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A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing

David E. Gordon, Gwendolyn M. Jang, Mehdi Bouhaddou, Jiewei Xu, Kirsten Obernier, Matthew J. O’Meara, Jeffrey Z. Guo, Danielle L. Swaney, Tia A. Tummino, Ruth Huettenhain, Robyn M. Kaake, Alicia L. Richards, Beril Tutuncuoglu, Helene Foussard, Jyoti Batra, Kelsey Haas, Maya Modak, Minkyu Kim, Paige Haas, Benjamin J. Polacco, Hannes Braberg, Jacqueline M. Fabius, Manon Eckhardt, Margaret Soucheray, Melanie J. Bennett, Merve Cakir, Michael J. McGregor, Qiongyu Li, Zun Zar Chi Naing, Yuan Zhou, Shiming Peng, Ilsa T. Kirby, James E. Melnyk, John S. Chorba, Kevin Lou, Shizhong A. Dai, Wenqi Shen, Ying Shi, Ziyang Zhang, Inigo Barrio-Hernandez, Danish Memon, Claudia Hernandez-Armenta, Christopher J.P. Mathy, Tina Perica, Kala B. Pilla, Sai J. Ganesan, Daniel J. Saltzberg, Rakesh Ramachandran, Xi Liu, Sara B. Rosenthal, Lorenzo Calviello, Srivats Venkataramanan, Jose Liboy-Lugo, Yizhu Lin, Stephanie A. Wankowicz, Markus Bohn, Phillip P. Sharp, Raphael Trenker, Janet M. Young, Devin A. Cavero, Joseph Hiatt, Theodore L. Roth, Ujjwal Rathore, Advait Subramanian, Julia Noack, Mathieu Hubert, Ferdinand Roesch, Thomas Vallet, Björn Meyer, Kris M. White, Lisa Miorin, Oren S. Rosenberg, Kliment A Verba, David Agard, Melanie Ott, Michael Emerman, Davide Ruggero, Adolfo García-Sastre, Natalia Jura, Mark von Zastrow, Jack Taunton, Alan Ashworth, Olivier Schwartz, Marco Vignuzzi, Christophe d’Enfert, Shaeri Mukherjee, Matt Jacobson, Harmit S. Malik, Danica G. Fujimori, Trey Ideker, Charles S. Craik, Stephen Floor, James S. Fraser, John Gross, Andrej Sali, Tanja Kortemme, Pedro Beltrao, Kevan Shokat, Brian K. Shoichet, Nevan J. Krogan

Preprint posted on March 27, 2020 https://www.biorxiv.org/content/10.1101/2020.03.22.002386v3

Article now published in Nature at http://dx.doi.org/10.1038/s41586-020-2286-9

Can the SARS-CoV-2 pandemic be slowed? Systematic discovery of the SARS-CoV-2 human interactome.

Selected by Robert Mahen

SARS-CoV-2, the causative agent of COVID-19, has infected in excess of 1.5 million people worldwide and is implicated in >90000 deaths. The pandemic has forced half the planet into lockdown, with further socioeconomic costs yet to be fully incurred. There are no efficient COVID-19 antivirals or vaccines and few people are immune thus far. The scientific community – and indeed the world – is looking in haste for treatments with which to slow the pandemic. It is, as British prime minister Boris Johnson put it, the health crisis of a generation.

How viruses hijack cellular systems is a fascinating cell biological question. With the SARS-CoV-2 pandemic it is now an issue viscerally impacting our daily lives.

 

Key findings

Gordon et al., have produced the first systematic analysis of which human proteins SARS-CoV-2 may interact with during infection. Almost all SARS-CoV-2 viral genes were cloned and expressed in human HEK293T cells as 2xStrep-tag fusion proteins. The 27 tagged viral proteins were analysed with affinity-purification mass spectrometry, isolating them from lysates and determining the human proteins associating with them (Figure 1). Thus, 332 human proteins interacting with SARS-CoV-2 viral genes were discovered, providing a first SARS-CoV-2 human protein interactome.

Figure 1. Selected panels taken directly from Gordon et al., 2020. (a) Table of the SARS-CoV-2 proteins, including molecular weight, sequence similarity with the SARS-CoV homolog, and inferred function based on the SARS-CoV homolog. (b) Experimental workflow for expressing each 2xStrep tagged SARS-CoV-2 fusion protein in biological triplicate in HEK293T cells, followed by affinity purification-mass spectrometry, and PPI scoring to identify 332 high confidence protein-protein interactions. Made available by a CC-BY 4.0 international license.

 

What does this interactome tell us about the biology of the virus? Gene ontology enrichment analysis showed that the viral interactome involves a variety of cellular processes (Figure 2), including transcription, translation, ubiquitination regulation, nuclear import and mitochondrial function. Some SARS-CoV-2-human protein-protein interactions appear to be related to viral immune evasion, such as interactions of Nsp13 with interferon signalling components or the interaction of structural protein N with stress granule protein G3BP1. Others may be related to viral entry and replication, with strong representation from intracellular membrane traffic (Nsp7 with Rabs, Nsp10 with AP2, E with AP3, and Orf3a with the HOPS complex). Coronaviruses gain cellular entry through the S glycoprotein which mediates viral and host cell membrane fusion events (Li et al., 2003). S protein was found associated with the GOLGA7-ZDHHC5 acyl-transferase complex, which may mediate S palmitoylation on its cytosolic tail, as has already been shown for SARS-CoV-1 (Petit et al., 2007).

Whilst these interactions suggest how intracellular membrane trafficking pathways may be rewired to favour the virus, other viral protein interactions are equally interesting yet require more imagination to understand their relevance. For example, Nsp13 interacts with multiple different proteins involved in microtubule-based processes, both at the centrosome, (CEP250, CEP68, AKAP9) and Golgi (Golgins). Why Nsp13 – the viral helicase/triphosphatase (Ivanov et al., 2004) – binds to multiple different centrosome associated genes is just one of a number of interesting questions amongst this dataset.

 

Figure 2. A selected panel from Gordon et al., 2020. Gene Ontology (GO) enrichment analysis performed on the human interacting proteins of each viral protein. The top GO term of each viral protein was selected for visualization. Made available by a CC-BY 4.0 international license.

 

Gordon et al., searched for small molecules targeting the newly identified SARS-CoV-2 human protein interactome, using chemoinformatics databases and analyses, to find 62 drugs targeting the virus-host interactome. Notably, 27 of these are already FDA approved, providing a realistic possibility of using them in patients rapidly. Included in this list are some possible COVID-19 treatments that have been widely discussed in the weeks since the pandemic erupted, such as chloroquine (Wang et al., 2020).

 

Why I chose this preprint

This preprint identifies crucial SARS-CoV-2 protein-protein interactions which can now be further tested for their relevance to the therapy and biology of COVID-19. It provides insight into how the virus is currently wreaking havoc with cellular systems, inside hundreds of thousands of infected individuals worldwide.

The collaborative and open nature of this work, and speed at which it has been produced is impressive – the first COVID-19 cases were around mid-December 2019 (Zhou et al., 2020) and the first version of this work was shared 22nd March 2020. This preprint illustrates how preprint servers are a major medium by which scientific work can be rapidly disseminated and evaluated, and how fast knowledge can be gained in response to a crisis.

 

Questions

There are a significant number of biological processes represented in the interactome, some of which suggest intuitive hypotheses for future work, and others which seem more complex to understand. Is there a part of this network which most surprised you in the context of protein-protein interactions found in other viruses?

You looked at the similarity of the SARS-CoV-2 interactome to ten other viruses. Would a close comparison to a matched SARS-CoV-1 dataset be informative?

You used individually expressed viral genes. Would you expect similar results in the context of a whole virus? For example, the S protein seems to have relatively few interactors, and I’m curious as to whether this could be related to differential functions as a heterologously expressed protein versus a virion?

Given the impressive size and speed of this collaborative effort, I’m particularly curious to know what your next steps might be. You mention genetic and compound screening to verify the results?

 

References

Ivanov KA, Thiel V, Dobbe JC, van der Meer Y, Snijder EJ, Ziebuhr J. Multiple enzymatic activities associated with severe acute respiratory syndrome coronavirus helicase. J Virol. 2004 Jun;78(11):5619-32.

Li W, Moore MJ, Vasilieva N, Sui J, Wong SK, Berne MA, Somasundaran M, Sullivan JL, Luzuriaga K, Greenough TC, Choe H, Farzan M. Angiotensin-converting enzyme 2 is a functional receptor for the SARS coronavirus. Nature. 2003 Nov 27;426(6965):450-4.

Petit CM, Chouljenko VN, Iyer A, Colgrove R, Farzan M, Knipe DM, Kousoulas KG. Palmitoylation of the cysteine-rich endodomain of the SARS-coronavirus spike glycoprotein is important for spike-mediated cell fusion. Virology. 2007 Apr 10;360(2):264-74.

Wang M, Cao R, Zhang L, Yang X, Liu J, Xu M, Shi Z, Hu Z, Zhong W, Xiao G. Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro. Cell Res. 2020 Mar;30(3):269-271.

Zhou P, Yang XL, Wang XG, Hu B, Zhang L, Zhang W, Si HR, Zhu Y, Li B, Huang CL, Chen HD, Chen J, Luo Y, Guo H, Jiang RD, Liu MQ, Chen Y, Shen XR, Wang X, Zheng XS, Zhao K, Chen QJ, Deng F, Liu LL, Yan B, Zhan FX, Wang YY, Xiao GF, Shi ZL. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature. 2020 Mar;579(7798):270-273.

Tags: coronaviridae, coronavirus, covid-19, interactome, mass spectrometry, pandemic, sars-cov-2

Posted on: 9th April 2020 , updated on: 10th April 2020

doi: https://doi.org/10.1242/prelights.18355

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  • Author's response

    Manon Eckhardt shared

    Questions

    There are a significant number of biological processes represented in the interactome, some of which suggest intuitive hypotheses for future work, and others which seem more complex to understand. Is there a part of this network which most surprised you in the context of protein-protein interactions found in other viruses?

    There are so many exciting interactions in this map, that I have a hard time choosing only one. But if you press me: one of the first things that caught our eyes in the map was the interaction of Orf10 with members of the ubiquitination pathway. Ubiquitination serves as a cellular label to mark proteins for removal. Many viruses interact with and use the ubiquitin system to get rid of human proteins that would otherwise restrict virus replication. We’re excited to work with our collaborators on figuring out if this is something that SARS-CoV-2 does, too. Because there are drugs that can inhibit the ubiquitin system, this might offer an avenue to throw a wrench into the virus’ inactivation of a natural cellular defence.     

    You looked at the similarity of the SARS-CoV-2 interactome to ten other viruses. Would a close comparison to a matched SARS-CoV-1 dataset be informative?

    Definitely! In addition to comparing the interactions of SARS-CoV-1, we’re also interested in comparing it to other coronaviruses that cause less severe disease as well as the ‘ancestral’ bat coronavirus. Finding both differences and commonalities between these networks would be really interesting: Differences in how these viruses interact with cellular proteins might help us understand on why some cause more severe disease than others. Our biggest hope, however, would be to find common targets that would allow the development of drugs effective against all coronaviruses – possibly including future emerging viruses to prevent the next coronavirus pandemic. It’s so important to have hope in these times – just to be clear, though, this will require a big effort of many labs in the science community and isn’t something that’s right around the corner quite yet. 

    You used individually expressed viral genes. Would you expect similar results in the context of a whole virus? For example, the S protein seems to have relatively few interactors, and I’m curious as to whether this could be related to differential functions as a heterologously expressed protein versus a virion?

    This is an important question, and highlights the fact that our interaction map is only the first step in learning more about this novel virus. From our previous virus-host maps we have learned that about 20% of interactors we identify in this kind of setting have an impact on virus replication in primary model systems. Although this might sound lower than what you would hope for, it still offers a great shot at a large number of possible new drug targets. And the best part is that every promising drug we found to hit our interactome is currently being tested by members and collaborators of the QBI Coronavirus Research Group (QCRG). In fact, we are hoping to include some of these results in the next revision of the paper – so stay tuned!    

    Given the impressive size and speed of this collaborative effort, I’m particularly curious to know what your next steps might be. You mention genetic and compound screening to verify the results?

    The collaborative nature of our project is definitely what excites me most in these challenging times. I’ve long been a proponent of open and collaborative science, and am very glad that my work in building a team that can quickly gear up to stem such a big project is paying off. As soon as we got the first results of the interactome in, we connected with experts in other fields, and very quickly created the above-mentioned QBI Coronavirus Research Group (QCRG). The core group of 22 labs across the SF Bay Area has quickly grown to include important collaborators across the world – most importantly virology labs at Mt. Sinai in NYC and the Institut Pasteur in Paris, France, who started testing the effects of the drugs we predicted to have an impact on virus replication. It is super important to remember that our map is only the first step, and that all results have to be verified. There are so many promising avenues suggested by our work – too many for a single lab to work on, especially given the urgency of the current situation. Seeing how well QCRG scientists are working together, and the wider scientific community in general, makes me very hopeful for a future of more powerful team science.  

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