Relating characteristics of global biodiversity targets to reported progress

To inform governmental discussions on the nature of a revised Strategic Plan for Biodiversity of the Convention on Biological Diversity (CBD), we reviewed the relevant literature and assessed the framing of the 20 Aichi Biodiversity Targets in the current strategic plan. We asked international experts from nongovernmental organizations, academia, government agencies, international organizations, research institutes, and the CBD to score the Aichi Targets and their constituent elements against a set of specific, measurable, ambitious, realistic, unambiguous, scalable, and comprehensive criteria (SMART based, excluding time bound because all targets are bound to 2015 or 2020). We then investigated the relationship between these expert scores and reported progress toward the target elements by using the findings from 2 global progress assessments (Global Biodiversity Outlook and the Intergovernmental Science‐Policy Platform on Biodiversity and Ecosystem Services). We analyzed the data with ordinal logistic regressions. We found significant positive relationships (p < 0.05) between progress and the extent to which the target elements were perceived to be measurable, realistic, unambiguous, and scalable. There was some evidence of a relationship between progress and specificity of the target elements, but no relationship between progress and ambition. We are the first to show associations between progress and the extent to which the Aichi Targets meet certain SMART criteria. As negotiations around the post‐2020 biodiversity framework proceed, decision makers should strive to ensure that new or revised targets are effectively structured and clearly worded to allow the translation of targets into actionable policies that can be successfully implemented nationally, regionally, and globally

Predicting cell types and genetic variations contributing to disease by combining GWAS and epigenetic data

Genome-wide association studies (GWASs) identify single nucleotide polymorphisms (SNPs) that are enriched in individuals suffering from a given disease. Most disease-associated SNPs fall into non-coding regions, so that it is not straightforward to infer phenotype or function; moreover, many SNPs are in tight genetic linkage, so that a SNP identified as associated with a particular disease may not itself be causal, but rather signify the presence of a linked SNP that is functionally relevant to disease pathogenesis. Here, we present an analysis method that takes advantage of the recent rapid accumulation of epigenomics data to address these problems for some SNPs. Using asthma as a prototypic example; we show that non-coding disease-associated SNPs are enriched in genomic regions that function as regulators of transcription, such as enhancers and promoters. Identifying enhancers based on the presence of the histone modification marks such as H3K4me1 in different cell types, we show that the location of enhancers is highly cell-type specific. We use these findings to predict which SNPs are likely to be directly contributing to disease based on their presence in regulatory regions, and in which cell types their effect is expected to be detectable. Moreover, we can also predict which cell types contribute to a disease based on overlap of the disease-associated SNPs with the locations of enhancers present in a given cell type. Finally, we suggest that it will be possible to re-analyze GWAS studies with much higher power by limiting the SNPs considered to those in coding or regulatory regions of cell types relevant to a given disease

The Worldvolume Action of Kink Solitons in AdS Spacetime

A formalism is presented for computing the higher-order corrections to the worldvolume action of co-dimension one solitons. By modifying its potential, an explicit "kink" solution of a real scalar field in AdS spacetime is found. The formalism is then applied to explicitly compute the kink worldvolume action to quadratic order in two expansion parameters--associated with the hypersurface fluctuation length and the radius of AdS spacetime respectively. Two alternative methods are given for doing this. The results are expressed in terms of the trace of the extrinsic curvature and the intrinsic scalar curvature. In addition to conformal Galileon interactions, we find a non-Galileon term which is never sub-dominant. This method can be extended to any conformally flat bulk spacetime.Comment: 32 pages, 3 figures, typos corrected and additional comments adde

Genome size evolution at the speciation level: The cryptic species complex Brachionus plicatilis (Rotifera)

<p>Abstract</p> <p>Background</p> <p>Studies on genome size variation in animals are rarely done at lower taxonomic levels, e.g., slightly above/below the species level. Yet, such variation might provide important clues on the tempo and mode of genome size evolution. In this study we used the flow-cytometry method to study the evolution of genome size in the rotifer <it>Brachionus plicatilis</it>, a cryptic species complex consisting of at least 14 closely related species.</p> <p>Results</p> <p>We found an unexpectedly high variation in this species complex, with genome sizes ranging approximately seven-fold (haploid '1C' genome sizes: 0.056-0.416 pg). Most of this variation (67%) could be ascribed to the major clades of the species complex, i.e. clades that are well separated according to most species definitions. However, we also found substantial variation (32%) at lower taxonomic levels - within and among genealogical species - and, interestingly, among species pairs that are not completely reproductively isolated. In one genealogical species, called <it>B</it>. 'Austria', we found greatly enlarged genome sizes that could roughly be approximated as multiples of the genomes of its closest relatives, which suggests that whole-genome duplications have occurred early during separation of this lineage. Overall, genome size was significantly correlated to egg size and body size, even though the latter became non-significant after controlling for phylogenetic non-independence.</p> <p>Conclusions</p> <p>Our study suggests that substantial genome size variation can build up early during speciation, potentially even among isolated populations. An alternative, but not mutually exclusive interpretation might be that reproductive isolation tends to build up unusually slow in this species complex.</p

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

Histone Modifications at Human Enhancers Reflect Global Cell-Type-Specific Gene Expression

The human body is composed of diverse cell types with distinct functions. Although it is known that lineage specification depends on cell-specific gene expression, which in turn is driven by promoters, enhancers, insulators and other cis-regulatory DNA sequences for each gene1, 2, 3, the relative roles of these regulatory elements in this process are not clear. We have previously developed a chromatin-immunoprecipitation-based microarray method (ChIP-chip) to locate promoters, enhancers and insulators in the human genome4, 5, 6. Here we use the same approach to identify these elements in multiple cell types and investigate their roles in cell-type-specific gene expression. We observed that the chromatin state at promoters and CTCF-binding at insulators is largely invariant across diverse cell types. In contrast, enhancers are marked with highly cell-type-specific histone modification patterns, strongly correlate to cell-type-specific gene expression programs on a global scale, and are functionally active in a cell-type-specific manner. Our results define over 55,000 potential transcriptional enhancers in the human genome, significantly expanding the current catalogue of human enhancers and highlighting the role of these elements in cell-type-specific gene expression

Is Body Fat a Predictor of Race Time in Female Long-Distance Inline Skaters?

Purpose: The aim of this study was to evaluate predictor variables of race time in female ultra-endurance inliners in the longest inline race in Europe. Methods: We investigated the association between anthropometric and training characteristics and race time for 16 female ultraendurance inline skaters, at the longest inline marathon in Europe, the ‘Inline One-eleven’ over 111 km in Switzerland, using bi- and multivariate analysis. Results: The mean (SD) race time was 289.7 (54.6) min. The bivariate analysis showed that body height (r=0.61), length of leg (r=0.61), number of weekly inline skating training sessions (r=-0.51)and duration of each training unit (r=0.61) were significantly correlated with race time. Stepwise multiple regressions revealed that body height, duration of each training unit, and age were the best variables to predict race time. Conclusion: Race time in ultra-endurance inline races such as the ‘Inline One-eleven’ over 111 km might be predicted by the following equation (r2 = 0.65): Race time (min) = -691.62 + 521.71 (body height, m) + 0.58 (duration of each training unit, min) + 1.78 (age, yrs) for female ultra-endurance inline skaters

CHD7 Targets Active Gene Enhancer Elements to Modulate ES Cell-Specific Gene Expression

CHD7 is one of nine members of the chromodomain helicase DNA–binding domain family of ATP–dependent chromatin remodeling enzymes found in mammalian cells. De novo mutation of CHD7 is a major cause of CHARGE syndrome, a genetic condition characterized by multiple congenital anomalies. To gain insights to the function of CHD7, we used the technique of chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP–Seq) to map CHD7 sites in mouse ES cells. We identified 10,483 sites on chromatin bound by CHD7 at high confidence. Most of the CHD7 sites show features of gene enhancer elements. Specifically, CHD7 sites are predominantly located distal to transcription start sites, contain high levels of H3K4 mono-methylation, found within open chromatin that is hypersensitive to DNase I digestion, and correlate with ES cell-specific gene expression. Moreover, CHD7 co-localizes with P300, a known enhancer-binding protein and strong predictor of enhancer activity. Correlations with 18 other factors mapped by ChIP–seq in mouse ES cells indicate that CHD7 also co-localizes with ES cell master regulators OCT4, SOX2, and NANOG. Correlations between CHD7 sites and global gene expression profiles obtained from Chd7+/+, Chd7+/−, and Chd7−/− ES cells indicate that CHD7 functions at enhancers as a transcriptional rheostat to modulate, or fine-tune the expression levels of ES–specific genes. CHD7 can modulate genes in either the positive or negative direction, although negative regulation appears to be the more direct effect of CHD7 binding. These data indicate that enhancer-binding proteins can limit gene expression and are not necessarily co-activators. Although ES cells are not likely to be affected in CHARGE syndrome, we propose that enhancer-mediated gene dysregulation contributes to disease pathogenesis and that the critical CHD7 target genes may be subject to positive or negative regulation

Occupancy Classification of Position Weight Matrix-Inferred Transcription Factor Binding Sites

BACKGROUND: Computational prediction of Transcription Factor Binding Sites (TFBS) from sequence data alone is difficult and error-prone. Machine learning techniques utilizing additional environmental information about a predicted binding site (such as distances from the site to particular chromatin features) to determine its occupancy/functionality class show promise as methods to achieve more accurate prediction of true TFBS in silico. We evaluate the Bayesian Network (BN) and Support Vector Machine (SVM) machine learning techniques on four distinct TFBS data sets and analyze their performance. We describe the features that are most useful for classification and contrast and compare these feature sets between the factors. RESULTS: Our results demonstrate good performance of classifiers both on TFBS for transcription factors used for initial training and for TFBS for other factors in cross-classification experiments. We find that distances to chromatin modifications (specifically, histone modification islands) as well as distances between such modifications to be effective predictors of TFBS occupancy, though the impact of individual predictors is largely TF specific. In our experiments, Bayesian network classifiers outperform SVM classifiers. CONCLUSIONS: Our results demonstrate good performance of machine learning techniques on the problem of occupancy classification, and demonstrate that effective classification can be achieved using distances to chromatin features. We additionally demonstrate that cross-classification of TFBS is possible, suggesting the possibility of constructing a generalizable occupancy classifier capable of handling TFBS for many different transcription factors