The cofactor-dependent folding mechanism of Drosophila cryptochrome revealed by single-molecule pulling experiments.

The link between cofactor binding and protein activity is well-established. However, how cofactor interactions modulate folding of large proteins remains unknown. We use optical tweezers, clustering and global fitting to dissect the folding mechanism of Drosophila cryptochrome (dCRY), a 542-residue protein that binds FAD, one of the most chemically and structurally complex cofactors in nature. We show that the first dCRY parts to fold are independent of FAD, but later steps are FAD-driven as the remaining polypeptide folds around the cofactor. FAD binds to largely unfolded intermediates, yet with association kinetics above the diffusion-limit. Interestingly, not all FAD moieties are required for folding: whereas the isoalloxazine ring linked to ribitol and one phosphate is sufficient to drive complete folding, the adenosine ring with phosphates only leads to partial folding. Lastly, we propose a dCRY folding model where regions that undergo conformational transitions during signal transduction are the last to fold.

A Bayesian two-step integrative procedure incorporating prior knowledge for the identification of miRNA-mRNAs involved in hepatocellular carcinoma.

Recent studies have confirmed the role of miRNA regulation of gene expression in oncogenesis for various cancers. In parallel, prior knowledge about relationships between miRNA and mRNA have been accumulated from biological experiments or statistical analyses. Improved identification of disease-associated miRNA-mRNA pairs may be achieved by incorporating prior knowledge into integrative genomic analyses. In this study we focus on 39 patients with hepatocellular carcinoma (HCC) and 25 patients with liver cirrhosis and use a flexible Bayesian two-step integrative method. We found 66 significant miRNA-mRNA pairs, several of which contain molecules that have previously been identified as potential biomarkers. These results demonstrate the utility of the proposed approach in providing a better understanding of relationships between different biological levels, thereby giving insights into the biological mechanisms underlying the diseases, while providing a better selection of biomarkers that may serve as diagnostic, prognostic, or therapeutic biomarker candidates.

Maternal-fetal genetic interactions, imprinting, and risk of placental abruption.

RESULTS: Abruption cases were more likely to experience preeclampsia, have shorter gestational age, and deliver infants with lower birthweight compared with controls. Models with MFGI effects provided improved fit than models with only maternal and fetal genotype main effects for SNP rs12530904 (p-value = 1.2e-04) in calcium/calmodulin-dependent protein kinase [CaM kinase] II beta (CAMK2B), and, SNP rs73136795 (p-value = 1.9e-04) in peroxisome proliferator-activated receptor-gamma (PPARG), both MB genes. We identified 320 SNPs in 45 maternally-imprinted genes (including potassium voltage-gated channel subfamily Q member 1 [KCNQ1], neurotrimin [NTM], and, ATPase phospholipid transporting 10 A [ATP10A]) associated with abruption. Top hits included rs2012323 (p-value = 1.6E-16) and rs12221520 (p-value1.3e-13) in KCNQ1, rs8036892 (p-value = 9.3E-17) and rs188497582 in ATP10A, rs12589854 (p-value = 2.9E-11) and rs80203467 (p-value = 4.6e-11) in maternally expressed 8, small nucleolar RNA host (MEG8), and rs138281088 in solute carrier family 22 member 2 (SLC22A2) (p-value = 6.8e-9). CONCLUSIONS: We identified novel PA-related maternal-fetal MB gene interactions and imprinting effects that highlight the role of the fetus in PA risk development. Findings can inform mechanistic investigations to understand the pathogenesis of PA.

Integrative Analysis of DNA Methylation and microRNA Expression Reveals Mechanisms of Racial Heterogeneity in Hepatocellular Carcinoma.

Pathologic alterations in epigenetic regulation have long been considered a hallmark of many cancers, including hepatocellular carcinoma (HCC). In a healthy individual, the relationship between DNA methylation and microRNA (miRNA) expression maintains a fine balance; however, disruptions in this harmony can aid in the genesis of cancer or the propagation of existing cancers. The balance between DNA methylation and microRNA expression and its potential disturbance in HCC can vary by race. There is emerging evidence linking epigenetic events including DNA methylation and miRNA expression to cancer disparities. In this paper, we evaluate the epigenetic mechanisms of racial heterogenity in HCC through an integrated analysis of DNA methylation, miRNA, and combined regulation of gene expression. Specifically, we generated DNA methylation, mRNA-seq, and miRNA-seq data through the analysis of tumor and adjacent non-tumor liver tissues from African Americans (AA) and European Americans (EA) with HCC. Using mixed ANOVA, we identified cytosine-phosphate-guanine (CpG) sites, mRNAs, and miRNAs that are significantly altered in HCC vs. adjacent non-tumor tissue in a race-specific manner. We observed that the methylome was drastically changed in EA with a significantly larger number of differentially methylated and differentially expressed genes than in AA. On the other hand, the miRNA expression was altered to a larger extent in AA than in EA. Pathway analysis functionally linked epigenetic regulation in EA to processes involved in immune cell maturation, inflammation, and vascular remodeling. In contrast, cellular proliferation, metabolism, and growth pathways are found to predominate in AA as a result of this epigenetic analysis. Furthermore, through integrative analysis, we identified significantly differentially expressed genes in HCC with disparate epigenetic regulation, associated with changes in miRNA expression for AA and DNA methylation for EA.

Race and Patient-reported Symptoms in Adherence to Adjuvant Endocrine Therapy: A Report from the Women's Hormonal Initiation and Persistence Study.

BACKGROUND: Adjuvant endocrine therapy (AET) improves outcomes in women with hormone receptor-positive (HR+) breast cancer. Suboptimal AET adherence is common, but data are lacking about symptoms and adherence in racial/ethnic minorities. We evaluated adherence by race and the relationship between symptoms and adherence. METHODS: The Women's Hormonal Initiation and Persistence study included women diagnosed with nonrecurrent HR+ breast cancer who initiated AET. AET adherence was captured using validated items. Data regarding patient (e.g., race), medication-related (e.g., symptoms), cancer care delivery (e.g., communication), and clinicopathologic factors (e.g., chemotherapy) were collected via surveys and medical charts. Multivariable logistic regression models were employed to calculate odds ratios and 95% confidence intervals (CIs) associated with adherence. RESULTS: Of the 570 participants, 92% were privately insured and nearly one of three were Black. Thirty-six percent reported nonadherent behaviors. In multivariable analysis, women less likely to report adherent behaviors were Black (vs. White; OR, 0.43; 95% CI, 0.27-0.67; P < 0.001) and with greater symptom burden (OR, 0.98; 95% CI, 0.96-1.00; P < 0.05). Participants more likely to be adherent were overweight (vs. normal weight) (OR, 1.58; 95% CI, 1.04-2.43; P < 0.05), sat ≤ 6 hours a day (vs. ≥6 hours; OR, 1.83; 95% CI, 1.25-2.70; P < 0.01), and were taking aromatase inhibitors (vs. tamoxifen; OR, 1.91; 95% CI, 1.28-2.87; P < 0.01). CONCLUSIONS: Racial differences in AET adherence were observed. Longitudinal assessments of symptom burden are needed to better understand this dynamic process and factors that may explain differences in survivor subgroups. IMPACT: Future interventions should prioritize Black survivors and women with greater symptom burden.

Bayesian variable selection for high dimensional predictors and self-reported outcomes.

BACKGROUND: The onset of silent diseases such as type 2 diabetes is often registered through self-report in large prospective cohorts. Self-reported outcomes are cost-effective; however, they are subject to error. Diagnosis of silent events may also occur through the use of imperfect laboratory-based diagnostic tests. In this paper, we describe an approach for variable selection in high dimensional datasets for settings in which the outcome is observed with error. METHODS: We adapt the spike and slab Bayesian Variable Selection approach in the context of error-prone, self-reported outcomes. The performance of the proposed approach is studied through simulation studies. An illustrative application is included using data from the Women's Health Initiative SNP Health Association Resource, which includes extensive genotypic (>900,000 SNPs) and phenotypic data on 9,873 African American and Hispanic American women. RESULTS: Simulation studies show improved sensitivity of our proposed method when compared to a naive approach that ignores error in the self-reported outcomes. Application of the proposed method resulted in discovery of several single nucleotide polymorphisms (SNPs) that are associated with risk of type 2 diabetes in a dataset of 9,873 African American and Hispanic participants in the Women's Health Initiative. There was little overlap among the top ranking SNPs associated with type 2 diabetes risk between the racial groups, adding support to previous observations in the literature of disease associated genetic loci that are often not generalizable across race/ethnicity populations. The adapted Bayesian variable selection algorithm is implemented in R. The source code for the simulations are available in the Supplement. CONCLUSIONS: Variable selection accuracy is reduced when the outcome is ascertained by error-prone self-reports. For this setting, our proposed algorithm has improved variable selection performance when compared to approaches that neglect to account for the error-prone nature of self-reports.

Identification of miRNA-mRNA associations in hepatocellular carcinoma using hierarchical integrative model.

BACKGROUND: The established role miRNA-mRNA regulation of gene expression has in oncogenesis highlights the importance of integrating miRNA with downstream mRNA targets. These findings call for investigations aimed at identifying disease-associated miRNA-mRNA pairs. Hierarchical integrative models (HIM) offer the opportunity to uncover the relationships between disease and the levels of different molecules measured in multiple omic studies. METHODS: The HIM model we formulated for analysis of mRNA-seq and miRNA-seq data can be specified with two levels: (1) a mechanistic submodel relating mRNAs to miRNAs, and (2) a clinical submodel relating disease status to mRNA and miRNA, while accounting for the mechanistic relationships in the first level. RESULTS: mRNA-seq and miRNA-seq data were acquired by analysis of tumor and normal liver tissues from 30 patients with hepatocellular carcinoma (HCC). We analyzed the data using HIM and identified 157 significant miRNA-mRNA pairs in HCC. The majority of these molecules have already been independently identified as being either diagnostic, prognostic, or therapeutic biomarker candidates for HCC. These pairs appear to be involved in processes contributing to the pathogenesis of HCC involving inflammation, regulation of cell cycle, apoptosis, and metabolism. For further evaluation of our method, we analyzed miRNA-seq and mRNA-seq data from TCGA network. While some of the miRNA-mRNA pairs we identified by analyzing both our and TCGA data are previously reported in the literature and overlap in regulation and function, new pairs have been identified that may contribute to the discovery of novel targets. CONCLUSION: The results strongly support the hypothesis that miRNAs are important regulators of mRNAs in HCC. Furthermore, these results emphasize the biological relevance of studying miRNA-mRNA pairs.

Metabolomic Analysis of Liver Tissues for Characterization of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) causes more than half a million annual deaths worldwide. Understanding the mechanisms contributing to HCC development is highly desirable for improved surveillance, diagnosis, and treatment. Liver tissue metabolomics has the potential to reflect the physiological changes behind HCC development. Also, it allows identification of biomarker candidates for future evaluation in biofluids and investigation of racial disparities in HCC. Tumor and nontumor tissues from 40 patients were analyzed by both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) platforms to increase the metabolome coverage. The levels of the metabolites extracted from solid liver tissue of the HCC area and adjacent non-HCC area were compared. Among the analytes detected by GC-MS and LC-MS with significant alterations, 18 were selected based on biological relevance and confirmed metabolite identification. These metabolites belong to TCA cycle, glycolysis, purines, and lipid metabolism and have been previously reported in liver metabolomic studies where high correlation with HCC progression is implied. We demonstrated that metabolites related to HCC pathogenesis can be identified through liver tissue metabolomic analysis. Additionally, this study has enabled us to identify race-specific metabolites associated with HCC.

Adherence to Adjuvant Endocrine Therapy in Insured Black and White Breast Cancer Survivors: Exploring Adherence Measures in Patient Data.

BACKGROUND: Adjuvant endocrine therapy (AET) is a critical therapy in that it improves survival in women with hormone receptor-positive (HR+) breast cancer (BC), but adherence to AET is suboptimal. The purpose of this study was to fill scientific gaps about predictors of adherence to AET among black and white women diagnosed with BC. OBJECTIVE: To assess AET adherence in black and white insured women using multiple measures, including one that uses an innovative statistical approach. METHODS: Black and white women newly diagnosed with HR+ BC were identified from 2 health maintenance organizations. Pharmacy records captured the type of oral AET prescriptions and all fill dates. Multivariable logistic regression was used to identify predictors of adherence defined in terms of proportion of days covered (PDC; ≥ 80%) and medication gap of ≤ 10 days. A zero-inflated negative binomial (ZINB) regression model was used to identify variables associated with the total number of days of medication gaps. RESULTS: 1,925 women met inclusion criteria. 80% were PDC adherent (> 80%); 44% had a medication gap of ≤ 10 days; and 24% had no medication gap days. Race and age were significant in all multivariable models. Black women were less likely to be adherent based on PDC than white women (OR = 0.72, 95% CI = 0.57-0.90, P < 0.01), and they were less likely to have a medication gap of ≤ 10 days (OR = 0.65, 95% CI = 0.54-0.79, P < 0.001). Women aged 25-49 years were less likely to be PDC adherent than women aged 65-93 years (OR = 0.65, 95% CI = 0.48-0.87, P < 0.001). In the ZINB model, women were without their medication for an average of 37 days (SD = 50.5). CONCLUSIONS: Racial disparities in adherence to AET in the study highlight a need for interventions among insured women. Using various measures of adherence may help better understand this multidimensional concept. There might be benefits from using both more common dichotomous measures (e.g., PDC) and integrating novel statistical approaches to allow tailoring adherence to patterns within a specific sample. DISCLOSURES: This research was funded by the National Institutes of Health (R01CA154848). It was also supported in part by the NIH-NCI Cancer Center Support Grant P30 CA016059, the Laboratory of Telomere Health P30 CA51008, and the TSA Award No. UL1TR002649 from the National Center for Advancing Translational Sciences. The contents of this study are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences or the National Institutes of Health. Bosworth reports grants from Sanofi, Otsuka, Johnson & Johnson, and Blue Cross/Blue Shield of NC and consulting fees from Sanofi and Otsuka. The other authors have nothing to disclose. The datasets generated during and/or analyzed during the current study are not publicly available due to privacy reasons but are available from the corresponding author on reasonable request. The author does not own these data. Data use was granted to the author as part of a data use agreement between specific agencies and organizations.

Physical activity and epigenetic biomarkers in maternal blood during pregnancy.

AIM: Investigate associations of leisure time physical activity (LTPA) with DNA methylation and miRNAs during pregnancy. Patients & methods: LTPA, candidate DNA methylation and circulating miRNAs were measured (average 15 weeks gestation) in pregnant women (n = 92). RESULTS: Each additional hour of prepregnancy LTPA duration was associated with hypermethylation in C1orf212 (ß = 0.137, 95% CI: 0.004-0.270) and higher circulating miR-146b-5p (ß = 0.084, 95% CI: 0.017-0.151). Each additional metabolic equivalent hour of early-pregnancy LTPA energy expenditure was associated with higher circulating miR-21-3p (ß = 0.431, 95% CI: 0.089-0.772) in women carrying female offspring, and lower circulating miR-146b-5p (ß = -0.285, 95% CI: -0.528 to -0.043) and miR-517-5p (ß = -0.406, 95% CI: -0.736 to -0.076) in women carrying male offspring. CONCLUSION: Our findings suggest that LTPA may influence maternal epigenetic biomarkers, possibly in an offspring sex-specific manner.

Identification of race-associated metabolite biomarkers for hepatocellular carcinoma in patients with liver cirrhosis and hepatitis C virus infection.

Disparities in hepatocellular carcinoma (HCC) incidence and survival have been observed between ethnic groups including African-Americans (AA) and European-Americans (EA). The evaluation of the changes in the levels of metabolites in samples stratified by race could provide a snapshot of ethnically diverse disease related pathways and identify reliable biomarkers. In this study, we considered AA and EA to investigate metabolites that may be associated with HCC in a race-specific manner. The levels of 46 metabolites in plasma samples, collected from patients recruited at MedStar Georgetown University Hospital, were analyzed by Agilent GC-qMS in selected ion monitoring (SIM) mode. A least absolute shrinkage and selection operator (LASSO) regression model was applied to select metabolites with significant changes in HCC vs. cirrhosis in three groups: (1) AA and EA combined; (2) AA separately; and (3) EA separately. In addition, metabolites that distinguish HCC cases from cirrhosis in these three groups were selected by excluding those without HCV infection. The performances of the metabolites selected by LASSO in each group were evaluated through a leave-one-out cross-validation. We identified race-specific metabolites that differentiated HCC cases from cirrhotic controls, yielding better area under the receiver operating characteristics (ROC) curve (AUC) compared to alpha-fetoprotein (AFP), the serological marker widely used for the diagnosis of HCC. This study sheds light on metabolites that could potentially be used as biomarkers for HCC by monitoring their levels in high-risk population of cirrhotic patients in a race-specific manner.

Initiation of Adjuvant Endocrine Therapy in Black and White Women With Breast Cancer.

BACKGROUND: Adjuvant endocrine therapy reduces risk of recurrence and mortality in women with hormone receptor-positive breast cancer, yet many women never initiate it. We examined the influence of race, sociocultural factors, and process-of-care factors on initiation of adjuvant endocrine therapy in a racially diverse sample. PATIENTS AND METHODS: Eligible women were originally recruited for the Narrowing the Gaps in Adjuvant Therapy Study (2006-2011). Sociocultural and process-of-care factors were collected via telephone surveys before adjuvant therapy. Clinical factors were abstracted from charts. Penalized LASSO (least absolute shrinkage and selection operator) logistic regression model was used to identify variables associated with initiation. RESULTS: Of the 270 women, 55.6% were black and the rest were white. Most women (74.8%) initiated therapy. A significant interaction (P = .008) was found between race and age. Black women aged ≤ 50 years had the lowest initiation (59.7%) compared to black women > 50 years (87.1%), white women ≤ 50 years (73.7%), or white women > 50 years (72.0%). Multivariate analysis found that younger black women exhibited a marginally higher risk of noninitiation compared to older black women. Additionally, ratings of financial access, presence of comorbidities, and levels of communication were all associated with endocrine therapy initiation. CONCLUSION: Black women ≤ 50 years of age and women with financial constraints may be important subgroups for interventions. Patient-provider communication appears to be an important leverage point to foster therapy uptake.

A Modified Random Survival Forests Algorithm for High Dimensional Predictors and Self-Reported Outcomes.

We present an ensemble tree-based algorithm for variable selection in high dimensional datasets, in settings where a time-to-event outcome is observed with error. The proposed methods are motivated by self-reported outcomes collected in large-scale epidemiologic studies, such as the Women's Health Initiative. The proposed methods equally apply to imperfect outcomes that arise in other settings such as data extracted from electronic medical records. To evaluate the performance of our proposed algorithm, we present results from simulation studies, considering both continuous and categorical covariates. We illustrate this approach to discover single nucleotide polymorphisms that are associated with incident Type II diabetes in the Women's Health Initiative. A freely available R package icRSF (R Core Team, 2018; Xu et al., 2018) has been developed to implement the proposed methods.

Circulating early- and mid-pregnancy microRNAs and risk of gestational diabetes.

AIMS: Epigenetic regulators, including microRNAs (miRNAs), are implicated in type 2 diabetes, but evidence linking circulating miRNAs in pregnancy and risk of gestational diabetes (GDM) is sparse. Potential modifiers, including pre-pregnancy overweight/obesity and offspring sex, are unexamined. We hypothesized that circulating levels of early-mid-pregnancy (range 7-23weeks of gestation) candidate miRNAs are related to subsequent development of GDM. We also hypothesized that miRNA-GDM associations might vary by pre-pregnancy body-mass index (ppBMI) or offspring sex. METHODS: In a case-control analysis (36GDM cases/80 controls) from the Omega study, a prospective cohort study of pregnancy complications, we measured early-mid-pregnancy plasma levels of 10miRNAs chosen for potential roles in pregnancy course and complications (miR-126-3p, -155-5p, -21-3p, -146b-5p, -210-3p, -222-3p, -223-3p, -517-5p, -518a-3p, and 29a-3p) using qRT-PCR. Logistic regression models adjusted for gestational age at blood draw (GA) were fit to compare circulating miRNAs between cases and controls. We repeated analyses among overweight/obese (ppBMI≥25kg/m2) or lean (ppBMI<25kg/m2) women, and women with male or female offspring separately. RESULTS: Mean age was 34.3years (cases) and 32.9years (controls). GA-adjusted miR-155-5p (ß=0.260/p=0.028) and -21-3p (ß=0.316/p=0.005) levels were positively associated with GDM. MiR-146b-5p (ß=0.266/p=0.068) and miR-517-5p (ß=0.196/p=0.074) were borderline. Associations of miR-21-3p and miR-210-3p with GDM were observed among overweight/obese but not lean women. Associations of six miRNAs (miR-155-5p, -21-3p, -146b-5p, -223-3p, -517-5p, and -29a-3p) with GDM were present only among women carrying male fetuses (all p<0.05). CONCLUSIONS: Circulating early-mid-pregnancy miRNAs are associated with GDM, particularly among women who are overweight/obese pre-pregnancy or pregnant with male offspring. This area has potential to clarify mechanisms underlying GDM pathogenesis and identify at-risk mothers earlier in pregnancy.

Multivariate Bayesian variable selection exploiting dependence structure among outcomes: Application to air pollution effects on DNA methylation.

The analysis of multiple outcomes is becoming increasingly common in modern biomedical studies. It is well-known that joint statistical models for multiple outcomes are more flexible and more powerful than fitting a separate model for each outcome; they yield more powerful tests of exposure or treatment effects by taking into account the dependence among outcomes and pooling evidence across outcomes. It is, however, unlikely that all outcomes are related to the same subset of covariates. Therefore, there is interest in identifying exposures or treatments associated with particular outcomes, which we term outcome-specific variable selection. In this work, we propose a variable selection approach for multivariate normal responses that incorporates not only information on the mean model, but also information on the variance-covariance structure of the outcomes. The approach effectively leverages evidence from all correlated outcomes to estimate the effect of a particular covariate on a given outcome. To implement this strategy, we develop a Bayesian method that builds a multivariate prior for the variable selection indicators based on the variance-covariance of the outcomes. We show via simulation that the proposed variable selection strategy can boost power to detect subtle effects without increasing the probability of false discoveries. We apply the approach to the Normative Aging Study (NAS) epigenetic data and identify a subset of five genes in the asthma pathway for which gene-specific DNA methylations are associated with exposures to either black carbon, a marker of traffic pollution, or sulfate, a marker of particles generated by power plants.

Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery.

Background: Metabolomics plays an important role in providing insight into the etiology and mechanisms of hepatocellular carcinoma (HCC). This is accomplished by a comprehensive analysis of patterns involved in metabolic alterations in human specimens. This study compares the levels of plasma metabolites in HCC cases versus cirrhotic patients and evaluates the ability of candidate metabolites in distinguishing the two groups. Also, it investigates the combined use of metabolites and clinical covariates for detection of HCC in patients with liver cirrhosis.Methods: Untargeted analysis of metabolites in plasma from 128 subjects (63 HCC cases and 65 cirrhotic controls) was conducted using gas chromatography coupled to mass spectrometry (GC-MS). This was followed by targeted evaluation of selected metabolites. LASSO regression was used to select a set of metabolites and clinical covariates that are associated with HCC. The performance of candidate biomarkers in distinguishing HCC from cirrhosis was evaluated through a leave-one-out cross-validation based on area under the receiver operating characteristics (ROC) curve.Results: We identified 11 metabolites and three clinical covariates that differentiated HCC cases from cirrhotic controls. Combining these features in a panel for disease classification using support vector machines (SVM) yielded better area under the ROC curve compared with alpha-fetoprotein (AFP).Conclusions: This study demonstrates the combination of metabolites and clinical covariates as an effective approach for early detection of HCC in patients with liver cirrhosis.Impact: Further investigation of these findings may improve understanding of HCC pathophysiology and possible implication of the metabolites in HCC prevention and diagnosis. Cancer Epidemiol Biomarkers Prev; 26(5); 675-83. ©2016 AACR.

Genetic variations related to maternal whole blood mitochondrial DNA copy number: a genome-wide and candidate gene study.

We conducted genome-wide (GWAS) and candidate gene association studies of maternal mitochondrial DNA copy number. Maternal peripheral blood was collected during labor and delivery admission from 471 participants of a placental abruption case-control study conducted in Lima, Peru. Single nucleotide polymorphism (SNP) genotyping was performed using the Illumina Cardio-Metabo Chip. Whole blood mitochondrial DNA (mtDNA) copy number was measured using qRT-PCR techniques. We evaluated 119,629 SNPs in the GWAS and 161 SNPs (in 29 mitochondrial biogenesis and oxidative phosphorylation genes) in the candidate association study. Top hits from GWAS and the candidate gene study were selected to compute weighted genetic risk scores (wGRS). Linear regression models were used to calculate effect size estimates and related nominal p values. The top hit in our GWAS was chr19:51063065 in FOXA3 (empirical p values = 2.20e - 6). A total of 134 SNPs had p values < 0.001 including rs17111633 in CNNM1 (p values = 6.32e - 6) and chr19:51083059 in MYPOP (p values = 3.23e - 5). In the candidate association study, several SNPs in PPARG, PRKCA, SP1 and THRB were associated with mtDNA copy number (p values < 0.05). mtDNA copy number was significantly associated with wGRS based on top GWAS hits (ß = 0.49, 95% CI:0.38-0.60, p < 0.001). Variations in nuclear DNA are potentially associated with maternal mtDNA copy number.

Maternal pre-pregnancy body mass index and circulating microRNAs in pregnancy.

BACKGROUND: Maternal pre-pregnancy overweight and obese status has been associated with a number of pregnancy complications and adverse offspring outcomes. Mechanisms for observed associations, however, are largely unknown. We investigated associations of pre-pregnancy body mass index with early-mid pregnancy epigenetic biomarkers, circulating microRNAs. METHODS: Peripheral blood was collected from participants (16-27 weeks gestation) of two multi-racial pregnancy cohorts, the Omega Study and the Pregnancy Outcomes and Community Health Study. Plasma miRNA expression was characterised using epigenome-wide (319 miRNAs) profiling among 20 pregnant women in each cohort. Cohort-specific linear regression models that included the predictor (pre-pregnancy body mass index), the outcome (microRNA expression), and adjustment factors (maternal age, gestational age at blood collection, and race) were fit. RESULTS: Expression of 27 miRNAs was positively associated with pre-pregnancy body mass index in both cohorts (p-values <0.05). A number of these differentially expressed miRNAs have previously been associated with adipogenesis (e.g. let-7d*, miR-103-2*, -130b, -146b-5-p, -29c, and -26b). Identified miRNAs as well as their experimentally validated targets participate in pathways that involve organismal injury, reproductive system disease, connective tissue disorders, cancer, cellular development, growth and proliferation. CONCLUSION: Pre-pregnancy body mass index is associated with circulating miRNAs in early-mid pregnancy.

Placental genetic variations in circadian clock-related genes increase the risk of placental abruption.

The genetic architecture of placental abruption (PA) remains poorly understood. We examined variations in SNPs of circadian clock-related genes in placenta with PA risk. We also explored placental and maternal genomic contributions to PA risk. Placental genomic DNA samples were isolated from 280 PA cases and 244 controls. Genotyping was performed using the Illumina Cardio-MetaboChip. We examined 116 SNPs in 13 genes known to moderate circadian rhythms. Logistic regression models were fit to estimate odds ratios (ORs). The combined effect of multiple SNPs on PA risk was estimated using a weighted genetic risk score. We examined independent and joint associations of wGRS derived from placental and maternal genomes with PA. Seven SNPs in five genes (ARNTL2, CRY2, DEC1, PER3 and RORA), in the placental genome, were associated with PA risk. Each copy of the minor allele (G) of a SNP in the RORA gene (rs2899663) was associated with a 30% reduced odds of PA (95% CI 0.52-0.95). The odds of PA increased with increasing placental-wGRS (Ptrend<0.001). The ORs were 1.00, 2.16, 3.24 and 4.48 across quartiles. Associations persisted after the maternal-wGRS was included in the model. There was evidence of an additive contribution of placental and maternal genetic contributions to PA risk. Participants with placental- and maternal-wGRS in the highest quartile, compared with those in the lowest quartile, had a 15.57-fold (95% CI 3.34-72.60) increased odds of PA. Placental variants in circadian clock-related genes are associated with PA risk; and the association persists after control of genetic variants in the maternal genome.

Candidate Gene and MicroRNA Expression in Fetal Membranes and Preterm Delivery Risk.

We investigated candidate gene and microRNA (miRNA) expression in amnion and chorion in relation to risk of preterm delivery (PTD). Amnion and chorion were separated from placenta and collected at delivery from participants who delivered at term (N = 10) and from participants who delivered preterm following spontaneous labor (sPTL-PTD; N = 10), premature rupture of membranes (PPROM-PTD; N = 10), and preeclampsia (PE-PTD; N = 10). Expression of genes (metalloproteinase [MMP] 2, MMP-9, and tissue inhibitors of MMP-1) and miRNAs (miR-199a*, -202*, -210, -214, -223, and -338) was profiled using quantitative real-time polymerase chain reaction approaches. Adjusted multinomial logistic regression models were used to calculate relative risk ratios (RRR), 95% confidence intervals, and P values. Among controls, the expression of miR-199a*, -202*, and -214 was lower in the amnion compared with their expression in the chorion, whereas the expression of miR-210 was higher in the amnion compared with its expression in the chorion (all P values < .05). In the amnion, MMP-9 expression was associated with PTD risk (overall P value = .0092), and MMP-9 expression was positively associated with the risk of PPROM-PTD (RRR: 31.10) and inversely associated with the risk of PE-PTD (RRR:6.55e-6), although individual associations were not statistically significant. In addition, in the amnion, the expression of miR-210 (RRR: 0.45; overall P value = .0039) was inversely associated with the risk of PE-PTD, and miR-223 was inversely associated with all subtypes of PTD (overall P value = .0400). The amnion and chorion differ in their miRNA expression. The expression of MMP-9, miR-210, and -223 in the amnion is associated with PTD risk.