Coexpression network based on natural variation in human gene expression reveals gene interactions and functions

Genes interact in networks to orchestrate cellular processes. Analysis of these networks provides insights into gene interactions and functions. Here, we took advantage of normal variation in human gene expression to infer gene networks, which we constructed using correlations in expression levels of more than 8.5 million gene pairs in immortalized B cells from three independent samples. The resulting networks allowed us to identify biological processes and gene functions. Among the biological pathways, we found processes such as translation and glycolysis that co-occur in the same subnetworks. We predicted the functions of poorly characterized genes, including CHCHD2 and TMEM111, and provided experimental evidence that TMEM111 is part of the endoplasmic reticulum-associated secretory pathway. We also found that IFIH1, a susceptibility gene of type 1 diabetes, interacts with YES1, which plays a role in glucose transport. Furthermore, genes that predispose to the same diseases are clustered nonrandomly in the coexpression network, suggesting that networks can provide candidate genes that influence disease susceptibility. Therefore, our analysis of gene coexpression networks offers information on the role of human genes in normal and disease processes.The functions of many human genes are unknown. It is not unusual that when one searches the literature on a gene, one fails to find any papers that provide information on its biological roles. Identifying gene function is difficult, especially if no hints, such as homologies to known genes, are available to direct the search. However, since genes work by interacting with other genes, we may learn about their functions through their neighboring genes (Stuart et al. 2003; Ayroles et al. 2009). Identifying gene function is increasingly important; in the last several years, genome-wide association studies (GWAS) have identified DNA variants that are associated with common complex diseases. But for many of these studies, the functional links between the susceptibility genes and the diseases are unknown.In this study, we used correlations in expression levels of more than 8.5 million human gene pairs in immortalized B cells from three data sets to infer gene coexpression networks. The resulting gene networks were based on correlations between genes that were found reproducibly in the three data sets. This provided us with gene networks in which we had high confidence in the gene correlations. We then used the networks to identify key biological processes and interactions among those processes in our cells. Then, we identified the functions of 36 human genes with no known functions and four genes that have been implicated in GWAS as susceptibility genes for common human diseases, including IFIH1, which was recently found to be associated with type 1 diabetes.     

The use of logic relationships to model colon cancer gene expression networks with mRNA microarray data

The ultimate goal of genomics research is to describe the network of molecules and interactions that govern all biological functions and disease processes in cells. Nonlinear interactions among genes in terms of their logic relationships play a key role for deciphering the networks of molecules that underlie cellular function. We present a method based on a graph coloring scheme and information theory to identify the gene expression network with lower and higher order logic interactions of genes. The analysis of oncogenes and suppressor genes from a colon cancer mRNA microarray dataset identifies a gene expression network with directionality and weights that reflects intracellular communication pathways. The success of the proposed method in mining hidden, complicated gene interactions and reliably interpreting experimental results suggests that the proposed method is a useful tool for understanding cancer systems. Extension of this method holds the potential to be fruitful for understanding other complex, nonsymmetric systems.     

Application of gene expression profiling for validating models of human breast cancer

While classical histopathologic approaches are invaluable in classifying tumors and understanding aspects of cellular interactions, genomic approaches provide a means to molecularly dissect tumorigenesis. The relationship of gene expression to the development of neoplasia remains an area of intensive research. With the advent of large-scale genomic platforms, alterations in gene expression can be related to the morphological development of cancer. The feasibility of using large-scale genomic analysis platforms has dramatically changed the landscape of biological sciences, as cellular processes must be considered in the context of complex networks. Alterations in gene expression must now be understood in a systems approach in which the relationships between genes expression changes are studied by considering the interplay of multiple regulatory networks. Ultimately, such changes must be understood at the protein level. We have begun to apply this technology to determine changes in gene expression that differentiate various types of mammary cancers that arise in mouse models that have been initiated by different genetic alterations. Ultimately, a molecular catalogue of similarities and differences between rodent and human tumors can be created which will serve to validate or credential particular models for specific experimental purposes, such as preclinical testing. These approaches have led to new insights into molecular pathways involved in oncogenesis, new classifications of human breast cancer, and the identification of new genes that may be relevant to understanding and treating human cancer.     

蛋白质相互作用组学研究策略最新进展

随着人类基因组测序工作的完成,新的生物学后基因组时代已经来临。在蛋白质组和蛋白质组学相继提出的基础上,人类从蛋白质水平全面揭示生命本质的研究进入一个全新的领域—蛋白质相互作用组。在众多研究蛋白质相互作用组的技术中,串联亲和纯化(TAP)技术由于能够在不改变蛋白质生理条件下对其高通量进行研究,渐渐成为该领域的一个重要工具。本综述在介绍常用蛋白质相互作用研究方法的基础上,着重介绍TAP法的原理及其应用。     

Membrane-type 1 matrix metalloproteinase cytoplasmic tail binding protein-1 (MTCBP-1) acts as an eukaryotic aci-reductone dioxygenase (ARD) in the methionine salvage pathway

MTCBP-1 was identified as a protein that binds the cytoplasmic tail of membrane-type 1 matrix metalloproteinase (MT1-MMP/MMP-14). Since MTCBP-1 has a putative beta-barrel structure, it is presumably a member of the recently proposed cupin superfamily that contains tremendously diverged functions of proteins in spite of their well-conserved beta-barrel structure. MTCBP-1 shows significant homology to the bacterial aci-reductone dioxygenase (ARD) in the cupin family, which is an enzyme in the methionine salvage pathway (MTA cycle). Since it is difficult to speculate the functions of cupin proteins simply based on their sequence homology, we examined whether the eukaryotic ARD homologs surely function in the methionine metabolism. Under sulfur-depleted conditions, yeast could grow when substrate of MTA cycle was provided. Disruption of the yeast ARD homolog, YMR009w gene, abolished ability of the cells to grow in this culture condition. Re-expression of either the YMR009w or MTCBP-1 gene restored the cell growth. Mutation analysis revealed that the glutamic acid residue in the beta-barrel fold and the N-terminal extension from the beta-barrel fold were found to be important for the activity to restore the growth. Thus, MTCBP-1 isolated as a binding protein for MT1-MMP was demonstrated to function as an ARD-like enzyme in the MTA cycle in yeast.     

Integrative mining of traditional Chinese medicine literature and MEDLINE for functional gene networks

OBJECTIVE: The amount of biomedical data in different disciplines is growing at an exponential rate. Integrating these significant knowledge sources to generate novel hypotheses for systems biology research is difficult. Traditional Chinese medicine (TCM) is a completely different discipline, and is a complementary knowledge system to modern biomedical science. This paper uses a significant TCM bibliographic literature database in China, together with MEDLINE, to help discover novel gene functional knowledge. MATERIALS AND METHODS: We present an integrative mining approach to uncover the functional gene relationships from MEDLINE and TCM bibliographic literature. This paper introduces TCM literature (about 50,000 records) as one knowledge source for constructing literature-based gene networks. We use the TCM diagnosis, TCM syndrome, to automatically congregate the related genes. The syndrome-gene relationships are discovered based on the syndrome-disease relationships extracted from TCM literature and the disease-gene relationships in MEDLINE. Based on the bubble-bootstrapping and relation weight computing methods, we have developed a prototype system called MeDisco/3S, which has name entity and relation extraction, and online analytical processing (OLAP) capabilities, to perform the integrative mining process. RESULTS: We have got about 200,000 syndrome-gene relations, which could help generate syndrome-based gene networks, and help analyze the functional knowledge of genes from syndrome perspective. We take the gene network of Kidney-Yang Deficiency syndrome (KYD syndrome) and the functional analysis of some genes, such as CRH (corticotropin releasing hormone), PTH (parathyroid hormone), PRL (prolactin), BRCA1 (breast cancer 1, early onset) and BRCA2 (breast cancer 2, early onset), to demonstrate the preliminary results. The underlying hypothesis is that the related genes of the same syndrome will have some biological functional relationships, and will constitute a functional network. CONCLUSION: This paper presents an approach to integrate TCM literature and modern biomedical data to discover novel gene networks and functional knowledge of genes. The preliminary results show that the novel gene functional knowledge and gene networks, which are worthy of further investigation, could be generated by integrating the two complementary biomedical data sources. It will be a promising research field through integrative mining of TCM and modern life science literature.     

Correlations between gene expression and gene conservation in fission yeast

Genes can be expressed at a wide range of levels, and they show different degrees of cross-species conservation. We compared gene expression levels to gene conservation by integrating microarray data from fission yeast (Schizosaccharomyces pombe) with lists of "core" genes (present in worm and budding and fission yeasts), "yeast-specific" genes (present in budding and fission yeasts, but not in worm), and "pombe-specific" genes (present in fission yeast only). Whereas a disproportionate number of core genes are highly expressed in vegetatively growing cells, many pombe-specific genes are expressed at lower levels. This bias is less pronounced in cells undergoing sexual development, when many pombe-specific genes become highly expressed. This implies that organism-specific proteins are more likely to function during specialized processes such as cellular differentiation. Accordingly, pombe-specific genes were overrepresented among genes induced during sexual development; they were particularly enriched in a group of genes induced during meiotic prophase, when homologous chromosomes pair and recombine. This raises the possibility that organism-specific genes with functions in meiotic prophase favor speciation by preventing fruitful meiosis between closely related organisms. Finally, the set of genes induced late during sexual differentiation, at the time of spore formation, was enriched in yeast-specific genes, indicating that these genes play specialized roles in ascospore development.     

Computationally analyzing the possible biological function of YJL103C--an ORF potentially involved in the regulation of energy process in yeast

Although the complete genomes of a number of organisms have been sequenced, the biological functions of many genes are still not known. Because experimentally studying the functions of those genes one by one requires tremendous time, it is vital to use published resources like microarray gene expression data for computational analysis of gene functions. One example is YJL103C, a yeast gene of unknown function in the Saccharomyces Genome Database (SGD). It is possible to quickly infer its biological function by computational analysis. In this study, we present an efficient model to explore the biological function of a novel gene using microarray data. We showed that the expression pattern of YJL103C is most similar to the genes in the energy group and respiratory chain subgroup. We further found that YJL103C contains a HAP2,3,4 box in its promoter region and a cytochrome C heme-binding signature in its protein sequence. Our findings define a potential role for YJL103C in the regulation of energy metabolism, specifically in the process of oxidative phosphorylation. Similar bioinformatics methods can be applied to infer the biological functions of other novel genes in organisms for which microarray data are available. In this work, we selected a single gene of unknown function as a case study. By focusing on the power of computer analysis and bioinformatics on the available microarray data, we have determined the likely biological function of YJL103C. Our study provides a method by which to explore the potential function of other genes currently annotated as having an unknown function in any organism for which global gene expression data are available.     

Age-dependent changes of gene expression in the Drosophila head

Previous gene expression profiling studies in Drosophila have provided clues for understanding the aging process at the gene expression level. For a detailed understanding, studies of specific regions of the body are necessary. We therefore employed microarray analysis to examine gene expression changes in the Drosophila head during aging. Six hundred and eighty-four of the 5405 genes present in the microarray showed significant age-dependent changes as determined by significance analysis of microarray (SAM) (q < 0.05). The biological significance of the changes was analyzed using the gene annotations provided by the Gene Ontology Consortium. Major changes involved genes affecting energy metabolism (proton transport, energy pathways, oxidative phosphorylation) and neuronal function, especially responses to light. Genes involved in protein catabolism and several other metabolic processes also showed age-dependent changes. Most of the changes were reductions in gene expression and occurred before day 13 of adult life. After day 13, the age-dependent gene expression changes were relatively smaller than earlier life. Interestingly, the two biological processes of major gene expression changes are related to the two known environmental changes that increase life span in Drosophila: caloric restriction and light reduction. Our findings suggest that light signaling and energy metabolism may be important biological processes affected by aging and be interesting targets for the further investigation related to the longevity in Drosophila.     

Finding disease similarity based on implicit semantic similarity

Genomics has contributed to a growing collection of gene-function and gene-disease annotations that can be exploited by informatics to study similarity between diseases. This can yield insight into disease etiology, reveal common pathophysiology and/or suggest treatment that can be appropriated from one disease to another. Estimating disease similarity solely on the basis of shared genes can be misleading as variable combinations of genes may be associated with similar diseases, especially for complex diseases. This deficiency can be potentially overcome by looking for common biological processes rather than only explicit gene matches between diseases. The use of semantic similarity between biological processes to estimate disease similarity could enhance the identification and characterization of disease similarity. We present functions to measure similarity between terms in an ontology, and between entities annotated with terms drawn from the ontology, based on both co-occurrence and information content. The similarity measure is shown to outperform other measures used to detect similarity. A manually curated dataset with known disease similarities was used as a benchmark to compare the estimation of disease similarity based on gene-based and Gene Ontology (GO) process-based comparisons. The detection of disease similarity based on semantic similarity between GO Processes (Recall=55%, Precision=60%) performed better than using exact matches between GO Processes (Recall=29%, Precision=58%) or gene overlap (Recall=88% and Precision=16%). The GO-Process based disease similarity scores on an external test set show statistically significant Pearson correlation (0.73) with numeric scores provided by medical residents. GO-Processes associated with similar diseases were found to be significantly regulated in gene expression microarray datasets of related diseases.     

Global mapping of gene/protein interactions in PubMed abstracts: a framework and an experiment with P53 interactions

Gene/protein interactions provide critical information for a thorough understanding of cellular processes. Recently, considerable interest and effort has been focused on the construction and analysis of genome-wide gene networks. The large body of biomedical literature is an important source of gene/protein interaction information. Recent advances in text mining tools have made it possible to automatically extract such documented interactions from free-text literature. In this paper, we propose a comprehensive framework for constructing and analyzing large-scale gene functional networks based on the gene/protein interactions extracted from biomedical literature repositories using text mining tools. Our proposed framework consists of analyses of the network topology, network topology-gene function relationship, and temporal network evolution to distill valuable information embedded in the gene functional interactions in the literature. We demonstrate the application of the proposed framework using a testbed of P53-related PubMed abstracts, which shows that the literature-based P53 networks exhibit small-world and scale-free properties. We also found that high degree genes in the literature-based networks have a high probability of appearing in the manually curated database and genes in the same pathway tend to form local clusters in our literature-based networks. Temporal analysis showed that genes interacting with many other genes tend to be involved in a large number of newly discovered interactions.     

Precedence Temporal Networks to represent temporal relationships in gene expression data

The reconstruction of gene regulatory networks from gene expression time series is nowadays an interesting research challenge. A key problem in this kind of analysis is the automated extraction of precedence and synchronization between interesting patterns assumed by genes over time.The present work introduces Precedence Temporal Networks (PTN), a novel method to extract and visualize temporal relationships between genes. PTNs are a special kind of temporal network where nodes represent temporal patterns while edges identify precedence or synchronization relationships between the nodes.The method is tested on two case studies: the expression of a subset of genes in the soil amoeba Dictyostelium discoideum and of a set of well-studied genes involved in the human cell cycle regulation. The extracted networks reflect the capability of the algorithm to clearly reconstruct the timing of the considered gene sets, highlighting different stages in Dictyostelium development and in the cell cycle, respectively.     

Predicting gene ontology biological process from temporal gene expression patterns

The aim of the present study was to generate hypotheses on the involvement of uncharacterized genes in biological processes. To this end, supervised learning was used to analyze microarray-derived time-series gene expression data. Our method was objectively evaluated on known genes using cross-validation and provided high-precision Gene Ontology biological process classifications for 211 of the 213 uncharacterized genes in the data set used. In addition, new roles in biological process were hypothesized for known genes. Our method uses biological knowledge expressed by Gene Ontology and generates a rule model associating this knowledge with minimal characteristic features of temporal gene expression profiles. This model allows learning and classification of multiple biological process roles for each gene and can predict participation of genes in a biological process even though the genes of this class exhibit a wide variety of gene expression profiles including inverse coregulation. A considerable number of the hypothesized new roles for known genes were confirmed by literature search. In addition, many biological process roles hypothesized for uncharacterized genes were found to agree with assumptions based on homology information. To our knowledge, a gene classifier of similar scope and functionality has not been reported earlier.     

Protein-protein interactions: Methods,databases, and applications in virus-host study

Almost all the cellular processes in a living system are controlled by proteins: They regulate gene expression, catalyze chemical reactions, transport small molecules across membranes, and transmit signal across membranes. Even, a viral infection is often initiated through virus-host protein interactions. Protein-protein interactions (PPIs) are the physical contacts between two or more proteins and they represent complex biological functions. Nowadays, PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins. Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets. In this review, we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies. Here, we present a short but comprehensive review on PPIs, including the experimental and computational methods of finding PPIs, the databases dedicated to virus-host PPIs, and the associated various applications in protein interaction networks of some lethal viruses with their hosts.     

Knowledge-assisted recognition of cluster boundaries in gene expression data

BACKGROUND AND MOTIVATION: DNA microarray technology has made it possible to determine the expression levels of thousands of genes in parallel under multiple experimental conditions. Genome-wide analyses using DNA microarrays make a great contribution to the exploration of the dynamic state of genetic networks, and further lead to the development of new disease diagnosis technologies. An important step in the analysis of gene expression data is to classify genes with similar expression patterns into the same groups. To this end, hierarchical clustering algorithms have been widely used. Major advantages of hierarchical clustering algorithms are that investigators do not need to specify the number of clusters in advance and results are presented visually in the form of a dendrogram. However, since traditional hierarchical clustering methods simply provide results on the statistical characteristics of expression data, biological interpretations of the resulting clusters are not easy, and it requires laborious tasks to unveil hidden biological processes regulated by members in the clusters. Therefore, it has been a very difficult routine for experts. OBJECTIVE: Here, we propose a novel algorithm in which cluster boundaries are determined by referring to functional annotations stored in genome databases. MATERIALS AND METHODS: The algorithm first performs hierarchical clustering of gene expression profiles. Then, the cluster boundaries are determined by the Variance Inflation Factor among the Gene Function Vectors, which represents distributions of gene functions in each cluster. Our algorithm automatically specifies a cutoff that leads to functionally independent agglomerations of genes on the dendrogram derived from similarities among gene expression patterns. Finally, each cluster is annotated according to dominant gene functions within the respective cluster. RESULTS AND CONCLUSIONS: In this paper, we apply our algorithm to two gene expression datasets related to cell cycle and cold stress response in budding yeast Saccharomyces cerevisiae. As a result, we show that the algorithm enables us to recognize cluster boundaries characterizing fundamental biological processes such as the Early G1, Late G1, S, G2 and M phases in cell cycles, and also provides novel annotation information that has not been obtained by traditional hierarchical clustering methods. In addition, using formal cluster validity indices, high validity of our algorithm is verified by the comparison through other popular clustering algorithms, K-means, self-organizing map and AutoClass.     

A method for clustering gene expression data based on graph structure

Recently, gene expression data under various conditions have largely been obtained by the utilization of the DNA microarrays and oligonucleotide arrays. There have been emerging demands to analyze the function of genes from the gene expression profiles. For clustering genes from their expression profiles, hierarchical clustering has been widely used. The clustering method represents the relationships of genes as a tree structure by connecting genes using their similarity scores based on the Pearson correlation coefficient. But the clustering method is sensitive to experimental noise. To cope with the problem, we propose another type of clustering method (the p-quasi complete linkage clustering). We apply this method to the gene expression data of yeast cell-cycles and human lung cancer. The effectiveness of our method is demonstrated by comparing clustering results with other methods.