Diagnostic anoctamin‐5 protein defect in patients with ANO5‐mutated muscular dystrophy
Bibliographic record
Abstract
AIMS: Previously, detection of ANO5 protein has been complicated by unspecific antibodies, most of which have not identified the correct protein. The aims of the study were to specify ANO5 protein expression in human skeletal muscle, and to investigate if the ANO5 protein levels are affected by different ANO5 mutations in anoctaminopathy patients. METHODS: Four different antibodies were tested for ANO5 specificity. A sample preparation method compatible with membrane proteins, combined with tissue fractionation was used to determine ANO5 expression in cell cultures expressing ANO5, in normal muscles and eight patient biopsies with six different ANO5 mutations in homozygous or compound heterozygous states, and in other dystrophies. RESULTS: Only one specific monoclonal N-terminal ANO5 antibody was efficient in detecting the protein, showing that ANO5 is expressed as a single 107 kD polypeptide in human skeletal muscle. The truncating mutations c.191dupA and c.1261C>T were found to abolish ANO5 expression, whereas the studied point mutations had variable effects; however, all the ANO5 mutations resulted in clearly reduced ANO5 expression in the patient muscle membrane fraction. Attempts to detect ANO5 using immunohistochemistry were not yet successful. CONCLUSIONS: The data presented here indicate that the ANO5 protein expression is decreased in ANO5-mutated muscular dystrophy and that most of the non-truncating pathogenic ANO5 mutations likely destabilize the protein and cause its degradation. The method described here allows direct analysis of human ANO5 protein, which can be used in diagnostics, for evaluating the pathogenicity of the potentially harmful ANO5 variants of uncertain significance.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".