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Evidence for the presence of disease-perturbed networks in prostate cancer cells by genomic and proteomic analyses

ABSTRACT: Not available

INSTRUMENT(S): instrument model

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Attila Csordas

PROVIDER: PRD000471 | Pride | 2012-07-26

REPOSITORIES: Pride

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			Action	DRS
	PRIDE_Exp_Complete_Ac_17932.pride.mgf.gz	Mgf
	PRIDE_Exp_Complete_Ac_17932.pride.mztab.gz	Mztab
	PRIDE_Exp_Complete_Ac_17932.xml.gz	Xml
	PRIDE_Exp_Complete_Ac_17933.pride.mgf.gz	Mgf
	PRIDE_Exp_Complete_Ac_17933.pride.mztab.gz	Mztab

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Publications

Evidence for the presence of disease-perturbed networks in prostate cancer cells by genomic and proteomic analyses: a systems approach to disease.

Lin Biaoyang B White James T JT Lu Wei W Xie Tao T Utleg Angelita G AG Yan Xiaowei X Yi Eugene C EC Shannon Paul P Khrebtukova Irina I Lange Paul H PH Goodlett David R DR Zhou Daixing D Vasicek Thomas J TJ Hood Leroy L

Cancer research 20050401 8

Prostate cancer is initially responsive to androgen ablation therapy and progresses to androgen-unresponsive states that are refractory to treatment. The mechanism of this transition is unknown. A systems approach to disease begins with the quantitative delineation of the informational elements (mRNAs and proteins) in various disease states. We employed two recently developed high-throughput technologies, massively parallel signature sequencing (MPSS) and isotope-coded affinity tag, to gain a co ...[more]

PMID: 15833837

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Integrated genomic and proteomic analyses of a systematically perturbed metabolic network

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Identification of androgen-coregulated protein networks from the microsomes of human prostate cancer cells

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2012-07-26 | PRD000483 | Pride

Paxillin regulated genomic networks in prostate cancer.

Project description:Paxillin is extensively involved in focal adhesion signaling and kinase signaling throughout the plasma membrane and cytoplasm. However, recent studies in prostate cancer suggest that paxillin also plays a critical role in regulating gene expression within the nucleus, serving as a liaison between cytoplasmic and nuclear MAPK and Androgen Receptor (AR) signaling. Here we used RNA-seq to examine the paxillin-regulated transcriptome in several human prostate cancer cell lines. First, we examined paxillin effects on androgen-mediated transcription in control or paxillin-depleted AR-positive LNCaP and C4-2 human prostate cancer cells. In androgen-dependent LNCaP cells, we found over 1000 paxillin-dependent androgen-responsive genes, some of which are involved in endocrine therapy resistance. Most paxillin-dependent AR-mediated genes in LNCaP cells were no longer paxillin-dependent in androgen-sensitive, castration-resistant C4-2 cells, suggesting that castration-resistance may markedly alter paxillin effects on genomic AR signaling. To examine the paxillin-regulated transcriptome in the absence of androgen signaling, we performed RNA-seq in AR-negative PC3 human prostate cancer cells. Paxillin enhanced several pro-proliferative pathways, including the CyclinD/Rb/E2F and DNA replication/repair pathways. Additionally, paxillin suppressed pro-apoptotic genes, including CASP1 and TNFSF10. Quantitative PCR confirmed that these pathways are similarly regulated by paxillin in LNCaP and C4-2 cells. Functional studies showed that, while paxillin stimulated cell proliferation, it had minimum effect on apoptosis. Thus, paxillin appears to be an important transcriptional regulator in prostate cancer, and analysis of its transcriptome might lead to novel approaches toward the diagnosis and treatment of this important disease.

| S-EPMC6802295 | biostudies-literature

Integrating Gene and Protein Expression Reveals Perturbed Functional Networks in Alzheimer's Disease.

Project description:Asymptomatic and symptomatic Alzheimer's disease (AD) subjects may present with equivalent neuropathological burdens but have significantly different antemortem cognitive decline rates. Using the transcriptome as a proxy for functional state, we selected 414 expression profiles of symptomatic AD subjects and age-matched non-demented controls from a community-based neuropathological study. By combining brain tissue-specific protein interactomes with gene networks, we identified functionally distinct composite clusters of genes that reveal extensive changes in expression levels in AD. Global expression for clusters broadly corresponding to synaptic transmission, metabolism, cell cycle, survival, and immune response were downregulated, while the upregulated cluster included largely uncharacterized processes. We propose that loss of EGR3 regulation mediates synaptic deficits by targeting the synaptic vesicle cycle. Our results highlight the utility of integrating protein interactions with gene perturbations to generate a comprehensive framework for characterizing alterations in the molecular network as applied to AD.

| S-EPMC7503200 | biostudies-literature

Optimal control nodes in disease-perturbed networks as targets for combination therapy.

Project description:Most combination therapies are developed based on targets of existing drugs, which only represent a small portion of the human proteome. We introduce a network controllability-based method, OptiCon, for de novo identification of synergistic regulators as candidates for combination therapy. These regulators jointly exert maximal control over deregulated genes but minimal control over unperturbed genes in a disease. Using data from three cancer types, we show that 68% of predicted regulators are either known drug targets or have a critical role in cancer development. Predicted regulators are depleted for known proteins associated with side effects. Predicted synergy is supported by disease-specific and clinically relevant synthetic lethal interactions and experimental validation. A significant portion of genes regulated by synergistic regulators participate in dense interactions between co-regulated subnetworks and contribute to therapy resistance. OptiCon represents a general framework for systemic and de novo identification of synergistic regulators underlying a cellular state transition.

| S-EPMC6522545 | biostudies-literature

Genomic analyses of Symbiomonas scintillans show no evidence for endosymbiotic bacteria but does reveal the presence of giant viruses.

Project description:Symbiomonas scintillans Guillou et Chrétiennot-Dinet, 1999 is a tiny (1.4 μm) heterotrophic microbial eukaryote. The genus was named based on the presence of endosymbiotic bacteria in its endoplasmic reticulum, however, like most such endosymbionts neither the identity nor functional association with its host were known. We generated both amplification-free shotgun metagenomics and whole genome amplification sequencing data from S. scintillans strains RCC257 and RCC24, but were unable to detect any sequences from known lineages of endosymbiotic bacteria. The absence of endobacteria was further verified with FISH analyses. Instead, numerous contigs in assemblies from both RCC24 and RCC257 were closely related to prasinoviruses infecting the green algae Ostreococcus lucimarinus, Bathycoccus prasinos, and Micromonas pusilla (OlV, BpV, and MpV, respectively). Using the BpV genome as a reference, we assembled a near-complete 190 kbp draft genome encoding all hallmark prasinovirus genes, as well as two additional incomplete assemblies of closely related but distinct viruses from RCC257, and three similar draft viral genomes from RCC24, which we collectively call SsVs. A multi-gene tree showed the three SsV genome types branched within highly supported clades with each of BpV2, OlVs, and MpVs, respectively. Interestingly, transmission electron microscopy also revealed a 190 nm virus-like particle similar the morphology and size of the endosymbiont originally reported in S. scintillans. Overall, we conclude that S. scintillans currently does not harbour an endosymbiotic bacterium, but is associated with giant viruses.

| S-EPMC11008856 | biostudies-literature

Prediction of perturbed proton transfer networks.

Project description:The transfer of protons through proton translocating channels is a complex process, for which direct samplings of different protonation states and side chain conformations in a transition network calculation provide an efficient, bias-free description. In principle, a new transition network calculation is required for every unsampled change in the system of interest, e.g. an unsampled protonation state change, which is associated with significant computational costs. Transition networks void of or including an unsampled change are termed unperturbed or perturbed, respectively. Here, we present a prediction method, which is based on an extensive coarse-graining of the underlying transition networks to speed up the calculations. It uses the minimum spanning tree and a corresponding sensitivity analysis of an unperturbed transition network as initial guess and refinement parameter for the determination of an unknown, perturbed transition network. Thereby, the minimum spanning tree defines a sub-network connecting all nodes without cycles and minimal edge weight sum, while the sensitivity analysis analyzes the stability of the minimum spanning tree towards individual edge weight reductions. Using the prediction method, we are able to reduce the calculation costs in a model system by up to 80%, while important network properties are maintained in most predictions.

| S-EPMC6291078 | biostudies-literature

Paxillin regulates genomic networks in prostate cancer

Project description:This SuperSeries is composed of the SubSeries listed below.

2019-08-01 | GSE131731 | GEO

Genome-wide expression profiling reveals transcriptomic variation and perturbed gene networks in androgen-dependent and androgen-independent prostate cancer cells.

Project description:Previously, we have developed a unique in vitro LNCaP cell model, which includes androgen-dependent (LNCaP-C33), androgen-independent (LNCaP-C81) and an intermediate phenotype (LNCaP-C51) cell lines resembling the stages of prostate cancer progression to hormone independence. This model is advantageous in overcoming the heterogeneity associated with the prostate cancer up to a certain extent. We characterized and compared the gene expression profiles in LNCaP-C33 (androgen-dependent) and LNCaP-C81 (androgen-independent) cells using Affymetrix GeneChip array analyses. Multiple genes were identified exhibiting differential expression during androgen-independent progression. Among the important genes upregulated in androgen-independent cells were PCDH7, TPTE, TSPY, EPHA3, HGF, MET, EGF, TEM8, etc., whereas many candidate tumor suppressor genes (HTATIP2, CDKN2A, CDKN2B, CDKN1C, TP53, TP73, ICAM1, SOCS1/2, SPRY2, PPP2CA, PPP3CA, etc.) were decreased. Pathway prediction analysis identified important gene networks associated with growth-promoting and apoptotic signaling that were perturbed during androgen-independent progression. Further investigation of one of the genes, PPP2CA, which encodes the catalytic subunit of a serine phosphatase PP2A, a potent tumor suppressor, revealed that its expression was decreased in prostate cancer compared to adjacent normal/benign tissue. Furthermore, the downregulated expression of PPP2CA was significantly correlated with tumor stage and Gleason grade. Future studies on the identified differentially expressed genes and signaling pathways may be helpful in understanding the biology of prostate cancer progression and prove useful in developing novel prognostic biomarkers and therapy for androgen-refractory prostate cancer.

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Discovering networks of perturbed biological processes in hepatocyte cultures.

Project description:The liver plays a vital role in glucose homeostasis, the synthesis of bile acids and the detoxification of foreign substances. Liver culture systems are widely used to test adverse effects of drugs and environmental toxicants. The two most prevalent liver culture systems are hepatocyte monolayers (HMs) and collagen sandwiches (CS). Despite their wide use, comprehensive transcriptional programs and interaction networks in these culture systems have not been systematically investigated. We integrated an existing temporal transcriptional dataset for HM and CS cultures of rat hepatocytes with a functional interaction network of rat genes. We aimed to exploit the functional interactions to identify statistically significant linkages between perturbed biological processes. To this end, we developed a novel approach to compute Contextual Biological Process Linkage Networks (CBPLNs). CBPLNs revealed numerous meaningful connections between different biological processes and gene sets, which we were successful in interpreting within the context of liver metabolism. Multiple phenomena captured by CBPLNs at the process level such as regulation, downstream effects, and feedback loops have well described counterparts at the gene and protein level. CBPLNs reveal high-level linkages between pathways and processes, making the identification of important biological trends more tractable than through interactions between individual genes and molecules alone. Our approach may provide a new route to explore, analyze, and understand cellular responses to internal and external cues within the context of the intricate networks of molecular interactions that control cellular behavior.

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