Biology subjects

Wildman, D. E.

Publications and source records attributed to Wildman, D. E..

10 recordsLinked to original sources

bioRxiv2025

Miniaturized Workflow for Transcriptomic Profiling of Urinary Extracellular RNA during Pregnancy

Urine contains extracellular RNA (exRNA) carried by extracellular vesicles (EVs) and other biomolecular complexes. There is currently a need for studies focused on female cohorts to develop new methods for non-invasive analysis of biofluids to create reference profiles and for identification of biomarkers of reproductive and pregnancy disorders. The objective of this study was therefore to identify optimal methods for transcriptomic profiling of urine by testing different exRNA isolation and scalable library preparation methods that enable detection of biomarkers that reflect pregnancy-associated changes in the placenta and maternal tissues. RNA was extracted from pooled and individual urine samples obtained from normal non-pregnant and pregnant females, as well as males, using input volumes of either 0.6 mL, 1 mL, or 4 mL. Samples were extracted using methods that focused either on isolating vesicular (EV-associated) or total (EV-associated and non EV-associated) exRNA. Small RNA libraries (n=208) were prepared using the NEBNext Small RNA Library Prep kit and long RNA libraries (n=97) were prepared using the SMART-Seq v4 Ultra Low Input RNA or the SMARTer Stranded Total RNA-Seq Kit v2 Pico Input kits (Takara). Principal component analysis showed that the greatest source of variance amongst technical replicates of small RNA libraries (n=176 which passed quality control) was exRNA isolation method, and amongst long RNA libraries (n=97 which passed quality control) was library preparation method. Long RNA libraries prepared from exRNA extracted using miRCURY showed that the SMART-Seq v4 method yielded significantly more uniquely mapped reads compared to the Pico v2 method (p<0.05). We have established a scalable pipeline for small and long RNA-Seq profiling of exRNA in urine in a reproducible manner, which we used to identify differentially expressed urinary exRNAs in pregnancy, and will enable transcriptomic profiling of urinary exRNA in disorders of pregnancy, including preeclampsia.

genetics↗

bioRxiv2024

Time-varying mediation analysis for incomplete data with application to DNA methylation study for PTSD

DNA methylation (DNAm) has been shown to mediate causal effects from traumatic experiences to post-traumatic stress disorder (PTSD). However, the scientific question about whether the mediation effect changes over time remains unclear. In this paper, we develop time-varying structural equation models to identify cytosine-phosphate-guanine (CpG) sites where DNAm mediates the effect of trauma exposure on PTSD, and to capture dynamic changes in mediation effects. The proposed methodology is motivated by the Detroit Neighborhood Health Study (DNHS) with high-dimensional and longitudinal DNAm measurements. To handle the non-monotone missing DNAm in the dataset, we propose a novel Longitudinal Multiple Imputation (LMI) method utilizing dependency among repeated measurements, and employ the generalized method of moments to integrate the multiple imputations. Simulations confirm that the proposed method outperforms existing approaches in various longitudinal settings. In DNHS data analysis, our method identifies several CpG sites where DNAm exhibits dynamic mediation effects. Some of the corresponding genes have been shown to be associated with PTSD in the existing literature, and our findings on their time-varying effects could deepen the understanding of the mediation role of DNAm on the causal path from trauma exposure to PTSD risk.

bioinformatics↗

bioRxiv2020

Neighborhood environment, social cohesion, and epigenetic aging

Living in adverse neighborhood environments have been linked to increased risk of aging-related diseases and mortality; however, the biological mechanisms explaining this observation remain poorly understood. DNA methylation (DNAm), a proposed biomarker of biological aging responsive to environmental stressors, offers promising insight into molecular pathways. We examined associations of three measures of neighborhood conditions (poverty, quality, and social cohesion) with three different epigenetic clocks (Horvath, Hannum, and Levine) using data from the Detroit Neighborhood Health Study (n=158). Using linear regression models, we evaluated associations in the total sample and stratified by gender and social cohesion. Differential effects by gender were found between men and women. Neighborhood poverty was associated with PhenoAge acceleration among women, but not among men (women: {beta} = 1.4; 95% CI: -0.4, 3.3 vs. men: {beta} = -0.3; 95% CI: -2.2, 1.5) in fully adjusted models. In models stratified on social cohesion, association of neighborhood poverty and quality with accelerated DNAm aging remained elevated for residents living in neighborhoods with lower social cohesion, but were null for those living in neighborhoods with higher social cohesion. Our study suggests that living in adverse neighborhood conditions can speed up epigenetic aging, while positive neighborhood characteristics may buffer effects.

genomics↗

bioRxiv2020

The Impact of Psychopathology, Social Adversity and Stress-relevant DNAm on Prospective Risk for Post-traumatic Stress: A Machine Learning Approach

BackgroundA range of factors have been identified that contribute to greater incidence, severity, and prolonged course of post-traumatic stress disorder (PTSD), including: comorbid and/or prior psychopathology; social adversity such as low socioeconomic position, perceived discrimination, and isolation; and biological factors such as genomic variation at glucocorticoid receptor regulatory network (GRRN) genes. This complex etiology and clinical course make identification of people at higher risk of PTSD challenging. Here we leverage machine learning (ML) approaches to identify a core set of factors that may together predispose persons to PTSD. MethodsWe used multiple ML approaches to assess the relationship among DNA methylation (DNAm) at GRRN genes, prior psychopathology, social adversity, and prospective risk for PTS severity (PTSS). ResultsML models predicted prospective risk of PTSS with high accuracy. The Gradient Boost approach was the top-performing model with mean absolute error of 0.135, mean square error of 0.047, root mean square error of 0.217, and R2 of 95.29%. Prior PTSS ranked highest in predicting the prospective risk of PTSS, accounting for >88% of the prediction. The top ranked GRRN CpG site was cg05616442, in AKT1, and the top ranked social adversity feature was loneliness. ConclusionMultiple factors including prior PTSS, social adversity, and DNAm play a role in predicting prospective risk of PTSS. ML models identified factors accounting for increased PTSS risk with high accuracy, which may help to target risk factors that reduce the likelihood or course of PTSD, potentially pointing to approaches that can lead to early intervention.

bioinformatics↗

bioRxiv2020

Psychosocial experiences modulate asthma-associated genes through gene-environment interactions

Social interactions and the overall psychosocial environment have a demonstrated impact on health, particularly for people living in disadvantaged urban areas. Here we investigated the effect of psychosocial experiences on gene expression in peripheral blood immune cells of children with asthma in Metro Detroit. Using RNA-sequencing and a new machine learning approach, we identified transcriptional signatures of 20 variables including psychosocial factors, blood cell composition and asthma symptoms. Importantly, we found 174 genes associated with asthma that are regulated by psychosocial factors, and 349 significant gene-environment interactions for gene expression levels. These results demonstrate that immune gene expression mediates the link between negative psychosocial experiences and asthma risk.

genomics↗

bioRxiv2019

Methylomic profiles reveal sex-specific shifts in leukocyte composition associated with post-traumatic stress disorder

Post-traumatic stress disorder (PTSD) is a debilitating mental disorder precipitated by trauma exposure. However, only some persons exposed to trauma develop PTSD. There are sex differences in risk; twice as many women as men, develop a lifetime diagnosis of PTSD. Methylomic profiles derived from peripheral blood are well-suited for investigating PTSD because DNA methylation (DNAm) encodes individual response to trauma and may play a key role in the immune dysregulation characteristic of PTSD pathophysiology. In the current study, we leveraged recent methodological advances to investigate sex-specific shifts in DNAm-based leukocyte composition that are associated with lifetime PTSD. We estimated leukocyte composition on a combined methylation array dataset (483 participants, [~]450k CpG sites) consisting of two civilian cohorts, the Detroit Neighborhood Health Study and Grady Trauma Project. Sex-stratified Mann-Whitney U test and two-way ANCOVA revealed that lifetime PTSD was associated with a small but significant elevation in monocyte proportions in males, but not in females (Holm-adjusted p-val < 0.05). No difference in monocyte proportions was observed between current and remitted PTSD cases in males, suggesting that this sex-specific peak shift reflects a long-standing trait of lifetime history of PTSD, rather than current state of PTSD. Associations with lifetime PTSD or PTSD status were not observed in any other leukocyte subtype and our finding in monocytes was confirmed using cell estimates based on a different deconvolution algorithm, suggesting that our sex-specific findings are robust across cell estimation approaches. Overall, our main finding of elevated monocyte proportions in males, but not in females with lifetime history of PTSD provides evidence for a sex-specific shift in peripheral blood leukocyte composition that is detectable in methylomic profiles and that reflects long-standing changes associated with PTSD diagnosis.

genomics↗

bioRxiv2018

Computational performance and accuracy of Sentieon DNASeq variant calling workflow

As reliable, efficient genome sequencing becomes more ubiquitous, the need for similarly reliable and efficient variant calling becomes increasingly important. The Genome Analysis Toolkit (GATK), maintained by the Broad Institute, is currently the widely accepted standard for variant calling software. However, alternative solutions may provide faster variant calling without sacrificing accuracy. One such alternative is Sentieon DNASeq, a toolkit analogous to GATK but built on a highly optimized backend. We evaluated the DNASeq single-sample variant calling pipeline in comparison to that of GATK. Our results confirm the near-identical accuracy of the two software packages, showcase perfect scalability and great speed from Sentieon, and describe computational performance considerations for the deployment of Sentieon DNASeq.

bioinformatics↗

bioRxiv2018

DNA Methylation Explains a Subset of Placental Gene Expression Differences Based on Ancestry and Altitude

AbstractObjectives: The most pronounced effect of high altitude (>2700m) on reproductive outcomes is reduced birth weight. Indigenous Bolivians (Andean Native Americans) residing for generations at high altitudes have higher birth weights relative to more recent migrants of primarily European ancestry. Previous research demonstrated that the placenta is a key contributor to the preservation of Andean birth weight at high altitude. Our current research investigated how gene expression and epigenetics contributes to the conservation of birth weight at high altitude by examining mRNA expression and DNA methylation differences between placentas of Andeans and those of European ancestry residing at high and low altitude.\n\nMethods: Genome-wide mRNA expression and DNA methylation of villous placenta tissue was quantified utilizing microarray technology. Subjects were of Andean and European ancestry and resident at high (3600m) or low (400m) altitudes. Differentially expressed genes (DEGs) associated with altitude or ancestry were identified (FDR<0.1, |fold change|>1.25). To predict which DEGs could be regulated by methylation we tested for correlation between gene expression and methylation values.\n\nResults: 69 DEGs associated with altitude (n=36) or ancestry (n=34) were identified. Altitude-associated DEGs included members of the AP-1 transcription factor family. Ancestry-associated DEGs were implicated in inflammatory pathways and associated with pro-angiogenic macrophages. More ancestry-associated DEGs correlated significantly (n=17) (FDR<0.1) with promoter or gene body methylation (p=0.0242) when compared to altitude associated DEGs (n=8).\n\nConclusions: Compared to altitude-associated DEGs, methylation regulates more ancestry-associated DEGs, potentially allowing for rapid modification in the expression of inflammatory genes to attract pro-angiogenic macrophages as a means of promoting placental capillary growth in Andeans, regardless of altitude.

genomics↗

bioRxiv2018

Performance benchmarking of GATK3.8 and GATK4

Use of the Genome Analysis Toolkit (GATK) continues to be the standard practice in genomic variant calling in both research and the clinic. Recently the toolkit has been rapidly evolving. Significant computational performance improvements have been introduced in GATK3.8 through collaboration with Intel in 2017. The first release of GATK4 in early 2018 revealed significant rewrites in the code base, as the stepping stone toward a Spark implementation. As the software continues to be a moving target for optimal deployment in highly productive environments, we present a detailed analysis of these improvements, to help the community stay abreast with changes in performance. We re-evaluated the options previously identified as advantageous, such as threading, parallel garbage collection, I/O options and data-level parallelization. Based on our results, we consider the performance and cost trade-offs of using GATK3.8 and GATK4 for different types of analyses.

bioinformatics↗

bioRxiv2017

The Core Transcriptome Of Mammalian Placentas And The Divergence Of Expression With Placental Shape

IntroductionThe placenta is arguably the most anatomically variable organ in mammals even though its primary function is conserved.\n\nMethodUsing RNA-Seq, we measured the expression profiles of 55 term placentas of 14 species of mammals representing all major eutherian superordinal clades and marsupials, and compared the evolution of expression across clades.\n\nResultsWe identified a set of 115 core genes which is expressed (FPKM[≥] 10) in all eutherian placentas, including genes with immune-modulating properties (ANXA2, ANXA1, S100A11, S100A10, and LGALS1), cell-cell interactions (LAMC1, LUM, and LGALS1), invasion (GRB2 and RALB) and syncytialization (ANXA5 and ANXA1). We also identified multiple pre-eclampsia associated genes which are differentially expressed in Homo sapiens when compared to the other 13 species. Multiple genes are significantly associated with placenta morphology, including EREG and WNT5A which are both associated with placental shape.\n\nDiscussion115 genes are important for the core functions of the placenta in all eutherian species analyzed. The molecular functions and pathways enriched in the core placenta align with the evolutionarily conserved functionality of the placenta.

genomics↗