A human functional protein interaction network and its application to cancer data analysis

Wu, Guanming; Feng, Xin; Stein, Lincoln

doi:10.1186/gb-2010-11-5-r53

Cited by 613 publications

(596 citation statements)

References 89 publications

(103 reference statements)

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“…It was constructed by merging the Functional Interaction Network (FIN) by Reactome 15 and information on transcription regulation in the form of set of transcription factor regulons (i.e. sets of targeted genes) assembled from public available resources, such as ChEA, Transfac, and Jaspar.…”

Section: Construction Of Prior Knowledge Network (Pkn)mentioning

confidence: 99%

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

et al. 2015

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Identification of signaling pathways that are functional in a specific biological context is a major challenge in systems biology, and could be instrumental to the study of complex diseases and various aspects of drug discovery. Recent approaches have attempted to combine gene expression data with prior knowledge of protein connectivity in the form of a PPI network, and employ computational methods to identify subsets of the protein-protein-interaction (PPI) network that are functional, based on the data at hand. However, the use of undirected networks limits the mechanistic insight that can be drawn, since it does not allow for following mechanistically signal transduction from one node to the next. To address this important issue, we used a directed, signaling network as a scaffold to represent protein connectivity, and implemented an Integer Linear Programming (ILP) formulation to model the rules of signal transduction from one node to the next in the network. We then optimized the structure of the network to best fit the gene expression data at hand. We illustrated the utility of ILP modeling with a case study of drug induced lung injury. We identified the modes of action of 200 lung toxic drugs based on their gene expression profiles and, subsequently, merged the drug specific pathways to construct a signaling network that captured the mechanisms underlying Drug Induced Lung Disease (DILD). We further demonstrated the predictive power and biological relevance of the DILD network by applying it to identify drugs with relevant pharmacological mechanisms for treating lung injury. Insight, innovation, integrationIn this manuscript we introduce a novel approach for the identification of signaling pathways that are functional in a specific biological context, by leveraging gene expression data and prior knowledge of protein connectivity. In more detail, we introduce a linear programming formulation to model signal transduction from one node to the next in a Prior Knowledge Network (PKN), and by minimizing the mismatch between model predictions and experimental data, we are able to identify subsets of the PKN that are most probably functional in the specific biological context. More specifically, we address the problem of identifying the modes of action of drugs that have been reported to induce respiratory side effects, based on their gene expression profiles, and subsequently, merge the drug specific pathways together to construct a signaling network that captures the signaling mechanisms underlying Drug Induced Lung Disease (DILD). Moreover, to demonstrate the predictive power and biological relevance of the DILD network, we use it to suggest potential drug repositioning for treating lung injury.

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Section: Construction Of Prior Knowledge Network (Pkn)mentioning

confidence: 99%

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

et al. 2015

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“…We surveyed 789 genes (listed in Table S2) thought to be involved in RAS-ERK pathway regulation from the literature and the GeneAssist pathway Altas. A protein functional interaction (FI) network was also used to analyze sequencing data (53). Seven known NS-associated genes (PTPN11, KRAS, NRAS, RAF1, BRAF, SOS1, and SHOC2) served as seeds to find intermediate-related genes, such that the seeds were connected by intermediate genes either known to be direct interactors or interactors that require one linker (Fig.…”

Section: Methodsmentioning

confidence: 99%

Next-generation sequencing identifies rare variants associated with Noonan syndrome

Chen

Yin

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Noonan syndrome (NS) is a relatively common genetic disorder, characterized by typical facies, short stature, developmental delay, and cardiac abnormalities. Known causative genes account for 70-80% of clinically diagnosed NS patients, but the genetic basis for the remaining 20-30% of cases is unknown. We performed nextgeneration sequencing on germ-line DNA from 27 NS patients lacking a mutation in the known NS genes. We identified gainof-function alleles in Ras-like without CAAX 1 (RIT1) and mitogenactivated protein kinase kinase 1 (MAP2K1) and previously unseen loss-of-function variants in RAS p21 protein activator 2 (RASA2) that are likely to cause NS in these patients. Expression of the mutant RASA2, MAP2K1, or RIT1 alleles in heterologous cells increased RAS-ERK pathway activation, supporting a causative role in NS pathogenesis. Two patients had more than one disease-associated variant. Moreover, the diagnosis of an individual initially thought to have NS was revised to neurofibromatosis type 1 based on an NF1 nonsense mutation detected in this patient. Another patient harbored a missense mutation in NF1 that resulted in decreased protein stability and impaired ability to suppress RAS-ERK activation; however, this patient continues to exhibit a NS-like phenotype. In addition, a nonsense mutation in RPS6KA3 was found in one patient initially diagnosed with NS whose diagnosis was later revised to Coffin-Lowry syndrome. Finally, we identified other potential candidates for new NS genes, as well as potential carrier alleles for unrelated syndromes. Taken together, our data suggest that nextgeneration sequencing can provide a useful adjunct to RASopathy diagnosis and emphasize that the standard clinical categories for RASopathies might not be adequate to describe all patients.human genetics | developmental diseases | whole exome sequencing | PTPN11 | RAS

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“…Both methods confirmed the expectation that NKX3.1 was the most abundant protein identified in the FLAG affinity purifications ( Figure 1C, D). We also performed Reactome Functional Interaction analysis to construct a functional interaction network of NKX3.1 binding proteins derived from manually curated literature data 32 . The network was clustered into modules and enriched functional pathways/reactions were identified ( Figure 2A).…”

Section: Resultsmentioning

confidence: 99%

“…The NKX3.1 interactome was analyzed with the Cytoscape Reactome FI plugin 32 . The list of NKX3.1 interacting proteins was loaded into Cytoscape and used to build Reactome networks allowing linker genes.…”

Section: Methodsmentioning

confidence: 99%

Systems analysis of the prostate tumor suppressor NKX3.1 supports roles in DNA repair and luminal cell differentiation

Yang

Chung²,

et al. 2014

F1000Res

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NKX3.1 is a homeobox transcription factor whose function as a prostate tumor suppressor remains insufficiently understood because neither the transcriptional program governed by NKX3.1, nor its interacting proteins have been fully revealed. Using affinity purification and mass spectrometry, we have established an extensive NKX3.1 interactome which contains the DNA repair proteins Ku70, Ku80, and PARP, thus providing a molecular underpinning to previous reports implicating NKX3.1 in DNA repair. Transcriptomic profiling of NKX3.1-negative prostate epithelial cells acutely expressing NKX3.1 revealed a rapid and complex response that is a near mirror image of the gene expression signature of human prostatic intraepithelial neoplasia (PIN). Pathway and network analyses suggested that NKX3.1 actuates a cellular reprogramming toward luminal cell differentiation characterized by suppression of pro-oncogenic c-MYC and interferon-STAT signaling and activation of tumor suppressor pathways. Consistently, ectopic expression of NKX3.1 conferred a growth arrest depending on TNFα and JNK signaling. We propose that the tumor suppressor function of NKX3.1 entails a transcriptional program that maintains the differentiation state of secretory luminal cells and that disruption of NKX3.1 contributes to prostate tumorigenesis by permitting luminal cell de-differentiation potentially augmented by defects in DNA repair.

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A human functional protein interaction network and its application to cancer data analysis

Abstract: A high-quality human functional protein interaction network is constructed. Its utility is demonstrated in the identification of cancer candidate genes.

Cited by 613 publications

References 89 publications

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

Next-generation sequencing identifies rare variants associated with Noonan syndrome

Systems analysis of the prostate tumor suppressor NKX3.1 supports roles in DNA repair and luminal cell differentiation

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