1,945
Views
1
CrossRef citations to date
0
Altmetric
Research Paper

Circulating small RNA signatures differentiate accurately the subtypes of muscular dystrophies: small-RNA next-generation sequencing analytics and functional insights

, , , , , , , , , , , , , , , , & show all
Pages 507-518 | Received 14 Jul 2021, Accepted 23 Mar 2022, Published online: 07 Apr 2022

References

  • Emery AE. The muscular dystrophies. Lancet. 2002;359(9307):687–695.
  • Yoshioka M, Okuno T, Honda Y, et al. Central nervous system involvement in progressive muscular dystrophy. Arch Dis Child. 1980;55(8):589–594.
  • Hoffman EP, Brown RH Jr., Kunkel LM. Dystrophin: the protein product of the Duchenne muscular dystrophy locus. Cell. 1987;51(6):919–928.
  • Hathout Y, Seol H, Han MHJ, et al. Clinical utility of serum biomarkers in Duchenne muscular dystrophy. Clin Proteomics. 2016;13(1):9.
  • Harper PS. Postoperative complications in myotonic dystrophy. Lancet. 1989;2(8674):1269.
  • Aslanidis C, Jansen G, Amemiya C, et al. Cloning of the essential myotonic dystrophy region and mapping of the putative defect. Nature. 1992;355(6360):548–551.
  • Larkin K, Fardaei M. Myotonic dystrophy–a multigene disorder. Brain Res Bull. 2001;56(3–4):389–395.
  • Day JW, Ricker K, Jacobsen JF, et al. Myotonic dystrophy type 2: molecular, diagnostic and clinical spectrum. Neurology. 2003;60(4):657–664.
  • Sarfarazi M, Wijmenga C, Upadhyaya M, et al. Regional mapping of facioscapulohumeral muscular dystrophy gene on 4q35: combined analysis of an international consortium. Am J Hum Genet. 1992;51(2):396–403.
  • Steel D, Main M, Manzur A, et al. Clinical features of facioscapulohumeral muscular dystrophy 1 in childhood. Dev Med Child Neurol. 2019;61(8):964–971.
  • Wijmenga C, Padberg GW, Moerer P, et al. Mapping of facioscapulohumeral muscular dystrophy gene to chromosome 4q35-qter by multipoint linkage analysis and in situ hybridization. Genomics. 1991;9(4):570–575.
  • Tawil R, Kissel JT, Heatwole C, et al. Evidence-based guideline summary: evaluation, diagnosis, and management of facioscapulohumeral muscular dystrophy: report of the guideline development, dissemination, and implementation subcommittee of the American academy of neurology and the practice issues review panel of the American association of neuromuscular & electrodiagnostic medicine. Neurology. 2015;85(4):357–364.
  • Wagner KR. Facioscapulohumeral Muscular Dystrophies. Continuum (Minneap Minn). 2019;25(6):1662–1681.
  • Tawil R, van der Maarel SM, Tapscott SJ. Facioscapulohumeral dystrophy: the path to consensus on pathophysiology. Skelet Muscle. 2014;4(1):12.
  • de Greef JC, Lemmers RJLF, Camano P, et al. Clinical features of facioscapulohumeral muscular dystrophy 2. Neurology. 2010;75(17):1548–1554.
  • Vissing J. Limb girdle muscular dystrophies: classification, clinical spectrum and emerging therapies. Curr Opin Neurol. 2016;29(5):635–641.
  • Iyadurai SJ, Kissel JT. The limb-girdle muscular dystrophies and the dystrophinopathies. Continuum (Minneap Minn). 2016;22(6):1954–1977. Muscle and Neuromuscular Junction Disorders.
  • Lasa-Elgarresta J, Mosqueira-Martín L, Naldaiz-Gastesi N, et al. Calcium mechanisms in limb-girdle muscular dystrophy with CAPN3 mutations. Int J Mol Sci. 2019;20(18):4548.
  • Pegoraro E, and Hoffman EP Limb-girdle muscular dystrophy overview - RETIRED CHAPTER, FOR HISTORICAL REFERENCE ONLY. In: Adam MP (ed.). GeneReviews((R)). Seattle (WA): GeneReviews((R))[Internet]; 2000.
  • Guiraud S, Aartsma-Rus A, Vieira NM, et al. The pathogenesis and therapy of muscular dystrophies. Annu Rev Genomics Hum Genet. 2015;16(1):281–308.
  • Wicklund MP, Kissel JT. The limb-girdle muscular dystrophies. Neurol Clin. 2014;32(3):729–749. ix.
  • Vihola A, Bassez G, Meola G, et al. Histopathological differences of myotonic dystrophy type 1 (DM1) and PROMM/DM2. Neurology. 2003;60(11):1854–1857.
  • Chemello F, Wang Z, Li H, et al. Degenerative and regenerative pathways underlying Duchenne muscular dystrophy revealed by single-nucleus RNA sequencing. Proc Natl Acad Sci U S A. 2020;117(47):29691–29701.
  • Banerji CRS, Henderson D, Tawil RN, et al. Skeletal muscle regeneration in facioscapulohumeral muscular dystrophy is correlated with pathological severity. Hum Mol Genet. 2020;29(16):2746–2760.
  • Mercuri E, Muntoni F. Muscular dystrophies. Lancet. 2013;381(9869):845–860.
  • Machuca-Tzili L, Brook D, Hilton-Jones D. Clinical and molecular aspects of the myotonic dystrophies: a review. Muscle Nerve. 2005;32(1):1–18.
  • Lovering RM, Porter NC, Bloch RJ. The muscular dystrophies: from genes to therapies. Phys Ther. 2005;85(12):1372–1388.
  • Kirby TJ, Chaillou T, McCarthy JJ. The role of microRNAs in skeletal muscle health and disease. Front Biosci (Landmark Ed). 2015;20(1):37–77.
  • Koutsoulidou A, Mastroyiannopoulos NP, Furling D, et al. Expression of miR-1, miR-133a, miR-133b and miR-206 increases during development of human skeletal muscle. BMC Dev Biol. 2011;11(1):34.
  • Koutalianos D, Koutsoulidou A, Mastroyiannopoulos NP, et al. MyoD transcription factor induces myogenesis by inhibiting Twist-1 through miR-206. J Cell Sci. 2015;128(19):3631–3645.
  • Masi LN, Serdan TDA, Levada-Pires AC, et al. Regulation of gene expression by exercise-related micrornas. Cell Physiol Biochem. 2016;39(6):2381–2397.
  • Hanke M, Hoefig K, Merz H, et al. A robust methodology to study urine microRNA as tumor marker: microRNA-126 and microRNA-182 are related to urinary bladder cancer. Urol Oncol. 2010;28(6):655–661.
  • Gilad S, Meiri E, Yogev Y, et al. Serum microRNAs are promising novel biomarkers. PLoS One. 2008;3(9):e3148.
  • Li LM, Hu Z-B, Zhou Z-X, et al. Serum microRNA profiles serve as novel biomarkers for HBV infection and diagnosis of HBV-positive hepatocarcinoma. Cancer Res. 2010;70(23):9798–9807.
  • Wang K, Zhang S, Marzolf B, et al. Circulating microRNAs, potential biomarkers for drug-induced liver injury. Proc Natl Acad Sci U S A. 2009;106(11):4402–4407.
  • Hrach HC, Mangone M. miRNA Profiling for early detection and treatment of duchenne muscular dystrophy. Int J Mol Sci. 2019;20(18):4638.
  • Dardiotis E, Aloizou A-M, Siokas V, et al. The role of microRNAs in patients with amyotrophic lateral sclerosis. J Mol Neurosci. 2018;66(4):617–628.
  • Kaifer KA, Villalón E, O’Brien BS, et al. AAV9-mediated delivery of miR-23a reduces disease severity in smn2B/-SMA model mice. Hum Mol Genet. 2019;28(19):3199–3210.
  • Chen TH. Circulating microRNAs as potential biomarkers and therapeutic targets in spinal muscular atrophy. Ther Adv Neurol Disord. 2020;13:1756286420979954.
  • Koutsoulidou A, Phylactou LA. Circulating biomarkers in muscular dystrophies: disease and therapy monitoring. Mol Ther Methods Clin Dev. 2020;18:230–239.
  • Ambrose KK, Ishak T, Lian L-H, et al. Deregulation of microRNAs in blood and skeletal muscles of myotonic dystrophy type 1 patients. Neurol India. 2017;65(3):512–517.
  • Koutsoulidou A, Kyriakides TC, Papadimas GK, et al. Elevated muscle-specific miRNAs in serum of myotonic dystrophy patients relate to muscle disease progress. PLoS One. 2015;10(4):e0125341.
  • Matsuzaka Y, Kishi S, Aoki Y, et al. Three novel serum biomarkers, miR-1, miR-133a, and miR-206 for limb-girdle muscular dystrophy, facioscapulohumeral muscular dystrophy, and becker muscular dystrophy. Environ Health Prev Med. 2014;19(6):452–458.
  • Anaya-Segura MA, Rangel-Villalobos H, Martínez-Cortés G, et al. Serum levels of microRNA-206 and novel mini-STR assays for carrier detection in duchenne muscular dystrophy. Int J Mol Sci. 2016;17(8).
  • Hu J, Kong M, Ye Y, et al. Serum miR-206 and other muscle-specific microRNAs as non-invasive biomarkers for Duchenne muscular dystrophy. J Neurochem. 2014;129(5):877–883.
  • Perfetti A, Greco S, Bugiardini E, et al. Plasma microRNAs as biomarkers for myotonic dystrophy type 1. Neuromuscul Disord. 2014;24(6):509–515.
  • Vignier N, Amor F, Fogel P, et al. Distinctive serum miRNA profile in mouse models of striated muscular pathologies. PLoS One. 2013;8(2):e55281.
  • Mizuno H, Nakamura A, Aoki Y, et al. Identification of muscle-specific microRNAs in serum of muscular dystrophy animal models: promising novel blood-based markers for muscular dystrophy. PLoS One. 2011;6(3):e18388.
  • Roberts TC, Blomberg KEM, McClorey G, et al. Expression analysis in multiple muscle groups and serum reveals complexity in the microRNA transcriptome of the mdx mouse with implications for therapy. Mol Ther Nucleic Acids. 2012;1:e39.
  • Cacchiarelli D, Legnini I, Martone J, et al. miRNAs as serum biomarkers for duchenne muscular dystrophy. EMBO Mol Med. 2011;3(5):258–265.
  • Roberts TC, Godfrey C, McClorey G, et al. Extracellular microRNAs are dynamic non-vesicular biomarkers of muscle turnover. Nucleic Acids Res. 2013;41(20):9500–9513.
  • Coenen-Stass AM, Betts CA, Lee YF, et al. Selective release of muscle-specific, extracellular microRNAs during myogenic differentiation. Hum Mol Genet. 2016;25(18):3960–3974.
  • Jeanson-Leh L, Lameth J, Krimi S, et al. Serum profiling identifies novel muscle miRNA and cardiomyopathy-related miRNA biomarkers in golden retriever muscular dystrophy dogs and duchenne muscular dystrophy patients. Am J Pathol. 2014;184(11):2885–2898.
  • Zaharieva IT, Calissano M, Scoto M, et al. Dystromirs as serum biomarkers for monitoring the disease severity in duchenne muscular Dystrophy. PLoS One. 2013;8(11):e80263.
  • Coenen-Stass AML, Sork H, Gatto S, et al. Comprehensive RNA-sequencing analysis in serum and muscle reveals novel small RNA signatures with biomarker potential for DMD. Mol Ther Nucleic Acids. 2018;13:1–15.
  • Koutsoulidou A, Photiades M, Kyriakides TC, et al. Identification of exosomal muscle-specific miRNAs in serum of myotonic dystrophy patients relating to muscle disease progress. Hum Mol Genet. 2017;26(17):3285–3302.
  • Perfetti A, Greco S, Cardani R, et al. Validation of plasma microRNAs as biomarkers for myotonic dystrophy type 1. Sci Rep. 2016;6(1):38174.
  • Meyer D, Dimitriadou E, Hornik K, et al. e1071: Misc functions of the Department of Statistics (e1071). TU Wien. 2014.
  • Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–140.
  • Kundu S, Aulchenko YS, van Duijn CM, et al. PredictABEL: an R package for the assessment of risk prediction models. Eur J Epidemiol. 2011;26(4):261–264.
  • Mi H, Muruganujan A, Ebert D, et al. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res. 2019;47(D1):D419–D426.
  • Mi H, Muruganujan A, Huang X, et al. Protocol update for large-scale genome and gene function analysis with the PANTHER classification system (v.14.0). Nat Protoc. 2019;14(3):703–721.
  • Sadik N, Cruz L, Gurtner A, et al. Extracellular RNAs: a new awareness of old perspectives. Methods Mol Biol. 2018;1740:1–15.
  • Roser AE, Caldi Gomes L, Schünemann J, et al. Circulating miRNAs as diagnostic biomarkers for parkinson’s disease. Front Neurosci. 2018;12:625.
  • Filipow S, Laczmanski L. Blood circulating miRNAs as cancer biomarkers for diagnosis and surgical treatment response. Front Genet. 2019;10:169.
  • Kovanda A, Leonardis L, Zidar J, et al. Differential expression of microRNAs and other small RNAs in muscle tissue of patients with ALS and healthy age-matched controls. Sci Rep. 2018;8(1):5609.
  • Masciullo M, Iannaccone E, Bianchi MLE, et al. Myotonic dystrophy type 1 and de novo FSHD mutation double trouble: a clinical and muscle MRI study. Neuromuscul Disord. 2013;23(5):427–431.
  • Li X, Li Y, Zhao L, et al. Circulating muscle-specific miRNAs in duchenne muscular dystrophy patients. Mol Ther Nucleic Acids. 2014;3:e177.
  • Coenen-Stass AML, Wood MJA, Roberts TC. Biomarker potential of extracellular miRNAs in duchenne muscular dystrophy. Trends Mol Med. 2017;23(11):989–1001.
  • Ayalon G, Davis JQ, Scotland PB, et al. An ankyrin-based mechanism for functional organization of dystrophin and dystroglycan. Cell. 2008;135(7):1189–1200.
  • Rouillard AD, Gundersen, GW, Fernandez, NF, et al. The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database (Oxford). 2016;2016. https://doi.org/10.1093/database/baw100
  • Arandel L, Espinoza MP, Matloka M, et al. Immortalized human myotonic dystrophy muscle cell lines to assess therapeutic compounds. Dis Model Mech. 2017;10(4):487–497.
  • Geng LN, Yao Z, Snider L, et al. DUX4 activates germline genes, retroelements, and immune mediators: implications for facioscapulohumeral dystrophy. Dev Cell. 2012;22(1):38–51.
  • Lau EC, Janson MM, Roesler MR, et al. Birth of a healthy infant following preimplantation PKHD1 haplotyping for autosomal recessive polycystic kidney disease using multiple displacement amplification. J Assist Reprod Genet. 2010;27(7):397–407.
  • Gourdon G, Meola G. Myotonic dystrophies: state of the art of new therapeutic developments for the CNS. Front Cell Neurosci. 2017;11:101.
  • Zouvelou V, Rentzos M, Zalonis I, et al. Cognitive impairment and cerebellar atrophy in typical onset 4Q35 fascioscapulohumeral dystrophy. Muscle Nerve. 2008;38(5):1523–1524.
  • Maccaroni K, Balzano E, Mirimao F, et al. Impaired replication timing promotes tissue-specific expression of common fragile sites. Genes (Basel). 2020;11(3):326.
  • Catani M, Dell’acqua F, Thiebaut de Schotten M. A revised limbic system model for memory, emotion and behaviour. Neurosci Biobehav Rev. 2013;37(8):1724–1737.
  • Anderson JL, Head SI, Rae C, et al. Brain function in duchenne muscular dystrophy. Brain. 2002;125(Pt 1):4–13.
  • Hinton VJ, Nereo NE, Fee RJ, et al. Social behavior problems in boys with duchenne muscular dystrophy. J Dev Behav Pediatr. 2006;27(6):470–476.
  • Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–2120.
  • Smith T, Heger A, Sudbery I. UMI-tools: modeling sequencing errors in unique molecular identifiers to improve quantification accuracy. Genome Res. 2017;27(3):491–499.
  • Andrews S FastQC: A Quality Control Tool for High Throughput Sequence Data. 2015. Last accessed 05 07 2021. Available from: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/.
  • Langmead B, Trapnell C, Pop M, et al. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10(3):R25.
  • Anders S, Pyl PT, Huber W. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166–169.
  • Griffiths-Jones S, Grocock RJ, van Dongen S, et al. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 2006;34(90001):D140–4. (Database issue).
  • Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11(3):R25.
  • Lund SP, Nettleton D, McCarthy DJ, et al. Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates. Statistical Applications in Genetics and Molecular Biology. 2012;11(5). DOI:10.1515/1544-6115.1826.
  • Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–2504.
  • Bindea G, Mlecnik B, Hackl H, et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 2009;25(8):1091–1093.
  • Bindea G, Galon J, Mlecnik B. CluePedia cytoscape plugin: pathway insights using integrated experimental and in silico data. Bioinformatics. 2013;29(5):661–663.
  • Lipscomb CE. Medical Subject Headings (MeSH). Bull Med Libr Assoc. 2012;88(3):265–6.
  • Duren W, Weymouth T, Hull T, et al. MetDisease–connecting metabolites to diseases via literature. Bioinformatics. 2014;30(15):2239–2241.