Robust structure-based resonance assignment for functional protein studies by NMR

High-throughput functional protein NMR studies, like protein interactions or dynamics, require an automated approach for the assignment of the protein backbone. With the availability of a growing number of protein 3D structures, a new class of automated approaches, called structure-based assignment, has been developed quite recently. Structure-based approaches use primarily NMR input data that are not based on J-coupling and for which connections between residues are not limited by through bonds magnetization transfer efficiency. We present here a robust structure-based assignment approach using mainly HN–HN NOEs networks, as well as 1H–15N residual dipolar couplings and chemical shifts. The NOEnet complete search algorithm is robust against assignment errors, even for sparse input data. Instead of a unique and partly erroneous assignment solution, an optimal assignment ensemble with an accuracy equal or near to 100% is given by NOEnet. We show that even low precision assignment ensembles give enough information for functional studies, like modeling of protein-complexes. Finally, the combination of NOEnet with a low number of ambiguous J-coupling sequential connectivities yields a high precision assignment ensemble. NOEnet will be available under: http://www.icsn.cnrs-gif.fr/download/nmr

Cys-Ph-TAHA: a lanthanide binding tag for RDC and PCS enhanced protein NMR

Here we present Cys-Ph-TAHA, a new nonadentate lanthanide tag for the paramagnetic labelling of proteins. The tag can be easily synthesized and is stereochemically homogenous over a wide range of temperatures, yielding NMR spectra with a single set of peaks. Bound to ubiquitin, it induced large residual dipolar couplings and pseudocontact shifts that could be measured easily and agreed very well with the protein structure. We show that Cys-Ph-TAHA can be used to label large proteins that are biochemically challenging such as the Lac repressor in a 90 kDa ternary complex with DNA and inducer

Thermal Perceptual Thresholds are typical in Autism Spectrum Disorder but Strongly Related to Intra-individual Response Variability

Individuals with autism spectrum disorder (ASD) are often reported to exhibit an apparent indifference to pain or temperature. Leading models suggest that this behavior is the result of elevated perceptual thresholds for thermal stimuli, but data to support these assertions are inconclusive. An alternative proposal suggests that the sensory features of ASD arise from increased intra-individual perceptual variability. In this study, we measured method-of-limits warm and cool detection thresholds in 142 individuals (83 with ASD, 59 with typical development [TD], aged 7–54 years), testing relationships with diagnostic group, demographics, and clinical measures. We also investigated the relationship between detection thresholds and a novel measure of intra-individual (trial-to-trial) threshold variability, a putative index of “perceptual noise.” This investigation found no differences in thermal detection thresholds between individuals with ASD and typical controls, despite large differences between groups in sensory reactivity questionnaires and modest group differences in intra-individual variability. Lower performance IQ, male sex, and higher intra-individual variability in threshold estimates were the most significant predictors of elevated detection thresholds. Although no psychophysical measure was significantly correlated with questionnaire measures of sensory hyporeactivity, large intra-individual variability may partially explain the elevated psychophysical thresholds seen in a subset of the ASD population

Gene Expression Programs of Mouse Endothelial Cells in Kidney Development and Disease

Endothelial cells are remarkably heterogeneous in both morphology and function, and they play critical roles in the formation of multiple organ systems. In addition endothelial cell dysfunction can contribute to disease processes, including diabetic nephropathy, which is a leading cause of end stage renal disease. In this report we define the comprehensive gene expression programs of multiple types of kidney endothelial cells, and analyze the differences that distinguish them. Endothelial cells were purified from Tie2-GFP mice by cell dissociation and fluorescent activated cell sorting. Microarrays were then used to provide a global, quantitative and sensitive measure of gene expression levels. We examined renal endothelial cells from the embryo and from the adult glomerulus, cortex and medulla compartments, as well as the glomerular endothelial cells of the db/db mutant mouse, which represents a model for human diabetic nephropathy. The results identified the growth factors, receptors and transcription factors expressed by these multiple endothelial cell types. Biological processes and molecular pathways were characterized in exquisite detail. Cell type specific gene expression patterns were defined, finding novel molecular markers and providing a better understanding of compartmental distinctions. Further, analysis of enriched, evolutionarily conserved transcription factor binding sites in the promoters of co-activated genes begins to define the genetic regulatory network of renal endothelial cell formation. Finally, the gene expression differences associated with diabetic nephropathy were defined, providing a global view of both the pathogenic and protective pathways activated. These studies provide a rich resource to facilitate further investigations of endothelial cell functions in kidney development, adult compartments, and disease

Structural Biology by NMR: Structure, Dynamics, and Interactions

The function of bio-macromolecules is determined by both their 3D structure and conformational dynamics. These molecules are inherently flexible systems displaying a broad range of dynamics on time-scales from picoseconds to seconds. Nuclear Magnetic Resonance (NMR) spectroscopy has emerged as the method of choice for studying both protein structure and dynamics in solution. Typically, NMR experiments are sensitive both to structural features and to dynamics, and hence the measured data contain information on both. Despite major progress in both experimental approaches and computational methods, obtaining a consistent view of structure and dynamics from experimental NMR data remains a challenge. Molecular dynamics simulations have emerged as an indispensable tool in the analysis of NMR data

A novel approach of homozygous haplotype sharing identifies candidate genes in autism spectrum disorder

Autism spectrum disorder (ASD) is a highly heritable disorder of complex and heterogeneous aetiology. It is primarily characterized by altered cognitive ability including impaired language and communication skills and fundamental deficits in social reciprocity. Despite some notable successes in neuropsychiatric genetics, overall, the high heritability of ASD (~90%) remains poorly explained by common genetic risk variants. However, recent studies suggest that rare genomic variation, in particular copy number variation, may account for a significant proportion of the genetic basis of ASD. We present a large scale analysis to identify candidate genes which may contain low-frequency recessive variation contributing to ASD while taking into account the potential contribution of population differences to the genetic heterogeneity of ASD. Our strategy, homozygous haplotype (HH) mapping, aims to detect homozygous segments of identical haplotype structure that are shared at a higher frequency amongst ASD patients compared to parental controls. The analysis was performed on 1,402 Autism Genome Project trios genotyped for 1 million single nucleotide polymorphisms (SNPs). We identified 25 known and 1,218 novel ASD candidate genes in the discovery analysis including CADM2, ABHD14A, CHRFAM7A, GRIK2, GRM3, EPHA3, FGF10, KCND2, PDZK1, IMMP2L and FOXP2. Furthermore, 10 of the previously reported ASD genes and 300 of the novel candidates identified in the discovery analysis were replicated in an independent sample of 1,182 trios. Our results demonstrate that regions of HH are significantly enriched for previously reported ASD candidate genes and the observed association is independent of gene size (odds ratio 2.10). Our findings highlight the applicability of HH mapping in complex disorders such as ASD and offer an alternative approach to the analysis of genome-wide association data