Faculty Opinions recommendation of Maternal Whole Blood Gene Expression at 18 and 28 Weeks of Gestation Associated with Spontaneous Preterm Birth in Asymptomatic Women.

Author(s):  
Jeff Keelan
PLoS ONE ◽  
2016 ◽  
Vol 11 (6) ◽  
pp. e0155191 ◽  
Author(s):  
Yujing J. Heng ◽  
Craig E. Pennell ◽  
Sheila W. McDonald ◽  
Angela E. Vinturache ◽  
Jingxiong Xu ◽  
...  

PLoS ONE ◽  
2014 ◽  
Vol 9 (5) ◽  
pp. e96901 ◽  
Author(s):  
Yujing Jan Heng ◽  
Craig Edward Pennell ◽  
Hon Nian Chua ◽  
Jonathan Edward Perkins ◽  
Stephen James Lye

2020 ◽  
Author(s):  
Adi L. Tarca ◽  
Bálint Ármin Pataki ◽  
Roberto Romero ◽  
Marina Sirota ◽  
Yuanfang Guan ◽  
...  

AbstractIdentification of pregnancies at risk of preterm birth (PTB), the leading cause of newborn deaths, remains challenging given the syndromic nature of the disease. We report a longitudinal multi-omics study coupled with a DREAM challenge to develop predictive models of PTB. We found that whole blood gene expression predicts ultrasound-based gestational ages in normal and complicated pregnancies (r=0.83), as well as the delivery date in normal pregnancies (r=0.86), with an accuracy comparable to ultrasound. However, unlike the latter, transcriptomic data collected at <37 weeks of gestation predicted the delivery date of one third of spontaneous (sPTB) cases within 2 weeks of the actual date. Based on samples collected before 33 weeks in asymptomatic women we found expression changes preceding preterm prelabor rupture of the membranes that were consistent across time points and cohorts, involving, among others, leukocyte-mediated immunity. Plasma proteomic random forests predicted sPTB with higher accuracy and earlier in pregnancy than whole blood transcriptomic models (e.g. AUROC=0.76 vs. AUROC=0.6 at 27-33 weeks of gestation).


Allergy ◽  
2020 ◽  
Vol 75 (12) ◽  
pp. 3248-3260 ◽  
Author(s):  
Nathanaël Lemonnier ◽  
Erik Melén ◽  
Yale Jiang ◽  
Stéphane Joly ◽  
Camille Ménard ◽  
...  

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Chen Yao ◽  
Roby Joehanes ◽  
Rory Wilson ◽  
Toshiko Tanaka ◽  
Luigi Ferrucci ◽  
...  

Abstract Background DNA methylation is a key epigenetic modification that can directly affect gene regulation. DNA methylation is highly influenced by environmental factors such as cigarette smoking, which is causally related to chronic obstructive pulmonary disease (COPD) and lung cancer. To date, there have been few large-scale, combined analyses of DNA methylation and gene expression and their interrelations with lung diseases. Results We performed an epigenome-wide association study of whole blood gene expression in ~ 6000 individuals from four cohorts. We discovered and replicated numerous CpGs associated with the expression of cis genes within 500 kb of each CpG, with 148 to 1,741 cis CpG-transcript pairs identified across cohorts. We found that the closer a CpG resided to a transcription start site, the larger its effect size, and that 36% of cis CpG-transcript pairs share the same causal genetic variant. Mendelian randomization analyses revealed that hypomethylation and lower expression of CHRNA5, which encodes a smoking-related nicotinic receptor, are causally linked to increased risk of COPD and lung cancer. This putatively causal relationship was further validated in lung tissue data. Conclusions Our results provide a large and comprehensive association study of whole blood DNA methylation with gene expression. Expression platform differences rather than population differences are critical to the replication of cis CpG-transcript pairs. The low reproducibility of trans CpG-transcript pairs suggests that DNA methylation regulates nearby rather than remote gene expression. The putatively causal roles of methylation and expression of CHRNA5 in relation to COPD and lung cancer provide evidence for a mechanistic link between patterns of smoking-related epigenetic variation and lung diseases, and highlight potential therapeutic targets for lung diseases and smoking cessation.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sunita Chopra ◽  
Maria Moroni ◽  
Jaleal Sanjak ◽  
Laurel MacMillan ◽  
Bernadette Hritzo ◽  
...  

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.


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