Towards development of a statistical framework to evaluate myotonic dystrophy type 1 mRNA biomarkers in the context of a clinical trial

Kurkiewicz, Adam; Cooper, Anneli; McIlwaine, Emily; Cumming, Sarah A.; Adam, Berit; Krahe, Ralf; Puymirat, Jack; Schöser, Benedikt; Timchenko, Lubov; Ashizawa, Tetsuo; Thornton, Charles A.; Rogers, Simon; McClure, John; Monckton, Darren G.

doi:10.1371/journal.pone.0231000

Cited by 7 publications

(9 citation statements)

References 27 publications

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“…The DMBDI procedures for RNA isolation and microarray performing are described elsewhere [ 27 ]. We processed the raw CEL files using the “oligo” Bioconductor R package [ 121 ] with RMA normalization [ 122 ] and filtering out features with low median intensities (less than 4 in 6 samples).…”

Section: Methodsmentioning

confidence: 99%

“…Patients were divided into groups with mild (MIRS 1–2) and severe (MIRS 3–5) neuromuscular symptoms with different DMPK repeat length characteristics. In addition, we sought to independently validate any findings in an independent cohort namely, those with equivalent MIRS/ DMPK repeat length data from the Dystrophia Myotonica Biomarker Discovery Initiative (DMBDI) mRNA microarray datasets in blood [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

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Blood Transcriptome Profiling Links Immunity to Disease Severity in Myotonic Dystrophy Type 1 (DM1)

Nieuwenhuis

Widomska

Blom³

et al. 2022

IJMS

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The blood transcriptome was examined in relation to disease severity in type I myotonic dystrophy (DM1) patients who participated in the Observational Prolonged Trial In DM1 to Improve QoL- Standards (OPTIMISTIC) study. This sought to (a) ascertain if transcriptome changes were associated with increasing disease severity, as measured by the muscle impairment rating scale (MIRS), and (b) establish if these changes in mRNA expression and associated biological pathways were also observed in the Dystrophia Myotonica Biomarker Discovery Initiative (DMBDI) microarray dataset in blood (with equivalent MIRS/DMPK repeat length). The changes in gene expression were compared using a number of complementary pathways, gene ontology and upstream regulator analyses, which suggested that symptom severity in DM1 was linked to transcriptomic alterations in innate and adaptive immunity associated with muscle-wasting. Future studies should explore the role of immunity in DM1 in more detail to assess its relevance to DM1.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blood Transcriptome Profiling Links Immunity to Disease Severity in Myotonic Dystrophy Type 1 (DM1)

Nieuwenhuis

Widomska

Blom³

et al. 2022

IJMS

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“…Mutated transcripts are mainly localized in the nucleus, where they form RNA foci and sequester RBPs such as MBNL1, -2, and -3; HNRNPH1; CUGBP1; and STAU1 [ 117 , 118 ]. This leads to the trans-deregulation of RNA metabolism-especially APA, miRNA processing, and alternative splicing processes [ 119 , 120 ]. MBNL’s implications in alternative splicing and APA are well-documented [ 121 , 122 , 123 , 124 ].…”

Section: Repeat Expansion Diseasesmentioning

confidence: 99%

Implications of Poly(A) Tail Processing in Repeat Expansion Diseases

Joachimiak

Ciesiolka

Figura

et al. 2022

Cells

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Repeat expansion diseases are a group of more than 40 disorders that affect mainly the nervous and/or muscular system and include myotonic dystrophies, Huntington’s disease, and fragile X syndrome. The mutation-driven expanded repeat tract occurs in specific genes and is composed of tri- to dodeca-nucleotide-long units. Mutant mRNA is a pathogenic factor or important contributor to the disease and has great potential as a therapeutic target. Although repeat expansion diseases are quite well known, there are limited studies concerning polyadenylation events for implicated transcripts that could have profound effects on transcript stability, localization, and translation efficiency. In this review, we briefly present polyadenylation and alternative polyadenylation (APA) mechanisms and discuss their role in the pathogenesis of selected diseases. We also discuss several methods for poly(A) tail measurement (both transcript-specific and transcriptome-wide analyses) and APA site identification—the further development and use of which may contribute to a better understanding of the correlation between APA events and repeat expansion diseases. Finally, we point out some future perspectives on the research into repeat expansion diseases, as well as APA studies.

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“…Longer CTG expansions are associated with greater symptom severity and earlier age at onset. To directly quantify the treatment effect by the reduction of the CTG repeat, Kurkiewicz et al (2020) developed a model based on partial least squares regression (PLSR), that is able to predict the size of the DM1CTG repeat and the effect that has on mRNA expression.…”

Section: Epigenetics and Ai In Rds: Existing Literaturementioning

confidence: 99%

“…Supervised analysis has been restricted to biological problems in which a good balance between variables and data is observed ( Esteban-Medina et al, 2019 ). In this work, most supervised tools have been applied for diagnosis ( Mo et al, 2014 ; Crippa et al, 2014 ; Fu et al, 2014 ; Farh et al, 2015 ; McMaster et al, 2018 ; Aref-Eshghi et al, 2019 ; Pranckėnienė et al, 2019 ; Cochran et al, 2020 ; Kurkiewicz et al, 2020 ). In RDs, small sample sizes allied to complex levels of data structure and differences among the characteristics of patients can hinder the application of AI tools.…”

Section: Challenges and Future Perspectivesmentioning

confidence: 99%

Artificial Intelligence in Epigenetic Studies: Shedding Light on Rare Diseases

Brasil¹,

Neves²,

Rijoff³

et al. 2021

Front. Mol. Biosci.

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More than 7,000 rare diseases (RDs) exist worldwide, affecting approximately 350 million people, out of which only 5% have treatment. The development of novel genome sequencing techniques has accelerated the discovery and diagnosis in RDs. However, most patients remain undiagnosed. Epigenetics has emerged as a promise for diagnosis and therapies in common disorders (e.g., cancer) with several epimarkers and epidrugs already approved and used in clinical practice. Hence, it may also become an opportunity to uncover new disease mechanisms and therapeutic targets in RDs. In this “big data” age, the amount of information generated, collected, and managed in (bio)medicine is increasing, leading to the need for its rapid and efficient collection, analysis, and characterization. Artificial intelligence (AI), particularly deep learning, is already being successfully applied to analyze genomic information in basic research, diagnosis, and drug discovery and is gaining momentum in the epigenetic field. The application of deep learning to epigenomic studies in RDs could significantly boost discovery and therapy development. This review aims to collect and summarize the application of AI tools in the epigenomic field of RDs. The lower number of studies found, specific for RDs, indicate that this is a field open to expansion, following the results obtained for other more common disorders.

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Towards development of a statistical framework to evaluate myotonic dystrophy type 1 mRNA biomarkers in the context of a clinical trial

Cited by 7 publications

References 27 publications

Blood Transcriptome Profiling Links Immunity to Disease Severity in Myotonic Dystrophy Type 1 (DM1)

Blood Transcriptome Profiling Links Immunity to Disease Severity in Myotonic Dystrophy Type 1 (DM1)

Implications of Poly(A) Tail Processing in Repeat Expansion Diseases

Artificial Intelligence in Epigenetic Studies: Shedding Light on Rare Diseases

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