DAXX promotes centromeric stability independently of ATRX by preventing the accumulation of R-loop-induced DNA double-stranded breaks

Maintaining chromatin integrity at the repetitive non-coding DNA sequences underlying centromeres is crucial to prevent replicative stress, DNA breaks and genomic instability. The concerted action of transcriptional repressors, chromatin remodelling complexes and epigenetic factors controls transcription and chromatin structure in these regions. The histone chaperone complex ATRX/DAXX is involved in the establishment and maintenance of centromeric chromatin through the deposition of the histone variant H3.3. ATRX and DAXX have also evolved mutually-independent functions in transcription and chromatin dynamics. Here, using paediatric glioma and pancreatic neuroendocrine tumor cell lines, we identify a novel ATRX-independent function for DAXX in promoting genome stability by preventing transcription-associated R-loop accumulation and DNA double-strand break formation at centromeres. This function of DAXX required its interaction with histone H3.3 but was independent of H3.3 deposition and did not reflect a role in the repression of centromeric transcription. DAXX depletion mobilized BRCA1 at centromeres, in line with BRCA1 role in counteracting centromeric R-loop accumulation. Our results provide novel insights into the mechanisms protecting the human genome from chromosomal instability, as well as potential perspectives in the treatment of cancers with DAXX alterations

Trajectories of Big Five Personality Traits: A Coordinated Analysis of 16 Longitudinal Samples

This study assessed change in self‐reported Big Five personality traits. We conducted a coordinated integrative data analysis using data from 16 longitudinal samples, comprising a total sample of over 60 000 participants. We coordinated models across multiple datasets and fit identical multi‐level growth models to assess and compare the extent of trait change over time. Quadratic change was assessed in a subset of samples with four or more measurement occasions. Across studies, the linear trajectory models revealed declines in conscientiousness, extraversion, and openness. Non‐linear models suggested late‐life increases in neuroticism. Meta‐analytic summaries indicated that the fixed effects of personality change are somewhat heterogeneous and that the variability in trait change is partially explained by sample age, country of origin, and personality measurement method. We also found mixed evidence for predictors of change, specifically for sex and baseline age. This study demonstrates the importance of coordinated conceptual replications for accelerating the accumulation of robust and reliable findings in the lifespan developmental psychological sciences. © 2020 European Association of Personality PsychologyPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/156004/1/per2259.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/156004/2/per2259-sup-0001-Data_S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/156004/3/per2259-sup-0002-Open_Practices_Disclosure_Form.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/156004/4/per2259_am.pd

Personality Predicts Mortality Risk: An Integrative Data Analysis of 15 International Longitudinal Studies

This study examined the Big Five personality traits as predictors of mortality risk, and smoking as a mediator of that association. Replication was built into the fabric of our design: we used a Coordinated Analysis with 15 international datasets, representing 44,094 participants. We found that high neuroticism and low conscientiousness, extraversion, and agreeableness were consistent predictors of mortality across studies. Smoking had a small mediating effect for neuroticism. Country and baseline age explained variation in effects: studies with older baseline age showed a pattern of protective effects (HR<1.00) for openness, and U.S. studies showed a pattern of protective effects for extraversion. This study demonstrated coordinated analysis as a powerful approach to enhance replicability and reproducibility, especially for aging-related longitudinal research.Funding support for this project was provided by the National Institute on Aging: P01-AG043362 (Integrative Analysis of Longitudinal Studies of Aging (IALSA), [Scott M. Hofer (PI)]), and Daniel K. Mroczek (CoInvestigator and Project Leader of the IALSA Personality & Health Project, as well as R01-AG018436 [Personality & Well-Being Trajectories in Adulthood, Daniel K. Mroczek, PI])

Is healthy neuroticism associated with health behaviors? A coordinated integrative data analysis

Current literature suggests that neuroticism is positively associated with maladaptive life choices, likelihood of disease, and mortality. However, recent research has identified circumstances under which neuroticism is associated with positive outcomes. The current project examined whether “healthy neuroticism”, defined as the interaction of neuroticism and conscientiousness, was associated with the following health behaviors: smoking, alcohol consumption, and physical activity. Using a pre-registered multi-study coordinated integrative data analysis (IDA) approach, we investigated whether “healthy neuroticism” predicted the odds of engaging in each of the aforementioned activities. Each study estimated identical models, using the same covariates and data transformations, enabling optimal comparability of results. These results were then meta-analyzed in order to estimate an average (N-weighted) effect and to ascertain the extent of heterogeneity in the effects. Overall, these results suggest that neuroticism alone was not related to health behaviors, while individuals higher in conscientiousness were less likely to be smokers or drinkers, and more likely to engage in physical activity. In terms of the healthy neuroticism interaction of neuroticism and conscientiousness, significant interactions for smoking and physical activity suggest that the association between neuroticism and health behaviors was smaller among those high in conscientiousness. These findings lend credence to the idea that healthy neuroticism may be linked to certain health behaviors and that these effects are generalizable across several heterogeneous samples

Is healthy neuroticism associated with longevity? A coordinated integrative data analysis

Early investigations of the neuroticism by conscientiousness interaction with regards to health have been promising, but to date, there have been no systematic investigations of this interaction that account for the various personality measurement instruments, varying populations, or aspects of health. The current study - the second of three - uses a coordinated analysis approach to test the impact of the neuroticism by conscientiousness interaction on the prevalence and incidence of chronic conditions. Using 15 pre-existing longitudinal studies (N > 49,375), we found that conscientiousness did not moderate the relationship between neuroticism and having hypertension (OR = 1.00,95%CI[0.98,1.02]), diabetes (OR = 1.02[0.99,1.04]), or heart disease (OR = 0.99[0.97,1.01]). Similarly, we found that conscientiousness did not moderate the prospective relationship between neuroticism and onset of hypertension (OR = 0.98,[0.95,1.01]), diabetes (OR = 0.99[0.94,1.05]), or heart disease (OR = 0.98[0.94,1.03]). Heterogeneity of effect sizes was largely nonsignificant, with one exception, indicating that the effects are consistent between datasets. Overall, we conclude that there is no evidence that healthy neuroticism, operationalized as the conscientiousness by neuroticism interaction, buffers against chronic conditions

Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease

We identified rare coding variants associated with Alzheimer’s disease (AD) in a 3-stage case-control study of 85,133 subjects. In stage 1, 34,174 samples were genotyped using a whole-exome microarray. In stage 2, we tested associated variants (P<1×10-4) in 35,962 independent samples using de novo genotyping and imputed genotypes. In stage 3, an additional 14,997 samples were used to test the most significant stage 2 associations (P<5×10-8) using imputed genotypes. We observed 3 novel genome-wide significant (GWS) AD associated non-synonymous variants; a protective variant in PLCG2 (rs72824905/p.P522R, P=5.38×10-10, OR=0.68, MAFcases=0.0059, MAFcontrols=0.0093), a risk variant in ABI3 (rs616338/p.S209F, P=4.56×10-10, OR=1.43, MAFcases=0.011, MAFcontrols=0.008), and a novel GWS variant in TREM2 (rs143332484/p.R62H, P=1.55×10-14, OR=1.67, MAFcases=0.0143, MAFcontrols=0.0089), a known AD susceptibility gene. These protein-coding changes are in genes highly expressed in microglia and highlight an immune-related protein-protein interaction network enriched for previously identified AD risk genes. These genetic findings provide additional evidence that the microglia-mediated innate immune response contributes directly to AD development

Proceedings of the 3rd Biennial Conference of the Society for Implementation Research Collaboration (SIRC) 2015: advancing efficient methodologies through community partnerships and team science

It is well documented that the majority of adults, children and families in need of evidence-based behavioral health interventionsi do not receive them [1, 2] and that few robust empirically supported methods for implementing evidence-based practices (EBPs) exist. The Society for Implementation Research Collaboration (SIRC) represents a burgeoning effort to advance the innovation and rigor of implementation research and is uniquely focused on bringing together researchers and stakeholders committed to evaluating the implementation of complex evidence-based behavioral health interventions. Through its diverse activities and membership, SIRC aims to foster the promise of implementation research to better serve the behavioral health needs of the population by identifying rigorous, relevant, and efficient strategies that successfully transfer scientific evidence to clinical knowledge for use in real world settings [3]. SIRC began as a National Institute of Mental Health (NIMH)-funded conference series in 2010 (previously titled the “Seattle Implementation Research Conference”; $150,000 USD for 3 conferences in 2011, 2013, and 2015) with the recognition that there were multiple researchers and stakeholdersi working in parallel on innovative implementation science projects in behavioral health, but that formal channels for communicating and collaborating with one another were relatively unavailable. There was a significant need for a forum within which implementation researchers and stakeholders could learn from one another, refine approaches to science and practice, and develop an implementation research agenda using common measures, methods, and research principles to improve both the frequency and quality with which behavioral health treatment implementation is evaluated. SIRC’s membership growth is a testament to this identified need with more than 1000 members from 2011 to the present.ii SIRC’s primary objectives are to: (1) foster communication and collaboration across diverse groups, including implementation researchers, intermediariesi, as well as community stakeholders (SIRC uses the term “EBP champions” for these groups) – and to do so across multiple career levels (e.g., students, early career faculty, established investigators); and (2) enhance and disseminate rigorous measures and methodologies for implementing EBPs and evaluating EBP implementation efforts. These objectives are well aligned with Glasgow and colleagues’ [4] five core tenets deemed critical for advancing implementation science: collaboration, efficiency and speed, rigor and relevance, improved capacity, and cumulative knowledge. SIRC advances these objectives and tenets through in-person conferences, which bring together multidisciplinary implementation researchers and those implementing evidence-based behavioral health interventions in the community to share their work and create professional connections and collaborations

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation