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Record W3137370550 · doi:10.1038/s41419-021-03513-1

Inhibition of HECT E3 ligases as potential therapy for COVID-19

2021· article· en· W3137370550 on OpenAlexaff
Giuseppe Novelli, Jing Liu, Michela Biancolella, Tonino Alonzi, Antonio Novelli, J. J. Patten, Dario Cocciadiferro, Emanuele Agolini, Vito Luigi Colona, Barbara Rizzacasa, Rosalinda Giannini, Benedetta Bigio, Delia Goletti, Maria Rosaria Capobianchi, Sandro Grelli, Justin David Mann, Trevor D. McKee, Ke Cheng, Fatima Amanat, Florian Krammer, Andrea Guarracino, Gerardo Pepe, Carlo Tomino, Yacine Tandjaoui-Lambiotte, Y. Uzunhan, Sarah Tubiana, Jade Ghosn, Luigi D. Notarangelo, Helen C. Su, Laurent Abel, Aurélie Cobat, Gai Elhanan, Joseph J. Grzymski, Andrea Latini, Sachdev S. Sidhu, Suresh Kumar Jain, Robert A. Davey, Jean‐Laurent Casanova, Wenyi Wei, Pier Paolo Pandolfi

Bibliographic record

VenueCell Death and Disease · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsUniversity of Toronto
FundersNational Institutes of HealthUniversité de ParisAssistance publique-Hôpitaux de ParisInstitut National de la Santé et de la Recherche MédicaleUniversité Claude Bernard Lyon 1NHLBI Division of Intramural ResearchUniversität BaselFondation pour la Recherche MédicaleSt. Giles FoundationMercatus Center, George Mason UniversityCentre National de la Recherche ScientifiqueGeorge Mason UniversityInstitut de Médecine et d'Epidémiologie AppliquéeYale UniversityNational Center for Advancing Translational SciencesNational Human Genome Research InstituteAgence Nationale de la RechercheGeorgia Clinical and Translational Science AllianceNational Institute of Allergy and Infectious DiseasesHoward Hughes Medical Institute
KeywordsNEDD4Ubiquitin ligaseUbiquitinBiologyVP40CullinGeneticsVirologyGeneRNA

Abstract

fetched live from OpenAlex

SARS-CoV-2 is responsible for the ongoing world-wide pandemic which has already taken more than two million lives. Effective treatments are urgently needed. The enzymatic activity of the HECT-E3 ligase family members has been implicated in the cell egression phase of deadly RNA viruses such as Ebola through direct interaction of its VP40 Protein. Here we report that HECT-E3 ligase family members such as NEDD4 and WWP1 interact with and ubiquitylate the SARS-CoV-2 Spike protein. Furthermore, we find that HECT family members are overexpressed in primary samples derived from COVID-19 infected patients and COVID-19 mouse models. Importantly, rare germline activating variants in the NEDD4 and WWP1 genes are associated with severe COVID-19 cases. Critically, I3C, a natural NEDD4 and WWP1 inhibitor from Brassicaceae, displays potent antiviral effects and inhibits viral egression. In conclusion, we identify the HECT family members of E3 ligases as likely novel biomarkers for COVID-19, as well as new potential targets of therapeutic strategy easily testable in clinical trials in view of the established well-tolerated nature of the Brassicaceae natural compounds.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.271
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations56
Published2021
Admission routes1
Has abstractyes

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