymes of the germination step. The changes in gene expression levels of mas1 and icl1 were found parallel to the rise in these enzymes’ activities. It was determined similar increases in root and coleoptile lengths and total soluble protein and total carbohydrate contents. The most remarkable changes were detected in 10−8 mol·L−1 progesterone-treated seedlings. These results clearly indicate that progesterone stimulates mitochondrial respiration by inducing biochemical and molecular parameters and thus accelerates seed germination thanks to the activation of other pathways related to mitochondrial respiration.

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.

scholarly journals Genome-Wide Expression and Alternative Splicing in Domesticated Sunflowers (Helianthus annuus L.) under Flooding Stress

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 92
Author(s):  
Joon Seon Lee ◽  
Lexuan Gao ◽  
Laura Melissa Guzman ◽  
Loren H. Rieseberg
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Mixed Model ◽  
Agricultural Land ◽  
Alcohol Fermentation ◽  
Expression Levels ◽  
Flooding Stress ◽  
Genome Wide ◽  
And Control ◽  
Gene Expression Levels

Approximately 10% of agricultural land is subject to periodic flooding, which reduces the growth, survivorship, and yield of most crops, reinforcing the need to understand and enhance flooding resistance in our crops. Here, we generated RNA-Seq data from leaf and root tissue of domesticated sunflower to explore differences in gene expression and alternative splicing (AS) between a resistant and susceptible cultivar under both flooding and control conditions and at three time points. Using a combination of mixed model and gene co-expression analyses, we were able to separate general responses of sunflower to flooding stress from those that contribute to the greater tolerance of the resistant line. Both cultivars responded to flooding stress by upregulating expression levels of known submergence responsive genes, such as alcohol dehydrogenases, and slowing metabolism-related activities. Differential AS reinforced expression differences, with reduced AS frequencies typically observed for genes with upregulated expression. Significant differences were found between the genotypes, including earlier and stronger upregulation of the alcohol fermentation pathway and a more rapid return to pre-flooding gene expression levels in the resistant genotype. Our results show how changes in the timing of gene expression following both the induction of flooding and release from flooding stress contribute to increased flooding tolerance.

Estimation of Expression Levels in Spotted Microarrays with Saturated Pixels

2007 ◽  
Vol 6 (1) ◽  
Author(s):  
Chris A Glasbey ◽  
Thorsten Forster ◽  
Peter Ghazal
Keyword(s):  
Gene Expression ◽  
Linear Model ◽  
Principal Components ◽  
Laser Scanning ◽  
Dynamic Range ◽  
Biological Data ◽  
Hyperbolic Model ◽  
Expression Levels ◽  
Biased Estimates ◽  
Gene Expression Levels

Digital images obtained by the laser scanning of spotted microarrays often include saturated pixel values. These arise when the scan settings are sufficiently high and some pixels exceed the limit L=65535 and are instead set to L. Failure to adjust for this censoring leads to biased estimates of gene expression levels. To impute censored values, we propose a linear model based on the principal components of uncensored spots on the same array. This is computationally fast, flexible to adapt to distinctive spot shapes and profiles on different arrays, and is shown to be more effective than the polynomial-hyperbolic model in correcting for the bias. The application to biological data demonstrates the potential for enhancing the dynamic range of detection. Fortran90 subroutines implementing these methods are available at http://www.bioss.ac.uk/~chris.

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