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Exploring the pre-immune landscape of antigen-specific T cells

Overview of attention for article published in Genome Medicine, August 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

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Title
Exploring the pre-immune landscape of antigen-specific T cells
Published in
Genome Medicine, August 2018
DOI 10.1186/s13073-018-0577-7
Pubmed ID
Authors

Mikhail V. Pogorelyy, Alla D. Fedorova, James E. McLaren, Kristin Ladell, Dmitri V. Bagaev, Alexey V. Eliseev, Artem I. Mikelov, Anna E. Koneva, Ivan V. Zvyagin, David A. Price, Dmitry M. Chudakov, Mikhail Shugay

Abstract

Adaptive immune responses to newly encountered pathogens depend on the mobilization of antigen-specific clonotypes from a vastly diverse pool of naive T cells. Using recent advances in immune repertoire sequencing technologies, models of the immune receptor rearrangement process, and a database of annotated T cell receptor (TCR) sequences with known specificities, we explored the baseline frequencies of T cells specific for defined human leukocyte antigen (HLA) class I-restricted epitopes in healthy individuals. We used a database of TCR sequences with known antigen specificities and a probabilistic TCR rearrangement model to estimate the baseline frequencies of TCRs specific to distinct antigens epitopespecificT-cells. We verified our estimates using a publicly available collection of TCR repertoires from healthy individuals. We also interrogated a database of immunogenic and non-immunogenic peptides is used to link baseline T-cell frequencies with epitope immunogenicity. Our findings revealed a high degree of variability in the prevalence of T cells specific for different antigens that could be explained by the physicochemical properties of the corresponding HLA class I-bound peptides. The occurrence of certain rearrangements was influenced by ancestry and HLA class I restriction, and umbilical cord blood samples contained higher frequencies of common pathogen-specific TCRs. We also identified a quantitative link between specific T cell frequencies and the immunogenicity of cognate epitopes presented by defined HLA class I molecules. Our results suggest that the population frequencies of specific T cells are strikingly non-uniform across epitopes that are known to elicit immune responses. This inference leads to a new definition of epitope immunogenicity based on specific TCR frequencies, which can be estimated with a high degree of accuracy in silico, thereby providing a novel framework to integrate computational and experimental genomics with basic and translational research efforts in the field of T cell immunology.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 97 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 97 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 27%
Student > Ph. D. Student 22 23%
Student > Master 15 15%
Student > Bachelor 10 10%
Other 6 6%
Other 10 10%
Unknown 8 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 23 24%
Immunology and Microbiology 19 20%
Agricultural and Biological Sciences 18 19%
Medicine and Dentistry 10 10%
Chemistry 3 3%
Other 13 13%
Unknown 11 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 19. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 January 2024.
All research outputs
#2,010,766
of 26,184,649 outputs
Outputs from Genome Medicine
#438
of 1,637 outputs
Outputs of similar age
#39,556
of 346,770 outputs
Outputs of similar age from Genome Medicine
#6
of 22 outputs
Altmetric has tracked 26,184,649 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,637 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.4. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 346,770 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.