Spinal muscular atrophy (SMA) is an autosomal-recessive neuromuscular disorder, characterized by the degeneration of the α-motor neurons of the ventral horns of the spinal cord. The severity of the infantile forms is classically ranked into types I–III, according to the age of onset and the maximum motor milestone achieved (Mercuri et al., 2020). According to the conventional classification, the onset of the condition in type I is below 6 months of age and patients do not acquire the sitting position; in type II, symptoms occur within 18 months and children do not acquire autonomous ambulation. Type III is the most variable phenotype; developmental phases are comparable to that of the general population, the onset is over 18 months. Type III is subclassified into -a and -b, based on onset below or over 3 years of age (Zerres and Rudnik-Schöneborn, 1995). An additional form, with onset over 18 years of age, is usually reported as type IV: the outcome is variable and the degree of disability is usually mild. Indeed, SMA phenotype is better depicted as a continuous spectrum: for this reason, patients are preferably stratified according to a decimal classification, available for types I and II only (Dubowitz, 1995; Main et al., 2003).

Irrespective of the phenotypic severity, SMA patients have the same genetic defect: the homozygous loss of the SMN1 gene, located in 5q13 (Lefebvre et al., 1995). In the same region, a hypomorphic allele of SMN1 is present (SMN2), which produces insufficient levels of the SMN protein. Due to an alternative splicing, SMN2 is mainly transcribed into an isoform lacking exon 7 (SMN-del7) and then translated into an unstable protein (Mercuri et al., 2020). The number of SMN2 genes is variable in patients, generally 2–4; SMN2 copy number is the only consistent phenotypic modifier known to date, grossly and inversely related to disease severity (Calucho et al., 2018). The efficiency of exon 7 inclusion in SMN2 mRNA can be enhanced by two relatively rare variants (rs121909192 and rs1454173648), more commonly found in the less severely affected patients (Vezain et al., 2010; Wu et al., 2017).

The molecular pathophysiology of SMA is largely unknown: even though the SMN protein is ubiquitously expressed and has a housekeeping function in splicing regulation, the second motor neuron is the main target cell of the disease (Beattie and Kolb, 2018). However, a growing bulk of evidence supports the pathogenic role of skeletal muscle or even of the whole motor arch (Bricceno et al., 2014; Martínez-Hernández et al., 2014; Ripolone et al., 2015).

Physiological and pathological degeneration of skeletal muscle in SMA has been intensively investigated, also focusing on the role of microRNAs (miRs or miRNAs), 20-22mers non-coding RNAs. miRs are involved in most cellular processes: these regulate gene expression through mRNA degradation or translational inhibition, mainly by binding cis-regulatory elements present in the 3′UTR of mRNAs (Bartel, 2009; Vidigal and Ventura, 2015). Among miRs, the myomiRs play a pivotal role in regulating myogenesis and muscle degeneration (including hsa-miR-1, miR-206, miR-133a, and miR-133b) (Horak et al., 2016).

miRs have been intensively studied also as potential biomarkers in several conditions, including SMA (Kariyawasam et al., 2019). This topic has become even more relevant after the development and registration of the first effective treatments, which have led to a revolutionary change of perspective for patients (Mercuri et al., 2020). The results of presymptomatic treatments (Mercuri et al., 2020) are prompting the development of newborn screening programs, already started in some countries worldwide (Dangouloff et al., 2020), and are making compelling the need of prognostic biomarkers to predict the progression trajectories of patients identified at birth. Besides the SMN2 products, either transcripts or protein, (Tiziano et al., 2013; Tiziano et al., 2019; Crawford et al., 2012), few SMN-independent biomarkers have been evaluated so far, with discordant results; these include the SMA-MAP, creatinine, neurofilament dosage, and a few miRs (Kariyawasam et al., 2019; Kobayashi et al., 2013; Alves et al., 2020; Darras et al., 2019).

In the present study, we have first used whole miRNome sequencing of muscle specimens and cultures from SMA patients and controls to identify a specific signature of the disease and investigate on the muscle involvement in the pathogenic process. Subsequently, the levels of deregulated miRs have been evaluated in serum samples of patients and controls as SMN-independent biomarkers.

We identified three deregulated miRs (miR-181a-5p, miR-324-5p, miR-451a: SMA-miRs) that have been integrated in a composite score (SMA-score), including SMN2 full-length (SMN2-fl) transcript levels, SMN2 copy number and age at sampling, markedly improving the phenotypic predictive value of SMN2 copy number assessment alone.

The landscape of SMA has been so revolutionized over the last few years by the availability of effective treatments that the solution of past issues has become more urgent and novel ones have come to the surface. Firstly, the usual clinical classification is unsatisfactory for several reasons: (1) the treatment of patients has uncovered novel phenotypes that do not fall in any of the classical forms (Mercuri et al., 2020); (2) the spreading of newborn screening programs is changing the diagnosis of SMA into that of subjects with a genetic defect who might or not develop signs of the condition. Secondly, the identification of prognostic response and predictive biomarkers has become even more urgent since (1) the available outcome measures may not be sensitive enough to detect slight improvements that may still be clinically relevant and (2) the molecular effect of treatments that target CNS only cannot be evaluated peripherally.

The only genomic biomarker with clinical relevance is the determination of SMN2 copy number, alongside two alternative splicing-modulating variants (rs121909192 and rs1454173648; Vezain et al., 2010; Wu et al., 2017) even though some other modifier genes have been reported (Kariyawasam et al., 2019). In this study, we have exploited an unbiased approach to identify deregulated muscular miRs that could be dosed in serum samples as candidate biomarkers for SMA; at the same time, high-throughput data might provide hints on the possible pathogenic role of skeletal muscle. miRs and neurodegeneration processes are tightly related: the depletion of DICER in mice leads to a phenotype resembling SMA (Gonçalves et al., 2018); DROSHA is downregulated in motor neurons of a SMA model (Haramati et al., 2010). In this latter study, the depletion of the bouquet of expressed miRs was interpreted as due to a global deregulation of miR biogenesis. In our study, less than 10% of miRs (100/1115 annotated miRs) were significantly deregulated in patients’ muscle biopsies. This discrepancy might be related to the obvious difference in the tissues analyzed. More importantly, while we used human tissues, the motor neurons studied by Goncalves et al. were from the Taiwanese murine model of SMA that displays very low SMN levels, incomparable with those found in patients.

The involvement of skeletal muscle in SMA is unquestionable; to discern whether active or passive, we used the dual approach of muscle cell cultures and biopsies. While alterations found in vivo only could have been either primitive or secondary to the denervation/atrophy processes, the alterations found also in cultured cells point to a primary defect related to SMN deficiency.

In cell cultures, the number of differentially expressed miRs was smaller than in muscle samples. This finding could be related to the higher variability of cultured cells compared to the in vivo specimens. Interestingly, of the five miRs shared in all groups of samples (hsa-miR-1, -133a, -133b, -204-5p, -208b all upregulated), four belonged to the myomiRs: miR-1 and miR-133 family levels inversely correlate with myogenic differentiation/proliferation status (Kirby and McCarthy, 2013); miR-208b overexpression favors proliferation to differentiation during skeletal muscle development (Fu et al., 2020). Globally, these data suggest a primary muscular defect in SMA, determining a delay in the differentiation path from myoblasts to maturely innervated myofibers. The maturation defect of SMA skeletal muscle has been historically reported in morphological studies ahead of the identification of SMN1 (Fidziańska et al., 1990) and has been confirmed in more recent studies on human (Martínez-Hernández et al., 2014) and murine tissues (Bricceno et al., 2014). Additionally, Houzelle et al. have very recently shown that hsa-miR-204-5p and -133b levels inversely correlate with the mitochondrial activity in skeletal muscle (Houzelle et al., 2020), suggesting that these miRs could be involved in the mitochondrial depletion observed in SMA (Ripolone et al., 2015). How miR modulation occurs is unknown: SMN is not directly binding miRs even though the SMN complex can assemble with these small RNAs (Chen and Chen, 2019); conversely, in silico predictions (Dweep et al., 2014) indicate that miR-133a, -133b, and -204-5p bind the 3′-UTR of SMN2, and thus might downregulate SMN transcript/protein levels.

In this study, we used the unbiased dataset above to identify candidate SMN-independent biomarkers. We first tested >100 miRs in a small cohort of patients and controls to get the shortlist of potential biomarkers. Most miRs were not detectable in serum, indicating the integrity of the sarcolemma in SMA, differently from other neuromuscular conditions such as Duchenne muscular dystrophy (Cacchiarelli et al., 2011). Only three miRs were overexpressed in serum of patients (miR-181a-5p, miR-324-5p, and miR-451a, the SMA-miRs), suggesting that these might be actively secreted by skeletal muscle. We can hypothesize that these miRs, which do not modulate SMN2, may exert a loco-regional effect in muscle and have, at the same time, an at-distance-effect following secretion.

Regarding the possible pathophysiological function of the SMA-miRs, to our knowledge, few data are available on the role of miR-324-5p in skeletal muscle: a single study reported the upregulation in human CD56+ myoblasts, during differentiation (Dmitriev et al., 2013), again supporting the immaturity of SMA skeletal muscle. The putative role of the secreted miR-324-5p remains unknown: Sun et al., 2019 reported an essential function of this miR in synapsis formation. Intriguingly, we observed that even if the intrathecal administration of the specific antagomir did not improve the survival of SMNΔ7 mice, however, it induced a significant transient increase in body weight (Figure 4—figure supplement 1).

More information is available regarding the role of miR-451a: an increase was found in aged muscle (Mercken et al., 2013), whereas the downregulation occurred during endurance training (Zacharewicz et al., 2013), suggesting that miR-451a levels may be inversely related to muscle mass. However, in our cohort, this miR was upregulated in patients independently of the degree of the atrophic process. Some studies have reported the simultaneous modulation of miR-451a and miR-181a-5p: in acute exercise both miRs are upregulated, whereas in aging these displayed an opposite trend (Zacharewicz et al., 2013), suggesting that the modulation in SMA is independent of the reduced mobility of patients; interestingly, we found that the expression levels of the two miRs are related (Figure 3—figure supplement 1). miR-181a-5p displays the more intriguing profile in terms of putative involvement in SMA: Ouyang et al., 2012 reported a worsening of brain injury following the increase of miR levels in a mouse model of stroke, whereas the depletion accelerated the recovery. More importantly, Benigni et al., 2016 found an increase in miR-181a levels in CSF of amyotrophic lateral sclerosis patients. We argue that miR-181a-5p might be a part of a retrograde signaling system from skeletal muscle, which may accelerate motor neuron loss in SMA. To test this hypothesis, we treated SMNΔ7 mice with both mimic and anti-miR-181a-5p without increasing SMN levels. miR-181a-5p modulation alone was not sufficient to significantly improve the survival of affected mice even if some changes in Kaplan–Meier curves were observed in a subset of animals (Figure 4).

If confirmed in other studies with more therapeutic or pathogenic purposes, our data suggest that systemic therapeutic approaches increasing SMN levels also in skeletal muscle may provide additional benefits to SMA patients, and that miR-181a-5p (and/or miR324-5p) modulation might be a potential target for combinatorial treatments in addition to SMN modulation.

To the best of our knowledge, the miRs we have identified in the present study have not been described in SMA so far. Previously, other miRs have been found differentially expressed in animal models or in serum samples of patients (miR-9, -19a-3p, -23a-3p, -132, -146a, -183, -206, -431) (Kye et al., 2014; Valsecchi et al., 2015; Murdocca et al., 2016; Catapano et al., 2016; Wertz et al., 2016; O’Hern et al., 2017; Sison et al., 2017; Kaifer et al., 2019; Bonanno et al., 2020; Haramati et al., 2010). Of these (Table 1), three (miR-132, -183, -431) were not differentially expressed in muscle biopsies and had not been described at the time of study design, thus have not been tested here; among the others, three were almost undetectable in serum samples of our cohorts (miR-9, -19a-3p, -146a) and two were not differentially expressed (miR-23a-3p, -206). The discrepancy between our and previous data may be ascribed to the different technical approaches used: as in the case of SMN2 mRNAs (Tiziano et al., 2010), the results of absolute qPCR, differently from the relative approaches, are not affected by the expression levels of endogenous genes and calibrators. Moreover, in the case of low-copy number transcripts, the relative approaches may magnify even small differences in expression levels, which may be statistically significant but of doubtful biological meaning.

One of the most relevant results of our study is the development of the SMA-score. The global spreading of newborn screening programs for SMA has made compelling the identification of tools with good predictive power of the clinical severity (Dangouloff et al., 2020). So far, the stop-or-go to the treatment of presymptomatic patients is uniquely based on SMN2 copy number assessment, which is roughly predictive of the clinical severity in individual patients. Even more critical are patients with three SMN2 copies whose severity may range from type I to type III (1.9 to 3b in our cohort).

Two SMN2 variants (rs121909192 and rs1454173648) modulate the inclusion efficiency of exon 7 into mature mRNA, (Vezain et al., 2010; Wu et al., 2017), but these variants are relatively rare in patients. Additional SMN2 variants have been very recently described (Blasco-Pérez et al., 2021); however, the frequency and the functional effect of these variants have not been elucidated yet. For these reasons, more functional and dynamic molecular markers could reasonably improve the prediction of the severity. The inclusion of serum SMA-miRs, whole-blood SMN2-fl levels, and age has markedly increased the accuracy of the severity prediction of SMN2 copy number alone, from about 52% to about 75%. The age effect might be related to the physiological modulation of SMN2-fl levels over time, as previously reported (Crawford et al., 2012). In patients with three SMN2 copies, the SMA-score was significantly related to the decimal classification of patients, even if with about 30% strength (Figure 5—figure supplement 1C). The increase in the population size might improve these results.

The main drawback of our results is related to the cross-sectional nature of the present study. While we have previously shown that SMN2-fl levels are stable in untreated patients for >1 year (Tiziano et al., 2019), longitudinal data on SMA-miR stability are lacking. In any case, repeated samplings could not be feasible in our study since almost all patients (except for the few type I subjects) were treated with SMN-modifying compounds (such as salbutamol; Tiziano et al., 2019; Tiziano et al., 2010; Angelozzi et al., 2008) or new experimental treatments. Also, the effect of SMN-modifying treatments on SMA-miRs is unknown. The collection of longitudinal data would be highly desirable, namely in presymptomatic patients, identified in our and other newborn screening projects (Dangouloff et al., 2020). If the accuracy will be confirmed in replicative studies, the SMA-score might be included in the clinical routine, as part of the prognostic process, once newborn testing will be universally available.

All data generated or analysed during this study are included in the manuscript and supporting files. Raw sequencing data are available at NCBI-SRA database; BioProject PRJNA748014.

1. 1. Alves CRR
   2. Zhang R
   3. Johnstone AJ
   4. Garner R
   5. Nwe PH
   6. Siranosian JJ
   7. Swoboda KJ

   (2020) Serum creatinine is a biomarker of progressive denervation in spinal muscular atrophy

   Neurology 94:e921–e931.

   https://doi.org/10.1212/WNL.0000000000008762

   • Google Scholar
2. 1. Angelozzi C
   2. Borgo F
   3. Tiziano FD
   4. Martella A
   5. Neri G
   6. Brahe C

   (2008) Salbutamol increases SMN mRNA and protein levels in spinal muscular atrophy cells

   Journal of Medical Genetics 45:29–31.

   https://doi.org/10.1136/jmg.2007.051177

   • Google Scholar
3. 1. Bartel DP

   (2009) MicroRNAs: target recognition and regulatory functions

   Cell 136:215–233.

   https://doi.org/10.1016/j.cell.2009.01.002

   • Google Scholar
4. 1. Beattie CE
   2. Kolb SJ

   (2018) Spinal muscular atrophy: Selective motor neuron loss and global defect in the assembly of ribonucleoproteins

   Brain Research 1693:92–97.

   https://doi.org/10.1016/j.brainres.2018.02.022

   • Google Scholar
5. 1. Benigni M
   2. Ricci C
   3. Jones AR
   4. Giannini F
   5. Al-Chalabi A
   6. Battistini S

   (2016) Identification of miRNAs as Potential Biomarkers in Cerebrospinal Fluid from Amyotrophic Lateral Sclerosis Patients

   Neuromol Med 18:551–560.

   https://doi.org/10.1007/s12017-016-8396-8

   • Google Scholar
6. 1. Blasco-Pérez L
   2. Paramonov I
   3. Leno J
   4. Bernal S
   5. Alias L
   6. Fuentes-Prior P
   7. Cuscó I
   8. Tizzano EF

   (2021) Beyond copy number: A new, rapid, and versatile method for sequencing the entire smn2 gene in SMA patients

   Human Mutation 42:787–795.

   https://doi.org/10.1002/humu.24200

   • PubMed
   • Google Scholar
7. 1. Bonanno S
   2. Marcuzzo S
   3. Malacarne C
   4. Giagnorio E
   5. Masson R
   6. Zanin R
   7. Arnoldi MT
   8. Andreetta F
   9. Simoncini O
   10. Venerando A
   11. Gellera C
   12. Pantaleoni C
   13. Mantegazza R
   14. Bernasconi P
   15. Baranello G
   16. Maggi L

   (2020) Circulating MYOMIRS as potential biomarkers to monitor response to nusinersen in pediatric SMA patients

   Biomedicines 8:E21.

   https://doi.org/10.3390/biomedicines8020021

   • PubMed
   • Google Scholar
8. 1. Bricceno KV
   2. Martinez T
   3. Leikina E
   4. Duguez S
   5. Partridge TA
   6. Chernomordik LV
   7. Fischbeck KH
   8. Sumner CJ
   9. Burnett BG

   (2014) Survival motor neuron protein deficiency impairs myotube formation by altering myogenic gene expression and focal adhesion dynamics

   Human Molecular Genetics 23:4745–4757.

   https://doi.org/10.1093/hmg/ddu189

   • PubMed
   • Google Scholar
9. 1. Cacchiarelli D
   2. Legnini I
   3. Martone J
   4. Cazzella V
   5. D’Amico A
   6. Bertini E
   7. Bozzoni I

   (2011) miRNAs as serum biomarkers for Duchenne muscular dystrophy

   EMBO Molecular Medicine 3:258–265.

   https://doi.org/10.1002/emmm.201100133

   • PubMed
   • Google Scholar
10. 1. Calucho M
    2. Bernal S
    3. Alías L
    4. March F
    5. Venceslá A
    6. Rodríguez-Álvarez FJ
    7. Aller E
    8. Fernández RM
    9. Borrego S
    10. Millán JM
    11. Hernández-Chico C
    12. Cuscó I
    13. Fuentes-Prior P
    14. Tizzano EF

    (2018) Correlation between SMA type and SMN2 copy number revisited: An analysis of 625 unrelated spanish patients and a compilation of 2834 reported cases

    Neuromuscular Disorders 28:208–215.

    https://doi.org/10.1016/j.nmd.2018.01.003

    • PubMed
    • Google Scholar
11. 1. Catapano F
    2. Zaharieva I
    3. Scoto M
    4. Marrosu E
    5. Morgan J
    6. Muntoni F
    7. Zhou H

    (2016) Altered Levels of MicroRNA-9, -206, and -132 in Spinal Muscular Atrophy and Their Response to Antisense Oligonucleotide Therapy

    Molecular Therapy. Nucleic Acids 5:e331.

    https://doi.org/10.1038/mtna.2016.47

    • PubMed
    • Google Scholar
12. 1. Chen TH
    2. Chen JA

    (2019) Multifaceted roles of micrornas: From motor neuron generation in embryos to degeneration in spinal muscular atrophy

    eLife 8:e50848.

    https://doi.org/10.7554/eLife.50848

    • PubMed
    • Google Scholar
13. 1. Crawford TO
    2. Paushkin SV
    3. Kobayashi DT
    4. Forrest SJ
    5. Joyce CL
    6. Finkel RS
    7. Kaufmann P
    8. Swoboda KJ
    9. Tiziano D
    10. Lomastro R
    11. Li RH
    12. Trachtenberg FL
    13. Plasterer T
    14. Chen KS
    15. Pilot Study of Biomarkers for Spinal Muscular Atrophy Trial Group

    (2012) Evaluation of SMN protein, transcript, and copy number in the biomarkers for spinal muscular atrophy (Bforsma) clinical study

    PLOS ONE 7:e33572.

    https://doi.org/10.1371/journal.pone.0033572

    • PubMed
    • Google Scholar
14. 1. Dangouloff T
    2. Burghes A
    3. Tizzano EF
    4. Servais L
    5. Dangouloff T
    6. Burghes A
    7. Bertini E
    8. Boemer F
    9. Hiligsmann M
    10. Mueller-Felber W
    11. Tiziano D
    12. Young P
    13. Tizzano EF
    14. Servais L
    15. Germanenko O
    16. de Lemus M
    17. Ouillade L
    18. Rucinski K
    19. Stephenson K
    20. Farwell W
    21. Gorni K
    22. Hoffmann F
    23. Hjort M
    24. Kausar I

    (2020) 244th ENMC international workshop: Newborn screening in spinal muscular atrophy May 10–12, 2019, Hoofdorp, The Netherlands

    Neuromuscular Disorders 30:93–103.

    https://doi.org/10.1016/j.nmd.2019.11.002

    • Google Scholar
15. 1. Darras BT
    2. Crawford TO
    3. Finkel RS
    4. Mercuri E
    5. De Vivo DC
    6. Oskoui M
    7. Tizzano EF
    8. Ryan MM
    9. Muntoni F
    10. Zhao G
    11. Staropoli J
    12. McCampbell A
    13. Petrillo M
    14. Stebbins C
    15. Fradette S
    16. Farwell W
    17. Sumner CJ

    (2019) Neurofilament as a potential biomarker for spinal muscular atrophy

    Annals of Clinical and Translational Neurology 6:932–944.

    https://doi.org/10.1002/acn3.779

    • PubMed
    • Google Scholar
16. 1. Dmitriev P
    2. Barat A
    3. Polesskaya A
    4. O’Connell MJ
    5. Robert T
    6. Dessen P
    7. Walsh TA
    8. Lazar V
    9. Turki A
    10. Carnac G
    11. Laoudj-Chenivesse D
    12. Lipinski M
    13. Vassetzky YS

    (2013) Simultaneous mirna and mrna transcriptome profiling of human myoblasts reveals a novel set of myogenic differentiation-associated mirnas and their target genes

    BMC Genomics 14:265.

    https://doi.org/10.1186/1471-2164-14-265

    • PubMed
    • Google Scholar
17. 1. Dubowitz V

    (1995) Chaos in the classification of SMA: A possible resolution

    Neuromuscular Disorders 5:3–5.

    https://doi.org/10.1016/0960-8966(94)00075-k

    • PubMed
    • Google Scholar
18. Book

    1. Dweep H
    2. Gretz N
    3. Sticht C

    (2014) RNA mapping

    In: Alvarez ML, Nourbakhsh M, editors. Methods in Molecular Biology, Vol. 1182. New York, NY: Springer. pp. 289–305.

    https://doi.org/10.1007/978-1-4939-1062-5_25

    • Google Scholar
19. 1. Fidziańska A
    2. Goebel HH
    3. Warlo I

    (1990) Acute infantile spinal muscular atrophy. Muscle apoptosis as a proposed pathogenetic mechanism

    Brain 113 ( Pt 2):433–445.

    https://doi.org/10.1093/brain/113.2.433

    • PubMed
    • Google Scholar
20. 1. Fu L
    2. Wang H
    3. Liao Y
    4. Zhou P
    5. Xu Y
    6. Zhao Y
    7. Xie S
    8. Zhao S
    9. Li X

    (2020) Mir-208b modulating skeletal muscle development and energy homoeostasis through targeting distinct targets

    RNA Biology 17:743–754.

    https://doi.org/10.1080/15476286.2020.1728102

    • PubMed
    • Google Scholar
21. 1. Glascock JJ
    2. Osman EY
    3. Coady TH
    4. Rose FF
    5. Shababi M
    6. Lorson CL

    (2011) Delivery of therapeutic agents through intracerebroventricular (ICV) and intravenous (IV) injection in mice

    Journal of Visualized Experiments 1:2968.

    https://doi.org/10.3791/2968

    • PubMed
    • Google Scholar
22. 1. Gonçalves I
    2. Brecht J
    3. Thelen MP
    4. Rehorst WA
    5. Peters M
    6. Lee HJ
    7. Motameny S
    8. Torres-Benito L
    9. Ebrahimi-Fakhari D
    10. Kononenko NL
    11. Altmüller J
    12. Vilchez D
    13. Sahin M
    14. Wirth B
    15. Kye MJ

    (2018) Neuronal activity regulates drosha via autophagy in spinal muscular atrophy

    Scientific Reports 8:7907.

    https://doi.org/10.1038/s41598-018-26347-y

    • PubMed
    • Google Scholar
23. 1. Haramati S
    2. Chapnik E
    3. Sztainberg Y
    4. Eilam R
    5. Zwang R
    6. Gershoni N
    7. McGlinn E
    8. Heiser PW
    9. Wills AM
    10. Wirguin I
    11. Rubin LL
    12. Misawa H
    13. Tabin CJ
    14. Brown R
    15. Chen A
    16. Hornstein E

    (2010) MIRNA malfunction causes spinal motor neuron disease

    PNAS 107:13111–13116.

    https://doi.org/10.1073/pnas.1006151107

    • PubMed
    • Google Scholar
24. 1. Horak M
    2. Novak J
    3. Bienertova-Vasku J

    (2016) Muscle-specific micrornas in skeletal muscle development

    Developmental Biology 410:1–13.

    https://doi.org/10.1016/j.ydbio.2015.12.013

    • PubMed
    • Google Scholar
25. 1. Houzelle A
    2. Dahlmans D
    3. Nascimento EBM
    4. Schaart G
    5. Jörgensen JA
    6. Moonen-Kornips E
    7. Kersten S
    8. Wang X
    9. Hoeks J

    (2020) MicroRNA-204-5p modulates mitochondrial biogenesis in C2C12 myotubes and associates with oxidative capacity in humans

    Journal of Cellular Physiology 235:9851–9863.

    https://doi.org/10.1002/jcp.29797

    • PubMed
    • Google Scholar
26. 1. Kaifer KA
    2. Villalón E
    3. O’Brien BS
    4. Sison SL
    5. Smith CE
    6. Simon ME
    7. Marquez J
    8. O’Day S
    9. Hopkins AE
    10. Neff R
    11. Rindt H
    12. Ebert AD
    13. Lorson CL

    (2019) Aav9-mediated delivery of mir-23a reduces disease severity in SMN2B/-SMA model mice

    Human Molecular Genetics 28:3199–3210.

    https://doi.org/10.1093/hmg/ddz142

    • PubMed
    • Google Scholar
27. 1. Kariyawasam DST
    2. D’Silva A
    3. Lin C
    4. Ryan MM
    5. Farrar MA

    (2019) Biomarkers and the development of a personalized medicine approach in spinal muscular atrophy

    Frontiers in Neurology 10:898.

    https://doi.org/10.3389/fneur.2019.00898

    • PubMed
    • Google Scholar
28. 1. Kirby TJ
    2. McCarthy JJ

    (2013) Micrornas in skeletal muscle biology and exercise adaptation

    Free Radical Biology & Medicine 64:95–105.

    https://doi.org/10.1016/j.freeradbiomed.2013.07.004

    • PubMed
    • Google Scholar
29. 1. Kobayashi DT
    2. Shi J
    3. Stephen L
    4. Ballard KL
    5. Dewey R
    6. Mapes J
    7. Chung B
    8. McCarthy K
    9. Swoboda KJ
    10. Crawford TO
    11. Li R
    12. Plasterer T
    13. Joyce C
    14. Biomarkers for Spinal Muscular Atrophy Study Group
    15. Chung WK
    16. Kaufmann P
    17. Darras BT
    18. Finkel RS
    19. Sproule DM
    20. Martens WB
    21. McDermott MP
    22. De Vivo DC
    23. Pediatric Neuromuscular Clinical Research Network
    24. Walker MG
    25. Chen KS

    (2013) SMA-MAP: A plasma protein panel for spinal muscular atrophy

    PLOS ONE 8:e60113.

    https://doi.org/10.1371/journal.pone.0060113

    • PubMed
    • Google Scholar
30. 1. Kye MJ
    2. Niederst ED
    3. Wertz MH
    4. Gonçalves I
    5. Akten B
    6. Dover KZ
    7. Peters M
    8. Riessland M
    9. Neveu P
    10. Wirth B
    11. Kosik KS
    12. Sardi SP
    13. Monani UR
    14. Passini MA
    15. Sahin M

    (2014) SMN regulates axonal local translation via MIR-183/MTOR pathway

    Human Molecular Genetics 23:6318–6331.

    https://doi.org/10.1093/hmg/ddu350

    • PubMed
    • Google Scholar
31. 1. Lefebvre S
    2. Bürglen L
    3. Reboullet S
    4. Clermont O
    5. Burlet P
    6. Viollet L
    7. Benichou B
    8. Cruaud C
    9. Millasseau P
    10. Zeviani M

    (1995) Identification and characterization of a spinal muscular atrophy-determining gene

    Cell 80:155–165.

    https://doi.org/10.1016/0092-8674(95)90460-3

    • PubMed
    • Google Scholar
32. 1. Main M
    2. Kairon H
    3. Mercuri E
    4. Muntoni F

    (2003) The Hammersmith Functional Motor Scale for Children with Spinal Muscular Atrophy: a Scale to Test Ability and Monitor Progress in Children with Limited Ambulation

    European Journal of Paediatric Neurology 7:155–159.

    https://doi.org/10.1016/s1090-3798(03)00060-6.S

    • Google Scholar
33. 1. Martínez-Hernández R
    2. Bernal S
    3. Alias L
    4. Tizzano EF

    (2014) Abnormalities in Early Markers of Muscle Involvement Support a Delay in Myogenesis in Spinal Muscular Atrophy

    Journal of Neuropathology and Experimental Neurology 73:559–567.

    https://doi.org/10.1097/NEN.0000000000000078

    • Google Scholar
34. 1. Mercken EM
    2. Majounie E
    3. Ding J
    4. Guo R
    5. Kim J
    6. Bernier M
    7. Mattison J
    8. Cookson MR
    9. Gorospe M
    10. de Cabo R
    11. Abdelmohsen K

    (2013) Age-associated miRNA Alterations in Skeletal Muscle from Rhesus Monkeys reversed by caloric restriction

    Aging 5:692–703.

    https://doi.org/10.18632/aging.100598

    • Google Scholar
35. 1. Mercuri E
    2. Pera MC
    3. Scoto M
    4. Finkel R
    5. Muntoni F

    (2020) Spinal muscular atrophy — insights and challenges in the treatment era

    Nature Reviews. Neurology 16:706–715.

    https://doi.org/10.1016/S1474-4422(12)70061-3

    • Google Scholar
36. 1. Murdocca M
    2. Ciafrè SA
    3. Spitalieri P
    4. Talarico RV
    5. Sanchez M
    6. Novelli G
    7. Sangiuolo F

    (2016) SMA human ipsc-derived motor neurons show perturbed differentiation and reduced mir-335-5p expression

    International Journal of Molecular Sciences 17:1231.

    https://doi.org/10.3390/ijms17081231

    • PubMed
    • Google Scholar
37. 1. Ouyang Y-B
    2. Lu Y
    3. Yue S
    4. Xu L-J
    5. Xiong X-X
    6. White RE
    7. Sun X
    8. Giffard RG

    (2012) miR-181 regulates GRP78 and influences outcome from cerebral ischemia in vitro and in vivo

    Neurobiology of Disease 45:555–563.

    https://doi.org/10.1016/j.nbd.2011.09.012

    • PubMed
    • Google Scholar
38. 1. O’Hern PJ
    2. do Carmo G Gonçalves I
    3. Brecht J
    4. López Soto EJ
    5. Simon J
    6. Chapkis N
    7. Lipscombe D
    8. Kye MJ
    9. Hart AC

    (2017) Decreased microrna levels lead to deleterious increases in neuronal m2 muscarinic receptors in spinal muscular atrophy models

    eLife 6:e20752.

    https://doi.org/10.7554/eLife.20752

    • PubMed
    • Google Scholar
39. 1. Ripolone M
    2. Ronchi D
    3. Violano R
    4. Vallejo D
    5. Fagiolari G
    6. Barca E
    7. Lucchini V
    8. Colombo I
    9. Villa L
    10. Berardinelli A
    11. Balottin U
    12. Morandi L
    13. Mora M
    14. Bordoni A
    15. Fortunato F
    16. Corti S
    17. Parisi D
    18. Toscano A
    19. Sciacco M
    20. DiMauro S
    21. Comi GP
    22. Moggio M

    (2015) Impaired Muscle Mitochondrial Biogenesis and Myogenesis in Spinal Muscular Atrophy

    JAMA Neurology 72:666–675.

    https://doi.org/10.1001/jamaneurol.2015.0178

    • PubMed
    • Google Scholar
40. 1. Sison SL
    2. Patitucci TN
    3. Seminary ER
    4. Villalon E
    5. Lorson CL
    6. Ebert AD

    (2017) Astrocyte-produced miR-146a as a mediator of motor neuron loss in spinal muscular atrophy

    Human Molecular Genetics 26:3409–3420.

    https://doi.org/10.1093/hmg/ddx230

    • Google Scholar
41. 1. Sun C
    2. Zhu L
    3. Ma R
    4. Ren J
    5. Wang J
    6. Gao S
    7. Yang D
    8. Ning K
    9. Ling B
    10. Lu B
    11. Chen X
    12. Xu J

    (2019) Astrocytic miR-324-5p is essential for synaptic formation by suppressing the secretion of CCL5 from astrocytes

    Cell Death & Disease 10:141.

    https://doi.org/10.1038/s41419-019-1329-3

    • Google Scholar
42. 1. Tiziano FD
    2. Pinto AM
    3. Fiori S
    4. Lomastro R
    5. Messina S
    6. Bruno C
    7. Pini A
    8. Pane M
    9. D’Amico A
    10. Ghezzo A
    11. Bertini E
    12. Mercuri E
    13. Neri G
    14. Brahe C

    (2010) SMN transcript levels in leukocytes of SMA patients determined by absolute real-time PCR

    European Journal of Human Genetics 18:52–58.

    https://doi.org/10.1038/ejhg.2009.116

    • Google Scholar
43. 1. Tiziano FD
    2. Lomastro R
    3. Di Pietro L
    4. Barbara Pasanisi M
    5. Fiori S
    6. Angelozzi C
    7. Abiusi E
    8. Angelini C
    9. Sorarù G
    10. Gaiani A
    11. Mongini T
    12. Vercelli L
    13. Vasco G
    14. Vita G
    15. Luca Vita G
    16. Messina S
    17. Politano L
    18. Passamano L
    19. Di Gregorio G
    20. Montomoli C
    21. Orsi C
    22. Campanella A
    23. Mantegazza R
    24. Morandi L

    (2013) Clinical and molecular cross-sectional study of a cohort of adult type III spinal muscular atrophy patients: clues from a biomarker study

    European Journal of Human Genetics 21:630–636.

    https://doi.org/10.1038/ejhg.2012.233

    • Google Scholar
44. 1. Tiziano FD
    2. Lomastro R
    3. Abiusi E
    4. Pasanisi MB
    5. Di Pietro L
    6. Fiori S
    7. Baranello G
    8. Angelini C
    9. Sorarù G
    10. Gaiani A
    11. Mongini T
    12. Vercelli L
    13. Mercuri E
    14. Vasco G
    15. Pane M
    16. Vita G
    17. Vita G
    18. Messina S
    19. Petillo R
    20. Passamano L
    21. Politano L
    22. Campanella A
    23. Mantegazza R
    24. Morandi L

    (2019) Longitudinal evaluation of SMN levels as biomarker for spinal muscular atrophy: results of a phase IIb double-blind study of salbutamol

    Journal of Medical Genetics 56:293–300.

    https://doi.org/10.1136/jmedgenet-2018-105482

    • Google Scholar
45. 1. Valsecchi V
    2. Boido M
    3. De Amicis E
    4. Piras A
    5. Vercelli A
    6. Pignataro G

    (2015) Expression of muscle-specific MIRNA 206 in the progression of disease in a murine SMA model

    PLOS ONE 10:e0128560.

    https://doi.org/10.1371/journal.pone.0128560

    • PubMed
    • Google Scholar
46. 1. Vezain M
    2. Saugier-Veber P
    3. Goina E
    4. Touraine R
    5. Manel V
    6. Toutain A
    7. Fehrenbach S
    8. Frébourg T
    9. Pagani F
    10. Tosi M
    11. Martins A

    (2010) A rare SMN2 variant in a previously unrecognized composite splicing regulatory element induces exon 7 inclusion and reduces the clinical severity of spinal muscular atrophy

    Human Mutation 31:E1110–E1125.

    https://doi.org/10.1002/humu.21173

    • Google Scholar
47. 1. Vidigal JA
    2. Ventura A

    (2015) The biological functions of mirnas: Lessons from in vivo studies

    Trends in Cell Biology 25:137–147.

    https://doi.org/10.1016/j.tcb.2014.11.004

    • PubMed
    • Google Scholar
48. 1. Wertz MH
    2. Winden K
    3. Neveu P
    4. Ng S-Y
    5. Ercan E
    6. Sahin M

    (2016) Cell-type-specific MIR-431 dysregulation in a motor neuron model of spinal muscular atrophy

    Human Molecular Genetics 25:2168–2181.

    https://doi.org/10.1093/hmg/ddw084

    • PubMed
    • Google Scholar
49. 1. Wu X
    2. Wang SH
    3. Sun J
    4. Krainer AR
    5. Hua Y
    6. Prior TW

    (2017) A-44g transition in SMN2 inTron 6 protects patients with spinal muscular atrophy

    Human Molecular Genetics 26:2768–2780.

    https://doi.org/10.1093/hmg/ddx166

    • PubMed
    • Google Scholar
50. 1. Zacharewicz E
    2. Lamon S
    3. Russell AP

    (2013) Micrornas in skeletal muscle and their regulation with exercise, ageing, and disease

    Frontiers in Physiology 4:266.

    https://doi.org/10.3389/fphys.2013.00266

    • PubMed
    • Google Scholar
51. 1. Zanotti S
    2. Saredi S
    3. Ruggieri A
    4. Fabbri M
    5. Blasevich F
    6. Romaggi S
    7. Morandi L
    8. Mora M

    (2007) Altered extracellular matrix transcript expression and protein modulation in primary Duchenne muscular dystrophy myotubes

    Matrix Biology 26:615–624.

    https://doi.org/10.1016/j.matbio.2007.06.004

    • PubMed
    • Google Scholar
52. 1. Zerres K
    2. Rudnik-Schöneborn S

    (1995) Natural history in proximal spinal muscular atrophy. Clinical analysis of 445 patients and suggestions for a modification of existing classifications

    Archives of Neurology 52:518–523.

    https://doi.org/10.1001/archneur.1995.00540290108025

    • PubMed
    • Google Scholar

Author details

1. Emanuela Abiusi

   Department of Life Sciences and Public Health, Section of Genomic Medicine, Università cattolica del Sacro Cuore, Roma, Italy

   Contribution

   Data curation, Investigation, Writing - original draft

   Contributed equally with

   Paola Infante

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9028-012X

2. Paola Infante

   Center For Life Nano Science@Sapienza, Istituto Italiano di Tecnologia; Department of Molecular Medicine, Università degli Studi di Roma "La Sapienza", Roma, Italy, Roma, Italy

   Contribution

   Investigation, Methodology, Data curation

   Contributed equally with

   Emanuela Abiusi

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0682-3916

3. Cinzia Cagnoli

   Clinical and Experimental Epileptology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy, Roma, Italy

   Contribution

   Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6863-6687

4. Ludovica Lospinoso Severini

   Department of Molecular Medicine, Università degli Studi di Roma "La Sapienza", Roma, Italy

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1032-5496

5. Marika Pane

   1. Pediatric Neurology, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS-Università Cattolica del Sacro Cuore, Rome, Italy
   2. Centro Clinico Nemo, Fondazione Policlinico Universitario A. Gemelli IRCCS-Università Cattolica del Sacro Cuore, Roma, Italy

   Contribution

   Investigation, Supervision

   Competing interests

   No competing interests declared

6. Giorgia Coratti

   Centro Clinico Nemo, Fondazione Policlinico Universitario A. Gemelli IRCCS-Università Cattolica del Sacro Cuore, Roma, Italy

   Contribution

   Investigation

   Competing interests

   No competing interests declared

7. Maria Carmela Pera

   Centro Clinico Nemo, Fondazione Policlinico Universitario A. Gemelli IRCCS-Università Cattolica del Sacro Cuore, Roma, Italy

   Contribution

   Investigation

   Competing interests

   No competing interests declared

8. Adele D'Amico

   Unit of Neuromuscular and Neurodegenerative Disorders, Dept. Neurosciences, Bambino Gesu' Children's Hospital IRCCS, Roma, Italy

   Contribution

   Investigation

   Competing interests

   No competing interests declared

9. Federica Diano

   Department of Life Sciences and Public Health, Section of Genomic Medicine, Università cattolica del Sacro Cuore, Roma, Italy

   Contribution

   Investigation

   Competing interests

   No competing interests declared

10. Agnese Novelli

    Department of Life Sciences and Public Health, Section of Genomic Medicine, Università cattolica del Sacro Cuore, Roma, Italy

    Contribution

    Investigation

    Competing interests

    No competing interests declared

11. Serena Spartano

    Department of Life Sciences and Public Health, Section of Genomic Medicine, Università cattolica del Sacro Cuore, Roma, Italy

    Contribution

    Investigation, Software

    Competing interests

    No competing interests declared

12. Stefania Fiori

    Department of Life Sciences and Public Health, Section of Genomic Medicine, Università cattolica del Sacro Cuore, Roma, Italy

    Contribution

    Investigation

    Competing interests

    No competing interests declared

13. Giovanni Baranello

    Department of Pediatric Neuroscience, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy

    Contribution

    Investigation

    Competing interests

    No competing interests declared

14. Isabella Moroni

    Neuromuscular Diseases and Neuroimmunology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy

    Contribution

    Investigation

    Competing interests

    No competing interests declared

15. Marina Mora

    Neuromuscular Diseases and Neuroimmunology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy

    Contribution

    Investigation

    Competing interests

    No competing interests declared

16. Maria Barbara Pasanisi

    Neuromuscular Diseases and Neuroimmunology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy

    Contribution

    Investigation

    Competing interests

    No competing interests declared

17. Krizia Pocino

    Department of Medical and Surgical Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Roma, Italy

    Contribution

    Investigation

    Competing interests

    No competing interests declared

18. Loredana Le Pera

    1. Bioenergetics and Molecular Biotechnologies (IBIOM), CNR-Institute of Biomembranes, Bari, Italy
    2. CNR-Institute of Molecular Biology and Pathology (IBPM), Rome, Italy

    Contribution

    Investigation, Software

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0076-9878

19. Davide D'Amico

    Amazentis SA, EPFL Innovation Park, Losanne, Switzerland

    Contribution

    Investigation, Software

    Competing interests

    Davide D'Amico is affiliated with Amazentis SA. The author has no financial interests to declare. At the time of study developement, Dr. D'Amico had an academic affiliation

20. Lorena Travaglini

    Unit of Neuromuscular and Neurodegenerative Disorders, Dept. Neurosciences, Bambino Gesu' Children's Hospital IRCCS, Roma, Italy

    Contribution

    Investigation

    Competing interests

    No competing interests declared

21. Francesco Ria

    1. Department of Translational Medicine and Surgery, Section of General Pathology, Università Cattolica del Sacro Cuore, Roma, Italy
    2. Fondazione Policlinico Universitario A. Gemelli - IRCCS, Rome, Italy

    Contribution

    Investigation

    Competing interests

    No competing interests declared

22. Claudio Bruno

    Center of Translational and Experimental Myology, IRCCS Istituto Giannina Gaslini, Genova, Italy

    Contribution

    Investigation

    Competing interests

    No competing interests declared

23. Denise Locatelli

    Clinical and Experimental Epileptology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy, Roma, Italy

    Contribution

    Investigation

    Competing interests

    No competing interests declared

24. Enrico Silvio Bertini

    Unit of Neuromuscular and Neurodegenerative Disorders, Dept. Neurosciences, Bambino Gesu' Children's Hospital IRCCS, Roma, Italy

    Contribution

    Investigation

    Competing interests

    No competing interests declared

25. Lucia Ovidia Morandi

    Neuromuscular Diseases and Neuroimmunology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy

    Contribution

    Investigation, Supervision

    Competing interests

    No competing interests declared

26. Eugenio Mercuri

    1. Pediatric Neurology, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS-Università Cattolica del Sacro Cuore, Rome, Italy
    2. Centro Clinico Nemo, Fondazione Policlinico Universitario A. Gemelli IRCCS-Università Cattolica del Sacro Cuore, Roma, Italy

    Contribution

    Investigation, Supervision

    Competing interests

    No competing interests declared

27. Lucia Di Marcotullio

    1. Department of Molecular Medicine, Università degli Studi di Roma "La Sapienza", Roma, Italy
    2. Laboratory affiliated to Istituto Pasteur Italia-Fondazione Cenci Bolognetti, Department of Molecular Medicine, Sapienza University, Rome, Italy

    Contribution

    Project administration, Supervision, Writing – review and editing, Conceptualization, Data curation, Funding acquisition

    Contributed equally with

    Francesco Danilo Tiziano

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-0274-7178

28. Francesco Danilo Tiziano

    1. Department of Life Sciences and Public Health, Section of Genomic Medicine, Università cattolica del Sacro Cuore, Roma, Italy
    2. Unit of Medical Genetics, Department of Laboratory science and Infectious Diseases, Fondazione Policlinico Universitario IRCCS “A. Gemelli”, Rome, Italy

    Contribution

    Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Writing – review and editing

    Contributed equally with

    Lucia Di Marcotullio

    For correspondence

    francescodanilo.tiziano@unicatt.it

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-5545-6158

• 1,058

  views

• 183

  downloads

• 19

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.