Data from: The Drosophila Genetic Reference Panel (DGRP) on locomotor activity across different environmental conditions

In nature, organisms are exposed to variable and occasionally stressful environmental conditions. Responses to diurnal and seasonal fluctuations, such as temperature and food accessibility, involve adaptive behavioral and physiological changes. While much work has been done on understanding the genetic architecture and evolutionary potential of stress tolerance traits under constant thermal conditions, there has been less focus on the quantitative genetic background in variable environments. In this study, we use the Drosophila Genetic Reference Panel (DGRP) to investigate locomotor activity, a key behavioral trait, under variable natural thermal conditions during the summer in a temperate environment. Male flies from 100 DGRP lines were exposed to natural thermal and light conditions in Drosophila activity monitors across three experimental days. We found that activity was highly temperature- and time-dependent and varied between lines both within and between days. Further, we observed variation in genetic and environmental variance components, with low to moderate estimates of the heritability for locomotor activity, consistently peaking in the afternoons. Moreover, we showed that the estimated genetic correlations of locomotor activity between two time points decreased as the absolute differences in ambient temperature was increased. In conclusion, we find that the genetic background for locomotor activity is environment specific and we conclude that more variable and unpredictable future temperatures will likely have a strong impact on the evolutionary trajectories of behavioral traits in ectotherms

Additional file 3 of Comparative proteomics analysis in different stages of urothelial bladder cancer for identification of potential biomarkers: highlighted role for antioxidant activity

Supplementary Table S3: Reactome topology pathways obtained from over-representation analysi

Additional file 3 of Comparative proteomics analysis in different stages of urothelial bladder cancer for identification of potential biomarkers: highlighted role for antioxidant activity

Supplementary Table S3: Reactome topology pathways obtained from over-representation analysi

Histological and MRI brain atlas of the common shrew, Sorex araneus, with brain region-specific gene expression profiles

:una

Gene expression comparisons between captive and wild shrew brains reveal captivity effects

Compared to their free-ranging counterparts, wild animals in captivity experience different conditions with lasting physiological and behavioral effects. Although shifts in gene expression are expected to occur upstream of these phenotypes, we found no previous gene expression comparisons of captive vs. free-ranging mammals. We assessed gene expression profiles of three brain regions (cortex, olfactory bulb, and hippocampus) of wild shrews (Sorex araneus) compared to shrews kept in captivity for two months and undertook sample drop-out to examine robustness given limited sample sizes. Consistent with captivity effects, we found hundreds of differentially expressed genes in all three brain regions, 104 overlapping across all three, that enriched pathways associated with neurodegenerative disease, oxidative phosphorylation, and genes encoding ribosomal proteins. In the shrew, transcriptomic changes detected under captivity resemble responses in several human pathologies, including major depressive disorder and neurodegeneration. While interpretations of individual genes are tempered by small sample sizes, we propose captivity influences brain gene expression and function and can confound analyses of natural processes in wild individuals under captive conditions

Additional file 2 of Comparative proteomics analysis in different stages of urothelial bladder cancer for identification of potential biomarkers: highlighted role for antioxidant activity

Supplementary Table S2: The list of GO enriched in 12 identified protein

Additional file 2 of Predicting the presence of coronary plaques featuring high-risk characteristics using polygenic risk scores and targeted proteomics in patients with suspected coronary artery disease

Additional file 2: Table S4. Summary level data of number of risk factors, proteomics and GPSMult stratified by case/control status

Additional file 2 of Predicting the presence of coronary plaques featuring high-risk characteristics using polygenic risk scores and targeted proteomics in patients with suspected coronary artery disease

Additional file 2: Table S4. Summary level data of number of risk factors, proteomics and GPSMult stratified by case/control status

Predicting the presence of coronary plaques featuring high-risk characteristics using polygenic risk scores and targeted proteomics in patients with suspected coronary artery disease

Abstract Background The presence of coronary plaques with high-risk characteristics is strongly associated with adverse cardiac events beyond the identification of coronary stenosis. Testing by coronary computed tomography angiography (CCTA) enables the identification of high-risk plaques (HRP). Referral for CCTA is presently based on pre-test probability estimates including clinical risk factors (CRFs); however, proteomics and/or genetic information could potentially improve patient selection for CCTA and, hence, identification of HRP. We aimed to (1) identify proteomic and genetic features associated with HRP presence and (2) investigate the effect of combining CRFs, proteomics, and genetics to predict HRP presence. Methods Consecutive chest pain patients (n = 1462) undergoing CCTA to diagnose obstructive coronary artery disease (CAD) were included. Coronary plaques were assessed using a semi-automatic plaque analysis tool. Measurements of 368 circulating proteins were obtained with targeted Olink panels, and DNA genotyping was performed in all patients. Imputed genetic variants were used to compute a multi-trait multi-ancestry genome-wide polygenic score (GPSMult). HRP presence was defined as plaques with two or more high-risk characteristics (low attenuation, spotty calcification, positive remodeling, and napkin ring sign). Prediction of HRP presence was performed using the glmnet algorithm with repeated fivefold cross-validation, using CRFs, proteomics, and GPSMult as input features. Results HRPs were detected in 165 (11%) patients, and 15 input features were associated with HRP presence. Prediction of HRP presence based on CRFs yielded a mean area under the receiver operating curve (AUC) ± standard error of 73.2 ± 0.1, versus 69.0 ± 0.1 for proteomics and 60.1 ± 0.1 for GPSMult. Combining CRFs with GPSMult increased prediction accuracy (AUC 74.8 ± 0.1 (P = 0.004)), while the inclusion of proteomics provided no significant improvement to either the CRF (AUC 73.2 ± 0.1, P = 1.00) or the CRF + GPSMult (AUC 74.6 ± 0.1, P = 1.00) models, respectively. Conclusions In patients with suspected CAD, incorporating genetic data with either clinical or proteomic data improves the prediction of high-risk plaque presence. Trial registration https://clinicaltrials.gov/ct2/show/NCT02264717 (September 2014)

Data from: Environmental variation partitioned into separate heritable components

Trait variation is normally separated into genetic and environmental components, yet genetic factors also control the expression of environmental variation, encompassing plasticity across environmental gradients and within-environment responses. We defined four components of environmental variation: plasticity across environments, variability in plasticity, variation within environments, and differences in within-environment variation across environments. We assessed these components for cold tolerance across five rearing temperatures using the Drosophila melanogaster Genetic Reference Panel (DGRP). The four components were found to be heritable, and genetically correlated to different extents. By whole genome single marker regression, we detected multiple candidate genes controlling the four components and showed limited overlap in genes affecting them. Using the binary UAS-GAL4 system, we functionally validated the effects of a subset of candidate genes affecting each of the four components of environmental variation and also confirmed the genetic and phenotypic correlations obtained from the DGRP in distinct genetic backgrounds. We delineate selection targets associated with environmental variation and the constraints acting upon them, providing a framework for evolutionary and applied studies on environmental sensitivity. Based on our results we suggest that the traditional quantitative genetic view of environmental variation and genotype-by-environment interactions needs revisiting