Longitudinal Associations Between MicroRNAs and Weight in the Diabetes Prevention Program.

OBJECTIVE: Circulating microRNAs show cross-sectional associations with overweight and obesity. Few studies provided data to differentiate between a snapshot perspective on these associations versus how microRNAs characterize prodromal risk from disease pathology and complications. This study assessed longitudinal relationships between circulating microRNAs and weight at multiple time-points in the Diabetes Prevention Program trial. RESEARCH DESIGN AND METHODS: A subset of participants (n=150) from the Diabetes Prevention Program were included. MicroRNAs were measured from banked plasma using a Fireplex Assay. We used generalized linear mixed models to evaluate relationships between microRNAs and changes in weight at baseline, year-1, and year-2. Logistic regression was used to evaluate whether microRNAs at baseline were associated with weight change after 2 years. RESULTS: In fully adjusted models that included relevant covariates, seven miRs (i.e., miR-126, miR-15a, miR-192, miR-23a, and miR-27a) were statistically associated with weight over 2 years. MiR-197 and miR-320a remained significant after adjustment for multiple comparisons. Baseline levels of let-7f, miR-17, and miR-320c were significantly associated with 3% weight loss after 2 years in fully adjusted models. DISCUSSION: This study provided evidence for longitudinal relationships between circulating microRNAs and weight. Because microRNAs characterize the combined effects of genetic determinants and responses to behavioral determinants, they may provide insights about the etiology of overweight and obesity in the context or risk for common, complex diseases. Additional studies are needed to validate the potential genes and biological pathways that might be targeted by these microRNA biomarkers and have mechanistic implications for weight loss and disease prevention.

Cannabis use trajectories over time in relation to minority stress and gender among sexual and gender minority people.

Substance use disparities among sexual and gender minority (SGM) people are attributed to minority stress, but few studies have examined minority stress and cannabis use over time or investigated differences in cannabis use trajectories by less-studied gender subgroups. We examined if longitudinal cannabis use trajectories are related to baseline minority stressors and if gender differences persisted after accounting for minority stress. Cannabis use risk was measured annually over four years (2017-2021) within a longitudinal cohort study of SGM adults in the United States (N = 11,813). Discrimination and victimization, internalized stigma, disclosure and concealment, and safety and acceptance comprised minority stress (n = 5,673). Latent class growth curve mixture models identified five cannabis use trajectories: 'low or no risk', 'low moderate risk', 'high moderate risk', 'steep risk increase', and 'highest risk'. Participants who reported past-year discrimination and/or victimization at baseline had greater odds of membership in any cannabis risk category compared to the 'low risk' category (odds ratios [OR] 1.17-1.33). Internalized stigma was related to 'high moderate' and 'highest risk' cannabis use (ORs 1.27-1.38). After accounting for minority stress, compared to cisgender men, gender expansive people and transgender men had higher odds of 'low moderate risk' (ORs 1.61, 1.67) or 'high moderate risk' (ORs 2.09, 1.99), and transgender men had higher odds of 'highest risk' (OR 2.36) cannabis use. This study indicates minority stress is related to prospective cannabis use risk trajectories among SGM people, and transgender men and gender expansive people have greater odds of trajectories reflecting cannabis use risk.

Artificial intelligence-based epigenomic, transcriptomic and histologic signatures of tobacco use in oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) biomarker studies rarely employ multi-omic biomarker strategies and pertinent clinicopathologic characteristics to predict mortality. In this study we determine for the first time a combined epigenetic, gene expression, and histology signature that differentiates between patients with different tobacco use history (heavy tobacco use with ≥10 pack years vs. no tobacco use). Using The Cancer Genome Atlas (TCGA) cohort (n = 257) and an internal cohort (n = 40), we identify 3 epigenetic markers (GPR15, GNG12, GDNF) and 13 expression markers (IGHA2, SCG5, RPL3L, NTRK1, CD96, BMP6, TFPI2, EFEMP2, RYR3, DMTN, GPD2, BAALC, and FMO3), which are dysregulated in OSCC patients who were never smokers vs. those who have a ≥ 10 pack year history. While mortality risk prediction based on smoking status and clinicopathologic covariates alone is inaccurate (c-statistic = 0.57), the combined epigenetic/expression and histologic signature has a c-statistic = 0.9409 in predicting 5-year mortality in OSCC patients.

Single-Cell Molecular Profiling of Head and Neck Squamous Cell Carcinoma Reveals Five Dysregulated Signaling Pathways Associated With Circulating Tumor Cells.

OBJECTIVES: To determine the dysregulated signaling pathways of head and neck squamous cell carcinoma associated with circulating tumor cells (CTCs) via single-cell molecular characterization. INTRODUCTION: Head and neck squamous cell carcinoma (HNSCC) has a significant global burden and is a disease with poor survival. Despite trials exploring new treatment modalities to improve disease control rates, the 5 year survival rate remains low at only 60%. Most cancer malignancies are reported to progress to a fatal phase due to the metastatic activity derived from treatment-resistant cancer cells, regarded as one of the most significant obstacles to develope effective cancer treatment options. However, the molecular profiles of cancer cells have not been thoroughly studied. METHODS: Here, we examined in-situ HNSCC tumors and pairwisely followed up with the downstream circulating tumor cells (CTCs)-based on the surrogate biomarkers to detect metastasis that is established in other cancers - not yet being fully adopted in HNSCC treatment algorithms. RESULTS: Specifically, we revealed metastatic HNSCC patients have complex CTCs that could be defined through gene expression and mutational gene profiling derived from completed single-cell RNASeq (scRNASeq) that served to confirm molecular pathways inherent in these CTCs. To enhance the reliability of our findings, we cross-validated those molecular profiles with results from previously published studies. CONCLUSION: Thus, we identified 5 dysregulated signaling pathways in CTCs to derive HNSCC biomarker panels for screening HNSCC in situ tumors.

ObjectivesInvestigating the dysregulated signaling pathways of head and neck squamous cell carcinoma (HNSCC) linked with circulating tumor cells (CTCs) using single-cell molecular characterization.IntroductionHNSCC poses a significant global health burden with poor survival rates despite advancements in treatment. Metastatic activity from treatment-resistant cancer cells remains a major challenge in developing effective treatments. However, the molecular profiles of cancer cells, particularly CTCs, are not well-understood.MethodsWe analyzed in-situ HNSCC tumors and corresponding CTCs using surrogate biomarkers to detect metastasis, a technique not widely used in HNSCC treatment protocols.ResultsOur study revealed complex CTCs in metastatic HNSCC patients characterized by gene expression and mutational gene profiling via single-cell RNASeq (scRNASeq). These profiles confirmed molecular pathways inherent in CTCs, further validated by previous research.ConclusionThrough our research, we identified five dysregulated signaling pathways in CTCs, suggesting potential biomarker panels for HNSCC screening in situ tumors.

Perineural invasion is associated with function-evoked pain and altered extracellular matrix in patients with head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is painful, and perineural invasion (PNI) has been associated with worst pain. Pain due to HNSCC is diverse and may vary based on clinicopathological factors. This study aims to characterize different pain patterns linked with PNI, its influence on daily functioning, and gain insights into molecular changes and pathways associated with PNI-related pain in HNSCC patients. We conducted a cross-sectional study across three medical centers (n=114), assessing pain phenotypes and their impact on daily functioning using two self-reported pain questionnaires, given to patients prior to their cancer surgery. Furthermore, we conducted RNA-seq analysis utilizing the TCGA dataset of HNSCC tumor from patients (n=192) to identify genes relevant to both PNI and pain. Upon adjusting for demographic and clinicopathological variables using linear regression models, we found that PNI independently predicted function-evoked pain according to the UCSF Oral Cancer Pain Questionnaire, as well as the worst pain intensity reported in the Brief Pain Inventory. Distinct pain patterns were observed to be associated with daily activities in varying manners. Our molecular analyses revealed significant disruptions in pathways associated with extracellular matrix (ECM) structure and organization. The top differentially expressed genes linked to the ECM are implicated in cancer development, pain, and neurodegenerative diseases. Our data underscore the importance of properly categorizing pain phenotypes in future studies aiming to uncover mechanistic underpinnings of pain. Additionally, we have compiled a list of genes of interest that could serve as targets for both cancer and cancer pain management. PERSPECTIVE: PNI independently predicts function-evoked pain. Different pain phenotypes affect daily activities differently. We identified a list of candidate genes involved in extracellular matrix structure and function that can be targeted for both cancer and cancer pain control.

A positive affect intervention alters leukocyte DNA methylation in sexual minority men with HIV who use methamphetamine.

OBJECTIVE: This epigenomics sub-study embedded within a randomized controlled trial examined whether an evidenced-based behavioral intervention model that decreased stimulant use altered leukocyte DNA methylation (DNAm). METHODS: Sexual minority men with HIV who use methamphetamine were randomized to a five-session positive affect intervention (n = 32) or an attention-control condition (n = 21), both delivered during three months of contingency management for stimulant abstinence. All participants exhibited sustained HIV virologic control - an HIV viral load less than 40 copies/mL at baseline and six months post-randomization. The Illumina EPIC BeadChip measured leukocyte methylation of cytosine-phosphate-guanosine (CpG) sites mapping onto five a priori candidate genes of interest (i.e., ADRB2, BDNF, FKBP5, NR3C1, OXTR). Functional DNAm pathways and soluble markers of immune dysfunction were secondary outcomes. RESULTS: Compared to the attention-control condition, the positive affect intervention significantly decreased methylation of CpG sites on genes that regulate ß2 adrenergic and oxytocin receptors. There was an inconsistent pattern for the direction of the intervention effects on methylation of CpG sites on genes for glucocorticoid receptors and brain-derived neurotrophic factor. Pathway analyses adjusting for the false discovery rate (padj < 0.05) revealed significant intervention-related alterations in DNAm of Reactome pathways corresponding to neural function as well as dopamine, glutamate, and serotonin release. Positive affect intervention effects on DNAm were accompanied by significant reductions in the self-reported frequency of stimulant use. CONCLUSIONS: There is an epigenetic signature of an evidence-based behavioral intervention model that reduced stimulant use, which will guide the identification of biomarkers for treatment responses.

DNA methylation-based telomere length is associated with HIV infection, physical frailty, cancer, and all-cause mortality.

Telomere length (TL) is an important indicator of cellular aging. Shorter TL is associated with several age-related diseases including coronary heart disease, heart failure, diabetes, osteoporosis, and cancer. Recently, a DNA methylation-based TL (DNAmTL) estimator has been developed as an alternative method for directly measuring TL. In this study, we examined the association of DNAmTL with cancer prevalence and mortality risk among people with and without HIV in the Veterans Aging Cohort Study Biomarker Cohort (VACS, N = 1917) and Women's Interagency HIV Study Cohort (WIHS, N = 481). We profiled DNAm in whole blood (VACS) or in peripheral blood mononuclear cells (WIHS) using an array-based method. Cancer prevalence was estimated from electronic medical records and cancer registry data. The VACS Index was used as a measure of physiologic frailty. Models were adjusted for self-reported race and ethnicity, batch, smoking status, alcohol consumption, and five cell types (CD4, CD8, NK, B cell, and monocyte). We found that people with HIV had shorter average DNAmTL than those without HIV infection [beta = -0.25, 95% confidence interval (-0.32, -0.18), p = 1.48E-12]. Greater value of VACS Index [beta = -0.002 (-0.003, -0.001), p = 2.82E-05] and higher cancer prevalence [beta = -0.07 (-0.10, -0.03), p = 1.37E-04 without adjusting age] were associated with shortened DNAmTL. In addition, one kilobase decrease in DNAmTL was associated with a 40% increase in mortality risk [hazard ratio: 0.60 (0.44, 0.82), p = 1.42E-03]. In summary, HIV infection, physiologic frailty, and cancer are associated with shortening DNAmTL, contributing to an increased risk of all-cause mortality.

Stimulator of Interferon Genes Pathway Activation through the Controlled Release of STINGel Mediates Analgesia and Anti-Cancer Effects in Oral Squamous Cell Carcinoma.

Oral squamous cell carcinoma (OSCC) presents significant treatment challenges due to its poor survival and intense pain at the primary cancer site. Cancer pain is debilitating, contributes to diminished quality of life, and causes opioid tolerance. The stimulator of interferon genes (STING) agonism has been investigated as an anti-cancer strategy. We have developed STINGel, an extended-release formulation that prolongs the availability of STING agonists, which has demonstrated an enhanced anti-tumor effect in OSCC compared to STING agonist injection. This study investigates the impact of intra-tumoral STINGel on OSCC-induced pain using two separate OSCC models and nociceptive behavioral assays. Intra-tumoral STINGel significantly reduced mechanical allodynia in the orofacial cancer model and alleviated thermal and mechanical hyperalgesia in the hind paw model. To determine the cellular signaling cascade contributing to the antinociceptive effect, we performed an in-depth analysis of immune cell populations via single-cell RNA-seq. We demonstrated an increase in M1-like macrophages and N1-like neutrophils after STINGel treatment. The identified regulatory pathways controlled immune response activation, myeloid cell differentiation, and cytoplasmic translation. Functional pathway analysis demonstrated the suppression of translation at neuron synapses and the negative regulation of neuron projection development in M2-like macrophages after STINGel treatment. Importantly, STINGel treatment upregulated TGF-ß pathway signaling between various cell populations and peripheral nervous system (PNS) macrophages and enhanced TGF-ß signaling within the PNS itself. Overall, this study sheds light on the mechanisms underlying STINGel-mediated antinociception and anti-tumorigenic impact.

Factors Associated With the Cardiovascular Health of Black and Latino Adults With Type 2 Diabetes.

AIMS: The purpose of this study was to assess the levels of cardiovascular health (CVH) of Black and Latino adults with type 2 diabetes (T2D) and examine the association of individual and microsystem level factors with their CVH score. METHODS: This was a cross-sectional design in 60 Black and Latino Adults aged 18-40 with T2D. Data were collected on sociodemographic, individual (sociodemographic, diabetes self-management, sleep disturbance, depressive symptoms, quality of life, and the inflammatory biomarkers IL-6 and hs-CRP) and microsystem factors (family functioning), and American Heart Association's Life's Simple 7 metrics of CVH. Factors significantly associated with the CVH score in the bivariate analyses were entered into a linear regression model. RESULTS: The sample had a mean age 34 ± 5 years and was primarily female (75%) with a mean CVH score was 8.6 ± 2.2 (possible range of 0-14). The sample achieved these CVH factors at ideal levels: body mass index <25 kg/m2 (8%); blood pressure <120/80 (42%); hemoglobin A1c < 7% (57%); total cholesterol <200 mg/dL (83%); healthy diet (18%); never or former smoker > one year (95%); and physical activity (150 moderate-to-vigorous minutes/week; 45%). In the multivariable model, two factors were significantly associated with cardiovascular health: hs-CRP (B = -0.11621, p < .0001) and the general health scale (B = 0.45127, p = .0013). CONCLUSIONS: This sample had an intermediate level of CVH, with inflammation and general health associated with overall CVH score.

Computationally inferred cell-type specific epigenome-wide DNA methylation analysis unveils distinct methylation patterns among immune cells for HIV infection in three cohorts.

BACKGROUND: Epigenome-wide association studies (EWAS) have identified CpG sites associated with HIV infection in blood cells in bulk, which offer limited knowledge of cell-type specific methylation patterns associated with HIV infection. In this study, we aim to identify differentially methylated CpG sites for HIV infection in immune cell types: CD4+ T-cells, CD8+ T-cells, B cells, Natural Killer (NK) cells, and monocytes. METHODS: Applying a computational deconvolution method, we performed a cell-type based EWAS for HIV infection in three independent cohorts (Ntotal = 1,382). DNA methylation in blood or in peripheral blood mononuclear cells (PBMCs) was profiled by an array-based method and then deconvoluted by Tensor Composition Analysis (TCA). The TCA-computed CpG methylation in each cell type was first benchmarked by bisulfite DNA methylation capture sequencing in a subset of the samples. Cell-type EWAS of HIV infection was performed in each cohort separately and a meta-EWAS was conducted followed by gene set enrichment analysis. RESULTS: The meta-analysis unveiled a total of 2,021 cell-type unique significant CpG sites for five inferred cell types. Among these inferred cell-type unique CpG sites, the concordance rate in the three cohorts ranged from 96% to 100% in each cell type. Cell-type level meta-EWAS unveiled distinct patterns of HIV-associated differential CpG methylation, where 74% of CpG sites were unique to individual cell types (false discovery rate, FDR <0.05). CD4+ T-cells had the largest number of unique HIV-associated CpG sites (N = 1,624) compared to any other cell type. Genes harboring significant CpG sites are involved in immunity and HIV pathogenesis (e.g. CD4+ T-cells: NLRC5, CX3CR1, B cells: IFI44L, NK cells: IL12R, monocytes: IRF7), and in oncogenesis (e.g. CD4+ T-cells: BCL family, PRDM16, monocytes: PRDM16, PDCD1LG2). HIV-associated CpG sites were enriched among genes involved in HIV pathogenesis and oncogenesis that were enriched among interferon-α and -γ, TNF-α, inflammatory response, and apoptotic pathways. CONCLUSION: Our findings uncovered computationally inferred cell-type specific modifications in the host epigenome for people with HIV that contribute to the growing body of evidence regarding HIV pathogenesis.

Substance Use Over Time Among Sexual and Gender Minority People: Differences at the Intersection of Sex and Gender.

Purpose: Sexual and gender minority (SGM) people are at greater risk for substance use than heterosexual and cisgender people, but most prior work is limited by cross-sectional analyses or the examination of single substance use. This study examined substance use over time among SGM people to identify patterns of polysubstance use at the intersection of sex and gender. Methods: Data were collected annually over 4 years from SGM respondents (n = 11,822) in The Population Research in Identity and Disparities for Equality (PRIDE) Study. Differences in substance use patterns (any prior 30-day use of 15 substances) by gender subgroup were examined with latent class analysis, and multinomial regression models tested relationships between gender subgroup and substance use. Results: Eight classes of substance use were observed. The three most common patterns were low substance use (49%), heavy episodic alcohol use (≥5 alcoholic drinks on one occasion) with some cannabis and tobacco use (14%), and cannabis use with some tobacco and declining heavy episodic alcohol use (13%). Differences observed included lower odds of patterns defined by heavy episodic alcohol use with some cannabis and tobacco use in all gender subgroups relative to cisgender men and persons with low substance use (odds ratios [ORs] 0.26-0.60). Gender expansive people assigned female at birth, gender expansive people assigned male at birth, and transgender men had greater odds of reporting cannabis use with small percentages of heavy episodic alcohol and tobacco use (ORs: 1.41-1.60). Conclusion: This study suggests that there are unique patterns of polysubstance use over time among gender subgroups of SGM people.

Multi-Tiered Assessment of Gene Expression Provides Evidence for Mechanisms That Underlie Risk for Type 2 Diabetes.

Introduction: Integrated transcriptome and microRNA differential gene expression (DEG) analyses may help to explain type 2 diabetes (T2D) pathogenesis in at-risk populations. The purpose of this study was to characterize DEG in banked biospecimens from underactive adult participants who responded to a randomized clinical trial measuring the effects of lifestyle interventions on T2D risk factors. DEGs were further examined within the context of annotated biological pathways. Methods: Participants (n = 52) in a previously completed clinical trial that assessed a 12-week behavioural intervention for T2D risk reduction were included. Participants who showed >6mg/dL decrease in fasting blood glucose were identified as responders. Gene expression was measured by RNASeq, and overrepresentation analysis within KEGG pathways and weighted gene correlation network analysis (WGCNA) were performed. Results: No genes remained significantly differentially expressed after correction for multiple comparisons. One module derived by WGCNA related to body mass index was identified, which contained genes located in KEGG pathways related to known mechanisms underlying risk for T2D as well as pathways related to neurodegeneration and protein misfolding. A network analysis showed indirect connections between genes in this module and islet amyloid polypeptide (IAPP), which has previously been hypothesized as a mechanism for T2D. Discussion: We validated prior studies that showed pathways related to metabolism, inflammation/immunity, and endocrine/hormone function are related to risk for T2D. We identified evidence for new potential mechanisms that include protein misfolding. Additional studies are needed to determine whether these are potential therapeutic targets to decrease risk for T2D.

MicroRNA biomarkers target genes and pathways associated with type 2 diabetes.

AIMS/HYPOTHESIS: Our prior analysis of the Diabetes Prevention Program study identified a subset of five miRNAs that predict incident type 2 diabetes. The purpose of this study was to identify mRNAs and biological pathways targeted by these five miRNAs to elucidate potential mechanisms of risk and responses to the tested interventions. METHODS: Using experimentally validated data from miRTarBase version 8.0 and R (2021), we identified mRNAs with strong evidence to be regulated by individual or combinations of the five predictor miRNAs. Overrepresentation of the mRNA targets was assessed in pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation database. RESULTS: The five miRNAs targeted 167 pathways and 122 mRNAs. Nine of the pathways have known associations with type 2 diabetes: Insulin signaling, Insulin resistance, Diabetic cardiomyopathy, Type 2 diabetes, AGE-RAGE signaling in diabetic complications, HIF-1 signaling, TGF-beta signaling, PI3K/Akt signaling, and Adipocytokine signaling pathways. Vascular endothelial growth factor A (VEGFA) has prior genetic associations with risk for type 2 diabetes and was the most commonly targeted mRNA for this set of miRNAs. CONCLUSIONS/INTERPRETATION: These findings show that miRNA predictors of incident type 2 diabetes target mRNAs and pathways known to underlie risk for type 2 diabetes. Future studies should evaluate miRNAs as potential therapeutic targets for preventing and treating type 2 diabetes.

Variations in Genes Encoding Human Papillomavirus Binding Receptors and Susceptibility to Cervical Precancer.

BACKGROUND: Cervical cancer oncogenesis starts with human papillomavirus (HPV) cell entry after binding to host cell surface receptors; however, the mechanism is not fully known. We examined polymorphisms in receptor genes hypothesized to be necessary for HPV cell entry and assessed their associations with clinical progression to precancer. METHODS: African American women (N = 1,728) from the MACS/WIHS Combined Cohort Study were included. Two case-control study designs were used-cases with histology-based precancer (CIN3+) and controls without; and cases with cytology-based precancer [high-grade squamous intraepithelial lesions (HSIL)] and controls without. SNPs in candidate genes (SDC1, SDC2, SDC3, SDC4, GPC1, GPC2, GPC3, GPC4, GPC5, GPC6, and ITGA6) were genotyped using an Illumina Omni2.5-quad beadchip. Logistic regression was used to assess the associations in all participants and by HPV genotypes, after adjusting for age, human immunodeficiency virus serostatus, CD4 T cells, and three principal components for ancestry. RESULTS: Minor alleles in SNPs rs77122854 (SDC3), rs73971695, rs79336862 (ITGA6), rs57528020, rs201337456, rs11987725 (SDC2), rs115880588, rs115738853, and rs9301825 (GPC5) were associated with increased odds of both CIN3+ and HSIL, whereas, rs35927186 (GPC5) was found to decrease the odds for both outcomes (P value ≤ 0.01). Among those infected with Alpha-9 HPV types, rs722377 (SDC3), rs16860468, rs2356798 (ITGA6), rs11987725 (SDC2), and rs3848051 (GPC5) were associated with increased odds of both precancer outcomes. CONCLUSIONS: Polymorphisms in genes that encode binding receptors for HPV cell entry may play a role in cervical precancer progression. IMPACT: Our findings are hypothesis generating and support further exploration of mechanisms of HPV entry genes that may help prevent progression to cervical precancer.

Co-occurrence of injection drug use and hepatitis C increases epigenetic age acceleration that contributes to all-cause mortality among people living with HIV.

Co-occurrence of injection drug use (IDU) and hepatitis C virus infection (HCV) is common in people living with HIV (PLWH) and leads to significantly increased mortality. Epigenetic clocks derived from DNA methylation (DNAm) are associated with disease progression and all-cause mortality. In this study, we hypothesized that epigenetic age mediates the relationships between the co-occurrence of IDU and HCV with mortality risk among PLWH. We tested this hypothesis in the Veterans Aging Cohort Study (n = 927) by using four established epigenetic clocks of DNAm age (i.e., Horvath, Hannum, Pheno, Grim). Compared to individuals without IDU and HCV (IDU-HCV-), participants with IDU and HCV (IDU+HCV+) showed a 2.23-fold greater risk of mortality estimated using a Cox proportional hazards model (hazard ratio: 2.23; 95% confidence interval: 1.62-3.09; p = 1.09E-06). IDU+HCV+ was associated with a significantly increased epigenetic age acceleration (EAA) measured by 3 out of 4 epigenetic clocks, adjusting for demographic and clinical variables (Hannum: p = 8.90E-04, Pheno: p = 2.34E-03, Grim: p = 3.33E-11). Furthermore, we found that epigenetic age partially mediated the relationship between IDU+HCV+ and all-cause mortality, up to a 13.67% mediation proportion. Our results suggest that comorbid IDU with HCV increases EAA in PLWH that partially mediates the increased mortality risk.

Prediction of Weight Loss to Decrease the Risk for Type 2 Diabetes Using Multidimensional Data in Filipino Americans: Secondary Analysis.

BACKGROUND: Type 2 diabetes (T2D) has an immense disease burden, affecting millions of people worldwide and costing billions of dollars in treatment. As T2D is a multifactorial disease with both genetic and nongenetic influences, accurate risk assessments for patients are difficult to perform. Machine learning has served as a useful tool in T2D risk prediction, as it can analyze and detect patterns in large and complex data sets like that of RNA sequencing. However, before machine learning can be implemented, feature selection is a necessary step to reduce the dimensionality in high-dimensional data and optimize modeling results. Different combinations of feature selection methods and machine learning models have been used in studies reporting disease predictions and classifications with high accuracy. OBJECTIVE: The purpose of this study was to assess the use of feature selection and classification approaches that integrate different data types to predict weight loss for the prevention of T2D. METHODS: The data of 56 participants (ie, demographic and clinical factors, dietary scores, step counts, and transcriptomics) were obtained from a previously completed randomized clinical trial adaptation of the Diabetes Prevention Program study. Feature selection methods were used to select for subsets of transcripts to be used in the selected classification approaches: support vector machine, logistic regression, decision trees, random forest, and extremely randomized decision trees (extra-trees). Data types were included in different classification approaches in an additive manner to assess model performance for the prediction of weight loss. RESULTS: Average waist and hip circumference were found to be different between those who exhibited weight loss and those who did not exhibit weight loss (P=.02 and P=.04, respectively). The incorporation of dietary and step count data did not improve modeling performance compared to classifiers that included only demographic and clinical data. Optimal subsets of transcripts identified through feature selection yielded higher prediction accuracy than when all available transcripts were included. After comparison of different feature selection methods and classifiers, DESeq2 as a feature selection method and an extra-trees classifier with and without ensemble learning provided the most optimal results, as defined by differences in training and testing accuracy, cross-validated area under the curve, and other factors. We identified 5 genes in two or more of the feature selection subsets (ie, CDP-diacylglycerol-inositol 3-phosphatidyltransferase [CDIPT], mannose receptor C type 2 [MRC2], PAT1 homolog 2 [PATL2], regulatory factor X-associated ankyrin containing protein [RFXANK], and small ubiquitin like modifier 3 [SUMO3]). CONCLUSIONS: Our results suggest that the inclusion of transcriptomic data in classification approaches for prediction has the potential to improve weight loss prediction models. Identification of which individuals are likely to respond to interventions for weight loss may help to prevent incident T2D. Out of the 5 genes identified as optimal predictors, 3 (ie, CDIPT, MRC2, and SUMO3) have been previously shown to be associated with T2D or obesity. TRIAL REGISTRATION: ClinicalTrials.gov NCT02278939; https://clinicaltrials.gov/ct2/show/NCT02278939.

Review of databases for experimentally validated human microRNA-mRNA interactions.

MicroRNAs (miRs) may contribute to disease etiology by influencing gene expression. Numerous databases are available for miR target prediction and validation, but their functionality is varied, and outputs are not standardized. The purpose of this review is to identify and describe databases for cataloging validated miR targets. Using Tools4miRs and PubMed, we identified databases with experimentally validated targets, human data, and a focus on miR-messenger RNA (mRNA) interactions. Data were extracted about the number of times each database was cited, the number of miRs, the target genes, the interactions per database, experimental methodology and key features of each database. The search yielded 10 databases, which in order of most cited to least were: miRTarBase, starBase/The Encyclopedia of RNA Interactomes, DIANA-TarBase, miRWalk, miRecords, miRGator, miRSystem, miRGate, miRSel and targetHub. Findings from this review suggest that the information presented within miR target validation databases can be enhanced by adding features such as flexibility in performing queries in multiple ways, downloadable data, ongoing updates and integrating tools for further miR-mRNA target interaction analysis. This review is designed to aid researchers, especially those new to miR bioinformatics tools, in database selection and to offer considerations for future development and upkeep of validation tools. Database URL http://mirtarbase.cuhk.edu.cn/.

Cell-type specific EWAS identifies genes involved in HIV pathogenesis and oncogenesis among people with HIV infection.

Epigenome-wide association studies (EWAS) of heterogenous blood cells have identified CpG sites associated with chronic HIV infection, which offer limited knowledge of cell-type specific methylation patterns associated with HIV infection. Applying a computational deconvolution method validated by capture bisulfite DNA methylation sequencing, we conducted a cell type-based EWAS and identified differentially methylated CpG sites specific for chronic HIV infection among five immune cell types in blood: CD4+ T-cells, CD8+ T-cells, B cells, Natural Killer (NK) cells, and monocytes in two independent cohorts (N total =1,134). Differentially methylated CpG sites for HIV-infection were highly concordant between the two cohorts. Cell-type level meta-EWAS revealed distinct patterns of HIV-associated differential CpG methylation, where 67% of CpG sites were unique to individual cell types (false discovery rate, FDR <0.05). CD4+ T-cells had the largest number of HIV-associated CpG sites (N=1,472) compared to any other cell type. Genes harboring statistically significant CpG sites are involved in immunity and HIV pathogenesis (e.g. CX3CR1 in CD4+ T-cells, CCR7 in B cells, IL12R in NK cells, LCK in monocytes). More importantly, HIV-associated CpG sites were overrepresented for hallmark genes involved in cancer pathology ( FDR <0.05) (e.g. BCL family, PRDM16, PDCD1LGD, ESR1, DNMT3A, NOTCH2 ). HIV-associated CpG sites were enriched among genes involved in HIV pathogenesis and oncogenesis such as Kras-signaling, interferon-α and -γ, TNF-α, inflammatory, and apoptotic pathways. Our findings are novel, uncovering cell-type specific modifications in the host epigenome for people with HIV that contribute to the growing body of evidence regarding pathogen-induced epigenetic oncogenicity, specifically on HIV and its comorbidity with cancers.

Prediction Performance of Feature Selectors and Classifiers on Highly Dimensional Transcriptomic Data for Prediction of Weight Loss in Filipino Americans at Risk for Type 2 Diabetes.

Background: Accurate prediction of risk for chronic diseases like type 2 diabetes (T2D) is challenging due to the complex underlying etiology. Integration of more complex data types from sensors and leveraging technologies for collection of -omics datasets may provide greater insights into the specific risk profile for complex diseases.Methods: We performed a literature review to identify feature selection methods and machine learning models for prediction of weight loss in a previously completed clinical trial (NCT02278939) of a behavioral intervention for weight loss in Filipinos at risk for T2D. Features included demographic and clinical characteristics, dietary factors, physical activity, and transcriptomics.Results: We identified four feature selection methods: Correlation-based Feature Subset Selection (CfsSubsetEval) with BestFirst, Kolmogorov-Smirnov (KS) test with correlation featureselection (CFS), DESeq2, and max-relevance-min-relevance (MRMR) with linear forward search and mutual information (MI) and four machine learning algorithms: support vector machine, decision tree, random forest, and extra trees that are applicable to prediction of weight loss using the specified feature types.Conclusion: More accurate prediction of risk for T2D and other complex conditions may be possible by leveraging complex data types from sensors and -omics datasets. Emerging methods for feature selection and machine learning algorithms make this type of modeling feasible.