scholarly journals Non-parallel transcriptional divergence during parallel adaptation

2019 ◽  
Author(s):  
Eva K Fischer ◽  
Youngseok Song ◽  
Kimberly A Hughes ◽  
Wen Zhou ◽  
Kim L Hoke

AbstractHow underlying mechanisms bias evolution toward predictable outcomes remains an area of active debate. In this study, we leveraged phenotypic plasticity and parallel adaptation across independent lineages of Trinidadian guppies (Poecilia reticulata) to assess the predictability of gene expression evolution during parallel adaptation. Trinidadian guppies have repeatedly and independently adapted to high- and low-predation environments in the wild. We combined this natural experiment with a laboratory breeding design to attribute transcriptional variation to the genetic influences of population of origin and developmental plasticity in response to rearing with or without predators. We observed substantial gene expression plasticity as well as the evolution of expression plasticity itself across populations. Genes exhibiting expression plasticity within populations were more likely to also differ in expression between populations, with the direction of population differences more likely to be opposite those of plasticity. While we found more overlap than expected by chance in genes differentially expressed between high- and low-predation populations from distinct evolutionary lineages, the majority of differentially expressed genes were not shared between lineages. Our data suggest alternative transcriptional configurations associated with shared phenotypes, highlighting a role for transcriptional flexibility in the parallel phenotypic evolution of a species known for rapid adaptation.

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ying Xie ◽  
Xiaofeng Hang ◽  
Wensheng Xu ◽  
Jing Gu ◽  
Yuanjing Zhang ◽  
...  

Abstract Background Most of the biological functions of circular RNAs (circRNAs) and the potential underlying mechanisms in hepatocellular carcinoma (HCC) have not yet been discovered. Methods In this study, using circRNA expression data from HCC tumor tissues and adjacent tissues from the Gene Expression Omnibus database, we identified out differentially expressed circRNAs and verified them by qRT-PCT. Functional experiments were performed to evaluate the effects of circFAM13B in HCC in vitro and in vivo. Results We found that circFAM13B was the most significantly differentially expressed circRNA in HCC tissue. Subsequently, in vitro and in vivo studies also demonstrated that circFAM13B promoted the proliferation of HCC. Further studies revealed that circFAM13B, a sponge of miR-212, is involved in the regulation of E2F5 gene expression by competitively binding to miR-212, inhibits the activation of the P53 signalling pathway, and promotes the proliferation of HCC cells. Conclusions Our findings revealed the mechanism underlying the regulatory role played by circFAM13B, miR-212 and E2F5 in HCC. This study provides a new theoretical basis and novel target for the clinical prevention and treatment of HCC.


BMC Genomics ◽  
2014 ◽  
Vol 15 (1) ◽  
pp. 693 ◽  
Author(s):  
Huan Yang ◽  
Dawei Li ◽  
Chao Cheng

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

2008 ◽  
Vol 4 (1) ◽  
pp. 159 ◽  
Author(s):  
Itay Tirosh ◽  
Adina Weinberger ◽  
Dana Bezalel ◽  
Mark Kaganovich ◽  
Naama Barkai

2020 ◽  
Author(s):  
Soumitra Pal ◽  
Brian Oliver ◽  
Teresa M. Przytycka

AbstractWhile DNA sequence evolution has been well studied, the expression of genes is also subject to evolution. Yet the evolution of gene expression is currently not well understood. In recent years, new tissue/organ specific gene expression datasets spanning several organisms across the tree of life, have become available providing the opportunity to study gene expression evolution in more detail. However, while a theoretical model to study evolution of continuous traits exist, in practice computational methods often cannot distinguish, with confidence, between alternative evolutionary scenarios. This lack of power has been attributed to the modest number of species with available expression data.To solve this challenge, we introduce EvoGeneX, a computationally efficient method to uncover the mode of gene expression evolution based on the Ornstein-Uhlenbeck process. Importantly, EvoGeneX in addition to modelling expression variations between species, models within species variation. To estimate the within species variation, EvoGeneX formally incorporates the data from biological replicates as a part of the mathematical model. We show that by modelling the within species diversity EvoGeneX significantly outperforms the currently available computational method. In addition, to facilitate comparative analysis of gene expression evolution, we introduce a new approach to measure the dynamics of evolutionary divergence of a group of genes.We used EvoGeneX to analyse the evolution of expression across different organs, species and sexes of the Drosophila genus. Our analysis revealed differences in the evolutionary dynamics of male and female gonads, and uncovered examples of adaptive evolution of genes expressed in the head and in the thorax.


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