Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome

Huttlin, Edward L.; Bruckner, Raphael J.; Navarrete‐Perea, Jose; Cannon, Joe R.; Baltier, Kurt; Gebreab, Fana; Gygi, Melanie P.; Thornock, Alexandra; Zarraga, Gabriela; Tam, Stanley; Szpyt, John; Panov, Alexandra; Parzen, Hannah; Fu, Sipei; Golbazi, Arvene; Maenpaa, Eila; Stricker, Keegan; Thakurta, Sanjukta Guha; Rad, Ramin; Pan, Joshua; Nusinow, David P.; Paulo, João A.; Schweppe, Devin K.; Vaites, Laura Pontano; Harper, J. Wade; Gygi, Steven P.

doi:10.1101/2020.01.19.905109

Cited by 159 publications

(250 citation statements)

References 75 publications

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“…interactions from over 5,000 proteins (and counting) using the Comparative Proteomics Analysis (ComPASS) methodology 24,25 . For smaller datasets, however, while aggressive statistical filters ensure that only high-quality interactors are reported, they can also lead to few identified prey 26,27 .…”

Section: Introductionmentioning

confidence: 99%

Bait Correlation Improves Interactor Identification by Tandem Mass Tag-Affinity Purification-Mass Spectrometry

et al. 2020

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The quantitative multiplexing capacity of isobaric Tandem Mass Tags (TMT) has increased the throughput of affinity purification mass spectrometry (AP-MS) to characterize protein interaction networks of immunoprecipitated bait proteins. However, variable bait levels between replicates can convolute interactor identification. We compared the Student's t-test and Pearson's R correlation as methods to generate t-statistics and assessed the significance of interactors following TMT-AP-MS. Using a simple linear model of protein recovery in immunoprecipitates to simulate reporter ion ratio distributions, we found that correlation-derived t-statistics protect against bait variance while robustly controlling Type I errors (false positives). We experimentally determined the performance of these two approaches for determining t-statistics under two experimental conditions: irreversible prey association to the Hsp40 mutant DNAJB8 H31Q followed by stringent washing, and reversible association to 14-3-3z with gentle washing. Correlation-derived t-statistics performed at least as well as Student's t-statistics for each sample, and with substantial improvement in performance for experiments with high bait level variance. Deliberately varying bait levels over a large range fails to improve selectivity but does increase robustness between runs. The use of correlation-derived t-statistics should improve identification of interactors using TMT-AP-MS. Data are available via ProteomeXchange with identifier PXD016613.

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Section: Introductionmentioning

confidence: 99%

Bait Correlation Improves Interactor Identification by Tandem Mass Tag-Affinity Purification-Mass Spectrometry

et al. 2020

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“…We verified whether any of the 2,000 novel PPIs came up in recent interactome maps HuRI (HI-Union) 14 and BioPlex 15 . While there was no overlap with HuRI union dataset, there were 8 PPIs in the BioPlex map (ADAM9-ADAM32, P3H3-OS9, PVR-NECTIN2, SRRM2-SNIP1, PABPC4-LUC7L2, PRKACA-AKAP1, NDUFA13-ECSIT, and NPTX1-NPTX2).…”

Section: Resultsmentioning

confidence: 88%

Interactome of SARS-CoV-2 / nCoV19 modulated host proteins with computationally predicted PPIs

Karunakaran

Ganapathiraju

2020

Preprint

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World over, people are looking for solutions to tackle the pandemic coronavirus disease (COVID-19) caused by the virus SARS-CoV-2/nCoV-19. Notable contributions in biomedical field have been characterizing viral genomes, host transcriptomes and proteomes, repurposable drugs and vaccines. In one such study, 332 human proteins targeted by nCoV19 were identified. We expanded this set of host proteins by constructing their protein interactome, including in it not only the known protein-protein interactions (PPIs) but also novel, hitherto unknown PPIs predicted with our High-precision Protein-Protein Interaction Prediction (HiPPIP) model that was shown to be highly accurate. In fact, one of the earliest discoveries made possible by HiPPIP is related to activation of immunity upon viral infection. We found that several interactors of the host proteins are differentially expressed upon viral infection, are related to highly relevant pathways, and that the novel interaction of NUP98 with CHMP5 may activate an antiviral mechanism leading to disruption of viral budding. We are making the interactions available as downloadable files to facilitate future systems biology studies and also on a web-server at http://hagrid.dbmi.pitt.edu/corona that allows not only keyword search but also queries such as “PPIs where one protein is associated with ‘virus’ and the interactors with 'pulmonary'”.

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“…13 PPI from Affinity-Purification Mass Spectrometry experiments were obtained from the BIOPLEX database. 14…”

Section: Identification Of Gene-disease Associationsmentioning

confidence: 99%

Combining Gene–Disease Associations with Single-Cell Gene Expression Data Provides Anatomy-Specific Subnetworks in Age-Related Macular Degeneration

Luthert

Kiel

2020

Network and Systems Medicine

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Background: Age-related macular degeneration (AMD) is the most common cause of visual impairment in the developed world. Despite some treatment options for late AMD, there is no intervention that blocks early AMD proceeding to the late and blinding forms. This is partly due to the lack of precise drug targets, despite great advances in genetics, epidemiology, and protein-protein interaction (PPI) networks proposed to be driving the disease pathology. A systems approach to narrow down PPI networks to specific protein drug targets would provide new therapeutic options. Materials and Methods: In this study we analyzed single cell RNAseq (RNA sequencing) datasets of 17 cell types present in choroidal, retinal pigment epithelium (RPE), and neural retina (NR) tissues to explore if a more granular analysis incorporating different cell types exposes more specific pathways and relationships. Furthermore, we developed a novel and systematic gene ontology database (SysGO) to explore if a subcellular classification of processes will further enhance the understanding of the pathogenesis of this complex disorder and its comorbidities with other age-related diseases. Results: We found that 57% of the AMD (risk) genes are among the top 25% expressed genes in *1 of the 17 choroidal/RPE/NR cell types, and 9% were among the top 1% of expressed genes. Using SysGO, we identified an enrichment of AMD genes in cell membrane and extracellular anatomical locations, and we found both functional enrichments (e.g., cell adhesion) and cell types (e.g., fibroblasts, microglia) not previously associated with AMD pathogenesis. We reconstructed PPI networks among the top expressed AMD genes for all 17 choroidal/RPE/NR cell types, which provides molecular and anatomical definitions of AMD phenotypes that can guide therapeutic approaches to target this complex disease. Conclusion: We provide mechanism-based AMD endophenotypes that can be exploited in vitro, using computational models and for drug discovery/repurposing.

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Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome

Cited by 159 publications

References 75 publications

Bait Correlation Improves Interactor Identification by Tandem Mass Tag-Affinity Purification-Mass Spectrometry

Bait Correlation Improves Interactor Identification by Tandem Mass Tag-Affinity Purification-Mass Spectrometry

Interactome of SARS-CoV-2 / nCoV19 modulated host proteins with computationally predicted PPIs

Combining Gene–Disease Associations with Single-Cell Gene Expression Data Provides Anatomy-Specific Subnetworks in Age-Related Macular Degeneration

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