Universality, limits and predictability of gold-medal performances at the Olympic Games

Inspired by the Games held in ancient Greece, modern Olympics represent the world's largest pageant of athletic skill and competitive spirit. Performances of athletes at the Olympic Games mirror, since 1896, human potentialities in sports, and thus provide an optimal source of information for studying the evolution of sport achievements and predicting the limits that athletes can reach. Unfortunately, the models introduced so far for the description of athlete performances at the Olympics are either sophisticated or unrealistic, and more importantly, do not provide a unified theory for sport performances. Here, we address this issue by showing that relative performance improvements of medal winners at the Olympics are normally distributed, implying that the evolution of performance values can be described in good approximation as an exponential approach to an a priori unknown limiting performance value. This law holds for all specialties in athletics-including running, jumping, and throwing-and swimming. We present a self-consistent method, based on normality hypothesis testing, able to predict limiting performance values in all specialties. We further quantify the most likely years in which athletes will breach challenging performance walls in running, jumping, throwing, and swimming events, as well as the probability that new world records will be established at the next edition of the Olympic Games.Comment: 8 pages, 3 figures, 1 table. Supporting information files and data are available at filrad.homelinux.or

Improved annotation of 3' untranslated regions and complex loci by combination of strand-specific direct RNA sequencing, RNA-seq and ESTs

The reference annotations made for a genome sequence provide the framework for all subsequent analyses of the genome. Correct annotation is particularly important when interpreting the results of RNA-seq experiments where short sequence reads are mapped against the genome and assigned to genes according to the annotation. Inconsistencies in annotations between the reference and the experimental system can lead to incorrect interpretation of the effect on RNA expression of an experimental treatment or mutation in the system under study. Until recently, the genome-wide annotation of 3-prime untranslated regions received less attention than coding regions and the delineation of intron/exon boundaries. In this paper, data produced for samples in Human, Chicken and A. thaliana by the novel single-molecule, strand-specific, Direct RNA Sequencing technology from Helicos Biosciences which locates 3-prime polyadenylation sites to within +/- 2 nt, were combined with archival EST and RNA-Seq data. Nine examples are illustrated where this combination of data allowed: (1) gene and 3-prime UTR re-annotation (including extension of one 3-prime UTR by 5.9 kb); (2) disentangling of gene expression in complex regions; (3) clearer interpretation of small RNA expression and (4) identification of novel genes. While the specific examples displayed here may become obsolete as genome sequences and their annotations are refined, the principles laid out in this paper will be of general use both to those annotating genomes and those seeking to interpret existing publically available annotations in the context of their own experimental dataComment: 44 pages, 9 figure

The Escherichia coli transcriptome mostly consists of independently regulated modules

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome

Cancer somatic mutations cluster in a subset of regulatory sites predicted from the ENCODE data

Background: Transcriptional regulation of gene expression is essential for cellular differentiation and function, and defects in the process are associated with cancer. The ENCODE project has mapped potential regulatory sites across the complete genome in many cell types, and these regions have been shown to harbour many of the somatic mutations that occur in cancer cells, suggesting that their effects may drive cancer initiation and development. The ENCODE data suggests a very large number of regulatory sites, and methods are needed to identify those that are most relevant and to connect them to the genes that they control. Methods: Predictive models of gene expression were developed by integrating the ENCODE data for regulation, including transcription factor binding and DNase1 hypersensitivity, with RNA-seq data for gene expression. A penalized regression method was used to identify the most predictive potential regulatory sites for each transcript. Known cancer somatic mutations from the COSMIC database were mapped to potential regulatory sites, and we examined differences in the mapping frequencies associated with sites chosen in regulatory models and other (rejected) sites. The effects of potential confounders, for example replication timing, were considered. Results: Cancer somatic mutations preferentially occupy those regulatory regions chosen in our models as most predictive of gene expression. Conclusion: Our methods have identified a significantly reduced set of regulatory sites that are enriched in cancer somatic mutations and are more predictive of gene expression. This has significance for the mechanistic interpretation of cancer mutations, and the understanding of genetic regulation

Control of intestinal stem cell function and proliferation by mitochondrial pyruvate metabolism.

Most differentiated cells convert glucose to pyruvate in the cytosol through glycolysis, followed by pyruvate oxidation in the mitochondria. These processes are linked by the mitochondrial pyruvate carrier (MPC), which is required for efficient mitochondrial pyruvate uptake. In contrast, proliferative cells, including many cancer and stem cells, perform glycolysis robustly but limit fractional mitochondrial pyruvate oxidation. We sought to understand the role this transition from glycolysis to pyruvate oxidation plays in stem cell maintenance and differentiation. Loss of the MPC in Lgr5-EGFP-positive stem cells, or treatment of intestinal organoids with an MPC inhibitor, increases proliferation and expands the stem cell compartment. Similarly, genetic deletion of the MPC in Drosophila intestinal stem cells also increases proliferation, whereas MPC overexpression suppresses stem cell proliferation. These data demonstrate that limiting mitochondrial pyruvate metabolism is necessary and sufficient to maintain the proliferation of intestinal stem cells

Attention-dependent modulation of cortical taste circuits revealed by granger causality with signal-dependent noise

We show, for the first time, that in cortical areas, for example the insular, orbitofrontal, and lateral prefrontal cortex, there is signal-dependent noise in the fMRI blood-oxygen level dependent (BOLD) time series, with the variance of the noise increasing approximately linearly with the square of the signal. Classical Granger causal models are based on autoregressive models with time invariant covariance structure, and thus do not take this signal-dependent noise into account. To address this limitation, here we describe a Granger causal model with signal-dependent noise, and a novel, likelihood ratio test for causal inferences. We apply this approach to the data from an fMRI study to investigate the source of the top-down attentional control of taste intensity and taste pleasantness processing. The Granger causality with signal-dependent noise analysis reveals effects not identified by classical Granger causal analysis. In particular, there is a top-down effect from the posterior lateral prefrontal cortex to the insular taste cortex during attention to intensity but not to pleasantness, and there is a top-down effect from the anterior and posterior lateral prefrontal cortex to the orbitofrontal cortex during attention to pleasantness but not to intensity. In addition, there is stronger forward effective connectivity from the insular taste cortex to the orbitofrontal cortex during attention to pleasantness than during attention to intensity. These findings indicate the importance of explicitly modeling signal-dependent noise in functional neuroimaging, and reveal some of the processes involved in a biased activation theory of selective attention

Genetic determinants of co-accessible chromatin regions in activated T cells across humans.

Over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4+ T cells in up to 105 healthy donors. We found that regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution, consistent with the three-dimensional chromatin organization measured by in situ Hi-C in T cells. Fifteen percent of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (local-ATAC-QTLs). Local-ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression

Modulation of enhancer looping and differential gene targeting by Epstein-Barr virus transcription factors directs cellular reprogramming

Epstein-Barr virus (EBV) epigenetically reprogrammes B-lymphocytes to drive immortalization and facilitate viral persistence. Host-cell transcription is perturbed principally through the actions of EBV EBNA 2, 3A, 3B and 3C, with cellular genes deregulated by specific combinations of these EBNAs through unknown mechanisms. Comparing human genome binding by these viral transcription factors, we discovered that 25% of binding sites were shared by EBNA 2 and the EBNA 3s and were located predominantly in enhancers. Moreover, 80% of potential EBNA 3A, 3B or 3C target genes were also targeted by EBNA 2, implicating extensive interplay between EBNA 2 and 3 proteins in cellular reprogramming. Investigating shared enhancer sites neighbouring two new targets (WEE1 and CTBP2) we discovered that EBNA 3 proteins repress transcription by modulating enhancer-promoter loop formation to establish repressive chromatin hubs or prevent assembly of active hubs. Re-ChIP analysis revealed that EBNA 2 and 3 proteins do not bind simultaneously at shared sites but compete for binding thereby modulating enhancer-promoter interactions. At an EBNA 3-only intergenic enhancer site between ADAM28 and ADAMDEC1 EBNA 3C was also able to independently direct epigenetic repression of both genes through enhancer-promoter looping. Significantly, studying shared or unique EBNA 3 binding sites at WEE1, CTBP2, ITGAL (LFA-1 alpha chain), BCL2L11 (Bim) and the ADAMs, we also discovered that different sets of EBNA 3 proteins bind regulatory elements in a gene and cell-type specific manner. Binding profiles correlated with the effects of individual EBNA 3 proteins on the expression of these genes, providing a molecular basis for the targeting of different sets of cellular genes by the EBNA 3s. Our results therefore highlight the influence of the genomic and cellular context in determining the specificity of gene deregulation by EBV and provide a paradigm for host-cell reprogramming through modulation of enhancer-promoter interactions by viral transcription factors

A Cis-Regulatory Map of the Drosophila Genome

Systematic annotation of gene regulatory elements is a major challenge in genome science. Direct mapping of chromatin modification marks and transcriptional factor binding sites genome-wide1, 2 has successfully identified specific subtypes of regulatory elements3. In Drosophila several pioneering studies have provided genome-wide identification of Polycomb response elements4, chromatin states5, transcription factor binding sites6, 7, 8, 9, RNA polymerase II regulation8 and insulator elements10; however, comprehensive annotation of the regulatory genome remains a significant challenge. Here we describe results from the modENCODE cis-regulatory annotation project. We produced a map of the Drosophila melanogaster regulatory genome on the basis of more than 300 chromatin immunoprecipitation data sets for eight chromatin features, five histone deacetylases and thirty-eight site-specific transcription factors at different stages of development. Using these data we inferred more than 20,000 candidate regulatory elements and validated a subset of predictions for promoters, enhancers and insulators in vivo. We identified also nearly 2,000 genomic regions of dense transcription factor binding associated with chromatin activity and accessibility. We discovered hundreds of new transcription factor co-binding relationships and defined a transcription factor network with over 800 potential regulatory relationships

Properties of local interactions and their potential value in complementing genome-wide association studies

Local interactions between neighbouring SNPs are hypothesized to be able to capture variants missing from genome-wide association studies (GWAS) via haplotype effects but have not been thoroughly explored. We have used a new high-throughput analysis tool to probe this underexplored area through full pair-wise genome scans and conventional GWAS in diastolic and systolic blood pressure and six metabolic traits in the Northern Finland Birth Cohort 1966 (NFBC1966) and the Atherosclerosis Risk in Communities study cohort (ARIC). Genome-wide significant interactions were detected in ARIC for systolic blood pressure between PLEKHA7 (a known GWAS locus for blood pressure) and GPR180 (which plays a role in vascular remodelling), and also for triglycerides as local interactions within the 11q23.3 region (replicated significantly in NFBC1966), which notably harbours several loci (BUD13, ZNF259 and APOA5) contributing to triglyceride levels. Tests of the local interactions within the 11q23.3 region conditional on the top GWAS signal suggested the presence of two independent functional variants, each with supportive evidence for their roles in gene regulation. Local interactions captured 9 additional GWAS loci identified in this study (3 significantly replicated) and 73 from previous GWAS (24 in the eight traits and 49 in related traits). We conclude that the detection of local interactions requires adequate SNP coverage of the genome and that such interactions are only likely to be detectable between SNPs in low linkage disequilibrium. Analysing local interactions is a potentially valuable complement to GWAS and can provide new insights into the biology underlying variation in complex traits