BackgroundNetwork is a useful way for presenting many types of biological data including protein-protein interactions, gene regulations, cellular pathways, and signal transductions. We can measure nodes by their network features to infer their importance in the network, and it can help us identify central elements of biological networks.ResultsWe introduce a novel Cytoscape plugin cytoHubba for ranking nodes in a network by their network features. CytoHubba provides 11 topological analysis methods including Degree, Edge Percolated Component, Maximum Neighborhood Component, Density of Maximum Neighborhood Component, Maximal Clique Centrality and six centralities (Bottleneck, EcCentricity, Closeness, Radiality, Betweenness, and Stress) based on shortest paths. Among the eleven methods, the new proposed method, MCC, has a better performance on the precision of predicting essential proteins from the yeast PPI network.ConclusionsCytoHubba provide a user-friendly interface to explore important nodes in biological networks. It computes all eleven methods in one stop shopping way. Besides, researchers are able to combine cytoHubba with and other plugins into a novel analysis scheme. The network and sub-networks caught by this topological analysis strategy will lead to new insights on essential regulatory networks and protein drug targets for experimental biologists. According to cytoscape plugin download statistics, the accumulated number of cytoHubba is around 6,700 times since 2010.

One major task in the post-genome era is to reconstruct proteomic and genomic interacting networks using high-throughput experiment data. To identify essential nodes/hubs in these interactomes is a way to decipher the critical keys inside biochemical pathways or complex networks. These essential nodes/hubs may serve as potential drug-targets for developing novel therapy of human diseases, such as cancer or infectious disease caused by emerging pathogens. Hub Objects Analyzer (Hubba) is a web-based service for exploring important nodes in an interactome network generated from specific small- or large-scale experimental methods based on graph theory. Two characteristic analysis algorithms, Maximum Neighborhood Component (MNC) and Density of Maximum Neighborhood Component (DMNC) are developed for exploring and identifying hubs/essential nodes from interactome networks. Users can submit their own interaction data in PSI format (Proteomics Standards Initiative, version 2.5 and 1.0), tab format and tab with weight values. User will get an email notification of the calculation complete in minutes or hours, depending on the size of submitted dataset. Hubba result includes a rank given by a composite index, a manifest graph of network to show the relationship amid these hubs, and links for retrieving output files. This proposed method (DMNC || MNC) can be applied to discover some unrecognized hubs from previous dataset. For example, most of the Hubba high-ranked hubs (80% in top 10 hub list, and >70% in top 40 hub list) from the yeast protein interactome data (Y2H experiment) are reported as essential proteins. Since the analysis methods of Hubba are based on topology, it can also be used on other kinds of networks to explore the essential nodes, like networks in yeast, rat, mouse and human. The website of Hubba is freely available at http://hub.iis.sinica.edu.tw/Hubba.

Phosphaturic mesenchymal tumors typically cause paraneoplastic osteomalacia, chiefly as a result of FGF23 secretion. In a prior study, we identified FN1-FGFR1 fusion in 9 of 15 phosphaturic mesenchymal tumors. In this study, a total of 66 phosphaturic mesenchymal tumors and 7 tumors resembling phosphaturic mesenchymal tumor but without known phosphaturia were studied. A novel FN1-FGF1 fusion gene was identified in two cases without FN1-FGFR1 fusion by RNA sequencing and cross-validated with direct sequencing and western blot. Fluorescence in situ hybridization analyses revealed FN1-FGFR1 fusion in 16 of 39 (41%) phosphaturic mesenchymal tumors and identified an additional case with FN1-FGF1 fusion. The two fusion genes were mutually exclusive. Combined with previous data, the overall prevalence of FN1-FGFR1 and FN1-FGF1 fusions was 42% (21/50) and 6% (3/50), respectively. FGFR1 immunohistochemistry was positive in 82% (45/55) of phosphaturic mesenchymal tumors regardless of fusion status. By contrast, 121 cases of potential morphologic mimics (belonging to 13 tumor types) rarely expressed FGFR1, the main exceptions being solitary

Phosphaturic mesenchymal tumours (PMTs) are uncommon soft tissue and bone tumours that typically cause hypophosphataemia and tumour-induced osteomalacia (TIO) through secretion of phosphatonins including fibroblast growth factor 23 (FGF23). PMT has recently been accepted by the World Health Organization as a formal tumour entity. The genetic basis and oncogenic pathways underlying its tumourigenesis remain obscure. In this study, we identified a novel FN1-FGFR1 fusion gene in three out of four PMTs by next-generation RNA sequencing. The fusion transcripts and proteins were subsequently confirmed with RT-PCR and western blotting. Fluorescence in situ hybridization analysis showed six cases with FN1-FGFR1 fusion out of an additional 11 PMTs. Overall, nine out of 15 PMTs (60%) harboured this fusion. The FN1 gene possibly provides its constitutively active promoter and the encoded protein's oligomerization domains to overexpress and facilitate the activation of the FGFR1 kinase domain. Interestingly, unlike the prototypical leukaemia-inducing FGFR1 fusion genes, which are ligand-independent, the FN1-FGFR1 chimeric protein was predicted to preserve its ligand-binding domains, suggesting an advantage of the presence of its ligands (such as FGF23 secreted at high levels by the tumour) in the activation of the chimeric receptor tyrosine kinase, thus effecting an autocrine or a paracrine mechanism of tumourigenesis.

Genetic research on influenza virus biology has been informed in large part by nucleotide variants present in seasonal or pandemic samples, or individual mutants generated in the laboratory, leaving a substantial part of the genome uncharacterized. Here, we have developed a single-nucleotide resolution genetic approach to interrogate the fitness effect of point mutations in 98% of the amino acid positions in the influenza A virus hemagglutinin (HA) gene. Our HA fitness map provides a reference to identify indispensable regions to aid in drug and vaccine design as targeting these regions will increase the genetic barrier for the emergence of escape mutations. This study offers a new platform for studying genome dynamics, structure-function relationships, virus-host interactions, and can further rational drug and vaccine design. Our approach can also be applied to any virus that can be genetically manipulated.