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Record W2687206218 · doi:10.1371/journal.pbio.2001930

MAIT cells launch a rapid, robust and distinct hyperinflammatory response to bacterial superantigens and quickly acquire an anergic phenotype that impedes their cognate antimicrobial function: Defining a novel mechanism of superantigen-induced immunopathology and immunosuppression

2017· article· en· W2687206218 on OpenAlexafffund
Christopher R. Shaler, Joshua Choi, Patrick T. Rudak, Arash Memarnejadian, Peter A. Szabo, Mauro Enrique Tun‐Abraham, Jamie Rossjohn, Alexandra J. Corbett, James McCluskey, John K. McCormick, Olivier Lantz, Roberto Hernandez‐Alejandro, S. M. Mansour Haeryfar

Bibliographic record

VenuePLoS Biology · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsLawson Health Research InstituteWestern University
FundersCanadian Institutes of Health Research
KeywordsSuperantigenBiologyImmunologyPopulationCytokineCytokine stormTumor necrosis factor alphaImmune systemStreptococcus pyogenesToxic shock syndromeT cellMicrobiologyStaphylococcus aureusMedicine

Abstract

fetched live from OpenAlex

Superantigens (SAgs) are potent exotoxins secreted by Staphylococcus aureus and Streptococcus pyogenes. They target a large fraction of T cell pools to set in motion a "cytokine storm" with severe and sometimes life-threatening consequences typically encountered in toxic shock syndrome (TSS). Given the rapidity with which TSS develops, designing timely and truly targeted therapies for this syndrome requires identification of key mediators of the cytokine storm's initial wave. Equally important, early host responses to SAgs can be accompanied or followed by a state of immunosuppression, which in turn jeopardizes the host's ability to combat and clear infections. Unlike in mouse models, the mechanisms underlying SAg-associated immunosuppression in humans are ill-defined. In this work, we have identified a population of innate-like T cells, called mucosa-associated invariant T (MAIT) cells, as the most powerful source of pro-inflammatory cytokines after exposure to SAgs. We have utilized primary human peripheral blood and hepatic mononuclear cells, mouse MAIT hybridoma lines, HLA-DR4-transgenic mice, MAIThighHLA-DR4+ bone marrow chimeras, and humanized NOD-scid IL-2Rγnull mice to demonstrate for the first time that: i) mouse and human MAIT cells are hyperresponsive to SAgs, typified by staphylococcal enterotoxin B (SEB); ii) the human MAIT cell response to SEB is rapid and far greater in magnitude than that launched by unfractionated conventional T, invariant natural killer T (iNKT) or γδ T cells, and is characterized by production of interferon (IFN)-γ, tumor necrosis factor (TNF)-α and interleukin (IL)-2, but not IL-17A; iii) high-affinity MHC class II interaction with SAgs, but not MHC-related protein 1 (MR1) participation, is required for MAIT cell activation; iv) MAIT cell responses to SEB can occur in a T cell receptor (TCR) Vβ-specific manner but are largely contributed by IL-12 and IL-18; v) as MAIT cells are primed by SAgs, they also begin to develop a molecular signature consistent with exhaustion and failure to participate in antimicrobial defense. Accordingly, they upregulate lymphocyte-activation gene 3 (LAG-3), T cell immunoglobulin and mucin-3 (TIM-3), and/or programmed cell death-1 (PD-1), and acquire an anergic phenotype that interferes with their cognate function against Klebsiella pneumoniae and Escherichia coli; vi) MAIT cell hyperactivation and anergy co-utilize a signaling pathway that is governed by p38 and MEK1/2. Collectively, our findings demonstrate a pathogenic, rather than protective, role for MAIT cells during infection. Furthermore, we propose a novel mechanism of SAg-associated immunosuppression in humans. MAIT cells may therefore provide an attractive therapeutic target for the management of both early and late phases of severe SAg-mediated illnesses.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.230
Teacher spread0.203 · 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 designBench or experimental
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".

Quick stats

Citations151
Published2017
Admission routes2
Has abstractyes

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