Effects of gene therapy on cardiovascular symptoms of lysosomal storage diseases

Poletto, Édina; Pasqualim, Gabriela; Giugliani, Roberto; Matte, Úrsula da Silveira; Baldo, Guilherme

doi:10.1590/1678-4685-gmb-2018-0100

Cited by 6 publications

(3 citation statements)

References 153 publications

(172 reference statements)

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“…The IDUA gene encodes alpha-L-iduronidase, which degrades glycosaminoglycans (GAGs) in the lysosome. Mutations in the IDUA gene cause mucopolysaccharidosis type I (MPS I), a lysosomal storage disease 40 . These findings also highlight the power of pQTL to help in determining the causal gene in a multi-loci genomic region associated with disease risk.…”

Section: Mendelian Randomization To Identify Novel Biomarkers and Drumentioning

confidence: 99%

Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traits

Yang

Farias

Ibáñez

et al. 2020

Preprint

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Expression quantitative trait loci (eQTL) mapping has successfully resolved some genome-wide association study (GWAS) loci for complex traits. However, there is a need for implementing additional "omic" approaches to untangle additional loci and provide a biological context for GWAS signals. We generated a detailed landscape of the genomic architecture of protein levels in multiple neurologically relevant tissues (brain, cerebrospinal fluid (CSF) and plasma), by profiling thousands of proteins in a large and well-characterized cohort. We identified 274, 127 and 32 protein quantitative loci (pQTL) for CSF, plasma and brain respectively. We demonstrated that cis-pQTL are more likely to be shared across tissues but trans-pQTL are tissue-specific. Between 78% to 87% of pQTL are not eQTL, indicating that protein levels have a different genetic architecture than gene expression. By combining our pQTL with Mendelian Randomization approaches we identified potential novel biomarkers and drug targets for neurodegenerative diseases including Alzheimer disease and frontotemporal dementia. In the context of personalized medicine, these results highlight the need for implementing additional functional genomic approaches beyond gene expression in order to understand the biology of complex traits, and to identify novel biomarkers and potential drug targets for those traits.

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Section: Mendelian Randomization To Identify Novel Biomarkers and Drumentioning

confidence: 99%

Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traits

Yang

Farias

Ibáñez

et al. 2020

Preprint

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“…The first attempts at in vivo gene therapy used adenovirus because of its versatility. [ 77 ] However, toxicity reports led to the withdrawal of this approach. Hepatocellular necrosis emerged as a relevant drawback.…”

Section: Treatmentmentioning

confidence: 99%

Cardiovascular Involvement in Fabry’s Disease: New Advances in Diagnostic Strategies, Outcome Prediction and Management

et al. 2023

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Cardiovascular involvement is common in Fabry’s disease and is the leading cause of morbidity and mortality. The research is focused on identifying diagnostic clues suggestive of cardiovascular involvement in the preclinical stage of the disease through clinical and imaging markers. Different pathophysiologically driven therapies are currently or will soon be available for the treatment of Fabry’s disease, with the most significant benefit observed in the early stages of the disease. Thus, early diagnosis and risk stratification for adverse outcomes are crucial to determine when to start an aetiological treatment. This review describes the cardiovascular involvement in Fabry’s disease, focusing on the advances in diagnostic strategies, outcome prediction and disease management.

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“…The IDUA gene encodes alpha-L-iduronidase, which degrades glycosaminoglycans (GAGs) in the lysosome. Mutations in the IDUA gene cause mucopolysaccharidosis type I (MPS I), a lysosomal storage disease 46 . These findings also highlight the power of pQTL to help in determining the causal gene in a multi-loci genomic region associated with disease risk.…”

Section: Mendelian Randomization To Identify Novel Biomarkers and Drumentioning

confidence: 99%

Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traits

Cruchaga¹,

Yang²,

Farias³

et al. 2020

Preprint

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Understanding the tissue-specific genetic architecture of protein levels is instrumental to understand the biology of health and disease. We generated a genomic atlas of protein levels in multiple neurologically relevant tissues (380 brain, 835 cerebrospinal fluid (CSF) and 529 plasma), by profiling thousands of proteins (713 CSF, 931 plasma and 1079 brain) in a large and well-characterized cohort. We identified 274, 127 and 32 protein quantitative loci (pQTL) for CSF, plasma and brain respectively. cis-pQTL were more likely to be shared across tissues but trans-pQTL tend to be tissue-specific. Between 44% to 68.2% of the pQTL do not colocalize with expression, splicing, methylation or histone QTLs, indicating that protein levels have a different genetic architecture to those that regulate gene expression. By combining our pQTL with Mendelian Randomization approaches we identified potential novel biomarkers and drug targets for neurodegenerative diseases including Alzheimer disease and frontotemporal dementia. Here we present the first multi-tissue study yielding hundred of novel pQTLs. This data will be instrumental to identify the functional gene from GWAS signals, identify novel biological protein-protein interactions, identify novel potential biomarkers and drug targets for complex traits.

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Effects of gene therapy on cardiovascular symptoms of lysosomal storage diseases

Cited by 6 publications

References 153 publications

Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traits

Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traits

Cardiovascular Involvement in Fabry’s Disease: New Advances in Diagnostic Strategies, Outcome Prediction and Management

Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traits

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