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Mosquito-borne diseases, including dengue, malaria, and lymphatic filariasis, exact a devastating toll on global health and economics, killing or debilitating millions every year (54). Mosquito innate immune responses are at the forefront of concerted research efforts aimed at defining potential target genes that could be manipulated to engineer pathogen resistance in vector populations. We aimed to describe the pivotal role that circulating blood cells (

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Distal hereditary motor neuropathy is a heterogeneous group of inherited neuropathies characterized by distal limb muscle weakness and atrophy. Although at least 15 genes have been implicated in distal hereditary motor neuropathy, the genetic causes remain elusive in many families. To identify an additional causal gene for distal hereditary motor neuropathy, we performed exome sequencing for two affected individuals and two unaffected members in a Taiwanese family with an autosomal dominant distal hereditary motor neuropathy in which mutations in common distal hereditary motor neuropathy-implicated genes had been excluded. The exome sequencing revealed a heterozygous mutation, c.770A > G (p.His257Arg), in the cytoplasmic tryptophanyl-tRNA synthetase (TrpRS) gene (WARS) that co-segregates with the neuropathy in the family. Further analyses of WARS in an additional 79 Taiwanese pedigrees with inherited neuropathies and 163 index cases from Australian, European, and Korean distal hereditary motor neuropathy families identified the same mutation in another Taiwanese distal hereditary motor neuropathy pedigree with different ancestries and one additional Belgian distal hereditary motor neuropathy family of Caucasian origin. Cell transfection studies demonstrated a dominant-negative effect of the p.His257Arg mutation on aminoacylation activity of TrpRS, which subsequently compromised protein synthesis and reduced cell viability. His257Arg TrpRS also inhibited neurite outgrowth and led to neurite degeneration in the neuronal cell lines and rat motor neurons. Further in vitro analyses showed that the WARS mutation could potentiate the angiostatic activities of TrpRS by enhancing its interaction with vascular endothelial-cadherin. Taken together, these findings establish WARS as a gene whose mutations may cause distal hereditary motor neuropathy and alter canonical and non-canonical functions of TrpRS.

BackgroundRas is frequently mutated in a variety of human cancers, including lung cancer, leading to constitutive activation of MAPK signaling. Despite decades of research focused on the Ras oncogene, Ras-targeted phosphorylation events and signaling pathways have not been described on a proteome-wide scale.Methodology/Principal FindingsBy functional phosphoproteomics, we studied the molecular mechanics of oncogenic Ras signaling using a pathway-based approach. We identified Ras-regulated phosphorylation events (n = 77) using label-free comparative proteomics analysis of immortalized human bronchial epithelial cells with and without the expression of oncogenic Ras. Many were newly identified as potential targets of the Ras signaling pathway. A majority (∼60%) of the Ras-targeted events consisted of a [pSer/Thr]-Pro motif, indicating the involvement of proline-directed kinases. By integrating the phosphorylated signatures into the Pathway Interaction Database, we further inferred Ras-regulated pathways, including MAPK signaling and other novel cascades, in governing diverse functions such as gene expression, apoptosis, cell growth, and RNA processing. Comparisons of Ras-regulated phosphorylation events, pathways, and related kinases in lung cancer-derived cells supported a role of oncogenic Ras signaling in lung adenocarcinoma A549 and H322 cells, but not in large cell carcinoma H1299 cells.Conclusions/SignificanceThis study reveals phosphorylation events, signaling networks, and molecular functions that are regulated by oncogenic Ras. The results observed in this study may aid to extend our knowledge on Ras signaling in lung cancer.

Background Susceptibility genes for migraine, despite it being a highly prevalent and disabling neurological disorder, have not been analyzed in Asians by genome-wide association study (GWAS). Methods We conducted a two-stage case-control GWAS to identify susceptibility genes for migraine without aura in Han Chinese residing in Taiwan. In the discovery stage, we genotyped 1005 clinic-based Taiwanese migraine patients and 1053 population-based sex-matched controls using Axiom Genome-Wide CHB Array. In the replication stage, we genotyped 27 single-nucleotide polymorphisms with p < 10 in 1120 clinic-based migraine patients and 604 sex-matched normal controls by using Sequenom. Variants at LRP1, TRPM8, and PRDM, which have been replicated in Caucasians, were also genotyped. Results We identified a novel susceptibility locus (rs655484 in DLG2) that reached GWAS significance level for migraine risk in Han Chinese ( p = 1.45 × 10, odds ratio [OR] = 2.42), and also another locus (rs3781545in GFRA1) with suggestive significance ( p = 1.27 × 10, OR = 1.38). In addition, we observed positive association signals with a similar trend to the associations identified in Caucasian GWASs for rs10166942 in TRPM8 (OR = 1.33, 95% confidence interval [CI] = 1.14-1.54, P = 9.99 × 10; risk allele: T) and rs1172113 in LRP1 (OR = 1.23, 95% CI = 1.04-1.45, P = 2.9 × 10; risk allele: T). Conclusion The present study is the first migraine GWAS conducted in Han-Chinese and Asians. The newly identified susceptibility genes have potential implications in migraine pathogenesis. DLG2 is involved in glutamatergic neurotransmission, and GFRA1 encodes GDNF receptors that are abundant in CGRP-containing trigeminal neurons. Furthermore, positive association signals for TRPM8 and LRP1 suggest the possibility for common genetic contributions across ethnicities.

BackgroundGenome-wide disease-gene finding approaches may sometimes provide us with a long list of candidate genes. Since using pure experimental approaches to verify all candidates could be expensive, a number of network-based methods have been developed to prioritize candidates. Such tools usually have a set of parameters pre-trained using available network data. This means that re-training network-based tools may be required when existing biological networks are updated or when networks from different sources are to be tried.ResultsWe developed a parameter-free method, interconnectedness (ICN), to rank candidate genes by assessing the closeness of them to known disease genes in a network. ICN was tested using 1,993 known disease-gene associations and achieved a success rate of ~44% using a protein-protein interaction network under a test scenario of simulated linkage analysis. This performance is comparable with those of other well-known methods and ICN outperforms other methods when a candidate disease gene is not directly linked to known disease genes in a network. Interestingly, we show that a combined scoring strategy could enable ICN to achieve an even better performance (~50%) than other methods used alone.ConclusionsICN, a user-friendly method, can well complement other network-based methods in the context of prioritizing candidate disease genes.

BackgroundRecombinant protein production is a useful biotechnology to produce a large quantity of highly soluble proteins. Currently, the most widely used production system is to fuse a target protein into different vectors in Escherichia coli (E. coli). However, the production efficacy of different vectors varies for different target proteins. Trial-and-error is still the common practice to find out the efficacy of a vector for a given target protein. Previous studies are limited in that they assumed that proteins would be over-expressed and focused only on the solubility of expressed proteins. In fact, many pairings of vectors and proteins result in no expression.ResultsIn this study, we applied machine learning to train prediction models to predict whether a pairing of vector-protein will express or not express in E. coli. For expressed cases, the models further predict whether the expressed proteins would be soluble. We collected a set of real cases from the clients of our recombinant protein production core facility, where six different vectors were designed and studied. This set of cases is used in both training and evaluation of our models. We evaluate three different models based on the support vector machines (SVM) and their ensembles. Unlike many previous works, these models consider the sequence of the target protein as well as the sequence of the whole fusion vector as the features. We show that a model that classifies a case into one of the three classes (no expression, inclusion body and soluble) outperforms a model that considers the nested structure of the three classes, while a model that can take advantage of the hierarchical structure of the three classes performs slight worse but comparably to the best model. Meanwhile, compared to previous works, we show that the prediction accuracy of our best method still performs the best. Lastly, we briefly present two methods to use the trained model in the design of the recombinant protein production systems to improve the chance of high soluble protein production.ConclusionIn this paper, we show that a machine learning approach to the prediction of the efficacy of a vector for a target protein in a recombinant protein production system is promising and may compliment traditional knowledge-driven study of the efficacy. We will release our program to share with other labs in the public domain when this paper is published.