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Record W7161937866 · doi:10.82308/24364

Genetics of hereditary spastic paraplegia

2021· dissertation· en· W7161937866 on OpenAlexaboutno aff
Parizad Varghaei

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldNeuroscience
TopicHereditary Neurological Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsHereditary spastic paraplegiaGenetic heterogeneitySanger sequencingExome sequencingSpasticityGeneGenetic counselingGenetic testingSpastic

Abstract

fetched live from OpenAlex

Background: Hereditary spastic paraplegias (HSPs) are a heterogeneous group of rare neurodegenerative disorders characterized by lower limb spasticity and weakness, with over 80 causative genes and loci described. The most common type of HSP, caused by heterozygous SPAST mutations, is spastic paraplegia type 4 (SPG4), with highly heterogeneous clinical manifestations.A genetic diagnosis cannot be made for over half of HSP cases, even with the use of whole exome sequencing (WES). There are some potential explanations for these undiagnosed cases: WES cannot detect some genetic alterations such as copy number variations (CNVs); variants may be in new genes or genes associated with other conditions, such as GCH1, which has been described in dopa-responsive dystonia and Parkinson’s disease.Objectives: The general objective of this study was to better understand HSP and its novel features and improve its genetic diagnostic yield. Aim 1) Studying the genotype-phenotype correlation and clarifying novel clinical and genetic aspects of SPG4. Aim 2) Identifying the genetic diagnosis of three genetically unsolved HSP cases and introducing possible treatment options.Methods: As a part of CanHSP, a Canadian consortium for the study of HSP, 696 HSP patients from 431 families were recruited and assessed. HSP-gene panel sequencing was performed on 379 cases, and 400 patients from 291 families underwent WES. To analyze the WES data, a list of HSP-related genes or genes associated with similar neurological disorders was used. The suspicious mutations were validated by Sanger sequencing. For detection of CNVs, Multiplex ligation-dependent probe amplification was used.Results: In the SPG4 study, 157 SPG4 patients from 65 families were identified to carry 41 different SPAST mutations, 6 of which were never reported before, as well as 6 CNVs. We reported three de novo cases, a family with probable compound heterozygous mutation, a case with homozygous mutation, three cases with pathogenic synonymous mutations, and novel or rarely reported signs and symptoms seen in SPG4 patients.Among undiagnosed cases, we identified three patients with heterozygous GCH1 mutations: monozygotic twins carrying a novel, in-frame deletion, p.(Ser77_Leu82del); and a case with a p.(Val205Glu) variant. The variants were predicted to be likely pathogenic and pathogenic respectively. All patients presented with childhood-onset lower limb spasticity, abnormal plantar responses, and hyperreflexia. The monozygotic twins presented with different manifestations, and both responded well to levodopa treatment. Structural analysis of the variants indicated a disruptive effect, and pathway enrichment analysis suggested that GCH1 shares processes and pathways with other HSP-associated genes.Conclusion: As a heterogeneous type of HSP, SPG4 could present with diverse clinical manifestations and genetic features. In some cases, CNVs, de novo mutation, pathogenic synonymous mutations, and biallelic inheritance should be considered.We suggest considering mutation in genes associated with other disorders, such as GCH1, in the diagnosis process of patients presenting with HSP symptoms, as well as levodopa trials in their treatment. The clinical differences seen between the monozygotic twins could suggest the role of environmental factors, epigenetics, and stochasticity in the presentation of HSP

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.035
GPT teacher head0.282
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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