FaaPred: A SVM-Based Prediction Method for Fungal Adhesins and Adhesin-Like Proteins

Adhesion constitutes one of the initial stages of infection in microbial diseases and is mediated by adhesins. Hence, identification and comprehensive knowledge of adhesins and adhesin-like proteins is essential to understand adhesin mediated pathogenesis and how to exploit its therapeutic potential. However, the knowledge about fungal adhesins is rudimentary compared to that of bacterial adhesins. In addition to host cell attachment and mating, the fungal adhesins play a significant role in homotypic and xenotypic aggregation, foraging and biofilm formation. Experimental identification of fungal adhesins is labor- as well as time-intensive. In this work, we present a Support Vector Machine (SVM) based method for the prediction of fungal adhesins and adhesin-like proteins. The SVM models were trained with different compositional features, namely, amino acid, dipeptide, multiplet fractions, charge and hydrophobic compositions, as well as PSI-BLAST derived PSSM matrices. The best classifiers are based on compositional properties as well as PSSM and yield an overall accuracy of 86%. The prediction method based on best classifiers is freely accessible as a world wide web based server at http://bioinfo.icgeb.res.in/faap. This work will aid rapid and rational identification of fungal adhesins, expedite the pace of experimental characterization of novel fungal adhesins and enhance our knowledge about role of adhesins in fungal infections.     

An Attempt at a Molecular Prediction of Metastasis in Patients with Primary Cutaneous Melanoma

Background

Current prognostic clinical and morphological parameters are insufficient to accurately predict metastasis in individual melanoma patients. Several studies have described gene expression signatures to predict survival or metastasis of primary melanoma patients, however the reproducibility among these studies is disappointingly low.

Methodology/Principal Findings

We followed extended REMARK/Gould Rothberg criteria to identify gene sets predictive for metastasis in patients with primary cutaneous melanoma. For class comparison, gene expression data from 116 patients with clinical stage I/II (no metastasis) and 72 with III/IV primary melanoma (with metastasis) at time of first diagnosis were used. Significance analysis of microarrays identified the top 50 differentially expressed genes. In an independent data set from a second cohort of 28 primary melanoma patients, these genes were analyzed by multivariate Cox regression analysis and leave-one-out cross validation for association with development of metastatic disease. In a multivariate Cox regression analysis, expression of the genes Ena/vasodilator-stimulated phosphoprotein-like (EVL) and CD24 antigen gave the best predictive value (p = 0.001; p = 0.017, respectively). A multivariate Cox proportional hazards model revealed these genes as a potential independent predictor, which may possibly add (both p = 0.01) to the predictive value of the most important morphological indicator, Breslow depth.

Conclusion/Significance

Combination of molecular with morphological information may potentially enable an improved prediction of metastasis in primary melanoma patients. A strength of the gene expression set is the small number of genes, which should allow easy reevaluation in independent data sets and adequately designed clinical trials.     

Supervised Learning Classification Models for Prediction of Plant Virus Encoded RNA Silencing Suppressors

Viral encoded RNA silencing suppressor proteins interfere with the host RNA silencing machinery, facilitating viral infection by evading host immunity. In plant hosts, the viral proteins have several basic science implications and biotechnology applications. However in silico identification of these proteins is limited by their high sequence diversity. In this study we developed supervised learning based classification models for plant viral RNA silencing suppressor proteins in plant viruses. We developed four classifiers based on supervised learning algorithms: J48, Random Forest, LibSVM and Naïve Bayes algorithms, with enriched model learning by correlation based feature selection. Structural and physicochemical features calculated for experimentally verified primary protein sequences were used to train the classifiers. The training features include amino acid composition; auto correlation coefficients; composition, transition, and distribution of various physicochemical properties; and pseudo amino acid composition. Performance analysis of predictive models based on 10 fold cross-validation and independent data testing revealed that the Random Forest based model was the best and achieved 86.11% overall accuracy and 86.22% balanced accuracy with a remarkably high area under the Receivers Operating Characteristic curve of 0.95 to predict viral RNA silencing suppressor proteins. The prediction models for plant viral RNA silencing suppressors can potentially aid identification of novel viral RNA silencing suppressors, which will provide valuable insights into the mechanism of RNA silencing and could be further explored as potential targets for designing novel antiviral therapeutics. Also, the key subset of identified optimal features may help in determining compositional patterns in the viral proteins which are important determinants for RNA silencing suppressor activities. The best prediction model developed in the study is available as a freely accessible web server pVsupPred at http://bioinfo.icgeb.res.in/pvsup/.     

An efficiency analysis of high-order combinations of gene–gene interactions using multifactor-dimensionality reduction

Background

Multifactor dimensionality reduction (MDR) is widely used to analyze interactions of genes to determine the complex relationship between diseases and polymorphisms in humans. However, the astronomical number of high-order combinations makes MDR a highly time-consuming process which can be difficult to implement for multiple tests to identify more complex interactions between genes. This study proposes a new framework, named fast MDR (FMDR), which is a greedy search strategy based on the joint effect property.

Results

Six models with different minor allele frequencies (MAFs) and different sample sizes were used to generate the six simulation data sets. A real data set was obtained from the mitochondrial D-loop of chronic dialysis patients. Comparison of results from the simulation data and real data sets showed that FMDR identified significant gene–gene interaction with less computational complexity than the MDR in high-order interaction analysis.

Conclusion

FMDR improves the MDR difficulties associated with the computational loading of high-order SNPs and can be used to evaluate the relative effects of each individual SNP on disease susceptibility. FMDR is freely available at http://bioinfo.kmu.edu.tw/FMDR.rar.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1717-8) contains supplementary material, which is available to authorized users.     

Melanoma invasion – current knowledge and future directions

The acquisition of invasive behaviour is the key transition in the progression of benign melanocyte hyperplasia to life threatening melanoma. Understanding this transition and the mechanisms of invasion are the key to understanding why malignant melanoma is such a devastating disease and will aid treatment strategies. Underlying the invasive behaviour is increased cell motility caused by changes in cytoskeletal organization and altered contacts with the extra‐cellular matrix (ECM). In addition, changes in the interactions of melanoma cells with keratinocytes and fibroblasts enable them to survive and proliferate outside their normal epidermal location. Proteomic and genomic initiatives are greatly increasing our knowledge of which gene products are deregulated in invasive and metastatic melanoma; however, the next challenge is to understand how these genes promote the invasion of melanoma cells. In recent years new models have been developed that more closely recapitulate the conditions of melanoma invasion in vivo. It is hoped that these models will give us a better understanding of how the genes implicated in melanoma progression affect the motility of melanoma cells and their interactions with the ECM, stromal cells and blood vessels. This review will summarise our current understanding of melanoma invasion and focus on the new model systems that can be used to study melanoma.     

Melanoma invasion - current knowledge and future directions

The acquisition of invasive behaviour is the key transition in the progression of benign melanocyte hyperplasia to life threatening melanoma. Understanding this transition and the mechanisms of invasion are the key to understanding why malignant melanoma is such a devastating disease and will aid treatment strategies. Underlying the invasive behaviour is increased cell motility caused by changes in cytoskeletal organization and altered contacts with the extra-cellular matrix (ECM). In addition, changes in the interactions of melanoma cells with keratinocytes and fibroblasts enable them to survive and proliferate outside their normal epidermal location. Proteomic and genomic initiatives are greatly increasing our knowledge of which gene products are deregulated in invasive and metastatic melanoma; however, the next challenge is to understand how these genes promote the invasion of melanoma cells. In recent years new models have been developed that more closely recapitulate the conditions of melanoma invasion in vivo. It is hoped that these models will give us a better understanding of how the genes implicated in melanoma progression affect the motility of melanoma cells and their interactions with the ECM, stromal cells and blood vessels. This review will summarise our current understanding of melanoma invasion and focus on the new model systems that can be used to study melanoma.     

CanGeneBase (CGB) ‐ a database on cancer related genes

The advent of genomic and proteomic technologies in this post-genomic era has urged the researchers to develop novel research strategies against cancer by targeting the human genes that would greatly facilitate to identify more promising treatment and to develop accurate early diagnosis for cancer. To harness the power of cancer genetic information towards better treatment we have developed a cancer gene database called CanGeneBase (CGB). It is a comprehensive data collection of cancer-related genes with the intention of helping the researchers to stay on a single platform to gain exclusive information on the genes of their interest. According to the Cancer Gene Data Curation Project, about 4,700 genes have been identified as being related to cancer. The present CanGeneBase covers about 12 different types of cancer which includes 190 unique gene entries. Each entry encompasses about 33 useful parameters to provide detailed information about specific gene. CanGeneBase is made in such a way that it can be easily accessed by either gene symbol or by the type of cancer.

Availability

The database is freely available at http://122.165.25.137/bioinfo/cancerdb/     

Prioritizing Candidate Disease Metabolites Based on Global Functional Relationships between Metabolites in the Context of Metabolic Pathways

Identification of key metabolites for complex diseases is a challenging task in today''s medicine and biology. A special disease is usually caused by the alteration of a series of functional related metabolites having a global influence on the metabolic network. Moreover, the metabolites in the same metabolic pathway are often associated with the same or similar disease. Based on these functional relationships between metabolites in the context of metabolic pathways, we here presented a pathway-based random walk method called PROFANCY for prioritization of candidate disease metabolites. Our strategy not only takes advantage of the global functional relationships between metabolites but also sufficiently exploits the functionally modular nature of metabolic networks. Our approach proved successful in prioritizing known metabolites for 71 diseases with an AUC value of 0.895. We also assessed the performance of PROFANCY on 16 disease classes and found that 4 classes achieved an AUC value over 0.95. To investigate the robustness of the PROFANCY, we repeated all the analyses in two metabolic networks and obtained similar results. Then we applied our approach to Alzheimer''s disease (AD) and found that a top ranked candidate was potentially related to AD but had not been reported previously. Furthermore, our method was applicable to prioritize the metabolites from metabolomic profiles of prostate cancer. The PROFANCY could identify prostate cancer related-metabolites that are supported by literatures but not considered to be significantly differential by traditional differential analysis. We also developed a freely accessible web-based and R-based tool at http://bioinfo.hrbmu.edu.cn/PROFANCY.     

Modeling cancer progression via pathway dependencies

Cancer is a heterogeneous disease often requiring a complexity of alterations to drive a normal cell to a malignancy and ultimately to a metastatic state. Certain genetic perturbations have been implicated for initiation and progression. However, to a great extent, underlying mechanisms often remain elusive. These genetic perturbations are most likely reflected by the altered expression of sets of genes or pathways, rather than individual genes, thus creating a need for models of deregulation of pathways to help provide an understanding of the mechanisms of tumorigenesis. We introduce an integrative hierarchical analysis of tumor progression that discovers which a priori defined pathways are relevant either throughout or in particular steps of progression. Pathway interaction networks are inferred for these relevant pathways over the steps in progression. This is followed by the refinement of the relevant pathways to those genes most differentially expressed in particular disease stages. The final analysis infers a gene interaction network for these refined pathways. We apply this approach to model progression in prostate cancer and melanoma, resulting in a deeper understanding of the mechanisms of tumorigenesis. Our analysis supports previous findings for the deregulation of several pathways involved in cell cycle control and proliferation in both cancer types. A novel finding of our analysis is a connection between ErbB4 and primary prostate cancer.     

SANTA: Quantifying the Functional Content of Molecular Networks

Linking networks of molecular interactions to cellular functions and phenotypes is a key goal in systems biology. Here, we adapt concepts of spatial statistics to assess the functional content of molecular networks. Based on the guilt-by-association principle, our approach (called SANTA) quantifies the strength of association between a gene set and a network, and functionally annotates molecular networks like other enrichment methods annotate lists of genes. As a general association measure, SANTA can (i) functionally annotate experimentally derived networks using a collection of curated gene sets and (ii) annotate experimentally derived gene sets using a collection of curated networks, as well as (iii) prioritize genes for follow-up analyses. We exemplify the efficacy of SANTA in several case studies using the S. cerevisiae genetic interaction network and genome-wide RNAi screens in cancer cell lines. Our theory, simulations, and applications show that SANTA provides a principled statistical way to quantify the association between molecular networks and cellular functions and phenotypes. SANTA is available from http://bioconductor.org/packages/release/bioc/html/SANTA.html.     

An Evaluation of Methods for Inferring Boolean Networks from Time-Series Data

Regulatory networks play a central role in cellular behavior and decision making. Learning these regulatory networks is a major task in biology, and devising computational methods and mathematical models for this task is a major endeavor in bioinformatics. Boolean networks have been used extensively for modeling regulatory networks. In this model, the state of each gene can be either ‘on’ or ‘off’ and that next-state of a gene is updated, synchronously or asynchronously, according to a Boolean rule that is applied to the current-state of the entire system. Inferring a Boolean network from a set of experimental data entails two main steps: first, the experimental time-series data are discretized into Boolean trajectories, and then, a Boolean network is learned from these Boolean trajectories. In this paper, we consider three methods for data discretization, including a new one we propose, and three methods for learning Boolean networks, and study the performance of all possible nine combinations on four regulatory systems of varying dynamics complexities. We find that employing the right combination of methods for data discretization and network learning results in Boolean networks that capture the dynamics well and provide predictive power. Our findings are in contrast to a recent survey that placed Boolean networks on the low end of the “faithfulness to biological reality” and “ability to model dynamics” spectra. Further, contrary to the common argument in favor of Boolean networks, we find that a relatively large number of time points in the time-series data is required to learn good Boolean networks for certain data sets. Last but not least, while methods have been proposed for inferring Boolean networks, as discussed above, missing still are publicly available implementations thereof. Here, we make our implementation of the methods available publicly in open source at http://bioinfo.cs.rice.edu/.     

Targeted Chromosomal Translocations and Essential Gene Knockout Using CRISPR/Cas9 Technology in Caenorhabditis elegans

Many genes play essential roles in development and fertility; their disruption leads to growth arrest or sterility. Genetic balancers have been widely used to study essential genes in many organisms. However, it is technically challenging and laborious to generate and maintain the loss-of-function mutations of essential genes. The CRISPR/Cas9 technology has been successfully applied for gene editing and chromosome engineering. Here, we have developed a method to induce chromosomal translocations and produce genetic balancers using the CRISPR/Cas9 technology and have applied this approach to edit essential genes in Caenorhabditis elegans. The co-injection of dual small guide RNA targeting genes on different chromosomes resulted in reciprocal translocation between nonhomologous chromosomes. These animals with chromosomal translocations were subsequently crossed with animals that contain normal sets of chromosomes. The F1 progeny were subjected to a second round of Cas9-mediated gene editing. Through this method, we successfully produced nematode strains with specified chromosomal translocations and generated a number of loss-of-function alleles of two essential genes (csr-1 and mes-6). Therefore, our method provides an easy and efficient approach to generate and maintain loss-of-function alleles of essential genes with detailed genetic background information.     

Multi-Edge Gene Set Networks Reveal Novel Insights into Global Relationships between Biological Themes

Curated gene sets from databases such as KEGG Pathway and Gene Ontology are often used to systematically organize lists of genes or proteins derived from high-throughput data. However, the information content inherent to some relationships between the interrogated gene sets, such as pathway crosstalk, is often underutilized. A gene set network, where nodes representing individual gene sets such as KEGG pathways are connected to indicate a functional dependency, is well suited to visualize and analyze global gene set relationships. Here we introduce a novel gene set network construction algorithm that integrates gene lists derived from high-throughput experiments with curated gene sets to construct co-enrichment gene set networks. Along with previously described co-membership and linkage algorithms, we apply the co-enrichment algorithm to eight gene set collections to construct integrated multi-evidence gene set networks with multiple edge types connecting gene sets. We demonstrate the utility of approach through examples of novel gene set networks such as the chromosome map co-differential expression gene set network. A total of twenty-four gene set networks are exposed via a web tool called MetaNet, where context-specific multi-edge gene set networks are constructed from enriched gene sets within user-defined gene lists. MetaNet is freely available at http://blaispathways.dfci.harvard.edu/metanet/.     

Coordination of Recombination with Meiotic Progression in the Caenorhabditis elegans Germline by KIN-18, a TAO Kinase That Regulates the Timing of MPK-1 Signaling

Meiosis is a tightly regulated process requiring coordination of diverse events. A conserved ERK/MAPK-signaling cascade plays an essential role in the regulation of meiotic progression. The Thousand And One kinase (TAO) kinase is a MAPK kinase kinase, the meiotic role of which is unknown. We have analyzed the meiotic functions of KIN-18, the homolog of mammalian TAO kinases, in Caenorhabditis elegans. We found that KIN-18 is essential for normal meiotic progression; mutants exhibit accelerated meiotic recombination as detected both by analysis of recombination intermediates and by crossover outcome. In addition, ectopic germ-cell differentiation and enhanced levels of apoptosis were observed in kin-18 mutants. These defects correlate with ectopic activation of MPK-1 that includes premature, missing, and reoccurring MPK-1 activation. Late progression defects in kin-18 mutants are suppressed by inhibiting an upstream activator of MPK-1 signaling, KSR-2. However, the acceleration of recombination events observed in kin-18 mutants is largely MPK-1-independent. Our data suggest that KIN-18 coordinates meiotic progression by modulating the timing of MPK-1 activation and the progression of recombination events. The regulation of the timing of MPK-1 activation ensures the proper timing of apoptosis and is required for the formation of functional oocytes. Meiosis is a conserved process; thus, revealing that KIN-18 is a novel regulator of meiotic progression in C. elegans would help to elucidate TAO kinase’s role in germline development in higher eukaryotes.