Survival analysis of microarray expression data by transformation models

Many microarray experiments involve examining the time elapsed prior to the occurrence of a specific event. One purpose of these studies is to relate the gene expressions to the survival times. The Cox proportional hazards model has been the major tool for analyzing such data. The transformation model provides a viable alternative to the classical Cox's model. We investigate the use of transformation models in microarray survival data in this paper. The transformation model, which can be viewed as a generalization of proportional hazards model and the proportional odds model, is more robust than the proportional hazards model, because it is not susceptible to erroneous results for cases when the assumption of proportional hazards is violated. We analyze a gene expression dataset from Beer et al. [Beer, D.G., Kardia, S.L., Huang, C.C., Giordano, T.J., Levin, A.M., Misek, D.E., Lin, L., Chen, G., Gharib, T.G., Thomas, D.G., Lizyness, M.L., Kuick, R., Hayasaka, S., Taylor, J.M., Iannettoni, M.D., Orringer, M.B., Hanash, S., 2002. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat. Med. 8 (8), 816-824] and show that the transformation model provides higher prediction precision than the proportional hazards model.     

A robust and efficient method for estimating enzyme complex abundance and metabolic flux from expression data

A major theme in constraint-based modeling is unifying experimental data, such as biochemical information about the reactions that can occur in a system or the composition and localization of enzyme complexes, with high-throughput data including expression data, metabolomics, or DNA sequencing. The desired result is to increase predictive capability and improve our understanding of metabolism. The approach typically employed when only gene (or protein) intensities are available is the creation of tissue-specific models, which reduces the available reactions in an organism model, and does not provide an objective function for the estimation of fluxes. We develop a method, flux assignment with LAD (least absolute deviation) convex objectives and normalization (FALCON), that employs metabolic network reconstructions along with expression data to estimate fluxes. In order to use such a method, accurate measures of enzyme complex abundance are needed, so we first present an algorithm that addresses quantification of complex abundance. Our extensions to prior techniques include the capability to work with large models and significantly improved run-time performance even for smaller models, an improved analysis of enzyme complex formation, the ability to handle large enzyme complex rules that may incorporate multiple isoforms, and either maintained or significantly improved correlation with experimentally measured fluxes. FALCON has been implemented in MATLAB and ATS, and can be downloaded from: https://github.com/bbarker/FALCON. ATS is not required to compile the software, as intermediate C source code is available. FALCON requires use of the COBRA Toolbox, also implemented in MATLAB.     

Gene network coherence based on prior knowledge using direct and indirect relationships

Gene networks (GNs) have become one of the most important approaches for modeling biological processes. They are very useful to understand the different complex biological processes that may occur in living organisms. Currently, one of the biggest challenge in any study related with GN is to assure the quality of these GNs. In this sense, recent works use artificial data sets or a direct comparison with prior biological knowledge. However, these approaches are not entirely accurate as they only take into account direct gene–gene interactions for validation, leaving aside the weak (indirect) relationships.We propose a new measure, named gene network coherence (GNC), to rate the coherence of an input network according to different biological databases. In this sense, the measure considers not only the direct gene–gene relationships but also the indirect ones to perform a complete and fairer evaluation of the input network. Hence, our approach is able to use the whole information stored in the networks. A GNC JAVA-based implementation is available at: http://fgomezvela.github.io/GNC/.The results achieved in this work show that GNC outperforms the classical approaches for assessing GNs by means of three different experiments using different biological databases and input networks. According to the results, we can conclude that the proposed measure, which considers the inherent information stored in the direct and indirect gene–gene relationships, offers a new robust solution to the problem of GNs biological validation.     

Weighted edge based clustering to identify protein complexes in protein–protein interaction networks incorporating gene expression profile

Protein complex detection from protein–protein interaction (PPI) network has received a lot of focus in recent years. A number of methods identify protein complexes as dense sub-graphs using network information while several other methods detect protein complexes based on topological information. While the methods based on identifying dense sub-graphs are more effective in identifying protein complexes, not all protein complexes have high density. Moreover, existing methods focus more on static PPI networks and usually overlook the dynamic nature of protein complexes. Here, we propose a new method, Weighted Edge based Clustering (WEC), to identify protein complexes based on the weight of the edge between two interacting proteins, where the weight is defined by the edge clustering coefficient and the gene expression correlation between the interacting proteins. Our WEC method is capable of detecting highly inter-connected and co-expressed protein complexes. The experimental results of WEC on three real life data shows that our method can detect protein complexes effectively in comparison with other highly cited existing methods.Availability: The WEC tool is available at http://agnigarh.tezu.ernet.in/~rosy8/shared.html.     

Gene selection from microarray data for cancer classification--a machine learning approach

A DNA microarray can track the expression levels of thousands of genes simultaneously. Previous research has demonstrated that this technology can be useful in the classification of cancers. Cancer microarray data normally contains a small number of samples which have a large number of gene expression levels as features. To select relevant genes involved in different types of cancer remains a challenge. In order to extract useful gene information from cancer microarray data and reduce dimensionality, feature selection algorithms were systematically investigated in this study. Using a correlation-based feature selector combined with machine learning algorithms such as decision trees, nave Bayes and support vector machines, we show that classification performance at least as good as published results can be obtained on acute leukemia and diffuse large B-cell lymphoma microarray data sets. We also demonstrate that a combined use of different classification and feature selection approaches makes it possible to select relevant genes with high confidence. This is also the first paper which discusses both computational and biological evidence for the involvement of zyxin in leukaemogenesis.     

Computational identification of circular RNAs based on conformational and thermodynamic properties in the flanking introns

Circular RNAs (circRNAs) were found more than 30 years ago, but have been treated as molecular flukes in a long time. Combining deep sequencing studies with bioinformatics technique, thousands of endogenous circRNAs have been found in mammalian cells, and some researchers have proved that several circRNAs act as competing endogenous RNAs (ceRNAs) to regulate gene expression. However, the mechanism by which the precursor mRNA to be transformed into a circular RNA or a linear mRNA is largely unknown. In this paper, we attempted to bioinformatically identify shared genomic features that might further elucidate the mechanism of formation and proposed a SVM-based model to distinguish circRNAs from non-circularized, expressed exons. Firstly, conformational and thermodynamic dinucleotide properties in the flanking introns were extracted as potential features. Secondly, two feature selection methods were applied to gain the optimal feature subset. Our 10-fold cross-validation results showed that the model can be used to distinguish circRNAs from non-circularized, expressed exons with an Sn of 0.884, Sp of 0.900, ACC of 0.892, MCC of 0.784, respectively. The identification results suggest that conformational and thermodynamic properties in the flanking introns are closely related to the formation of circRNAs. Datasets and the tool involved in this paper are all available at https://sourceforge.net/projects/predicircrnatool/files/.     

A novel fuzzy set based multifactor dimensionality reduction method for detecting gene–gene interaction

BackgroundGene-gene interaction (GGI) is one of the most popular approaches for finding the missing heritability of common complex traits in genetic association studies. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. In order to identify the best interaction model associated with disease susceptibility, MDR compares all possible genotype combinations in terms of their predictability of disease status from a simple binary high(H) and low(L) risk classification. However, this simple binary classification does not reflect the uncertainty of H/L classification.MethodsWe regard classifying H/L as equivalent to defining the degree of membership of two risk groups H/L. By adopting the fuzzy set theory, we propose Fuzzy MDR which takes into account the uncertainty of H/L classification. Fuzzy MDR allows the possibility of partial membership of H/L through a membership function which transforms the degree of uncertainty into a [0,1] scale. The best genotype combinations can be selected which maximizes a new fuzzy set based accuracy measure.ResultsTwo simulation studies are conducted to compare the power of the proposed Fuzzy MDR with that of MDR. Our results show that Fuzzy MDR has higher power than MDR. We illustrate the proposed Fuzzy MDR by analysing bipolar disorder (BD) trait of the WTCCC dataset to detect GGI associated with BD.ConclusionsWe propose a novel Fuzzy MDR method to detect gene–gene interaction by taking into account the uncertainly of H/L classification and show that it has higher power than MDR. Fuzzy MDR can be easily extended to handle continuous phenotypes as well. The program written in R for the proposed Fuzzy MDR is available at https://statgen.snu.ac.kr/software/FuzzyMDR.     

A computational model for predicting fusion peptide of retroviruses

As a pivotal domain within envelope protein, fusion peptide (FP) plays a crucial role in pathogenicity and therapeutic intervention. Taken into account the limited FP annotations in NCBI database and absence of FP prediction software, it is urgent and desirable to develop a bioinformatics tool to predict new putative FPs (np-FPs) in retroviruses. In this work, a sequence-based FP model was proposed by combining Hidden Markov Method with similarity comparison. The classification accuracies are 91.97% and 92.31% corresponding to 10-fold and leave-one-out cross-validation. After scanning sequences without FP annotations, this model discovered 53,946 np-FPs. The statistical results on FPs or np-FPs reveal that FP is a conserved and hydrophobic domain. The FP software programmed for windows environment is available at https://sourceforge.net/projects/fptool/files/?source=navbar.     

Analysis of the NCI-60 dataset for cancer-related microRNA and mRNA using expression profiles

BackgroundRecent studies have indicated that microRNA (miRNA) may play an oncogenic or tumor suppressor role in human cancer. To study the regulatory role of miRNAs in tumorigenesis, an integrated platform has been set up to provide a user friendly interface for query. The main advantage of the present platform is that all the miRNA target genes’ information and disease records are drawn from experimentally verified or high confidence records.ResultsMiRNA target gene results are annotated with reference to the disease gene as well as the pathway database. The correlation strength between miRNA and target gene expression profile is quantified by computing the correlation coefficient using the NCI-60 expression profiling data. Comprehensive analysis of the NCI-60 data found that the cumulative percentage of negative correlation coefficients for cleavage regulation is slightly higher than its positive counterpart; which indicated that the mRNA degradation mechanism is slightly dominant. In addition, the RNAHybrid and TargetScans scores are computed which potentially served as quantitative estimators for miRNA–mRNA binding events.Three scores are defined for each miRNA–mRNA pair, which are based on the disease gene and pathway information. These three scores allow user to sort out high confidence cancer-related miRNA–mRNA pairs.Statistical tests were applied to investigate the relations of three chromosomal features, i.e., CpG island, fragile site, and miRNA cluster, with cancer-related miRNAs. A web-based interface has been set up for query, which can be accessed at: http://ppi.bioinfo.asia.edu.tw/mirna_target/ConclusionsThe main advantage of the present platform on miRNA–mRNA targeting information is that all the target genes’ information and disease records are experimentally verified. Although this may limit the number of miRNA–mRNA relationships, the results provided here are more solid and have fewer false positive events. Certain novel cancer-related miRNA–mRNA pairs are identified and confirmed in the literature. Fisher's exact test suggests that CpG island and fragile site associated miRNAs tend to associate with cancer formation. In summary, the present platform provides an easy means of investigating cancer-related miRNAs.     

Identifying microRNAs involved in cancer pathway using support vector machines

Since Ambros’ discovery of small non-protein coding RNAs in the early 1990s, the past two decades have seen an upsurge in the number of reports of predicted microRNAs (miR), which have been implicated in various functions. The correlation of miRs with cancer has spurred the usage of this class of non-coding RNAs in various cancer therapies, although most of them are at trial stages. However, the experimental identification of a miR to be associated with cancer is still an elaborate, time-consuming process. To aid this process of miR association, we undertook an in-silico study involving the identification of global signatures in experimentally validated microRNAs associated with cancer. Subsequently, a support vector machine based two-step binary classifier system has been trained and modeled from the features extracted from the above study. A total of 60 distinguishing features were selected and ranked to form the feature set for classification – 26 of these extracted from the miR sequence itself, and the remainder from the thermodynamics of folding and the hybridized miRNA–mRNA structure. The two step classifier model – miRSEQ and miRINT had reasonably good performance measures with fairly high values of Matthew’s correlation coefficient (MCC) values ranging from 0.72 to 0.82 (availability: https://sites.google.com/site/sumitslab/tools).     

DEEPAligner: Deep encoding of pathways to align epigenetic signatures

Background and objectiveRecently, differential DNA Methylation is known to affect the regulatory mechanism of biological pathways. A pathway encompasses a set of interacting genes or gene products that altogether perform a given biological function. Pathways often encode strong methylation signatures that are capable of distinguishing biologically distinct subtypes. Even though Next Generation Sequencing techniques such as MeDIP-seq and MBD-isolated genome sequencing (MiGS) allow for genome-wide identification of clinical and biological subtypes, there is a pressing need for computational methods to compare epigenetic signatures across pathways.MethodsA novel alignment method, called DEEPAligner (Deep Encoded Epigenetic Pathway Aligner), is proposed in this paper that finds functionally consistent and topologically sound alignments of epigenetic signatures from pathway networks. A deep embedding framework is used to obtain epigenetic signatures from pathways which are then aligned for functional consistency and local topological similarity.ResultsExperiments on four benchmark cancer datasets reveal epigenetic signatures that are conserved in cancer-specific and across-cancer subtypes.ConclusionThe proposed deep embedding framework obtains highly coherent signatures that are aligned for biological as well as structural orthology. Comparison with state-of-the-art network alignment methods clearly suggest that the proposed method obtains topologically and functionally more consistent alignments.Availabilityhttp://bdbl.nitc.ac.in/DEEPAligner     

PPI‐G4 Glycodendrimers Upregulate TRAIL‐Induced Apoptosis in Chronic Lymphocytic Leukemia Cells

Although chronic lymphocytic leukemia (CLL) is the most common adult leukemia in Western world, it remains incurable with conventional chemotherapeutic agents. Tumor necrosis factor (TNF)‐related apoptosis‐inducing ligand (TRAIL) is an antitumor candidate in cancer therapy. This study examines the proapoptotic effects of poly(propylene imine) (PPI) glycodendrimers modified with the maltotriose residues (PPI‐G4‐OS‐Mal‐III and PPI‐G4‐DS‐Mal‐III) on the TNF family in CLL cells. The combination of an understanding of the signaling pathways associated with CLL and the development of a molecular profiling is a key issue for the design of personalized approaches to therapy. Gene expression is determined with two‐color microarray 8 × 60K. The findings indicate that PPI‐G4‐OS/DS‐Mal‐III affect gene expression from the TRAIL apoptotic pathway and exert a strong effect on CLL cells comparable with fludarabine. Dendrimer‐targeted technology may well prove to bridge the gap between the ineffective treatment of today and the effective personalized therapy of the future.

     

Effect of heating rate and γ-Radiation on the structure of As4SexTe6−x Glasses

The phenomena accompanying the temperature-induced structural changes in five As₄Se_xTe_6–x glasses, withx=1 tox=5, were examined and are discussed. Differential thermal analysis traces of each glass composition at different heating rates from 2 to 50 deg/min were obtained and interpreted. The effect of the Se/Te ratio on the crystallization behaviour is discussed. It is interesting to note that the compositional dependence of the overall behaviour of the crystallization activation energy (E) seems to be similar to that of both the melting point (T_m) and the thermal conductivity () for the investigated glasses. Created structural defects due to gamma-irradiation have some effects on the crystallization process.

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Zusammenfassung Die die temperaturbedingten strukturellen Veränderungen von 5 Glasern der allgemeinen Zusammensetzung As₄Se_xTe_6–x (x=1–5) begleitenden Phänomene wurden untersucht und diskutiert. Die von jedem Glas bei unterschiedlichen Aufheizgeschwindigkeiten zwischen 2 und 50 Grad pro Minute erhaltenen DTA-Kurven werden interpretiert. Der Effekt des Se/Te-Verhältnisses auf das Kristallisationsverhalten wird diskutiert. Von Interesse ist, daß die Abhängigkeit der Aktivierungsenergie (E) der Kristallisation von der Zusammensetzung der des Schmelzpunktes (Tm) und der Wärmeleitfähigkeit (*) der untersuchten Gläser ähnelt. Durch y-Bestrahlung hervorgerufene strukturelle Defekte haben einen gewissen Einfluß auf den Kristallisationsprozeß.

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, As₄Se_x,Te_6–x, c x=1–5. ( 2 50°/) . Se/Te . , , (T _m ) (). , -, .

     

PPM-Dom: A novel method for domain position prediction

Domains are the structural basis of the physiological functions of proteins, and the prediction of which is an advantageous process on the study of protein structure and function. This article proposes a new complete automatic prediction method, PPM-Dom (Domain Position Prediction Method), for predicting the particular positions of domains in a target protein via its atomic coordinate. The presented method integrates complex networks, community division, and fuzzy mean operator (FMO). The whole sequences are divided into potential domain regions by the complex network and community division, and FMO allows the final determination for the domain position. This method will suffice to predict regions that will form a domain structure and those that are unstructured based on completely new atomic coordinate information of the query sequence, and be able to separate different domains in the same query sequence from each other. On evaluating the performance using an independent testing dataset, PPM-Dom reached 91.41% for prediction accuracy, 96.12% for sensitivity and 92.86% for specificity. The tool bag of PPM-Dom is freely available at http://cic.scu.edu.cn/bioinformatics/PPMDom.zip.     

ISDTool: A computational model for predicting immunosuppressive domain of HERVs

Human endogenous retroviruses (HERVs) have been found to act as etiological cofactors in several chronic diseases, including cancer, autoimmunity and neurological dysfunction. Immunosuppressive domain (ISD) is a conserved region of transmembrane protein (TM) in envelope gene (env) of retroviruses. In vitro and vivo, evidence has shown that retroviral TM is highly immunosuppressive and a synthetic peptide (CKS-17) that shows homology to ISD inhibits immune function. ISD is probably a potential pathogenic element in HERVs. However, only less than one hundred ISDs of HERVs have been annotated by researchers so far, and universal software for domain prediction could not achieve sufficient accuracy for specific ISD. In this paper, a computational model is proposed to identify ISD in HERVs based on genome sequences only. It has a classification accuracy of 97.9% using Jack-knife test. 117 HERVs families were scanned with the model, 1002 new putative ISDs have been predicted and annotated in the human chromosomes. This model is also applicable to search for ISDs in human T-lymphotropic virus (HTLV), simian T-lymphotropic virus (STLV) and murine leukemia virus (MLV) because of the evolutionary relationship between endogenous and exogenous retroviruses. Furthermore, software named ISDTool has been developed to facilitate the application of the model. Datasets and the software involved in the paper are all available at https://sourceforge.net/projects/isdtool/files/ISDTool-1.0.     

Use of 5‐Fluorouracil Loaded Micelles and Cisplatin in Thermosensitive Chitosan Hydrogel as an Efficient Therapy against Colorectal Peritoneal Carcinomatosis

Colorectal peritoneal carcinomatosis (CRPC) is a common systemic metastasis of intra‐abdominal cancers. Intraperitoneal chemotherapy against CRPC is at present the preferred treatment. The aim of this study is to develop a novel hydrogel drug delivery system through the combination of 5‐fluorouracil (5‐FU) loaded polymeric micelles and cisplatin (DDP) in biodegradable thermosensitive chitosan (CS) hydrogel. The prepared CS hydrogel drug is a free‐flowing solution at room temperature and forms a stationary gel at body temperature. Therefore, a CRPC mouse model is established to investigate the antitumor activity of CS hydrogel drug system. The results suggest that intraperitoneal administration of CS hydrogel drug can inhibit tumor growth and metastasis, and prolong survival time compared with other groups, thus improving the chemotherapeutic effect. Ki‐67 immunohistochemical analysis reveals that tumors in the CS hydrogel drug group has lower cell proliferation in contrast to other groups (P < 0.001). Furthermore, hematoxylin‐eosin staining of liver and lung tissue indicates that the CS hydrogel drug has also a certain inhibitory effect on colorectal cancer metastasis to the liver and lung. Hence, the work highlights the potential clinical applications of the CS hydrogel drug.

     

Tailoring Disorder and Dimensionality: Strategies for Improved Solid Oxide Fuel Cell Electrolytes

Towards cooler solid oxide fuel cells: Disorder and dimensionality (see picture) play an important role in determining ion mobility of bulk and artificially nanolayered materials and should be considered in the design of new electrolytes with enhanced conductivity.

     

Comprehensive comparison of two protein family of P-ATPases (13A1 and 13A3) in insects

The P-type ATPases (P-ATPases) are present in all living cells where they mediate ion transport across membranes on the expense of ATP hydrolysis. Different ions which are transported by these pumps are protons like calcium, sodium, potassium, and heavy metals such as manganese, iron, copper, and zinc. Maintenance of the proper gradients for essential ions across cellular membranes makes P-ATPases crucial for cell survival. In this study, characterization of two families of P-ATPases including P-ATPase 13A1 and P-ATPase 13A3 protein was compared in two different insect species from different orders. According to the conserved motifs found with MEME, nine motifs were shared by insects of 13A1 family but eight in 13A3 family. Seven different insect species from 13A1 and five samples from 13A3 family were selected as the representative samples for functional and structural analyses. The structural and functional analyses were performed with ProtParam, SOPMA, SignalP 4.1, TMHMM 2.0, ProtScale and ProDom tools in the ExPASy database. The tertiary structure of Bombus terrestris as a sample of each family of insects were predicted by the Phyre2 and TM-score servers and their similarities were verified by SuperPose server. The tertiary structures were predicted via the “c3b9bA” model (PDB Accession Code: 3B9B) in P-ATPase 13A1 family and “c2zxeA” model (PDB Accession Code: 2ZXE) in P-ATPase 13A3 family. A phylogenetic tree was constructed with MEGA 6.06 software using the Neighbor-joining method. According to the results, there was a high identity of P-ATPase families so that they should be derived from a common ancestor however they belonged to separate groups. In protein–protein interaction analysis by STRING 10.0, six common enriched pathways of KEGG were identified in B. terrestris in both families. The obtained data provide a background for bioinformatic studies of the function and evolution of other insects and organisms.