Spotlite: Web Application and Augmented Algorithms for Predicting Co-Complexed Proteins from Affinity Purification – Mass Spectrometry Data

Protein-protein interactions defined by affinity purification and mass spectrometry (APMS) approaches suffer from high false discovery rates. Consequently, the candidate interaction lists must be pruned of contaminants before network construction and interpretation, historically an expensive and time-intensive task. In recent years, numerous computational methods have been developed to identify genuine interactions from hundreds revealed by APMS experiments. Here, comparative analysis of several popular algorithms revealed complementarity in their classification accuracies, which is supported by their divergent scoring strategies. As such, we used two accurate and computationally efficient methods as features for machine learning using the Random Forest algorithm. Additionally, we developed novel mathematical models to include a variety of indirect data, such as mRNA co-expression, gene ontologies and homologous protein interactions as features within the classification problem. We show that our method, which we call Spotlite, outperforms existing methods on four diverse and public APMS datasets. Because implementation of existing APMS scoring methods requires computational expertise beyond many laboratories, we created a user-friendly and fast web application for APMS data scoring, analysis, annotation and network visualization, for use on new and existing data (http://152.19.87.94:8080/spotlite). The utility of Spotlite and its visualization platform for revealing physical, functional and disease-relevant characteristics within APMS data is established through a focused analysis of the KEAP1 E3 ubiquitin ligase

Proteomic Analysis of Ubiquitin Ligase KEAP1 Reveals Associated Proteins That Inhibit NRF2 Ubiquitination

Somatic mutations in the KEAP1 ubiquitin ligase or its substrate NRF2 (NFE2L2) commonly occur in human cancer, resulting in constitutive NRF2-mediated transcription of cytoprotective genes. However, many tumors display high NRF2 activity in the absence of mutation, supporting the hypothesis that alternative mechanisms of pathway activation exist. Previously, we and others discovered that via a competitive binding mechanism, the proteins WTX (AMER1), PALB2 and SQSTM1 bind KEAP1 to activate NRF2. Proteomic analysis of the KEAP1 protein interaction network revealed a significant enrichment of associated proteins containing an ETGE amino acid motif, which matches the KEAP1 interaction motif found in NRF2. Like WTX, PALB2, and SQSTM1, we found that the dipeptidyl peptidase 3 (DPP3) protein binds KEAP1 via an ‘ETGE’ motif to displace NRF2, thus inhibiting NRF2 ubiquitination and driving NRF2-dependent transcription. Comparing the spectrum of KEAP1 interacting proteins with the genomic profile of 178 squamous cell lung carcinomas characterized by The Cancer Genome Atlas revealed amplification and mRNA over-expression of the DPP3 gene in tumors with high NRF2 activity but lacking NRF2 stabilizing mutations. We further show that tumor-derived mutations in KEAP1 are hypomorphic with respect to NRF2 inhibition and that DPP3 over-expression in the presence of these mutants further promotes NRF2 activation. Collectively, our findings further support the competition model of NRF2 activation and suggest that ‘ETGE’-containing proteins like DPP3 contribute to NRF2 activity in cancer

Cancer-Derived Mutations in KEAP1 Impair NRF2 Degradation but not Ubiquitination

NRF2 is a transcription factor that mediates stress responses. Oncogenic mutations in NRF2 localize to one of its two binding interfaces with KEAP1, an E3 ubiquitin ligase that promotes proteasome-dependent degradation of NRF2. Somatic mutations in KEAP1 occur commonly in human cancer, where KEAP1 may function as a tumor suppressor. These mutations distribute throughout the KEAP1 protein but little is known about their functional impact. In this study, we characterized 18 KEAP1 mutations defined in a lung squamous cell carcinoma tumor set. Four mutations behaved as wild-type KEAP1, thus are likely passenger events. R554Q, W544C, N469fs, P318fs, and G333C mutations attenuated binding and suppression of NRF2 activity. The remaining mutations exhibited hypomorphic suppression of NRF2, binding both NRF2 and CUL3. Proteomic analysis revealed that the R320Q, R470C, G423V, D422N, G186R, S243C, and V155F mutations augmented the binding of KEAP1 and NRF2. Intriguingly, these 'super-binder' mutants exhibited reduced degradation of NRF2. Cell-based and in vitro biochemical analyses demonstrated that despite its inability to suppress NRF2 activity, the R320Q 'superbinder' mutant maintained the ability to ubiquitinate NRF2. These data strengthen the genetic interactions between KEAP1 and NRF2 in cancer and provide new insight into KEAP1 mechanics

Spotlite: Web Application and Augmented Algorithms for Predicting Co-Complexed Proteins from Affinity Purification – Mass Spectrometry Data

Protein–protein interactions defined by affinity purification and mass spectrometry (APMS) suffer from high false discovery rates. Consequently, lists of potential interactions must be pruned of contaminants before network construction and interpretation, historically an expensive, time-intensive, and error-prone task. In recent years, numerous computational methods were developed to identify genuine interactions from the hundreds of candidates. Here, comparative analysis of three popular algorithms, HGSCore, CompPASS, and SAINT, revealed complementarity in their classification accuracies, which is supported by their divergent scoring strategies. We improved each algorithm by an average area under a receiver operating characteristics curve increase of 16% by integrating a variety of indirect data known to correlate with established protein–protein interactions, including mRNA coexpression, gene ontologies, domain–domain binding affinities, and homologous protein interactions. Each APMS scoring approach was incorporated into a separate logistic regression model along with the indirect features; the resulting three classifiers demonstrate improved performance on five diverse APMS data sets. To facilitate APMS data scoring within the scientific community, we created Spotlite, a user-friendly and fast web application. Within Spotlite, data can be scored with the augmented classifiers, annotated, and visualized (http://cancer.unc.edu/majorlab/software.php). The utility of the Spotlite platform to reveal physical, functional, and disease-relevant characteristics within APMS data is established through a focused analysis of the KEAP1 E3 ubiquitin ligase

Proteomic Analysis of Ubiquitin Ligase KEAP1 Reveals Associated Proteins That Inhibit NRF2 Ubiquitination

Somatic mutations in the KEAP1 ubiquitin ligase or its substrate NRF2 (NFE2L2) commonly occur in human cancer, resulting in constitutive NRF2-mediated transcription of cytoprotective genes. However, many tumors display high NRF2 activity in the absence of mutation, supporting the hypothesis that alternative mechanisms of pathway activation exist. Previously, we and others discovered that via a competitive binding mechanism, the proteins WTX (AMER1), PALB2 and SQSTM1 bind KEAP1 to activate NRF2. Proteomic analysis of the KEAP1 protein interaction network revealed a significant enrichment of associated proteins containing an ETGE amino acid motif, which matches the KEAP1 interaction motif found in NRF2. Like WTX, PALB2, and SQSTM1, we found that the dipeptidyl peptidase 3 (DPP3) protein binds KEAP1 via an ‘ETGE’ motif to displace NRF2, thus inhibiting NRF2 ubiquitination and driving NRF2-dependent transcription. Comparing the spectrum of KEAP1 interacting proteins with the genomic profile of 178 squamous cell lung carcinomas characterized by The Cancer Genome Atlas revealed amplification and mRNA over-expression of the DPP3 gene in tumors with high NRF2 activity but lacking NRF2 stabilizing mutations. We further show that tumor-derived mutations in KEAP1 are hypomorphic with respect to NRF2 inhibition and that DPP3 over-expression in the presence of these mutants further promotes NRF2 activation. Collectively, our findings further support the competition model of NRF2 activation and suggest that ‘ETGE’-containing proteins like DPP3 contribute to NRF2 activity in cancer

Cancer-Derived Mutations in KEAP1 Impair NRF2 Degradation but not Ubiquitination

NRF2 is a transcription factor that mediates stress responses. Oncogenic mutations in NRF2 localize to one of its two binding interfaces with KEAP1, an E3 ubiquitin ligase that promotes proteasome-dependent degradation of NRF2. Somatic mutations in KEAP1 occur commonly in human cancer, where KEAP1 may function as a tumor suppressor. These mutations distribute throughout the KEAP1 protein but little is known about their functional impact. In this study, we characterized 18 KEAP1 mutations defined in a lung squamous cell carcinoma tumor set. Four mutations behaved as wild-type KEAP1, thus are likely passenger events. R554Q, W544C, N469fs, P318fs, and G333C mutations attenuated binding and suppression of NRF2 activity. The remaining mutations exhibited hypomorphic suppression of NRF2, binding both NRF2 and CUL3. Proteomic analysis revealed that the R320Q, R470C, G423V, D422N, G186R, S243C, and V155F mutations augmented the binding of KEAP1 and NRF2. Intriguingly, these 'super-binder' mutants exhibited reduced degradation of NRF2. Cell-based and in vitro biochemical analyses demonstrated that despite its inability to suppress NRF2 activity, the R320Q 'superbinder' mutant maintained the ability to ubiquitinate NRF2. These data strengthen the genetic interactions between KEAP1 and NRF2 in cancer and provide new insight into KEAP1 mechanics

Regulation of endocytosis via the oxygen-sensing pathway

Tumor hypoxia is associated with disease progression, resistance to conventional cancer therapies and poor prognosis. Hypoxia, by largely unknown mechanisms, leads to deregulated accumulation of and signaling via receptor tyrosine kinases (RTKs) that are critical for driving oncogenesis. Here, we show that hypoxia or loss of von Hippel-Lindau protein--the principal negative regulator of hypoxia-inducible factor (HIF)--prolongs the activation of epidermal growth factor receptor that is attributable to lengthened receptor half-life and retention in the endocytic pathway. The deceleration in endocytosis is due to the attenuation of Rab5-mediated early endosome fusion via HIF-dependent downregulation of a critical Rab5 effector, rabaptin-5, at the level of transcription. Primary kidney and breast tumors with strong hypoxic signatures show significantly lower expression of rabaptin-5 RNA and protein. These findings reveal a general role of the oxygen-sensing pathway in endocytosis and support a model in which tumor hypoxia or oncogenic activation of HIF prolongs RTK-mediated signaling by delaying endocytosis-mediated deactivation of receptors

Spotlite: Web Application and Augmented Algorithms for Predicting Co-Complexed Proteins from Affinity Purification – Mass Spectrometry Data

Protein-protein interactions defined by affinity purification and mass spectrometry (APMS) approaches suffer from high false discovery rates. Consequently, the candidate interaction lists must be pruned of contaminants before network construction and interpretation, historically an expensive and time-intensive task. In recent years, numerous computational methods have been developed to identify genuine interactions from hundreds revealed by APMS experiments. Here, comparative analysis of several popular algorithms revealed complementarity in their classification accuracies, which is supported by their divergent scoring strategies. As such, we used two accurate and computationally efficient methods as features for machine learning using the Random Forest algorithm. Additionally, we developed novel mathematical models to include a variety of indirect data, such as mRNA co-expression, gene ontologies and homologous protein interactions as features within the classification problem. We show that our method, which we call Spotlite, outperforms existing methods on four diverse and public APMS datasets. Because implementation of existing APMS scoring methods requires computational expertise beyond many laboratories, we created a user-friendly and fast web application for APMS data scoring, analysis, annotation and network visualization, for use on new and existing data (http://152.19.87.94:8080/spotlite). The utility of Spotlite and its visualization platform for revealing physical, functional and disease-relevant characteristics within APMS data is established through a focused analysis of the KEAP1 E3 ubiquitin ligase