The Inferred Cardiogenic Gene Regulatory Network in the Mammalian Heart

Cardiac development is a complex, multiscale process encompassing cell fate adoption, differentiation and morphogenesis. To elucidate pathways underlying this process, a recently developed algorithm to reverse engineer gene regulatory networks was applied to time-course microarray data obtained from the developing mouse heart. Approximately 200 genes of interest were input into the algorithm to generate putative network topologies that are capable of explaining the experimental data via model simulation. To cull specious network interactions, thousands of putative networks are merged and filtered to generate scale-free, hierarchical networks that are statistically significant and biologically relevant. The networks are validated with known gene interactions and used to predict regulatory pathways important for the developing mammalian heart. Area under the precision-recall curve and receiver operator characteristic curve are 9% and 58%, respectively. Of the top 10 ranked predicted interactions, 4 have already been validated. The algorithm is further tested using a network enriched with known interactions and another depleted of them. The inferred networks contained more interactions for the enriched network versus the depleted network. In all test cases, maximum performance of the algorithm was achieved when the purely data-driven method of network inference was combined with a data-independent, functional-based association method. Lastly, the network generated from the list of approximately 200 genes of interest was expanded using gene-profile uniqueness metrics to include approximately 900 additional known mouse genes and to form the most likely cardiogenic gene regulatory network. The resultant network supports known regulatory interactions and contains several novel cardiogenic regulatory interactions. The method outlined herein provides an informative approach to network inference and leads to clear testable hypotheses related to gene regulation

Elevated n-terminal pro-brain natriuretic peptide is associated with mortality in tobacco smokers independent of airflow obstruction

Background: Tobacco use is associated with an increased prevalence of cardiovascular disease. N-terminal pro-brain natiuretic peptide (NT-proBNP), a widely available biomarker that is associated with cardiovascular outcomes in other conditions, has not been investigated as a predictor of mortality in tobacco smokers. We hypothesized that NT-proBNP would be an independent prognostic marker in a cohort of well-characterized tobacco smokers without known cardiovascular disease. Methods: Clinical data from 796 subjects enrolled in two prospective tobacco exposed cohorts was assessed to determine factors associated with elevated NT-proBNP and the relationship of these factors and NT-proBNP with mortality. Results: Subjects were followed for a median of 562 (IQR 252 - 826) days. Characteristics associated with a NT-proBNP above the median (≥49 pg/mL) were increased age, female gender, and decreased body mass index. By time-to-event analysis, an NT-proBNP above the median (≥49 pg/mL) was a significant predictor of mortality (log rank p = 0.02). By proportional hazard analysis controlling for age, gender, cohort, and severity of airflow obstruction, an elevated NT-proBNP level (≥49 pg/mL) remained an independent predictor of mortality (HR = 2.19, 95% CI 1.07-4.46, p = 0.031). Conclusions: Elevated NT-proBNP is an independent predictor of mortality in tobacco smokers without known cardiovascular disease, conferring a 2.2 fold increased risk of death. Future studies should assess the ability of this biomarker to guide further diagnostic testing and to direct specific cardiovascular risk reduction inventions that may positively impact quality of life and survival. © 2011 Stamm et al

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead