An epigenetic small molecule screen to target abnormal nuclear morphology in human cells.

Irregular nuclear shapes are a hallmark of human cancers. Recent studies suggest that alterations to chromatin regulators may cause irregular nuclear morphologies. Here we screened an epigenetic small molecule library consisting of 145 compounds against chromatin regulators for their ability to revert abnormal nuclear shapes that were induced by gene knockdown in noncancerous MCF10A human mammary breast epithelial cells. We leveraged a previously validated quantitative Fourier approach to quantify the elliptical Fourier coefficient (EFC ratio) as a measure of nuclear irregularities, which allowed us to perform rigorous statistical analyses of screening data. Top hit compounds fell into three major mode of action categories, targeting three separate epigenetic modulation routes: 1) histone deacetylase inhibitors, 2) bromodomain and extraterminal domain protein inhibitors, and 3) methyl-transferase inhibitors. Some of the top hit compounds were also efficacious in reverting nuclear irregularities in MDA-MB-231 triple negative breast cancer cells and in PANC-1 pancreatic cancer cells in a cell-type-dependent manner. Regularization of nuclear shapes was compound-specific, cell-type specific, and dependent on the specific molecular perturbation that induced nuclear irregularities. Our approach of targeting nuclear abnormalities may be potentially useful in screening new types of cancer therapies targeted toward chromatin structure.

A generalized likelihood-based Bayesian approach for scalable joint regression and covariance selection in high dimensions.

The paper addresses joint sparsity selection in the regression coefficient matrix and the error precision (inverse covariance) matrix for high-dimensional multivariate regression models in the Bayesian paradigm. The selected sparsity patterns are crucial to help understand the network of relationships between the predictor and response variables, as well as the conditional relationships among the latter. While Bayesian methods have the advantage of providing natural uncertainty quantification through posterior inclusion probabilities and credible intervals, current Bayesian approaches either restrict to specific sub-classes of sparsity patterns and/or are not scalable to settings with hundreds of responses and predictors. Bayesian approaches which only focus on estimating the posterior mode are scalable, but do not generate samples from the posterior distribution for uncertainty quantification. Using a bi-convex regression based generalized likelihood and spike-and-slab priors, we develop an algorithm called Joint Regression Network Selector (JRNS) for joint regression and covariance selection which (a) can accommodate general sparsity patterns, (b) provides posterior samples for uncertainty quantification, and (c) is scalable and orders of magnitude faster than the state-of-the-art Bayesian approaches providing uncertainty quantification. We demonstrate the statistical and computational efficacy of the proposed approach on synthetic data and through the analysis of selected cancer data sets. We also establish high-dimensional posterior consistency for one of the developed algorithms.

High-Dimensional Posterior Consistency in Bayesian Vector Autoregressive Models.

Vector autoregressive (VAR) models aim to capture linear temporal interdependencies amongst multiple time series. They have been widely used in macroeconomics and financial econometrics and more recently have found novel applications in functional genomics and neuroscience. These applications have also accentuated the need to investigate the behavior of the VAR model in a high-dimensional regime, which provides novel insights into the role of temporal dependence for regularized estimates of the model's parameters. However, hardly anything is known regarding properties of the posterior distribution for Bayesian VAR models in such regimes. In this work, we consider a VAR model with two prior choices for the autoregressive coefficient matrix: a non-hierarchical matrix-normal prior and a hierarchical prior, which corresponds to an arbitrary scale mixture of normals. We establish posterior consistency for both these priors under standard regularity assumptions, when the dimension p of the VAR model grows with the sample size n (but still remains smaller than n). A special case corresponds to a shrinkage prior that introduces (group) sparsity in the columns of the model coefficient matrices. The performance of the model estimates are illustrated on synthetic and real macroeconomic data sets.

Reimbursement patterns for neurosurgery: Analysis of the NERVES survey results from 2011-2016.

OBJECTIVE: In response to rising national health expenditures, the Patient Protection and Affordable Care Act (ACA) was passed in 2010, with major provisions implemented in 2014. Due to increasing concerns about workload and compensation among neurosurgeons, we evaluated trends in neurosurgical reimbursement, productivity and compensation before and after the implementation of the major provisions of the ACA. PATIENTS AND METHODS: Results from Neurosurgery Executives' Resource Value and Education Society (NERVES) annual surveys were collected, representing data from 2011 to 2016. Responses from different practice settings across the six years were categorized into groups, and inverse variance-weighted averaging was performed within the frameworks of a one-way ANOVA model with year. Data from 2011 to 2013 and 2014-2016 were analyzed similarly for differences among practice setting and region. RESULTS: The NERVES survey response rates ranged from 20% to 36%. Median values for compensation decreased by 3.66%, 6.42%, and 10.34% within private, hospital, and academic practices respectively after 2014 although these trends did not reach statistical significance. Median work RVUs had a trend to decrease by 5.67%, 13.08%, and 19.44% within private, hospital, and academic practices respectively after 2014. Academic practices showed statistically significant decreases in annual total RVUs, total gross charges and collections. CONCLUSION: These data demonstrate neurosurgical reimbursement and productivity have trended down during a time that increases in productivity and reimbursement were predicted. This phenomenon is most notable in academic practices compared to private or hospital based practices. Prospective analyses of the impact of healthcare policy reform on neurosurgical productivity are urgently needed.

Modeling correlated marker effects in genome-wide prediction via Gaussian concentration graph models.

In genome-wide prediction, independence of marker allele substitution effects is typically assumed; however, since early stages in the evolution of this technology it has been known that nature points to correlated effects. In statistics, graphical models have been identified as a useful and powerful tool for covariance estimation in high dimensional problems and it is an area that has recently experienced a great expansion. In particular, Gaussian concentration graph models (GCGM) have been widely studied. These are models in which the distribution of a set of random variables, the marker effects in this case, is assumed to be Markov with respect to an undirected graph G. In this paper, Bayesian (Bayes G and Bayes G-D) and frequentist (GML-BLUP) methods adapting the theory of GCGM to genome-wide prediction were developed. Different approaches to define the graph G based on domain-specific knowledge were proposed, and two propositions and a corollary establishing conditions to find decomposable graphs were proven. These methods were implemented in small simulated and real datasets. In our simulations, scenarios where correlations among allelic substitution effects were expected to arise due to various causes were considered, and graphs were defined on the basis of physical marker positions. Results showed improvements in correlation between phenotypes and predicted additive genetic values and accuracies of predicted additive genetic values when accounting for partially correlated allele substitution effects. Extensions to the multiallelic loci case were described and some possible refinements incorporating more flexible priors in the Bayesian setting were discussed. Our models are promising because they allow incorporation of biological information in the prediction process, and because they are more flexible and general than other models accounting for correlated marker effects that have been proposed previously.

Tautomeric equilibria of isoguanine and related purine analogs.

Nucleobase pairs in DNA match hydrogen-bond donor and acceptor groups on the nucleobases. However, these can adopt more than one tautomeric form, and can consequently pair with nucleobases other than their canonical complements, possibly a source of natural mutation. These issues are now being re-visited by synthetic biologists increasing the number of replicable pairs in DNA by exploiting unnatural hydrogen bonding patterns, where tautomerism can also create mutation. Here, we combine spectroscopic measurements on methylated analogs of isoguanine tautomers and tautomeric mixtures with statistical analyses to a set of isoguanine analogs, the complement of isocytosine, the 5th and 6th "letters" in DNA.

Joint genome-wide prediction in several populations accounting for randomness of genotypes: A hierarchical Bayes approach. II: Multivariate spike and slab priors for marker effects and derivation of approximate Bayes and fractional Bayes factors for the complete family of models.

This study corresponds to the second part of a companion paper devoted to the development of Bayesian multiple regression models accounting for randomness of genotypes in across population genome-wide prediction. This family of models considers heterogeneous and correlated marker effects and allelic frequencies across populations, and has the ability of considering records from non-genotyped individuals and individuals with missing genotypes in any subset of loci without the need for previous imputation, taking into account uncertainty about imputed genotypes. This paper extends this family of models by considering multivariate spike and slab conditional priors for marker allele substitution effects and contains derivations of approximate Bayes factors and fractional Bayes factors to compare models from part I and those developed here with their null versions. These null versions correspond to simpler models ignoring heterogeneity of populations, but still accounting for randomness of genotypes. For each marker loci, the spike component of priors corresponded to point mass at 0 in RS, where S is the number of populations, and the slab component was a S-variate Gaussian distribution, independent conditional priors were assumed. For the Gaussian components, covariance matrices were assumed to be either the same for all markers or different for each marker. For null models, the priors were simply univariate versions of these finite mixture distributions. Approximate algebraic expressions for Bayes factors and fractional Bayes factors were found using the Laplace approximation. Using the simulated datasets described in part I, these models were implemented and compared with models derived in part I using measures of predictive performance based on squared Pearson correlations, Deviance Information Criterion, Bayes factors, and fractional Bayes factors. The extensions presented here enlarge our family of genome-wide prediction models making it more flexible in the sense that it now offers more modeling options.

Joint genome-wide prediction in several populations accounting for randomness of genotypes: A hierarchical Bayes approach. I: Multivariate Gaussian priors for marker effects and derivation of the joint probability mass function of genotypes.

It is important to consider heterogeneity of marker effects and allelic frequencies in across population genome-wide prediction studies. Moreover, all regression models used in genome-wide prediction overlook randomness of genotypes. In this study, a family of hierarchical Bayesian models to perform across population genome-wide prediction modeling genotypes as random variables and allowing population-specific effects for each marker was developed. Models shared a common structure and differed in the priors used and the assumption about residual variances (homogeneous or heterogeneous). Randomness of genotypes was accounted for by deriving the joint probability mass function of marker genotypes conditional on allelic frequencies and pedigree information. As a consequence, these models incorporated kinship and genotypic information that not only permitted to account for heterogeneity of allelic frequencies, but also to include individuals with missing genotypes at some or all loci without the need for previous imputation. This was possible because the non-observed fraction of the design matrix was treated as an unknown model parameter. For each model, a simpler version ignoring population structure, but still accounting for randomness of genotypes was proposed. Implementation of these models and computation of some criteria for model comparison were illustrated using two simulated datasets. Theoretical and computational issues along with possible applications, extensions and refinements were discussed. Some features of the models developed in this study make them promising for genome-wide prediction, the use of information contained in the probability distribution of genotypes is perhaps the most appealing. Further studies to assess the performance of the models proposed here and also to compare them with conventional models used in genome-wide prediction are needed.

RasGRP1 promotes amphetamine-induced motor behavior through a Rhes interaction network ("Rhesactome") in the striatum.

The striatum of the brain coordinates motor function. Dopamine-related drugs may be therapeutic to patients with striatal neurodegeneration, such as Huntington's disease (HD) and Parkinson's disease (PD), but these drugs have unwanted side effects. In addition to stimulating the release of norepinephrine, amphetamines, which are used for narcolepsy and attention-deficit/hyperactivity disorder (ADHD), trigger dopamine release in the striatum. The guanosine triphosphatase Ras homolog enriched in the striatum (Rhes) inhibits dopaminergic signaling in the striatum, is implicated in HD and L-dopa-induced dyskinesia, and has a role in striatal motor control. We found that the guanine nucleotide exchange factor RasGRP1 inhibited Rhes-mediated control of striatal motor activity in mice. RasGRP1 stabilized Rhes, increasing its synaptic accumulation in the striatum. Whereas partially Rhes-deficient (Rhes+/-) mice had an enhanced locomotor response to amphetamine, this phenotype was attenuated by coincident depletion of RasGRP1. By proteomic analysis of striatal lysates from Rhes-heterozygous mice with wild-type or partial or complete knockout of Rasgrp1, we identified a diverse set of Rhes-interacting proteins, the "Rhesactome," and determined that RasGRP1 affected the composition of the amphetamine-induced Rhesactome, which included PDE2A (phosphodiesterase 2A; a protein associated with major depressive disorder), LRRC7 (leucine-rich repeat-containing 7; a protein associated with bipolar disorder and ADHD), and DLG2 (discs large homolog 2; a protein associated with chronic pain). Thus, this Rhes network provides insight into striatal effects of amphetamine and may aid the development of strategies to treat various neurological and psychological disorders associated with the striatal dysfunction.

On the Bayesness, minimaxity and admissibility of point estimators of allelic frequencies.

In this paper, decision theory was used to derive Bayes and minimax decision rules to estimate allelic frequencies and to explore their admissibility. Decision rules with uniformly smallest risk usually do not exist and one approach to solve this problem is to use the Bayes principle and the minimax principle to find decision rules satisfying some general optimality criterion based on their risk functions. Two cases were considered, the simpler case of biallelic loci and the more complex case of multiallelic loci. For each locus, the sampling model was a multinomial distribution and the prior was a Beta (biallelic case) or a Dirichlet (multiallelic case) distribution. Three loss functions were considered: squared error loss (SEL), Kulback-Leibler loss (KLL) and quadratic error loss (QEL). Bayes estimators were derived under these three loss functions and were subsequently used to find minimax estimators using results from decision theory. The Bayes estimators obtained from SEL and KLL turned out to be the same. Under certain conditions, the Bayes estimator derived from QEL led to an admissible minimax estimator (which was also equal to the maximum likelihood estimator). The SEL also allowed finding admissible minimax estimators. Some estimators had uniformly smaller variance than the MLE and under suitable conditions the remaining estimators also satisfied this property. In addition to their statistical properties, the estimators derived here allow variation in allelic frequencies, which is closer to the reality of finite populations exposed to evolutionary forces.