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Record W1976302032 · doi:10.1038/nature12979

IL-35-producing B cells are critical regulators of immunity during autoimmune and infectious diseases

2014· article· en· W1976302032 on OpenAlexafffund
Ping Shen, Toralf Roch, Vicky Lampropoulou, Richard A. O’Connor, Ulrik Stervbo, Ellen Hilgenberg, Stefanie Ries, Van Duc Dang, Yarúa Jaimes, Capucine Daridon, Rui Li, Luc Jouneau, Pierre Boudinot, Siska Wilantri, Imme Sakwa, Yusei Miyazaki, Melanie D. Leech, Rhoanne C. McPherson, Stefan Wirtz, Markus F. Neurath, Kai Hoehlig, Edgar Meinl, Andreas Grützkau, Joachim R. Grün, Katharina Horn, Anja A. Kühl, Thomas Dörner, Amit Bar‐Or, Stefan H. E. Kaufmann, Stephen M. Anderton, Simon Fillatreau

Bibliographic record

VenueNature · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsMontreal Neurological Institute and Hospital
FundersMedical Research CouncilCanadian Institutes of Health ResearchInstitut National de la Recherche AgronomiqueDeutsche ForschungsgemeinschaftWellcome Trust
KeywordsRegulatory B cellsBiologyImmunityImmunologyExperimental autoimmune encephalomyelitisB cellSalmonella entericaImmune systemAntigenMicrobiologyInterleukin 10SalmonellaAntibodyBacteria

Abstract

fetched live from OpenAlex

B cells can secrete IL-35 upon activation, and subsequently contribute negatively to the regulation of immunity, such as T-cell-mediated autoimmunity or anti-microbial immunity, and a characterization of these cells raises new questions about possible independent roles for IL-10- and IL-35-expressing plasma cells as regulatory cells. This study identifies interleukin-35 (IL-35)-producing B cells as novel negative regulators of immunity. Mice with B cells unable to produce IL-35 proved susceptible to induced autoimmune disease and at the same time showed increased resistance to Salmonella infection. This finding points to IL-35 production by B cells as a potential therapeutic target for autoimmune and infectious diseases. B lymphocytes have critical roles as positive and negative regulators of immunity. Their inhibitory function has been associated primarily with interleukin 10 (IL-10) because B-cell-derived IL-10 can protect against autoimmune disease and increase susceptibility to pathogens1,2. Here we identify IL-35-producing B cells as key players in the negative regulation of immunity. Mice in which only B cells did not express IL-35 lost their ability to recover from the T-cell-mediated demyelinating autoimmune disease experimental autoimmune encephalomyelitis (EAE). In contrast, these mice displayed a markedly improved resistance to infection with the intracellular bacterial pathogen Salmonella enterica serovar Typhimurium as shown by their superior containment of the bacterial growth and their prolonged survival after primary infection, and upon secondary challenge, compared to control mice. The increased immunity found in mice lacking IL-35 production by B cells was associated with a higher activation of macrophages and inflammatory T cells, as well as an increased function of B cells as antigen-presenting cells (APCs). During Salmonella infection, IL-35- and IL-10-producing B cells corresponded to two largely distinct sets of surface-IgM+CD138hiTACI+CXCR4+CD1dintTim1int plasma cells expressing the transcription factor Blimp1 (also known as Prdm1). During EAE, CD138+ plasma cells were also the main source of B-cell-derived IL-35 and IL-10. Collectively, our data show the importance of IL-35-producing B cells in regulation of immunity and highlight IL-35 production by B cells as a potential therapeutic target for autoimmune and infectious diseases. This study reveals the central role of activated B cells, particularly plasma cells, and their production of cytokines in the regulation of immune responses in health and disease.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.221
Teacher spread0.218 · 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

Citations1,016
Published2014
Admission routes2
Has abstractno

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