Regulation of PPARγ2 Stability and Activity by SHP-1.

The protein tyrosine phosphatase Src homology region 2 domain-containing phosphatase-1 (SHP-1) plays an important role in modulating glucose and lipid homeostasis. We previously suggested a potential role of SHP-1 in the regulation of peroxisome proliferator-activated receptor γ2 (PPARγ2) expression and activity but the mechanisms were unexplored. PPARγ2 is the master regulator of adipogenesis, but how its activity is regulated by tyrosine phosphorylation is largely unknown. Here, we found that SHP-1 binds to PPARγ2 primarily via its N-terminal SH2-domain. We confirmed the phosphorylation of PPARγ2 on tyrosine-residue 78 (Y78), which was reduced by SHP-1 in vitro resulting in decreased PPARγ2 stability. Loss of SHP-1 led to elevated, agonist-induced expression of the classical PPARγ2 targets FABP4 and CD36, concomitant with increased lipid content in cells expressing PPARγ2, an effect blunted by abrogation of PPARγ2 phosphorylation. Collectively, we discovered that SHP-1 affects the stability of PPARγ2 through dephosphorylation thereby influencing adipogenesis.

Path Tracing in 2D, 3D, and Physicalized Networks.

It is common to advise against using 3D to visualize abstract data such as networks, however Ware and Mitchell's 2008 study showed that path tracing in a network is less error prone in 3D than in 2D. It is unclear, however, if 3D retains its advantage when the 2D presentation of a network is improved using edge-routing, and when simple interaction techniques for exploring the network are available. We address this with two studies of path tracing under new conditions. The first study was preregistered, involved 34 users, and compared 2D and 3D layouts that the user could rotate and move in virtual reality with a handheld controller. Error rates were lower in 3D than in 2D, despite the use of edge-routing in 2D and the use of mouse-driven interactive highlighting of edges. The second study involved 12 users and investigated data physicalization, comparing 3D layouts in virtual reality versus physical 3D printouts of networks augmented with a Microsoft HoloLens headset. No difference was found in error rate, but users performed a variety of actions with their fingers in the physical condition which can inform new interaction techniques.

Improved prediction of fracture risk leveraging a genome-wide polygenic risk score.

BACKGROUND: Accurately quantifying the risk of osteoporotic fracture is important for directing appropriate clinical interventions. While skeletal measures such as heel quantitative speed of sound (SOS) and dual-energy X-ray absorptiometry bone mineral density are able to predict the risk of osteoporotic fracture, the utility of such measurements is subject to the availability of equipment and human resources. Using data from 341,449 individuals of white British ancestry, we previously developed a genome-wide polygenic risk score (PRS), called gSOS, that captured 25.0% of the total variance in SOS. Here, we test whether gSOS can improve fracture risk prediction. METHODS: We examined the predictive power of gSOS in five genome-wide genotyped cohorts, including 90,172 individuals of European ancestry and 25,034 individuals of Asian ancestry. We calculated gSOS for each individual and tested for the association between gSOS and incident major osteoporotic fracture and hip fracture. We tested whether adding gSOS to the risk prediction models had added value over models using other commonly used clinical risk factors. RESULTS: A standard deviation decrease in gSOS was associated with an increased odds of incident major osteoporotic fracture in populations of European ancestry, with odds ratios ranging from 1.35 to 1.46 in four cohorts. It was also associated with a 1.26-fold (95% confidence interval (CI) 1.13-1.41) increased odds of incident major osteoporotic fracture in the Asian population. We demonstrated that gSOS was more predictive of incident major osteoporotic fracture (area under the receiver operating characteristic curve (AUROC) = 0.734; 95% CI 0.727-0.740) and incident hip fracture (AUROC = 0.798; 95% CI 0.791-0.805) than most traditional clinical risk factors, including prior fracture, use of corticosteroids, rheumatoid arthritis, and smoking. We also showed that adding gSOS to the Fracture Risk Assessment Tool (FRAX) could refine the risk prediction with a positive net reclassification index ranging from 0.024 to 0.072. CONCLUSIONS: We generated and validated a PRS for SOS which was associated with the risk of fracture. This score was more strongly associated with the risk of fracture than many clinical risk factors and provided an improvement in risk prediction. gSOS should be explored as a tool to improve risk stratification to identify individuals at high risk of fracture.

Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study.

BACKGROUND: Since screening programs identify only a small proportion of the population as eligible for an intervention, genomic prediction of heritable risk factors could decrease the number needing to be screened by removing individuals at low genetic risk. We therefore tested whether a polygenic risk score for heel quantitative ultrasound speed of sound (SOS)-a heritable risk factor for osteoporotic fracture-can identify low-risk individuals who can safely be excluded from a fracture risk screening program. METHODS AND FINDINGS: A polygenic risk score for SOS was trained and selected in 2 separate subsets of UK Biobank (comprising 341,449 and 5,335 individuals). The top-performing prediction model was termed "gSOS", and its utility in fracture risk screening was tested in 5 validation cohorts using the National Osteoporosis Guideline Group clinical guidelines (N = 10,522 eligible participants). All individuals were genome-wide genotyped and had measured fracture risk factors. Across the 5 cohorts, the average age ranged from 57 to 75 years, and 54% of studied individuals were women. The main outcomes were the sensitivity and specificity to correctly identify individuals requiring treatment with and without genetic prescreening. The reference standard was a bone mineral density (BMD)-based Fracture Risk Assessment Tool (FRAX) score. The secondary outcomes were the proportions of the screened population requiring clinical-risk-factor-based FRAX (CRF-FRAX) screening and BMD-based FRAX (BMD-FRAX) screening. gSOS was strongly correlated with measured SOS (r2 = 23.2%, 95% CI 22.7% to 23.7%). Without genetic prescreening, guideline recommendations achieved a sensitivity and specificity for correct treatment assignment of 99.6% and 97.1%, respectively, in the validation cohorts. However, 81% of the population required CRF-FRAX tests, and 37% required BMD-FRAX tests to achieve this accuracy. Using gSOS in prescreening and limiting further assessment to those with a low gSOS resulted in small changes to the sensitivity and specificity (93.4% and 98.5%, respectively), but the proportions of individuals requiring CRF-FRAX tests and BMD-FRAX tests were reduced by 37% and 41%, respectively. Study limitations include a reliance on cohorts of predominantly European ethnicity and use of a proxy of fracture risk. CONCLUSIONS: Our results suggest that the use of a polygenic risk score in fracture risk screening could decrease the number of individuals requiring screening tests, including BMD measurement, while maintaining a high sensitivity and specificity to identify individuals who should be recommended an intervention.

Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models.

Complex traits are known to be influenced by a combination of environmental factors and rare and common genetic variants. However, detection of such multivariate associations can be compromised by low statistical power and confounding by population structure. Linear mixed effects models (LMM) can account for correlations due to relatedness but have not been applicable in high-dimensional (HD) settings where the number of fixed effect predictors greatly exceeds the number of samples. False positives or false negatives can result from two-stage approaches, where the residuals estimated from a null model adjusted for the subjects' relationship structure are subsequently used as the response in a standard penalized regression model. To overcome these challenges, we develop a general penalized LMM with a single random effect called ggmix for simultaneous SNP selection and adjustment for population structure in high dimensional prediction models. We develop a blockwise coordinate descent algorithm with automatic tuning parameter selection which is highly scalable, computationally efficient and has theoretical guarantees of convergence. Through simulations and three real data examples, we show that ggmix leads to more parsimonious models compared to the two-stage approach or principal component adjustment with better prediction accuracy. Our method performs well even in the presence of highly correlated markers, and when the causal SNPs are included in the kinship matrix. ggmix can be used to construct polygenic risk scores and select instrumental variables in Mendelian randomization studies. Our algorithms are available in an R package available on CRAN (https://cran.r-project.org/package=ggmix).

A method for genome-wide genealogy estimation for thousands of samples.

Knowledge of genome-wide genealogies for thousands of individuals would simplify most evolutionary analyses for humans and other species, but has remained computationally infeasible. We have developed a method, Relate, scaling to >10,000 sequences while simultaneously estimating branch lengths, mutational ages and variable historical population sizes, as well as allowing for data errors. Application to 1,000 Genomes Project haplotypes produces joint genealogical histories for 26 human populations. Highly diverged lineages are present in all groups, but most frequent in Africa. Outside Africa, these mainly reflect ancient introgression from groups related to Neanderthals and Denisovans, while African signals instead reflect unknown events unique to that continent. Our approach allows more powerful inferences of natural selection than has previously been possible. We identify multiple regions under strong positive selection, and multi-allelic traits including hair color, body mass index and blood pressure, showing strong evidence of directional selection, varying among human groups.

PRS-on-Spark (PRSoS): a novel, efficient and flexible approach for generating polygenic risk scores.

BACKGROUND: Polygenic risk scores (PRS) describe the genomic contribution to complex phenotypes and consistently account for a larger proportion of variance in outcome than single nucleotide polymorphisms (SNPs) alone. However, there is little consensus on the optimal data input for generating PRS, and existing approaches largely preclude the use of imputed posterior probabilities and strand-ambiguous SNPs i.e., A/T or C/G polymorphisms. Our ability to predict complex traits that arise from the additive effects of a large number of SNPs would likely benefit from a more inclusive approach. RESULTS: We developed PRS-on-Spark (PRSoS), a software implemented in Apache Spark and Python that accommodates different data inputs and strand-ambiguous SNPs to calculate PRS. We compared performance between PRSoS and an existing software (PRSice v1.25) for generating PRS for major depressive disorder using a community cohort (N = 264). We found PRSoS to perform faster than PRSice v1.25 when PRS were generated for a large number of SNPs (~ 17 million SNPs; t = 42.865, p = 5.43E-04). We also show that the use of imputed posterior probabilities and the inclusion of strand-ambiguous SNPs increase the proportion of variance explained by a PRS for major depressive disorder (from 4.3% to 4.8%). CONCLUSIONS: PRSoS provides the user with the ability to generate PRS using an inclusive and efficient approach that considers a larger number of SNPs than conventional approaches. We show that a PRS for major depressive disorder that includes strand-ambiguous SNPs, calculated using PRSoS, accounts for the largest proportion of variance in symptoms of depression in a community cohort, demonstrating the utility of this approach. The availability of this software will help users develop more informative PRS for a variety of complex phenotypes.

Agreement in DNA methylation levels from the Illumina 450K array across batches, tissues, and time.

Epigenome-wide association studies (EWAS) have focused primarily on DNA methylation as a chemically stable and functional epigenetic modification. However, the stability and accuracy of the measurement of methylation in different tissues and extraction types is still being actively studied, and the longitudinal stability of DNA methylation in commonly studied peripheral tissues is of great interest. Here, we used data from two studies, three tissue types, and multiple time points to assess the stability of DNA methylation measured with the Illumina Infinium HumanMethylation450 BeadChip array. Redundancy analysis enabled visual assessment of agreement of replicate samples overall and showed good agreement after removing effects of tissue type, age, and sex. At the probe level, analysis of variance contrasts separating technical and biological replicates clearly showed better agreement between technical replicates versus longitudinal samples, and suggested increased stability for buccal cells versus blood or blood spots. Intraclass correlations (ICCs) demonstrated that inter-individual variability is of similar magnitude to within-sample variability at many probes; however, as inter-individual variability increased, so did ICC. Furthermore, we were able to demonstrate decreasing agreement in methylation levels with time, despite a maximal sampling interval of only 576 days. Finally, at 6 popular candidate genes, there was a large range of stability across probes. Our findings highlight important sources of technical and biological variation in DNA methylation across different tissues over time. These data will help to inform longitudinal sampling strategies of future EWAS.

Integration of "omics" Data and Phenotypic Data Within a Unified Extensible Multimodal Framework.

Analysis of "omics" data is often a long and segmented process, encompassing multiple stages from initial data collection to processing, quality control and visualization. The cross-modal nature of recent genomic analyses renders this process challenging to both automate and standardize; consequently, users often resort to manual interventions that compromise data reliability and reproducibility. This in turn can produce multiple versions of datasets across storage systems. As a result, scientists can lose significant time and resources trying to execute and monitor their analytical workflows and encounter difficulties sharing versioned data. In 2015, the Ludmer Centre for Neuroinformatics and Mental Health at McGill University brought together expertise from the Douglas Mental Health University Institute, the Lady Davis Institute and the Montreal Neurological Institute (MNI) to form a genetics/epigenetics working group. The objectives of this working group are to: (i) design an automated and seamless process for (epi)genetic data that consolidates heterogeneous datasets into the LORIS open-source data platform; (ii) streamline data analysis; (iii) integrate results with provenance information; and (iv) facilitate structured and versioned sharing of pipelines for optimized reproducibility using high-performance computing (HPC) environments via the CBRAIN processing portal. This article outlines the resulting generalizable "omics" framework and its benefits, specifically, the ability to: (i) integrate multiple types of biological and multi-modal datasets (imaging, clinical, demographics and behavioral); (ii) automate the process of launching analysis pipelines on HPC platforms; (iii) remove the bioinformatic barriers that are inherent to this process; (iv) ensure standardization and transparent sharing of processing pipelines to improve computational consistency; (v) store results in a queryable web interface; (vi) offer visualization tools to better view the data; and (vii) provide the mechanisms to ensure usability and reproducibility. This framework for workflows facilitates brain research discovery by reducing human error through automation of analysis pipelines and seamless linking of multimodal data, allowing investigators to focus on research instead of data handling.

The performance of a new local false discovery rate method on tests of association between coronary artery disease (CAD) and genome-wide genetic variants.

The maximum entropy (ME) method is a recently-developed approach for estimating local false discovery rates (LFDR) that incorporates external information allowing assignment of a subset of tests to a category with a different prior probability of following the null hypothesis. Using this ME method, we have reanalyzed the findings from a recent large genome-wide association study of coronary artery disease (CAD), incorporating biologic annotations. Our revised LFDR estimates show many large reductions in LFDR, particularly among the genetic variants belonging to annotation categories that were known to be of particular interest for CAD. However, among SNPs with rare minor allele frequencies, the reductions in LFDR were modest in size.

Gene networks show associations with seed region connectivity.

Primary patterns in adult brain connectivity are established during development by coordinated networks of transiently expressed genes; however, neural networks remain malleable throughout life. The present study hypothesizes that structural connectivity from key seed regions may induce effects on their connected targets, which are reflected in gene expression at those targeted regions. To test this hypothesis, analyses were performed on data from two brains from the Allen Human Brain Atlas, for which both gene expression and DW-MRI were available. Structural connectivity was estimated from the DW-MRI data and an approach motivated by network topology, that is, weighted gene coexpression network analysis (WGCNA), was used to cluster genes with similar patterns of expression across the brain. Group exponential lasso models were then used to predict gene cluster expression summaries as a function of seed region structural connectivity patterns. In several gene clusters, brain regions located in the brain stem, diencephalon, and hippocampal formation were identified that have significant predictive power for these expression summaries. These connectivity-associated clusters are enriched in genes associated with synaptic signaling and brain plasticity. Furthermore, using seed region based connectivity provides a novel perspective in understanding relationships between gene expression and connectivity. Hum Brain Mapp 38:3126-3140, 2017. © 2017 Wiley Periodicals, Inc.

Hepatocyte-specific Ptpn6 deletion promotes hepatic lipid accretion, but reduces NAFLD in diet-induced obesity: potential role of PPARγ.

UNLABELLED: Hepatocyte-specific Shp1 knockout mice (Ptpn6(H-KO)) are protected from hepatic insulin resistance evoked by high-fat diet (HFD) feeding for 8 weeks. Unexpectedly, we report herein that Ptpn6(H-KO) mice fed an HFD for up to 16 weeks are still protected from insulin resistance, but are more prone to hepatic steatosis, as compared with their HFD-fed Ptpn6(f/f) counterparts. The livers from HFD-fed Ptpn6(H-KO) mice displayed 1) augmented lipogenesis, marked by increased expression of several hepatic genes involved in fatty acid biosynthesis, 2) elevated postprandial fatty acid uptake, and 3) significantly reduced lipid export with enhanced degradation of apolipoprotein B (ApoB). Despite more extensive hepatic steatosis, the inflammatory profile of the HFD-fed Ptpn6(H-KO) liver was similar (8 weeks) or even improved (16 weeks) as compared to their HFD-fed Ptpn6(f/f) littermates, along with reduced hepatocellular damage as revealed by serum levels of hepatic enzymes. Interestingly, comparative microarray analysis revealed a significant up-regulation of peroxisome proliferator-activated receptor gamma (PPARγ) gene expression, confirmed by quantitative polymerase chain reaction. Elevated PPARγ nuclear activity also was observed and found to be directly regulated by Shp1 in a cell-autonomous manner. CONCLUSION: These findings highlight a novel role for hepatocyte Shp1 in the regulation of PPARγ and hepatic lipid metabolism. Shp1 deficiency prevents the development of severe hepatic inflammation and hepatocellular damage in steatotic livers, presenting hepatocyte Shp1 as a potential novel mediator of nonalcoholic fatty liver diseases in obesity.

Fibrin gel improves the survival of transplanted myoblasts.

Duchenne muscular dystrophy (DMD) is the most frequent muscular dystrophy in children and young adults. Currently, there is no cure for the disease. The transplantation of healthy myoblasts is an experimental therapeutic strategy, since it could restore the expression of dystrophin in DMD muscles. Nevertheless, this cellular therapy is limited by immune reaction, low migration of the implanted cells, and high early cell death that could be at least partially due to anoikis. To avoid the lack of attachment of the cells to an extracellular matrix after the transplantation, which is the cause of anoikis, we tested the use of a fibrin gel for myoblast transplantation. In vitro, three concentrations of fibrinogen were compared (3, 20, and 50 mg/ml) to form a fibrin gel. A stiffer fibrin gel leads to less degradability and less proliferation of the cells. A concentration of 3 mg/ml fibrin gel enhanced the differentiation of the myoblasts earlier as a culture in monolayer. Human myoblasts were also transplanted in muscles of Rag/mdx mice in a fibrin gel or in a saline solution (control). The use of 3 mg/ml fibrin gel for cell transplantation increased not only the survival of the cells as measured after 5 days but also the number of fibers expressing dystrophin after 21 days, compared to the control. Moreover, the fibrin gel was also compared to a prosurvival cocktail. The survival of the myoblasts at 5 days was increased in both conditions compared to the control but the efficacy of the prosurvival cocktail was not significantly higher than the fibrin gel.

Pharmacological inhibition of stearoyl-CoA desaturase 1 improves insulin sensitivity in insulin-resistant rat models.

Stearoyl-CoA Desaturase 1 (SCD1) is a central enzyme that catalyzes the biosynthesis of monounsaturated fatty acids from saturated fatty acids. SCD1 is an emerging target in obesity and insulin resistance due to the improved metabolic profile obtained when the enzyme is genetically inactivated. Here, we have investigated if the pharmacological inhibition of SCD1 could elicit the same profile. We have identified a small molecule, GSK993 and characterized it as a potent and orally available SCD1 inhibitor. In Zucker(fa/fa) rats, GSK993 exerted a marked reduction in hepatic lipids as well as a significant improvement of glucose tolerance. Furthermore, in a diet-induced insulin resistant rat model, GSK993 induced a very strong reduction in Triton-induced hepatic Very Low Density Lipoprotein-Triglyceride production. In addition, following a hyperinsulinemic-euglycemic clamp in GSK993-treated animals, we observed an improvement in the whole body insulin sensitivity as reflected by an increase in the glucose infusion rate. Taken together, these findings demonstrate that the pharmacological inhibition of SCD1 translates into improved lipid and glucose metabolic profiles and raises the interest of SCD1 inhibitors as potential new drugs for the treatment of insulin resistance.

Substituted 2-[(4-aminomethyl)phenoxy]-2-methylpropionic acid PPARalpha agonists. 1. Discovery of a novel series of potent HDLc raising agents.

The peroxisome proliferator activated receptors PPARalpha, PPARgamma, and PPARdelta are ligand-activated transcription factors that play a key role in lipid homeostasis. The fibrates raise circulating levels of high-density lipoprotein cholesterol and lower levels of triglycerides in part through their activity as PPARalpha agonists; however, the low potency and restricted selectivity of the fibrates may limit their efficacy, and it would be desirable to develop more potent and selective PPARalpha agonists. Modification of the selective PPARdelta agonist 1 (GW501516) so as to incorporate the 2-aryl-2-methylpropionic acid group of the fibrates led to a marked shift in potency and selectivity toward PPARalpha agonism. Optimization of the series gave 25a, which shows EC50 = 4 nM on PPARalpha and at least 500-fold selectivity versus PPARdelta and PPARgamma. Compound 25a (GW590735) has been progressed to clinical trials for the treatment of diseases of lipid imbalance.