GREMA: modelling of emulated gene regulatory networks with confidence levels based on evolutionary intelligence to cope with the underdetermined problem.

MOTIVATION: Non-linear ordinary differential equation (ODE) models that contain numerous parameters are suitable for inferring an emulated gene regulatory network (eGRN). However, the number of experimental measurements is usually far smaller than the number of parameters of the eGRN model that leads to an underdetermined problem. There is no unique solution to the inference problem for an eGRN using insufficient measurements. RESULTS: This work proposes an evolutionary modelling algorithm (EMA) that is based on evolutionary intelligence to cope with the underdetermined problem. EMA uses an intelligent genetic algorithm to solve the large-scale parameter optimization problem. An EMA-based method, GREMA, infers a novel type of gene regulatory network with confidence levels for every inferred regulation. The higher the confidence level is, the more accurate the inferred regulation is. GREMA gradually determines the regulations of an eGRN with confidence levels in descending order using either an S-system or a Hill function-based ODE model. The experimental results showed that the regulations with high-confidence levels are more accurate and robust than regulations with low-confidence levels. Evolutionary intelligence enhanced the mean accuracy of GREMA by 19.2% when using the S-system model with benchmark datasets. An increase in the number of experimental measurements may increase the mean confidence level of the inferred regulations. GREMA performed well compared with existing methods that have been previously applied to the same S-system, DREAM4 challenge and SOS DNA repair benchmark datasets. AVAILABILITY AND IMPLEMENTATION: All of the datasets that were used and the GREMA-based tool are freely available at https://nctuiclab.github.io/GREMA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Association of Toll-like 4 receptor gene polymorphism (rs4986790, rs4986791) with the risk of urinary tract infection: A systematic review and meta-analysis.

Recently published studies had shown that there may be a potential link between the Single nucleotide polymorphism (SNP) of Toll-like receptor-4 (TLR4), and the risk of urinary tract infection (UTI); however, no consensus was reached. To further understand the relationship between TLR SNPs and urinary tract infections, we searched for related studies published in PubMed, EMBASE, and Web of Science before October 30, 2018, for further systematic review and meta-analysis. Our study accrued 10 case-control studies, which included 1476 urinary tract infection patients and 1449 healthy controls in TLR4(rs4986790, rs4986791). R3.4.2 and Stata 15.0 software were used for the analysis. In general, there was no statistically significant association between rs4986790 and urinary tract infection in the four genetic models. However, in the subgroup analysis, the Asian population showed significantly difference in the allelic model (G vs A: OR = 1.88 [95% CI:1.42-2.49], P = .03). In addition, there were also significant differences in the dominant model (GG + AG vs AA OR = 1.97 [95% CI:1.46-2.66], P = .01). Due to the small number of available literatures, no meaningful conclusion can be drawn regarding the relationship between TLR4 (rs4986791) and the risk of urinary tract infections in general. Nevertheless, our meta-analysis shows that in Asian populations, TLR4 (rs4986790) may be associated with risk of urinary tract infection.

PredCRP: predicting and analysing the regulatory roles of CRP from its binding sites in Escherichia coli.

Cyclic AMP receptor protein (CRP), a global regulator in Escherichia coli, regulates more than 180 genes via two roles: activation and repression. Few methods are available for predicting the regulatory roles from the binding sites of transcription factors. This work proposes an accurate method PredCRP to derive an optimised model (named PredCRP-model) and a set of four interpretable rules (named PredCRP-ruleset) for predicting and analysing the regulatory roles of CRP from sequences of CRP-binding sites. A dataset consisting of 169 CRP-binding sites with regulatory roles strongly supported by evidence was compiled. The PredCRP-model, using 12 informative features of CRP-binding sites, and cooperating with a support vector machine achieved a training and test accuracy of 0.98 and 0.93, respectively. PredCRP-ruleset has two activation rules and two repression rules derived using the 12 features and the decision tree method C4.5. This work further screened and identified 23 previously unobserved regulatory interactions in Escherichia coli. Using quantitative PCR for validation, PredCRP-model and PredCRP-ruleset achieved a test accuracy of 0.96 (=22/23) and 0.91 (=21/23), respectively. The proposed method is suitable for designing predictors for regulatory roles of all global regulators in Escherichia coli. PredCRP can be accessed at https://github.com/NctuICLab/PredCRP .

The loss-of-function mutations and down-regulated expression of ASB3 gene promote the growth and metastasis of colorectal cancer cells.

BACKGROUND: Ankyrin repeat and SOCS box protein 3 (ASB3) is a member of ASB family and contains ankyrin repeat sequence and SOCS box domain. Previous studies indicated that it mediates the ubiquitination and degradation of tumor necrosis factor receptor 2 and is likely involved in inflammatory responses. However, its effects on oncogenesis are unclear. This study aimed to investigate the effects of ASB3 on the growth and metastasis of colorectal cancer (CRC). METHODS: We used next-generation sequencing or Sanger sequencing to detect ASB3 mutations in CRC specimens or cell lines, and used real-time quantitative polymerase chain reaction, Western blotting, and immunohistochemical or immunofluorescence assay to determine gene expression. We evaluated cell proliferation by MTT and colony formation assays, tested cell cycle distribution by flow cytometry, and assessed cell migration and invasion by transwell and wound healing assays. We also performed nude mouse experiments to evaluate tumorigenicity and hepatic metastasis potential of tumor cells. RESULTS: We found that ASB3 gene was frequently mutated (5.3%) and down-regulated (70.4%) in CRC cases. Knockdown of endogenous ASB3 expression promoted CRC cell proliferation, migration, and invasion in vitro and facilitated tumorigenicity and hepatic metastasis in vivo. Conversely, the ectopic overexpression of wild-type ASB3, but not that of ASB3 mutants that occurred in clinical CRC tissues, inhibited tumor growth and metastasis. Further analysis showed that ASB3 inhibited CRC metastasis likely by retarding epithelial-mesenchymal transition, which was characterized by the up-regulation of ß-catenin and E-cadherin and the down-regulation of transcription factor 8, N-cadherin, and vimentin. CONCLUSION: ASB3 dysfunction resulted from gene mutations or down-regulated expression frequently exists in CRC and likely plays a key role in the pathogenesis and progression of CRC.

ESA-UbiSite: accurate prediction of human ubiquitination sites by identifying a set of effective negatives.

Motivation: Numerous ubiquitination sites remain undiscovered because of the limitations of mass spectrometry-based methods. Existing prediction methods use randomly selected non-validated sites as non-ubiquitination sites to train ubiquitination site prediction models. Results: We propose an evolutionary screening algorithm (ESA) to select effective negatives among non-validated sites and an ESA-based prediction method, ESA-UbiSite, to identify human ubiquitination sites. The ESA selects non-validated sites least likely to be ubiquitination sites as training negatives. Moreover, the ESA and ESA-UbiSite use a set of well-selected physicochemical properties together with a support vector machine for accurate prediction. Experimental results show that ESA-UbiSite with effective negatives achieved 0.92 test accuracy and a Matthews's correlation coefficient of 0.48, better than existing prediction methods. The ESA increased ESA-UbiSite's test accuracy from 0.75 to 0.92 and can improve other post-translational modification site prediction methods. Availability and Implementation: An ESA-UbiSite-based web server has been established at http://iclab.life.nctu.edu.tw/iclab_webtools/ESAUbiSite/ . Contact: syho@mail.nctu.edu.tw. Supplementary information: Supplementary data are available at Bioinformatics online.

Long-term prognostic implications and therapeutic target role of hexokinase II in patients with nasopharyngeal carcinoma.

Tumor cells preferentially use anaerobic glycolysis rather than oxidative phosphorylation to generate energy. Hexokinase II (HK-II) is necessary for anaerobic glycolysis and displays aberrant expression in malignant cells. The current study aimed to evaluate the role of HK-II in the survival and biological function of nasopharyngeal carcinoma (NPC). Our study demonstrated that high expression of HK-II was associated with poor survival outcomes in NPC patients. When using 3-BrOP (an HK-II inhibitor) to repress glycolysis, cell proliferation and invasion were attenuated, accompanied by the induction of apoptosis and cell cycle arrest at the G1 stage. Furthermore, 3-BrOP synergized with cisplatin (DDP) to induce NPC cell death. Collectively, we provided that the aberrant expression of HK-II was associated with the malignant phenotype of NPC. A combined treatment modality that targets glycolysis with DDP holds promise for the treatment of NPC patients.

Prediction of linear B-cell epitopes of hepatitis C virus for vaccine development.

BACKGROUND: High genetic heterogeneity in the hepatitis C virus (HCV) is the major challenge of the development of an effective vaccine. Existing studies for developing HCV vaccines have mainly focused on T-cell immune response. However, identification of linear B-cell epitopes that can stimulate B-cell response is one of the major tasks of peptide-based vaccine development. Owing to the variability in B-cell epitope length, the prediction of B-cell epitopes is much more complex than that of T-cell epitopes. Furthermore, the motifs of linear B-cell epitopes in different pathogens are quite different (e. g. HCV and hepatitis B virus). To cope with this challenge, this work aims to propose an HCV-customized sequence-based prediction method to identify B-cell epitopes of HCV. RESULTS: This work establishes an experimentally verified dataset comprising the B-cell response of HCV dataset consisting of 774 linear B-cell epitopes and 774 non B-cell epitopes from the Immune Epitope Database. An interpretable rule mining system of B-cell epitopes (IRMS-BE) is proposed to select informative physicochemical properties (PCPs) and then extracts several if-then rule-based knowledge for identifying B-cell epitopes. A web server Bcell-HCV was implemented using an SVM with the 34 informative PCPs, which achieved a training accuracy of 79.7% and test accuracy of 70.7% better than the SVM-based methods for identifying B-cell epitopes of HCV and the two general-purpose methods. This work performs advanced analysis of the 34 informative properties, and the results indicate that the most effective property is the alpha-helix structure of epitopes, which influences the connection between host cells and the E2 proteins of HCV. Furthermore, 12 interpretable rules are acquired from top-five PCPs and achieve a sensitivity of 75.6% and specificity of 71.3%. Finally, a conserved promising vaccine candidate, PDREMVLYQE, is identified for inclusion in a vaccine against HCV. CONCLUSIONS: This work proposes an interpretable rule mining system IRMS-BE for extracting interpretable rules using informative physicochemical properties and a web server Bcell-HCV for predicting linear B-cell epitopes of HCV. IRMS-BE may also apply to predict B-cell epitopes for other viruses, which benefits the improvement of vaccines development of these viruses without significant modification. Bcell-HCV is useful for identifying B-cell epitopes of HCV antigen to help vaccine development, which is available at http://e045.life.nctu.edu.tw/BcellHCV.

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes.

BACKGROUND: Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only. RESULTS: This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of the 20 amino acids) to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (Pkd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn. CONCLUSIONS: The proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein complexes. The characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes.

Performance evaluation of the section of 3D reconstruction based on different PET/CT image fusion sequence.

In recent years, positron emission tomography imaging (PET) and Computed tomography (CT) fusion images can be observed metabolic information, they can get a more accurate spatial information. People have to construct 3D models in the first place when they try to examine images from different angles. Once a cross-section which we want to inspect has been revealed, it can be observed from any angles. However, a issue people encounter in above-mentioned procedures is that they have to either process images fusion at the beginning and then reconstruct the 3D models with these images to generate section or rebuilt the 3D models with these images and fuse section images as the second step. The main objective of this research is to discriminate the divergences and merits between two types of procedures.This research discovers that two different procedures will exactly bring about dissimilar types of images. Therefore, this research particularly aims at the analysis of two images and evaluates the extent of fringe and remaining information. We calculates entropy and standard deviation of the images. Nevertheless, finding section on 3D models first and fusing images secondly will generate the images which retain more information.

Evaluation of bile reflux in HIDA images based on fluid mechanics.

We propose a new method to help physicians assess, using a hepatobiliary iminodiacetic acid scan image, whether or not there is bile reflux into the stomach. The degree of bile reflux is an important index for clinical diagnosis of stomach diseases. The proposed method applies image-processing technology combined with a hydrodynamic model to determine the extent of bile reflux or whether the duodenum is also folded above the stomach. This condition in 2D dynamic images suggests that bile refluxes into the stomach, when endoscopy shows no bile reflux. In this study, we used optical flow to analyze images from Tc99m-diisopropyl iminodiacetic acid cholescintigraphy (Tc99m-DISIDA) to ascertain the direction and velocity of bile passing through the pylorus. In clinical diagnoses, single photon emission computed tomography (SPECT) is the main clinical tool for evaluating functional images of hepatobiliary metabolism. Computed tomography (CT) shows anatomical images of the external contours of the stomach, liver, and biliary extent. By exploiting the functional fusion of the two kinds of medical image, physicians can obtain a more accurate diagnosis. We accordingly reconstructed 3D images from SPECT and CT to help physicians choose which cross sections to fuse with software and to help them more accurately diagnose the extent and quantity of bile reflux.

Neuropilin 1 is an entry factor that promotes EBV infection of nasopharyngeal epithelial cells.

Epstein-Barr virus (EBV) is implicated as an aetiological factor in B lymphomas and nasopharyngeal carcinoma. The mechanisms of cell-free EBV infection of nasopharyngeal epithelial cells remain elusive. EBV glycoprotein B (gB) is the critical fusion protein for infection of both B and epithelial cells, and determines EBV susceptibility of non-B cells. Here we show that neuropilin 1 (NRP1) directly interacts with EBV gB(23-431). Either knockdown of NRP1 or pretreatment of EBV with soluble NRP1 suppresses EBV infection. Upregulation of NRP1 by overexpression or EGF treatment enhances EBV infection. However, NRP2, the homologue of NRP1, impairs EBV infection. EBV enters nasopharyngeal epithelial cells through NRP1-facilitated internalization and fusion, and through macropinocytosis and lipid raft-dependent endocytosis. NRP1 partially mediates EBV-activated EGFR/RAS/ERK signalling, and NRP1-dependent receptor tyrosine kinase (RTK) signalling promotes EBV infection. Taken together, NRP1 is identified as an EBV entry factor that cooperatively activates RTK signalling, which subsequently promotes EBV infection in nasopharyngeal epithelial cells.

Rule-based knowledge acquisition method for promoter prediction in human and Drosophila species.

The rapid and reliable identification of promoter regions is important when the number of genomes to be sequenced is increasing very speedily. Various methods have been developed but few methods investigate the effectiveness of sequence-based features in promoter prediction. This study proposes a knowledge acquisition method (named PromHD) based on if-then rules for promoter prediction in human and Drosophila species. PromHD utilizes an effective feature-mining algorithm and a reference feature set of 167 DNA sequence descriptors (DNASDs), comprising three descriptors of physicochemical properties (absorption maxima, molecular weight, and molar absorption coefficient), 128 top-ranked descriptors of 4-mer motifs, and 36 global sequence descriptors. PromHD identifies two feature subsets with 99 and 74 DNASDs and yields test accuracies of 96.4% and 97.5% in human and Drosophila species, respectively. Based on the 99- and 74-dimensional feature vectors, PromHD generates several if-then rules by using the decision tree mechanism for promoter prediction. The top-ranked informative rules with high certainty grades reveal that the global sequence descriptor, the length of nucleotide A at the first position of the sequence, and two physicochemical properties, absorption maxima and molecular weight, are effective in distinguishing promoters from non-promoters in human and Drosophila species, respectively.

A DFT study on structures, frontier molecular orbitals and UV-vis spectra of RuX(PPh3)(NHCPh2)L (X=Tp and Cp; L=Cl and N3).

Geometry optimization for RuX(PPh3)(NHCPh2)(L) (X=hydridotris(pyrazolyl)borate (Tp) and cyclopentadiene (Cp); L=Cl and N3) are investigated by using density functional theory (DFT) with DZVP2/DZVP all-electron mixed basis sets and compared with available experimental values, and the calculated structures are in very good agreement with experimental data. The frontier molecular orbitals (FMOs) and electronic transitions have been investigated as well. Our calculations show that the π electron-rich ligand (N3) may increase the energies of occupied orbitals and reduce the energy gap of the HOMO-LUMO (ΔEL-H) in these ruthenium based complexes. The simulated UV-vis spectra of these complexes in methanol have been studied with time-dependent density functional theory (TD-DFT), and conductor-like polarizable continuum model (CPCM) was employed to account for the solvent effects. Our results show that a number of absorption peaks are found in the visible region (400-800 nm) with non-zero oscillator strengths. The strongest adsorption feature is associated to a transition from HOMO-2 to LUMO, which is assigned to metal-to-ligand charge transfer (MLCT) or metal/ligand-to-ligand charge transfer (MLCT/LLCT) depending on co-ligands. In addition, the Cp group increases electron-accept ability and results in red shift due to its π electron-rich and π donor characters. According to our results, these ruthenium based complexes are good candidates for dye-sensitized solar cell owing to their absorption intensities and rich absorption bands in the visible region.

Structures, molecular orbitals and UV-vis spectra investigations on Br2C6H4: a computational study.

The dibromobenzenes (1,2-, 1,3- and 1,4-Br2C6H4) have been studied by theoretical methods. The structures of these species are optimized and the structural characteristics are determined by density functional theory (DFT) and the second order Møller-Plesset perturbation theory (MP2) levels. The geometrical structures of Br2C6H4 show a little distortion of benzene ring due to the substitution of highly electronegativity of bromine atoms. The electronegativity of bromine atoms in 1,4-Br2C6H4 is predicted to be more negative than 1,2- and 1,3-Br2C6H4. In addition, dipole moment and frontier molecular orbitals (FMOs) of these Br2C6H4 are performed as well. The 1,4-Br2C6H4 is slightly more reactive than 1,2- and 1,3-Br2C6H4 because of its small HOMO-LUMO energy gap. The simulated UV-vis spectra are investigated by time-dependent density functional theory (TD-DFT) approach, which are in excellent agreement with the available experimental value. Our calculations show that a few of absorption features are between 140nm and 250nm, which is in ultraviolet C range, and the red shift of 1,3- and 1,4-Br2C6H4 are predicted. Moreover, the UV absorption features of these Br2C6H4 in water or methanol are predicted to be more intense than in gas phase due to solvent effect.

FRKAS: knowledge acquisition using a fuzzy rule base approach to insight of DNA-binding domains/proteins.

Numerous prediction methods of DNA-binding domains/proteins were proposed by identifying informative features and designing effective classifiers. These researches reveal that the DNA-protein binding mechanism is complicated and existing accurate predictors such as support vector machine (SVM) with position specific scoring matrices (PSSMs) are regarded as black-box methods which are not easily interpretable for biologists. In this study, we propose an ensemble fuzzy rule base classifier consisting of a set of interpretable fuzzy rule classifiers (iFRCs) using informative physicochemical properties as features. In designing iFRCs, feature selection, membership function design, and fuzzy rule base generation are all simultaneously optimized using an intelligent genetic algorithm (IGA). IGA maximizes prediction accuracy, minimizes the number of features selected, and minimizes the number of fuzzy rules to generate an accurate and concise fuzzy rule base. Benchmark datasets of DNA-binding domains are used to evaluate the proposed ensemble classifier of 30 iFRCs. Each iFRC has a mean test accuracy of 77.46%, and the ensemble classifier has a test accuracy of 83.33%, where the method of SVM with PSSMs has the accuracy of 82.81%. The physicochemical properties of the first two ranks according to their contribution are positive charge and Van Der Waals volume. Charge complementarity between protein and DNA is thought to be important in the first step of recognition between protein and DNA. The amino acid residues of binding peptides have larger Van Der Waals volumes and positive charges than those of non-binding ones. The proposed knowledge acquisition method by establishing a fuzzy rule-based classifier can also be applicable to predict and analyze other protein functions from sequences.

Land use change and landslide characteristics analysis for community-based disaster mitigation.

On August 8, 2009, Typhoon Morakot brought heavy rain to Taiwan, causing numerous landslides and debris flows in the Taihe village area of Meishan Township, Chiayi County, in south-central Taiwan. In the Taihe land is primary used for agriculture and land use management may be a factor in the area's landslides. This study explores Typhoon Morakot-induced landslides and land use changes between 1999 and 2009 using GIS with the aid of field investigation. Spot 5 satellite images with a resolution of 2.5 m are used for landslide interpretation and manually digitalized in GIS. A statistical analysis for landslide frequency-area distribution was used to identify the landslide characteristics associated with different types of land use. There were 243 landslides with a total area of 2.75 km(2) in the study area. The area is located in intrinsically fragile combinations of sandstone and shale. Typhoon Morakot-induced landslides show a power-law distribution in the study area. Landslides were mainly located in steep slope areas containing natural forest and in areas planted with bamboo, tea, and betel nut. Land covered with natural forest shows the highest landslide ratio, followed by bamboo, betel nut, and tea. Landslides thus show a higher ratio in areas planted with shallow root vegetation such as bamboo, betel nut, and tea. Furthermore, the degree of basin development is proportional to the landslide ratio. The results show that a change in vegetation cover results in a modified landslide area and frequency and changed land use areas have higher landslide ratios than non-changed. Land use management and community-based disaster prevention are needed in mountainous areas of Taiwan for hazard mitigation.

Ranking Gene Ontology terms for predicting non-classical secretory proteins in eukaryotes and prokaryotes.

Protein secretion is an important biological process for both eukaryotes and prokaryotes. Several sequence-based methods mainly rely on utilizing various types of complementary features to design accurate classifiers for predicting non-classical secretory proteins. Gene Ontology (GO) terms are increasing informative in predicting protein functions. However, the number of used GO terms is often very large. For example, there are 60,020 GO terms used in the prediction method Euk-mPLoc 2.0 for subcellular localization. This study proposes a novel approach to identify a small set of m top-ranked GO terms served as the only type of input features to design a support vector machine (SVM) based method Sec-GO to predict non-classical secretory proteins in both eukaryotes and prokaryotes. To evaluate the Sec-GO method, two existing methods and their used datasets are adopted for performance comparisons. The Sec-GO method using m=436 GO terms yields an independent test accuracy of 96.7% on mammalian proteins, much better than the existing method SPRED (82.2%) which uses frequencies of tri-peptides and short peptides, secondary structure, and physicochemical properties as input features of a random forest classifier. Furthermore, when applying to Gram-positive bacterial proteins, the Sec-GO with m=158 GO terms has a test accuracy of 94.5%, superior to NClassG+ (90.0%) which uses SVM with several feature types, comprising amino acid composition, di-peptides, physicochemical properties and the position specific weighting matrix. Analysis of the distribution of secretory proteins in a GO database indicates the percentage of the non-classical secretory proteins annotated by GO is larger than that of classical secretory proteins in both eukaryotes and prokaryotes. Of the m top-ranked GO features, the top-four GO terms are all annotated by such subcellular locations as GO:0005576 (Extracellular region). Additionally, the method Sec-GO is easily implemented and its web tool of prediction is available at iclab.life.nctu.edu.tw/secgo.

Prediction and analysis of protein solubility using a novel scoring card method with dipeptide composition.

BACKGROUND: Existing methods for predicting protein solubility on overexpression in Escherichia coli advance performance by using ensemble classifiers such as two-stage support vector machine (SVM) based classifiers and a number of feature types such as physicochemical properties, amino acid and dipeptide composition, accompanied with feature selection. It is desirable to develop a simple and easily interpretable method for predicting protein solubility, compared to existing complex SVM-based methods. RESULTS: This study proposes a novel scoring card method (SCM) by using dipeptide composition only to estimate solubility scores of sequences for predicting protein solubility. SCM calculates the propensities of 400 individual dipeptides to be soluble using statistic discrimination between soluble and insoluble proteins of a training data set. Consequently, the propensity scores of all dipeptides are further optimized using an intelligent genetic algorithm. The solubility score of a sequence is determined by the weighted sum of all propensity scores and dipeptide composition. To evaluate SCM by performance comparisons, four data sets with different sizes and variation degrees of experimental conditions were used. The results show that the simple method SCM with interpretable propensities of dipeptides has promising performance, compared with existing SVM-based ensemble methods with a number of feature types. Furthermore, the propensities of dipeptides and solubility scores of sequences can provide insights to protein solubility. For example, the analysis of dipeptide scores shows high propensity of α-helix structure and thermophilic proteins to be soluble. CONCLUSIONS: The propensities of individual dipeptides to be soluble are varied for proteins under altered experimental conditions. For accurately predicting protein solubility using SCM, it is better to customize the score card of dipeptide propensities by using a training data set under the same specified experimental conditions. The proposed method SCM with solubility scores and dipeptide propensities can be easily applied to the protein function prediction problems that dipeptide composition features play an important role. AVAILABILITY: The used datasets, source codes of SCM, and supplementary files are available at http://iclab.life.nctu.edu.tw/SCM/.

Direct sequencing and characterization of a clinical isolate of Epstein-Barr virus from nasopharyngeal carcinoma tissue by using next-generation sequencing technology.

Epstein-Barr virus (EBV)-encoded molecules have been detected in the tumor tissues of several cancers, including nasopharyngeal carcinoma (NPC), suggesting that EBV plays an important role in tumorigenesis. However, the nature of EBV with respect to genome width in vivo and whether EBV undergoes clonal expansion in the tumor tissues are still poorly understood. In this study, next-generation sequencing (NGS) was used to sequence DNA extracted directly from the tumor tissue of a patient with NPC. Apart from the human sequences, a clinically isolated EBV genome 164.7 kb in size was successfully assembled and named GD2 (GenBank accession number HQ020558). Sequence and phylogenetic analyses showed that GD2 was closely related to GD1, a previously assembled variant derived from a patient with NPC. GD2 contains the most prevalent EBV variants reported in Cantonese patients with NPC, suggesting that it might be the prevalent strain in this population. Furthermore, GD2 could be grouped into a single subtype according to common classification criteria and contains only 6 heterozygous point mutations, suggesting the monoclonal expansion of GD2 in NPC. This study represents the first genome-wide analysis of a clinical isolate of EBV directly extracted from NPC tissue. Our study reveals that NGS allows the characterization of genome-wide variations of EBV in clinical tumors and provides evidence of monoclonal expansion of EBV in vivo. The pipeline could also be applied to the study of other pathogen-related malignancies. With additional NGS studies of NPC, it might be possible to uncover the potential causative EBV variant involved in NPC.

Advancement and prospects of tumor gene therapy.

Gene therapy is one of the most attractive fields in tumor therapy. In past decades, significant progress has been achieved. Various approaches, such as viral and non-viral vectors and physical methods, have been developed to make gene delivery safer and more efficient. Several therapeutic strategies have evolved, including gene-based (tumor suppressor genes, suicide genes, antiangiogenic genes, cytokine and oxidative stress-based genes) and RNA-based (antisense oligonucleotides and RNA interference) approaches. In addition, immune response-based strategies (dendritic cell- and T cell-based therapy) are also under investigation in tumor gene therapy. This review highlights the progress and recent developments in gene delivery systems, therapeutic strategies, and possible clinical directions for gene therapy.