Evolutionary History of the Helicobacter pylori Genome: Implications for Gastric Carcinogenesis

The genome of the bacterium Helicobacter pylori has evolved over the millennia since its migration out of Africa along with its human host approximately 60,000 years ago. Human migrations, after thousands of years of permanent settlement in those lands, resulted in seven prototypes of genetic populations of H. pylori with distinct geographical distributions. In all continents, present day isolates of H. pylori have molecular markers that reflect population migrations. The colonization of the Americas as well as the slave trade introduced European and African strains to the New World. The relationship between H. pylori genome and gastric cancer rates is linked to the presence of the cagA gene, but the knowledge on this subject is incomplete because other genes may be involved in certain populations. A new situation for Homo sapiens is the absence of H. pylori colonization in certain, mostly affluent, populations, apparently brought about by improved home sanitation and widespread use of antibiotics during the last decades. The disappearance of H. pylori from the human microbiota may be linked to emerging epidemics of esophageal adenocarcinoma, some allergic diseases such as asthma and some autoimmune disorders

A model-based approach to selection of tag SNPs

BACKGROUND: Single Nucleotide Polymorphisms (SNPs) are the most common type of polymorphisms found in the human genome. Effective genetic association studies require the identification of sets of tag SNPs that capture as much haplotype information as possible. Tag SNP selection is analogous to the problem of data compression in information theory. According to Shannon's framework, the optimal tag set maximizes the entropy of the tag SNPs subject to constraints on the number of SNPs. This approach requires an appropriate probabilistic model. Compared to simple measures of Linkage Disequilibrium (LD), a good model of haplotype sequences can more accurately account for LD structure. It also provides a machinery for the prediction of tagged SNPs and thereby to assess the performances of tag sets through their ability to predict larger SNP sets. RESULTS: Here, we compute the description code-lengths of SNP data for an array of models and we develop tag SNP selection methods based on these models and the strategy of entropy maximization. Using data sets from the HapMap and ENCODE projects, we show that the hidden Markov model introduced by Li and Stephens outperforms the other models in several aspects: description code-length of SNP data, information content of tag sets, and prediction of tagged SNPs. This is the first use of this model in the context of tag SNP selection. CONCLUSION: Our study provides strong evidence that the tag sets selected by our best method, based on Li and Stephens model, outperform those chosen by several existing methods. The results also suggest that information content evaluated with a good model is more sensitive for assessing the quality of a tagging set than the correct prediction rate of tagged SNPs. Besides, we show that haplotype phase uncertainty has an almost negligible impact on the ability of good tag sets to predict tagged SNPs. This justifies the selection of tag SNPs on the basis of haplotype informativeness, although genotyping studies do not directly assess haplotypes. A software that implements our approach is available

Multivariate Analysis and Visualization of Splicing Correlations in Single-Gene Transcriptomes

BACKGROUND: RNA metabolism, through 'combinatorial splicing', can generate enormous structural diversity in the proteome. Alternative domains may interact, however, with unpredictable phenotypic consequences, necessitating integrated RNA-level regulation of molecular composition. Splicing correlations within transcripts of single genes provide valuable clues to functional relationships among molecular domains as well as genomic targets for higher-order splicing regulation. RESULTS: We present tools to visualize complex splicing patterns in full-length cDNA libraries. Developmental changes in pair-wise correlations are presented vectorially in 'clock plots' and linkage grids. Higher-order correlations are assessed statistically through Monte Carlo analysis of a log-linear model with an empirical-Bayes estimate of the true probabilities of observed and unobserved splice forms. Log-linear coefficients are visualized in a 'spliceprint,' a signature of splice correlations in the transcriptome. We present two novel metrics: the linkage change index, which measures the directional change in pair-wise correlation with tissue differentiation, and the accuracy index, a very simple goodness-of-fit metric that is more sensitive than the integrated squared error when applied to sparsely populated tables, and unlike chi-square, does not diverge at low variance. Considerable attention is given to sparse contingency tables, which are inherent to single-gene libraries. CONCLUSION: Patterns of splicing correlations are revealed, which span a broad range of interaction order and change in development. The methods have a broad scope of applicability, beyond the single gene – including, for example, multiple gene interactions in the complete transcriptome

Human Development Sector Africa Region The World Bank

Human development is crucial to confronting the many economic and social ills facing Sub-Saharan Africa (SSA). And within the context of human development, addressing the thorny issue of child labor is vital to the development of many of Africa’s youngest citizens, who will determine the future of SSA. With more than a third of Africa’s children not attending school, and most of them working, the child labor issue will be central in the fight against poverty and destitution. This paper looks at the question of child labor in its totality—cultural, social, and economic. In order to design effective interventions, the development community—including the Bank—must understand the complex problems linked to child labor and school attendance in many African countries. The paper argues for a comprehensive strategy rather than targeted approaches, and details some innovative instruments to address child labor. The World Bank is committed to supporting measures that reduce harmful effects of child labor, and to exploring fully the human capital potential in Africa. The authors hope that this paper will encourage future discussions on policies and strategies to address Africa’s development challenges—especially human capital issues—in an effort to reduce poverty and spur economic growth