Rice Galaxy: An open resource for plant science

Background: Rice molecular genetics, breeding, genetic diversity, and allied research (such as rice-pathogen interaction) have adopted sequencing technologies and high-density genotyping platforms for genome variation analysis and gene discovery. Germplasm collections representing rice diversity, improved varieties, and elite breeding materials are accessible through rice gene banks for use in research and breeding, with many having genome sequences and high-density genotype data available. Combining phenotypic and genotypic information on these accessions enables genome-wide association analysis, which is driving quantitative trait loci discovery and molecular marker development. Comparative sequence analyses across quantitative trait loci regions facilitate the discovery of novel alleles. Analyses involving DNA sequences and large genotyping matrices for thousands of samples, however, pose a challenge to non−computer savvy rice researchers. Findings: The Rice Galaxy resource has shared datasets that include high-density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from 9 published rice genomes. The Rice Galaxy web server and deployment installer includes tools for designing single-nucleotide polymorphism assays, analyzing genome-wide association studies, population diversity, rice−bacterial pathogen diagnostics, and a suite of published genomic prediction methods. A prototype Rice Galaxy compliant to Open Access, Open Data, and Findable, Accessible, Interoperable, and Reproducible principles is also presented. Conclusions: Rice Galaxy is a freely available resource that empowers the plant research community to perform state-of-the-art analyses and utilize publicly available big datasets for both fundamental and applied science

Effects of a school-based intervention on active commuting to school and health-related fitness

Background: Active commuting to school has declined over time, and interventions are needed to reverse this trend. The main objective was to investigate the effects of a school-based intervention on active commuting to school and health-related fitness in school-age children of Southern Spain. Methods: A total of 494 children aged 8 to 11 years were invited to participate in the study. The schools were non-randomly allocated (i.e., school level allocation) into the experimental group (EG) or the control group (CG). The EG received an intervention program for 6 months (a monthly activity) focused on increasing the level of active commuting to school and mainly targeting children’s perceptions and attitudes. Active commuting to school and health-related fitness (i.e., cardiorespiratory fitness, muscular fitness and speed-agility), were measured at baseline and at the end of the intervention. Children with valid data on commuting to school at baseline and follow-up, sex, age and distance from home to school were included in the final analysis (n = 251). Data was analyzed through a factorial ANOVA and the Bonferroni post-hoc test. Results: At follow up, the EG had higher rates of cycling to school than CG for boys only (p = 0.04), but not for walking to school for boys or girls. The EG avoided increases in the rates of passive commuting at follow up, which increased in the CG among girls for car (MD = 1.77; SE = 0.714; p = 0.010) and bus (MD = 1.77; SE = 0.714; p = 0.010) modes. Moreover, we observed significant interactions and main effects between independent variables (study group, sex and assessment time point) on health-related fitness (p < 0.05) over the 6-month period between groups, with higher values in the control group (mainly in boys). Conclusion: A school-based intervention focused on increasing active commuting to school was associated with increases in rates of cycling to school among boys, but not for walking to school or health-related fitness. However, the school-based intervention avoided increases in rates of passive commuting in the experimental group, which were significantly increased in girls of the control group

Side effects of analgesia may significantly reduce quality of life in symptomatic multiple myeloma: a cross-sectional prevalence study

Background Pain is a common symptom in patients with multiple myeloma (MM). Many patients are dependent on analgesics and in particular opioids, but there is limited information on the impact of these drugs and their side effects on health-related quality of life (HRQoL). Method In a cross-sectional study, semi-structured interviews were performed in 21 patients attending the hospital with symptomatic MM on pain medications. HRQoL was measured using items 29 and 30 of the European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30. Results Patients were able to recall a median of two (range 0–4) analgesics. They spontaneously identified a median of two (range 1–5) side effects attributable to their analgesic medications. Patients’ assessment of HRQoL based on the EORTC QLQ-C30 questions 29/30 was mean 48.3 (95 % CI; 38.7–57.9) out of 100. Patients’ assessment of their HRQoL in the hypothetical situation, in which they would not experience any side effects from analgesics, was significantly higher: 62.6 (53.5–71.7) (t test, p=0.001). Conclusion This study provides, for the first time, evidence that side effects of analgesics are common in symptomatic MM and may result in a statistically and clinically significant reduction of self-reported HRQoL

The Tuberculin Skin Test versus QuantiFERON TB Gold® in Predicting Tuberculosis Disease in an Adolescent Cohort Study in South Africa

Setting: This study was conducted in a high tuberculosis (TB) burden area in Worcester, South Africa, with a notified all TB incidence rate of 1,400/100,000. Main Objective: To compare the predictive value of a baseline tuberculin skin test (TST) with that of the QuantiFERON TB Gold (In-tube) assay (QFT) for subsequent microbiologically confirmed TB disease among adolescents. Methods: Adolescents aged 12-18 years were recruited from high schools in the study area. At baseline, blood was drawn for QFT and a TST administered. Participants were followed up for up to 3.8 years for incident TB disease (median 2.4 years). Results: After exclusions, 5244 (82.4%) of 6,363 adolescents enrolled, were analysed. The TB incidence rate was 0.60 cases per 100 person years (pyrs) (95% CI 0.43-0.82) for baseline TST positive (>= 5 mm) participants and 0.64 cases per 100 pyrs (95% CI 0.45-0.87) for baseline QFT positive participants. TB incidence rates were 0.22 per 100 pyrs (0.11-0.39) and 0.22 per 100 pyrs (0.12-0.38) among those with a negative baseline TST and QFT respectively. Sensitivity for incident TB disease was 76.9% for TST and 75.0% for QFT (p = 0.81). Positive predictive value was 1.4% for TST and 1.5% for QFT. Conclusion: Positive TST and QFT tests were moderately sensitive predictors of progression to microbiologically confirmed TB disease. There was no significant difference in the predictive ability of these tests for TB disease amongst adolescents in this high burden setting. Therefore, these findings do not support use of QFT in preference to TST to predict the risk of TB disease in this study populatio

Information Routing Driven by Background Chatter in a Signaling Network

Living systems are capable of processing multiple sources of information simultaneously. This is true even at the cellular level, where not only coexisting signals stimulate the cell, but also the presence of fluctuating conditions is significant. When information is received by a cell signaling network via one specific input, the existence of other stimuli can provide a background activity –or chatter– that may affect signal transmission through the network and, therefore, the response of the cell. Here we study the modulation of information processing by chatter in the signaling network of a human cell, specifically, in a Boolean model of the signal transduction network of a fibroblast. We observe that the level of external chatter shapes the response of the system to information carrying signals in a nontrivial manner, modulates the activity levels of the network outputs, and effectively determines the paths of information flow. Our results show that the interactions and node dynamics, far from being random, confer versatility to the signaling network and allow transitions between different information-processing scenarios

Simulating rewetting events in intermittent rivers and ephemeral streams: A global analysis of leached nutrients and organic matter

Climate change and human pressures are changing the global distribution and the ex‐ tent of intermittent rivers and ephemeral streams (IRES), which comprise half of the global river network area. IRES are characterized by periods of flow cessation, during which channel substrates accumulate and undergo physico‐chemical changes (precon‐ ditioning), and periods of flow resumption, when these substrates are rewetted and release pulses of dissolved nutrients and organic matter (OM). However, there are no estimates of the amounts and quality of leached substances, nor is there information on the underlying environmental constraints operating at the global scale. We experi‐ mentally simulated, under standard laboratory conditions, rewetting of leaves, river‐ bed sediments, and epilithic biofilms collected during the dry phase across 205 IRES from five major climate zones. We determined the amounts and qualitative character‐ istics of the leached nutrients and OM, and estimated their areal fluxes from riverbeds. In addition, we evaluated the variance in leachate characteristics in relation to selected environmental variables and substrate characteristics. We found that sediments, due to their large quantities within riverbeds, contribute most to the overall flux of dis‐ solved substances during rewetting events (56%–98%), and that flux rates distinctly differ among climate zones. Dissolved organic carbon, phenolics, and nitrate contrib‐ uted most to the areal fluxes. The largest amounts of leached substances were found in the continental climate zone, coinciding with the lowest potential bioavailability of the leached OM. The opposite pattern was found in the arid zone. Environmental vari‐ ables expected to be modified under climate change (i.e. potential evapotranspiration, aridity, dry period duration, land use) were correlated with the amount of leached sub‐ stances, with the strongest relationship found for sediments. These results show that the role of IRES should be accounted for in global biogeochemical cycles, especially because prevalence of IRES will increase due to increasing severity of drying event

Analysis of protein-coding genetic variation in 60,706 humans

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. We describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of truncating variants with 72% having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human “knockout” variants in protein-coding genes

Type 2 Diabetes Variants Disrupt Function of SLC16A11 through Two Distinct Mechanisms

Type 2 diabetes (T2D) affects Latinos at twice the rate seen in populations of European descent. We recently identified a risk haplotype spanning SLC16A11 that explains ∼20% of the increased T2D prevalence in Mexico. Here, through genetic fine-mapping, we define a set of tightly linked variants likely to contain the causal allele(s). We show that variants on the T2D-associated haplotype have two distinct effects: (1) decreasing SLC16A11 expression in liver and (2) disrupting a key interaction with basigin, thereby reducing cell-surface localization. Both independent mechanisms reduce SLC16A11 function and suggest SLC16A11 is the causal gene at this locus. To gain insight into how SLC16A11 disruption impacts T2D risk, we demonstrate that SLC16A11 is a proton-coupled monocarboxylate transporter and that genetic perturbation of SLC16A11 induces changes in fatty acid and lipid metabolism that are associated with increased T2D risk. Our findings suggest that increasing SLC16A11 function could be therapeutically beneficial for T2D. Video Abstract [Figure presented] Keywords: type 2 diabetes (T2D); genetics; disease mechanism; SLC16A11; MCT11; solute carrier (SLC); monocarboxylates; fatty acid metabolism; lipid metabolism; precision medicin