Evidence map›Paper›PMID 37628888›Full record

ArticleInternational journal of molecular sciences2023

Modeling Sarcoglycanopathy in

Francesco Dalla Barba, Michela Soardi, Leila Mouhib, Giovanni Risato, Eylem Emek Akyürek, Tyrone Lucon-Xiccato, Martina Scano, Alberto Benetollo, Roberta Sacchetto, Isabelle Richard and 4 more

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 3 institutions in 3 countries.

Francesco Dalla BarbaDepartment of Biomedical Sciences, University of Padova, Via U. Bassi 58/b, 35131 Padova, Italy.ORCID 0009-0008-3277-4242
Michela SoardiDepartment of Biomedical Sciences, University of Padova, Via U. Bassi 58/b, 35131 Padova, Italy.
Leila MouhibDepartment of Biomedical Sciences, University of Padova, Via U. Bassi 58/b, 35131 Padova, Italy.
Giovanni RisatoDepartment of Biology, University of Padova, Via U. Bassi 58/b, 35131 Padova, Italy.ORCID 0000-0003-0651-7409
Eylem Emek AkyürekDepartment of Comparative Biomedicine and Food Science, University of Padova, Agripolis, Legnaro, 35020 Padova, Italy.ORCID 0000-0002-7054-5116
Tyrone Lucon-XiccatoDepartment of Life Sciences and Biotechnology, University of Ferrara, Via Luigi Borsari 46, 44121 Ferrara, Italy.
Martina ScanoDepartment of Biomedical Sciences, University of Padova, Via U. Bassi 58/b, 35131 Padova, Italy.ORCID 0000-0002-8761-1730
Alberto BenetolloDepartment of Biomedical Sciences, University of Padova, Via U. Bassi 58/b, 35131 Padova, Italy.ORCID 0000-0002-1848-0677
Roberta SacchettoDepartment of Comparative Biomedicine and Food Science, University of Padova, Agripolis, Legnaro, 35020 Padova, Italy.
Isabelle RichardGenethon, F-91002 Evry, France.ORCID 0000-0002-6505-446X
Francesco ArgentonDepartment of Biology, University of Padova, Via U. Bassi 58/b, 35131 Padova, Italy.ORCID 0000-0002-0803-8236
Cristiano BertolucciDepartment of Life Sciences and Biotechnology, University of Ferrara, Via Luigi Borsari 46, 44121 Ferrara, Italy.ORCID 0000-0003-0252-3107
Marcello CarottiDepartment of Biomedical Sciences, University of Padova, Via U. Bassi 58/b, 35131 Padova, Italy.ORCID 0000-0002-7742-339X
Dorianna SandonàDepartment of Biomedical Sciences, University of Padova, Via U. Bassi 58/b, 35131 Padova, Italy.ORCID 0000-0002-9908-2442
University of Padua · ITUniversity of Ferrara · ITInserm · FR

Funding

Association Françoise contre les Myopathies 18620Association Françoise contre les Myopathies 23765Muscular Dystrophy Association 577888: doi.org/10.55762/pc.gr.81540University of Ferrara research grants FAR2022
6 · The paper itself

Abstract

Sarcoglycanopathies, also known as limb girdle muscular dystrophy 3-6, are rare muscular dystrophies characterized, although heterogeneous, by high disability, with patients often wheelchair-bound by late adolescence and frequently developing respiratory and cardiac problems. These diseases are currently incurable, emphasizing the importance of effective treatment strategies and the necessity of animal models for drug screening and therapeutic verification. Using the CRISPR/Cas9 genome editing technique, we generated and characterized δ-sarcoglycan and β-sarcoglycan knockout zebrafish lines, which presented a progressive disease phenotype that worsened from a mild larval stage to distinct myopathic features in adulthood. By subjecting the knockout larvae to a viscous swimming medium, we were able to anticipate disease onset. The δ-SG knockout line was further exploited to demonstrate that a δ-SG missense mutant is a substrate for endoplasmic reticulum-associated degradation (ERAD), indicating premature degradation due to protein folding defects. In conclusion, our study underscores the utility of zebrafish in modeling sarcoglycanopathies through either gene knockout or future knock-in techniques. These novel zebrafish lines will not only enhance our understanding of the disease's pathogenic mechanisms, but will also serve as powerful tools for phenotype-based drug screening, ultimately contributing to the development of a cure for sarcoglycanopathies.

Indexed as

Muscular Dystrophies, Limb-GirdleSarcoglycanopathiesAnimalsDrug Evaluation, PreclinicalEndoplasmic Reticulum-Associated DegradationLarvaZebrafishanimal modelsgenome editingknockoutlimb girdle muscular dystrophiesβ-sarcoglycanδ-sarcoglycan

Identifiers

PMID37628888
PMCPMC10454440
OpenAlexW4385759542

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.