Probabilistic Interaction Network of Evidence Algorithm and its Application to Complete Labeling of Peak Lists from Protein NMR Spectroscopy

The process of assigning a finite set of tags or labels to a collection of observations, subject to side conditions, is notable for its computational complexity. This labeling paradigm is of theoretical and practical relevance to a wide range of biological applications, including the analysis of data from DNA microarrays, metabolomics experiments, and biomolecular nuclear magnetic resonance (NMR) spectroscopy. We present a novel algorithm, called Probabilistic Interaction Network of Evidence (PINE), that achieves robust, unsupervised probabilistic labeling of data. The computational core of PINE uses estimates of evidence derived from empirical distributions of previously observed data, along with consistency measures, to drive a fictitious system M with Hamiltonian H to a quasi-stationary state that produces probabilistic label assignments for relevant subsets of the data. We demonstrate the successful application of PINE to a key task in protein NMR spectroscopy: that of converting peak lists extracted from various NMR experiments into assignments associated with probabilities for their correctness. This application, called PINE-NMR, is available from a freely accessible computer server (http://pine.nmrfam.wisc.edu). The PINE-NMR server accepts as input the sequence of the protein plus user-specified combinations of data corresponding to an extensive list of NMR experiments; it provides as output a probabilistic assignment of NMR signals (chemical shifts) to sequence-specific backbone and aliphatic side chain atoms plus a probabilistic determination of the protein secondary structure. PINE-NMR can accommodate prior information about assignments or stable isotope labeling schemes. As part of the analysis, PINE-NMR identifies, verifies, and rectifies problems related to chemical shift referencing or erroneous input data. PINE-NMR achieves robust and consistent results that have been shown to be effective in subsequent steps of NMR structure determination

Structural analysis of haemoglobin binding by HpuA from the Neisseriaceae family

The Neisseriaceae family of bacteria causes a range of diseases including meningitis, septicaemia, gonorrhoea and endocarditis, and extracts haem from haemoglobin as an important iron source within the iron-limited environment of its human host. Herein we report crystal structures of apo- and haemoglobin-bound HpuA, an essential component of this haem import system. The interface involves long loops on the bacterial receptor that present hydrophobic side chains for packing against the surface of haemoglobin. Interestingly, our structural and biochemical analyses of Kingella denitrificans and Neisseria gonorrhoeae HpuA mutants, although validating the interactions observed in the crystal structure, show how Neisseriaceae have the fascinating ability to diversify functional sequences and yet retain the haemoglobin binding function. Our results present the first description of HpuA’s role in direct binding of haemoglobin

Fine mapping of a linkage peak with integration of lipid traits identifies novel coronary artery disease genes on chromosome 5

Coronary artery disease (CAD), and one of its intermediate risk factors, dyslipidemia, possess a demonstrable genetic component, although the genetic architecture is incompletely defined. We previously reported a linkage peak on chromosome 5q31-33 for early-onset CAD where the strength of evidence for linkage was increased in families with higher mean low density lipoprotein-cholesterol (LDL-C). Therefore, we sought to fine-map the peak using association mapping of LDL-C as an intermediate disease-related trait to further define the etiology of this linkage peak. The study populations consisted of 1908 individuals from the CATHGEN biorepository of patients undergoing cardiac catheterization; 254 families (N = 827 individuals) from the GENECARD familial study of early-onset CAD; and 162 aorta samples harvested from deceased donors. Linkage disequilibrium-tagged SNPs were selected with an average of one SNP per 20 kb for 126.6-160.2 MB (region of highest linkage) and less dense spacing (one SNP per 50 kb) for the flanking regions (117.7-126.6 and 160.2-167.5 MB) and genotyped on all samples using a custom Illumina array. Association analysis of each SNP with LDL-C was performed using multivariable linear regression (CATHGEN) and the quantitative trait transmission disequilibrium test (QTDT; GENECARD). SNPs associated with the intermediate quantitative trait, LDL-C, were then assessed for association with CAD (i.e., a qualitative phenotype) using linkage and association in the presence of linkage (APL; GENECARD) and logistic regression (CATHGEN and aortas)

T-cell identity and epigenetic memory

T-cell development endows cells with a flexible range of effector differentiation options, superimposed on a stable core of lineage-specific gene expression that is maintained while access to alternative hematopoietic lineages is permanently renounced. This combination of features could be explained by environmentally responsive transcription factor mobilization overlaying an epigenetically stabilized base gene expression state. For example, "poising" of promoters could offer preferential access to T-cell genes, while repressive histone modifications and DNA methylation of non-T regulatory genes could be responsible for keeping non-T developmental options closed. Here, we critically review the evidence for the actual deployment of epigenetic marking to support the stable aspects of T-cell identity. Much of epigenetic marking is dynamically maintained or subject to rapid modification by local action of transcription factors. Repressive histone marks are used in gene-specific ways that do not fit a simple, developmental lineage-exclusion hierarchy. We argue that epigenetic analysis may achieve its greatest impact for illuminating regulatory biology when it is used to locate cis-regulatory elements by catching them in the act of mediating regulatory change