scholarly journals Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single cell resolution

2021 ◽  
Author(s):  
Justyna A Resztak ◽  
Julong Wei ◽  
Samuele Zilioli ◽  
Edward Sendler ◽  
Adnan Alazizi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Genetic Regulation ◽  
Transcriptional Response ◽  
Response Dynamics ◽  
Gene Expression Response ◽  
Expression Variability ◽  
Fine Grained ◽  
Response Status ◽  
Expression Response

Synthetic glucocorticoids are used to treat many immune conditions, such as asthma and severe COVID-19. Single cell data capture fine-grained details of transcriptional variability and dynamics to gain a better understanding of the molecular underpinnings of inter-individual variation in drug response. We used single cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated PBMCs from African American donors. We employed novel statistical approaches to calculate a mean-independent measure of gene expression variability and a measure of transcriptional response pseudotime. We demonstrated that glucocorticoids reverse the effects of immune stimulation on both gene expression mean and variability. Our novel measure of gene expression response dynamics separated cells by response status and identified dynamic transcriptional patterns along the response pseudotime. We identified genetic variants regulating gene expression mean and variability, including treatment-specific effects, and demonstrated widespread genetic regulation of the transcriptional dynamics of the gene expression response.

scholarly journals MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hengshi Yu ◽  
Joshua D. Welch
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Data Generation ◽  
Gene Expression Response ◽  
Adversarial Networks ◽  
Cell Gene Expression ◽  
Expression Response ◽  
Cell Gene

AbstractDeep generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs) generate and manipulate high-dimensional images. We systematically assess the complementary strengths and weaknesses of these models on single-cell gene expression data. We also develop MichiGAN, a novel neural network that combines the strengths of VAEs and GANs to sample from disentangled representations without sacrificing data generation quality. We learn disentangled representations of three large single-cell RNA-seq datasets and use MichiGAN to sample from these representations. MichiGAN allows us to manipulate semantically distinct aspects of cellular identity and predict single-cell gene expression response to drug treatment.

Gene expression response of mouse lung, liver and white adipose tissue to β-carotene supplementation, knockout of Bcmo1 and sex

2011 ◽  
Vol 55 (10) ◽  
pp. 1466-1474 ◽  
Author(s):  
Yvonne G. J. van Helden ◽  
Roger W. L. Godschalk ◽  
Johannes von Lintig ◽  
Georg Lietz ◽  
Jean-Francois Landrier ◽  
...  
Keyword(s):  
Gene Expression ◽  
Adipose Tissue ◽  
White Adipose Tissue ◽  
Mouse Lung ◽  
Gene Expression Response ◽  
Expression Response ◽  
Β Carotene

Gene expression response of the alga Fucus virsoides (Fucales, Ochrophyta) to glyphosate solution exposure

2020 ◽  
Vol 267 ◽  
pp. 115483
Author(s):  
Marco Gerdol ◽  
Andrea Visintin ◽  
Sara Kaleb ◽  
Francesca Spazzali ◽  
Alberto Pallavicini ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Response ◽  
Expression Response

scholarly journals VExD: A curated resource for human gene expression following viral infection

2021 ◽  
Author(s):  
Phillip J Dexheimer ◽  
Mario Pujato ◽  
Krishna Roskin ◽  
Matthew T Weirauch
Keyword(s):  
Gene Expression ◽  
Viral Infection ◽  
Human Gene ◽  
Application Programming Interface ◽  
Gene Expression Response ◽  
Human Gene Expression ◽  
Link Type ◽  
Uniform Manner ◽  
Expression Response ◽  
Virus Expression

AbstractMotivationHuman viruses cause significant mortality, morbidity, and economic disruption worldwide. The human gene expression response to viral infection can yield important insights into the detrimental effects to the host. To date, hundreds of studies have performed genome-scale profiling of the effect of viral infection on human gene expression. However, no resource exists that aggregates human expression results across multiple studies, viruses, and tissue types.ResultsWe developed the Virus Expression Database (VExD), a comprehensive curated resource of transcriptomic studies of viral infection in human cells. We have processed all studies within VExD in a uniform manner, allowing users to easily compare human gene expression changes across conditions.Availability and ImplementationVExD is freely accessible at https://vexd.cchmc.org for all modern web browsers. An Application Programming Interface (API) for VExD is also available. The source code is available at https://github.com/pdexheimer/[email protected], [email protected]

scholarly journals Disruption of TFIIH activities generates a stress gene expression response and reveals possible new targets against cancer

2019 ◽  
Author(s):  
Maritere Urioistegui-Arcos ◽  
Rodrigo Aguayo-Ortiz ◽  
María del Pilar Valencia-Morales ◽  
Erika Melchy-Pérez ◽  
Yvonne Rosenstein ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumour Growth ◽  
Transformed Cells ◽  
Rna Seq ◽  
Gene Expression Response ◽  
New Targets ◽  
Increased Sensitivity ◽  
Promoter Occupancy ◽  
Expression Response ◽  
Stress Gene

AbstractDisruption of the enzymatic activities of the transcription factor TFIIH by Triptolide (TPL) or THZ1 could be used against cancer. Here, we used an oncogenesis model to compare the effect of TFIIH inhibitors between transformed cells and their progenitors. We report that tumour cells exhibited highly increased sensitivity to TPL or THZ1 and that the combination of both had an additive effect. TPL affects the interaction between XPB and P52, causing a reduction in the levels of XPB, P52, and P8, but not other TFIIH subunits. RNA-Seq and RNAPII-ChIP-Seq experiments showed that although the levels of many transcripts were reduced, the levels of a significant number were increased after TPL treatment, with maintained or increased RNAPII promoter occupancy. A significant number of these genes encode for factors that have been related to tumour growth and metastasis. Some of these genes were also overexpressed in response to THZ1, which depletion enhances the toxicity of TPL and are possible new targets against cancer.

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