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Review ARTICLE

Front. Neurol., 19 August 2019 | https://doi.org/10.3389/fneur.2019.00898

Biomarkers and the Development of a Personalized Medicine Approach in Spinal Muscular Atrophy

Didu S. T. Kariyawasam1,2, Arlene D'Silva2, Cindy Lin3, Monique M. Ryan4 and Michelle A. Farrar1,2*
  • 1Department of Neurology, Sydney Children's Hospital, Sydney, NSW, Australia
  • 2School of Women's and Children's Health, University of New South Wales Medicine, University of New South Wales, Sydney, NSW, Australia
  • 3Department of Neurophysiology, Brain and Mind Center, University of Sydney, Sydney, NSW, Australia
  • 4Department of Neurology, Murdoch Children's Research Institute, Royal Children's Hospital, University of Melbourne, Melbourne, VIC, Australia

Recent unprecedented advances in treatment for spinal muscular atrophy (SMA) enabled patients to access the first approved disease modifying therapy for the condition. There are however many uncertainties, regarding timing of treatment initiation, response to intervention, treatment effects and long-term outcomes, which are complicated by the evolving phenotypes seen in the post-treatment era for patients with SMA. Biomarkers of disease, with diagnostic, prognostic, predictive, and pharmacodynamic value are thus urgently required, to facilitate a wider understanding in this dynamic landscape. A spectrum of these candidate biomarkers, will be evaluated in this review, including genetic, epigenetic, proteomic, electrophysiological, and imaging measures. Of these, SMN2 appears to be the most significant modifier of phenotype to date, and its use in prognostication shows considerable clinical utility. Longitudinal studies in patients with SMA highlight an emerging role of circulatory markers such as neurofilament, in tracking disease progression and response to treatment. Furthermore, neurophysiological biomarkers such as CMAP and MUNE values show considerable promise in the real word setting, in following the dynamic response and output of the motor unit to therapeutic intervention. The specific value for these possible biomarkers across diagnosis, prognosis, prediction of treatment response, efficacy, and safety will be central to guide future patient-targeted treatments, the design of clinical trials, and understanding of the pathophysiological mechanisms of disease and intervention.

Introduction

Spinal muscular atrophy (SMA) is characterized by progressive loss of motor neurons in the brainstem and spinal cord resulting in muscle weakness (1). It is the leading inherited cause of infant mortality with severity ranging from progressive infantile paralysis and premature death (Type I) to limited motor neuron loss and normal life expectancy (Type IV) (2, 3). SMA is caused by homozygous disruption in the survival motor neuron gene 1 (SMN1) (4, 5), whereas the disease severity is mainly influenced by the number of SMN2 gene copies (6). A diagnosis of SMA has a profound impact on patients and their families (7, 8).

Recent advances in the demonstrated therapeutic efficacy of novel genetic and molecular therapies for SMA are fueling an unprecedented upsurge in clinical treatment (9). Phenotypic heterogeneity, that is inherent to this condition, may result in difficulties in providing early and accurate diagnosis, prognosis, assessment of disease activity and monitoring of treatment response. Within this dynamic setting the need for biomarkers to provide an objective measure is never more essential, to facilitate decision-making in clinical pathways for patients and guide therapeutic interventions in a tailored way (Figure 1).

FIGURE 1
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Figure 1. The utility of biomarkers in SMA treatment; current and future applications.

Biomarkers may serve different purposes, but ideally share common key qualities (Table 1). These include stability in healthy individuals, with significantly different levels in disease cohorts to identify affected individuals. High degrees of sensitivity, specificity, precision, and reproducibility are also vital in an efficacious biomarker. In addition, biomarkers should reflect disease pathology, rather than disease epiphenomena and ideally be measured with ease, speed, and minimum expense in the target population.

TABLE 1
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Table 1. The classification of biomarkers.

This review will focus on the spectrum of candidate biomarkers in SMA, explore their role in facilitating our understanding of pathogenic mechanisms of disease and their clinical, pharmacological, and therapeutic utility. A variety of approaches are in early stages of discovery and development, encompassing biomolecular assays in serum and cerebrospinal fluid, as well as novel and conventional electrophysiological and neuroimaging assessments (Figure 2). A comprehensive understanding of factors that modify biological and pathogenic processes in SMA is therefore essential to realizing and curating efficacious biomarkers in this disorder.

FIGURE 2
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Figure 2. Potential electrophysiological, radiological, and circulating biomarkers for SMA.

Circulating Biomarkers

Circulatory biomarkers have garnered significant attention to date, as tools for biomarker guided therapy in SMA. Their role spans a spectrum from prognostication, prediction of treatment response and monitoring the effects of therapeutic agents.

Survival Motor Neuron Protein: The Cornerstone of SMA

SMA is caused by insufficient levels of the survival motor neuron (SMN) protein, due to biallelic SMN1 deletion or mutation. The severity of SMA varies across a spectrum and is modified by the number of copies of the paralogous SMN2 gene in humans, with the major difference conferred by a C to T nucleotide change in exon 7 (10, 11). This nucleotide change, though translationally silent, results in predominant skipping of exon 7 during SMN2 pre-mRNA splicing, giving rise to a truncated transcript and protein (12, 13). Alternative splicing enables ~10% of SMN2 transcripts to include exon 7 and produce a small amount of functional SMN (11, 14). SMN RNA and protein are ubiquitously expressed and have multiple roles in normal biological processes. These include general “housekeeping” and cell specific roles in ribonucleoprotein assembly, RNA metabolism (15), macromolecular trafficking, actin dynamics, and signal transduction (16). Alterations at any level of transcription, translation or splicing can lead to dysregulation of pathways involved in SMN protein production and potentially modify disease phenotype.

Therapeutic development has focused on augmenting SMN. The first approved drug for SMA (nusinersen), is an intrathecally delivered antisense oligonucleotide (ASO). Nusinersen was developed to alter the splicing of SMN2 pre-mRNA by promoting inclusion of SMN2 exon 7 by sequestering an inhibitory cis-element called Intronic Splicing Silencer N1 or ISS-N1, thus increasing concentrations of functional SMN protein (17, 18). Onasemnogene Abeparvovec (Zolgensma) is a one-time SMN1 gene replacement therapy that may be administered intravenously or intrathecally (19). Several additional SMN induction therapies are currently in development, including systemic small molecules, such as risdiplam (20).

SMN protein levels are also dependent on degradation pathways. As such, the pathogenesis of SMA has been linked to mutations in the ubiquitin activating enzyme (UBA1) gene, encoding UBA1 that plays a crucial role in the ubiquitin proteasome system (UPS). Levels of SMN protein can be regulated by the UPS also making this a potential therapeutic target for SMA (21, 22). Previous studies have shown that pharmacological inhibition of the proteasome and UPS downstream targets can lead to phenotypic improvements in SMA mice (2224). Findings from an animal study have identified an important role of SMN in the maintenance of ubiquitin homeostasis with decreased levels of UPS as a driving factor in SMA pathogenesis (24). There is a need for future studies to evaluate if different aspects of the UPS that are perturbed in SMA could act as potential drug targets independently or in combination with other SMN dependent strategies. With this clarification, ubiquitin pathways may in the future be proposed as putative mechanistic biomarkers of pharmacodynamic response to SMN enhancing therapies.

SMN protein is an obvious pharmacodynamic biomarker that can be measured from biological samples. This is aligned with therapeutic approaches designed to increase SMN levels. For example, preliminary data from clinical trials evaluating risdiplam, show >2-fold increase in SMN2 protein levels in participants with SMA (20, 25). Similar results have been seen with salbutamol; a compound that increases SMN2 full length transcript and SMN protein. In this study, a subjective improvement of motor function was noted in all patients with a statistically significant improvement in validated functional scores in a proportion of patients (26). This suggests target engagement and a potential method of tracking treatment response. Future studies are needed to determine the utility of SMN protein levels as a biomarker to guide dose optimization or frequency of the therapeutic regime.

There are limitations associated with SMN protein being used as a sole universal biomarker. For example, in one murine study, levels were unchanged in mice who were treated with ASO therapy, compared to an untreated cohort. Potentially, this could be attributed to utilizing blood assays to evaluate protein levels, when determining effects of CSF directed therapies (27). In a prospective analysis of SMN transcript and protein levels, as anticipated, plasma SMN protein was significantly lower in SMA samples compared to control. But, SMN protein levels did not correlate with SMN2 copy number, disease severity or motor function (28). This lack of correlation has been attributed to modulation of SMN at the post-transcriptional level. Further studies have focused on assays of SMN protein levels in specific cell lines to circumvent variabilities of levels found in blood profiles. For example, spot analysis in peripheral blood nuclear cells, particularly CD33++ cells, has been shown to be a potential parameter of functional SMN protein levels (29). Additional validation studies will be necessary to demonstrate the efficacy of spot analysis.

Neurofilaments

Neurofilaments (NF) are cytoplasmic proteins abundantly expressed in axons that have recently been recognized as promising diagnostic, prognostic, and monitoring biomarkers in a range of neurological disorders associated with axon loss (3032). Initial discovery studies focused on modulation of SMN2 encoded transcripts in children with SMA identifying NFs as potential biomarkers of disease activity and therapeutic response (33, 34). Across large nusinersen clinical trials with SMA types 1, 2 and presymptomatic infants (2 or 3 SMN2 copies), plasma phosphorylated neurofilament heavy chain (pNF-H) differentiated SMA individuals from healthy controls. However, conclusions were limited by a small number of healthy pediatric age-matched controls (34, 35). Treatment initiation with nusinersen was associated with rapid decline followed by stabilization of pNF-H at levels close to those of healthy controls. pNF-H declined with advancing age in untreated patients, possibly due to reductions in motor neuron pool or disease activity. This raises uncertainty about its ability to demarcate whether the decline is due to physiological aging or a surrogate marker for treatment response. Serum neurofilament light chain (NfL) levels have also been evaluated in clinical trials investigating the safety and efficacy of branaplam, a small-molecule RNA splicing modulator. Preliminary results identified an inverse correlation between pre-treatment NfL levels and motor function scores in participants with SMA type I (36).

It is not yet clear which proteins released from motor neurons (NfL, Nf-H, or others) will be more sensitive in detecting the earliest stages of degeneration. These may have utility in the “presymptomatic” patient, helping guide decisions regarding when to start treatment. In addition, these may serve as pharmacodynamic markers, to verify suppression of continuing degeneration. Further evaluation of NFs across SMA populations and evaluation of potential correlations with efficacy outcomes is required.

Genetic Modifiers

Genetic modifiers may enhance or suppress the effects of pathogenic mutations. Genetic modifiers also improve our mechanistic understanding of SMA and may identify novel targets for therapeutic intervention and future combination regimens. This knowledge serves to enhance our understanding of prognostic biomarkers in SMA, providing information on disease evolution and phenotypic severity.

SMN2

The SMN2 copy number is the most important modifier of clinical course in SMA, correlating inversely with age of symptom onset and severity (3739), albeit with limitations in precision. Epidemiological studies demonstrate that more than 95% of individuals with ≤2 copies of SMN2 have SMA type 1 with symptom onset in the first 6 months of life. Furthermore, <5% have SMA types 2 and 3 with symptom onset in early childhood (4042). All major phenotypes of SMA are encompassed with ≤3 SMN2 copies (40).

With the advent of newborn screening for SMA, SMN2 copy number is emerging as a vital marker to guide the type and extent of intervention and stratify newborn patients to differing treatment arms. For example, there is consensus among experts that pre-symptomatic infants with ≤3 copies of SMN2 should be promptly treated with disease modifying therapy, as they are predicted to have early onset forms (43). However, SMN2 copy number has limitations, lacking precision as a biomarker of disease onset and prognosis. Theoretically, SMN2 copy number acts in a dose-dependent manner to ameliorate the SMA phenotype. However, in observational studies, those with higher copy numbers do not always have a mild disease phenotype (44). This inherent limitation of SMN2 as a prognostic biomarker, leads some experts to argue for disease-modifying treatment in all presymptomatic newborns with ≤4 SMN2 copy numbers as identified through newborn screening programmes. Considerable overlap in copy number exists amongst phenotypic subgroups of patients with SMA (40). Furthermore, discordance in phenotype and response to therapy is noted in siblings with the same copy number, showing that there are other modifiers of disease at work in these individuals (45, 46). For example, sequence variations within the SMN2 gene may positively modify phenotype, particularly the c.859G>C variant which increases inclusion of exon 7 and the amount of full length SMN transcript (47, 48). The latter is being used to stratify data analysis in current clinical trials (NCT03505099). Additional variants in SMN2 introns 6 and 7 have been shown to alter the incorporation of exon 7 (49). Intron 6 variants (A-44G, A-549G, and C-1897T) have also been associated with milder SMA phenotypes in further studies (49, 50).

Plastin 3

Plastin 3 (PLS3) is a calcium dependent F-actin-binding protein (51). The latter forms an integral part of the axonal cytoskeleton and is thus crucial in a spectrum of cellular pathways, from axonal maturation to vesicular migration and endocytosis. PLS3 is a gender-specific, positive modifier, altering severity of SMA phenotype, in post-pubertal female patients only (52, 53). In murine models of SMA, PLS3 overexpression delays axonal pruning and rescues neuromuscular junction (NMJ) function (54). The molecular basis of PLS3 overexpression in unaffected individuals is unknown. Additionally, the degree to which PLS3 expression modifies SMA phenotype remains contentious, in part due to experiments in SMA mice showing no survival or electrophysiological benefit of PLS3 over-expression (55). A recent study analyzed effect of PLS3 on a panel of six potential biomarkers in mice (27). PLS3 overexpression neither influenced the SMN level nor the other experimental biomarkers, supporting the hypothesis that it acts as an independent protective modifier. Further studies are essential to translate these pre-clinical findings to the clinical sphere before the utility of PLS3 as a biomarker for SMA can be fully elucidated.

Coronin 1C

Another phenotypic modifier is Coronin 1C that acts simultaneously on axons and muscles of the motor unit, by interacting with PLS3, in a calcium dependent manner to increase the amount of the F-actin (56). Additionally, PLS3 and Coronin 1C co-localize and work together in growth cones, axonal compartments and cell membranes of motor neurons (57). Both PLS3 and Coronin 1C rescue reduced vesicular pools in presynaptic terminals and restore NMJ function by facilitating endocytosis (55), leading to increased levels of F-actin. Consequently, in SMN depleted animal models where abnormal axonal branching and premature truncation are noted, this restoration of F-actin dynamics, ameliorate these structural anomalies and the SMA phenotype (56). Identification of this PLS3 interacting protein highlights the role of actin dynamics in pathomechanisms of SMA. Further research into protective modifiers such as PLS3 and Coronin 1C will provide improved prognostic genetic modifiers helping stratify disease severity and revealing potential targets for therapeutic intervention.

Splicing Regulators as Modifiers of Phenotype

Aberrant splicing plays a significant role in SMA pathogenesis. Consequently, the development of biomarkers that accurately capture splicing events would greatly advance understanding of the control of SMN gene expression. Splicing regulators such as microRNA, epigenetic modifications and long non-coding RNA (lncRNA) may emerge as putative biomarkers.

MicroRNA

MicroRNAs (miRNAs) regulate gene expression. Studies have shown their potential as non-invasive biomarkers in SMA (58). Differential expression of miRNAs (miR-9, miR-206, and miR-132) have been reported in spinal cord, skeletal muscle and serum from SMA and control mice, and in serum samples only from SMA and control patients. The SMA mice cohort presented with different severities (severe SMA-I and mild SMA-III) at different disease stages (presymptomatic, mid-symptomatic, and late stage). Serum miRNAs were altered prior to neuroanatomical changes in spinal cord and skeletal muscle at the presymptomatic stage. Especially, miR-132 was found to be most responsive to systemic ASO treatment in the severe mouse model (58). Thus, these experimental parameters may form the basis for an early diagnostic biomarker, particularly in the latent phase of disease, which can be used to track treatment response to SMN2 enhancing therapies.

Epigenetic Modifications: Methylation

Studies suggest that epigenetic effects such as SMN2 methylation may regulate SMA disease phenotype by modulating its transcription (5961). Genome wide methylation studies have determined differences in methylation patterns in certain genes, suggesting involvement of their proteins in pathogenesis of SMA (62). Discordant sibling pairs with identical SMN genotypes, suggest that epigenetic modification may control individual variations in the SMN2 function (59).

Long Non-coding RNAs

Recent advances have demonstrated that a significant portion of the genome is actively transcribed as non-coding RNA molecules. lncRNAs may be potential biomarkers of neuronal dysfunction due to their role in regulation of biological processes, and as “fine-tuners” of gene expression. SMA has long been considered not only a motor neuronal disease, but a process of anomalous neurodevelopment (6366). The understanding of lncRNAs in SMA is still very limited. As proof of concept, one study has reported that targeting lncRNA to transcriptionally activate SMN2, in combination with SMN2 splicing modification, ameliorates SMA. This demonstrates the promise of combinatorial ASO therapy in SMA (67). Future studies are needed to elucidate the prospect of lncRNAs as diagnostic and prognostic biomarkers and therapeutic targets.

Electrophysiological Biomarkers

Serum biomarkers in SMA are in early phases of discovery and validation. Conversely, electrophysiological biomarkers have been studied, validated, and used globally in clinical trials in neuromuscular disease to assess the functional status of the motor unit pool in vivo (Figure 3) (68, 69).

FIGURE 3
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Figure 3. Healthy vs. SMA motor units: (A) healthy muscles are of uniform fiber size innervated by motor neurons. Neurophysiological measures show a high CMAP, full complement of incremental motor units and a silent EMG in relaxed muscle. (B) SMA muscle shows a mixture of small, denervated muscle fibers, and large hypertrophic units secondary to collateral reinnervation. Neurophysiological measures show a reduced CMAP, reduction in incremental motor unit number and fibrillations on EMG.

Compound Muscle Action Potential and Electromyography

A compound muscle action potential (CMAP) is obtained by providing a supramaximal stimulus to a motor nerve. The muscle response recorded is an indirect measure of the number of intact and available motor neurons, representing total functional status of the motor unit (70, 71). Electromyography (EMG) fibrillation potentials and positive sharp waves are spontaneous action potentials recorded from a relaxed muscle and generated by denervated muscle fibers (72). These parameters are proposed as easily collated potential biomarkers of disease in patients with SMA (73), measured with equipment used in the course of routine neurophysiology studies.

In murine models of presymptomatic SMA type 1, CMAP values are initially comparable to healthy controls. Subsequently a significant and rapid decline in CMAP correlates with a period of functional motor loss (disease onset) in affected mice (71). Similarly, a significant reduction in CMAP amplitude compared to healthy controls is noted in murine models of SMA type 2 and accompanied by needle EMG fibrillation potentials (71). When SMN restoration therapies in the form of ASOs are introduced, CMAP initially stabilizes, recovering gradually over time. Electrophysiological recovery correlates with functional recovery (71).

Clinical studies have also characterized CMAP as a surrogate marker of disease onset, status, and progression. A precipitous decline in CMAP is associated with sudden and significant functional decline in studies of prenatally defined patients (42). An ulnar CMAP ≤1.5 mV and evidence of denervation on EMG has thus been proposed as a threshold to consider disease onset in presymptomatic newborns, genetically confirmed with SMA. These electrophysiological parameters have also been incorporated into clinical trial design, to characterize the magnitude of motor neuron reserve at the time of treatment initiation in presymptomatic infants (18, 74).

The utility of the CMAP as a surrogate marker of disease status is replicated across all phenotypes of SMA. CMAP amplitude is found to discriminate between ambulant and non-ambulant patients in one cohort study (75). In patients with SMA significant reductions in CMAP correlate with reduced functional motor scores reproducibly over a number of studies (76, 77). Thus, CMAP measurement can potentially facilitate stratification of disease severity, as an adjunct to SMN2 copy number. Patients with severe forms of SMA (≤2 copies of SMN2) show smaller and sometimes undetectable CMAP amplitudes, compared to those with milder genotypes (>2 copies of SMN2) (78).

The CMAP also holds potential as a biomarker of disease progression with patterns of decline dependent on phenotype. In a large natural history study of patients with SMA, overall CMAP declined significantly over the 5-year study period. In SMA type 1, this decline was predominantly in the first month of life, with relative stability thereafter. Patients with type II SMA have modest ongoing decline in CMAP amplitudes, whilst patients with SMA type III have relatively stable CMAPs throughout the study interval (42).

Longitudinal changes in CMAP have been examined as a secondary outcome measure and possible predictive biomarker of response to disease-modifying agents in clinical trials. The ENDEAR (phase 3 nusinersen) reported that motor milestone response occurred in 41% of patients, and with CMAP response in 36% treated infants. CMAP could therefore be adjunctive to longitudinal changes in functional motor scores to assess treatment response to therapeutic intervention (18). In children with later onset SMA, treatment with nusinersen over several years produced improvements in motor function, concomitant with stable CMAP readings (79). Further detailed neurophysiological assessments may provide a deeper understanding of treatment response.

The CMAP is also being explored as a putative prognostic biomarker. Natural history studies have shown that maximal baseline CMAP has a relative prognostic value in determining functional outcome in untreated patients with SMA (42). This putative prognostic biomarker may determine the severity of pre-existing denervation and be useful in setting realistic goals for therapeutic intervention (42). However, baseline CMAP has limitations in predicting other important clinical outcomes, with studies determining that it does not correlate with risk and age of death or use of permanent ventilation (78).

Standardized operating protocols are essential to ensure the CMAP is a feasible, valid, reliable, and reproducible outcome measure. Limiting factors such as background noise, inconsistent electrode placement and contact, may produce large errors, particularly in the small CMAP measurements evident in late disease stages (75, 79, 80). Patient tolerance and cooperation (to avoid movement and EMG artifact) is also critical to success. Specificity of these measures is also low. Localizing areas of pathology within the motor unit and consequently pinpointing targets of therapeutic intervention, is impossible with such qualitative markers. Dysfunction at any point along the motor unit can contribute to changes in CMAP amplitudes and development of fibrillation potentials (81). Additionally, CMAP amplitudes may be preserved despite loss of motor neurons due to compensatory changes (collateral reinnervation). This is especially noted in earlier stages prior to loss of significant proportions of motor units (68, 82). Therefore, other measures are essential to complement CMAP to determine the motor neuron input to muscles and show progression of disease through falling numbers of motor units (42).

Motor Unit Number Estimation and Single Motor Unit Potentials

Motor unit number estimation (MUNE) provides an assessment of axon number and the capacity for reinnervation represented by size of the average single motor unit potential (SMUP). These parameters are linked closely to pathophysiology in SMA (42, 68, 8385). Furthermore, in comparison to clinical measures of strength and function in SMA, MUNE as an objective measure is not limited by effort, fatigue, contractures, or developmental stage. Multiple ways of estimating MUNE exist. An incremental MUNE technique uses increasing strengths of submaximal stimuli to recruit motor units into the firing pool to determine the average SMUP (86). Traditional MUNE methods are derived from the basis that maximum CMAP is a composite measure of the number of functional motor units and the average amplitude made by a single motor unit (87).

In rodent models of SMA, MUNE values are significantly reduced. Declines in MUNE correlate directly with emergence of the motor phenotype (71). Administration of ASOs correlate with improvement in phenotype and MUNE values, reflecting preservation of the threatened motor unit pool (71, 88). This effect is enhanced at points distant to the time of administration, possibly depicting ongoing effects of intervention (71).

MUNE is proposed as a diagnostic marker of disease onset, as values in symptomatic children with SMA remain significantly below that of healthy infants (42). In small numbers of pre-symptomatic neonates and infants, changes in MUNE value were highly sensitive to early deterioration in the motor unit pool and a precursor to manifestation of clinical symptoms (18, 42). MUNE may also be helpful in the broad stratification of SMA phenotype, with lower values generally noted in the more severe forms (SMA type I), compared to milder phenotypes (SMA type III) (89). However, considerable overlap exists in MUNE values between SMA phenotypes (42), and thus its use for predicting disease severity may have limitations.

In some studies, MUNE values appear to be highly dependent on and inversely related to disease duration in untreated patients (42), however stability in the chronic phase may be apparent for many individuals. Other studies demonstrate increases in MUNE in untreated subjects while CMAPs remain stable longitudinally. This leads the suggestion that new motor unit development may occur as a compensatory response to motor unit loss (73). Alternatively, these findings may be linked to a normal developmental, maturational process, causing collateral innervation with polysynaptic connections amongst motor units, which are pruned, becoming monosynaptic as motor units physiologically mature (90). In treated cohorts, MUNE has been assessed over the longer term in phase 1/2 studies of nusinersen, demonstrating relative stability of values in children with SMA type II, compared with longitudinal declines in SMA type III, despite improvements in functional scores in the latter group (79). These findings may be explained by the focus of MUNE on distal muscles such that it may not reflect the proximal effects of novel therapies. For example, distal reinnervation may be difficult to achieve, especially where a high demand for SMN protein exists to maintain and increase motor unit capacity, in the growing phase of a child with later onset forms of SMA (79). Because of these differing findings, MUNE may not be helpful when used in isolation as a surrogate biomarker of disease progression and response to treatment. A more personalized approach, accounting for disease duration and severity may enable MUNE to be more judiciously used as a precision biomarker. Additional studies are vital to further understand MUNE values within the dynamic setting of pathological and normal developmental pathways of neuronal denervation/reinnervation, and to establish lower-limit normative values (91).

Variability in MUNE may be influenced by the operator's expertise, such that standardized procedures and training are critical. Different MUNE techniques have been developed, varying in procedures and the manner that SMUPs are calculated. The methodology of deriving MUNE may thus change the sensitivity, specificity, and reproducibility of this biomarker. Early MUNE methods were derived from the basis that the maximum CMAP is a composite measure of the number of functional motor units and the average amplitude made by a single motor unit as it is “recruited” into firing by submaximal stimulation (86). Average SMUP size is traditionally derived by increasing stimulus size to recruit increasing numbers of individual motor units into the firing pool and averaging the amplitude of these units (incremental method). In SMA, this method of deriving MUNE correlates well with functional motor scores, particularly sensitive to severity of weakness. Average SMUP size shows negative correlation to motor score, perhaps denoting a process of reinnervation with increasing severity of weakness. These findings confirm the incremental method's potential utility in showing motor neuron loss and tracking decompensation/compensation changes through SMUP (85). Longitudinal studies are awaited to confirm these results.

There are inherent limitations of incremental methods. These affect the accuracy of motor unit estimation (92). Other methodologies such as multi-point method samples the first all-or-none SMUP response to a low-level stimulus, over repeated points along the motor nerve to collate an average sample of SMUPs (93). An adapted system, combining incremental stimulus and multiple point analysis is increasingly used to improve estimate accuracy. Novel, computerized methods of MUNE are coming to the fore including the MScan fit and MUNIX methods, to improve analysis accuracy and time, and circumvent biases incurred by operator input, which are intrinsic weaknesses noted in traditional methods (94, 95). Their clinical role is yet to be elucidated in terms of sensitivity, specificity, and reproducibility in healthy controls and for individuals with SMA.

Neuroimaging Biomarkers

Electrical Impedance Myography

Electrical impedance myography (EIM) summates surface muscle action potentials derived from direct application of a low-intensity, high frequency stimulus to muscle. The method involves quantitative measurement of changing parameters in the muscle's extra and intracellular fluid resistance, and cell membrane reactance (as a measure of its capacitance), to a sinusoidal current, applied at varying frequencies (96). This measure reflects the changing intrinsic properties of the muscle (97), denoting tissue quality, including changes secondary to fibrosis, denervation, and edema (98). The adult onset disease amyotrophic lateral sclerosis (ALS) has acted as a model for biomarker exploration in disease of upper and lower motor neurons. Findings in this condition may therefore be extrapolated to other motor neuron pathologies such as SMA. In studies of disease progression in ALS, this method correlates well with traditional MUNE methods, and validated functional motor scores. It is sensitive to subtle disease progression (99), easy to perform with little technical training required, and allows proximal muscles to be sampled. Use of EIM was purported to reduce sample size significantly in one multi-center trial (100). Its use in tracking therapeutic interventions secondary to muscle-specific therapies such as myostatin inhibitors is also theoretically possible. However, its utility has significant limitations. For instance, its specificity as a diagnostic identifier of ALS, when compared to ALS-mimics, is low (100). Furthermore, in a small clinical trial of EIM in children with SMA, this parameter remained static over the study period, when compared to healthy controls where EIM showed non-mass dependant muscle maturation (101). It is difficult to know if this reflects lack of muscle maturation, absence of decline in muscle fibers, or a rate of decline that counteracts maturation potential in disease cohorts (101).

MRI and Ultrasound

Magnetic resonance imaging (MRI) of skeletal muscle has been used in small numbers of pre-clinical and clinical studies to provide alternative markers of disease. Muscle composition changes with disease duration in SMA (102). Changes in parameters such as muscle fat fraction correlate well with validated functional motor scores in non-SMA motor neuropathies and are highly sensitive to disease progression (103). Ultrasound offers a different modality for assessment of muscle composition in patients with motor neuropathies. Muscle thickness and echo intensity have been reviewed to a limited extent in ALS, where one study showed use in prognosticating survival (104). Longitudinal studies have noted a reduction in hand muscle cross-sectional area in ALS disease cohorts (105), but these changes correlate poorly with functional abilities and may therefore not be a sensitive representative of disease progress (106).

Significant work needs to be done before MRI or US can be purported as a suitable method for producing viable biomarkers in SMA. Firstly, it is necessary to trace changes in muscle composition and architecture in healthy controls over time, to establish normal age-dependant baselines. Furthermore, standardized protocols encompassing type and number of muscles assessed, techniques for evaluation, and comparability of equipment used is pivotal before the feasibility and reproducibility of these biomarkers can be elucidated.

Exploratory Biomarkers of the Neuromuscular Junction

Repetitive Nerve Stimulation

Whilst SMA is considered a primary neurogenic process, it is increasingly recognized that it is associated with concomitant dysfunction at the level of the NMJ, secondary to lack of the SMN protein required for normal NMJ development and maturation (107109). Abnormalities in NMJ impulse transmission may be responsible for significant degrees of fatigability commonly reported by affected individuals, and clinically observed when using validated measures of endurance such as the 6-min walk test (110112). Therefore, an electrophysiological biomarker such as repetitive nerve stimulation (RNS) to determine the presence and extent of NMJ dysfunction (113), may facilitate a different modality of looking at pathophysiology and response to novel therapies. In a recent study of SMA cohorts, a pathological decrement on low frequency RNS was noted in 49% of patients with SMA but not in healthy controls or patients with other motor neuron diseases (108). This decrement was independent of SMA subgroup, clinical score, and disease duration (108). Therapies directed toward augmentation of NMJ impulse transmission provide a novel target, with pyridostigmine currently being assessed in a phase II clinical trial (NCT02941328) (114). Thus, RNS may be helpful as a biomarker of treatment response in therapies that augment NMJ function.

Conclusion

Within the changing SMA therapeutic landscape there are many current clinical uncertainties, including determination of optimal timings, regimen, mode, and response to treatment. The target treatment population and prognostication of meaningful endpoints for patients also require definition. A repertoire of validated biomarkers is essential. This will enable a comprehensive evaluation of SMA and facilitate clinical decision-making across diagnosis, prognosis, pharmacotherapy, and support future research advances. While numerous genetic, epigenetic, proteomic, electrophysiological, and imaging biomarkers have been suggested, future studies are needed to determine reproducibility across the SMA population. Thus far, SMN2 appears to be a highly valuable prognostic biomarker in SMA. Its utility includes stratification of patients for treatment in research and clinical spheres. Better understanding of other genetic and epigenetic modifiers would facilitate an individualized approach to prognostication. More recently, neurofilament has been proposed as a pharmacodynamic biomarker of disease response to treatment and is being investigated across the phenotypic spectrum of SMA. Neurophysiological parameters of motor unit function include CMAP and MUNE assessments. These have been used primarily as surrogate markers of disease onset and to track disease progression in treated and untreated cohorts. Emerging data suggests that they may also have a potential role in disease prognostication and help predict treatment responders. Nevertheless, there may not be a “one size fits for all” biomarker. Different biomarkers may be required to assess specific questions related to disease progression, treatment efficacy, safety, and prognostic endpoints. Suitable biomarkers may change depending on the therapeutic target of medical intervention, as increasing number of agents come to the fore and compete for use in patients with SMA. As a collective, the ultimate aim of these biomarkers is to enable a personalized approach to management, facilitating a smooth and optimal pathway for the patient through their clinical journey.

Author Contributions

DK, AD'S, and MF planned the manuscript. DK and AD'S executed and prepared the first and subsequent drafts of the manuscript. CL, MR, and MF contributed to manuscript revision. All authors read and approved the submitted version.

Disclosure

MF and MR had received honoraria for scientific advisory boards from Biogen, Roche and AveXis. AD'S received funding from the Sydney Children's Hospital Foundation Researcher Grant (RG182501).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors acknowledge Dr. Kate Carey for her contribution to the preparation of figures in the manuscript. AD'S acknowledges funding from the Sydney Children's Hospital Foundation. DK acknowledges funding from the Freedman Family Foundation.

References

1. Bharucha-Goebel D, Kaufmann P. Treatment advances in spinal muscular atrophy. Curr Neurol Neurosci Rep. (2017) 17:91. doi: 10.1007/s11910-017-0798-y

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Groen EJN, Talbot K, Gillingwater TH. Advances in therapy for spinal muscular atrophy: promises and challenges. Nat Rev Neurol. (2018) 14:214. doi: 10.1038/nrneurol.2018.4

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Darras BT, Markowitz JA, Monani UR, De Vivo DC. Chapter 8 - Spinal muscular atrophies. In: Darras BT, Jones HR, Ryan MM, De Vivo DC, editors. Neuromuscular Disorders of Infancy, Childhood, and Adolescence. 2nd ed. San Diego, CA: Academic Press (2015) 117–45. doi: 10.1016/B978-0-12-417044-5.00008-1

CrossRef Full Text | Google Scholar

4. Farrar MA, Kiernan MC. The genetics of spinal muscular atrophy: progress and challenges. Neurotherapeutics. (2015) 12:290–302. doi: 10.1007/s13311-014-0314-x

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Lefebvre S, Bürglen L, Reboullet S, Clermont O, Burlet P, Viollet L, et al. Identification and characterization of a spinal muscular atrophy-determining gene. Cell. (1995) 80:155–65. doi: 10.1016/0092-8674(95)90460-3

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Feldkötter M, Schwarzer V, Wirth R, Wienker TF, Wirth B. Quantitative analyses of SMN1 and SMN2 based on real-time lightcycler PCR: fast and highly reliable carrier testing and prediction of severity of spinal muscular atrophy. Am J Hum Genet. (2002) 70:358–68. doi: 10.1086/338627

PubMed Abstract | CrossRef Full Text | Google Scholar

7. Wan HWY, Carey KA, D'Silva A, Kasparian NA, Farrar MA. “Getting ready for the adult world”: how adults with spinal muscular atrophy perceive and experience healthcare, transition and well-being. Orphanet J Rare Dis. (2019) 14:74. doi: 10.1186/s13023-019-1052-2

CrossRef Full Text | Google Scholar

8. Farrar MA, Carey KA, Paguinto S-G, Chambers G, Kasparian NA. Financial, opportunity and psychosocial costs of spinal muscular atrophy: an exploratory qualitative analysis of Australian carer perspectives. BMJ Open. (2018) 8:e020907. doi: 10.1136/bmjopen-2017-020907

PubMed Abstract | CrossRef Full Text | Google Scholar

9. Kariyawasam D, Carey KA, Jones KJ, Farrar MA. New and developing therapies in spinal muscular atrophy. Paediatr Respir Rev. (2018) 28:3–10. doi: 10.1016/j.prrv.2018.03.003

PubMed Abstract | CrossRef Full Text | Google Scholar

10. Monani UR, Lorson CL, Parsons DW, Prior TW, Androphy EJ, Burghes AH, et al. A single nucleotide difference that alters splicing patterns distinguishes the SMA gene SMN1 from the copy gene SMN2. Hum Mol Genet. (1999) 8:1177–83. doi: 10.1093/hmg/8.7.1177

PubMed Abstract | CrossRef Full Text | Google Scholar

11. Cartegni L, Krainer AR. Disruption of an SF2/ASF-dependent exonic splicing enhancer in SMN2 causes spinal muscular atrophy in the absence of SMN1. Nat Genet. (2002) 30:377–84. doi: 10.1038/ng854

PubMed Abstract | CrossRef Full Text | Google Scholar

12. Gennarelli M, Lucarelli M, Capon F, Pizzuti A, Merlini L, Angelini C, et al. Survival motor neuron gene transcript analysis in muscles from spinal muscular atrophy patients. Biochem Biophys Res Commun. (1995) 213:342–8. doi: 10.1006/bbrc.1995.2135

PubMed Abstract | CrossRef Full Text | Google Scholar

13. Burnett BG, Munoz E, Tandon A, Kwon DY, Sumner CJ, Fischbeck KH. Regulation of SMN protein stability. Mol Cell Biol. (2009) 29:1107–15. doi: 10.1128/MCB.01262-08

PubMed Abstract | CrossRef Full Text | Google Scholar

14. Kashima T, Manley JL. A negative element in SMN2 exon 7 inhibits splicing in spinal muscular atrophy. Nat Genet. (2003) 34:460–3. doi: 10.1038/ng1207

PubMed Abstract | CrossRef Full Text | Google Scholar

15. Li DK, Tisdale S, Lotti F, Pellizzoni L. SMN control of RNP assembly: from post-transcriptional gene regulation to motor neuron disease. Semin Cell Dev Biol. (2014) 32:22–9. doi: 10.1016/j.semcdb.2014.04.026

PubMed Abstract | CrossRef Full Text | Google Scholar

16. Chaytow H, Huang Y-T, Gillingwater TH, Faller KME. The role of survival motor neuron protein (SMN) in protein homeostasis. Cell Mol Life Sci. (2018) 75:3877–94. doi: 10.1007/s00018-018-2849-1

PubMed Abstract | CrossRef Full Text | Google Scholar

17. Mercuri E, Darras BT, Chiriboga CA, Day JW, Campbell C, Connolly AM, et al. Nusinersen versus sham control in later-onset spinal muscular atrophy. N Engl J Med. (2018) 378:625–35. doi: 10.1056/NEJMoa1710504

PubMed Abstract | CrossRef Full Text | Google Scholar

18. Finkel RS, Mercuri E, Darras BT, Connolly AM, Kuntz NL, Kirschner J, et al. Nusinersen versus sham control in infantile-onset spinal muscular atrophy. N Engl J Med. (2017) 377:1723–32. doi: 10.1056/NEJMoa1702752

PubMed Abstract | CrossRef Full Text | Google Scholar

19. Mendell JR, Al-Zaidy S, Shell R, Arnold WD, Rodino-Klapac LR, Prior TW, et al. Single-dose gene-replacement therapy for spinal muscular atrophy. N Engl J Med. (2017) 377:1713–22. doi: 10.1056/NEJMoa1706198

PubMed Abstract | CrossRef Full Text | Google Scholar

20. Poirier A, Weetall M, Heinig K, Bucheli F, Schoenlein K, Alsenz J, et al. Risdiplam distributes and increases SMN protein in both the central nervous system and peripheral organs. Pharmacol Res Perspect. (2018) 6:e00447. doi: 10.1002/prp2.447

PubMed Abstract | CrossRef Full Text | Google Scholar

21. Hsu SH, Lai MC, Er TK, Yang SN, Hung CH, Tsai HH, et al. Ubiquitin carboxyl-terminal hydrolase L1 (UCHL1) regulates the level of SMN expression through ubiquitination in primary spinal muscular atrophy fibroblasts. Clin Chim Acta. (2010) 411:1920–8. doi: 10.1016/j.cca.2010.07.035

PubMed Abstract | CrossRef Full Text | Google Scholar

22. Chang HC, Hung WC, Chuang YJ, Jong YJ. Degradation of survival motor neuron (SMN) protein is mediated via the ubiquitin/proteasome pathway. Neurochem Int. (2004) 45:1107–12. doi: 10.1016/j.neuint.2004.04.005

PubMed Abstract | CrossRef Full Text | Google Scholar

23. Kwon DY, Motley WW, Fischbeck KH, Burnett BG. Increasing expression and decreasing degradation of SMN ameliorate the spinal muscular atrophy phenotype in mice. Hum Mol Genet. (2011) 20:3667–77. doi: 10.1093/hmg/ddr288

PubMed Abstract | CrossRef Full Text | Google Scholar

24. Wishart TM, Mutsaers CA, Riessland M, Reimer MM, Hunter G, Hannam ML, et al. Dysregulation of ubiquitin homeostasis and β-catenin signaling promote spinal muscular atrophy. J Clin Invest. (2014) 124:1821–34. doi: 10.1172/JCI71318

PubMed Abstract | CrossRef Full Text | Google Scholar

25. Mercuri E, Baranello G, Kirschner J, Servais L, Goemans N, Pera MC, et al. Update from SUNFISH part 1: safety, tolerability and PK/PD from the dose-finding study, including exploratory efficacy data in patients with Type 2 or 3 spinal muscular atrophy (SMA) treated with risdiplam (RG7916) (S25.007). Neurology. (2019) 92:S25.007.

Google Scholar

26. Tiziano FD, Lomastro R, Abiusi E, Pasanisi MB, Di Pietro L, Fiori S, et al. Longitudinal evaluation of SMN levels as biomarker for spinal muscular atrophy: results of a phase IIb double-blind study of salbutamol. J Med Genet. (2019) 56:293–300. doi: 10.1136/jmedgenet-2018-105482

PubMed Abstract | CrossRef Full Text | Google Scholar

27. Strathmann EA, Peters M, Hosseinibarkooie S, Rigo FW, Bennett CF, Zaworski PG, et al. Evaluation of potential effects of Plastin 3 overexpression and low-dose SMN-antisense oligonucleotides on putative biomarkers in spinal muscular atrophy mice. PLoS ONE. (2018) 13:e0203398. doi: 10.1371/journal.pone.0203398

PubMed Abstract | CrossRef Full Text | Google Scholar

28. Crawford TO, Paushkin SV, Kobayashi DT, Forrest SJ, Joyce CL, Finkel RS, et al. Evaluation of SMN protein, transcript, and copy number in the biomarkers for spinal muscular atrophy (BforSMA) clinical study. PLoS ONE. (2012) 7:e33572. doi: 10.1371/journal.pone.0033572

PubMed Abstract | CrossRef Full Text | Google Scholar

29. Otsuki N, Arakawa R, Kaneko K, Aoki R, Arakawa M, Saito K. A new biomarker candidate for spinal muscular atrophy: identification of a peripheral blood cell population capable of monitoring the level of survival motor neuron protein. PLoS ONE. (2018) 13:e0201764. doi: 10.1371/journal.pone.0201764

PubMed Abstract | CrossRef Full Text | Google Scholar

30. Yuan A, Rao MV, Veeranna, Nixon RA. Neurofilaments and neurofilament proteins in health and disease. Cold Spring Harb Perspect Biol. (2017) 9:a018309. doi: 10.1101/cshperspect.a018309

PubMed Abstract | CrossRef Full Text | Google Scholar

31. Weydt P, Oeckl P, Huss A, Muller K, Volk AE, Kuhle J, et al. Neurofilament levels as biomarkers in asymptomatic and symptomatic familial amyotrophic lateral sclerosis. Ann Neurol. (2016) 79:152–8. doi: 10.1002/ana.24552

PubMed Abstract | CrossRef Full Text | Google Scholar

32. Teunissen CE, Khalil M. Neurofilaments as biomarkers in multiple sclerosis. Mult Scler. (2012) 18:552–6. doi: 10.1177/1352458512443092

PubMed Abstract | CrossRef Full Text | Google Scholar

33. Winter B, Guenther R, Ludolph AC, Hermann A, Otto M, Wurster CD. Neurofilaments and tau in CSF in an infant with SMA type 1 treated with nusinersen. J Neurol Neurosurg Psychiatry. (2019) 1–2. doi: 10.1136/jnnp-2018-320033

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Darras BT, Crawford TO, Finkel RS, Mercuri E, De Vivo DC, Oskoui M, et al. Neurofilament as a potential biomarker for spinal muscular atrophy. Ann Clin Transl Neurol. (2019) 6:932–44. doi: 10.1002/acn3.779

PubMed Abstract | CrossRef Full Text | Google Scholar

35. Crawford T, Sumner C, Finkel R, De Vivo D, Oskoui M, Tizzano E, et al. SMA THERAPIES II AND BIOMARKERS: P.262Phosphorylated neurofilament heavy chain (pNF-H) levels in infants and children with SMA: evaluation of pNF-H as a potential biomarker of SMA disease activity. Neuromusc Disord. (2018) 28:S110–1. doi: 10.1016/j.nmd.2018.06.310

CrossRef Full Text | Google Scholar

36. Jullien de Pommerol H, Kieloch A, Leppert D, Peters T, Theil D, Valentin M, et al. SMA THERAPIES II AND BIOMARKERS: P.263Neurofilament light chain as a potential biomarker in spinal muscular atrophy. Neuromusc Disord. (2018) 28:S111. doi: 10.1016/j.nmd.2018.06.311

CrossRef Full Text | Google Scholar

37. Brkusanin M, Kosac A, Jovanovic V, Pesovic J, Brajuskovic G, Dimitrijevic N, et al. Joint effect of the SMN2 and SERF1A genes on childhood-onset types of spinal muscular atrophy in Serbian patients. J Hum Genet. (2015) 60:723–8. doi: 10.1038/jhg.2015.104

PubMed Abstract | CrossRef Full Text | Google Scholar

38. Fang P, Li L, Zeng J, Zhou WJ, Wu WQ, Zhong ZY, et al. Molecular characterization and copy number of SMN1, SMN2 and NAIP in Chinese patients with spinal muscular atrophy and unrelated healthy controls. BMC Musculoskelet Disord. (2015) 16:11. doi: 10.1186/s12891-015-0457-x

PubMed Abstract | CrossRef Full Text | Google Scholar

39. Stabley DL, Harris AW, Holbrook J, Chubbs NJ, Lozo KW, Crawford TO, et al. SMN1 and SMN2 copy numbers in cell lines derived from patients with spinal muscular atrophy as measured by array digital PCR. Mol Genet Genomic Med. (2015) 3:248–57. doi: 10.1002/mgg3.141

PubMed Abstract | CrossRef Full Text | Google Scholar

40. Calucho M, Bernal S, Alias L, March F, Vencesla A, Rodriguez-Alvarez FJ, et al. Correlation between SMA type and SMN2 copy number revisited: an analysis of 625 unrelated Spanish patients and a compilation of 2834 reported cases. Neuromuscul Disord. (2018) 28:208–15. doi: 10.1016/j.nmd.2018.01.003

PubMed Abstract | CrossRef Full Text | Google Scholar

41. Elsheikh B, Prior T, Zhang X, Miller R, Kolb SJ, Moore D, et al. An analysis of disease severity based on SMN2 copy number in adults with spinal muscular atrophy. Muscle Nerve. (2009) 40:652–6. doi: 10.1002/mus.21350

PubMed Abstract | CrossRef Full Text | Google Scholar

42. Swoboda KJ, Prior TW, Scott CB, McNaught TP, Wride MC, Reyna SP, et al. Natural history of denervation in SMA: relation to age, SMN2 copy number, and function. Ann Neurol. (2005) 57:704–12. doi: 10.1002/ana.20473

PubMed Abstract | CrossRef Full Text | Google Scholar

43. Glascock J, Sampson J, Haidet-Phillips A, Connolly A, Darras B, Day J, et al. Treatment algorithm for infants diagnosed with spinal muscular atrophy through newborn screening. J Neuromuscul Dis. (2018) 5:145–58. doi: 10.3233/JND-180304

PubMed Abstract | CrossRef Full Text | Google Scholar

44. Darras BT, De Vivo DC. Precious SMA natural history data. Neurology. (2018) 91:337. doi: 10.1212/WNL.0000000000006026

PubMed Abstract | CrossRef Full Text | Google Scholar

45. Cobben JM, van der Steege G, Grootscholten P, de Visser M, Scheffer H, Buys CH. Deletions of the survival motor neuron gene in unaffected siblings of patients with spinal muscular atrophy. Am J Hum Genet. (1995) 57:805–8.

PubMed Abstract | Google Scholar

46. McAndrew PE, Parsons DW, Simard LR, Rochette C, Ray PN, Mendell JR, et al. Identification of proximal spinal muscular atrophy carriers and patients by analysis of SMNT and SMNC gene copy number. Am J Hum Genet. (1997) 60:1411–22. doi: 10.1086/515465

PubMed Abstract | CrossRef Full Text | Google Scholar

47. Prior TW, Krainer AR, Hua Y, Swoboda KJ, Snyder PC, Bridgeman SJ, et al. A positive modifier of spinal muscular atrophy in the SMN2 gene. Am J Hum Genet. (2009) 85:408–13. doi: 10.1016/j.ajhg.2009.08.002

PubMed Abstract | CrossRef Full Text | Google Scholar

48. Bernal S, Alias L, Barcelo MJ, Also-Rallo E, Martinez-Hernandez R, Gamez J, et al. The c.859G>C variant in the SMN2 gene is associated with types II and III SMA and originates from a common ancestor. J Med Genet. (2010) 47:640–2. doi: 10.1136/jmg.2010.079004

PubMed Abstract | CrossRef Full Text | Google Scholar

49. Wu X, Wang SH, Sun J, Krainer AR, Hua Y, Prior TW. A-44G transition in SMN2 intron 6 protects patients with spinal muscular atrophy. Hum Mol Genet. (2017) 26:2768–80. doi: 10.1093/hmg/ddx166

PubMed Abstract | CrossRef Full Text | Google Scholar

50. Ruhno C, McGovern VL, Avenarius MR, Snyder PJ, Prior TW, Nery FC, et al. Complete sequencing of the SMN2 gene in SMA patients detects SMN gene deletion junctions and variants in SMN2 that modify the SMA phenotype. Hum Genet. (2019) 138:241–56. doi: 10.1007/s00439-019-01983-0

PubMed Abstract | CrossRef Full Text | Google Scholar

51. Delanote V, Vandekerckhove J, Gettemans J. Plastins: versatile modulators of actin organization in (patho)physiological cellular processes. Acta Pharmacol Sinica. (2005) 26:769–79. doi: 10.1111/j.1745-7254.2005.00145.x

PubMed Abstract | CrossRef Full Text | Google Scholar

52. Yanyan C, Yujin Q, Jinli B, Yuwei J, Hong W, Fang S. Correlation of PLS3 expression with disease severity in children with spinal muscular atrophy. J Hum Genet. (2014) 59:24–7. doi: 10.1038/jhg.2013.111

PubMed Abstract | CrossRef Full Text | Google Scholar

53. Stratigopoulos G, Lanzano P, Deng L, Guo J, Kaufmann P, Darras B, et al. Association of plastin 3 expression with disease severity in spinal muscular atrophy only in postpubertal females. Arch Neurol. (2010) 67:1252–6. doi: 10.1001/archneurol.2010.239

PubMed Abstract | CrossRef Full Text | Google Scholar

54. Ackermann B, Krober S, Torres-Benito L, Borgmann A, Peters M, Hosseini Barkooie SM, et al. Plastin 3 ameliorates spinal muscular atrophy via delayed axon pruning and improves neuromuscular junction functionality. Hum Mol Genet. (2013) 22:1328–47. doi: 10.1093/hmg/dds540

PubMed Abstract | CrossRef Full Text | Google Scholar

55. McGovern VL, Massoni-Laporte A, Wang X, Le TT, Le HT, Beattie CE, et al. Plastin 3 expression does not modify spinal muscular atrophy severity in the Δ7 SMA mouse. PLoS ONE. (2015) 10:e0132364. doi: 10.1371/journal.pone.0132364

PubMed Abstract | CrossRef Full Text | Google Scholar

56. Hosseinibarkooie S, Peters M, Torres-Benito L, Rastetter RH, Hupperich K, Hoffmann A, et al. The power of human protective modifiers: PLS3 and CORO1C unravel impaired endocytosis in spinal muscular atrophy and rescue SMA phenotype. Am J Hum Genet. (2016) 99:647–65. doi: 10.1016/j.ajhg.2016.07.014

PubMed Abstract | CrossRef Full Text | Google Scholar

57. Riessland M, Kaczmarek A, Schneider S, Swoboda KJ, Lohr H, Bradler C, et al. Neurocalcin delta suppression protects against spinal muscular atrophy in humans and across species by restoring impaired endocytosis. Am J Hum Genet. (2017) 100:297–315. doi: 10.1016/j.ajhg.2017.01.005

PubMed Abstract | CrossRef Full Text | Google Scholar

58. Catapano F, Zaharieva I, Scoto M, Marrosu E, Morgan J, Muntoni F, et al. Altered levels of microRNA-9,−206, and−132 in spinal muscular atrophy and their response to antisense oligonucleotide therapy. Mol Ther Nucleic Acids. (2016) 5:e331. doi: 10.1038/mtna.2016.47

PubMed Abstract | CrossRef Full Text | Google Scholar

59. Cao YY, Qu YJ, He SX, Li Y, Bai JL, Jin YW, et al. Association between SMN2 methylation and disease severity in Chinese children with spinal muscular atrophy. J Zhejiang Univ Sci B. (2016) 17:76–82. doi: 10.1631/jzus.B1500072

PubMed Abstract | CrossRef Full Text | Google Scholar

60. Hauke J, Riessland M, Lunke S, Eyupoglu IY, Blumcke I, El-Osta A, et al. Survival motor neuron gene 2 silencing by DNA methylation correlates with spinal muscular atrophy disease severity and can be bypassed by histone deacetylase inhibition. Hum Mol Genet. (2009) 18:304–17. doi: 10.1093/hmg/ddn357

PubMed Abstract | CrossRef Full Text | Google Scholar

61. Zheleznyakova GY, Voisin S, Kiselev AV, Sallman Almen M, Xavier MJ, Maretina MA, et al. Genome-wide analysis shows association of epigenetic changes in regulators of Rab and Rho GTPases with spinal muscular atrophy severity. Eur J Hum Genet. (2013) 21:988–93. doi: 10.1038/ejhg.2012.293

PubMed Abstract | CrossRef Full Text | Google Scholar

62. Maretina M, Egorova A, Baranov V, Kiselev A. DYNC1H1 gene methylation correlates with severity of spinal muscular atrophy. Ann Hum Genet. (2019) 83:73–81. doi: 10.1111/ahg.12288

PubMed Abstract | CrossRef Full Text | Google Scholar

63. Fenoglio C, Ridolfi E, Galimberti D, Scarpini E. An emerging role for long non-coding RNA dysregulation in neurological disorders. Int J Mol Sci. (2013) 14:20427–42. doi: 10.3390/ijms141020427

PubMed Abstract | CrossRef Full Text | Google Scholar

64. Chamberlain SJ, Brannan CI. The Prader-Willi syndrome imprinting center activates the paternally expressed murine Ube3a antisense transcript but represses paternal Ube3a. Genomics. (2001) 73:316–22. doi: 10.1006/geno.2001.6543

PubMed Abstract | CrossRef Full Text | Google Scholar

65. Faghihi MA, Modarresi F, Khalil AM, Wood DE, Sahagan BG, Morgan TE, et al. Expression of a noncoding RNA is elevated in Alzheimer's disease and drives rapid feed-forward regulation of beta-secretase. Nat Med. (2008) 14:723–30. doi: 10.1038/nm1784

PubMed Abstract | CrossRef Full Text | Google Scholar

66. Meng L, Ward AJ, Chun S, Bennett CF, Beaudet AL, Rigo F. Towards a therapy for Angelman syndrome by targeting a long non-coding RNA. Nature. (2015) 518:409–12. doi: 10.1038/nature13975

PubMed Abstract | CrossRef Full Text | Google Scholar

67. D'Ydewalle C, Ramos DM, Pyles NJ, Ng SY, Gorz M, Pilato CM, et al. The antisense transcript SMN-AS1 regulates SMN expression and is a novel therapeutic target for spinal muscular atrophy. Neuron. (2017) 93:66–79. doi: 10.1016/j.neuron.2016.11.033

PubMed Abstract | CrossRef Full Text | Google Scholar

68. Henderson RD, McCombe PA. Assessment of motor units in neuromuscular disease. Neurotherapeutics. (2017) 14:69–77. doi: 10.1007/s13311-016-0473-z

PubMed Abstract | CrossRef Full Text | Google Scholar

69. Farina D, Negro F, Muceli S, Enoka RM. Principles of motor unit physiology evolve with advances in technology. Physiology. (2016) 31:83–94. doi: 10.1152/physiol.00040.2015

PubMed Abstract | CrossRef Full Text | Google Scholar

70. Kimura J. Peripheral nerve conduction studies and neuromuscular junction testing. In: Eisen A, editor. Handbook of Clinical Neurophysiology. Iowa, IA: Elsevier (2004) 241–270. doi: 10.1016/S1567-4231(04)04012-2

CrossRef Full Text | Google Scholar

71. Arnold WD, Porensky PN, McGovern VL, Iyer CC, Duque S, Li X, et al. Electrophysiological biomarkers in spinal muscular atrophy: preclinical proof of concept. Ann Clin Transl Neurol. (2014) 1:34–44. doi: 10.1002/acn3.23

PubMed Abstract | CrossRef Full Text | Google Scholar

72. Mills KR. The basics of electromyography. J Neurol Neurosurg Psychiatry. (2005) 76:ii32–ii35. doi: 10.1136/jnnp.2005.069211

PubMed Abstract | CrossRef Full Text | Google Scholar

73. Kang PB, Gooch CL, McDermott MP, Darras BT, Finkel RS, Yang ML, et al. The motor neuron response to SMN1 deficiency in spinal muscular atrophy. Muscle Nerve. (2014) 49:636–44. doi: 10.1002/mus.23967

PubMed Abstract | CrossRef Full Text | Google Scholar

74. Bertini E, Hwu WL, Reyna SP, Farwell W, Gheuens S, Sun P, et al. Efficacy and safety of nusinersen in infants with presymptomatic spinal muscular atrophy (SMA): interim results from the NURTURE study. Eur J Paediatr Neurol. (2017) 21:e14. doi: 10.1016/j.ejpn.2017.04.1218

CrossRef Full Text | Google Scholar

75. Lewelt A, Krosschell KJ, Scott C, Sakonju A, Kissel JT, Crawford TO, et al. Compound muscle action potential and motor function in children with spinal muscular atrophy. Muscle Nerve. (2010) 42:703–8. doi: 10.1002/mus.21838

PubMed Abstract | CrossRef Full Text | Google Scholar

76. Kolb SJ, Coffey CS, Yankey JW, Krosschell K, Arnold WD, Rutkove SB, et al. Baseline results of the NeuroNEXT spinal muscular atrophy infant biomarker study. Ann Clin Transl Neurol. (2016) 3:132–45. doi: 10.1002/acn3.283

PubMed Abstract | CrossRef Full Text | Google Scholar

77. Farrar MA, Vucic S, Lin CSY, Park SB, Johnston HM, du Sart D, et al. Dysfunction of axonal membrane conductances in adolescents and young adults with spinal muscular atrophy. Brain. (2011) 134:3185–97. doi: 10.1093/brain/awr229

PubMed Abstract | CrossRef Full Text | Google Scholar

78. Kolb SJ, Coffey CS, Yankey JW, Krosschell K, Arnold WD, Rutkove SB, et al. Natural history of infantile-onset spinal muscular atrophy. Ann Neurol. (2017) 82:883–91. doi: 10.1002/ana.25101

PubMed Abstract | CrossRef Full Text | Google Scholar

79. Darras BT, Chiriboga CA, Iannaccone ST, Swoboda KJ, Montes J, Mignon L, et al. Nusinersen in later-onset spinal muscular atrophy: long-term results from the phase 1/2 studies. Neurology. (2019) 92:e2492–506. doi: 10.1212/WNL.0000000000007527

PubMed Abstract | CrossRef Full Text | Google Scholar

80. Arnold WD, Sheth KA, Wier CG, Kissel JT, Burghes AH, Kolb SJ. Electrophysiological motor unit number estimation (MUNE) measuring compound muscle action potential (CMAP) in mouse hindlimb muscles. J Vis Exp. (2015) e52899. doi: 10.3791/52899

PubMed Abstract | CrossRef Full Text | Google Scholar

81. Li J, Geisbush TR, Arnold WD, Rosen GD, Zaworski PG, Rutkove SB. A comparison of three electrophysiological methods for the assessment of disease status in a mild spinal muscular atrophy mouse model. PLoS ONE. (2014) 9:e111428. doi: 10.1371/journal.pone.0111428

PubMed Abstract | CrossRef Full Text | Google Scholar

82. Wang FC, Bouquiaux O, de Pasqua V, Delwaide PJ. Changes in motor unit numbers in patients with ALS: a longitudinal study using the adapted multiple point stimulation method. Amyotroph Lateral Scler Other Motor Neuron Disord. (2002) 3:31–8. doi: 10.1080/146608202317576516

PubMed Abstract | CrossRef Full Text | Google Scholar

83. Galea V, Fehlings D, Kirsch S, McComas A. Depletion and sizes of motor units in spinal muscular atrophy. Muscle Nerve. (2001) 24:1168–72. doi: 10.1002/mus.1128

PubMed Abstract | CrossRef Full Text | Google Scholar

84. Bromberg MB, Swoboda KJ. Motor unit number estimation in infants and children with spinal muscular atrophy. Muscle Nerve. (2002) 25:445–7. doi: 10.1002/mus.10050

PubMed Abstract | CrossRef Full Text | Google Scholar

85. Gawel M, Kostera-Pruszczyk A, Lusakowska A, Jedrzejowska M, Ryniewicz B, Lipowska M, et al. Motor unit loss estimation by the multipoint incremental MUNE method in children with spinal muscular atrophy–a preliminary study. Neuromuscul Disord. (2015) 25:216–21. doi: 10.1016/j.nmd.2014.11.012

PubMed Abstract | CrossRef Full Text | Google Scholar

86. Daube JR, Gooch C, Shefner J, Olney R, Felice K, Bromberg M. Motor unit number estimation (MUNE) with nerve conduction studies. Suppl Clin Neurophysiol. (2000) 53:112–5. doi: 10.1016/S1567-424X(09)70145-X

PubMed Abstract | CrossRef Full Text | Google Scholar

87. Daube JR. Motor unit number estimates–from A to Z. J Neurol Sci. (2006) 242:23–35. doi: 10.1016/j.jns.2005.11.011

PubMed Abstract | CrossRef Full Text | Google Scholar

88. Duque SI, Arnold WD, Odermatt P, Li X, Porensky PN, Schmelzer L, et al. A large animal model of spinal muscular atrophy and correction of phenotype. Ann Neurol. (2015) 77:399–414. doi: 10.1002/ana.24332

PubMed Abstract | CrossRef Full Text | Google Scholar

89. Bromberg MB, Swoboda KJ, Lawson VH. Counting motor units in chronic motor neuropathies. Exp Neurol. (2003) 184 (Suppl 1):S53–S57. doi: 10.1016/j.expneurol.2003.07.008

PubMed Abstract | CrossRef Full Text | Google Scholar

90. Arnold WD, Mo X, Kolb SJ, Burghes AH, Kissel JT. The motor neuron response to SMN1 deficiency in spinal muscular atrophy. Muscle Nerve. (2014) 50:457–8. doi: 10.1002/mus.24318

PubMed Abstract | CrossRef Full Text | Google Scholar

91. Gooch CL, Doherty TJ, Chan KM, Bromberg MB, Lewis RA, Stashuk DW, et al. Motor unit number estimation: a technology and literature review. Muscle Nerve. (2014) 50:884–93. doi: 10.1002/mus.24442

PubMed Abstract | CrossRef Full Text | Google Scholar

92. DeForest BA, Winslow J, Thomas CK. Improved motor unit number estimate when motor unit alternation is addressed. J Appl Physiol. (2018) 125:1131–40. doi: 10.1152/japplphysiol.00910.2017

PubMed Abstract | CrossRef Full Text | Google Scholar

93. Stein RB, Yang JF. Methods for estimating the number of motor units in human muscles. Ann Neurol. (1990) 28:487–95. doi: 10.1002/ana.410280404

PubMed Abstract | CrossRef Full Text | Google Scholar

94. Gunther R, Neuwirth C, Koch JC, Lingor P, Braun N, Untucht R, et al. Motor Unit Number Index (MUNIX) of hand muscles is a disease biomarker for adult spinal muscular atrophy. Clin Neurophysiol. (2019) 130:315–9. doi: 10.1016/j.clinph.2018.11.009

PubMed Abstract | CrossRef Full Text | Google Scholar

95. Jacobsen AB, Bostock H, Tankisi H. CMAP scan MUNE (MScan) - a novel motor unit number estimation (MUNE) method. J Vis Exp. (2018) 1–7. doi: 10.3791/56805

PubMed Abstract | CrossRef Full Text | Google Scholar

96. Rutkove SB, Aaron R, Shiffman CA. Localized bioimpedance analysis in the evaluation of neuromuscular disease. Muscle Nerve. (2002) 25:390–7. doi: 10.1002/mus.10048

PubMed Abstract | CrossRef Full Text | Google Scholar

97. Vucic S, Rutkove SB. Neurophysiological biomarkers in amyotrophic lateral sclerosis. Curr Opin Neurol. (2018) 31:640–7. doi: 10.1097/WCO.0000000000000593

PubMed Abstract | CrossRef Full Text | Google Scholar

98. Rutkove SB, Sanchez B. Electrical impedance methods in neuromuscular assessment: an overview. Cold Spring Harb Perspect Med. (2018) 8:a034405. doi: 10.1101/cshperspect.a034405

CrossRef Full Text | Google Scholar

99. Rutkove SB, Zhang H, Schoenfeld DA, Raynor EM, Shefner JM, Cudkowicz ME, et al. Electrical impedance myography to assess outcome in amyotrophic lateral sclerosis clinical trials. Clin Neurophysiol. (2007) 118:2413–8. doi: 10.1016/j.clinph.2007.08.004

PubMed Abstract | CrossRef Full Text | Google Scholar

100. Shefner JM, Rutkove SB, Caress JB, Benatar M, David WS, Cartwright MC, et al. Reducing sample size requirements for future ALS clinical trials with a dedicated electrical impedance myography system. Amyotroph Lateral Scler Frontotemporal Degener. (2018) 19:555–61. doi: 10.1080/21678421.2018.1510008

PubMed Abstract | CrossRef Full Text | Google Scholar

101. Rutkove SB, Gregas MC, Darras BT. Electrical impedance myography in spinal muscular atrophy: a longitudinal study. Muscle Nerve. (2012) 45:642–7. doi: 10.1002/mus.23233

PubMed Abstract | CrossRef Full Text | Google Scholar

102. Durmus H, Yilmaz R, Gulsen-Parman Y, Oflazer-Serdaroglu P, Cuttini M, Dursun M, et al. Muscle magnetic resonance imaging in spinal muscular atrophy type 3: selective and progressive involvement. Muscle Nerve. (2017) 55:651–6. doi: 10.1002/mus.25385

PubMed Abstract | CrossRef Full Text | Google Scholar

103. Forbes SC, Willcocks RJ, Rooney WD, Walter GA, Vandenborne K. MRI quantifies neuromuscular disease progression. Lancet Neurol. (2016) 15:26–8. doi: 10.1016/S1474-4422(15)00320-8

PubMed Abstract | CrossRef Full Text | Google Scholar

104. Arts IM, Overeem S, Pillen S, Schelhaas HJ, Zwarts MJ. Muscle ultrasonography to predict survival in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry. (2011) 82:552–4. doi: 10.1136/jnnp.2009.200519

PubMed Abstract | CrossRef Full Text | Google Scholar

105. Schreiber S, Dannhardt-Stieger V, Henkel D, Debska-Vielhaber G, Machts J, Abdulla S, et al. Quantifying disease progression in amyotrophic lateral sclerosis using peripheral nerve sonography. Muscle Nerve. (2016) 54:391–7. doi: 10.1002/mus.25066

PubMed Abstract | CrossRef Full Text | Google Scholar

106. De Carvalho M. Ultrasound in ALS: is it a sound method? Clin Neurophysiol. (2015) 126:651–2. doi: 10.1016/j.clinph.2014.08.002

PubMed Abstract | CrossRef Full Text | Google Scholar

107. Bowerman M, Murray LM, Beauvais A, Pinheiro B, Kothary R. A critical smn threshold in mice dictates onset of an intermediate spinal muscular atrophy phenotype associated with a distinct neuromuscular junction pathology. Neuromuscul Disord. (2012) 22:263–76. doi: 10.1016/j.nmd.2011.09.007

PubMed Abstract | CrossRef Full Text | Google Scholar

108. Wadman RI, Vrancken AF, van den Berg LH, van der Pol WL. Dysfunction of the neuromuscular junction in spinal muscular atrophy types 2 and 3. Neurology. (2012) 79:2050–5. doi: 10.1212/WNL.0b013e3182749eca

PubMed Abstract | CrossRef Full Text | Google Scholar

109. Arnold AS, Gueye M, Guettier-Sigrist S, Courdier-Fruh I, Coupin G, Poindron P, et al. Reduced expression of nicotinic AChRs in myotubes from spinal muscular atrophy I patients. Lab Invest. (2004) 84:1271–8. doi: 10.1038/labinvest.3700163

PubMed Abstract | CrossRef Full Text | Google Scholar

110. Pera MC, Luigetti M, Pane M, Coratti G, Forcina N, Fanelli L, et al. 6MWT can identify type 3 SMA patients with neuromuscular junction dysfunction. Neuromuscul Disord. (2017) 27:879–82. doi: 10.1016/j.nmd.2017.07.007

PubMed Abstract | CrossRef Full Text | Google Scholar

111. Montes J, McDermott MP, Martens WB, Dunaway S, Glanzman AM, Riley S, et al. Six-Minute Walk Test demonstrates motor fatigue in spinal muscular atrophy. Neurology. (2010) 74:833–8. doi: 10.1212/WNL.0b013e3181d3e308

PubMed Abstract | CrossRef Full Text | Google Scholar

112. Dunaway Young S, Montes J, Kramer SS, Podwika B, Rao AK, De Vivo DC. Perceived fatigue in spinal muscular atrophy: a pilot study. J Neuromuscul Dis. (2019) 6:109–17. doi: 10.3233/JND-180342

PubMed Abstract | CrossRef Full Text | Google Scholar

113. Bertorini TE. Chapter 2 - Neurologic evaluation and ancillary tests. In: Bertorini TE, editor. Neuromuscular Case Studies. Philadelphia, PA: Butterworth-Heinemann (2008) 27–76. doi: 10.1016/B978-0-7506-7332-7.50006-4

CrossRef Full Text | Google Scholar

114. Stam M, Wadman RI, Wijngaarde CA, Bartels B, Asselman FL, Otto LAM, et al. Protocol for a phase II, monocentre, double-blind, placebo-controlled, cross-over trial to assess efficacy of pyridostigmine in patients with spinal muscular atrophy types 2-4 (SPACE trial). BMJ Open. (2018) 8:e019932. doi: 10.1136/bmjopen-2017-019932

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: biomarker, spinal muscular atrophy, motor unit number estimation, compound muscle action potential, SMN2, neurofilament

Citation: Kariyawasam DST, D'Silva A, Lin C, Ryan MM and Farrar MA (2019) Biomarkers and the Development of a Personalized Medicine Approach in Spinal Muscular Atrophy. Front. Neurol. 10:898. doi: 10.3389/fneur.2019.00898

Received: 31 May 2019; Accepted: 02 August 2019;
Published: 19 August 2019.

Edited by:

Tim Hagenacker, Essen University Hospital, Germany

Reviewed by:

Antonio Di Muzio, Università degli Studi G. d'Annunzio Chieti e Pescara, Italy
Paola Sandroni, Mayo Clinic, United States
Basil T. Darras, Boston Children's Hospital, Harvard Medical School, United States

Copyright © 2019 Kariyawasam, D'Silva, Lin, Ryan and Farrar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Michelle A. Farrar, m.farrar@unsw.edu.au