Germline gain-of-function mutations in SOS1 cause Noonan syndrome

Noonan syndrome, the most common single-gene cause of congenital heart disease, is characterized by short stature, characteristic facies, learning problems and leukemia predisposition. Gain-of-function mutations in PTPN11, encoding the tyrosine phosphatase SHP2, cause approximately 50% of Noonan syndrome cases. SHP2 is required for RAS-ERK MAP kinase (MAPK) cascade activation, and Noonan syndrome mutants enhance ERK activation ex vivo and in mice. KRAS mutations account for <5% of cases of Noonan syndrome, but the gene(s) responsible for the remainder are unknown. We identified missense mutations in SOS1, which encodes an essential RAS guanine nucleotide-exchange factor (RAS-GEF), in approximately 20% of cases of Noonan syndrome without PTPN11 mutation. The prevalence of specific cardiac defects differs in SOS1 mutation-associated Noonan syndrome. Noonan syndrome-associated SOS1 mutations are hypermorphs encoding products that enhance RAS and ERK activation. Our results identify SOS1 mutants as a major cause of Noonan syndrome, representing the first example of activating GEF mutations associated with human disease and providing new insights into RAS-GEF regulation.     

Automatic patent document summarization for collaborative knowledge systems and services

Engineering and research teams often develop new products and technologies by referring to inventions described in patent databases. Efficient patent analysis builds R&D knowledge, reduces new product development time, increases market success, and reduces potential patent infringement. Thus, it is beneficial to automatically and systematically extract information from patent documents in order to improve knowledge sharing and collaboration among R&D team members. In this research, patents are summarized using a combined ontology based and TF-IDF concept clustering approach. The ontology captures the general knowledge and core meaning of patents in a given domain. Then, the proposed methodology extracts, clusters, and integrates the content of a patent to derive a summary and a cluster tree diagram of key terms. Patents from the International Patent Classification (IPC) codes B25C, B25D, B25F (categories for power hand tools) and B24B, C09G and H011 (categories for chemical mechanical polishing) are used as case studies to evaluate the compression ratio, retention ratio, and classification accuracy of the summarization results. The evaluation uses statistics to represent the summary generation and its compression ratio, the ontology based keyword extraction retention ratio, and the summary classification accuracy. The results show that the ontology based approach yields about the same compression ratio as previous non-ontology based research but yields on average an 11% improvement for the retention ratio and a 14% improvement for classification accuracy.     

Clustering patents using non-exhaustive overlaps

Patent documents are unique external sources of information that reveal the core technology underlying new inventions.Patents also serve as a strategic data source that can be mined to discover state-of-the-art technical development and subsequently help guide R&D investments.This research incorporates an ontology schema to extract and represent patent concepts.A clustering algorithm with non-exhaustive overlaps is proposed to overcome deficiencies with exhaustive clustering methods used in patent mining and technology discovery.The non-exhaustive clustering approach allows for the clustering of patent documents with overlapping technical findings and claims,a feature that enables the grouping of patents that define related key innovations.Legal advisors can use this approach to study potential cases of patent infringement or devise strategies to avoid litigation.The case study demonstrates the use of non-exhaustive overlaps algorithm by clustering US and Japan radio frequency identification (RFID) patents and by analyzing the legal implications of automated discovery of patent infringement.     

The enteric nervous system in gastrointestinal disease etiology

A highly conserved but convoluted network of neurons and glial cells, the enteric nervous system (ENS), is positioned along the wall of the gut to coordinate digestive processes and gastrointestinal homeostasis. Because ENS components are in charge of the autonomous regulation of gut function, it is inevitable that their dysfunction is central to the pathophysiology and symptom generation of gastrointestinal disease. While for neurodevelopmental disorders such as Hirschsprung, ENS pathogenesis appears to be clear-cut, the role for impaired ENS activity in the etiology of other gastrointestinal disorders is less established and is often deemed secondary to other insults like intestinal inflammation. However, mounting experimental evidence in recent years indicates that gastrointestinal homeostasis hinges on multifaceted connections between the ENS, and other cellular networks such as the intestinal epithelium, the immune system, and the intestinal microbiome. Derangement of these interactions could underlie gastrointestinal disease onset and elicit variable degrees of abnormal gut function, pinpointing, perhaps unexpectedly, the ENS as a diligent participant in idiopathic but also in inflammatory and cancerous diseases of the gut. In this review, we discuss the latest evidence on the role of the ENS in the pathogenesis of enteric neuropathies, disorders of gut–brain interaction, inflammatory bowel diseases, and colorectal cancer.

     

Yersinia pestis genome sequencing identifies patterns of global phylogenetic diversity

Plague is a pandemic human invasive disease caused by the bacterial agent Yersinia pestis. We here report a comparison of 17 whole genomes of Y. pestis isolates from global sources. We also screened a global collection of 286 Y. pestis isolates for 933 SNPs using Sequenom MassArray SNP typing. We conducted phylogenetic analyses on this sequence variation dataset, assigned isolates to populations based on maximum parsimony and, from these results, made inferences regarding historical transmission routes. Our phylogenetic analysis suggests that Y. pestis evolved in or near China and spread through multiple radiations to Europe, South America, Africa and Southeast Asia, leading to country-specific lineages that can be traced by lineage-specific SNPs. All 626 current isolates from the United States reflect one radiation, and 82 isolates from Madagascar represent a second radiation. Subsequent local microevolution of Y. pestis is marked by sequential, geographically specific SNPs.     

Interaction of reelin signaling and Lis1 in brain development

Loss-of-function mutations in RELN (encoding reelin) or PAFAH1B1 (encoding LIS1) cause lissencephaly, a human neuronal migration disorder. In the mouse, homozygous mutations in Reln result in the reeler phenotype, characterized by ataxia and disrupted cortical layers. Pafah1b1(+/-) mice have hippocampal layering defects, whereas homozygous mutants are embryonic lethal. Reln encodes an extracellular protein that regulates layer formation by interacting with VLDLR and ApoER2 (Lrp8) receptors, thereby phosphorylating the Dab1 signaling molecule. Lis1 associates with microtubules and modulates neuronal migration. We investigated interactions between the reelin signaling pathway and Lis1 in brain development. Compound mutant mice with disruptions in the Reln pathway and heterozygous Pafah1b1 mutations had a higher incidence of hydrocephalus and enhanced cortical and hippocampal layering defects. Dab1 and Lis1 bound in a reelin-induced phosphorylation-dependent manner. These data indicate genetic and biochemical interaction between the reelin signaling pathway and Lis1.     

Common variation at 10p12.31 near MLLT10 influences meningioma risk

To identify susceptibility loci for meningioma, we conducted a genome-wide association study of 859 affected individuals (cases) and 704 controls with validation in two independent sample sets totaling 774 cases and 1,764 controls. We identified a new susceptibility locus for meningioma at 10p12.31 (MLLT10, rs11012732, odds ratio = 1.46, P(combined) = 1.88 × 10(-14)). This finding advances our understanding of the genetic basis of meningioma development.