scholarly journals Directional selection at gene expression level contributes to the speciation of Asian rice cultivars

2021 ◽  
Author(s):  
Lihong Xie ◽  
Kehan Yu ◽  
Dongjing Chen
Keyword(s):  
Gene Expression ◽  
Oryza Sativa ◽  
Oryza Sativa L ◽  
Directional Selection ◽  
Expression Level ◽  
Stabilizing Selection ◽  
Rice Cultivars ◽  
Gene Expression Variation ◽  
Expression Variation ◽  
Asian Rice

Differences in expression levels play important roles in phenotypic variation across species, especially those closely related species with limited genomic differences. Therefore, studying gene evolution at expression level is important for illustrating phenotypic differentiation between species, such as the two Asian rice cultivars, Oryza sativa L. ssp. indica and Oryza sativa L. ssp. japonica. In this study, we evaluated the gene expression variation at inter-subspecies and intra-subspecies levels using transcriptome data from seedlings of three indica and japonica rice and defined four groups of genes under different natural selections. We found a substantial of genes (about 79%) that are under stabilizing selection at the expression level in both subspecies, while about 16% of genes are under directional selection. Genes under directional selection have higher expression level and lower expression variation than those under stabilizing selection, which suggest a potential explanation to subspecies adaptation to different environments and interspecific phenotypic differences. Subsequent functional enrichment analysis of genes under directional selection shows that indica rice have experienced the adaptation to environmental stresses, and also show differences in biosynthesis and metabolism pathways. Our study provides an avenue of investigating indica-japonica differentiation through gene expression variation, which may guide to rice breeding and yield improvement.

scholarly journals Evaluating intra- and inter-individual variation in the human placental transcriptome

2014 ◽  
Author(s):  
David A Hughes ◽  
Martin Kircher ◽  
Zhisong He ◽  
Song Guo ◽  
Genevieve L Fairbrother ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Phenotypic Trait ◽  
Stabilizing Selection ◽  
Biological Traits ◽  
Gene Expression Variation ◽  
Expression Variation ◽  
Biological Trait ◽  
Total Gene Expression ◽  
Genetic And Environmental Factors

Background: Gene expression variation is a phenotypic trait of particular interest as it represents the initial link between genotype and other phenotypes. Analyzing how such variation apportions among and within groups allows for the evaluation of how genetic and environmental factors influence such traits. It also provides opportunities to identify genes and pathways that may have been influenced by non-neutral processes. Here we use a population genetics framework and next generation sequencing to evaluate how gene expression variation is apportioned among four human groups in a natural biological tissue, the placenta. Results: We estimate that on average, 33.2%, 58.9% and 7.8% of the placental transcriptome is explained by variation within individuals, among individuals and among human groups, respectively. Additionally, when technical and biological traits are included in models of gene expression they account for roughly 2% of total gene expression variation. Notably, the variation that is significantly different among groups is enriched in biological pathways associated with immune response, cell signaling and metabolism. Many biological traits demonstrated correlated changes in expression in numerous pathways of potential interest to clinicians and evolutionary biologists. Finally, we estimate that the majority of the human placental transcriptome (65% of expressed genes) exhibits expression profiles consistent with neutrality; the remainder are consistent with stabilizing selection (26%), directional selection (4.9%), or diversifying selection (4.8%). Conclusion: We apportion placental gene expression variation into individual, population and biological trait factors and identify how each influence the transcriptome. Additionally, we advance methods to associate expression profiles with different forms of selection.

scholarly journals Massively parallel gene expression variation measurement of a synonymous codon library

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Alexander Schmitz ◽  
Fuzhong Zhang
Keyword(s):  
Gene Expression ◽  
Codon Usage ◽  
Single Cells ◽  
Massively Parallel ◽  
Protein Abundance ◽  
Translation Efficiency ◽  
Gene Expression Variation ◽  
Expression Variation ◽  
Change In Mean ◽  
Adaptation Index

Abstract Background Cell-to-cell variation in gene expression strongly affects population behavior and is key to multiple biological processes. While codon usage is known to affect ensemble gene expression, how codon usage influences variation in gene expression between single cells is not well understood. Results Here, we used a Sort-seq based massively parallel strategy to quantify gene expression variation from a green fluorescent protein (GFP) library containing synonymous codons in Escherichia coli. We found that sequences containing codons with higher tRNA Adaptation Index (TAI) scores, and higher codon adaptation index (CAI) scores, have higher GFP variance. This trend is not observed for codons with high Normalized Translation Efficiency Index (nTE) scores nor from the free energy of folding of the mRNA secondary structure. GFP noise, or squared coefficient of variance (CV2), scales with mean protein abundance for low-abundant proteins but does not change at high mean protein abundance. Conclusions Our results suggest that the main source of noise for high-abundance proteins is likely not originating at translation elongation. Additionally, the drastic change in mean protein abundance with small changes in protein noise seen from our library implies that codon optimization can be performed without concerning gene expression noise for biotechnology applications.

The effect of harvest date on yield loss of long and short-grain rice cultivars (Oryza sativa L.) in Northeast China

2021 ◽  
Vol 131 ◽  
pp. 126382
Author(s):  
Jinwu Wang ◽  
Xiaobo Sun ◽  
Yanan Xu ◽  
Qi Wang ◽  
Han Tang ◽  
...  
Keyword(s):  
Oryza Sativa ◽  
Northeast China ◽  
Yield Loss ◽  
Oryza Sativa L ◽  
Harvest Date ◽  
Rice Cultivars

scholarly journals Gene expression variation increase in trisomy 21 tissues

2008 ◽  
Vol 19 (6) ◽  
pp. 398-405 ◽  
Author(s):  
Ching Yu Chou ◽  
Li Yu Liu ◽  
Chien Yu Chen ◽  
Cheng Hsien Tsai ◽  
Hsiao Lin Hwa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Trisomy 21 ◽  
Gene Expression Variation ◽  
Expression Variation

Gene expression variation in natural populations of hexaploid and allododecaploid Spartina species (Poaceae)

2017 ◽  
Vol 303 (8) ◽  
pp. 1061-1079 ◽  
Author(s):  
Julie Ferreira de Carvalho ◽  
Julien Boutte ◽  
Pierre Bourdaud ◽  
Houda Chelaifa ◽  
Kader Ainouche ◽  
...  
Keyword(s):  
Gene Expression ◽  
Natural Populations ◽  
Gene Expression Variation ◽  
Expression Variation ◽  
Spartina Species

scholarly journals Population-scale long-read sequencing uncovers transposable elements contributing to gene expression variation and associated with adaptive signatures in Drosophila melanogaster

2021 ◽  
Author(s):  
Gabriel Rech ◽  
Santiago Radio ◽  
Sara Guirao-Rico ◽  
Laura Aguilera ◽  
Vivien Horvath ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Variation ◽  
Transposable Elements ◽  
Natural Populations ◽  
Gene Expression Variation ◽  
High Quality ◽  
Expression Variation ◽  
Climatic Regions ◽  
Genomic Studies ◽  
Reference Genomes

High quality reference genomes are crucial to understanding genome function, structure and evolution. The availability of reference genomes has allowed us to start inferring the role of genetic variation in biology, disease, and biodiversity conservation. However, analyses across organisms demonstrate that a single reference genome is not enough to capture the global genetic diversity present in populations. In this work, we generated 32 high-quality reference genomes for the well-known model species D. melanogaster and focused on the identification and analysis of transposable element variation as they are the most common type of structural variant. We showed that integrating the genetic variation across natural populations from five climatic regions increases the number of detected insertions by 58%. Moreover, 26% to 57% of the insertions identified using long-reads were missed by short-reads methods. We also identified hundreds of transposable elements associated with gene expression variation and new TE variants likely to contribute to adaptive evolution in this species. Our results highlight the importance of incorporating the genetic variation present in natural populations to genomic studies, which is essential if we are to understand how genomes function and evolve.

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