Search

Aim: Whole-genome methylation sequencing carries both DNA methylation and structural variant information (single nucleotide variant [SNV]; copy number variant [CNV]); however, limited data is available on the reliability of obtaining this information simultaneously from low-input DNA using various library preparation and sequencing protocols. Methods: A HapMap NA12878 sample was sequenced with three protocols (EM-sequencing, QIA-sequencing and Swift-sequencing) and their performance was compared on CpG methylation measurement and SNV and CNV detection. Results: At low DNA input (10–25 ng), EM-sequencing was superior in almost all metrics except CNV detection where all protocols were similar. EM-sequencing captured the highest number of CpGs and true SNVs. Conclusion: EM-sequencing is suitable to detect methylation, SNVs and CNVs from single sequencing with low-input DNA.

Nonbacterial thrombotic endocarditis is characterized by the presence of organized thrombi on cardiac valves, often associated with hypercoagulable states. There is a paucity of data regarding the predictors of mortality in patients with nonbacterial thrombotic endocarditis. Our primary aim was to identify predictors of in-hospital mortality in patients with nonbacterial thrombotic endocarditis.A systematic literature review of all published cases and case series was performed until May 2018 according to Preferred Reporting Items for Systematic Review and Meta-analyses statement guidelines. We applied random forest machine learning model to identify predictors of in-patient mortality in patients with nonbacterial thrombotic endocarditis.Our search generated a total of 163 patients (mean age, 46 ± 17 years; women, 69%) with newly diagnosed nonbacterial thrombotic endocarditis. The in-hospital mortality rate in the study cohort was 30%. Among the patients who died in the hospital, initial presentation of pulmonary embolism (12.2 vs. 2.6%), splenic (38.7 vs. 10.5%), and renal (40.8 vs. 9.6%) infarcts were higher compared to patients alive at the time of discharge. Higher rates of malignancy (71.4 vs. 39.4%, P = .0003) and lower rates of antiphospholipid syndrome (8.1 vs. 48.2%, P = .0001) were noted in deceased patients. Random forest machine learning analysis showed that older age, presence of antiphospholipid syndrome, splenic infarct, renal infarct, peripheral thromboembolism, pulmonary embolism, myocardial infarction, and mitral valve regurgitation were significantly associated with increased risk of in-hospital mortality.Patients admitted with nonbacterial thrombotic endocarditis have a high rate of in-hospital mortality. Factors including older age, presence of antiphospholipid syndrome, splenic/renal infarct, lower limb thromboembolism, pulmonary embolism, myocardial infarction, and mitral valve regurgitation were significantly associated with increased risk of in-hospital mortality in patients with nonbacterial thrombotic endocarditis.

National Cardiovascular Data Registry (NCDR)-based logistic regression model is available for clinicians to predict in-hospital all-cause mortality after a percutaneous coronary intervention (PCI). However, this model has never been used to predict long-term all-cause mortality after PCI. Therefore, we sought to test the ability of the NCDR model to predict the short- and long-term risk of all-cause mortality in patients undergoing PCI. All patients undergoing PCI in the Mayo Clinic Health System were enrolled in the Mayo Clinic CathPCI registry. Patient-level demographic, clinical, and angiographic data from January 2006 to December 2017 were extracted from the registry. Patients who underwent coronary artery bypass graft surgery (CABG) were excluded. The area under the receiver operator characteristic curve (AUC) was calculated to assess the ability of the NCDR model to predict outcomes of interest (6-month, 1-year, 2-year, and 5-year all-cause mortality) after PCI. A total of 17,356 unique patients were included for the final analysis after excluding 165 patients who underwent CABG surgery. The mean age was 66.9 ± 12.5 years, and 71% were men. The 6-month, 1-year, 2-year, and 5-year all-cause mortality rates were 4.2% (n = 737), 5.8% (n = 1,005), 8.06% (n = 1,399), and 14.2% (n = 2,472), respectively. The AUCs of the NCDR model to predict 6-month, 1-year, 2-year, and 5-year all-cause mortality were 0.84 (95% CI: 0.82–0.86), 0.82 (95% CI: 0.80–0.84), 0.80 (95% CI: 0.79–0.81), and 0.78 (95% CI: 0.77–0.79), respectively. The NCDR model was able to accurately predict both short- and long-term all-cause mortality after PCI.

Cardiac power to left ventricular mass (LVM) ratio, also termed cardiac efficiency (CE), reflects the rate of cardiac work delivered to the potential energy stored in LVM. We sought to assess the association between baseline resting CE and survival post transcatheter aortic valve replacement (TAVR).We retrospectively extracted data of patients who received TAVR in the Mayo Clinic Foundation with follow up data available at 1 year. Cardiac output was measured using Doppler echocardiography at baseline. CE was calculated using the formula, (cardiac output × mean arterial blood pressure)/(451 × LVM × 100) W/100 g. Survival score analysis was performed to identify cut off value for CE to identify the maximum difference in mortality in the study cohort. Patients were subsequently divided into 2 groups CE 0.38 W/100 g and CE ≥ 0.38 W/100 g. Survival was determined using Kaplan-Meier method.We included 954 patients in the final analysis. CE in group1 vs group 2 was 0.31 ± 0.05 W/100 g vs 0.59 ± 0.18 W/100 g. Patients in group1 were more likely to be male, had a higher prevalence of atrial fibrillation, prior myocardial infarction, mitral and tricuspid regurgitation. They also had a higher STS risk score, NYHA functional class, and lower aortic valve area. The remainder of the baseline characteristics was similar in both groups. A lower CE was associated with higher 1-year mortality following TAVR based on multivariate analysis. (Group1: 22.18% vs Group 2: 9.89%, p .0001).In our cohort, a low baseline CE (0.38 W/100 g) conferred higher mortality risk following TAVR.

High grade glioma (HGG) represents a group of devastating diseases with dismal prognosis. Surgical resection of the contrast enhancing (CE) region of HGG remains the mainstay of treatment, but recurrence inevitably arises from the unresected non-contrast enhancing (NE) region, surgically inaccessible due to cancer cell invasion into healthy brain tissue. Due to its critical role in recurrence, understanding of the NE region is central to the improvement of clinical outcomes. We reveal the biological characteristics of this region through image localized multi-regional sampling. We linked microenvironmental characteristics measured by multi-parametric MRI to genomic mutations and transcriptional phenotypes using mixed effect modeling which allowed us to control for individualized patient effects. We first confirmed that T2 is a significant indicator of IDH mutation status in the NE region, being the first description of such a relationship in a HGG cohort. We found the combination of EGFR amplification and CDKN2A homozygous loss was associated with a significantly lower mean diffusivity (MD) compared to double wild type tumors in the NE region, indicating the presence of greater cellular packing and proliferation in EGFR amplification/CDKN2A loss regions. Finally, using single cell pathway based tumor classifications, we showed that nK2, a DSC-MRI metric representing cell size heterogeneity, correlated positively with neuronal signature and negatively with glycolytic/plurimetabolic signature within the NE tumor, indicating that glycolytic/plurimetobolic tumors possessed a high amount of cell size heterogeneity compared to neuronal samples. This hypothesis was supported using digital reference object (DRO) modeling which confirmed that cell size and heterogeneity drove the differential nK2 signal between neuronal and glycolytic/plurimetabolic samples. We identified immune cell infiltrate as one possible mechanism of increased cell size heterogeneity using transcriptomic signature analysis which found more immune cell signatures within glycolytic/plurimetobolic tumors compared to neuronal. Collectively this study demonstrates the central role of multi-parametric MRI as a non-invasive measure of tumor biology and a tool for understanding the clinically critical NE region which can then inform new therapies targeting this region of HGG recurrence. Citation Format: Matthew Flick, Taylor Weiskittel, Kevin Meng-Lin, Fulvio D'Angelo, Francesca Caruso, Shannon Ensign, Mylan Blomquist, Luija Wang, Christopher Sereduk, Gustavo De Leon, Ashley Nespodzany, Javier Urcuyo, Ashlynn Gonzalez, Lee Curtin, Kyle Singleton, Aliya Anil, Natenael Simmineh, Erika Lewis, Teresa Noviello, Reyna Patel, Panwen Wang, Junwen Wang, Jennifer Eschbacher, Andrea Hawkins-Daarud, Pamela Jackson, Kris Smith, Peter Nakaji, Bernard Bendok, Richard Zimmerman, Chandan Krishna, Devi Patra, Naresh Patel, Mark Lyons, Matthew Neal, Kliment Donev, Maciej Mrugala, Alyx Porter, Scott Beeman, Yuxiang Zhou, Leslie Baxter, Christopher Plaisier, Jing Li, Hu Li, Anna Lasorella, Chad Quarles, Kristin Swanson, Michele Ceccarelli, Antonio Iavarone, Nhan Tran, Leland Hu. Multi-parametric MRI maps regional heterogeneity of high grade glioma phenotypes. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5621.

Alzheimer’s disease (AD) affects more than 1 in 9 people age 65 and older and becomes an urgent public health concern as the global population ages. In clinical practice, structural magnetic resonance imaging (sMRI) is the most accessible and widely used diagnostic imaging modality. Additionally, genome-wide association studies (GWAS) and transcriptomics – the study of gene expression – also play an important role in understanding AD etiology and progression. Sophisticated imaging genetics systems have been developed to discover genetic factors that consistently affect brain function and structure. However, most studies to date focused on the relationships between brain sMRI and GWAS or brain sMRI and transcriptomics. To our knowledge, few methods have been developed to discover and infer multimodal relationships among sMRI, GWAS, and transcriptomics. To address this, we propose a novel federated model, Genotype-Expression-Imaging Data Integration (GEIDI), to identify genetic and transcriptomic influences on brain sMRI measures. The relationships between brain imaging measures and gene expression are allowed to depend on a person’s genotype at the single-nucleotide polymorphism (SNP) level, making the inferences adaptive and personalized. We performed extensive experiments on publicly available Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Experimental results demonstrated our proposed method outperformed state-of-the-art expression quantitative trait loci (eQTL) methods for detecting genetic and transcriptomic factors related to AD and has stable performance when data are integrated from multiple sites. Our GEIDI approach may offer novel insights into the relationship among image biomarkers, genotypes, and gene expression and help discover novel genetic targets for potential AD drug treatments.

Background: Identifying patients at a high risk of mortality post percutaneous coronary intervention (PCI) is of vital clinical importance. We investigated the utility of machine learning algorithms to predict short and intermediate-term risk of all-cause mortality in patients undergoing PCI. Methods: Patient-level demographics, clinical, electrocardiographic ,echocardiographic and angiographic data from January 2006 to December 2017 were extracted from the Mayo Clinic CathPCI registry and clinical records. For patients with multiple PCI events, data collected at the time of the index PCI was used for analysis. Patients who underwent bailout coronary artery bypass graft surgery (CABG) prior to discharge were excluded. 306 variables were incorporated into random forest machine learning model (RF) to predict all-cause mortality at 6 months and 1 year after PCI. Ten-fold cross-validation repeated five times was used to optimize the hyperparameters and estimate its external performance. The National Cardiovascular Data Registry (NCDR) based logistic regression model was used for comparison. The area under receiver operator characteristic curves (AUC) was calculated to assess the ability of the models to predict all-cause mortality. Results: A total of 17356 unique patients were included for the final analysis after excluding 165 patients who underwent CABG surgery during the index hospitalization. The mean age was 66.9 ± 12.5 years;71% were male. Indications for PCI were ST-elevation myocardial infarction (9.4%), non-ST elevation myocardial infarction (12.9%), unstable angina (17.7%), and stable angina (52.8%) in the cohort. In-hospital, 6-month & 1 year mortality rates were 1.9%,4.2% & 5.8% respectively. The RF model was superior to the NCDR model in predicting inhospital, 6-month, 1 year mortality (pFigure 1 ). Conclusion: Machine learning is superior to NCDR model in predicting short and intermediate risk of all-cause mortality post PCI.

Background Long chain omega-3 polyunsaturated fatty acids (ω-3PUFA) supplementation in animal models of diet-induced obesity has consistently shown to improve insulin sensitivity. The same is not always reported in human studies with insulin resistant (IR) subjects with obesity. Objective We studied whether high-dose ω-3PUFA supplementation for 3 months improves insulin sensitivity and adipose tissue (AT) inflammation in IR subjects with obesity. Methods Thirteen subjects (BMI = 39.3 ± 1.6 kg/m2) underwent 80 mU/m2·min euglycemic-hyperinsulinemic clamp with subcutaneous (Sc) AT biopsy before and after 3 months of ω-3PUFA (DHA and EPA, 4 g/daily) supplementation. Cytoadipokine plasma profiles were assessed before and after ω-3PUFA. AT-specific inflammatory gene expression was evaluated on Sc fat biopsies. Microarray analysis was performed on the fat biopsies collected during the program. Results Palmitic and stearic acid plasma levels were significantly reduced (P P P Conclusion High dose of long chain ω-3PUFA supplementation modulates significant changes in plasma fatty acid profile, AT, and systemic inflammation. These findings are associated with significant improvement of insulin-stimulated glucose disposal. Unbiased microarray analysis of Sc fat biopsy identified APOE as among the most differentially regulated gene after ω-3PUFA supplementation. We speculate that ω-3PUFA increases macrophage-derived APOE mRNA levels with anti-inflammatory properties.

The advances of large-scale genomics studies have enabled compilation of cell type–specific, genome-wide DNA functional elements at high resolution. With the growing volume of functional annotation data and sequencing variants, existing variant annotation algorithms lack the efficiency and scalability to process big genomic data, particularly when annotating whole-genome sequencing variants against a huge database with billions of genomic features. Here, we develop VarNote to rapidly annotate genome-scale variants in large and complex functional annotation resources. Equipped with a novel index system and a parallel random-sweep searching algorithm, VarNote shows substantial performance improvements (two to three orders of magnitude) over existing algorithms at different scales. It supports both region-based and allele-specific annotations and introduces advanced functions for the flexible extraction of annotations. By integrating massive base-wise and context-dependent annotations in the VarNote framework, we introduce three efficient and accurate pipelines to prioritize the causal regulatory variants for common diseases, Mendelian disorders, and cancers.

INTRODUCTION The right ventricle (RV) strain measured by speckle tracking (RVS) is an echocardiographic parameter used to assess RV function. We compared RVS to RV fractional area change (FAC%), tricuspid annular plane systolic excursion (TAPSE) and Doppler tissue imaging-derived peak systolic velocity (S') in the assessment of right ventricular (RV) systolic function measured using cardiac magnetic resonance imaging (MRI). METHODS We enrolled consecutive patients who underwent cardiac MRI between Jan 2012 and Dec 2017 and a transthoracic echocardiogram (TTE) within 1 month of the MRI with no interval event. Baseline clinical characteristics and MRI parameters were extracted from chart review. Echocardiographic parameters were measured prospectively. TTE parameters including RVS, TAPSE, S', and FAC% were tested for accuracy to identify impaired RV EF (EF

BACKGROUNDRadiogenomics uses machine-learning (ML) to directly connect the morphologic and physiological appearance of tumors on clinical imaging with underlying genomic features. Despite extensive growth in the area of radiogenomics across many cancers, and its potential role in advancing clinical decision making, no published studies have directly addressed uncertainty in these model predictions.METHODSWe developed a radiogenomics ML model to quantify uncertainty using transductive Gaussian Processes (GP) and a unique dataset of 95 image-localized biopsies with spatially matched MRI from 25 untreated Glioblastoma (GBM) patients. The model generated predictions for regional EGFR amplification status (a common and important target in GBM) to resolve the intratumoral genetic heterogeneity across each individual tumor - a key factor for future personalized therapeutic paradigms. The model used probability distributions for each sample prediction to quantify uncertainty, and used transductive learning to reduce the overall uncertainty. We compared predictive accuracy and uncertainty of the transductive learning GP model against a standard GP model using leave-one-patient-out cross validation.RESULTSPredictive uncertainty informed the likelihood of achieving an accurate sample prediction. When stratifying predictions based on uncertainty, we observed substantially higher performance in the group cohort (75% accuracy, n=95) and amongst sample predictions with the lowest uncertainty (83% accuracy, n=72) compared to predictions with higher uncertainty (48% accuracy, n=23), due largely to data interpolation (rather than extrapolation).CONCLUSIONWe present a novel approach to quantify radiogenomics uncertainty to enhance model performance and clinical interpretability. This should help integrate more reliable radiogenomics models for improved medical decision-making.

Seventy-six FDA-approved oncology drugs and emerging therapeutics were evaluated in 25 multiple myeloma (MM) and 15 non-Hodgkin’s lymphoma cell lines and in 113 primary MM samples. Ex vivo drug sensitivities were mined for associations with clinical phenotype, cytogenetic, genetic mutation, and transcriptional profiles. In primary MM samples, proteasome inhibitors, dinaciclib, selinexor, venetoclax, auranofin, and histone deacetylating agents had the broadest cytotoxicity. Of interest, newly diagnosed patient samples were globally less sensitive especially to bromodomain inhibitors, inhibitors of receptor tyrosine kinases or non-receptor kinases, and DNA synthesis inhibitors. Clustering demonstrated six broad groupings of drug sensitivity linked with genomic biomarkers and clinical outcomes. For example, our findings mimic clinical observations of increased venetoclax responsiveness in t(11;14) patients but also identify an increased sensitivity profile in untreated patients, standard genetic risk, low plasma cell S-Phase, and in the absence of Gain(1q) and t(4;14). In contrast, increased ex vivo responsiveness to selinexor was associated with biomarkers of poor prognosis and later relapse patients. This “direct to drug” screening resource, paired with functional genomics, has the potential to successfully direct appropriate individualized therapeutic approaches in MM and to enrich clinical trials for likely responders.

Background Chromatin alterations are important mediators of gene expression changes. We have recently shown that activated non-canonical NF-κB signaling (RelB/p52) recruits histone acetyltransferase CBP and deacetylase HDAC1 to selectively acetylate H3K9 (H3K9ac) to induce expression of corticotropin-releasing hormone (CRH) and prostaglandin-endoperoxide synthase-2 (PTGS2) in the human placenta. Both of these genes play a role in initiating parturition in human pregnancy. Methods We performed chromatin immunoprecipitation followed by gene sequencing (ChIP-seq) in primary term human cytotrophoblast (CTB) with use of antibodies to RelB, CBP, HDAC1 and H3K9ac. We further associated these chromatin alterations with gene expression changes from mid-trimester to term in CTB by RNA sequencing (RNA-seq). Results We detected a genome-wide differential gene enrichment between mid-trimester and term human placenta. Pathway analysis identified that cytokine-cytokine receptor interaction, NF-κB, and TNF are the leading pathways enriched in term placenta and associated with these chromatin alterations. Discussions Our analysis has provided the first-time characterization of the key players of human placental origin with molecular changes resulting from chromatin modifications, which could drive human labor.

Aim To determine the frequency and risk factors for colorectal cancer (CRC) development among individuals with resected advanced adenoma (AA)/traditional serrated adenoma (TSA)/advanced sessile serrated adenoma (ASSA). Methods Data was collected from medical records of 14663 subjects found to have AA, TSA, or ASSA at screening or surveillance colonoscopy. Patients with inflammatory bowel disease or known genetic predisposition for CRC were excluded from the study. Factors associated with CRC developing after endoscopic management of high risk polyps were calculated in 4610 such patients who had at least one surveillance colonoscopy within 10 years following the original polypectomy of the incident advanced polyp. Results 84/4610 (1.8%) patients developed CRC at the polypectomy site within a median of 4.2 years (mean 4.89 years), and 1.2% (54/4610) developed CRC in a region distinct from the AA/TSA/ASSA resection site within a median of 5.1 years (mean 6.67 years). Approximately, 30% (25/84) of patients who developed CRC at the AA/TSA/ASSA site and 27.8% (15/54) of patients who developed CRC at another site had colonoscopy at recommended surveillance intervals. Increasing age; polyp size; male sex; right-sided location; high degree of dysplasia; higher number of polyps resected; and piecemeal removal were associated with an increased risk for CRC development at the same site as the index polyp. Increasing age; right-sided location; higher number of polyps resected and sessile endoscopic appearance of the index AA/TSA/ASSA were significantly associated with an increased risk for CRC development at a different site. Conclusion Recognition that CRC may develop following AA/TSA/ASSA removal is one step toward improving our practice efficiency and preventing a portion of CRC related morbidity and mortality.

Cardiac power index (CPI) is an integrative hemodynamic measure of cardiac pumping capability and is the product of the simultaneously measured mean arterial pressure and the cardiac output. We assessed the association between baseline resting CPI and survival post transcatheter aortic valve replacement (TAVR).We retrospectively abstracted data of patients who underwent TAVR at the Mayo Clinic Foundation with follow-up data available at 1 year. Baseline demographic, clinical, and echocardiographic data were abstracted. CPI was calculated using the formula, (cardiac output x mean arterial blood pressure) / (451 x body surface area) W/m². Patients were divided into CPI0.48 W/m² (group 1) and CPI ≥0.48 W/m² (group 2). Survival according to CPI was determined using Kaplan-Meier method. Multivariate Cox regression analysis was performed to adjust for covariates. Nine hundred and seventy-five patients were included in the final analysis. CPI in group 1 vs group 2 was 0.41 ± 0.05 W/m² vs 0.66 ± 0.14 W/m², respectively (P.001, two-sided t-test). Patients in group 1 were more likely to be male and to have a prior history of myocardial infarction, coronary revascularization, peripheral arterial disease, diabetes mellitus, transient ischemic attack, carotid artery disease, atrial fibrillation, lower left ventricular ejection fraction, and moderate to severe mitral and tricuspid regurgitation. After adjusting for baseline covariates, a lower CPI was associated with higher 1-year mortality among patients undergoing TAVR (24.39% in group 1 vs 8.28% in group 2; P.001).Low baseline CPI (0.48 W/m²) confers higher mortality risk among patients undergoing TAVR and provides additional prognostic information, which can help risk-stratify patients.

Purpose Cardiac power (CP) index is a product of mean arterial pressure (MAP) and cardiac output (CO). In aortic stenosis, however, MAP is not reflective of true left ventricular (LV) afterload. We evaluated the utility of a gradient-adjusted CP (GCP) index in predicting survival after transcatheter aortic valve replacement (TAVR), compared to CP alone. Materials and methods We included 975 patients who underwent TAVR with 1 year of follow-up. CP was calculated as (CO×MAP)/[451×body surface area (BSA)] (W/m²). GCP was calculated using augmented MAP by adding aortic valve mean gradient (AVMG) to systolic blood pressure (CP1), adding aortic valve maximal instantaneous gradient to systolic blood pressure (CP2), and adding AVMG to MAP (CP3). A multivariate Cox regression analysis was performed adjusting for baseline covariates. Receiver operator curves (ROC) for CP and GCP were calculated to predict survival after TAVR. Results The mortality rate at 1 year was 16%. The mean age and AVMG of the survivors were 81±9 years and 43±4 mm Hg versus 80±9 years and 42±13 mm Hg in the deceased group. The proportions of female patients were similar in both groups (p=0.7). Both CP and GCP were independently associated with survival at 1 year. The area under ROCs for CP, CP1, CP2, and CP3 were 0.67 [95% confidence interval (CI), 0.62-0.72], 0.65 (95% CI, 0.60-0.70), 0.66 (95% CI, 0.61-0.71), and 0.63 (95% CI 0.58-0.68), respectively. Conclusion GCP did not improve the accuracy of predicting survival post TAVR at 1 year, compared to CP alone.

Introduction Systolic right ventricular (RV) function has prognostic significance both in acquired and congenital heart diseases. However accurate assessment of RV ejection fraction (EF) with transthoracic echocardiography (TTE) remains challenging due to the complex geometric shape of the RV. Magnetic resonance imaging (MRI) remains the gold standard modality for assessment of RV function; however the cost and availability limits the regular use of MRI in clinical practice. The RV strain measured by speckle tracking (RVS) is a novel method of assessing RV EF. Purpose The purpose of the study was to assess the accuracy of RVS compared RV fractional area change (FAC%), tricuspid annular peak systolic excursion (TAPSE) and Doppler tissue imaging-derived isovolumic acceleration and peak systolic velocity (S') at the lateral tricuspid annulus in the assessment of impaired RV EF. Methods We retrospectively identified patients who had a cardiac MRI between 2012- 2017 and only included patients with a TTE within 1 month of the MRI for our final analysis. Baseline characteristics, TTE and MRI parameters were extracted from chart review. RV strain was measured retrospectively using ECHOINSIGHT software. Four TTE parameters including RVS, TAPSE, S' and FAC% were tested for accuracy to identify impaired RV EF (defined as EF Results The study cohort included 500 patients with mean age 55 yr ± 18 and right ventricular systolic pressure 33.7±13.6 mmHg. The proportion of female gender, diabetes, hypertension and obstructive sleep apnea were 38%, 15.6%, 43% & 19.8% respectively. The area under ROC for TAPSE, S', FAC% and RVS were 0.58 (95% confidence interval [CI] 0.51–0.65, p=0.03), 0.59 (95% CI 0.52–0.66, p=0.03), 0.67 (95% CI 0.61–0.73, p=0.03) and 0.70 (0.64–0.76, p=0.03) for assessing RV EF Conclusion Right ventricular strain most accurately detected impaired right ventricular ejection fraction as obtained by MRI. Acknowledgement/Funding None

Inflammation of adipose tissue (AT) is key for the development of insulin resistance (IR) in obesity. We previously showed that 3 months supplementation of fish oil (FO, 4g/day), a well-known anti-inflammatory agent, induced 25% improvement in glucose disposal during euglycemic-hyperinsulinemic clamp in 13 obese, insulin resistant subjects (ADA abstract Hernandez J., et al 2017). To explore the mechanism(s) linked to this improvement, we first performed a microarray analysis of subcutaneous (SC) AT samples pre and post FO supplementation. Functional analysis indicated anti-inflammatory pathways as the principal pathways to be enriched after FO supplementation. We identified APOE (1.87 Fold Change (FC) Post vs. Pre-FO, P=0.004) among the most significantly enhanced genes, as APOE was common to the top 5 enriched anti-inflammatory pathways. RT-PCR analysis confirmed the changes in expression levels of several genes in these pathways including APOE, APOC1, MS4A6E, MMP9, MMP7, DEFB132, C1QTNF7, and POPDC3, thereby validating the microarray findings. Plasma and SC AT RT-PCR analysis showed significantly decreased inflammatory markers (IL1B: -0.28 FC, P=0.04, MCP1: -0.44 FC, P=0.04, CD68: -0.36 FC P=0.01) post vs. pre-FO. Additionally, mRNA adiponectin levels increased (FC +0.45 Post vs. Pre, P=0.02), while leptin decreased (FC -.030 Post vs. Pre, P=0.02). Furthermore, ω3PUFA supplementation significantly reduced plasma FFA levels (Post-FO: 0.578+0.061mM vs. Pre-FO: 0.719 +0.055mM P Disclosure C. Rau: None. T. Li: None. J.D. Hernandez: None. M.Y. Masuda: None. X. Zhang: None. P. Wang: None. D.K. Coletta: None. E. DeFilippis: None. Funding Arizona Department of Health Services (14-00003606); Kathryn H. and Roger Penske Career Development Award

With the rapid advances in high-throughput sequencing technologies, exome sequencing and targeted region sequencing have become routine approaches for identifying mutations of inherited disorders in both genetics research and molecular diagnosis. There is an imminent need for comprehensive and easy-to-use downstream analysis tools to isolate causal mutations in exome sequencing studies. We have developed a user-friendly online framework, wKGGSeq, to provide systematic annotation, filtration, prioritization, and visualization functions for characterizing causal mutation(s) in exome sequencing studies of inherited disorders. wKGGSeq provides: (1) a novel strategy-based procedure for downstream analysis of a large amount of exome sequencing data and (2) a disease-targeted analysis procedure to facilitate clinical diagnosis of well-studied genetic diseases. In addition, it is also equipped with abundant online annotation functions for sequence variants. We demonstrate that wKGGSeq either outperforms or is comparable to two popular tools in several real exome sequencing samples. This tool will greatly facilitate the downstream analysis of exome sequencing data and can play a useful role for researchers and clinicians in identifying causal mutations of inherited disorders. The wKGGSeq is freely available at http://statgenpro.psychiatry.hku.hk/wkggseq or http://jjwanglab.org/wkggseq, and will be updated frequently.

The majority of colorectal cancer (CRC) arises from precursor lesions known as polyps. The molecular determinants that distinguish benign from malignant polyps remain unclear. To molecularly characterize polyps, we utilized Cancer Adjacent Polyp (CAP) and Cancer Free Polyp (CFP) patients. CAPs had tissues from the residual polyp of origin and contiguous cancer; CFPs had polyp tissues matched to CAPs based on polyp size, histology and dysplasia. To determine whether molecular features distinguish CAPs and CFPs, we conducted Whole Genome Sequencing, RNA-seq, and RRBS on over 90 tissues from 31 patients. CAPs had significantly more mutations, altered expression and hypermethylation compared to CFPs. APC was significantly mutated in both polyp groups, but mutations in TP53, FBXW7, PIK3CA, KIAA1804 and SMAD2 were exclusive to CAPs. We found significant expression changes between CAPs and CFPs in GREM1, IGF2, CTGF, and PLAU, and both expression and methylation alterations in FES and HES1. Integrative analyses revealed 124 genes with alterations in at least two platforms, and ERBB3 and E2F8 showed aberrations specific to CAPs across all platforms. These findings provide a resource of molecular distinctions between polyps with and without cancer, which have the potential to enhance the diagnosis, risk assessment and management of polyps.

Integration and analysis of high content omics data have been critical to the investigation of molecule interactions (e.g., DNA-protein, protein-protein, chemical-protein) in biological systems. Human proteomic strategies that provide enriched information on cell surface proteins can be utilized for repurposing of drug targets and discovery of disease biomarkers. Although several published resources have proved useful to the analysis of these interactions, our newly developed web-based platform Targets-search has the capability of integrating multiple types of omics data to unravel their association with diverse molecule interactions and disease. Here, we describe how to use Targets-search, for the integrated and systemic exploitation of surface proteins to identify potential drug targets, which can further be used to analyze gene regulation, protein networks, and possible biomarkers for diseases and cancers. To illustrate this process, we have taken data from Ewing's sarcoma to identify surface proteins differentially expressed in Ewing's sarcoma cells. These surface proteins were then analyzed to determine which ones were known drug targets. The information suggested putative targets for drug repurposing and subsequent analyses illustrated their regulation by the transcription factor EWSR1.

Integration and analysis of high content omics data have been critical to the investigation of molecule interactions (e.g., DNA-protein, protein-protein, chemical-protein) in biological systems. Human proteomic strategies that provide enriched information on cell surface proteins can be utilized for repurposing of drug targets and discovery of disease biomarkers. Although several published resources have proved useful to the analysis of these interactions, our newly developed web-based platform Targets-search has the capability of integrating multiple types of omics data to unravel their association with diverse molecule interactions and disease. Here, we describe how to use Targets-search, for the integrated and systemic exploitation of surface proteins to identify potential drug targets, which can further be used to analyze gene regulation, protein networks, and possible biomarkers for diseases and cancers. To illustrate this process, we have taken data from Ewing's sarcoma to identify surface proteins differentially expressed in Ewing's sarcoma cells. These surface proteins were then analyzed to determine which ones were known drug targets. The information suggested putative targets for drug repurposing and subsequent analyses illustrated their regulation by the transcription factor EWSR1.

Interactions among transcriptional factors (TFs), cofactors and other proteins or enzymes can affect transcriptional regulatory capabilities of eukaryotic organisms. Post-translational modifications (PTMs) cooperate with TFs and epigenetic alterations to constitute a hierarchical complexity in transcriptional gene regulation. While clearly implicated in biological processes, our understanding of these complex regulatory mechanisms is still limited and incomplete. Various online software have been proposed for uncovering transcriptional and epigenetic regulatory networks, however, there is a lack of effective web-based software capable of constructing underlying interactive organizations between post-translational and transcriptional regulatory components. Here, we present an open web server, post-translational hierarchical gene regulatory network (PTHGRN) to unravel relationships among PTMs, TFs, epigenetic modifications and gene expression. PTHGRN utilizes a graphical Gaussian model with partial least squares regression-based methodology, and is able to integrate protein-protein interactions, ChIP-seq and gene expression data and to capture essential regulation features behind high-throughput data. The server provides an integrative platform for users to analyze ready-to-use public high-throughput Omics resources or upload their own data for systems biology study. Users can choose various parameters in the method, build network topologies of interests and dissect their associations with biological functions. Application of the software to stem cell and breast cancer demonstrates that it is an effective tool for understanding regulatory mechanisms in biological complex systems. PTHGRN web server is publically available at web site http://www.byanbioinfo.org/pthgrn.

It remains challenging to predict regulatory variants in particular tissues or cell types due to highly context-specific gene regulation. By connecting large-scale epigenomic profiles to expression quantitative trait loci (eQTLs) in a wide range of human tissues/cell types, we identify critical chromatin features that predict variant regulatory potential. We present cepip, a joint likelihood framework, for estimating a variant's regulatory probability in a context-dependent manner. Our method exhibits significant GWAS signal enrichment and is superior to existing cell type-specific methods. Furthermore, using phenotypically relevant epigenomes to weight the GWAS single-nucleotide polymorphisms, we improve the statistical power of the gene-based association test., published_or_final_version

Supplemental Figures and Tables. Supplemental Figures S1â S14 and Tables S1â S14. (PDF 3992 kb)

The advances of high throughput profiling methods, such as microarray gene profiling and RNA-seq, have enabled researchers to identify thousands of differentially expressed genes under a certain perturbation. Much work has been done to understand the genetic factors that contribute to the expression changes by searching the over-represented regulatory motifs in the promoter regions of these genes. However, the changes could also be caused by epigenetic regulation, especially histone modifications, and no web server has been constructed to study the epigenetic factors responsible for gene expression changes. Here, we pre-sent a web tool for this purpose. Provided with different categories of genes (e.g., up-regulated, down-regulated or unchanged genes), the server will find epigenetic factors responsible for the difference among the categories and construct an epigenetic regulatory network. Furthermore, it will perform co-localization analyses between these epigenetic factors and transcription factors, which were collected from large scale experimental ChIP-seq or computational predicted data. In addition, for users who want to analyze dynamic change of a histone modification mark under different cell conditions, the server will find direct and indirect target genes of this mark by integrative analysis of experimental data and computational prediction, and present a regulatory network around this mark. Both networks can be visualized by a user friendly interface and the data are downloadable in batch. The server currently supports 12 cell types in human, including ESC and CD4+ T cells, and will expand as more public data are available. It also allows user to create a self-defined cell type, upload and analyze multiple ChIP-seq data. It is freely available to academic users at http://jjwanglab.org/EpiRegNet.

Cardiac Power to left ventricular mass (LVM) ratio, also termed cardiac efficiency (CE), reflects the rate of cardiac work delivered with respect to the potential energy stored in LVM. We sought to assess the association between baseline resting CE and survival post transcatheter aortic valve

Protein-protein docking has made much progress in recent years, but challenges still exist. Here we present the application of our docking approach HoDock in CAPRI. In this approach, a binding site prediction is implemented to reduce docking sampling space and filter out unreasonable docked structures, and a network-based enhanced combinatorial scoring function HPNCscore is used to evaluate the decoys. The experimental information was combined with the predicted binding site to pick out the most likely key binding site residues. We applied the HoDock method in the recent rounds of the CAPRI experiments, and got good results as predictors on targets 39, 40, and 41. We also got good results as scorers on targets 35, 37, 40, and 41. This indicates that our docking approach can contribute to the progress of protein-protein docking methods and to the understanding of the mechanism of protein-protein interactions.

Motivation: Prediction and prioritization of human non-coding regulatory variants is critical for understanding the regulatory mechanisms of disease pathogenesis and promoting personalized medicine. Existing tools utilize functional genomics data and evolutionary information to evaluate the pathogenicity or regulatory functions of non-coding variants. However, different algorithms lead to inconsistent and even conflicting predictions. Combining multiple methods may increase accuracy in regulatory variant prediction. Results: Here, we compiled an integrative resource for predictions from eight different tools on functional annotation of non-coding variants. We further developed a composite strategy to integrate multiple predictions and computed the composite likelihood of a given variant being regulatory variant. Benchmarked by multiple independent causal variants datasets, we demonstrated that our composite model significantly improves the prediction performance. Availability and Implementation: We implemented our model and scoring procedure as a tool, named PRVCS, which is freely available to academic and non-profit usage at http://jjwanglab.org/PRVCS. Contact: wang.junwen@mayo.edu, jliu@stat.harvard.edu, or limx54@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

Transcription factors (TFs) play an important role in gene regulation. The interconnections among TFs, chromatin interactions, epigenetic marks and cis-regulatory elements form a complex gene transcription apparatus. Our previous work, ChIP-Array, combined TF binding and transcriptome data to construct gene regulatory networks (GRNs). Here we present an enhanced version, ChIP-Array 2, to integrate additional types of omics data including long-range chromatin interaction, open chromatin region and histone modification data to dissect more comprehensive GRNs involving diverse regulatory components. Moreover, we substantially extended our motif database for human, mouse, rat, fruit fly, worm, yeast and Arabidopsis, and curated large amount of omics data for users to select as input or backend support. With ChIP-Array 2, we compiled a library containing regulatory networks of 18 TFs/chromatin modifiers in mouse embryonic stem cell (mESC). The web server and the mESC library are publicly free and accessible athttp://jjwanglab.org/chip-array.