Frequency and type of adverse analytical findings in athletics: Differences among disciplines.

Athletics is a highly diverse sport that contains a set of disciplines grouped into jumps, throws, races of varying distances, and combined events. From a physiological standpoint, the physical capabilities linked to success are quite different among disciplines, with varying involvements of muscle strength, muscle power, and endurance. Thus, the use of banned substances in athletics might be dictated by physical dimensions of each discipline. Thus, the aim of this investigation was to analyse the number and distribution of adverse analytical findings per drug class in athletic disciplines. The data included in this investigation were gathered from the Anti-Doping Testing Figure Report made available by the World Anti-Doping Agency (from 2016 to 2018). Interestingly, there were no differences in the frequency of adverse findings (overall, 0.95%, range from 0.77 to 1.70%) among disciplines despite long distance runners having the highest number of samples analysed per year ( 9812 samples/year). Sprinters and throwers presented abnormally high proportions of adverse analytical findings within the group of anabolic agents (p < 0.01); middle- and long-distance runners presented atypically high proportions of findings related to peptide hormones and growth factors (p < 0.01); racewalkers presented atypically high proportions of banned diuretics and masking agents (p = 0.05). These results suggest that the proportion of athletes that are using banned substances is similar among the different disciplines of athletics. However, there are substantial differences in the class of drugs more commonly used in each discipline. This information can be used to effectively enhance anti-doping testing protocols in athletics.post-print1.911 K

Genomic Convergence among ERRα, PROX1, and BMAL1 in the Control of Metabolic Clock Outputs

Metabolic homeostasis and circadian rhythms are closely intertwined biological processes. Nuclear receptors, as sensors of hormonal and nutrient status, are actively implicated in maintaining this physiological relationship. Although the orphan nuclear receptor estrogen-related receptor α (ERRα, NR3B1) plays a central role in the control of energy metabolism and its expression is known to be cyclic in the liver, its role in temporal control of metabolic networks is unknown. Here we report that ERRα directly regulates all major components of the molecular clock. ERRα-null mice also display deregulated locomotor activity rhythms and circadian period lengths under free-running conditions, as well as altered circulating diurnal bile acid and lipid profiles. In addition, the ERRα-null mice exhibit time-dependent hypoglycemia and hypoinsulinemia, suggesting a role for ERRα in modulating insulin sensitivity and glucose handling during the 24-hour light/dark cycle. We also provide evidence that the newly identified ERRα corepressor PROX1 is implicated in rhythmic control of metabolic outputs. To help uncover the molecular basis of these phenotypes, we performed genome-wide location analyses of binding events by ERRα, PROX1, and BMAL1, an integral component of the molecular clock. These studies revealed the existence of transcriptional regulatory loops among ERRα, PROX1, and BMAL1, as well as extensive overlaps in their target genes, implicating these three factors in the control of clock and metabolic gene networks in the liver. Genomic convergence of ERRα, PROX1, and BMAL1 transcriptional activity thus identified a novel node in the molecular circuitry controlling the daily timing of metabolic processes

Interrupting Malaria Transmission: Quantifying the Impact of Interventions in Regions of Low to Moderate Transmission

Malaria has been eliminated from over 40 countries with an additional 39 currently planning for, or committed to, elimination. Information on the likely impact of available interventions, and the required time, is urgently needed to help plan resource allocation. Mathematical modelling has been used to investigate the impact of various interventions; the strength of the conclusions is boosted when several models with differing formulation produce similar data. Here we predict by using an individual-based stochastic simulation model of seasonal Plasmodium falciparum transmission that transmission can be interrupted and parasite reintroductions controlled in villages of 1,000 individuals where the entomological inoculation rate is <7 infectious bites per person per year using chemotherapy and bed net strategies. Above this transmission intensity bed nets and symptomatic treatment alone were not sufficient to interrupt transmission and control the importation of malaria for at least 150 days. Our model results suggest that 1) stochastic events impact the likelihood of successfully interrupting transmission with large variability in the times required, 2) the relative reduction in morbidity caused by the interventions were age-group specific, changing over time, and 3) the post-intervention changes in morbidity were larger than the corresponding impact on transmission. These results generally agree with the conclusions from previously published models. However the model also predicted changes in parasite population structure as a result of improved treatment of symptomatic individuals; the survival probability of introduced parasites reduced leading to an increase in the prevalence of sub-patent infections in semi-immune individuals. This novel finding requires further investigation in the field because, if confirmed, such a change would have a negative impact on attempts to eliminate the disease from areas of moderate transmission

Predicting DNA-Binding Specificities of Eukaryotic Transcription Factors

Today, annotated amino acid sequences of more and more transcription factors (TFs) are readily available. Quantitative information about their DNA-binding specificities, however, are hard to obtain. Position frequency matrices (PFMs), the most widely used models to represent binding specificities, are experimentally characterized only for a small fraction of all TFs. Even for some of the most intensively studied eukaryotic organisms (i.e., human, rat and mouse), roughly one-sixth of all proteins with annotated DNA-binding domain have been characterized experimentally. Here, we present a new method based on support vector regression for predicting quantitative DNA-binding specificities of TFs in different eukaryotic species. This approach estimates a quantitative measure for the PFM similarity of two proteins, based on various features derived from their protein sequences. The method is trained and tested on a dataset containing 1 239 TFs with known DNA-binding specificity, and used to predict specific DNA target motifs for 645 TFs with high accuracy

Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts

The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions. The study of these maps provides opportunities for future toxicogenomics and drug discovery applications. We developed a computational framework to build disease-specific drug-protein connectivity maps. We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples. We described the development and application of this computational framework using Alzheimer's Disease (AD) as a primary example in three steps. First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins. Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies. Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature. We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment. Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts. To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results. Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for drug development, in which therapeutic/toxicological profiles of candidate drugs can be checked computationally before costly clinical trials begin

A Key Marine Diazotroph in a Changing Ocean: The Interacting Effects of Temperature, CO2 and Light on the Growth of Trichodesmium erythraeum IMS101

Trichodesmium is a globally important marine diazotroph that accounts for approximately 60-80% of marine biological N2 fixation and as such plays a key role in marine N and C cycles. We undertook a comprehensive assessment of how the growth rate of Trichodesmium erythraeum IMS101 was directly affected by the combined interactions of temperature, pCO2 and light intensity. Our key findings were: low pCO2 affected the lower temperature tolerance limit (Tmin) but had no effect on the optimum temperature (Topt) at which growth was maximal or the maximum temperature tolerance limit (Tmax); low pCO2 had a greater effect on the thermal niche width than low-light; the effect of pCO2 on growth rate was more pronounced at suboptimal temperatures than at supraoptimal temperatures; temperature and light had a stronger effect on the photosynthetic efficiency (Fv/Fm) than did CO2; and at Topt, the maximum growth rate increased with increasing CO2, but the initial slope of the growth-irradiance curve was not affected by CO2. In the context of environmental change, our results suggest that the (i) nutrient replete growth rate of Trichodesmium IMS101 would have been severely limited by low pCO2 at the last glacial maximum (LGM), (ii) future increases in pCO2 will increase growth rates in areas where temperature ranges between Tmin to Topt, but will have negligible effect at temperatures between Topt and Tmax, (iii) areal increase of warm surface waters (> 18°C) has allowed the geographic range to increase significantly from the LGM to present and that the range will continue to expand to higher latitudes with continued warming, but (iv) continued global warming may exclude Trichodesmium spp. from some tropical regions by 2100 where temperature exceeds Topt

MicroRNA Genes Derived from Repetitive Elements and Expanded by Segmental Duplication Events in Mammalian Genomes

MicroRNAs (miRNAs) are a class of small noncoding RNAs that regulate gene expression by targeting mRNAs for translation repression or mRNA degradation. Many miRNAs are being discovered and studied, but in most cases their origin, evolution and function remain unclear. Here, we characterized miRNAs derived from repetitive elements and miRNA families expanded by segmental duplication events in the human, rhesus and mouse genomes. We applied a comparative genomics approach combined with identifying miRNA paralogs in segmental duplication pair data in a genome-wide study to identify new homologs of human miRNAs in the rhesus and mouse genomes. Interestingly, using segmental duplication pair data, we provided credible computational evidence that two miRNA genes are located in the pseudoautosomal region of the human Y chromosome. We characterized all the miRNAs whether they were derived from repetitive elements or not and identified significant differences between the repeat-related miRNAs (RrmiRs) and non-repeat-derived miRNAs in (1) their location in protein-coding and intergenic regions in genomes, (2) the minimum free energy of their hairpin structures, and (3) their conservation in vertebrate genomes. We found some lineage-specific RrmiR families and three lineage-specific expansion families, and provided evidence indicating that some RrmiR families formed and expanded during evolutionary segmental duplication events. We also provided computational and experimental evidence for the functions of the conservative RrmiR families in the three species. Together, our results indicate that repetitive elements contribute to the origin of miRNAs, and large segmental duplication events could prompt the expansion of some miRNA families, including RrmiR families. Our study is a valuable contribution to the knowledge of evolution and function of non-coding region in genome