Given its significant role in the maintenance of genomic stability, histone methylation has been postulated to regulate DNA repair. Histone methylation mediates localization of 53BP1 to a DNA double-strand break (DSB) during homologous recombination repair, but a role in DSB repair by nonhomologous end-joining (NHEJ) has not been defined. By screening for histone methylation after DSB induction by ionizing radiation we found that generation of dimethyl histone H3 lysine 36 (H3K36me2) was the major event.Using a novel human cell system that rapidly generates a single defined DSB in the vast majority of cells, we found that the DNA repair protein Metnase (also SETMAR), which has a SET histone methylase domain, localized to an induced DSB and directly mediated the formation of H3K36me2 near the induced DSB. This dimethylation of H3K36 improved the association of early DNA repair components, including NBS1 and Ku70, with the induced DSB, and enhanced DSB repair. In addition, expression of JHDM1a (an H3K36me2 demethylase) or histone H3 in which K36 was mutated to A36 or R36 to prevent H3K36me2 formation decreased the association of early NHEJ repair components with an induced DSB and decreased DSB repair. Thus, these experiments define a histone methylation event that enhances DNA DSB repair by NHEJ.double-strand break | I-Sce-I | chromatin immunoprecipitation | MRN complex | mathematical modeling H istone methylation is highly regulated by a family of proteins termed histone methylases, which usually share a SET domain (1-3). Histone methylation plays a key role in chromatin remodeling and as such regulates transcription, replication, cell differentiation, genome stability, and apoptosis (1-3). Because of its role in replication and genome stability, histone methylation has been hypothesized to play an important role in DNA repair. DNA double-strand breaks (DSBs) are a cytotoxic form of DNA damage that disrupts many of the cellular functions regulated by histone methylation described above (4-6). Previous reports indicate that histone methylation may be important in DNA DSB repair by homologous recombination: The DSB repair component 53BP1, which is required for proper homologous recombination, is recruited to sites of damage by methylated histone H3 lysine 79 (H3K79) and histone H4 lysine 20 (H4K20) (7-9). However, neither H3K79 nor H4K20 methylation is induced by DNA damage (9), so other histone methylation events at sites of DNA damage have been sought. In addition, a mechanism by which histone methylation might regulate NHEJ DSB repair has yet to be defined. In this study, a survey of histone methylation events after DSB induction revealed that the major immediate H3 methylation event is H3K36me2.Metnase is a DNA DSB repair component that is a fusion of a SET histone methylase domain with a nuclease domain and a domain from a member of the transposase/integrase family (10-14). We showed previously that Metnase enhances nonhomologous end-joining (NHEJ) repair of, and survival after, DNA DSBs, and that its SET dom...

This document provides a comprehensive description of LSODE, a solver for initial value problems in ordinary differential equation systems. It is intended to bring together numerous materials documenting various aspects of LSODE, including technical reports on the methods used, published papers on LSODE, usage documentation contained within the LSODE source, and unpublished notes on algorithmic details. The three central chapters-n methods, code description, and code usage-are largely independent. Thus, for example, we intend that readers who are familiar with the solution methods and interested in how they are implemented in LSODE can read the Introduction and then chapter 3, Description of Code, without reading chapter 2, Description and Implementation of Methods. Similarly, those interested solely in how to use the code need read only the Introduction and then chapter 4, Description of Code Usage. In this case chapter 5, Example Problem, which illustrates code usage by means of a simple, stiff chemical kinetics problem, supplements chapter 4 and may be of further assistance. Although this document is intended mainly for users of LSODE, it can be used as supplementary reading material for graduate and advanced undergraduate courses on numerical methods. Engineers and scientists who use numerical solution methods for ordinary differential equations may also benefit from this document.

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Objective: Anxiety disorders are common and often disabling. The goal of this study was to examine the genetic architecture of anxiety disorders and anxiety symptoms, which are also frequently comorbid with other mental disorders, such as major depressive disorder.Methods: Using one of the world's largest biobanks including genetic, environmental, and medical information, the Million Veteran Program, the authors performed a genome-wide association study (GWAS) of a continuous trait for anxiety (based on score on the Generalized Anxiety Disorder 2-item scale [GAD-2], N=199,611) as the primary analysis and selfreport of physician diagnosis of anxiety disorder (N=224,330) as a secondary analysis. Results:The authors identified five genome-wide significant signals for European Americans and one for African Americans on GAD-2 score. The strongest were on chromosome 3 (rs4603973) near SATB1, a global regulator of gene expression, and on chromosome 6 (rs6557168) near ESR1, which encodes an estrogen receptor. The locus identified on chromosome 7 (rs56226325, MAF=0.17) near MAD1L1 was previously identified in GWASs of bipolar disorder and schizophrenia. The authors replicated these findings in the summary statistics of two major published GWASs for anxiety, and also found evidence of significant genetic correlation between the GAD-2 score results and previous GWASs for anxiety (r g =0.75), depression (r g =0.81), and neuroticism (r g =0.75).Conclusions: This is the largest GWAS of anxiety traits to date. The authors identified novel genome-wide significant associations near genes involved with global regulation of gene expression (SATB1) and the estrogen receptor alpha (ESR1). Additionally, the authors identified a locus (MAD1L1) that may have implications for genetic vulnerability across several psychiatric disorders. This work provides new insights into genetic risk mechanisms underpinning anxiety and related psychiatric disorders.

We conducted genome-wide association analyses in over 250,000 participants of European and African ancestry from the Million Veteran Program using electronic health record-validated posttraumatic stress disorder (PTSD) diagnosis and quantitative symptom phenotypes. Applying genome-wide multiple testing correction, we identified three significant loci in European case-control analyses and 15 loci in quantitative symptom analyses. Genomic structural equation modeling indicated tight coherence of a PTSD symptom factor that shares genetic variance with a distinct internalizing (mood-anxiety-neuroticism) factor. Partitioned heritability indicated enrichment in several cortical and subcortical regions, and imputed genetically regulated gene expression in these regions was used to identify potential drug repositioning candidates. These results validate the biological coherence of the PTSD syndrome, inform its relationship to comorbid anxiety and depressive disorders, and provide new considerations for treatment.

Dr. Stein owns founders shares and stock options in Resilience Therapeutics and has stock options in Oxeia Biopharmaceuticals. Data Availability The GWAS summary statistics generated during and/or analyzed during the current study are available via dbGAP; the dbGaP accession assigned to the Million Veteran Program is phs001672.v1.p. The website is: https://www.ncbi.nlm.nih.gov/projects/gap/cgibin/study.cgi?study_id=phs001672.v1.p1 Additionally, the data that support the findings of this study are available from the corresponding authors upon request.

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We developed an algorithm for identifying U.S. veterans with a history of posttraumatic stress disorder (PTSD), using the Department of Veterans Affairs (VA) electronic medical record (EMR) system. This work was motivated by the need to create a valid EMR-based phenotype to identify thousands of cases and controls for a genome-wide association study of PTSD in veterans. We used manual chart review (n = 500) as the gold standard. For both the algorithm and chart review, three classifications were possible: likely PTSD, possible PTSD, and likely not PTSD. We used Lasso regression with cross-validation to select statistically significant predictors of PTSD from the EMR and then generate a predicted probability score of being a PTSD case for every participant in the study population (range: 0-1.00). Comparing the performance of our probabilistic approach (Lasso algorithm) to a rule-based approach (International Classification of Diseases [ICD] algorithm), the Lasso algorithm showed modestly higher overall percent agreement with chart review than the ICD algorithm (80% vs. 75%), higher sensitivity (0.95 vs. 0.84), and higher accuracy (AUC = 0.95 vs. 0.90). We applied a 0.7 probability cut-point to the Lasso results to determine final PTSD case-control status for the VA population. The final algorithm had a 0.99 sensitivity, 0.99 specificity, 0.95 positive predictive value, and 1.00 negative predictive value for PTSD classification (grouping possible PTSD and likely not PTSD) as determined by chart review. This algorithm may be useful for other research and quality improvement endeavors within the VA.We would like to thank Dr. Joan Kaufman for conducting medical chart reviews during the first wave of reviews and for providing constructive feedback on the chart review protocol for CSP #575B. We would also like to thank Rebecca Song for performing a quality control check on all programs used for participant selection and analysis.Widespread implementation of electronic medical record (EMR) systems provides opportunities for transforming population-based research by enabling efficient, cost-effective collection of data on a large scale and thus helps to address a