Shu-Hwa Chen

Hubba: hub objects analyzer—a framework of interactome hubs identification for network biology

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.

Cloning of Two Crustacean Hyperglycemic Hormone Isoforms in Freshwater Giant Prawn (Macrobrachium rosenbergii): Evidence of Alternative Splicing

A full-length chh cDNA was cloned from the eyestalk of Macrobrachium rosenbergii. The 991-bp cDNA contains an open reading frame of 408 bp that encodes the prepro-CHH. The tissue-specific expression pattern was examined by reverse transcriptase-polymerase chain reaction. Positive signals were detected in the eyestalk, heart, gills, antennal glands, and thoracic ganglion but not in muscle and hepatopancreas. However, two types of products were observed. The nucleotide sequences revealed the existence of 2 chh transcripts, named chh and chh-l, respectively. Direct sequence evidence suggests that these two isoforms come from a Chh gene transcribed in an alternative splicing manner. The Mar-Chh gene consists of 4 exons. The eyestalk transcript (chh) contains exons I, II, and IV, whereas the chh-l transcript in heart, gills, antennal glands, and thoracic ganglion contains all 4 exons. The appearance of exon III in chh-l cDNA changes the sequence content in the latter half of the mature peptide, starting within the codon of the 40th residue, arginine. The amino acid sequence deduced from exon III matched no homologue in public protein databases, while the 2 cysteine residues in this segment preserved the positional conservation characters of CHH neuropeptide family members. The common organization of Chh genes between palaemonid, brachyuran, and astacus crustaceans suggests that the Chh gene has a 4-exon structure in these species.

cytoHubba: identifying hub objects and sub-networks from complex interactome

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.

High-throughput profiling of influenza A virus hemagglutinin gene at single-nucleotide resolution

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.

POWER: PhylOgenetic WEb Repeater—an integrated and user-optimized framework for biomolecular phylogenetic analysis

POWER, the PhylOgenetic WEb Repeater, is a web-based service designed to perform user-friendly pipeline phylogenetic analysis. POWER uses an open-source LAMP structure and infers genetic distances and phylogenetic relationships using well-established algorithms (ClustalW and PHYLIP). POWER incorporates a novel tree builder based on the GD library to generate a high-quality tree topology according to the calculated result. POWER accepts either raw sequences in FASTA format or user-uploaded alignment output files. Through a user-friendly web interface, users can sketch a tree effortlessly in multiple steps. After a tree has been generated, users can freely set and modify parameters, select tree building algorithms, refine sequence alignments or edit the tree topology. All the information related to input sequences and the processing history is logged and downloadable for the users reference. Furthermore, iterative tree construction can be performed by adding sequences to, or removing them from, a previously submitted job. POWER is accessible at .

PALM: A Paralleled and Integrated Framework for Phylogenetic Inference with Automatic Likelihood Model Selectors

Background Selecting an appropriate substitution model and deriving a tree topology for a given sequence set are essential in phylogenetic analysis. However, such time consuming, computationally intensive tasks rely on knowledge of substitution model theories and related expertise to run through all possible combinations of several separate programs. To ensure a thorough and efficient analysis and avert tedious manipulations of various programs, this work presents an intuitive framework, the phylogenetic reconstruction with automatic likelihood model selectors (PALM), with convincing, updated algorithms and a best-fit model selection mechanism for seamless phylogenetic analysis. Methodology As an integrated framework of ClustalW, PhyML, MODELTEST, ProtTest, and several in-house programs, PALM evaluates the fitness of 56 substitution models for nucleotide sequences and 112 substitution models for protein sequences with scores in various criteria. The input for PALM can be either sequences in FASTA format or a sequence alignment file in PHYLIP format. To accelerate the computing of maximum likelihood and bootstrapping, this work integrates MPICH2/PhyML, PalmMonitor and Palm job controller across several machines with multiple processors and adopts the task parallelism approach. Moreover, an intuitive and interactive web component, PalmTree, is developed for displaying and operating the output tree with options of tree rooting, branches swapping, viewing the branch length values, and viewing bootstrapping score, as well as removing nodes to restart analysis iteratively. Significance The workflow of PALM is straightforward and coherent. Via a succinct, user-friendly interface, researchers unfamiliar with phylogenetic analysis can easily use this server to submit sequences, retrieve the output, and re-submit a job based on a previous result if some sequences are to be deleted or added for phylogenetic reconstruction. PALM results in an inference of phylogenetic relationship not only by vanquishing the computation difficulty of ML methods but also providing statistic methods for model selection and bootstrapping. The proposed approach can reduce calculation time, which is particularly relevant when querying a large data set. PALM can be accessed online at http://palm.iis.sinica.edu.tw.

Identification of a novel FN1–FGFR1 genetic fusion as a frequent event in phosphaturic mesenchymal tumour

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 proteins 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. Copyright

A hub-attachment based method to detect functional modules from confidence-scored protein interactions and expression profiles

BackgroundMany research results show that the biological systems are composed of functional modules. Members in the same module usually have common functions. This is useful information to understand how biological systems work. Therefore, detecting functional modules is an important research topic in the post-genome era. One of functional module detecting methods is to find dense regions in Protein-Protein Interaction (PPI) networks. Most of current methods neglect confidence-scores of interactions, and pay little attention on using gene expression data to improve their results.ResultsIn this paper, we propose a novel hu b-attachment based method to detect functional modules from confidence-scored protein inte ractions and expression pr ofiles, and we name it HUNTER. Our method not only can extract functional modules from a weighted PPI network, but also use gene expression data as optional input to increase the quality of outcomes. Using HUNTER on yeast data, we found it can discover more novel components related with RNA polymerase complex than those existed methods from yeast interactome. And these new components show the close relationship with polymerase after functional analysis on Gene Ontology.ConclusionA C++ implementation of our prediction method, dataset and supplementary material are available at http://hub.iis.sinica.edu.tw/Hunter/. Our proposed HUNTER method has been applied on yeast data, and the empirical results show that our method can accurately identify functional modules. Such useful application derived from our algorithm can reconstruct the biological machinery, identify undiscovered components and decipher common sub-modules inside these complexes like RNA polymerases I, II, III.

Compartment‐specific transcriptomics in a reef‐building coral exposed to elevated temperatures

Although rising ocean temperatures threaten scleractinian corals and the reefs they construct, certain reef corals can acclimate to elevated temperatures to which they are rarely exposed in situ. Specimens of the model Indo‐Pacific reef coral Pocillopora damicornis collected from upwelling reefs of Southern Taiwan were previously found to have survived a 36‐week exposure to 30°C, a temperature they encounter infrequently and one that can elicit the breakdown of the coral–dinoflagellate (genus Symbiodinium) endosymbiosis in many corals of the Pacific Ocean. To gain insight into the subcellular pathways utilized by both the coral hosts and their mutualistic Symbiodinium populations to acclimate to this temperature, mRNAs from both control (27°C) and high (30°C)‐temperature samples were sequenced on an Illumina platform and assembled into a 236 435‐contig transcriptome. These P. damicornis specimens were found to be ~60% anthozoan and 40% microbe (Symbiodinium, other eukaryotic microbes, and bacteria), from an mRNA‐perspective. Furthermore, a significantly higher proportion of genes from the Symbiodinium compartment were differentially expressed after two weeks of exposure. Specifically, at elevated temperatures, Symbiodinium populations residing within the coral gastrodermal tissues were more likely to up‐regulate the expression of genes encoding proteins involved in metabolism than their coral hosts. Collectively, these transcriptome‐scale data suggest that the two members of this endosymbiosis have distinct strategies for acclimating to elevated temperatures that are expected to characterize many of Earths coral reefs in the coming decades.

Fly-DPI: database of protein interactomes for D. melanogaster in the approach of systems biology

BackgroundProteins control and mediate many biological activities of cells by interacting with other protein partners. This work presents a statistical model to predict protein interaction networks of Drosophila melanogaster based on insight into domain interactions.ResultsThree high-throughput yeast two-hybrid experiments and the collection in FlyBase were used as our starting datasets. The co-occurrences of domains in these interactive events are converted into a probability score of domain-domain interaction. These scores are used to infer putative interaction among all available open reading frames (ORFs) of fruit fly. Additionally, the likelihood function is used to estimate all potential protein-protein interactions.All parameters are successfully iterated and MLE is obtained for each pair of domains. Additionally, the maximized likelihood reaches its converged criteria and maintains the probability stable. The hybrid model achieves a high specificity with a loss of sensitivity, suggesting that the model may possess major features of protein-protein interactions. Several putative interactions predicted by the proposed hybrid model are supported by literatures, while experimental data with a low probability score indicate an uncertain reliability and require further proof of interaction.Fly-DPI is the online database used to present this work. It is an integrated proteomics tool with comprehensive protein annotation information from major databases as well as an effective means of predicting protein-protein interactions. As a novel search strategy, the ping-pong search is a naïve path map between two chosen proteins based on pre-computed shortest paths. Adopting effective filtering strategies will facilitate researchers in depicting the birds eye view of the network of interest. Fly-DPI can be accessed at http://flydpi.nhri.org.tw.ConclusionThis work provides two reference systems, statistical and biological, to evaluate the reliability of protein interaction. First, the hybrid model statistically estimates both experimental and predicted protein interaction relationships. Second, the biological information for filtering and annotation itself is a strong indicator for the reliability of protein-protein interaction. The space-temporal or stage-specific expression patterns of genes are also critical for identifying proteins involved in a particular situation.