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Enregistrement W4385072695 · doi:10.1093/micmic/ozad067.325

Diving into COVID-19: Visualizing SARS-CoV-2 Patient Proteins using Liquid-Electron Microscopy

2023· article· en· W4385072695 sur OpenAlexaff
Samantha Berry, Liza‐Anastasia DiCecco, Jennifer L. Gray, Jack Boylan, María Elena González Solares, Deborah F. Kelly

Notice bibliographique

RevueMicroscopy and Microanalysis · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueSARS-CoV-2 and COVID-19 Research
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésLibrary scienceState (computer science)GerontologyMedicineComputer science

Résumé

récupéré en direct d'OpenAlex

Globally, there have been more than 600 million confirmed cases of SARS-CoV-2, resulting in over 6.5 million deaths according to the World Health Organization [1]. This coronavirus disease impacts survivors as well, and in some cases can cause long-lasting fatigue, respiratory issues, and even cardiomyopathy. One silver lining of the pandemic was the accelerated research resulting in the development of mRNA vaccines to provide protection to individuals. This mRNA technology is based on the spike (S) protein structure which facilitates viral entry into the host cell. All current structures are created from recombinant proteins, however, and may lack specific features such as the furin cleavage site found in the native S protein [2]. Therefore, examining the native structure could reveal critical insights into SARS-CoV-2 infection mechanisms. Since the great “Resolution Revolution”, cryo-electron microscopy (EM) has established itself as a prominent technique for studying protein structure. But what does one use if proteins in action need to be captured? Cryo-EM can only show a vitrified snapshot of what could be occurring in the human body. Proteins are dynamic and flexible in nature, characteristics that need to be considered when choosing a method of study. This makes the introduction of liquid-EM, the room temperature correlate of cryo-EM, extremely timely. Liquid-EM is a novel imaging technique making waves across the imaging community. Instead of plunge-freezing in liquid ethane, samples need only be hermetically sealed in liquid cell for imaging. While utilized extensively in materials research, liquid-EM is emerging as a technique capabable of achieving comparable high-resolutions to cryo-EM while being able to also able to capture dynamics in real time. By imaging proteins in a fluid, near-native environment, many pertinent questions in life sciences can be addressed. Visualizing conformational changes in flexible proteins could be the key to understanding disease mechanisms, such as SARS-CoV-2 infections. In this work, we modeled viral COVID-19 patient proteins utilizing this novel liquid technique. This research aimed to model SARS-CoV-2 S protein structure obtained from patients in a fluidic, near-native environment. Proteins were extracted from PCR+ patient serum through a column purification technique. Another serum sample obtained from recently vaccinated individuals was also purified. To image the samples, we utilized our innovative microchip sandwich assembly [4]. In this setup, a liquid sample is deposited on a glow-discharged carbon-coated gold grid and allowed time to incubate. Next, a silicon microchip is placed on top and the sandwich is sealed with an autoloader clip. This setup allows for instantaneous imaging in a single-tilt holder, shown in Fig. 1A. Samples were imaged using a Talos F200C microscope. After images were collected, data was processed using single-particle analysis (SPA) in RELION to create 3D reconstructions of viral proteins. Map comparisons and model fitting was performed using ChimeraX. EM maps from both SARS-CoV-2 PCR+ patient serum and vaccinated serum were obtained, shown in Fig. 2. The S protein structure from infected patients was discerned at 4.3 Å resolution from 200,000 particles using C1 symmetry (Fig. 2D). The reconstruction indicates that the map does not contain a whole trimeric S protein, however, this result is feasible when taken into consideration that the protein was obtained from a patient. It was anticipated that the S protein would have undergone post-translational modifications or degradation from the individual’s immune response. Therefore, this provides a unique opportunity to examine how the body defends itself from viral invaders. Additionally, an S protein map from vaccinated persons was obtained at 4.8 Å resolution from 55,000 particles using C1 symmetry (Fig. 2E). From initial inspection, this S protein created from mRNA appears to only have a monomeric structure, as opposed to the trimeric structure of the native protein. By visualizing these proteins from inoculated individuals, a greater understanding of how mRNA vaccines play a role in active immunity can be obtained. Overall, results from this study not only shed light on SARS-CoV-2 key structures but also highlight liquid-EM as a highly capable technique to carry out structural biology studies. Microchip sandwich assembly for liquid-EM. (A) SiN microchip and grid are sealed at clipping station and imaged using single-tilt TEM holder. (B) Side view schematic of sandwich assembly. Thin liquid layer is ideal for smaller particles. Analysis of SARS-CoV-2 PCR+ and vaccinated patient serum. (A) Magnified view of S protein in liquid from PCR+ patients (scale bar 50 nm). (B) Magnified view of S protein in liquid from vaccinated individuals (scale bar 50 nm). (C) Fourier analysis showed images were stable and free of drift. (D) 3D reconstruction of native S protein revealed modifications from previous models. (E) 3D reconstruction of S protein produced in individuals that received mRNA vaccine shows monomeric structure.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,013

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,046
Tête enseignante GPT0,408
Écart entre enseignants0,362 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2023
Routes d'admission1
Résumé présentoui

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