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Record W2793231860 · doi:10.1101/286500

A mutation-independent approach via transcriptional upregulation of a disease modifier gene rescues muscular dystrophy <i>in vivo</i>

2018· preprint· en· W2793231860 on OpenAlexafffund
Dwi U. Kemaladewi, Prabhpreet S. Bassi, Kyle Lindsay, Steven Erwood, Ella Hyatt, Ryan M. Marks, Kinga I. Gawlik, Madeleine Durbeej, Evgueni A. Ivakine, Ronald D. Cohn

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2018
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsUniversity of TorontoHospital for Sick Children
FundersCure CMDCanadian Institutes of Health ResearchHospital for Sick ChildrenRare Disease FoundationNatural Sciences and Engineering Research Council of CanadaMuscular Dystrophy Association
KeywordsDownregulation and upregulationBiologyCongenital muscular dystrophyGenetic enhancementMuscular dystrophyCancer researchTransgeneCRISPRGeneMolecular biologyGenetics

Abstract

fetched live from OpenAlex

Introductory paragraph Identification of protective and/or pathogenic genetic modifiers provides important insight into the heterogeneity of disease presentations in individuals affected by neuromuscular disorders (NMDs), despite having well-defined pathogenic variants. Targeting modifier genes to improve disease phenotypes could be especially beneficial in cases where the causative genes are large, structurally complex and the mutations are heterogeneous. Here, we report a mutation-independent strategy to upregulate expression of a compensatory disease-modifying gene in Congenital Muscular Dystrophy type 1A (MDC1A) using a CRISPR/dCas9-based transcriptional activation system. MDC1A is caused by nonfunctional Laminin α2, which compromises muscle fibers stability and axon myelination in peripheral nerves 1 . Transgenic overexpression of Lama1 , encoding a structurally similar protein Laminin α1, ameliorates muscle wasting and paralysis in the MDC1A mouse models, demonstrating its role as a protective disease modifier 2 . Yet, upregulation of Lama1 as a postnatal gene therapy is hampered by its large size, which exceeds the current genome packaging capacity of clinically relevant delivery vehicles such as adeno-associated viral vectors (AAVs). In this study, we sought to upregulate Lama1 using CRISPR/dCas9-based transcriptional activation system, comprised of catalytically inactive S. aureus Cas9 (dCas9) fused to VP64 transactivation domains and sgRNAs targeting the Lama1 promoter. We first demonstrated robust upregulation of Lama1 in myoblasts, and following AAV9-mediated intramuscular delivery, in skeletal muscles of dy 2j /dy 2j mouse model of MDC1A. We therefore assessed whether systemic upregulation of Lama1 would yield therapeutic benefits in dy 2j /dy 2j mice. When the intervention was started early in pre-symptomatic dy 2j /dy 2j mice, Lama1 upregulation prevented muscle fibrosis and hindlimb paralysis. An important question for future therapeutic approaches for a variety of disorders concerns the therapeutic window and phenotypic reversibility. This is particularly true for muscular dystrophies as it has long been hypothesized that fibrotic changes in skeletal muscle represent an irreversible disease state that would impair any therapeutic intervention at advanced stages of the disease. Here, we demonstrate that dystrophic features and disease progression were significantly improved and partially reversed when the treatment was initiated in symptomatic 3-week old dy 2j /dy 2j mice with already-apparent hind limb paralysis and significant muscle fibrosis. Collectively, our data demonstrate the feasibility and therapeutic benefit of CRISPR/dCas9-mediated modulation of a disease modifier gene, which opens up an entirely new and mutation-independent treatment approach for all MDC1A patients. Moreover, this treatment strategy provides evidence that muscle fibrosis can be reversible, thus extending the therapeutic window for this disorder. Our data provide a proof-of-concept strategy that can be applied to a variety of disease modifier genes and a powerful therapeutic approach for various inherited and acquired diseases.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.009
GPT teacher head0.207
Teacher spread0.199 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

Quick stats

Citations4
Published2018
Admission routes2
Has abstractyes

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