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BACKGROUND: Asthma is a common respiratory disorder with a highly heterogeneous nature that remains poorly understood. The objective was to use whole genome sequencing (WGS) data to identify regions of common genetic variation contributing to lung function in individuals with a diagnosis of asthma. METHODS: WGS data were generated for 1,053 individuals from trios and extended pedigrees participating in the family-based Genetic Epidemiology of Asthma in Costa Rica study. Asthma affection status was defined through a physician’s diagnosis of asthma, and most participants with asthma also had airway hyperresponsiveness (AHR) to methacholine. Family-based association tests for single variants were performed to assess the associations with lung function phenotypes. RESULTS: A genome-wide significant association was identified between baseline FEV(1)/FVC ratio and a single-nucleotide polymorphism in the top hit cysteine-rich secretory protein LCCL domain-containing 2 (CRISPLD2) (rs12051168; P = 3.6 × 10(−8) in the unadjusted model) that retained suggestive significance in the covariate-adjusted model (P = 5.6 × 10(−6)). Rs12051168 was also nominally associated with other related phenotypes: baseline FEV(1) (P = 3.3 × 10(−3)), postbronchodilator (PB) FEV(1) (7.3 × 10(−3)), and PB FEV(1)/FVC ratio (P = 2.7 × 10(−3)). The identified baseline FEV(1)/FVC ratio and rs12051168 association was meta-analyzed and replicated in three independent cohorts in which most participants with asthma also had confirmed AHR (combined weighted z-score P = .015) but not in cohorts without information about AHR. CONCLUSIONS: These findings suggest that using specific asthma characteristics, such as AHR, can help identify more genetically homogeneous asthma subgroups with genotype-phenotype associations that may not be observed in all children with asthma. CRISPLD2 also may be important for baseline lung function in individuals with asthma who also may have AHR.

Background: Decay products of radioactive materials may attach to ambient fine particles and form radioactive aerosol. Internal ionizing radiation source from inhaled radioactive aerosol may contribute to the fine particulate matter (PM2.5)-inflammation pathway. However, few studies in humans have examined the associations. Objectives: To examine the associations between particle radioactivity and biomarkers of oxidative stress and inflammation among participants from the Framingham Offspring and Third Generation cohorts. Methods: We included 3996 participants who were not current smokers and lived within 50 km from our central air pollution monitoring station. We estimated regional mean gross beta radioactivity from monitors in the northeastern U.S. as a surrogate for ambient radioactive particles, and calculated the 1- to 28-day moving averages. We used linear regression models for fibrinogen, tumor necrosis factor α, interleukin-6, and myeloperoxidase which were measured once, and linear mixed effect models for 8-epi-prostaglandin F2α, C-reactive protein, intercellular adhesion molecule-1 (ICAM-1), monocyte chemoattractant protein-1 (MCP-1), P-selectin, and tumor necrosis factor receptor-2 that were measured up to twice, adjusting for demographics, individual- and area-level socioeconomic positions, time, meteorology, and PM2.5. We also examined whether the associations differed by median age, sex, diabetes status, PM2.5 levels, and black carbon levels. Results: The mean age was 54 years and 54% were women. An interquartile range (3 × 10−3 pCi/m3) higher beta radioactivity level at the 7-day moving average was associated with 5.09% (95% CI: 0.92, 9.43), 2.65% (1.10, 4.22), and 4.71% (95% CI: 3.01, 6.44) higher levels of interleukin-6, MCP-1, and P-selectin, but with 7.01% (95% CI: −11.64, −2.15) and 2.70% (95% CI: −3.97, −1.42) lower levels of 8-epi-prostaglandin F2α and ICAM-1, respectively. Conclusions: Regional mean particle radioactivity was positively associated with interleukin-6, MCP-1, and P-selectin, but negatively with ICAM-1 and 8-epi-prostaglandin F2α among our study participants. Keywords: Gross beta radiation, Particle radioactivity, Epidemiology, Environment, Inflammation, Oxidative stress

Human genetics studies have uncovered genetic variants that can be used to guide biological research and prioritize molecular targets for therapeutic intervention for complex diseases and metabolic conditions. We have identified a missense variant (P746S) in EDEM3 associated with lower blood triglyceride (TG) levels in >300,000 individuals. Functional analyses in cell and mouse models show that EDEM3 deficiency strongly increased the uptake of very low-density lipoprotein and thereby reduced the plasma TG level, as a result of up-regulated expression of LRP1 receptor. We demonstrate that EDEM3 deletion up-regulated the pathways for RNA and ER protein processing and transport, and consequently increased the cell surface mannose-containing glycoproteins, including LRP1. Metabolomics analyses reveal a cellular TG accumulation under EDEM3 deficiency, a profile consistent with individuals with carrying EDEM3 P746S. Our study identifies EDEM3 as a regulator of blood TG, and targeted inhibition of EDEM3 may provide a complementary approach for lowering elevated blood TG concentrations.

Summary Human genetics studies have uncovered genetic variants that can be used to guide biological research and prioritize molecular targets for therapeutic intervention for complex diseases. We have identified a missense variant (P746S) in EDEM3 associated with lower blood triglyceride (TG) levels in >300,000 individuals. Functional analyses in cell and mouse models show that EDEM3 deficiency strongly increased the uptake of very-low-density lipoprotein and thereby reduced the plasma TG level, as a result of up-regulated expression of LRP1 receptor. We demonstrate that EDEM3 deletion up-regulated the pathways for RNA and endoplasmic reticulum protein processing and transport, and consequently increased the cell surface mannose-containing glycoproteins, including LRP1. Metabolomics analyses reveal a cellular TG accumulation under EDEM3 deficiency, a profile consistent with individuals carrying EDEM3 P746S. Our study identifies EDEM3 as a regulator of blood TG, and targeted inhibition of EDEM3 may provide a complementary approach for lowering elevated blood TG concentrations., Graphical Abstract, Highlights • Genetic deficiency of EDEM3 leads to lower blood triglyceride (TG) level • EDEM3 deficiency increases VLDL uptake by up-regulating LRP1 receptor expression • Blood TG changes due to EDEM3 mutation correlate with the TG profile of EDEM3 KO cells, Genetics; Diabetology; Specialized Functions of Cells; Metabolomics

Background: Although growing evidence from trials and population-based studies has supported a protective role for flavonoids in relation to risk of certain chronic diseases, the underlying mechanisms remain unclear. Several previous studies focused on individual inflammatory biomarkers, but because of the limited specificity of any individual marker, an assessment of a combination of biomarkers may be more informative. Objective: We used an inflammation score (IS) that integrated 12 individual inflammatory biomarkers for the examination of associations with intakes of different flavonoid classes. Design: The study was a cross-sectional analysis of 2375 Framingham Heart Study Offspring Cohort participants. Intakes of total flavonoids and their classes (anthocyanins, flavonols, flavanones, flavan-3-ols, polymers, and flavones) were calculated from validated food-frequency questionnaires. Individual inflammatory biomarkers were ranked, standardized, and summed to derive an overall IS and subgroup scores of functionally related biomarkers. Results: In multivariate analyses, an inverse association between higher anthocyanin and flavonol intakes and IS was observed with a mean ± SE difference between quintile categories 5 and 1 of −1.48 ± 0.32 (P-trend ≤ 0.001) and −0.72 ± 0.33 (P-trend = 0.01), respectively. Results remained significant after additional adjustment for physical activity and vitamin C and fruit and vegetable intakes. Higher anthocyanin intake was inversely associated with all biomarker subgroups, whereas higher flavonol intake was associated only with lower cytokine and oxidative stress biomarker concentrations. In food-based analyses, higher intakes of apples and pears, red wine, and strawberries were associated with a lower IS with differences between quintiles 5 and 1 of −1.02 ± 0.43 (P = 0.006), −1.73 ± 0.39 (P < 0.001), and −0.44 ± 0.88 (P = 0.02), respectively. Although intakes of other classes were not associated with a reduction in overall IS, higher intakes of flavan-3-ols and their polymers were associated with a significant reduction in oxidative stress biomarkers. Conclusion: These findings provide evidence to suggest that an anti-inflammatory effect may be a key component underlying the reduction in risk of certain chronic diseases associated with higher intakes of anthocyanins and flavonols. The Framingham Offspring Study was registered at clinicaltrials.gov as {"type":"clinical-trial","attrs":{"text":"NCT00005121","term_id":"NCT00005121"}}NCT00005121 (Framingham Heart Study).

A sedentary lifestyle is associated with increased risk for hypertension. Smartwatches enable accurate measurement of habitual physical activity. We hypothesize that higher habitual physical activity is associated with lower home blood pressure (BP). Electronic Framingham Heart Study (eFHS)

Circulating interleukin-6 levels increase with advancing age and are a risk factor for various diseases and mortality. The characterization of gene expression profiles associated with interleukin-6 levels might suggest important molecular events underlying its regulation.We studied the association of transcriptional profiles with interleukin-6 levels in 2422 participants from the Framingham Heart Study Offspring Cohort using Affymetrix Human Exon 1.0 ST Array. We identified 4139 genes that were significantly associated with interleukin-6 levels (FDR0.05) after adjusting for age, sex and blood cell components. We then replicated 807 genes in the InCHIANTI study with 694 participants. Many of the top genes are involved in inflammation-related pathways or erythrocyte function, including JAK/Stat signaling pathway and interleukin-10 signaling pathway.We identified and replicated 807 genes that were associated with circulating interleukin-6 levels. Future characterization of interleukin-6 regulation networks may facilitate the identification of additional potential targets for treating inflammation-related diseases.

Background The human left and right atria have different susceptibilities to develop atrial fibrillation (AF). However, the molecular events related to structural and functional changes that enhance AF susceptibility are still poorly understood. Objective The purpose of this study was to characterize gene expression and genetic variation in human atria. Methods We studied the gene expression profiles and genetic variations in 53 left atrial and 52 right atrial tissue samples collected from the Myocardial Applied Genomics Network (MAGNet) repository. The tissues were collected from heart failure patients undergoing transplantation and from unused organ donor hearts with normal ventricular function. Gene expression was profiled using the Affymetrix GeneChip Human Genome U133A Array. Genetic variation was profiled using the Affymetrix Genome-Wide Human SNP Array 6.0. Results We found that 109 genes were differentially expressed between left and right atrial tissues. A total of 187 and 259 significant cis-associations between transcript levels and genetic variants were identified in left and right atrial tissues, respectively. We also found that a single nucleotide polymorphism at a known AF locus, rs3740293, was associated with the expression of MYOZ1 in both left and right atrial tissues. Conclusion We found a distinct transcriptional profile between the right and left atrium and extensive cis-associations between atrial transcripts and common genetic variants. Our results implicate MYOZ1 as the causative gene at the chromosome 10q22 locus for AF.

The immune system is characterized by high combinatorial complexity that necessitates the use of specialized computational tools for analysis of immunological data. Machine learning (ML) algorithms are used in combination with classical experimentation for the selection of vaccine targets and in computational simulations that reduce the number of necessary experiments. The development of ML algorithms requires standardized data sets, consistent measurement methods, and uniform scales. To bridge the gap between the immunology community and the ML community, we designed a repository for machine learning in immunology named Dana-Farber Repository for Machine Learning in Immunology (DFRMLI). This repository provides standardized data sets of HLA-binding peptides with all binding affinities mapped onto a common scale. It also provides a list of experimentally validated naturally processed T cell epitopes derived from tumor or virus antigens. The DFRMLI data were preprocessed and ensure consistency, comparability, detailed descriptions, and statistically meaningful sample sizes for peptides that bind to various HLA molecules. The repository is accessible at http://bio.dfci.harvard.edu/DFRMLI/.

Increasing antibiotic resistance has become a worldwide challenge to the clinical treatment of infectious diseases. The identification of antibiotic resistance proteins (ARPs) would be helpful in the discovery of new therapeutic targets and the design of novel drugs to control the potential spread of antibiotic resistance. In this work, a support vector machine (SVM)-based ARP prediction system was developed using 1308 ARPs and 15587 non-ARPs. Its performance was evaluated using 313 ARPs and 7156 non-ARPs. The computed prediction accuracy was 88.5% for ARPs and 99.2% for non-ARPs. A potential application of this method is the identification of ARPs non-homologous to proteins of known function. Further genome screening found that ca. 3.5% and 3.2% of proteins in Escherichia coli and Staphylococcus aureus, respectively, are potential ARPs. These results suggest the usefulness of SVMs for facilitating the identification of ARPs. The software can be accessed at SARPI (Server for Antibiotic Resistance Protein Identification).

Support vector machines (SVM) and other machine-learning (ML) methods have been explored as ligand-based virtual screening (VS) tools for facilitating lead discovery. While exhibiting good hit selection performance, in screening large compound libraries, these methods tend to produce lower hit-rate than those of the best performing VS tools, partly because their training-sets contain limited spectrum of inactive compounds. We tested whether the performance of SVM can be improved by using training-sets of diverse inactive compounds. In retrospective database screening of active compounds of single mechanism (HIV protease inhibitors, DHFR inhibitors, dopamine antagonists) and multiple mechanisms (CNS active agents) from large libraries of 2.986 million compounds, the yields, hit-rates, and enrichment factors of our SVM models are 52.4–78.0%, 4.7–73.8%, and 214–10,543, respectively, compared to those of 62–95%, 0.65–35%, and 20–1200 by structure-based VS and 55–81%, 0.2–0.7%, and 110–795 by other ligand-based VS tools in screening libraries of ≥1 million compounds. The hit-rates are comparable and the enrichment factors are substantially better than the best results of other VS tools. 24.3–87.6% of the predicted hits are outside the known hit families. SVM appears to be potentially useful for facilitating lead discovery in VS of large compound libraries.

Factor Xa (FXa) inhibitors have been explored as anticoagulants for treatment and prevention of thrombotic diseases. Molecular docking, pharmacophore, quantitative structure-activity relationships, and support vector machines (SVM) have been used for computer prediction of FXa inhibitors. These methods achieve promising prediction accuracies of 69-80% for FXa inhibitors and 85-99% for non-inhibitors. Prediction performance, particularly for inhibitors, may be further improved by exploring methods applicable to more diverse range of compounds and by using more appropriate set of molecular descriptors. We tested the capability of several machine learning methods (C4.5 decision tree, k-nearest neighbor, probabilistic neural network, and support vector machine) by using a much more diverse set of 1098 compounds (360 inhibitors and 738 non-inhibitors) than those in other studies. A feature selection method was used for selecting molecular descriptors appropriate for distinguishing FXa inhibitors and non-inhibitors. The prediction accuracies of these methods are 89.1-97.5% for FXa inhibitors and 92.3-98.1% for non-inhibitors. In particular, compared to other studies, support vector machine gives a substantially improved accuracy of 94.6% for FXa non-inhibitors and maintains a comparable accuracy of 98.1% for inhibitors, based-on a more rigorous test with more diverse range of compounds. Our study suggests that machine learning methods such as SVM are useful for facilitating the prediction of FXa inhibitors.

Identification and validation of viable targets is an important first step in drug discovery and new methods, and integrated approaches are continuously explored to improve the discovery rate and exploration of new drug targets. An in silico machine learning method, support vector machines, has been explored as a new method for predicting druggable proteins from amino acid sequence independent of sequence similarity, thereby facilitating the prediction of druggable proteins that exhibit no or low homology to known targets.

Drug resistance is of increasing concern in the treatment of infectious diseases and cancer. Mutation in drug-interacting disease proteins is one of the primary causes for resistance particularly against anti-infectious drugs. Prediction of resistance mutations in these proteins is valuable both for the molecular dissection of drug resistance mechanisms and for predicting features that guide the design of new agents to counter resistant strains. Several protein structure- and sequence-based computer methods have been explored for mechanistic study and prediction of resistance mutations. These methods and their usefulness are reviewed here.