Gene expression in precursor cells of prostate cancer associated with activin by combination of subtractive hybridization and microarray technologies

Prostatic intraepithelial neoplasia (PIN) is considered the pre-malignant stage of prostate carcinoma, but little is known of its initiation and evolution. The identification of genes associated with these precursors of prostate cancer may elucidate the pathways of the early oncogenesis of this disease. Previously, we have reported that activin, a member of the TGFbeta superfamily, acted as an inhibitory growth factor in prostate cancer. We used laser capture microdissection, mRNA-library amplification (RNA-PCR), subtractive hybridization, and complementary DNA microarray to examine gene expression profiles in activin-positive PIN, compared with activin-negative PIN. Subtractive hybridization showed that 28 genes were differentially expressed (13 and 15 genes were up- and down-regulated, respectively). Microarray analysis identified 29 and 56 more genes (4 times) up- and down-regulated, respectively, suggesting that DNA microarray is a more effective method in screening gene profiles. We have validated the known genes identified by both subtractive hybridization and microarray technologies, using Northern blot analysis in the mRNA libraries generated from cells microdissected from pathological slides. We have successfully showed that at least 13 genes are involved in activin-associated PIN. The evaluation of candidate genes that emerge from these experiments provides a rational approach to investigate those genes significant in evolution from PIN to prostate carcinoma.     

Evolutionarily Conserved Network Properties of Intrinsically Disordered Proteins

BackgroundIntrinsically disordered proteins (IDPs) lack a stable tertiary structure in isolation. Remarkably, however, a substantial portion of IDPs undergo disorder-to-order transitions upon binding to their cognate partners. Structural flexibility and binding plasticity enable IDPs to interact with a broad range of partners. However, the broader network properties that could provide additional insights into the functional role of IDPs are not known.ResultsHere, we report the first comprehensive survey of network properties of IDP-induced sub-networks in multiple species from yeast to human. Our results show that IDPs exhibit greater-than-expected modularity and are connected to the rest of the protein interaction network (PIN) via proteins that exhibit the highest betweenness centrality and connect to fewer-than-expected IDP communities, suggesting that they form critical communication links from IDP modules to the rest of the PIN. Moreover, we found that IDPs are enriched at the top level of regulatory hierarchy.ConclusionOverall, our analyses reveal coherent and remarkably conserved IDP-centric network properties, namely, modularity in IDP-induced network and a layer of critical nodes connecting IDPs with the rest of the PIN.     

Prostaglandin E(2) stimulates prostatic intraepithelial neoplasia cell growth through activation of the interleukin-6/GP130/STAT-3 signaling pathway.

Cyclooxygenase (COX)-2 expression and prostaglandin E(2) (PGE(2)) secretion are increased in prostatic intraepithelial neoplasia (PIN) and prostate cancer. PGE(2) biosynthesis by cyclooxygenase (COX)-2 plays a pivotal role in inflammation and carcinogenesis. One of the critical proinflammatory cytokines in the prostate is interleukin-6 (IL-6). We hypothesized that increased expression of COX-2, with resultant increased levels of PGE(2) in human PIN cells, activates the IL-6 signaling pathway. We demonstrate an autocrine upregulation of PGE(2) mediated by IL-6 in a human PIN cell line. We further demonstrate that PGE(2) stimulates soluble IL-6 receptor (sIL-6R) release, gp130 dimerization, Stat-3 protein phosphorylation, and DNA binding activity. These events, induced by PGE(2), lead to increased PIN cell growth. Treatment of PIN cells with a selective COX-2 inhibitor decreases cell growth. Finally, PGE(2)-stimulated PIN cell growth was abrogated by the addition of IL-6 neutralizing antibodies. These data provide mechanistic evidence that increased expression of COX-2/PGE(2) contributes to prostate cancer development and progression via activation of the IL-6 signaling pathway.     

Isolation and characterization of the major form of human MUC18 cDNA gene and correlation of MUC18 over-expression in prostate cancer cell lines and tissues with malignant progression.

Ectopical expression of huMUC18, a cell adhesion molecule in the immunoglobulin gene superfamily, causes a non-metastatic human melanoma cell line to become metastatic in a nude mouse system. To determine if MUC18 expression correlates with the development and malignant progression of prostate cancer, we investigated differential expression of human MUC18 (huMUC18) in normal prostate epithelial cells, prostate cancer cell lines, and prostatic normal and cancer tissues. We cloned and characterized the human MUC18 (huMUC18) cDNA gene from three human prostate cancer cell lines and three human melanoma cell lines. The cDNA sequences from the six human cancer cell lines were identical except differences in one to five nucleotides. The deduced amino acid sequences of the longest ORF were 646 amino acids that were identical in these cDNAs except for one to three amino acid residues. The amino acid sequences of all our huMUC18 cDNA genes are similar to that cloned by other group (GenBank access #M28882) except differences in the same seven amino acids. We conclude that huMUC18 cDNA gene reported here represents the gene product from a major allele. The MUC18 mRNA and protein was expressed in three metastatic prostate cancer cell lines (TSU-PR1, DU145, and PC-3), but not in one non-metastatic prostate cancer cell line (LNCaP.FGC). The expression of huMUC18 in these four cell lines is positively related to their extent of in vitro motility and invasiveness and in vivo metastasis in nude mice. HuMUC18 protein was also expressed at high levels in extracts prepared from tissue sample sections containing high grade prostatic intraepithelial neoplasia (PIN), but weakly expressed in extracts prepared from cultured primary normal prostatic epithelial cells and the normal prostate gland. Immunohistochemical analysis showed that huMUC18 was expressed at higher levels in the epithelial cells of high-grade PIN and prostatic carcinomas, and in cells of a perineural invasion, a lymph node, and a lung metastases compared to that in normal or benign hyperplastic epithelium (BPH). We therefore conclude that MUC18 expression is increased during prostate cancer initiation (high grade PIN) and progression to carcinoma, and in metastatic cell lines and metastatic carcinoma. Increased expression of MUC18 is implicated to play an important role in developing and malignant progression of human prostate cancer. Furthermore, the lacking of predominant cytoplasmic membrane expression of MUC18 appeared to correlate with malignant progression of prostate cancer.     

The Roles of Post-translational Modifications in the Context of Protein Interaction Networks

Among other effects, post-translational modifications (PTMs) have been shown to exert their function via the modulation of protein-protein interactions. For twelve different main PTM-types and associated subtypes and across 9 diverse species, we investigated whether particular PTM-types are associated with proteins with specific and possibly “strategic” placements in the network of all protein interactions by determining informative network-theoretic properties. Proteins undergoing a PTM were observed to engage in more interactions and positioned in more central locations than non-PTM proteins. Among the twelve considered PTM-types, phosphorylated proteins were identified most consistently as being situated in central network locations and with the broadest interaction spectrum to proteins carrying other PTM-types, while glycosylated proteins are preferentially located at the network periphery. For the human interactome, proteins undergoing sumoylation or proteolytic cleavage were found with the most characteristic network properties. PTM-type-specific protein interaction network (PIN) properties can be rationalized with regard to the function of the respective PTM-carrying proteins. For example, glycosylation sites were found enriched in proteins with plasma membrane localizations and transporter or receptor activity, which generally have fewer interacting partners. The involvement in disease processes of human proteins undergoing PTMs was also found associated with characteristic PIN properties. By integrating global protein interaction networks and specific PTMs, our study offers a novel approach to unraveling the role of PTMs in cellular processes.     

The Par-4/PTEN connection in tumor suppression

Tumor suppressors function in a coordinated regulatory network, and their inactivation is a key step in carcinogenesis. The tumor suppressor Par-4 is a novel integral player in the PTEN network. Thus, Par-4 is absent in a high percentage of human prostate carcinomas, and its loss is concomitantly associated with PTEN loss. Genetic ablation of Par-4 induces fully invasive prostate carcinomas in PTEN-heterozygous mice. In contrast, Par-4 deficiency alone, like PTEN heterozygosis, results in lesions that are unable to progress beyond the benign neoplastic stage known as PIN. At this PIN transition, the mutual induction of Par-4 and PTEN is an additional regulatory step in preventing cancer progression. Par-4 deficiency cooperates with PTEN haploinsufficiency in prostate cancer initiation and progression and their simultaneous inactivation, in addition to enhancing Akt activation, sets in motion a unique mechanism involving the synergistic activation of NF-κB. These results suggest that the concurrent interruption of complementary signaling pathways targeting PI3K/Akt and NF-κB activation could provide new and effective strategies for cancer therapy.     

Epigenetic heterogeneity of high-grade prostatic intraepithelial neoplasia: clues for clonal progression in prostate carcinogenesis

High-grade prostatic intraepithelial neoplasia (PIN) is the most likely precursor of prostate adenocarcinoma, but the frequency and timing of epigenetic changes found in prostate carcinogenesis has not been extensively documented. Thus, the promoters of three genes (APC, GSTP1, and RARbeta2) involved in prostate carcinogenesis were tested by quantitative methylation-specific PCR in tissue DNA from 30 prostate carcinomas, 128 high-grade PIN lesions, and 30 normal prostate tissue samples dissected from 30 radical prostatectomy specimens using laser capture microdissection. The percentage of methylated alleles (PMA) was calculated for each gene, and hierarchical cluster analysis was used to define the degree of similarity of epigenetic alterations among the various samples. We found that PMA values of APC and RARbeta2 were higher than those of GSTP1 in all three types of tissue samples and median PMA values for all three genes were higher in prostate cancer. By cluster analysis, 26 of 30 prostate carcinomas and 82 of 128 high-grade PIN lesions were grouped in the "high methylation" branch, whereas 24 of 30 normal prostate tissue samples were allocated in the "low methylation" branch. Although high-grade PIN lesions are epigenetically more similar to prostate carcinoma than to normal prostate tissue, paired prostate carcinoma and high-grade PIN lesions did not always segregate together. We concluded that APC and RARbeta2 hypermethylation is frequent in normal prostate tissue and the progressive enrichment in cells carrying methylated alleles observed in high-grade PIN and prostate carcinoma is consistent with clonal progression. Because GSTP1 promoter methylation is mainly observed in prostate carcinoma and some high-grade PIN lesions, it represents an important marker for the transition of in situ to invasive neoplasia.     

Schizophrenia Gene Networks and Pathways and Their Applications for Novel Candidate Gene Selection

Background

Schizophrenia (SZ) is a heritable, complex mental disorder. We have seen limited success in finding causal genes for schizophrenia from numerous conventional studies. Protein interaction network and pathway-based analysis may provide us an alternative and effective approach to investigating the molecular mechanisms of schizophrenia.

Methodology/Principal Findings

We selected a list of schizophrenia candidate genes (SZGenes) using a multi-dimensional evidence-based approach. The global network properties of proteins encoded by these SZGenes were explored in the context of the human protein interactome while local network properties were investigated by comparing SZ-specific and cancer-specific networks that were extracted from the human interactome. Relative to cancer genes, we observed that SZGenes tend to have an intermediate degree and an intermediate efficiency on a perturbation spreading throughout the human interactome. This suggested that schizophrenia might have different pathological mechanisms from cancer even though both are complex diseases. We conducted pathway analysis using Ingenuity System and constructed the first schizophrenia molecular network (SMN) based on protein interaction networks, pathways and literature survey. We identified 24 pathways overrepresented in SZGenes and examined their interactions and crosstalk. We observed that these pathways were related to neurodevelopment, immune system, and retinoic X receptor (RXR). Our examination of SMN revealed that schizophrenia is a dynamic process caused by dysregulation of the multiple pathways. Finally, we applied the network/pathway approach to identify novel candidate genes, some of which could be verified by experiments.

Conclusions/Significance

This study provides the first comprehensive review of the network and pathway characteristics of schizophrenia candidate genes. Our preliminary results suggest that this systems biology approach might prove promising for selection of candidate genes for complex diseases. Our findings have important implications for the molecular mechanisms for schizophrenia and, potentially, other psychiatric disorders.     

A Network Based Method for Analysis of lncRNA-Disease Associations and Prediction of lncRNAs Implicated in Diseases

Increasing evidence has indicated that long non-coding RNAs (lncRNAs) are implicated in and associated with many complex human diseases. Despite of the accumulation of lncRNA-disease associations, only a few studies had studied the roles of these associations in pathogenesis. In this paper, we investigated lncRNA-disease associations from a network view to understand the contribution of these lncRNAs to complex diseases. Specifically, we studied both the properties of the diseases in which the lncRNAs were implicated, and that of the lncRNAs associated with complex diseases. Regarding the fact that protein coding genes and lncRNAs are involved in human diseases, we constructed a coding-non-coding gene-disease bipartite network based on known associations between diseases and disease-causing genes. We then applied a propagation algorithm to uncover the hidden lncRNA-disease associations in this network. The algorithm was evaluated by leave-one-out cross validation on 103 diseases in which at least two genes were known to be involved, and achieved an AUC of 0.7881. Our algorithm successfully predicted 768 potential lncRNA-disease associations between 66 lncRNAs and 193 diseases. Furthermore, our results for Alzheimer''s disease, pancreatic cancer, and gastric cancer were verified by other independent studies.     

Modification of gene duplicability during the evolution of protein interaction network

Duplications of genes encoding highly connected and essential proteins are selected against in several species but not in human, where duplicated genes encode highly connected proteins. To understand when and how gene duplicability changed in evolution, we compare gene and network properties in four species (Escherichia coli, yeast, fly, and human) that are representative of the increase in evolutionary complexity, defined as progressive growth in the number of genes, cells, and cell types. We find that the origin and conservation of a gene significantly correlates with the properties of the encoded protein in the protein-protein interaction network. All four species preserve a core of singleton and central hubs that originated early in evolution, are highly conserved, and accomplish basic biological functions. Another group of hubs appeared in metazoans and duplicated in vertebrates, mostly through vertebrate-specific whole genome duplication. Such recent and duplicated hubs are frequently targets of microRNAs and show tissue-selective expression, suggesting that these are alternative mechanisms to control their dosage. Our study shows how networks modified during evolution and contributes to explaining the occurrence of somatic genetic diseases, such as cancer, in terms of network perturbations.     

Virtual identification of essential proteins within the protein interaction network of yeast

Topological analysis of large scale protein-protein interaction networks (PINs) is important for understanding the organizational and functional principles of individual proteins. The number of interactions that a protein has in a PIN has been observed to be correlated with its indispensability. Essential proteins generally have more interactions than the nonessential ones. We show here that the lethality associated with removal of a protein from the yeast proteome correlates with different centrality measures of the nodes in the PIN, such as the closeness of a protein to many other proteins, or the number of pairs of proteins which need a specific protein as an intermediary in their communications, or the participation of a protein in different protein clusters in the PIN. These measures are significantly better than random selection in identifying essential proteins in a PIN. Centrality measures based on graph spectral properties of the network, in particular the subgraph centrality, show the best performance in identifying essential proteins in the yeast PIN. Subgraph centrality gives important structural information about the role of individual proteins, and permits the selection of possible targets for rational drug discovery through the identification of essential proteins in the PIN.     

Mapping the protein interaction network in methicillin-resistant Staphylococcus aureus

Mortality attributable to infection with methicillin-resistant Staphylococcus aureus (MRSA) has now overtaken the death rate for AIDS in the United States, and advances in research are urgently needed to address this challenge. We report the results of the systematic identification of protein-protein interactions for the hospital-acquired strain MRSA-252. Using a high-throughput pull-down strategy combined with quantitative proteomics to distinguish specific from nonspecific interactors, we identified 13,219 interactions involving 608 MRSA proteins. Consecutive analyses revealed that this protein interaction network (PIN) exhibits scale-free organization with the characteristic presence of highly connected hub proteins. When clinical and experimental antimicrobial targets were queried in the network, they were generally found to occupy peripheral positions in the PIN with relatively few interacting partners. In contrast, the hub proteins identified in this MRSA PIN that are essential for network integrity and stability have largely been overlooked as drug targets. Thus, this empirical MRSA-252 PIN provides a rich source for identifying critical proteins essential for network stability, many of which can be considered as prospective antimicrobial drug targets.