Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts

The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions. The study of these maps provides opportunities for future toxicogenomics and drug discovery applications. We developed a computational framework to build disease-specific drug-protein connectivity maps. We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples. We described the development and application of this computational framework using Alzheimer''s Disease (AD) as a primary example in three steps. First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins. Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies. Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature. We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment. Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts. To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results. Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for drug development, in which therapeutic/toxicological profiles of candidate drugs can be checked computationally before costly clinical trials begin.     

Fuzzy J-Means and VNS methods for clustering genes from microarray data

MOTIVATION: In the interpretation of gene expression data from a group of microarray experiments that include samples from either different patients or conditions, special consideration must be given to the pleiotropic and epistatic roles of genes, as observed in the variation of gene coexpression patterns. Crisp clustering methods assign each gene to one cluster, thereby omitting information about the multiple roles of genes. RESULTS: Here, we present the application of a local search heuristic, Fuzzy J-Means, embedded into the variable neighborhood search metaheuristic for the clustering of microarray gene expression data. We show that for all the datasets studied this algorithm outperforms the standard Fuzzy C-Means heuristic. Different methods for the utilization of cluster membership information in determining gene coregulation are presented. The clustering and data analyses were performed on simulated datasets as well as experimental cDNA microarray data for breast cancer and human blood from the Stanford Microarray Database. AVAILABILITY: The source code of the clustering software (C programming language) is freely available from Nabil.Belacel@nrc-cnrc.gc.ca     

Superposition of transcriptional behaviors determines gene state

We introduce a novel technique to determine the expression state of a gene from quantitative information measuring its expression. Adopting a productive abstraction from current thinking in molecular biology, we consider two expression states for a gene--Up or Down. We determine this state by using a statistical model that assumes the data behaves as a combination of two biological distributions. Given a cohort of hybridizations, our algorithm predicts, for the single reading, the probability of each gene's being in an Up or a Down state in each hybridization. Using a series of publicly available gene expression data sets, we demonstrate that our algorithm outperforms the prevalent algorithm. We also show that our algorithm can be used in conjunction with expression adjustment techniques to produce a more biologically sound gene-state call. The technique we present here enables a routine update, where the continuously evolving expression level adjustments feed into gene-state calculations. The technique can be applied in almost any multi-sample gene expression experiment, and holds equal promise for protein abundance experiments.     

eGIFT: Mining Gene Information from the Literature

Background  

With the biomedical literature continually expanding, searching PubMed for information about specific genes becomes increasingly difficult. Not only can thousands of results be returned, but gene name ambiguity leads to many irrelevant hits. As a result, it is difficult for life scientists and gene curators to rapidly get an overall picture about a specific gene from documents that mention its names and synonyms.     

Attribute clustering for grouping, selection, and classification of gene expression data

This paper presents an attribute clustering method which is able to group genes based on their interdependence so as to mine meaningful patterns from the gene expression data. It can be used for gene grouping, selection, and classification. The partitioning of a relational table into attribute subgroups allows a small number of attributes within or across the groups to be selected for analysis. By clustering attributes, the search dimension of a data mining algorithm is reduced. The reduction of search dimension is especially important to data mining in gene expression data because such data typically consist of a huge number of genes (attributes) and a small number of gene expression profiles (tuples). Most data mining algorithms are typically developed and optimized to scale to the number of tuples instead of the number of attributes. The situation becomes even worse when the number of attributes overwhelms the number of tuples, in which case, the likelihood of reporting patterns that are actually irrelevant due to chances becomes rather high. It is for the aforementioned reasons that gene grouping and selection are important preprocessing steps for many data mining algorithms to be effective when applied to gene expression data. This paper defines the problem of attribute clustering and introduces a methodology to solving it. Our proposed method groups interdependent attributes into clusters by optimizing a criterion function derived from an information measure that reflects the interdependence between attributes. By applying our algorithm to gene expression data, meaningful clusters of genes are discovered. The grouping of genes based on attribute interdependence within group helps to capture different aspects of gene association patterns in each group. Significant genes selected from each group then contain useful information for gene expression classification and identification. To evaluate the performance of the proposed approach, we applied it to two well-known gene expression data sets and compared our results with those obtained by other methods. Our experiments show that the proposed method is able to find the meaningful clusters of genes. By selecting a subset of genes which have high multiple-interdependence with others within clusters, significant classification information can be obtained. Thus, a small pool of selected genes can be used to build classifiers with very high classification rate. From the pool, gene expressions of different categories can be identified.     

Books for free? How can this be?— A PubMed resource you may be overlooking

The NCBI (National Center for Biotechnology Information) at the National Institutes of Health collects a wide range of molecular biological data, and develops tools and databases to analyse and disseminate this information. Many life scientists are familiar with the website maintained by the NCBI (http://www.ncbi.nlm.nih.gov), because they use it to search GenBank for homologues of their genes of interest or to search the PubMed database for scientific literature of interest. There is also a database called the Bookshelf that includes searchable popular life science textbooks, medical and research reference books and NCBI reference materials. The Bookshelf can be useful for researchers and educators to find basic biological information. This article includes a representative list of the resources currently available on the Bookshelf, as well as instructions on how to access the information in these resources.     

Pop’s Pipes: poplar gene expression data analysis pipelines

We developed multiple gene expression pipelines and assembled them into a web-based tool called Pop’s Pipes to facilitate preprocessing and analysis of substantial poplar gene expression data. The input data can be spatiotemporal microarray and RNA-seq data from comparable tissues, time points, or treatment-vs-control conditions. Pop’s Pipes can be used to identify differentially expressed genes between one or multiple paired tissues, time points, or treatment-vs-control conditions in a single in silico analysis. The differentially expressed genes (DEGs) obtained for each comparison will be automatically analyzed by Pop’s Pipes for identifying significantly enriched gene ontologies and interpro protein domains. Also, significantly changed metabolic pathways across all input data sets will be identified. We also integrated a pipeline into Pop's Pipes for constructing any of three type gene ontology trees when a short list of gene ontologies from biological processes, molecular functions, or cellular components is used as an input. The resulting information from Pop’s Pipes enables scrutiny to create spatiotemporal models and hypotheses to understand how poplar develops and functions. Pop’s Pipes can analyze a microarray or RNA-seq data set with 10 time points in 4–10 h, with each time point containing three replicates of treatments and three controls. Such a data set usually takes a bioinformatician a few months to a year to analyze. Pop’s Pipes can thus save users tremendous amounts of research time when large numbers of comparative data need to be analyzed.     

Network-Based Integration of Disparate Omic Data To Identify "Silent Players" in Cancer

Development of high-throughput monitoring technologies enables interrogation of cancer samples at various levels of cellular activity. Capitalizing on these developments, various public efforts such as The Cancer Genome Atlas (TCGA) generate disparate omic data for large patient cohorts. As demonstrated by recent studies, these heterogeneous data sources provide the opportunity to gain insights into the molecular changes that drive cancer pathogenesis and progression. However, these insights are limited by the vast search space and as a result low statistical power to make new discoveries. In this paper, we propose methods for integrating disparate omic data using molecular interaction networks, with a view to gaining mechanistic insights into the relationship between molecular changes at different levels of cellular activity. Namely, we hypothesize that genes that play a role in cancer development and progression may be implicated by neither frequent mutation nor differential expression, and that network-based integration of mutation and differential expression data can reveal these “silent players”. For this purpose, we utilize network-propagation algorithms to simulate the information flow in the cell at a sample-specific resolution. We then use the propagated mutation and expression signals to identify genes that are not necessarily mutated or differentially expressed genes, but have an essential role in tumor development and patient outcome. We test the proposed method on breast cancer and glioblastoma multiforme data obtained from TCGA. Our results show that the proposed method can identify important proteins that are not readily revealed by molecular data, providing insights beyond what can be gleaned by analyzing different types of molecular data in isolation.     

MENT: Methylation and expression database of normal and tumor tissues

Integrated analysis of DNA methylation and gene expression can reveal specific epigenetic patterns that are important during carcinogenesis. We built an integrated database of DNA methylation and gene expression termed MENT (Methylation and Expression database of Normal and Tumor tissues) to provide researchers information on both DNA methylation and gene expression in diverse cancers. It contains integrated data of DNA methylation, gene expression, correlation of DNA methylation and gene expression in paired samples, and clinicopathological conditions gathered from the GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas). A user-friendly interface allows users to search for differential DNA methylation by either ‘gene search’ or ‘dataset search’. The ‘gene search’ returns which conditions are differentially methylated in a gene of interest, while ‘dataset search’ returns which genes are differentially methylated in a condition of interest based on filtering options such as direction, DM (differential methylation value), and p-value. MENT is the first database which provides both DNA methylation and gene expression information in diverse normal and tumor tissues. Its user-friendly interface allows users to easily search and view both DNA methylation and gene expression patterns. MENT is freely available at http://mgrc.kribb.re.kr:8080/MENT/.     

BCDB - A database for breast cancer research and information

In pursuit of a better updated source including 'omics' information for breast cancer, Breast Cancer Database (BCDB) has been developed to provide the researcher with the quick overview of the Breast cancer disease and other relevant information. This database comprises of myriad of information about genes involved in breast cancer, its functions and drug molecules which are currently being used in the treatment of breast cancer. The data available in BCDB is retrieved from the biomedical research literature. It facilitates the user to search information on gene, its location in chromosome, functions and its importance in cancer diseases. Broadly, this can be queried by giving gene name, protein name and drug name. This database is platform independent, user friendly and freely accessible through internet. The data present in BCDB is directly linked to other on-line resources such as NCBI, PDB and PubMed. Hence, it can act as a complete web resource comprising gene sequences, drug structures and literature information related to breast cancer, which is not available in any other breast cancer database. AVAILABILITY: The database is freely available at http://122.165.25.137/bioinfo/breastcancerdb/     

Gene expression analysis in sections and tissue microarrays of archival tissues by mRNA in situ hybridization

Altered expression of genes in diseased tissues can prognosticate a distinct natural progression of the disease as well as predict sensitivity or resistance to particular therapies. Archival tissues from patients with a known medical history and treatments are an invaluable resource to validate the utility of candidate genes for prognosis and prediction of therapy outcomes. However, stored tissues with associated long-term follow-up information typically are formalin-fixed, paraffin-embedded specimen and this can severely restrict the methods applicable for gene expression analysis. We report here on the utility of tissue microarrays (TMAs) that use valuable tissues sparingly and provide a platform for simultaneous analysis of gene expression in several hundred samples. In particular, we describe a stable method applicable to mRNA expression screening in such archival tissues. TMAs are constructed from sections of small drill cores, taken from tissue blocks of archival tissues and multiple samples can thus be arranged on a single microscope slide. We used mRNA in situ hybridization (ISH) on >500 full sections and >100 TMAs for >10 different cDNAs that yielded >10,000 data points. We provide detailed experimental protocols that can be implemented without major hurdles in a molecular pathology laboratory and discuss quantitative analysis and the advantages and limitations of ISH. We conclude that gene expression analysis in archival tissues by ISH is reliable and particularly useful when no protein detection methods are available for a candidate gene.     

Distinctive pharmacological differences between liver cancer cell lines HepG2 and Hep3B

As cellular models for in vitro liver cancer and toxicity studies, HepG2 and Hep3B are the two most frequently used liver cancer cell lines. Because of their similarities they are often treated as the same in experimental studies. However, there are many differences that have been largely over-sighted or ignored between them. In this review, we summarize the differences between HepG2 and Hep3B cell lines that can be found in the literature based on PubMed search. We particularly focus on the differential gene expression, differential drug responses (chemosensitivity, cell cycle and growth inhibition, and gene induction), signaling pathways associated with these differences, as well as the factors in governing these differences between HepG2 and Hep3B cell lines. Based on our analyses of the available data, we suggest that neither HBx nor p53 may be the crucial factor to determine the differences between HepG2 and Hep3B cell lines although HBx regulates the expression of the majority of genes that are differentially expressed between HepG2 and Hep3B. Instead, the different maturation stages in cancer development of the original specimen between HepG2 and Hep3B may be responsible for the differences between them. This review provides insight into the molecular mechanisms underlying the differences between HepG2 and Hep3B and help investigators especially the beginners in the areas of liver cancer research and drug metabolism to fully understand, and thus better use and interpret the data from these two cell lines in their studies.     

Harnessing the cellular immune system to the gene-prediction cart

Prediction of genes and verification of their bona fide expression in the cell are major challenges of the post-genomic era. Here, we demonstrate how information from the apparently unrelated field of cellular immunology can be recruited for these challenging tasks. The cellular immune system presents short peptides that are the degradation products of both foreign and self-proteins expressed in the cell. We carried out a comprehensive search comparing these peptides to all accumulated human sequence data. Our findings illustrate how these ‘presented self-peptides’ are informative for the identification of new genes, for hypothetical gene verification, for verifying gene expression at the protein level and for supporting splice junctions.