Evidence map›Paper›PMID 39455652›Full record

ArticleScientific reports2024

In-silico evaluation of the T-cell based immune response against SARS-CoV-2 omicron variants.

Shivangi Sharma, Diya Roy, Sarah Cherian

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Shivangi SharmaBioinformatics and Data Management Group, ICMR-National Institute of Virology, Pune, Maharashtra, 411001, India.
Diya RoyBioinformatics and Data Management Group, ICMR-National Institute of Virology, Pune, Maharashtra, 411001, India.
Sarah CherianBioinformatics and Data Management Group, ICMR-National Institute of Virology, Pune, Maharashtra, 411001, India. sarahcherian100@gmail.com.

Funding

Council of Scientific and Industrial Research, India 09/0698(13659)/2022-EMR-I
6 · The paper itself

Abstract

During of COVID-19 pandemic, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has continuously evolved, resulting in the emergence of several new variants of concerns (VOCs) with numerous mutations. These VOCs dominate in various regions due to increased transmissibility and antibody evasion, potentially reducing vaccine effectiveness. Nonetheless, it remains uncertain whether the recent SARS-CoV-2 VOCs have the ability to circumvent the T cell immunity elicited by either COVID-19 vaccination or natural infection. To address this, we conducted in-silico analysis to examine the impact of VOC-specific mutations at the epitope level and T cell cross-reactivity with the ancestral SARS-CoV-2. According to the in-silico investigation, T cell responses triggered by immunization or prior infections still recognize the variants in spite of mutations. These variants are expected to either maintain their dominant epitope HLA patterns or bind with new HLAs, unlike the epitopes of the ancestral strain. Our findings indicate that a significant proportion of immuno-dominant CD8 + and CD4 + epitopes are conserved across all the variants, implying that existing vaccines might maintain efficacy against new variations. However, further in-vitro and in-vivo studies are needed to validate the in-silico results and fully elucidate immune sensitivities to VOCs.

Indexed as

Computer SimulationCOVID-19Epitopes, T-LymphocyteMutationSARS-CoV-2CD4-Positive T-LymphocytesCD8-Positive T-LymphocytesCOVID-19 VaccinesCross ReactionsHumansT-LymphocytesCOVID-19 VaccinesEpitopes, T-Lymphocyte

Identifiers

PMID39455652
PMCPMC11511884

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.