Murine Cytomegalovirus Infection of Neural Stem Cells Alters Neurogenesis in the Developing Brain

Congenital cytomegalovirus (CMV) brain infection causes serious neuro-developmental sequelae including: mental retardation, cerebral palsy, and sensorineural hearing loss. But, the mechanisms of injury and pathogenesis to the fetal brain are not completely understood. The present study addresses potential pathogenic mechanisms by which this virus injures the CNS using a neonatal mouse model that mirrors congenital brain infection. This investigation focused on, analysis of cell types infected with mouse cytomegalovirus (MCMV) and the pattern of injury to the developing brain.We used our MCMV infection model and a multi-color flow cytometry approach to quantify the effect of viral infection on the developing brain, identifying specific target cells and the consequent effect on neurogenesis. In this study, we show that neural stem cells (NSCs) and neuronal precursor cells are the principal target cells for MCMV in the developing brain. In addition, viral infection was demonstrated to cause a loss of NSCs expressing CD133 and nestin. We also showed that infection of neonates leads to subsequent abnormal brain development as indicated by loss of CD24(hi) cells that incorporated BrdU. This neonatal brain infection was also associated with altered expression of Oct4, a multipotency marker; as well as down regulation of the neurotrophins BDNF and NT3, which are essential to regulate the birth and differentiation of neurons during normal brain development. Finally, we report decreased expression of doublecortin, a marker to identify young neurons, following viral brain infection.MCMV brain infection of newborn mice causes significant loss of NSCs, decreased proliferation of neuronal precursor cells, and marked loss of young neurons

Exome Chip Meta-analysis Fine Maps Causal Variants and Elucidates the Genetic Architecture of Rare Coding Variants in Smoking and Alcohol Use

BACKGROUND: Smoking and alcohol use have been associated with common genetic variants in multiple loci. Rare variants within these loci hold promise in the identification of biological mechanisms in substance use. Exome arrays and genotype imputation can now efficiently genotype rare nonsynonymous and loss of function variants. Such variants are expected to have deleterious functional consequences and to contribute to disease risk. METHODS: We analyzed similar to 250,000 rare variants from 16 independent studies genotyped with exome arrays and augmented this dataset with imputed data from the UK Biobank. Associations were tested for five phenotypes: cigarettes per day, pack-years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted stratified heritability analyses, single-variant tests, and gene-based burden tests of nonsynonymous/loss-of-function coding variants. We performed a novel fine-mapping analysis to winnow the number of putative causal variants within associated loci. RESULTS: Meta-analytic sample sizes ranged from 152,348 to 433,216, depending on the phenotype. Rare coding variation explained 1.1% to 2.2% of phenotypic variance, reflecting 11% to 18% of the total single nucleotide polymorphism heritability of these phenotypes. We identified 171 genome-wide associated loci across all phenotypes. Fine mapping identified putative causal variants with double base-pair resolution at 24 of these loci, and between three and 10 variants for 65 loci. Twenty loci contained rare coding variants in the 95% credible intervals. CONCLUSIONS: Rare coding variation significantly contributes to the heritability of smoking and alcohol use. Fine-mapping genome-wide association study loci identifies specific variants contributing to the biological etiology of substance use behavior.Peer reviewe

Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci

Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations withP <5 x 10(-8)in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P <5 x 10(-8)) in the discovery samples. Ten novel SNVs, including rs12616219 nearTMEM182, were followed-up and five of them (rs462779 inREV3L, rs12780116 inCNNM2, rs1190736 inGPR101, rs11539157 inPJA1, and rs12616219 nearTMEM182) replicated at a Bonferroni significance threshold (P <4.5 x 10(-3)) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, inCCDC141and two low-frequency SNVs inCEP350andHDGFRP2. Functional follow-up implied that decreased expression ofREV3Lmay lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.Peer reviewe

Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use

Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders. They are heritable and etiologically related behaviors that have been resistant to gene discovery efforts. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures

Gender differences in sleep disruption during COVID-19: cross-sectional analyses from two UK nationally representative surveys

Objective COVID-19 related measures have impacted sleep on a global level. We examine changes in sleep problems and duration focusing on gender differentials. Design Cross-sectional analyses using two nationally representative surveys collected during the first and second month after the 2020 lockdown in the UK. Setting and participants Participants (age 17 years and above) from the first wave of the Understanding Society COVID-19 Study are linked to the most recent wave before the pandemic completed during 2018 and 2019 (n=14 073). COVID-19 Survey Data was collected from 2 to 31 May 2020 (n=8547) with participants drawn from five nationally representative cohort studies in the UK. Analysis We conducted bivariate analyses to examine gender gaps in change in sleep problems and change in sleep duration overall and by other predictors. A series of multivariate ordinary least squares (OLS) regression models were estimated to explore predictors of change in sleep problems and change in sleep time. Results People in the UK on average experienced an increase in sleep loss during the first 4 weeks of the lockdown (mean=0.13, SD=0.9). Women report more sleep loss than men (coefficient=0.15, 95% CI 0.11 to 0.19). Daily sleep duration on average increased by ten minutes (mean=−0.16, SD=1.11), with men gaining eight more minutes of sleep per day than women (coefficient=0.13, 95% CI 0.09 to 0.17). Conclusion The COVID-19 related measures amplified traditional gender roles. Men’s sleep was more affected by changes in their financial situation and employment status related to the crisis, with women more influenced by their emotional reaction to the pandemic, feeling anxious and spending more time on family duties such as home schooling, unpaid domestic duties, nurturing and caregiving. Based on our findings, we provide policy advice of early, clear and better employment protection coverage of self-employed and precarious workers and employer recognition for parents

Factors affecting adherence to non-pharmaceutical interventions for COVID-19 infections in the first year of the pandemic in the UK

Objective Non-pharmaceutical interventions (NPIs), including wearing face covering/masks, social distancing and working from home, have been introduced to control SARS-CoV-2 infections. We provide individual-level empirical evidence of whether adherence reduces infections. Setting and participants The COVID-19 Infection Study (CIS) was used from 10 May 2020 to 2 February 2021 with 409 009 COVID-19 nose and throat swab tests nested in 72 866 households for 100 138 individuals in the labour force aged 18–64. Analysis ORs for a positive COVID-19 test were calculated using multilevel logistic regression models, stratified by sex and time, by an index of autonomy to abide by NPIs, adjusted for various socioeconomic and behavioural covariates. Results Inability to comply with NPIs predicted higher infections when individuals reported not wearing a face covering outside. The main effect for inability to comply was OR 0.79 (95% CI 0.67 to 0.92), for wearing face covering/masks was OR 0.29 (95% CI 0.15 to 0.56) and the interaction term being OR 1.25 (95% CI 1.07 to 1.46). The youngest age groups had a significantly higher risk of infection (OR 1.52, 95% CI 1.28 to 1.82) as did women in larger households (OR 1.04, 95% CI 1.02 to 1.06). Effects varied over time with autonomy to follow NPIs only significant in the pre-second lockdown May–November 2020 period. Wearing a face covering outside was a significant predictor of a lower chance of infection before mid-December 2020 when a stricter second lockdown was implemented (OR 0.44, 95% CI 0.27 to 0.73). Conclusion The protective effect of wearing a face covering/mask was the strongest for those who were the most unable to comply with NPIs. Higher infection rates were in younger groups and women in large households. Wearing a face covering or mask outside the home consistently and significantly predicted lower infection before the 2020 Christmas period and among women

ブンカ ノ ヒ ノ シャセツ ワ ジンカク チセイ ノ シンリガクテキ ケンキュウ ノ タイショウ ト ナリウルカ アサヒ シンブン シャセツ 1948ネン 1994ネン ノ ブンセキ ト コウサツ

The Genes for Good study uses social media to engage a large, diverse participant pool in genetics research and education. Health historyand daily tracking surveys are administered through a Facebook application, and participants who complete a minimum number of sur-veys are mailed a saliva sample kit (‘‘spit kit’’) to collect DNA for genotyping. As of March 2019, we engaged&gt;80,000 individuals, sentspit kits to&gt;32,000 individuals who met minimum participation requirements, and collected&gt;27,000 spit kits. Participants come fromall 50 states and include a diversity of ancestral backgrounds. Rates of important chronic health indicators are consistent with those esti-mated for the general U.S. population using more traditional study designs. However, our sample is younger and contains a greater per-centage of females than the general population. As one means of verifying data quality, we have replicated genome-wide associationstudies (GWASs) for exemplar traits, such as asthma, diabetes, body mass index (BMI), and pigmentation. The flexible framework ofthe web application makes it relatively simple to add new questionnaires and for other researchers to collaborate. We anticipate thatthe study sample will continue to grow and that future analyses may further capitalize on the strengths of the longitudinal data in com-bination with genetic information

Demographic science aids in understanding the spread and fatality rates of COVID-19

Governments around the world must rapidly mobilize and make difficult policy decisions to mitigate the coronavirus disease 2019 (COVID-19) pandemic. Because deaths have been concentrated at older ages, we highlight the important role of demography, particularly, how the age structure of a population may help explain differences in fatality rates across countries and how transmission unfolds. We examine the role of age structure in deaths thus far in Italy and South Korea and illustrate how the pandemic could unfold in populations with similar population sizes but different age structures, showing a dramatically higher burden of mortality in countries with older versus younger populations. This powerful interaction of demography and current age-specific mortality for COVID-19 suggests that social distancing and other policies to slow transmission should consider the age composition of local and national contexts as well as intergenerational interactions. We also call for countries to provide case and fatality data disaggregated by age and sex to improve real-time targeted forecasting of hospitalization and critical care needs