An Integrated Model of Multiple-Condition ChIP-Seq Data Reveals Predeterminants of Cdx2 Binding

Regulatory proteins can bind to different sets of genomic targets in various cell types or conditions. To reliably characterize such condition-specific regulatory binding we introduce MultiGPS, an integrated machine learning approach for the analysis of multiple related ChIP-seq experiments. MultiGPS is based on a generalized Expectation Maximization framework that shares information across multiple experiments for binding event discovery. We demonstrate that our framework enables the simultaneous modeling of sparse condition-specific binding changes, sequence dependence, and replicate-specific noise sources. MultiGPS encourages consistency in reported binding event locations across multiple-condition ChIP-seq datasets and provides accurate estimation of ChIP enrichment levels at each event. MultiGPS's multi-experiment modeling approach thus provides a reliable platform for detecting differential binding enrichment across experimental conditions. We demonstrate the advantages of MultiGPS with an analysis of Cdx2 binding in three distinct developmental contexts. By accurately characterizing condition-specific Cdx2 binding, MultiGPS enables novel insight into the mechanistic basis of Cdx2 site selectivity. Specifically, the condition-specific Cdx2 sites characterized by MultiGPS are highly associated with pre-existing genomic context, suggesting that such sites are pre-determined by cell-specific regulatory architecture. However, MultiGPS-defined condition-independent sites are not predicted by pre-existing regulatory signals, suggesting that Cdx2 can bind to a subset of locations regardless of genomic environment. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5.National Science Foundation (U.S.) (Graduate Research Fellowship under Grant 0645960)National Institutes of Health (U.S.) (grant P01 NS055923)Pennsylvania State University. Center for Eukaryotic Gene Regulatio

In Silico Evidence for Gluconeogenesis from Fatty Acids in Humans

The question whether fatty acids can be converted into glucose in humans has a long standing tradition in biochemistry, and the expected answer is “No”. Using recent advances in Systems Biology in the form of large-scale metabolic reconstructions, we reassessed this question by performing a global investigation of a genome-scale human metabolic network, which had been reconstructed on the basis of experimental results. By elementary flux pattern analysis, we found numerous pathways on which gluconeogenesis from fatty acids is feasible in humans. On these pathways, four moles of acetyl-CoA are converted into one mole of glucose and two moles of CO2. Analyzing the detected pathways in detail we found that their energetic requirements potentially limit their capacity. This study has many other biochemical implications: effect of starvation, sports physiology, practically carbohydrate-free diets of inuit, as well as survival of hibernating animals and embryos of egg-laying animals. Moreover, the energetic loss associated to the usage of gluconeogenesis from fatty acids can help explain the efficiency of carbohydrate reduced and ketogenic diets such as the Atkins diet

Poverty, dirt, infections and non-atopic wheezing in children from a Brazilian urban center

BACKGROUND: The causation of asthma is poorly understood. Risk factors for atopic and non-atopic asthma may be different. This study aimed to analyze the associations between markers of poverty, dirt and infections and wheezing in atopic and non-atopic children. METHODS: 1445 children were recruited from a population-based cohort in Salvador, Brazil. Wheezing was assessed using the ISAAC questionnaire and atopy defined as allergen-specific IgE ≥ 0.70 kU/L. Relevant social factors, environmental exposures and serological markers for childhood infections were investigated as risk factors using multivariate multinomial logistic regression. RESULTS: Common risk factors for wheezing in atopic and non-atopic children, respectively, were parental asthma and respiratory infection in early childhood. No other factor was associated with wheezing in atopic children. Factors associated with wheezing in non-atopics were low maternal educational level (OR 1.49, 95% CI 0.98-2.38), low frequency of room cleaning (OR 2.49, 95% CI 1.27-4.90), presence of rodents in the house (OR 1.48, 95% CI 1.06-2.09), and day care attendance (OR 1.52, 95% CI 1.01-2.29). CONCLUSIONS: Non-atopic wheezing was associated with risk factors indicative of poverty, dirt and infections. Further research is required to more precisely define the mediating exposures and the mechanisms by which they may cause non-atopic wheeze