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Record 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 on OpenAlexaff
Samantha Berry, Liza‐Anastasia DiCecco, Jennifer L. Gray, Jack Boylan, María Elena González Solares, Deborah F. Kelly

Bibliographic record

VenueMicroscopy and Microanalysis · 2023
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLibrary scienceState (computer science)GerontologyMedicineComputer science

Abstract

fetched live from 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.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.046
GPT teacher head0.408
Teacher spread0.362 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations1
Published2023
Admission routes1
Has abstractyes

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