Putative Autoantigen Leiomodin-1 Is Expressed in the Human Brain and in the Membrane Fraction of Newly Formed Neurons
Bibliographic record
Abstract
infection, but the mechanism driving this relationship is unclear. One hypothesis proposes that parasite-induced immune responses cross-react with human leiomodin-1 resulting in immune-mediated central nervous system (CNS) damage. However, as leiomodin-1 expression and epitope availability in human neurons remains uncharacterized, the relevance of leiomodin-1 autoimmunity is unknown. Leiomodin-1 transcript expression was assessed in silico using publicly available ribonucleic acid (RNA) sequencing databases and in tissue by in situ hybridization and quantitative polymerase chain reaction. Abundance and subcellular localization were examined by cell fractionation and immunoblotting. Leiomodin-1 transcripts were expressed in cells of the CNS, including neurons and astrocytes. Protein was detectable from all brain regions examined as well as from representative cell lines and in vitro differentiated neurons and astrocytes. Leiomodin-1 was expressed on the membrane of newly formed neurons, but not neural progenitor cells or mature neurons. Importantly, leiomodin-1 antibodies were only toxic to cells expressing leiomodin-1 on the membrane. Our findings provide evidence that leiomodin-1 is expressed in human neurons and glia. Furthermore, we show membrane expression mediates leiomodin-1 antibody toxicity, suggesting these antibodies may play a role in pathogenesis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".