scholarly journals Predictive combinatorial design of mRNA translation initiation regions for systematic optimization of gene expression levels

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Sang Woo Seo ◽  
Jae-Seong Yang ◽  
Han-Saem Cho ◽  
Jina Yang ◽  
Seong Cheol Kim ◽  
...  
Keyword(s):  
Gene Expression ◽  
Translation Initiation ◽  
Mrna Translation ◽  
Combinatorial Design ◽  
Expression Levels ◽  
Systematic Optimization ◽  
Gene Expression Levels

scholarly journals Codon usage is an important determinant of gene expression levels largely through its effects on transcription

2016 ◽  
Vol 113 (41) ◽  
pp. E6117-E6125 ◽  
Author(s):  
Zhipeng Zhou ◽  
Yunkun Dang ◽  
Mian Zhou ◽  
Lin Li ◽  
Chien-hung Yu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Codon Usage ◽  
Important Determinant ◽  
Mrna Translation ◽  
Mrna Levels ◽  
Protein Coding ◽  
Expression Levels ◽  
Highly Expressed Genes ◽  
The Impact ◽  
Gene Expression Levels

Codon usage biases are found in all eukaryotic and prokaryotic genomes, and preferred codons are more frequently used in highly expressed genes. The effects of codon usage on gene expression were previously thought to be mainly mediated by its impacts on translation. Here, we show that codon usage strongly correlates with both protein and mRNA levels genome-wide in the filamentous fungus Neurospora. Gene codon optimization also results in strong up-regulation of protein and RNA levels, suggesting that codon usage is an important determinant of gene expression. Surprisingly, we found that the impact of codon usage on gene expression results mainly from effects on transcription and is largely independent of mRNA translation and mRNA stability. Furthermore, we show that histone H3 lysine 9 trimethylation is one of the mechanisms responsible for the codon usage-mediated transcriptional silencing of some genes with nonoptimal codons. Together, these results uncovered an unexpected important role of codon usage in ORF sequences in determining transcription levels and suggest that codon biases are an adaptation of protein coding sequences to both transcription and translation machineries. Therefore, synonymous codons not only specify protein sequences and translation dynamics, but also help determine 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.

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.

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