scholarly journals Physical activity and blood gene expression profiles: the Norwegian Women and Cancer (NOWAC) Post-genome cohort

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Karina Standahl Olsen ◽  
Marko Lukic ◽  
Kristin Benjaminsen Borch
Keyword(s):  
Gene Expression ◽  
Physical Activity ◽  
Expression Profiles ◽  
Single Gene ◽  
Gene Expression Profiles ◽  
R Package ◽  
Middle Aged ◽  
Blood Gene Expression ◽  
Gene Sets ◽  
Gene Level

Abstract Objectives The influence of physical activity (PA) on the immune system has emerged as a new field of research. Regular PA may promote an anti-inflammatory state in the body, thus contributing to the down-regulation of pro-inflammatory processes related to the onset and progression of multiple diseases. We aimed to assess whether overall PA levels were associated with differences in blood gene expression profiles, in a cohort of middle-aged Norwegian women. We used information from 977 women included in the Norwegian Women and Cancer (NOWAC) Post-genome cohort. Information on PA and covariates was extracted from the NOWAC database. Blood samples were collected using the PAXgene Blood RNA collection system, and gene expression profiles were measured using Illumina microarrays. The R-package limma was used for the single-gene level analysis. For a target gene set analysis, we used the global test R-package with 48 gene sets, manually curated from the literature and relevant molecular databases. Results We found no associations between overall PA levels and gene expression profiles at the single-gene level. Similarly, no gene sets reached statistical significance at adjusted p < 0.05. In our analysis of healthy, middle-aged Norwegian women, self-reported overall PA was not associated with differences in blood gene expression profiles.

Effects of Physical Activity on Neurophysiological and Gene Expression Profiles in Chronic Back Pain

2020 ◽  
Vol 69 (1) ◽  
pp. 74-81
Author(s):  
Kyounghae Kim ◽  
Divya Ramesh ◽  
Mallory Perry ◽  
Katherine M. Bernier ◽  
Erin E. Young ◽  
...  
Keyword(s):  
Gene Expression ◽  
Physical Activity ◽  
Back Pain ◽  
Chronic Back Pain ◽  
Expression Profiles ◽  
Gene Expression Profiles

scholarly journals Whole Blood Gene Expression Profiles in Insulin Resistant Latinos with the Metabolic Syndrome

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e84002 ◽  
Author(s):  
Samantha E. Tangen ◽  
Darwin Tsinajinnie ◽  
Martha Nuñez ◽  
Gabriel Q. Shaibi ◽  
Lawrence J. Mandarino ◽  
...  
Keyword(s):  
Gene Expression ◽  
Metabolic Syndrome ◽  
Whole Blood ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Blood Gene Expression ◽  
Insulin Resistant ◽  
The Metabolic Syndrome

Predose Blood Gene Expression Profiles Might Identify the Individuals Susceptible to Carbon Tetrachloride–Induced Hepatotoxicity

2010 ◽  
Vol 115 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Jun-Won Yun ◽  
Tae-Ryong Lee ◽  
Chae-Wook Kim ◽  
Young-Ho Park ◽  
Jin-Ho Chung ◽  
...  
Keyword(s):  
Gene Expression ◽  
Carbon Tetrachloride ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Blood Gene Expression

scholarly journals A crowdsourced analysis to identifyab initiomolecular signatures predictive of susceptibility to viral infection

2018 ◽  
Author(s):  
Slim Fourati ◽  
Aarthi Talla ◽  
Mehrad Mahmoudian ◽  
Joshua G. Burkhart ◽  
Riku Klén ◽  
...  
Keyword(s):  
Gene Expression ◽  
Healthy Subjects ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Respiratory Viruses ◽  
Community Based ◽  
Blood Gene Expression ◽  
Predictors Of Response ◽  
Peripheral Blood Gene Expression ◽  
Physiologic Responses

AbstractRespiratory viruses are highly infectious; however, the variation of individuals’ physiologic responses to viral exposure is poorly understood. Most studies examining molecular predictors of response focus on late stage predictors, typically near the time of peak symptoms. To determine whether pre- or early post-exposure factors could predict response, we conducted a community-based analysis to identify predictors of resilience or susceptibility to several respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV) using peripheral blood gene expression profiles collected from healthy subjects prior to viral exposure, as well as up to 24 hours following exposure. This analysis revealed that it is possible to construct models predictive of symptoms using profiles even prior to viral exposure. Analysis of predictive gene features revealed little overlap among models; however, in aggregate, these genes were enriched for common pathways. Heme Metabolism, the most significantly enriched pathway, was associated with higher risk of developing symptoms following viral exposure.

scholarly journals BayICE: A hierarchical Bayesian deconvolution model with stochastic search variable selection

2019 ◽  
Author(s):  
An-Shun Tai ◽  
George C. Tseng ◽  
Wen-Ping Hsieh
Keyword(s):  
Gene Expression ◽  
Variable Selection ◽  
Immune Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
R Package ◽  
Stochastic Search ◽  
Hierarchical Bayesian ◽  
Stochastic Search Variable Selection ◽  
Search Variable

AbstractGene expression deconvolution is a powerful tool for exploring the microenvironment of complex tissues comprised of multiple cell groups using transcriptomic data. Characterizing cell activities for a particular condition has been regarded as a primary mission against diseases. For example, cancer immunology aims to clarify the role of the immune system in the progression and development of cancer through analyzing the immune cell components of tumors. To that end, many deconvolution methods have been proposed for inferring cell subpopulations within tissues. Nevertheless, two problems limit the practicality of current approaches. First, all approaches use external purified data to preselect cell type-specific genes that contribute to deconvolution. However, some types of cells cannot be found in purified profiles and the genes specifically over- or under-expressed in them cannot be identified. This is particularly a problem in cancer studies. Hence, a preselection strategy that is independent from deconvolution is inappropriate. The second problem is that existing approaches do not recover the expression profiles of unknown cells present in bulk tissues, which results in biased estimation of unknown cell proportions. Furthermore, it causes the shift-invariant property of deconvolution to fail, which then affects the estimation performance. To address these two problems, we propose a novel deconvolution approach, BayICE, which employs hierarchical Bayesian modeling with stochastic search variable selection. We develop a comprehensive Markov chain Monte Carlo procedure through Gibbs sampling to estimate cell proportions, gene expression profiles, and signature genes. Simulation and validation studies illustrate that BayICE outperforms existing deconvolution approaches in estimating cell proportions. Subsequently, we demonstrate an application of BayICE in the RNA sequencing of patients with non-small cell lung cancer. The model is implemented in the R package “BayICE” and the algorithm is available for download.

scholarly journals Supervised pathway analysis of blood gene expression profiles in Alzheimer's disease

2019 ◽  
Vol 84 ◽  
pp. 98-108 ◽  
Author(s):  
Elaheh Moradi ◽  
Mikael Marttinen ◽  
Tomi Häkkinen ◽  
Mikko Hiltunen ◽  
Matti Nykter
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Pathway Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Blood Gene Expression
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