scholarly journals Whole blood gene expression within days after total-body irradiation predicts long term survival in Gottingen minipigs

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sunita Chopra ◽  
Maria Moroni ◽  
Jaleal Sanjak ◽  
Laurel MacMillan ◽  
Bernadette Hritzo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Total Body Irradiation ◽  
Whole Blood ◽  
Radiation Effects ◽  
Expression Profiles ◽  
Mass Casualty ◽  
Machine Learning Algorithms ◽  
Term Survival ◽  
Blood Gene Expression ◽  
Total Body

AbstractGottingen minipigs mirror the physiological radiation response observed in humans and hence make an ideal candidate model for studying radiation biodosimetry for both limited-sized and mass casualty incidents. We examined the whole blood gene expression profiles starting one day after total-body irradiation with increasing doses of gamma-rays. The minipigs were monitored for up to 45 days or time to euthanasia necessitated by radiation effects. We successfully identified dose- and time-agnostic (over a 1–7 day period after radiation), survival-predictive gene expression signatures derived using machine-learning algorithms with high sensitivity and specificity. These survival-predictive signatures fare better than an optimally performing dose-differentiating signature or blood cellular profiles. These findings suggest that prediction of survival is a much more useful parameter for making triage, resource-utilization and treatment decisions in a resource-constrained environment compared to predictions of total dose received. It should hopefully be possible to build such classifiers for humans in the future.

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

scholarly journals Whole blood gene expression profiles distinguish clinical phenotypes of venous thromboembolism

2015 ◽  
Vol 135 (4) ◽  
pp. 659-665 ◽  
Author(s):  
Deborah A. Lewis ◽  
Sunil Suchindran ◽  
Michele G. Beckman ◽  
W. Craig Hooper ◽  
Althea M. Grant ◽  
...  
Keyword(s):  
Gene Expression ◽  
Venous Thromboembolism ◽  
Whole Blood ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Blood Gene Expression ◽  
Clinical Phenotypes

scholarly journals Associations between Serum Sex Hormone Concentrations and Whole Blood Gene Expression Profiles in the General Population

PLoS ONE ◽  
2015 ◽  
Vol 10 (5) ◽  
pp. e0127466 ◽  
Author(s):  
Robin Haring ◽  
Claudia Schurmann ◽  
Georg Homuth ◽  
Leif Steil ◽  
Uwe Völker ◽  
...  
Keyword(s):  
Gene Expression ◽  
General Population ◽  
Whole Blood ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Sex Hormone ◽  
Blood Gene Expression

Blood-based multi-tissue gene expression inference with Bayesian ridge regression

2020 ◽  
Vol 36 (12) ◽  
pp. 3788-3794
Author(s):  
Wenjian Xu ◽  
Xuanshi Liu ◽  
Fei Leng ◽  
Wei Li
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Ridge Regression ◽  
Whole Blood ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Blood Gene Expression ◽  
Tissue Specific ◽  
Tissue Gene Expression

Abstract Motivation Gene expression profiling is widely used in basic and cancer research but still not feasible in many clinical applications because tissues, such as brain samples, are difficult and not ethnical to collect. Gene expression in uncollected tissues can be computationally inferred using genotype and expression quantitative trait loci. No methods can infer unmeasured gene expression of multiple tissues with single tissue gene expression profile as input. Results Here, we present a Bayesian ridge regression-based method (B-GEX) to infer gene expression profiles of multiple tissues from blood gene expression profile. For each gene in a tissue, a low-dimensional feature vector was extracted from whole blood gene expression profile by feature selection. We used GTEx RNAseq data of 16 tissues to train inference models to capture the cross-tissue expression correlations between each target gene in a tissue and its preselected feature genes in peripheral blood. We compared B-GEX with least square regression, LASSO regression and ridge regression. B-GEX outperforms the other three models in most tissues in terms of mean absolute error, Pearson correlation coefficient and root-mean-squared error. Moreover, B-GEX infers expression level of tissue-specific genes as well as those of non-tissue-specific genes in all tissues. Unlike previous methods, which require genomic features or gene expression profiles of multiple tissues, our model only requires whole blood expression profile as input. B-GEX helps gain insights into gene expressions of uncollected tissues from more accessible data of blood. Availability and implementation B-GEX is available at https://github.com/xuwenjian85/B-GEX. Supplementary information Supplementary data are available at Bioinformatics online.

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