Evidence map›Paper›PMID 30144804›Full record

ArticleGenome medicine2018

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

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 and 2 more

Open access · goldAbstract read
In one paragraph

Article in Genome medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
2.9field-weighted citation impact, top 8% of its field
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

32 citing papers in PubMed, 52 citations in OpenAlex.

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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

12 authors at 3 institutions in 4 countries.

Mikhail V PogorelyyDepartment of Genomics of Adaptive Immunity, IBCH RAS, Moscow, Russia.
Alla D FedorovaDepartment of Genomics of Adaptive Immunity, IBCH RAS, Moscow, Russia.
James E McLarenDivision of Infection and Immunity, Cardiff University School of Medicine, Cardiff, UK.
Kristin LadellDivision of Infection and Immunity, Cardiff University School of Medicine, Cardiff, UK.
Dmitri V BagaevDepartment of Genomics of Adaptive Immunity, IBCH RAS, Moscow, Russia.
Alexey V EliseevDepartment of Genomics of Adaptive Immunity, IBCH RAS, Moscow, Russia.
Artem I MikelovDepartment of Genomics of Adaptive Immunity, IBCH RAS, Moscow, Russia.
Anna E KonevaDepartment of Genomics of Adaptive Immunity, IBCH RAS, Moscow, Russia.
Ivan V ZvyaginDepartment of Genomics of Adaptive Immunity, IBCH RAS, Moscow, Russia.
David A PriceDivision of Infection and Immunity, Cardiff University School of Medicine, Cardiff, UK.
Dmitry M ChudakovDepartment of Genomics of Adaptive Immunity, IBCH RAS, Moscow, Russia.
Mikhail ShugayDepartment of Genomics of Adaptive Immunity, IBCH RAS, Moscow, Russia. mikhail.shugay@gmail.com.ORCID 0000-0001-7826-7942
Cardiff University · GBSkolkovo Institute of Science and Technology · RUPirogov Russian National Research Medical University · RU

Funding

Russian Science Foundation 17-15-01495Wellcome Trust
6 · The paper itself

Abstract

backgroundAdaptive 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.

methodsWe 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.

resultsOur 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.

conclusionsOur 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.

Indexed as

EpitopesHistocompatibility Antigens Class IHumansModels, StatisticalPeptidesReceptors, Antigen, T-CellT-LymphocytesEpitopesHistocompatibility Antigens Class IPeptidesReceptors, Antigen, T-CellAntigenImmune repertoireImmunogenicityT cell receptor

Identifiers

PMID30144804
PMCPMC6109350
OpenAlexW2888248186

What OpenQuestion holds

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LicenceCC BY
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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.