Deficiency of MALT1 Paracaspase Activity Results in Unbalanced Regulatory and Effector T and B Cell Responses Leading to Multiorgan Inflammation
Bibliographic record
Abstract
The paracaspase MALT1 plays an important role in immune receptor-driven signaling pathways leading to NF-κB activation. MALT1 promotes signaling by acting as a scaffold, recruiting downstream signaling proteins, as well as by proteolytic cleavage of multiple substrates. However, the relative contributions of these two different activities to T and B cell function are not well understood. To investigate how MALT1 proteolytic activity contributes to overall immune cell regulation, we generated MALT1 protease-deficient mice (Malt1(PD/PD)) and compared their phenotype with that of MALT1 knockout animals (Malt1(-/-)). Malt1(PD/PD) mice displayed defects in multiple cell types including marginal zone B cells, B1 B cells, IL-10-producing B cells, regulatory T cells, and mature T and B cells. In general, immune defects were more pronounced in Malt1(-/-) animals. Both mouse lines showed abrogated B cell responses upon immunization with T-dependent and T-independent Ags. In vitro, inactivation of MALT1 protease activity caused reduced stimulation-induced T cell proliferation, impaired IL-2 and TNF-α production, as well as defective Th17 differentiation. Consequently, Malt1(PD/PD) mice were protected in a Th17-dependent experimental autoimmune encephalomyelitis model. Surprisingly, Malt1(PD/PD) animals developed a multiorgan inflammatory pathology, characterized by Th1 and Th2/0 responses and enhanced IgG1 and IgE levels, which was delayed by wild-type regulatory T cell reconstitution. We therefore propose that the pathology characterizing Malt1(PD/PD) animals arises from an immune imbalance featuring pathogenic Th1- and Th2/0-skewed effector responses and reduced immunosuppressive compartments. These data uncover a previously unappreciated key function of MALT1 protease activity in immune homeostasis and underline its relevance in human health and disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".