Bradley E. Aouizerat

Abstract

Micrornas (mirnas) may be considered impor- tant regulators of risk for type 2 diabetes (T2d). The aim of the present study was to identify novel sets of mirnas associ- ated 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 biolog- ical 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 mecha- nisms 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.

Abstract

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: A machine learning method was applied to select a set of chronological age-associated DNA methylation CpG sites from 1202 monocyte methylomes. Pearson correlation was tested between MonoDNAmAge and chronological age in three independent cohorts (Ntotal = 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: The MonoDNAmAge, comprised of 186 CpG sites, was moderately to strongly correlated with chronological age in the three cohorts (r = 0.90, p = 3.12E−181 in YSCCS; r = 0.54, p = 1.75E−96 in VACS; r = 0.66, p = 1.50E−60 in WIHS). More importantly, we found a nonlinear association between MonoDNAmAge and alcohol consumption (pmodel = 4.55E−08, (Formula presented.) = 7.80E−08 in YSCCS; pmodel = 1.85E−02, (Formula presented.) = 3.46E−02 in VACS). Heavy alcohol consumption increased EAAMonoDNAmAge up to 1.60 years while light alcohol consumption decreased EAAMonoDNAmAge up to 2.66 years. These results were corroborated by the four established epigenetic clocks (i.e., HorvathDNAmAge, HannumDNAmAge, PhenoDNAmAge, GrimDNAmAge). 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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

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.

Abstract

Introduction: MicroRNAs (miRs) may be important regulators of risk for type 2 diabetes (T2D). Circulating miRs may provide information about which individuals are at risk for T2D. The purpose of this study was to assess longitudinal associations between circulating miR expression and variability in fasting blood glucose (FBG) and to identify miR-targeted genes and biological pathways. Methods: Variability in FBG was estimated using standard deviation from participants (n = 20) in a previously completed yoga trial. Expression of 402 miRs was measured using hydrogel particle lithography. MirTarBase was used to identify mRNAs, and miRPathDB was used to identify pathways targeted by differentially expressed miRs. Results: Six circulating miRs (miR-192, miR-197, miR-206, miR-424, miR-486, and miR-93) were associated with variability in FBG and targeted 143 genes and 23 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Six mRNAs (AKT1, CCND1, ESR1, FASN, SMAD7, and VEGFA) were targeted by at least two miRs and four of those were located in miR-targeted KEGG pathways. Conclusions: Circulating miRs are associated with variability in FBG in individuals at risk for T2D. Further studies are needed to determine whether miRs may be prodromal biomarkers that can identify which individuals are at greatest risk to progress to T2D and which biological pathways underlie this risk.

Abstract

Background: Globally, type 2 diabetes is highly prevalent in individuals of Latino ancestry. The reasons underlying this high prevalence are not well understood, but both genetic and lifestyle factors are contributors. Circulating microRNAs are readily detectable in blood and are promising biomarkers to characterize biological responses (i.e., changes in gene expression) to lifestyle factors. Prior studies identified relationships between circulating microRNAs and risk for type 2 diabetes, but Latinos have largely been under-represented in these study samples. Aims/hypothesis: The aim of this study was to assess for differences in expression levels of three candidate microRNAs (miR-126, miR-146, miR-15) between individuals who had prediabetes compared to normal glycemic status and between individuals who self-identified with Latino ancestry in the United States (US) and native Mexicans living in or near Leon, Mexico. Methods: This was a cross-sectional study that included 45 Mexicans and 21 Latino participants from the US. Prediabetes was defined as fasting glucose 100–125 mg/dL or 2-h post-glucose challenge between 140 and 199 mg/dL. Expression levels of microRNAs from plasma were measured by qPCR. Linear and logistic regression models were used to assess relationships between individual microRNAs and glycemic status or geographic site. Results: None of the three microRNAs was associated with risk for type 2 diabetes. MiR-146a and miR-15 were significantly lower in the study sample from Mexico compared to the US. There was a significant interaction between miR-146a and BMI associated with fasting blood glucose. Conclusions/interpretation: This study did not replicate in Latinos prior observations from other racial groups of associations between miR-126, miR-146a, and miR-15 and risk for type 2 diabetes. Future studies should consider other microRNAs related to different biological pathways as possible biomarkers for type 2 diabetes in Latinos.

Abstract

Background: MicroRNAs may be important regulators of risk for type 2 diabetes. The purpose of this longitudinal observational study was to assess whether circulating microRNAs predicted improvements in fasting blood glucose, a major risk factor for type 2 diabetes, over 12 months. Methods: The study included participants (n = 82) from a previously completed trial that tested the effect of restorative yoga on individuals with prediabetes. Circulating microRNAs were measured using a flow cytometry miRNA assay. Linear models were used to determine the optimal sets of microRNA predictors overall and by intervention group. Results: Subsets of microRNAs were significant predictors of final fasting blood glucose after 12-months (R2 = 0.754, p < 0.001) and changes in fasting blood glucose over 12-months (R2 = 0.731, p < 0.001). Three microRNAs (let-7c, miR-363, miR-374b) were significant for the control group only, however there was no significant interaction by intervention group. Conclusions: Circulating microRNAs are significant predictors of fasting blood glucose in individuals with prediabetes. Among the identified microRNAs, several have previously been associated with risk for type 2 diabetes. This is one of the first studies to use a longitudinal design to assess whether microRNAs predict changes in fasting blood glucose over time. Further exploration of the function of the microRNAs included in these models may provide new insights about the complex etiology of type 2 diabetes and responses to behavioral risk reduction interventions. Trial registration: This study was a secondary analysis of a previously completed clinical trial that is registered at clinicaltrials.gov (NCT01024816) on December 3, 2009.

Abstract

Purpose: To examine a) whether there are significant differences in gut microbial diversity and in the abundance of gut microbial taxa; and b) differences in predicted functional pathways of the gut microbiome between those participants with high co-occurring symptoms and those with low co-occurring symptoms, prior to neoadjuvant chemotherapy and radiation therapy (CRT) for rectal cancer. Methods: Rectal cancer patients (n = 41) provided stool samples for 16 S rRNA gene sequencing and symptom ratings for fatigue, sleep disturbance, and depressive symptoms prior to CRT. Descriptive statistics were computed for symptoms. Gut microbiome data were analyzed using QIIME2, LEfSe, and the R statistical package. Results: Participants with high co-occurring symptoms (n = 19) had significantly higher bacterial abundances of Ezakiella, Clostridium sensu stricto, Porphyromonas, Barnesiella, Coriobacteriales Incertae Sedis, Synergistiaceae, Echerichia-Shigella, and Turicibacter compared to those with low co-occurring symptoms before CRT (n = 22). Biosynthesis pathways for lipopolysaccharide, L-tryptophan, and colanic acid building blocks were enriched in participants with high co-occurring symptoms. Participants with low co-occurring symptoms showed enriched abundances of Enterococcus and Lachnospiraceae, as well as pathways for β-D-glucoronosides, hexuronide/hexuronate, and nicotinate degradation, methanogenesis, and L-lysine biosynthesis. Conclusion: A number of bacterial taxa and predicted functional pathways were differentially abundant in patients with high co-occurring symptoms compared to those with low co-occurring symptoms before CRT for rectal cancer. Detailed examination of bacterial taxa and pathways mediating co-occurring symptoms is warranted.