The modulative effects of microcurrent electrical nerve stimulation on diabetic mice.

Diabetes (one of non-communicable diseases) is serious due to its complications, such like, cardiovascular ailments, neuropathy, nephropathy, retinopathy, wound gangrene and sexual impotence. Diabetes and associated chronic conditions are rapidly emerging as major health problems. In clinical, there were different drugs for diabetes treatment on different mechanisms. However, there were limited studies on the efficacy of electric stimulations on diabetes therapeutic application. In current study, we try to evaluate the effect of microcurrent electrical nerve stimulator (MENS) on diabetes modulation as an alternative medicine. A total of 36 male ICR mice of 6 weeks old were randomly divided into 4 groups [1] Control, [2] MENS only, [3] DM, [4] DM with MENS. During 8 weeks treatments, the diabetes-associated assessments included body weight, diet utilization, blood glucose measurement, other biochemistries and histopathological observations. The diabetes animal model induced by STZ had 180 mg/dl fasting blood glucose (GLU-AC) before MENS intervention. After 3 and 6 weeks administration, the GLU-AC of DM+MENS group significantly decreased 31.97% and 50.82% (P < 0.0001), respectively, as compared to DM group and the OGTT also demonstrated the similar significant results. The diabetic syndromes of polydipsia and polyphagia were also significantly ameliorated by MENS intervention. In other biochemical indexes, the glycated hemoglobin (HbA1c), hyperinsulinemia, liver functions (AST & ALT) and kidneys function (BUN & Creatinine) were also significantly mitigated by MENS under diabetes model. The histological observation also showed the MENS administration improved the diabetes-related pathological characteristics in liver, kidney and pancreas tissues. Our results suggest that administration of MENS could significantly improve diabetes animal model on blood sugar homeostasis, diabetic polydipsia, biochemistries, and tissue damage. In the health conditions, the MENS didn't exist obvious side effects on assessments. Therefore, the MENS could be potential on alternative medicine or supportive applications to future DM therapeutics.

Analysis of protein-protein interactions in cross-talk pathways reveals CRKL protein as a novel prognostic marker in hepatocellular carcinoma.

Deciphering the network of signaling pathways in cancer via protein-protein interactions (PPIs) at the cellular level is a promising approach but remains incomplete. We used an in situ proximity ligation assay to identify and quantify 67 endogenous PPIs among 21 interlinked pathways in two hepatocellular carcinoma (HCC) cells, Huh7 (minimally migratory cells) and Mahlavu (highly migratory cells). We then applied a differential network biology analysis and determined that the novel interaction, CRKL-FLT1, has a high centrality ranking, and the expression of this interaction is strongly correlated with the migratory ability of HCC and other cancer cell lines. Knockdown of CRKL and FLT1 in HCC cells leads to a decrease in cell migration via ERK signaling and the epithelial-mesenchymal transition process. Our immunohistochemical analysis shows high expression levels of the CRKL and CRKL-FLT1 pair that strongly correlate with reduced disease-free and overall survival in HCC patient samples, and a multivariate analysis further established CRKL and the CRKL-FLT1 as novel prognosis markers. This study demonstrated that functional exploration of a disease network with interlinked pathways via PPIs can be used to discover novel biomarkers.

EBARDenovo: highly accurate de novo assembly of RNA-Seq with efficient chimera-detection.

MOTIVATION: High-accuracy de novo assembly of the short sequencing reads from RNA-Seq technology is very challenging. We introduce a de novo assembly algorithm, EBARDenovo, which stands for Extension, Bridging And Repeat-sensing Denovo. This algorithm uses an efficient chimera-detection function to abrogate the effect of aberrant chimeric reads in RNA-Seq data. RESULTS: EBARDenovo resolves the complications of RNA-Seq assembly arising from sequencing errors, repetitive sequences and aberrant chimeric amplicons. In a series of assembly experiments, our algorithm is the most accurate among the examined programs, including de Bruijn graph assemblers, Trinity and Oases. AVAILABILITY AND IMPLEMENTATION: EBARDenovo is available at http://ebardenovo.sourceforge.net/. This software package (with patent pending) is free of charge for academic use only. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Analysis of schizophrenia and hepatocellular carcinoma genetic network with corresponding modularity and pathways: novel insights to the immune system.

BACKGROUND: Schizophrenic patients show lower incidences of cancer, implicating schizophrenia may be a protective factor against cancer. To study the genetic correlation between the two diseases, a specific PPI network was constructed with candidate genes of both schizophrenia and hepatocellular carcinoma. The network, designated schizophrenia-hepatocellular carcinoma network (SHCN), was analysed and cliques were identified as potential functional modules or complexes. The findings were compared with information from pathway databases such as KEGG, Reactome, PID and ConsensusPathDB. RESULTS: The functions of mediator genes from SHCN show immune system and cell cycle regulation have important roles in the eitology mechanism of schizophrenia. For example, the over-expressing schizophrenia candidate genes, SIRPB1, SYK and LCK, are responsible for signal transduction in cytokine production; immune responses involving IL-2 and TREM-1/DAP12 pathways are relevant for the etiology mechanism of schizophrenia. Novel treatments were proposed by searching the target genes of FDA approved drugs with genes in potential protein complexes and pathways. It was found that Vitamin A, retinoid acid and a few other immune response agents modulated by RARA and LCK genes may be potential treatments for both schizophrenia and hepatocellular carcinoma. CONCLUSIONS: This is the first study showing specific mediator genes in the SHCN which may suppress tumors. We also show that the schizophrenic protein interactions and modulation with cancer implicates the importance of immune system for etiology of schizophrenia.

Quantitative assessment of mitochondrial DNA copies from whole genome sequencing.

BACKGROUND: Mitochondrial dysfunction is associated with various aging diseases. The copy number of mtDNA in human cells may therefore be a potential biomarker for diagnostics of aging. Here we propose a new computational method for the accurate assessment of mtDNA copies from whole genome sequencing data. RESULTS: Two families of the human whole genome sequencing datasets from the HapMap and the 1000 Genomes projects were used for the accurate counting of mitochondrial DNA copy numbers. The results revealed the parental mitochondrial DNA copy numbers are significantly lower than that of their children in these samples. There are 8%~21% more copies of mtDNA in samples from the children than from their parents. The experiment demonstrated the possible correlations between the quantity of mitochondrial DNA and aging-related diseases. CONCLUSIONS: Since the next-generation sequencing technology strives to deliver affordable and non-biased sequencing results, accurate assessment of mtDNA copy numbers can be achieved effectively from the output of whole genome sequencing. We implemented the method as a software package MitoCounter with the source code and user's guide available to the public at http://sourceforge.net/projects/mitocounter/.

MetaABC--an integrated metagenomics platform for data adjustment, binning and clustering.

SUMMARY: MetaABC is a metagenomic platform that integrates several binning tools coupled with methods for removing artifacts, analyzing unassigned reads and controlling sampling biases. It allows users to arrive at a better interpretation via series of distinct combinations of analysis tools. After execution, MetaABC provides outputs in various visual formats such as tables, pie and bar charts as well as clustering result diagrams. AVAILABILITY: MetaABC source code and documentation are available at http://bits2.iis.sinica.edu.tw/MetaABC/ CONTACT: dywang@gate.sinica.edu.tw; hktsai@iis.sinica.edu.tw SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A quantitative analysis of monochromaticity in genetic interaction networks.

BACKGROUND: A genetic interaction refers to the deviation of phenotypes from the expected when perturbing two genes simultaneously. Studying genetic interactions help clarify relationships between genes, such as compensation and masking, and identify gene groups of functional modules. Recently, several genome-scale experiments for measuring quantitative (positive and negative) genetic interactions have been conducted. The results revealed that genes in the same module usually interact with each other in a consistent way (pure positive or negative); this phenomenon was designated as monochromaticity. Monochromaticity might be the underlying principle that can be utilized to unveil the modularity of cellular networks. However, no appropriate quantitative measurement for this phenomenon has been proposed. RESULTS: In this study, we propose the monochromatic index (MCI), which is able to quantitatively evaluate the monochromaticity of potential functional modules of genes, and the MCI was used to study genetic landscapes in different cellular subsystems. We demonstrated that MCI not only amend the deficiencies of MP-score but also properly incorporate the background effect. The results showed that not only within-complex but also between-complex connections present significant monochromatic tendency. Furthermore, we also found that significantly higher proportion of protein complexes are connected by negative genetic interactions in metabolic network, while transcription and translation system adopts relatively even number of positive and negative genetic interactions to link protein complexes. CONCLUSION: In summary, we demonstrate that MCI improves deficiencies suffered by MP-score, and can be used to evaluate monochromaticity in a quantitative manner. In addition, it also helps to unveil features of genetic landscapes in different cellular subsystems. Moreover, MCI can be easily applied to data produced by different types of genetic interaction methodologies such as Synthetic Genetic Array (SGA), and epistatic miniarray profile (E-MAP).

Construction and analysis of the protein-protein interaction networks for schizophrenia, bipolar disorder, and major depression.

BACKGROUND: Schizophrenia, bipolar disorder, and major depression are devastating mental diseases, each with distinctive yet overlapping epidemiologic characteristics. Microarray and proteomics data have revealed genes which expressed abnormally in patients. Several single nucleotide polymorphisms (SNPs) and mutations are associated with one or more of the three diseases. Nevertheless, there are few studies on the interactions among the disease-associated genes and proteins. RESULTS: This study, for the first time, incorporated microarray and protein-protein interaction (PPI) databases to construct the PPI network of abnormally expressed genes in postmortem brain samples of schizophrenia, bipolar disorder, and major depression patients. The samples were collected from Brodmann area (BA) 10 of the prefrontal cortex. Abnormally expressed disease genes were selected by t-tests comparing the disease and control samples. These genes were involved in housekeeping functions (e.g. translation, transcription, energy conversion, and metabolism), in brain specific functions (e.g. signal transduction, neuron cell differentiation, and cytoskeleton), or in stress responses (e.g. heat shocks and biotic stress).The diseases were interconnected through several "switchboard"-like nodes in the PPI network or shared abnormally expressed genes. A "core" functional module which consisted of a tightly knitted sub-network of clique-5 and -4s was also observed. These cliques were formed by 12 genes highly expressed in both disease and control samples. CONCLUSIONS: Several previously unidentified disease marker genes and drug targets, such as SBNO2 (schizophrenia), SEC24C (bipolar disorder), and SRRT (major depression), were identified based on statistical and topological analyses of the PPI network. The shared or interconnecting marker genes may explain the shared symptoms of the studied diseases. Furthermore, the "switchboard" genes, such as APP, UBC, and YWHAZ, are proposed as potential targets for developing new treatments due to their functional and topological significance.

Cliques in mitotic spindle network bring kinetochore-associated complexes to form dependence pathway.

The mitotic spindle is an essential molecular machine for chromosome segregation during mitosis. Achieving a better understanding of its organization at the topological level remains a daunting task. To determine the functional connections among 137 mitotic spindle proteins, a protein-protein interaction network among queries was constructed. Many hub proteins, which connect more than one query and serve as highly plausible candidates for expanding the mitotic spindle proteome, are ranked by conventional degree centrality and a new subnetwork specificity score. Evaluation of the ranking results by literature reviews and empirical verification of SEPT6, a novel top-ranked hub, suggests that the subnetwork specificity score could enrich for putative spindle-related proteins. Topological analysis of this expanded network shows the presence of 30 3-cliques and six 4-cliques (fully connected subgraphs) that, respectively, reside in eight kinetochore-associated complexes, of which seven are evolution conserved. Notably, these complexes strikingly form dependence pathways for the assembly of the kinetochore complex. These analyses indicate the feasibility of using network topology, i.e. cliques, to uncover novel pathways to accelerate our understanding of potential biological processes.

POINeT: protein interactome with sub-network analysis and hub prioritization.

BACKGROUND: Protein-protein interactions (PPIs) are critical to every aspect of biological processes. Expansion of all PPIs from a set of given queries often results in a complex PPI network lacking spatiotemporal consideration. Moreover, the reliability of available PPI resources, which consist of low- and high-throughput data, for network construction remains a significant challenge. Even though a number of software tools are available to facilitate PPI network analysis, an integrated tool is crucial to alleviate the burden on querying across multiple web servers and software tools. RESULTS: We have constructed an integrated web service, POINeT, to simplify the process of PPI searching, analysis, and visualization. POINeT merges PPI and tissue-specific expression data from multiple resources. The tissue-specific PPIs and the numbers of research papers supporting the PPIs can be filtered with user-adjustable threshold values and are dynamically updated in the viewer. The network constructed in POINeT can be readily analyzed with, for example, the built-in centrality calculation module and an integrated network viewer. Nodes in global networks can also be ranked and filtered using various network analysis formulas, i.e., centralities. To prioritize the sub-network, we developed a ranking filtered method (S3) to uncover potential novel mediators in the midbody network. Several examples are provided to illustrate the functionality of POINeT. The network constructed from four schizophrenia risk markers suggests that EXOC4 might be a novel marker for this disease. Finally, a liver-specific PPI network has been filtered with adult and fetal liver expression profiles. CONCLUSION: The functionalities provided by POINeT are highly improved compared to previous version of POINT. POINeT enables the identification and ranking of potential novel genes involved in a sub-network. Combining with tissue-specific gene expression profiles, PPIs specific to selected tissues can be revealed. The straightforward interface of POINeT makes PPI search and analysis just a few clicks away. The modular design permits further functional enhancement without hampering the simplicity. POINeT is available at (http://poinet.bioinformatics.tw/).

From midbody protein-protein interaction network construction to novel regulators in cytokinesis.

Midbody, a transient organelle-like structure, is known as central for abscission and is indispensable for termination of cytokinesis. Here, we used the midbody proteome inventories to construct the potential midbody protein-protein interaction (PPI) network. To delineate novel regulators participating in cytokinesis, the z-score, a standard statistic score, rather than hub degree was implemented to prioritize the novel hubs. Of these hubs, KIAA0133, SEPT1, KIAA1377, and CRMP-1 were localized to the midbody, whereas HTR3A and ICAM2 were associated with the cleavage furrow as examined by immunofluorescence. Knockdown of SEPT1 and KIAA1377 resulted in increasing numbers of cytokinesis defect cells, suggesting these newly identified hubs play critical roles in cytokinesis progression. Moreover, ectopic expression of CRMP-1 mutant in which Aurora-A phosphorylation sites have been replaced with Ala results in a cytokinesis defect. This subproteome network construction not only sheds light on the intimate interactions of the midbody proteomes, but also prioritizes novel hubs or protein complexes that may govern the process of cytokinesis.

VNTRDB: a bacterial variable number tandem repeat locus database.

Variable number tandem repeat-PCR (VNTR-PCR) is a novel method developed for molecular typing of microorganisms. This method has proven useful in epidemiological studies in medical microbiology. Although hundreds of bacterial genomes have been sequenced, variable number tandem repeats (TRs) derived from comparative genome analyses are scarce. This may hamper their application to the surveillance of bacteria in molecular epidemiology. Here, we present a freely accessible variable number tandem repeat database (VNTRDB) that is intended to be a resource for helping in the discovery of putatively polymorphic tandem repeat loci and to aid with assay design by providing the flanking sequences that can be used in subsequent PCR primer design. In order to reveal possible polymorphism, each TR locus was obtained by comparing the sequences between different sets of bacterial genera, species or strains. Through this comparison, TRs which are unique to a genus can also be identified. Moreover, a visualization tool is provided to ensure that the copy number and locus length of repeats are correct. The VNTRDB is available at http://vntr.csie.ntu.edu.tw/.

Co-expression of neighboring genes in the zebrafish (Danio rerio) genome.

Neighboring genes in the eukaryotic genome have a tendency to express concurrently, and the proximity of two adjacent genes is often considered a possible explanation for their co-expression behavior. However, the actual contribution of the physical distance between two genes to their co-expression behavior has yet to be defined. To further investigate this issue, we studied the co-expression of neighboring genes in zebrafish, which has a compact genome and has experienced a whole genome duplication event. Our analysis shows that the proportion of highly co-expressed neighboring pairs (Pearson's correlation coefficient R>0.7) is low (0.24% approximately 0.67%); however, it is still significantly higher than that of random pairs. In particular, the statistical result implies that the co-expression tendency of neighboring pairs is negatively correlated with their physical distance. Our findings therefore suggest that physical distance may play an important role in the co-expression of neighboring genes. Possible mechanisms related to the neighboring genes' co-expression are also discussed.

Evolutionary conservation of DNA-contact residues in DNA-binding domains.

BACKGROUND: DNA-binding proteins are of utmost importance to gene regulation. The identification of DNA-binding domains is useful for understanding the regulation mechanisms of DNA-binding proteins. In this study, we proposed a method to determine whether a domain or a protein can has DNA binding capability by considering evolutionary conservation of DNA-binding residues. RESULTS: Our method achieves high precision and recall for 66 families of DNA-binding domains, with a false positive rate less than 5% for 250 non-DNA-binding proteins. In addition, experimental results show that our method is able to identify the different DNA-binding behaviors of proteins in the same SCOP family based on the use of evolutionary conservation of DNA-contact residues. CONCLUSION: This study shows the conservation of DNA-contact residues in DNA-binding domains. We conclude that the members in the same subfamily bind DNA specifically and the members in different subfamilies often recognize different DNA targets. Additionally, we observe the co-evolution of DNA-contact residues and interacting DNA base-pairs.

Ortholog-based protein-protein interaction prediction and its application to inter-species interactions.

BACKGROUND: The rapid growth of protein-protein interaction (PPI) data has led to the emergence of PPI network analysis. Despite advances in high-throughput techniques, the interactomes of several model organisms are still far from complete. Therefore, it is desirable to expand these interactomes with ortholog-based and other methods. RESULTS: Orthologous pairs of 18 eukaryotic species were expanded and merged with experimental PPI datasets. The contributions of interologs from each species were evaluated. The expanded orthologous pairs enable the inference of interologs for various species. For example, more than 32,000 human interactions can be predicted. The same dataset has also been applied to the prediction of host-pathogen interactions. PPIs between P. falciparum calmodulin and several H. sapiens proteins are predicted, and these interactions may contribute to the maintenance of host cell Ca2+ concentration. Using comparisons with Bayesian and structure-based approaches, interactions between putative HSP40 homologs of P. falciparum and the H. sapiens TNF receptor associated factor family are revealed, suggesting a role for these interactions in the interference of the human immune response to P. falciparum. CONCLUSION: The PPI datasets are available from POINT http://point.bioinformatics.tw/ and POINeT http://poinet.bioinformatics.tw/. Further development of methods to predict host-pathogen interactions should incorporate multiple approaches in order to improve sensitivity, and should facilitate the identification of targets for drug discovery and design.

Reconstruction of human protein interolog network using evolutionary conserved network.

BACKGROUND: The recent increase in the use of high-throughput two-hybrid analysis has generated large quantities of data on protein interactions. Specifically, the availability of information about experimental protein-protein interactions and other protein features on the Internet enables human protein-protein interactions to be computationally predicted from co-evolution events (interolog). This study also considers other protein interaction features, including sub-cellular localization, tissue-specificity, the cell-cycle stage and domain-domain combination. Computational methods need to be developed to integrate these heterogeneous biological data to facilitate the maximum accuracy of the human protein interaction prediction. RESULTS: This study proposes a relative conservation score by finding maximal quasi-cliques in protein interaction networks, and considering other interaction features to formulate a scoring method. The scoring method can be adopted to discover which protein pairs are the most likely to interact among multiple protein pairs. The predicted human protein-protein interactions associated with confidence scores are derived from six eukaryotic organisms--rat, mouse, fly, worm, thale cress and baker's yeast. CONCLUSION: Evaluation results of the proposed method using functional keyword and Gene Ontology (GO) annotations indicate that some confidence is justified in the accuracy of the predicted interactions. Comparisons among existing methods also reveal that the proposed method predicts human protein-protein interactions more accurately than other interolog-based methods.

Detection of the inferred interaction network in hepatocellular carcinoma from EHCO (Encyclopedia of Hepatocellular Carcinoma genes Online).

BACKGROUND: The significant advances in microarray and proteomics analyses have resulted in an exponential increase in potential new targets and have promised to shed light on the identification of disease markers and cellular pathways. We aim to collect and decipher the HCC-related genes at the systems level. RESULTS: Here, we build an integrative platform, the Encyclopedia of Hepatocellular Carcinoma genes Online, dubbed EHCO http://ehco.iis.sinica.edu.tw, to systematically collect, organize and compare the pileup of unsorted HCC-related studies by using natural language processing and softbots. Among the eight gene set collections, ranging across PubMed, SAGE, microarray, and proteomics data, there are 2,906 genes in total; however, more than 77% genes are only included once, suggesting that tremendous efforts need to be exerted to characterize the relationship between HCC and these genes. Of these HCC inventories, protein binding represents the largest proportion (~25%) from Gene Ontology analysis. In fact, many differentially expressed gene sets in EHCO could form interaction networks (e.g. HBV-associated HCC network) by using available human protein-protein interaction datasets. To further highlight the potential new targets in the inferred network from EHCO, we combine comparative genomics and interactomics approaches to analyze 120 evolutionary conserved and overexpressed genes in HCC. 47 out of 120 queries can form a highly interactive network with 18 queries serving as hubs. CONCLUSION: This architectural map may represent the first step toward the attempt to decipher the hepatocarcinogenesis at the systems level. Targeting hubs and/or disruption of the network formation might reveal novel strategy for HCC treatment.

Bioinformatics approaches for disulfide connectivity prediction.

Protein structure prediction with computational methods has gained much attention in the research fields of protein engineering and protein folding studies. Due to the vastness of conformational space, one of the major tasks is to restrain the flexibility of protein structure and reduce the search space. Many studies have revealed that, with the information of disulfide connectivity available, the search in conformational space can be dramatically reduced and lead to significant improvements in the prediction accuracy. As a result, predicting disulfide connectivity using bioinformatics approaches is of great interest nowadays. In this mini-review, the prediction of disulfide connectivity in proteins will be discussed in four aspects: (1) how the problem formulated and the computational techniques used in the literatures; (2) the effects of the features adopted to encode the information and the biological meanings implied; (3) the problems encountered and limitations of disulfide connectivity prediction; and (4) the practical usages of predicted disulfide bond information in molecular simulation and the prospects in the future.

Disulfide connectivity prediction with 70% accuracy using two-level models.

Disulfide bridges stabilize protein structures covalently and play an important role in protein folding. Predicting disulfide connectivity precisely helps towards the solution of protein structure prediction. Previous methods for disulfide connectivity prediction either infer the bonding potential of cysteine pairs or rank alternative disulfide bonding patterns. As a result, these methods encode data according to cysteine pairs (pair-wise) or disulfide bonding patterns (pattern-wise). However, using either encoding scheme alone cannot fully utilize the local and global information of proteins, so the accuracies of previous methods are limited. In this work, we propose a novel two-level framework to predict disulfide connectivity. With this framework, both the pair-wise and pattern-wise encoding schemes are considered. Our models were validated on the datasets derived from SWISS-PROT 39 and 43, and the results demonstrate that our models can combine both local and global information. Compared to previous methods, significant improvements were obtained by our models. Our work may also provide insights to further improvements of disulfide connectivity prediction and increase its applicability in protein structure analysis and prediction.

Prevalence of intestinal parasitic infections among school children in capital areas of the Democratic Republic of São Tomé and Príncipe, West Africa.

BACKGROUND: Although the Democratic Republic of Sao Tome and Principe (DRSTP) has undertaken school children-based deworming programs against intestinal parasitic infections (IPIs) using a single dose of mebendazole annually since 2005, it remains unclear as to the outcome to date. The present study intends to investigate the recent IPIs status among school children living in capital areas of the DRSTP. METHODS: A total of 252 school children (121 boys and 131 girls) of grades 4 and 5 from 4 primary schools located in the capital areas participated in the present study and their fresh fecal specimens were examined for the presence of any parasites using the merthiolate-iodine-formaldehyde concentration method as conducted. RESULTS: The overall prevalence of IPIs was 64.7% (163/ 252). No significant gender difference in prevalence between boys (67.8%) and girls (61.8%) was found (p = 0.3). The majority of school children were infected with a single species of parasite (55.8%). Altogether, 12 different intestinal parasite species were identified in DRSTP school children, of which 9 species were pathogenic and the remaining 3 were non-pathogenic. CONCLUSION: Improving the detection method, sanitation facilities and personal hygiene as well as utilizing combined drugs are all important measures to greatly reduce IPIs in DRSTP school children.