AI cloud-based end-to-end technology for accurate, fast & affordable diagnosis for Spinal Muscular Atrophy in Paraguay
Date Issued
2024-12-01
Author(s)
Garrido Navas, Carmen
Martinez de Filartiga, Maria Teresa
Zarate, Ruth
Casartelli, Marco
Darío, Rubén
Chulián Prado Duré, Sara
Chaushevska, Marija
Madjarov, Gjorgji
Velkoski, Zoran
Kyriakidis, Chris
Abstract
Spinal Muscular Atrophy (SMA), a progressive, recessive neuromuscular disease with varying presentations of onset and severity, is
caused by bi-allelic mutations in the SMN1 gene (deletion of the gene in 95% of the cases). The severity is determined by the number
of SMN2 copies. SMN1 and SMN2 only have 5 different nucleotides in the whole sequence. Due to its high clinical and genetic
heterogeneity and low prevalence (1/10,000 births), diagnosis and treatment are highly challenging.
Genetic diagnosis is usually made using RT-PCR for SMN1 (and sometimes SMN2) after clinical symptoms suggest the condition. This
procedure is costly, slow, and inefficient, as many of the clinical symptoms overlap with other neuromuscular diseases (DMD, BMD, or
multiple sclerosis), increasing the misdiagnosis rate.
Our proposed solution combines targeted ONT sequencing and our Phivea® platform to discriminate between SMN1 and SMN2,
ascertain the number of copies per gene, and identify a point mutation (C>T) typically occurring in the telomeric region.
caused by bi-allelic mutations in the SMN1 gene (deletion of the gene in 95% of the cases). The severity is determined by the number
of SMN2 copies. SMN1 and SMN2 only have 5 different nucleotides in the whole sequence. Due to its high clinical and genetic
heterogeneity and low prevalence (1/10,000 births), diagnosis and treatment are highly challenging.
Genetic diagnosis is usually made using RT-PCR for SMN1 (and sometimes SMN2) after clinical symptoms suggest the condition. This
procedure is costly, slow, and inefficient, as many of the clinical symptoms overlap with other neuromuscular diseases (DMD, BMD, or
multiple sclerosis), increasing the misdiagnosis rate.
Our proposed solution combines targeted ONT sequencing and our Phivea® platform to discriminate between SMN1 and SMN2,
ascertain the number of copies per gene, and identify a point mutation (C>T) typically occurring in the telomeric region.