Reply: Autosomal recessive cerebellar ataxia caused by a homozygous mutation in<i>PMPCA</i>
Bibliographic record
Abstract
Sir, The importance of nuclear-encoded mitochondrial peptide processing to normal cerebellar function in humans has recently been recognized. We identified bi-allelic mutations in PMPCA in 17 patients with non-progressive cerebellar ataxia (NPCA) from four families, and demonstrated that the Ala377Thr mutation, found in the homozygous state in 16 patients in our series, impacts both the level of the alpha subunit encoded by PMPCA (α-MPP) and the function of mitochondrial processing peptidase ( Jobling et al. , 2015 ). In their Letter to the Editor, Choquet et al. (2016) report two siblings from a French Canadian family with cerebellar ataxia and homozygous c.766G>A (p.Val256Met) mutation in PMPCA . Functional studies on immortalized lymphoblasts revealed that the Val256Met mutation disrupts the normal processing of frataxin with accumulation of FXN42-210, similar to the Ala377Thr mutation in our patients. This additional family further confirms the importance of PMPCA and the mitochondrial protein precursor cleavage process in the pathogenesis of cerebellar ataxia. The Val256Met mutation does not appear to have an effect on the levels of α-MPP, which contrasts with the decreased levels of α-MPP caused by the Ala377Thr mutation. The reason for this is unexplained, and deserves further study. Of note, Agrawal et al. (2014) observed decreased levels of α-MPP in a patient who was compound heterozygous for c.1066G>A (p.G356S) and c.G1129G>A (p.A377T) mutations in PMPCA . Mature frataxin was reduced while unprocessed frataxin, presumably FXN42-210, was increased. Whether mutations in close proximity to the glycine-rich loop of α-MPP have a detrimental effect on α-MPP levels in addition to α-MPP function, and if so, the exact mechanism by which this occurs, remain to be elucidated. It is entirely possible that variants in unrelated genes may contribute to modifying the expression of the gene or function of the protein, and analysis of exome data, particularly in consanguineous families, should be performed with this in mind. The importance of accurate clinical phenotyping cannot be underestimated, as pointed out by Horvath and Chinnery (2015) . It is interesting that Choquet et al. (2016) consider their patients to have a milder phenotype compared to those reported with the A377T mutation, particularly as they describe their patients as having slowly progressive ataxia. In the absence of a detailed description of their patients’ clinical course over the years, preferably using a standardized means of clinical evaluation, as well as serial imaging, which demonstrates progressive cerebellar volume loss, it is difficult to determine whether the cerebellar symptoms in these patients are truly progressive. Nevertheless, we agree that an appreciation of the clinical variability within NPCA should be emphasized, as this subgroup of disorders—NPCA associated with a stable cerebellar atrophy pattern on brain imaging—is not well recognized. The majority of patients present with hypotonia and gross motor delay, with or without initial worsening of their symptoms, which then stabilize, and do not progress even after decades, as seen in the adult patients described in Jobling et al. (2015) . It is critical for clinicians to recognize this combination of variable clinical course in early childhood, followed by stabilization of the neurological symptoms, in addition to stable cerebellar atrophy on serial imaging as NPCA. Accurate recognition of this condition has significant implications for prognosis, genetic testing, and counselling for these patients and their families. The family described by Agrawal and colleagues (2014) appears to have a classical mitochondrial presentation including cardiomyopathy, seizures, visual impairment and hypotonia. As more patients are diagnosed with mutations in PMPCA , it is only a matter of time before the spectrum of clinical phenotypes associated with PMPCA mutations broadens.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.042 | 0.029 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".