scholarly journals Transcribed microsatellite allele lengths are often correlated with gene expression levels in natural sunflower populations

2018 ◽  
Author(s):  
Chathurani Ranathunge ◽  
Gregory L. Wheeler ◽  
Melody E. Chimahusky ◽  
Andy D. Perkins ◽  
Sreepriya Pramod ◽  
...  
Keyword(s):  
Gene Expression ◽  
Common Garden ◽  
Rna Seq ◽  
Motif Length ◽  
Microsatellite Allele ◽  
Regulatory Processes ◽  
Allele Length ◽  
Population Interaction ◽  
Cis And Trans ◽  
Gene Expression Levels

ABSTRACTMicrosatellites are common in most species. While an adaptive role for these highly mutable regions has been considered, little is known concerning their contribution towards phenotypic variation. We used populations of the common sunflower (Helianthus annuus) at two latitudes to quantify the effect of microsatellite allele length on phenotype at the level of gene expression. We conducted a common garden experiment with seed collected from sunflower populations in Kansas and Oklahoma followed by an RNA-Seq experiment on 95 individuals. The effect of microsatellite allele length on gene expression was assessed across 3325 microsatellites that could be consistently scored. Our study revealed 479 microsatellites at which allele length significantly correlates with gene expression (eSTRs). When irregular allele sizes not conforming to the motif length were removed, the number of eSTRs rose to 2379. The percentage of variation in gene expression explained by eSTRs ranged from 1–86% when controlling for population and allele-by-population interaction effects at the 479 eSTRs. Of these, 70.4% are in untranslated regions (UTRs). A Gene Ontology (GO) analysis revealed that eSTRs are significantly enriched for GO terms associated with cis- and trans-regulatory processes. These findings suggest that a substantial number of transcribed microsatellites can influence gene expression.

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 Association of intronic DNA methylation and hydroxymethylation alterations in the epigenetic etiology of dilated cardiomyopathy

2019 ◽  
Vol 317 (1) ◽  
pp. H168-H180 ◽  
Author(s):  
Ali M. Tabish ◽  
Mohammed Arif ◽  
Taejeong Song ◽  
Zaher Elbeck ◽  
Richard C. Becker ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Dilated Cardiomyopathy ◽  
Cardiac Remodeling ◽  
Rna Seq ◽  
Wild Type ◽  
Kegg Pathways ◽  
Wild Type Phenotype ◽  
Gene Expression Levels

In this study, we investigated the role of DNA methylation [5-methylcytosine (5mC)] and 5-hydroxymethylcytosine (5hmC), epigenetic modifications that regulate gene activity, in dilated cardiomyopathy (DCM). A MYBPC3 mutant mouse model of DCM was compared with wild type and used to profile genomic 5mC and 5hmC changes by Chip-seq, and gene expression levels were analyzed by RNA-seq. Both 5mC-altered genes (957) and 5hmC-altered genes (2,022) were identified in DCM hearts. Diverse gene ontology and KEGG pathways were enriched for DCM phenotypes, such as inflammation, tissue fibrosis, cell death, cardiac remodeling, cardiomyocyte growth, and differentiation, as well as sarcomere structure. Hierarchical clustering of mapped genes affected by 5mC and 5hmC clearly differentiated DCM from wild-type phenotype. Based on these data, we propose that genomewide 5mC and 5hmC contents may play a major role in DCM pathogenesis. NEW & NOTEWORTHY Our data demonstrate that development of dilated cardiomyopathy in mice is associated with significant epigenetic changes, specifically in intronic regions, which, when combined with gene expression profiling data, highlight key signaling pathways involved in pathological cardiac remodeling and heart contractile dysfunction.

scholarly journals Plant organ evolution revealed by phylotranscriptomics in Arabidopsis

2017 ◽  
Author(s):  
Li Lei ◽  
Joshua G Steffen ◽  
Edward J Osborne ◽  
Christopher Toomajian
Keyword(s):  
Gene Expression ◽  
Evolutionary History ◽  
Morphological Evolution ◽  
Sequence Divergence ◽  
Rna Seq ◽  
Plant Organ ◽  
New Perspective ◽  
Adaptive Signal ◽  
Floral Bud ◽  
Gene Expression Levels

ABSTRACTThe evolution of species’ phenotypes occurs through changes both in protein sequence and gene expression levels. Though much of plant morphological evolution can be explained by changes in gene expression, examining its evolution has challenges. To gain a new perspective on organ evolution in plants, we applied a phylotranscriptomics approach. We combined a phylostratigraphic approach with gene expression based on the strand-specific RNA-seq data from seedling, floral bud, and root of 19 Arabidopsis thaliana accessions to examine the age and sequence divergence of transcriptomes from these organs and how they adapted over time. Our results indicate that, among the sense and antisense transcriptomes of these organs, the sense transcriptomes of seedlings are the evolutionarily oldest across all accessions and are the most conserved in amino acid sequence for most accessions. In contrast, among the sense transcriptomes from these same organs, those from floral bud are evolutionarily youngest and least conserved in sequence for most accessions. Different organs have adaptive peaks at different stages in their evolutionary history, however, from the Magnoliophyta stage to the Brassicale stage, all three organs show a common adaptive signal. Our research is significant because it offers novel evolutionary insight on plant organs revealed by phylotranscriptomics.

scholarly journals Genetic Variants Associated mRNA Stability in Lung

2021 ◽  
Author(s):  
Jian-Rong Li ◽  
Mabel Tang ◽  
Yafang Li ◽  
Christopher I Amos ◽  
Chao Cheng
Keyword(s):  
Gene Expression ◽  
Mrna Stability ◽  
Genetic Variants ◽  
Molecular Mechanisms ◽  
Rna Binding ◽  
Tissue Expression ◽  
Rna Seq ◽  
Genetic Traits ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background: Expression quantitative trait loci (eQTLs) analyses have been widely used to identify genetic variants associated with gene expression levels to understand what molecular mechanisms underlie genetic traits. The resultant eQTLs might affect the expression of associated genes through transcriptional or post-transcriptional regulation. In this study, we attempt to distinguish these two types of regulation by identifying genetic variants associated with mRNA stability of genes (stQTLs).Results: Here, we presented a computational framework that take the advantage of recently developed methods to infer the mRNA stability of genes based on RNA-seq data and performed association analysis to identify stQTLs. Using the Genotype-Tissue Expression (GTEx) lung RNA-Seq data, we identified a total of 142,801 stQTLs for 3,942 genes and 186,132 eQTLs for 4,751 genes from 15,122,700 genetic variants for 13,476 genes, respectively. Interesting, our results indicated that stQTLs were enriched in the CDS and 3’UTR regions, while eQTLs are enriched in the CDS, 3’UTR, 5’UTR, and upstream regions. We also found that stQTLs are more likely than eQTLs to overlap with RNA binding protein (RBP) and microRNA (miRNA) binding sites. Our analyses demonstrate that simultaneous identification of stQTLs and eQTLs can provide more mechanistic insight on the association between genetic variants and gene expression levels.

ISOFORM ABUNDANCE INFERENCE PROVIDES A MORE ACCURATE ESTIMATION OF GENE EXPRESSION LEVELS IN RNA-SEQ

2010 ◽  
Vol 08 (supp01) ◽  
pp. 177-192 ◽  
Author(s):  
XI WANG ◽  
ZHENGPENG WU ◽  
XUEGONG ZHANG
Keyword(s):  
Gene Expression ◽  
High Throughput Sequencing ◽  
Transcript Abundance ◽  
Accurate Estimation ◽  
Rna Seq ◽  
Estimation Errors ◽  
Expression Levels ◽  
Or Gene ◽  
Read Distribution ◽  
Gene Expression Levels

Due to its unprecedented high-resolution and detailed information, RNA-seq technology based on next-generation high-throughput sequencing significantly boosts the ability to study transcriptomes. The estimation of genes' transcript abundance levels or gene expression levels has always been an important question in research on the transcriptional regulation and gene functions. On the basis of the concept of Reads Per Kilo-base per Million reads (RPKM), taking the union-intersection genes (UI-based) and summing up inferred isoform abundance (isoform-based) are the two current strategies to estimate gene expression levels, but produce different estimations. In this paper, we made the first attempt to compare the two strategies' performances through a series of simulation studies. Our results showed that the isoform-based method gives not only more accurate estimation but also has less uncertainty than the UI-based strategy. If taking into account the non-uniformity of read distribution, the isoform-based method can further reduce estimation errors. We applied both strategies to real RNA-seq datasets of technical replicates, and found that the isoform-based strategy also displays a better performance. For a more accurate estimation of gene expression levels from RNA-seq data, even if the abundance levels of isoforms are not of interest, it is still better to first infer the isoform abundance and sum them up to get the expression level of a gene as a whole.

scholarly journals The Genetic Architecture of Gene Expression Levels in Wild Baboons

2014 ◽  
Author(s):  
Jenny Tung ◽  
Xiang Zhou ◽  
Susan C Alberts ◽  
Matthew Stephens ◽  
Yoav Gilad
Keyword(s):  
Gene Expression ◽  
Genetic Architecture ◽  
Nonhuman Primates ◽  
Rna Seq ◽  
Gene Expression Variation ◽  
Data Set ◽  
Expression Levels ◽  
Expression Variation ◽  
Wild Baboons ◽  
Gene Expression Levels

Gene expression variation is well documented in human populations and its genetic architecture has been extensively explored. However, we still know little about the genetic architecture of gene expression variation in other species, particularly our closest living relatives, the nonhuman primates. To address this gap, we performed an RNA sequencing (RNA-seq)-based study of 63 wild baboons, members of the intensively studied Amboseli baboon population in Kenya. Our study design allowed us to measure gene expression levels and identify genetic variants using the same data set, enabling us to perform complementary mapping of putative cis-acting expression quantitative trait loci (eQTL) and measurements of allele-specific expression (ASE) levels. We discovered substantial evidence for genetic effects on gene expression levels in this population. Surprisingly, we found more power to detect individual eQTL in the baboons relative to a HapMap human data set of comparable size, probably as a result of greater genetic variation, enrichment of SNPs with high minor allele frequencies, and longer-range linkage disequilibrium in the baboons. eQTL were most likely to be identified for lineage-specific, rapidly evolving genes. Interestingly, genes with eQTL significantly overlapped between the baboon and human data sets, suggesting that some genes may tolerate more genetic perturbation than others, and that this property may be conserved across species. Finally, we used a Bayesian sparse linear mixed model to partition genetic, demographic, and early environmental contributions to variation in gene expression levels. We found a strong genetic contribution to gene expression levels for almost all genes, while individual demographic and environmental effects tended to be more modest. Together, our results establish the feasibility of eQTL mapping using RNA-seq data alone, and act as an important first step towards understanding the genetic architecture of gene expression variation in nonhuman primates.

scholarly journals Spatially varying cis-regulatory divergence inDrosophilaembryos elucidates cis-regulatory logic

2017 ◽  
Author(s):  
Peter A. Combs ◽  
Hunter B. Fraser
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Raw Material ◽  
Spatial Patterning ◽  
Rna Seq ◽  
Specific Expression ◽  
Complex Interactions ◽  
Allele Specific ◽  
Spatially Varying ◽  
Cis And Trans

AbstractSpatial patterning of gene expression is a key process in development—responsible for the incredible diversity of animal body plans—yet how it evolves is still poorly understood. Both cis- and trans-acting changes could accumulate and participate in complex interactions, so to isolate the cis-regulatory component of patterning evolution, we measured allele-specific spatial gene expression patterns inD. melanogaster×D. simulanshybrid embryos. RNA-seq of cryosectioned slices revealed 55 genes with strong spatially varying allele-specific expression, and several hundred more with weaker but significant spatial divergence. For example, we found thathunchback (hb), a major regulator of developmental patterning, had reduced expression specifically in the anterior tip ofD. simulansembryos. Mathematical modeling ofhbcis-regulation suggested that a mutation in a Bicoid binding site was responsible, which we verified using CRISPR-Cas9 genome editing. In sum, even comparing morphologically near-identical species we identified a substantial amount of spatial variation in gene expression, suggesting that development is robust to many such changes, but also that natural selection may have ample raw material for evolving new body plans via cis-regulatory divergence.

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