scholarly journals Amalgamated cross-species transcriptomes reveal organ-specific propensity in gene expression evolution

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Kenji Fukushima ◽  
David D. Pollock
Keyword(s):  
Gene Expression ◽  
Gene Duplication ◽  
Evolutionary Rate ◽  
Expression Patterns ◽  
Vertebrate Species ◽  
Rna Seq ◽  
Organ Specific ◽  
Tightly Coupled ◽  
Expression Evolution ◽  
Gene Expression Evolution

Abstract The origins of multicellular physiology are tied to evolution of gene expression. Genes can shift expression as organisms evolve, but how ancestral expression influences altered descendant expression is not well understood. To examine this, we amalgamate 1,903 RNA-seq datasets from 182 research projects, including 6 organs in 21 vertebrate species. Quality control eliminates project-specific biases, and expression shifts are reconstructed using gene-family-wise phylogenetic Ornstein–Uhlenbeck models. Expression shifts following gene duplication result in more drastic changes in expression properties than shifts without gene duplication. The expression properties are tightly coupled with protein evolutionary rate, depending on whether and how gene duplication occurred. Fluxes in expression patterns among organs are nonrandom, forming modular connections that are reshaped by gene duplication. Thus, if expression shifts, ancestral expression in some organs induces a strong propensity for expression in particular organs in descendants. Regardless of whether the shifts are adaptive or not, this supports a major role for what might be termed preadaptive pathways of gene expression evolution.

scholarly journals Organ-specific propensity drives patterns of gene expression evolution

2018 ◽  
Author(s):  
Kenji Fukushima ◽  
David D. Pollock
Keyword(s):  
Gene Expression ◽  
Gene Duplication ◽  
Evolutionary Rate ◽  
Expression Patterns ◽  
Vertebrate Species ◽  
Rna Seq ◽  
Organ Specific ◽  
Tightly Coupled ◽  
Expression Evolution ◽  
Gene Expression Evolution

AbstractThe origins of multicellular physiology are tied to evolution of gene expression. Genes can shift expression as organisms evolve, but how ancestral expression influences altered descendant expression is not well understood. To examine this, we amalgamated 1,903 RNA-seq datasets from 182 research projects, including 6 organs in 21 vertebrate species. Quality control eliminated project-specific biases, and expression shifts were reconstructed using gene-family-wise phylogenetic Ornstein–Uhlenbeck models. Expression shifts following gene duplication result in more drastic changes in expression properties than shifts without gene duplication. The expression properties were tightly coupled with protein evolutionary rate, depending on whether and how gene duplication occurred. Fluxes in expression patterns among organs were nonrandom, forming modular connections which were reshaped by gene duplication. Thus, if expression shifted, ancestral expression in some organs induces a strong propensity for expression in particular organs in descendants. This supports a major role for what might be termed “preadaptive” pathways of gene expression evolution.

scholarly journals Evolution of H3K27me3-marked chromatin is linked to gene expression evolution and to patterns of gene duplication and diversification

2014 ◽  
Vol 24 (7) ◽  
pp. 1115-1124 ◽  
Author(s):  
R. K. Arthur ◽  
L. Ma ◽  
M. Slattery ◽  
R. F. Spokony ◽  
A. Ostapenko ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Duplication ◽  
Expression Evolution ◽  
Gene Expression Evolution

scholarly journals Pervasive correlated evolution in gene expression shapes cell type transcriptomes

2016 ◽  
Author(s):  
Cong Liang ◽  
Jacob M. Musser ◽  
Alison Cloutier ◽  
Richard O. Prum ◽  
Günter P. Wagner
Keyword(s):  
Gene Expression ◽  
Hierarchical Clustering ◽  
Cell Types ◽  
Regulatory Sequences ◽  
Rna Seq ◽  
Cell Type ◽  
Correlated Evolution ◽  
Phylogenetic Methods ◽  
Expression Evolution ◽  
Gene Expression Evolution

AbstractThe evolution and diversification of cell types is a key means by which animal complexity evolves. Recently, hierarchical clustering and phylogenetic methods have been applied to RNA-seq data to infer cell type evolutionary history and homology. A major challenge for interpreting this data is that cell type transcriptomes may not evolve independently due to correlated changes in gene expression. This non-independence can arise for several reasons, such as when different tissues share common regulatory sequences for regulating genes expressed in multiple tissues, i.e. pleiotropic effects of mutations. We develop a model to estimate the level of correlated transcriptome evolution (LCE) and apply it to different datasets. The results reveal pervasive correlated transcriptome evolution among different cell and tissue types. In general, tissues related by morphology or developmental lineage exhibit higher LCE than more distantly related tissues. Analyzing new data collected from bird skin appendages suggests that LCE decreases with the phylogenetic age of tissues compared, with recently evolved tissues exhibiting the highest LCE. Furthermore, we show correlated evolution can alter patterns of hierarchical clustering, causing different tissue types from the same species to cluster together. Using a dataset with sufficient taxon sampling, we performed a gene-wise estimation of LCE, identifying genes that most strongly contribute to the correlated evolution signal. Removing genes with high LCE allows for accurate reconstruction of evolutionary relationships among tissue types. Our study provides a statistical method to measure and account for correlated gene expression evolution when interpreting comparative transcriptome data.

scholarly journals Combined Metabolome and Transcriptome Profiling Reveal Optimal Harvest Strategy Model Based on Different Production Purposes in Olive

Foods ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 360
Author(s):  
Guodong Rao ◽  
Jianguo Zhang ◽  
Xiaoxia Liu ◽  
Xue Li ◽  
Chenhe Wang
Keyword(s):  
Gene Expression ◽  
Olive Oil ◽  
Expression Patterns ◽  
Transcriptome Profiling ◽  
Minor Components ◽  
Harvest Time ◽  
Rna Seq ◽  
Optimal Harvest ◽  
Harvest Strategy ◽  
Different Color

Olive oil has been favored as high-quality edible oil because it contains balanced fatty acids (FAs) and high levels of minor components. The contents of FAs and minor components are variable in olive fruits of different color at harvest time, which render it difficult to determine the optimal harvest strategy for olive oil producing. Here, we combined metabolome, Pacbio Iso-seq, and Illumina RNA-seq transcriptome to investigate the association between metabolites and gene expression of olive fruits at harvest time. A total of 34 FAs, 12 minor components, and 181 other metabolites (including organic acids, polyols, amino acids, and sugars) were identified in this study. Moreover, we proposed optimal olive harvesting strategy models based on different production purposes. In addition, we used the combined Pacbio Iso-seq and Illumina RNA-seq gene expression data to identify genes related to the biosynthetic pathways of hydroxytyrosol and oleuropein. These data lay the foundation for future investigations of olive fruit metabolism and gene expression patterns, and provide a method to obtain olive harvesting strategies for different production purposes.

scholarly journals Bayesian Framework for Detecting Gene Expression Outliers in Individual Samples

2020 ◽  
pp. 160-170
Author(s):  
John Vivian ◽  
Jordan M. Eizenga ◽  
Holly C. Beale ◽  
Olena M. Vaske ◽  
Benedict Paten
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Rna Seq ◽  
Single Patient ◽  
Tissue Samples ◽  
Composite Tissue ◽  
Patient Sample ◽  
Statistical Framework ◽  
Therapeutic Leads ◽  
Upregulated Genes

PURPOSE Many antineoplastics are designed to target upregulated genes, but quantifying upregulation in a single patient sample requires an appropriate set of samples for comparison. In cancer, the most natural comparison set is unaffected samples from the matching tissue, but there are often too few available unaffected samples to overcome high intersample variance. Moreover, some cancer samples have misidentified tissues of origin or even composite-tissue phenotypes. Even if an appropriate comparison set can be identified, most differential expression tools are not designed to accommodate comparisons to a single patient sample. METHODS We propose a Bayesian statistical framework for gene expression outlier detection in single samples. Our method uses all available data to produce a consensus background distribution for each gene of interest without requiring the researcher to manually select a comparison set. The consensus distribution can then be used to quantify over- and underexpression. RESULTS We demonstrate this method on both simulated and real gene expression data. We show that it can robustly quantify overexpression, even when the set of comparison samples lacks ideally matched tissue samples. Furthermore, our results show that the method can identify appropriate comparison sets from samples of mixed lineage and rediscover numerous known gene-cancer expression patterns. CONCLUSION This exploratory method is suitable for identifying expression outliers from comparative RNA sequencing (RNA-seq) analysis for individual samples, and Treehouse, a pediatric precision medicine group that leverages RNA-seq to identify potential therapeutic leads for patients, plans to explore this method for processing its pediatric cohort.

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