Immunity in amyotrophic lateral sclerosis: blurred lines between excessive inflammation and inefficient immune responses
Bibliographic record
Abstract
Abstract Despite wide genetic, environmental and clinical heterogeneity in amyotrophic lateral sclerosis, a rapidly fatal neurodegenerative disease targeting motoneurons, neuroinflammation is a common finding. It is marked by local glial activation, T cell infiltration and systemic immune system activation. The immune system has a prominent role in the pathogenesis of various chronic diseases, hence some of them, including some types of cancer, are successfully targeted by immunotherapeutic approaches. However, various anti-inflammatory or immunosuppressive therapies in amyotrophic lateral sclerosis have failed. This prompted increased scrutiny over the immune-mediated processes underlying amyotrophic lateral sclerosis. Perhaps the biggest conundrum is that amyotrophic lateral sclerosis pathogenesis exhibits features of three otherwise distinct immune dysfunctions—excessive inflammation, autoimmunity and inefficient immune responses. Epidemiological and genome-wide association studies show only minimal overlap between amyotrophic lateral sclerosis and autoimmune diseases, so excessive inflammation is usually thought to be secondary to protein aggregation, mitochondrial damage or other stresses. In contrast, several recently characterized amyotrophic lateral sclerosis-linked mutations, including those in TBK1, OPTN, CYLD and C9orf72, could lead to inefficient immune responses and/or damage pile-up, suggesting that an innate immunodeficiency may also be a trigger and/or modifier of this disease. In such cases, non-selective immunosuppression would further restrict neuroprotective immune responses. Here we discuss multiple layers of immune-mediated neuroprotection and neurotoxicity in amyotrophic lateral sclerosis. Particular focus is placed on individual patient mutations that directly or indirectly affect the immune system, and the mechanisms by which these mutations influence disease progression. The topic of immunity in amyotrophic lateral sclerosis is timely and relevant, because it is one of the few common and potentially malleable denominators in this heterogenous disease. Importantly, amyotrophic lateral sclerosis progression has recently been intricately linked to patient T cell and monocyte profiles, as well as polymorphisms in cytokine and chemokine receptors. For this reason, precise patient stratification based on immunophenotyping will be crucial for efficient therapies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".