Comparative Study
doi: 10.1126/science.abe9403. Epub 2020 Oct 15.Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms
David E Gordon # 1 2 3 4, Joseph Hiatt # 1 4 5 6 7, Mehdi Bouhaddou # 1 2 3 4, Veronica V Rezelj # 8, Svenja Ulferts # 9, Hannes Braberg # 1 2 3 4, Alexander S Jureka # 10, Kirsten Obernier # 1 2 3 4, Jeffrey Z Guo # 1 2 3 4, Jyoti Batra # 1 2 3 4, Robyn M Kaake # 1 2 3 4, Andrew R Weckstein # 11, Tristan W Owens # 12, Meghna Gupta # 12, Sergei Pourmal # 12, Erron W Titus # 12, Merve Cakir # 1 2 3 4, Margaret Soucheray 1 2 3 4, Michael McGregor 1 2 3 4, Zeynep Cakir 1 2 3 4, Gwendolyn Jang 1 2 3 4, Matthew J O'Meara 13, Tia A Tummino 1 2 14, Ziyang Zhang 1 2 3 15, Helene Foussard 1 2 3 4, Ajda Rojc 1 2 3 4, Yuan Zhou 1 2 3 4, Dmitry Kuchenov 1 2 3 4, Ruth Hüttenhain 1 2 3 4, Jiewei Xu 1 2 3 4, Manon Eckhardt 1 2 3 4, Danielle L Swaney 1 2 3 4, Jacqueline M Fabius 1 2, Manisha Ummadi 1 2 3 4, Beril Tutuncuoglu 1 2 3 4, Ujjwal Rathore 1 2 3 4, Maya Modak 1 2 3 4, Paige Haas 1 2 3 4, Kelsey M Haas 1 2 3 4, Zun Zar Chi Naing 1 2 3 4, Ernst H Pulido 1 2 3 4, Ying Shi 1 2 3 15, Inigo Barrio-Hernandez 16, Danish Memon 16, Eirini Petsalaki 16, Alistair Dunham 16, Miguel Correa Marrero 16, David Burke 16, Cassandra Koh 8, Thomas Vallet 8, Jesus A Silvas 10, Caleigh M Azumaya 12, Christian Billesbølle 12, Axel F Brilot 12, Melody G Campbell 12 17, Amy Diallo 12, Miles Sasha Dickinson 12, Devan Diwanji 12, Nadia Herrera 12, Nick Hoppe 12, Huong T Kratochvil 12, Yanxin Liu 12, Gregory E Merz 12, Michelle Moritz 12, Henry C Nguyen 12, Carlos Nowotny 12, Cristina Puchades 12, Alexandrea N Rizo 12, Ursula Schulze-Gahmen 12, Amber M Smith 12, Ming Sun 12 18, Iris D Young 12, Jianhua Zhao 12, Daniel Asarnow 12, Justin Biel 12, Alisa Bowen 12, Julian R Braxton 12, Jen Chen 12, Cynthia M Chio 12, Un Seng Chio 12, Ishan Deshpande 12, Loan Doan 12, Bryan Faust 12, Sebastian Flores 12, Mingliang Jin 12, Kate Kim 12, Victor L Lam 12, Fei Li 12, Junrui Li 12, Yen-Li Li 12, Yang Li 12, Xi Liu 12, Megan Lo 12, Kyle E Lopez 12, Arthur A Melo 12, Frank R Moss 3rd 12, Phuong Nguyen 12, Joana Paulino 12, Komal Ishwar Pawar 12, Jessica K Peters 12, Thomas H Pospiech Jr 12, Maliheh Safari 12, Smriti Sangwan 12, Kaitlin Schaefer 12, Paul V Thomas 12, Aye C Thwin 12, Raphael Trenker 12, Eric Tse 12, Tsz Kin Martin Tsui 12, Feng Wang 12, Natalie Whitis 12, Zanlin Yu 12, Kaihua Zhang 12, Yang Zhang 12, Fengbo Zhou 12, Daniel Saltzberg 1 2 19; QCRG Structural Biology Consortium; Anthony J Hodder 20, Amber S Shun-Shion 20, Daniel M Williams 20, Kris M White 21 22, Romel Rosales 21 22, Thomas Kehrer 21 22, Lisa Miorin 21 22, Elena Moreno 21 22, Arvind H Patel 23, Suzannah Rihn 23, Mir M Khalid 4, Albert Vallejo-Gracia 4, Parinaz Fozouni 4 5 7, Camille R Simoneau 4 7, Theodore L Roth 5 6 7, David Wu 5 7, Mohd Anisul Karim 24 25, Maya Ghoussaini 24 25, Ian Dunham 16 25, Francesco Berardi 26, Sebastian Weigang 27, Maxime Chazal 28, Jisoo Park 29, James Logue 30, Marisa McGrath 30, Stuart Weston 30, Robert Haupt 30, C James Hastie 31, Matthew Elliott 31, Fiona Brown 31, Kerry A Burness 31, Elaine Reid 31, Mark Dorward 31, Clare Johnson 31, Stuart G Wilkinson 31, Anna Geyer 31, Daniel M Giesel 31, Carla Baillie 31, Samantha Raggett 31, Hannah Leech 31, Rachel Toth 31, Nicola Goodman 31, Kathleen C Keough 4, Abigail L Lind 4; Zoonomia Consortium; Reyna J Klesh 32, Kafi R Hemphill 33, Jared Carlson-Stevermer 34, Jennifer Oki 34, Kevin Holden 34, Travis Maures 34, Katherine S Pollard 4 35 36, Andrej Sali 1 2 14 19, David A Agard 1 2 12 37, Yifan Cheng 1 2 12 15 37, James S Fraser 1 2 12 19, Adam Frost 1 2 12 37, Natalia Jura 1 2 3 12 38, Tanja Kortemme 1 2 12 19 39, Aashish Manglik 1 2 12 14, Daniel R Southworth 1 12 37, Robert M Stroud 1 2 12 37, Dario R Alessi 31, Paul Davies 31, Matthew B Frieman 30, Trey Ideker 29 40, Carmen Abate 26, Nolwenn Jouvenet 27 28, Georg Kochs 27, Brian Shoichet 1 2 14, Melanie Ott 4 41, Massimo Palmarini 23, Kevan M Shokat 1 2 3 15, Adolfo García-Sastre 42 22 43 44, Jeremy A Rassen 45, Robert Grosse 46 47, Oren S Rosenberg 48 2 12 36 37 41, Kliment A Verba 48 2 12 14, Christopher F Basler 49, Marco Vignuzzi 50, Andrew A Peden 51, Pedro Beltrao 52, Nevan J Krogan 48 2 3 4 21
Collaborators, Affiliations
- PMID:33060197
- PMCID: PMC7808408
- DOI: 10.1126/science.abe9403
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Comparative Study
Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms
David E Gordon et al. Science..
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Abstract
The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a grave threat to public health and the global economy. SARS-CoV-2 is closely related to the more lethal but less transmissible coronaviruses SARS-CoV-1 and Middle East respiratory syndrome coronavirus (MERS-CoV). Here, we have carried out comparative viral-human protein-protein interaction and viral protein localization analyses for all three viruses. Subsequent functional genetic screening identified host factors that functionally impinge on coronavirus proliferation, including Tom70, a mitochondrial chaperone protein that interacts with both SARS-CoV-1 and SARS-CoV-2 ORF9b, an interaction we structurally characterized using cryo-electron microscopy. Combining genetically validated host factors with both COVID-19 patient genetic data and medical billing records identified molecular mechanisms and potential drug treatments that merit further molecular and clinical study.
Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.
Figures
(Left) Viral-human protein-protein interaction network mapping, viral protein localization studies, and functional genetic screens provide key insights into the shared and individual characteristics of each virus. (Right) Structural studies and hypothesis testing in clinical datasets demonstrate the utility of this approach for prioritizing therapeutic strategies. Nsp, nonstructural protein; ORF, open reading frame; ER, endoplasmic reticulum.
(A) Genome annotation of SARS-CoV-2, SARS-CoV-1, and MERS-CoV with putative protein coding genes highlighted. Intensity of filled color indicates the lowest sequence identity between SARS-CoV-2 and SARS-CoV-1 or between SARS-CoV-2 and MERS. (B toD) Genome annotation of structural protein genes for SARS-CoV-2 (B), SARS-CoV-1 (C), and MERS-CoV (D). Color intensity indicates sequence identity to specified virus. (E) Overview of comparative coronavirus analysis. Proteins from SARS-CoV-2, SARS-CoV-1, and MERS-CoV were analyzed for their protein interactions and subcellular localization, and these data were integrated for comparative host interaction network analysis, followed by functional, structural, and clinical data analyses for exemplary virus-specific and pan-viral interactions. The asterisk indicates that the SARS-CoV-2 interactome was previously published in a separate study (5). SARS, both SARS-CoV-1 and SARS-CoV-2; MERS, MERS-CoV; Nsp, nonstructural protein; ORF, open reading frame.
(A) Overview of experimental design to determine localization of Strep-tagged SARS-CoV-2, SARS-CoV-1, and MERS-CoV proteins in HeLaM cells (left) or of viral proteins upon SARS-CoV-2 infection in Caco-2 cells (right). (B) Relative localization for all coronavirus proteins across viruses expressed individually (blue color bar) or in SARS-CoV-2–infected cells (colored box outlines). (C andD) Localization of Nsp1 and ORF3a expressed individually (C) or during infection (D); for representative images of all tagged constructs and viral proteins imaged during infection, see figs. S8 to S14 and fig. S15, respectively. Scale bars, 10 μm. (E) Prey overlap per bait measured as Jaccard index comparing SARS-CoV-2 versus SARS-CoV-1 (red dots) and SARS-CoV-2 versus MERS-CoV (blue dots) for all viral baits (all), viral baits found in the same cellular compartment (yes), and viral baits found in different compartments (no).
(A) Clustering analysis (K-means) of interactors from SARS-CoV-2, SARS-CoV-1, and MERS-CoV, weighted according to the average between their MiST and SAINT scores (interaction scoreK). Included are only viral protein baits represented amongst all three viruses and interactions that pass the high-confidence scoring threshold for at least one virus. Seven clusters highlight all possible scenarios of shared versus individual interactions, and percentages of total interactions are noted. (B) GO enrichment analysis of each cluster from (A), with the top six most-significant terms per cluster. Color indicates −log10(q), and the number of genes with significant (q < 0.05; white) or nonsignificant enrichment (q > 0.05; gray) is shown. (C) Percentage of interactions for each viral protein belonging to each cluster identified in (A). (D) Correlation between protein sequence identity and PPI overlap (Jaccard index) comparing SARS-CoV-2 and SARS-CoV-1 (blue) or MERS-CoV (red). Interactions for PPI overlap are derived from the final thresholded list of interactions per virus. (E) GO biological process terms significantly enriched (q < 0.05) for all three virus PPIs with Jaccard index indicating overlap of genes from each term for pairwise comparisons between SARS-CoV-1 and SARS-CoV-2 (purple), SARS-CoV-1 and MERS-CoV (green), and SARS-CoV-2 and MERS-CoV (orange). (F) Fraction of shared preys between orthologous (blue) and nonorthologous (red) viral protein baits. (G) Heatmap depicting overlap in PPIs (Jaccard index) between each bait from SARS-CoV-2 and MERS-CoV. Baits in gray were not assessed, do not exist, or do not have high-confidence interactors in the compared virus. Nonorthologous bait interactions are highlighted with a red square. GO, gene ontology; PPI, protein-protein interaction; SARS2, SARS-CoV-2; SARS1, SARS-CoV-1; MERS, MERS-CoV.
(A) Flowchart depicting calculation of DIS values using the average between the SAINT and MiST scores between every bait (i) and prey (j) to derive interaction score (K). The DIS is the difference between the interaction scores from each virus. The modified DIS (SARS-MERS) compares the averageK from SARS-CoV-1 and SARS-CoV-2 to that of MERS-CoV (see Materials and methods). Only viral bait proteins shared between all three viruses are included. (B) Density histogram of the DISs for all comparisons. (C) Dot plot depicting the DISs of interactions from viral bait proteins shared between all three viruses, ordered left to right by the mean DIS per viral bait. (D) Virus-human PPI map depicting the SARS-MERS comparison [purple in (B) and (C)]. The network depicts interactions derived from cluster 2 (all three viruses), cluster 4 (SARS-CoV-1 and SARS-CoV-2), and cluster 5 (MERS-CoV only). Edge color denotes DIS: red indicates interactions specific to SARS-CoV-1 and SARS-CoV-2 but absent in MERS-CoV; blue indicates interactions specific to MERS-CoV but absent from both SARS-CoV-1 and SARS-CoV-2; and black indicates interactions shared between all three viruses. Human-human interactions (thin dark gray line) and proteins sharing the same protein complexes or biological processes (light yellow or light blue highlighting, respectively) are shown. Host-host physical interactions, protein complex definitions, and biological process groupings are derived from CORUM (46), GO (biological process), and manually curated from literature sources. Thin dashed gray lines are used to indicate the placement of node labels when adjacent node labels would have otherwise been obscured. DIS, differential interaction score; SARS2, SARS-CoV-2; SARS1, SARS-CoV-1; MERS, MERS-CoV; SARS, both SARS-CoV-1 and SARS-CoV-2.
(A) A549-ACE2 cells were transfected with siRNA pools targeting each of the human genes from the SARS-CoV-2 interactome, followed by infection with SARS-CoV-2 and virus quantification using RT-qPCR. Cell viability and knockdown efficiency in uninfected cells was determined in parallel. (B) Caco-2 cells with CRISPR knockouts (KO) of each human gene from the SARS-CoV-2 interactome were infected with SARS-CoV-2, and supernatants were serially diluted and plated onto Vero E6 cells for quantification. Viabilities of the uninfected CRISPR knockout cells after infection were determined in parallel by DAPI staining. (C andD) Plot of results from the infectivity screens in A549-ACE2 knockdown cells (C) and Caco-2 knockout cells (D) sorted byz-score (z < 0, decreased infectivity;z > 0 increased infectivity). Negative controls (nontargeting control for siRNA, nontargeted cells for CRISPR) and positive controls (ACE2 knockdown or knockout) are highlighted. (E) Results from both assays with potential hits highlighted in red (A549-ACE2), yellow (Caco-2), and orange (both). (F) Pan-coronavirus interactome reduced to human preys with significant increase (red nodes) or decrease (blue nodes) in SARS-CoV2 replication upon knockdown or knockout. Viral proteins baits from SARS-CoV-2 (red), SARS-CoV-1 (orange), and MERS-CoV (yellow) are represented as diamonds. The thickness of the edge indicates the strength of the PPI in spectral counts. KD, knockdown; KO, knockout; PPI, protein-protein interaction.
(A) ORF9b-Tom70 interaction is conserved between SARS-CoV-1 and SARS-CoV-2. (B) Viral titers in Caco-2 cells after CRISPR knockout ofTOMM70 or controls. (C) Coimmunoprecipitation of endogenous Tom70 with Strep-tagged ORF9b from SARS-CoV-1 and SARS-CoV-2; Nsp2 from SARS-CoV-1, SARS-CoV-2, and MERS-CoV; or vector control in HEK293T cells. Representative blots of whole-cell lysates and eluates after IP are shown. (D) Size exclusion chromatography traces (10/300 S200 increase) of ORF9b alone, Tom70 alone, and coexpressed ORF9b-Tom70 complex purified from recombinant expression inE. coli. Insert shows SDS-PAGE of the complex peak indicating presence of both proteins. (E) Immunostainings for Tom70 in HeLaM cells transfected with GFP-Strep and ORF9b from SARS-CoV-1 and SARS-CoV-2 (left) and mean fluorescence intensity ± SD values of Tom70 in GFP-Strep and ORF9b expressing cells (normalized to nontransfected cells) (right). Scale bar, 10 μm. (F) Flag-Tom70 expression levels in total cell lysates of HEK293T cells upon titration of cotransfected Strep-ORF9b from SARS-CoV-1 and SARS-CoV-2. (G) Immunostaining for ORF9b and Tom70 in Caco-2 cells infected with SARS-CoV-2 (left) and mean fluorescence intensity ± SD values of Tom70 in uninfected and SARS-CoV-2–infected cells (right). SARS2, SARS-CoV-2; SARS1, SARS-CoV-1; MERS, MERS-CoV; IP, immunoprecipitation. **P < 0.05, Student’st test.
(A) Surface representation of the ORF9b-Tom70 structure. Tom70 is depicted as molecular surface in green, ORF9b is depicted as ribbon in orange. Region in charcoal indicates Hsp70 or Hsp90 binding site on Tom70. (B) Magnified view of ORF9b-Tom70 interactions with interacting hydrophobic residues on Tom70 indicated and shown in spheres. The two phosphorylation sites on ORF9b, S50 and S53, are shown in yellow. (C) Ionic interactions between Tom70 and ORF9b are depicted as sticks. Highly conserved residues on Tom70 making hydrophobic interactions with ORF9b are depicted as spheres. (D) Diagram depicting secondary structure comparison of ORF9b as predicted by JPred server—as visualized in our structure—or as visualized in the previously crystallized dimer structure (PDB ID: 6Z4U) (16). Pink tubes indicate helices, charcoal arrows indicate beta strands, and the amino acid sequence for the region visualized in the cryo-EM structure is shown on top. (E) Predicted probability of having an internal MTS as output by TargetP server by serially running N-terminally truncated regions of SARS-CoV-2 ORF9b. Region visualized in the cryo-EM structure (amino acids 39 to 76) overlaps with the highest internal MTS probability region (amino acids 40 to 50). MTS, mitochondrial targeting signal. Single-letter abbreviations for the amino acid residues are as follows: A, Ala; C, Cys; D, Asp; E, Glu; F, Phe; G, Gly; H, His; I, Ile; K, Lys; L, Leu; M, Met; N, Asn; P, Pro; Q, Gln; R, Arg; S, Ser; T, Thr; V, Val; W, Trp; and Y, Tyr.
(A) IL17RA and ADAM9 are functional interactors of SARS-CoV-2 ORF8. Only interactors identified in the genetic screening are shown. (B) Coimmunoprecipitation of endogenous IL17RA with Strep-tagged ORF8 or EGFP with or without IL-17A treatment at different times. Overexpression was done in HEK293T cells. (C) Viral titer afterIL17RA or control knockdown in A549-ACE2 cells. (D) OR of membership in indicated cohorts by genetically predicted sIL17RA levels. SARS2, SARS-CoV-2; IP, immunoprecipitation; SD, standard deviation; OR, odds ratio; CI, confidence interval; sIL17RA, soluble IL17RA. *P < 0.05, unpairedt test. Error bars in (C) indicate SDs; in (D), they indicate 95% CIs.
(A) Schematic of retrospective real-world clinical data analysis of indomethacin use for outpatients with SARS-CoV-2. Plots show distribution of propensity scores (PSs) for all included patients (red, indomethacin users; blue, celecoxib users). For a full list of inclusion, exclusion, and matching criteria, see Materials and methods and table S11. (B) Effectiveness of indomethacin versus celecoxib in patients with confirmed SARS-CoV-2 infection treated in an outpatient setting. Average standardized absolute mean difference (ASAMD) is a measure of balance between indomethacin and celecoxib groups calculated as the mean of the absolute standardized difference for each PS factor (table S11);P value and ORs with 95% CIs are estimated using the Aetion Evidence Platform r4.6. No ASAMD was >0.1. (C) Schematic of retrospective real-world clinical data analysis of typical antipsychotic use for inpatients with SARS-CoV-2. Plots show distribution of PSs for all included patients (red, typical users; blue, atypical users). For a full list of inclusion, exclusion, and matching criteria see Materials and methods and table S11. (D) Effectiveness of typical versus atypical antipsychotics among hospitalized patients with confirmed SARS-CoV-2 infection treated in hospital. ASAMD is a measure of balance between typical and atypical groups calculated as the mean of the absolute standardized difference for each PS factor (table S11);P value and ORs with 95% CIs are estimated using the Aetion Evidence Platform r4.6. No ASAMD was >0.1.
Comment in
- Conserved host-pathogen interactions identify novel treatment options in betacoronavirus infections.Lukassen S, Eils R.Lukassen S, et al.Signal Transduct Target Ther. 2021 Feb 10;6(1):57. doi: 10.1038/s41392-021-00480-z.Signal Transduct Target Ther. 2021.PMID:33563888Free PMC article.No abstract available.
- Olanzapine, risperidone and quetiapine: Do these atypical antipsychotics have a protective effect for SARS-CoV-2?Prokopez CR, Farinola R, Vallejos M, Lopredo LS, Sfriso LE, Chiapella LC, Arce C, Corral RM, Cuesta MJ, Alomo M.Prokopez CR, et al.Schizophr Res. 2022 Mar;241:140-141. doi: 10.1016/j.schres.2022.01.035. Epub 2022 Jan 24.Schizophr Res. 2022.PMID:35123336Free PMC article.No abstract available.
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References
- Beigel J. H., Tomashek K. M., Dodd L. E., Mehta A. K., Zingman B. S., Kalil A. C., Hohmann E., Chu H. Y., Luetkemeyer A., Kline S., Lopez de Castilla D., Finberg R. W., Dierberg K., Tapson V., Hsieh L., Patterson T. F., Paredes R., Sweeney D. A., Short W. R., Touloumi G., Lye D. C., Ohmagari N., Oh M.-d., Ruiz-Palacios G. M., Benfield T., Fätkenheuer G., Kortepeter M. G., Atmar R. L., Creech C. B., Lundgren J., Babiker A. G., Pett S., Neaton J. D., Burgess T. H., Bonnett T., Green M., Makowski M., Osinusi A., Nayak S., Lane H. C., ACTT-1 Study Group Members , Remdesivir for the treatment of Covid-19—Final report. N. Engl. J. Med. 383, 1813–1826 (2020). 10.1056/NEJMoa2007764 - DOI - PMC - PubMed
- Gordon D. E., Jang G. M., Bouhaddou M., Xu J., Obernier K., White K. M., O’Meara M. J., Rezelj V. V., Guo J. Z., Swaney D. L., Tummino T. A., Hüttenhain R., Kaake R. M., Richards A. L., Tutuncuoglu B., Foussard H., Batra J., Haas K., Modak M., Kim M., Haas P., Polacco B. J., Braberg H., Fabius J. M., Eckhardt M., Soucheray M., Bennett M. J., Cakir M., McGregor M. J., Li Q., Meyer B., Roesch F., Vallet T., Mac Kain A., Miorin L., Moreno E., Naing Z. Z. C., Zhou Y., Peng S., Shi Y., Zhang Z., Shen W., Kirby I. T., Melnyk J. E., Chorba J. S., Lou K., Dai S. A., Barrio-Hernandez I., Memon D., Hernandez-Armenta C., Lyu J., Mathy C. J. P., Perica T., Pilla K. B., Ganesan S. J., Saltzberg D. J., Rakesh R., Liu X., Rosenthal S. B., Calviello L., Venkataramanan S., Liboy-Lugo J., Lin Y., Huang X.-P., Liu Y., Wankowicz S. A., Bohn M., Safari M., Ugur F. S., Koh C., Savar N. S., Tran Q. D., Shengjuler D., Fletcher S. J., O’Neal M. C., Cai Y., Chang J. C. J., Broadhurst D. J., Klippsten S., Sharp P. P., Wenzell N. A., Kuzuoglu-Ozturk D., Wang H.-Y., Trenker R., Young J. M., Cavero D. A., Hiatt J., Roth T. L., Rathore U., Subramanian A., Noack J., Hubert M., Stroud R. M., Frankel A. D., Rosenberg O. S., Verba K. A., Agard D. A., Ott M., Emerman M., Jura N., von Zastrow M., Verdin E., Ashworth A., Schwartz O., d’Enfert C., Mukherjee S., Jacobson M., Malik H. S., Fujimori D. G., Ideker T., Craik C. S., Floor S. N., Fraser J. S., Gross J. D., Sali A., Roth B. L., Ruggero D., Taunton J., Kortemme T., Beltrao P., Vignuzzi M., García-Sastre A., Shokat K. M., Shoichet B. K., Krogan N. J., A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature 583, 459–468 (2020). 10.1038/s41586-020-2286-9 - DOI - PMC - PubMed
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