Faculty Bibliography

BACKGROUND:Assessing the effect of alcohol consumption on biological age is essential for understanding alcohol use-related comorbidities and mortality. Previously developed epigenetic clocks are mainly based on DNA methylation in heterogeneous cell types, which provide limited knowledge on the impacts of alcohol consumption at the individual cellular level. Evidence shows that monocytes play an important role in both alcohol-induced pathophysiology and the aging process. In this study, we developed a novel monocyte-based DNA methylation clock (MonoDNAmAge) to assess the impact of alcohol consumption on monocyte age. METHODS: = 2242). Using the MonoDNAmAge clock and four established clocks (i.e., HorvathDNAmAge, HannumDNAmAge, PhenoDNAmAge, GrimDNAmAge), we then evaluated the effect of alcohol consumption on epigenetic aging in the three cohorts [i.e., Yale Stress Center Community Study (YSCCS), Veteran Aging Cohort Study (VACS), Women's Interagency HIV Study (WIHS)] using linear and quadratic models. RESULTS: CONCLUSIONS:The results suggest a nonlinear relationship between alcohol consumption and its effects on epigenetic age. Considering adverse effects of alcohol consumption on health, nonlinear effects of alcohol use should be interpreted with caution. The findings, for the first time, highlight the complex effects of alcohol consumption on biological aging.

MicroRNAs (miRNAs) may be considered important regulators of risk for type 2 diabetes (T2D). The aim of the present study was to identify novel sets of miRNAs associated with T2D risk, as well as their gene and pathway targets. Circulating miRNAs (n=59) were measured in plasma from participants in a previously completed clinical trial (n=82). An agnostic statistical approach was applied to identify novel sets of miRNAs with optimal co‑expression patterns. In silico analyses were used to identify the messenger RNA and biological pathway targets of the miRNAs within each factor. A total of three factors of miRNAs were identified, containing 18, seven and two miRNAs each. Eight biological pathways were revealed to contain genes targeted by the miRNAs in all three factors, 38 pathways contained genes targeted by the miRNAs in two factors, and 55, 18 and two pathways were targeted by the miRNAs in a single factor, respectively (all q<0.05). The pathways containing genes targeted by miRNAs in the largest factor shared a common theme of biological processes related to metabolism and inflammation. By contrast, the pathways containing genes targeted by miRNAs in the second largest factor were related to endocrine function and hormone activity. The present study focused on the pathways uniquely targeted by each factor of miRNAs in order to identify unique mechanisms that may be associated with a subset of individuals. Further exploration of the genes and pathways related to these biological themes may provide insights about the subtypes of T2D and lead to the identification of novel therapeutic targets.

Here we report three epigenome-wide association studies (EWAS) of DNA methylation on self-reported race, global genetic ancestry, and local genetic ancestry in admixed Americans from three sets of samples, including internal and external replications (N_{total }= 1224). Our EWAS on local ancestry (LA) identified the largest number of ancestry-associated DNA methylation sites and also featured the highest replication rate. Furthermore, by incorporating ancestry origins of genetic variations, we identified 36 methylation quantitative trait loci (meQTL) clumps for LA-associated CpGs that cannot be captured by a model that assumes identical genetic effects across ancestry origins. Lead SNPs at 152 meQTL clumps had significantly different genetic effects in the context of an African or European ancestry background. Local ancestry information enables superior capture of ancestry-associated methylation signatures and identification of ancestry-specific genetic effects on DNA methylation. These findings highlight the importance of incorporating local ancestry for EWAS in admixed samples from multi-ancestry cohorts.

BACKGROUND:Gene expression is regulated by transcription factors, cofactors, and epigenetic mechanisms. Coexpressed genes indicate similar functional categories and gene networks. Detecting gene-gene coexpression is important for understanding the underlying mechanisms of cellular function and human diseases. A common practice of identifying coexpressed genes is to test the correlation of expression in a set of genes. In single-cell RNA-seq data, an important challenge is the abundance of zero values, so-called "dropout", which results in biased estimation of gene-gene correlations for downstream analyses. In recent years, efforts have been made to recover coexpressed genes in scRNA-seq data. Here, our goal is to detect coexpressed gene pairs to reduce the "dropout" effect in scRNA-seq data using a novel graph-based k-partitioning method by merging transcriptomically similar cells. RESULTS:We observed that the number of zero values was reduced among the merged transcriptomically similar cell clusters. Motivated by this observation, we leveraged a graph-based algorithm and develop an R package, scCorr, to recover the missing gene-gene correlation in scRNA-seq data that enables the reliable acquisition of cluster-based gene-gene correlations in three independent scRNA-seq datasets. The graphically partitioned cell clusters did not change the local cell community. For example, in scRNA-seq data from peripheral blood mononuclear cells (PBMCs), the gene-gene correlation estimated by scCorr outperformed the correlation estimated by the nonclustering method. Among 85 correlated gene pairs in a set of 100 clusters, scCorr detected 71 gene pairs, while the nonclustering method detected only 4 pairs of a dataset from PBMCs. The performance of scCorr was comparable to those of three previously published methods. As an example of downstream analysis using scCorr, we show that scCorr accurately identified a known cell type (i.e., CD4+ T cells) in PBMCs with a receiver operating characteristic area under the curve of 0.96. CONCLUSIONS:Our results demonstrate that scCorr is a robust and reliable graph-based method for identifying correlated gene pairs, which is fundamental to network construction, gene-gene interaction, and cellular omic analyses. scCorr can be quickly and easily implemented to minimize zero values in scRNA-seq analysis and is freely available at https://github.com/CBIIT-CGBB/scCorr .

Prior studies focused on circulating microRNAs and the risk for complex diseases have shown inconsistent findings. The majority of studies focused on European and East Asian racial or ethnic groups, however, ancestry was not typically reported. We evaluated the risk for type 2 diabetes as an exemplar to show that race and ethnic group may contribute to inconsistent validation of previous findings of associations with microRNAs.

PURPOSE:Gestational diabetes (GDM) is associated with increased risk for preterm birth and related complications for both the pregnant person and newborn. Changes in gene expression have the potential to characterize complex interactions between genetic and behavioral/environmental risk factors for GDM. Our goal was to summarize the state of the science about changes in gene expression and GDM. DESIGN:The systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. METHODS:PubMed articles about humans, in English, from any date were included if they described mRNA transcriptome or microRNA findings from blood samples in adults with GDM compared with adults without GDM. RESULTS:Sixteen articles were found representing 1355 adults (n=674 with GDM, n=681 controls) from 12 countries. Three studies reported transcriptome results and thirteen reported microRNA findings. Identified pathways described various aspects of diabetes pathogenesis, including glucose and insulin signaling, regulation, and transport; natural killer cell mediated cytotoxicity; and fatty acid biosynthesis and metabolism. Studies described 135 unique miRNAs that were associated with GDM, of which eight (miR-16-5p, miR-17-5p, miR-20a-5p, miR-29a-3p, miR-195-5p, miR-222-3p, miR-210-3p, and miR-342-3p) were described in 2 or more studies. Findings suggest that miRNA levels vary based on the time in pregnancy when GDM develops, the time point at which they were measured, sex assigned at birth of the offspring, and both the pre-pregnancy and gestational body mass index of the pregnant person. CONCLUSIONS:The mRNA, miRNA, gene targets, and pathways identified in this review contribute to our understanding of GDM pathogenesis; however, further research is warranted to validate previous findings. In particular, longitudinal repeated-measures designs are needed that control for participant characteristics (e.g., weight), use standardized data collection methods and analysis tools, and are sufficiently powered to detect differences between subgroups. Findings may be used to improve early diagnosis, prevention, medication choice and/or clinical treatment of patients with GDM.

BACKGROUND:At the start of the COVID-19 pandemic, HIV experts suggested that an increase in mental health diagnoses and substance use among people living with HIV (PLHIV) may be an unintended consequence of COVID-19 mitigation efforts (e.g., limiting social contact). We evaluated short-term trajectories in binge drinking, marijuana, and recreational drug use in a prospective cohort of PLHIV. METHODS:Data (N = 2121 PLHIV) consist of survey responses on substance use behaviors from two pre-COVID-19 (October 2018-September 2019) and one COVID-19-era (April 2020-September 2020) timepoints within the MACS/WIHS Combined Cohort Study (MWCCS). We conducted group-based trajectory models, triangulated with generalized linear mixed models, to assess changes in binge drinking, daily marijuana use, and recreational drug use at the start of the pandemic. Controlling for age and race/ethnicity, we tested whether trajectories differed by sex and early-pandemic depressive symptoms, loneliness, and social support. RESULTS:Group-based trajectory models yielded two trajectory groups for binge drinking (none vs. any), marijuana (none/infrequent vs. daily), and recreational drug use (none vs. any). Binge drinking and recreational drug use decreased at the beginning of the pandemic. Generalized linear mixed model supported these trends. Consistent with prior research, male sex and having depressive symptoms early pandemic were positively associated with each substance use outcomes. Social support was inversely associated with recreational drug use. CONCLUSIONS:Contrary to hypotheses, problematic substance use behaviors decreased from pre-pandemic to the post-pandemic follow-up in our sample of PLHIV. Ongoing surveillance is needed to assess whether this pattern persists as the pandemic continues.

BACKGROUND:Oral squamous cell carcinoma (OSCC) has poor survival rates. There is a pressing need to develop more precise risk assessment methods to tailor clinical treatment. Epigenome-wide association studies in OSCC have not produced a viable biomarker. These studies have relied on methylation array platforms, which are limited in their ability to profile the methylome. In this study, we use MethylCap-Seq (MC-Seq), a comprehensive methylation quantification technique, and brush swab samples, to develop a noninvasive, readily translatable approach to profile the methylome in OSCC patients. METHODS:Three OSCC patients underwent collection of cancer and contralateral normal tissue and brush swab biopsies, totaling 4 samples for each patient. Epigenome-wide DNA methylation quantification was performed using the SureSelectXT Methyl-Seq platform. DNA quality and methylation site resolution were compared between brush swab and tissue samples. Correlation and methylation value difference were determined for brush swabs vs. tissues for each respective patient and site (i.e., cancer or normal). Correlations were calculated between cancer and normal tissues and brush swab samples for each patient to determine the robustness of DNA methylation marks using brush swabs in clinical biomarker studies. RESULTS:There were no significant differences in DNA yield between tissue and brush swab samples. Mapping efficiency exceeded 90% across all samples, with no differences between tissue and brush swabs. The average number of CpG sites with at least 10x depth of coverage was 2,716,674 for brush swabs and 2,903,261 for tissues. Matched tissue and brush swabs had excellent correlation (r = 0.913 for cancer samples and r = 0.951 for normal samples). The methylation profile of the top 1000 CpGs was significantly different between cancer and normal samples (mean p-value = 0.00021) but not different between tissues and brush swabs (mean p-value = 0.11). CONCLUSIONS:Our results demonstrate that MC-Seq is an efficient platform for epigenome profiling in cancer biomarker studies, with broader methylome coverage than array-based platforms. Brush swab biopsy provides adequate DNA yield for MC-Seq, and taken together, our findings set the stage for development of a non-invasive methylome quantification technique for oral cancer with high translational potential.

OBJECTIVES:This study describes prevention behavior and psychosocial health among people living with HIV (PLHIV) and HIV-negative people during the early wave of the coronavirus disease 2019 (COVID-19) pandemic in the United States. We assessed differences by HIV status and associations between social disruption and psychosocial health. DESIGN:A cross-sectional telephone/videoconference administered survey of 3411 PLHIV and HIV-negative participants in the Multicenter AIDS Cohort Study/WIHS Combined Cohort Study (MWCCS). METHODS:An instrument combining new and validated measures was developed to assess COVID-19 prevention efforts, social disruptions (loss of employment, childcare, health insurance, and financial supports), experiences of abuse, and psychosocial health. Interviews were performed between April and June 2020. Associations between social disruptions and psychosocial health were explored using multivariable logistic regression, adjusting for sociodemographics and HIV status. RESULTS:Almost all (97.4%) participants reported COVID-19 prevention behavior; 40.1% participants reported social disruptions, and 34.3% reported health care appointment disruption. Men living with HIV were more likely than HIV-negative men to experience social disruptions (40.6% vs. 32.9%; P < 0.01), whereas HIV-negative women were more likely than women with HIV to experience social disruptions (51.1% vs. 39.8%, P < 0.001). Participants who experienced ≥2 social disruptions had significantly higher odds of depression symptoms [aOR = 1.32; 95% confidence interval (CI): 1.12 to 1.56], anxiety (aOR = 1.63; 95% CI: 1.17 to 2.27), and social support dissatisfaction (aOR = 1.81; 95% CI: 1.26 to 2.60). CONCLUSIONS:This study builds on emerging literature demonstrating the psychosocial health impact related to the COVID-19 pandemic by providing context specific to PLHIV. The ongoing pandemic requires structural and social interventions to decrease social disruption and address psychosocial health needs among the most vulnerable populations.