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Background: Late-life exposure to ambient air pollution is a modifiable risk factor for dementia, but epidemiological studies have shown inconsistent evidence for cognitive decline. Air quality (AQ) improvement has been associated with improved cardiopulmonary health and decreased mortality, but to the best of our knowledge, no studies have examined the association with cognitive function. We examined whether AQ improvement was associated with slower rate of cognitive decline in older women aged 74 to 92 years., Methods and Findings: We studied a cohort of 2,232 women residing in the 48 contiguous US states that were recruited from more than 40 study sites located in 24 states and Washington, DC from the Women's Health Initiative (WHI) Memory Study (WHIMS)-Epidemiology of Cognitive Health Outcomes (WHIMS-ECHO) study. They were predominantly non-Hispanic White women and were dementia free at baseline in 2008 to 2012. Measures of annual (2008 to 2018) cognitive function included the modified Telephone Interview for Cognitive Status (TICSm) and the telephone-based California Verbal Learning Test (CVLT). We used regionalized universal kriging models to estimate annual concentrations (1996 to 2012) of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) at residential locations. Estimates were aggregated to the 3-year average immediately preceding (recent exposure) and 10 years prior to (remote exposure) WHIMS-ECHO enrollment. Individual-level improved AQ was calculated as the reduction from remote to recent exposures. Linear mixed effect models were used to examine the associations between improved AQ and the rates of cognitive declines in TICSm and CVLT trajectories, adjusting for sociodemographic (age; geographic region; race/ethnicity; education; income; and employment), lifestyle (physical activity; smoking; and alcohol), and clinical characteristics (prior hormone use; hormone therapy assignment; depression; cardiovascular disease (CVD); hypercholesterolemia; hypertension; diabetes; and body mass index [BMI]). For both PM2.5 and NO2, AQ improved significantly over the 10 years before WHIMS-ECHO enrollment. During a median of 6.2 (interquartile range [IQR] = 5.0) years of follow-up, declines in both general cognitive status (β = -0.42/year, 95% CI: -0.44, -0.40) and episodic memory (β = -0.59/year, 95% CI: -0.64, -0.54) were observed. Greater AQ improvement was associated with slower decline in TICSm (βPM2.5improvement = 0.026 per year for improved PM2.5 by each IQR = 1.79 μg/m3 reduction, 95% CI: 0.001, 0.05; βNO2improvement = 0.034 per year for improved NO2 by each IQR = 3.92 parts per billion [ppb] reduction, 95% CI: 0.01, 0.06) and CVLT (βPM2.5 improvement = 0.070 per year for improved PM2.5 by each IQR = 1.79 μg/m3 reduction, 95% CI: 0.02, 0.12; βNO2improvement = 0.060 per year for improved NO2 by each IQR = 3.97 ppb reduction, 95% CI: 0.005, 0.12) after adjusting for covariates. The respective associations with TICSm and CVLT were equivalent to the slower decline rate found with 0.9 to 1.2 and1.4 to 1.6 years of younger age and did not significantly differ by age, region, education, Apolipoprotein E (ApoE) e4 genotypes, or cardiovascular risk factors. The main limitations of this study include measurement error in exposure estimates, potential unmeasured confounding, and limited generalizability., Conclusions: In this study, we found that greater improvement in long-term AQ in late life was associated with slower cognitive declines in older women. This novel observation strengthens the epidemiologic evidence of an association between air pollution and cognitive aging., Competing Interests: The authors have declared that no competing interests exist.

Observing organisms that evolve in response to strong selection over very short time scales allows the determination of the molecular mechanisms underlying adaptation. Although dissecting these molecular mechanisms is expensive and time-consuming, general patterns can be detected from repeated experiments, illuminating the biological processes involved in evolutionary adaptation. The bacteriophage φX174 was grown for 50 days in replicate chemostats under two culture conditions: Escherichia coli C as host growing at 37°C and Salmonella typhimurium as host growing at 43.5°C. After 50 days, greater than 20 substitutions per chemostat had risen to detectable levels. Of the 97 substitutions, four occurred in all four chemostats, five arose in both culture conditions, eight arose in only the high temperature S. typhimurium chemostats, and seven arose only in the E. coli chemostats. The remaining substitutions were detected only in a single chemostat, however, almost half of these have been seen in other similar experiments. Our findings support previous studies that host recognition and capsid stability are two biological processes that are modified during adaptation to novel hosts and high temperature. Based upon the substitutions shared across both environments, it is apparent that genome replication and packaging are also affected during adaptation to the chemostat environment, rather than to temperature or host per se. This environment is characterized by a large number of phage and very few hosts, leading to competition among phage within the host. We conclude from these results that adaptation to a high density environment selects for changes in genome replication at both protein and DNA sequence levels.

Background: In hepatocellular carcinoma (HCC) genes predictive of survival have been found in both adjacent normal (AN) and tumor (TU) tissues. The relationships between these two sets of predictive genes and the general process of tumorigenesis and disease progression remains unclear., Methodology/principal Findings: Here we have investigated HCC tumorigenesis by comparing gene expression, DNA copy number variation and survival using ∼250 AN and TU samples representing, respectively, the pre-cancer state, and the result of tumorigenesis. Genes that participate in tumorigenesis were defined using a gene-gene correlation meta-analysis procedure that compared AN versus TU tissues. Genes predictive of survival in AN (AN-survival genes) were found to be enriched in the differential gene-gene correlation gene set indicating that they directly participate in the process of tumorigenesis. Additionally the AN-survival genes were mostly not predictive after tumorigenesis in TU tissue and this transition was associated with and could largely be explained by the effect of somatic DNA copy number variation (sCNV) in cis and in trans. The data was consistent with the variance of AN-survival genes being rate-limiting steps in tumorigenesis and this was confirmed using a treatment that promotes HCC tumorigenesis that selectively altered AN-survival genes and genes differentially correlated between AN and TU., Conclusions/significance: This suggests that the process of tumor evolution involves rate-limiting steps related to the background from which the tumor evolved where these were frequently predictive of clinical outcome. Additionally treatments that alter the likelihood of tumorigenesis occurring may act by altering AN-survival genes, suggesting that the process can be manipulated. Further sCNV explains a substantial fraction of tumor specific expression and may therefore be a causal driver of tumor evolution in HCC and perhaps many solid tumor types.

Despite decades of research in defining sleep-wake properties in mammals, little is known about the nature or identity of genes that regulate sleep, a fundamental behaviour that in humans occupies about one-third of the entire lifespan. While genome-wide association studies in humans and quantitative trait loci (QTL) analyses in mice have identified candidate genes for an increasing number of complex traits and genetic diseases, the resources and time-consuming process necessary for obtaining detailed quantitative data have made sleep seemingly intractable to similar large-scale genomic approaches. Here we describe analysis of 20 sleep-wake traits from 269 mice from a genetically segregating population that reveals 52 significant QTL representing a minimum of 20 genomic loci. While many (28) QTL affected a particular sleep-wake trait (e.g., amount of wake) across the full 24-hr day, other loci only affected a trait in the light or dark period while some loci had opposite effects on the trait during the light vs. dark. Analysis of a dataset for multiple sleep-wake traits led to previously undetected interactions (including the differential genetic control of number and duration of REM bouts), as well as possible shared genetic regulatory mechanisms for seemingly different unrelated sleep-wake traits (e.g., number of arousals and REM latency). Construction of a Bayesian network for sleep-wake traits and loci led to the identification of sub-networks of linkage not detectable in smaller data sets or limited single-trait analyses. For example, the network analyses revealed a novel chain of causal relationships between the chromosome 17@29cM QTL, total amount of wake, and duration of wake bouts in both light and dark periods that implies a mechanism whereby overall sleep need, mediated by this locus, in turn determines the length of each wake bout. Taken together, the present results reveal a complex genetic landscape underlying multiple sleep-wake traits and emphasize the need for a systems biology approach for elucidating the full extent of the genetic regulatory mechanisms of this complex and universal behavior.

Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process.