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Babić, M.; Banović, M.; Berečić, I.; Banić, T.; Babić Leko, M.; Ulamec, M.; Junaković, A.; Kopić, J.; Sertić, J.; Barišić, N.; et al. Spinal Muscular Atrophy Molecular Biomarkers. Encyclopedia. Available online: https://encyclopedia.pub/entry/47969 (accessed on 16 November 2024).
Babić M, Banović M, Berečić I, Banić T, Babić Leko M, Ulamec M, et al. Spinal Muscular Atrophy Molecular Biomarkers. Encyclopedia. Available at: https://encyclopedia.pub/entry/47969. Accessed November 16, 2024.
Babić, Marija, Maria Banović, Ivana Berečić, Tea Banić, Mirjana Babić Leko, Monika Ulamec, Alisa Junaković, Janja Kopić, Jadranka Sertić, Nina Barišić, et al. "Spinal Muscular Atrophy Molecular Biomarkers" Encyclopedia, https://encyclopedia.pub/entry/47969 (accessed November 16, 2024).
Babić, M., Banović, M., Berečić, I., Banić, T., Babić Leko, M., Ulamec, M., Junaković, A., Kopić, J., Sertić, J., Barišić, N., & Šimić, G. (2023, August 11). Spinal Muscular Atrophy Molecular Biomarkers. In Encyclopedia. https://encyclopedia.pub/entry/47969
Babić, Marija, et al. "Spinal Muscular Atrophy Molecular Biomarkers." Encyclopedia. Web. 11 August, 2023.
Spinal Muscular Atrophy Molecular Biomarkers
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This entry is adapted from the peer-reviewed paper 10.3390/jcm12155060

Spinal muscular atrophy (SMA) is a progressive degenerative illness that affects 1 in every 6 to 11,000 live births. This autosomal recessive disorder is caused by homozygous deletion or mutation of the SMN1 gene (survival motor neuron). As a backup, the SMN1 gene has the SMN2 gene, which produces only 10% of the functional SMN protein. Nusinersen and risdiplam, the first FDA-approved medications, act as SMN2 pre-mRNA splicing modifiers and enhance the quantity of SMN protein produced by this gene. The emergence of new therapies for SMA has increased the demand for good prognostic and pharmacodynamic (response) biomarkers in SMA.

spinal muscular atrophy survival motor neuron 1 protein pharmacological biomarkers

1. Spinal Muscular Atrophy

Spinal muscular atrophy (SMA) is a progressive degenerative disease characterized by muscle weakness and atrophy. It is an autosomal recessive disorder, affecting 1 in 6–11,000 live births [1]. SMA is caused by a mutation or homozygous deletion of the SMN1 gene (survival motor neuron) found on chromosome 5q11.2–q13.3. In 95% of individuals, SMA is caused by the homozygous deletion of exon 7 in the SMN1 gene, while homozygous point mutation or heterozygous mutation (deletion and point mutation) is detected in less than 2% of patients [2]. Multiple cellular processes, including RNA metabolism, ribonucleoprotein assembly, trafficking, signal transduction, and actin dynamics [3][4], are dependent on the SMN protein, which is essential for the normal functioning of the cell. Based on the age at which the first signs and symptoms of the disease appear, SMA can be divided into five subtypes. SMA type 1 (Werdnig–Hoffmann disease) is the most frequent form of SMA, with an incidence of 50−60% [5]. It presents in the first 6 months of infancy with generalized hypotonia, muscle weakness, poor motor abilities, areflexia, swallowing, feeding, and breathing difficulties, respiratory failure, and premature death before the age of 2 years. In the natural course of the disease, only 8% survive until the age of 20 months, and survivors never sit unassisted. In SMA type 2, the first signs appear at the age of 7 to 18 months. These children never walk unsupported and suffer from progressive scoliosis and persistent respiratory insufficiency that may limit their life expectancy. Signs of progressive muscle weakness in SMA type 3 (Kugelberg-Welander disease) develop after 18 months. SMA type 3 is separated into two subgroups: SMA3a manifests before the third year of life, whereas SMA3b develops after the age of 3 years. SMA type 4 is the least frequent and is characterized by mild hypotonia and the slow progression of proximal muscle weakness, with the onset occurring in the second or third decade of life. SMA type 0 manifests with fetal hypomotility or akinesia prenatally, and these children most often die within the first several days of life due to severe generalized hypotonia, muscle weakness, and respiratory and cardiovascular failure [5].
According to recommendations published in 2018 [6], all patients with SMA should undergo a neurological examination every six months that includes a functional, scale-based assessment of motor function, evaluation of respiratory functions, ventilatory and nutritional support, and assessment of somatic development (weight, length, and head circumference). The Expert Consensus Agreement for SMA, CareMArtCARE—a platform used to collect real-life outcome data of patients with spinal muscular atrophy—is based on the use of several functional motor scales for patients with SMA related to age and the results of measurement scores. Scales are used as clinical tools and as functional outcome measures for clinical trials as well. The choice of scale is predominantly determined by the type of SMA and the patient’s age. The Children’s Hospital of Philadelphia Infant Test of Neuromuscular Disorders (CHOP-INTEND) motor skills evaluation scale is used in children under 2 years of age and in patients without the ability to sit independently older than 2 years of age. This 16-item scale evaluates limb flexion and extension, head stabilization, spontaneous movements, and hand grip strength while holding a toy or the examiner’s finger [7]. On the scale, each item is scored from 0 to 4, and the highest possible score is 64. The examination provides physicians with information regarding whether a patient’s abilities correspond to her/his clinical condition [7]. The Bayley Scales of Infant and Toddler Development, third edition (BSID-III), is used for the assessment of developmental functioning in infants and toddlers and involves five domains: cognition, language, social-emotional, and motor and adaptive behavior. The Hammersmith Infant Neurological Examination Section (HINE) is divided into three sections, encompassing 37 items and enabling the quantification of neurologic function assessment in infants. HINE-2 (score 0–26) enables the assessment of motor function development based on the achievement of eight motor milestones: voluntary grasp, kicking, head control, rolling, sitting, crawling, standing, and walking [5].
The Hammersmith Functional Motor Scale (HFMS) is intended for patients with SMA types 2 and 3, as well as those older than 2 years. It consists of 20 items that are scored from 0 to 2 points, depending on whether the patient performs them without assistance, with assistance from others, or is unable to perform them [8]. The maximum number of points is 40. The Hammersmith Functional Motor Scale Expanded (HFMSE) was devised as a modification of the HFMS [9] to adapt the HFMS to patients with SMA types 2 and 3 older than 2 years with the ability to sit, as well as to patients with a CHOP-INTEND score >50. It consists of 33 items that are scored from 0 to 2 points, and the maximum possible score is 66 [9]. To evaluate the motor function of the upper limbs, the Revised Upper Limb Module (RULM) (score 0–37) is used in patients older than 2 years of age with the ability to sit in a wheelchair. In ambulant patients older than 3 years of age, the 6- or 2-Minute-Walk Test (6MWT) is performed and additionally used as an endurance test [5].
Additional scales used for the assessment of motor functions are muscle function measurement scales (MFM-20), the Medical Research Council (MRC) test scale, and the revised Amyotrophic Lateral Sclerosis (ALS) functional rating scale [5].

2. Diagnosis and Prognosis of SMA Using Molecular Biomarkers

Biomarkers are measurable indicators of a specific biological condition. Biomarkers can be measured in body fluids (blood, CSF, and urine), but electrophysiological and neuroimaging techniques are also considered biomarkers [10][11][12]. A strong biomarker has a sensitivity and specificity of at least 85 percent and a correlation with disease progression. Availability, reproducibility, and non-invasiveness are additional substantial characteristics of potential biomarkers [13][14]. Biomarkers can be divided into two categories: biomarkers of disease and exposure. Biomarkers of disease include diagnostic, prognostic, and state biomarkers as well as pharmacodynamic (response) biomarkers, whereas biomarkers of exposure are used to estimate disease risk factors [15][16].
With a growing understanding of the etiology and pathogenesis of SMA and the emergence of new therapeutics, it has become necessary to monitor the progression of the disease and the response to treatment. To establish a diagnosis of SMA, the deletion of the SMN1 gene and the number of copies of the SMN2 gene are crucial. The role of SMN2 as a biological marker is also significant in the natural course of the disease (patients who have not yet begun treatment), as the number of SMN2 gene copies is a prognostic factor and a modifier of disease severity (that predicts the severity of the disease’s natural progression) [17]. Early SMA onset is associated with a lower SMN2 copy number, which has an impact on reduced survival, while later SMA onset is associated with more than 2 SMN2 copies, and motor milestones achieved in the natural course are related to longer survival. The SMN2 copy number has no impact on functional motor decline or treatment outcome.
SMN, mRNA, and SMN protein. SMN, mRNA, and SMN protein are produced by transcription and translation of the SMN2 gene in SMA patients. Different research groups have measured the levels of SMN, mRNA, and SMN protein in the blood and, less frequently, in the CSF [18][19][20]. Despite the fact that SMN, mRNA, and protein levels do not change with disease progression, they provide valuable insight into the present disease state [21].
Neurofilaments. Neurofilaments (Nfs) are proteins of the neuronal cytoskeleton that maintain the axon’s structural integrity. Neurofilaments are heteropolymers made up of four subunits: heavy, medium, and light neurofilament chains (NfH, NfM, and NfL; neurofilament heavy, medium, and light chains, respectively), α-internexin (in the central nervous system), and peripherin (in the peripheral nervous system) [22]. There is an increase in the concentration of Nfs in the interstitium, CSF, and peripheral circulation if there is neuronal damage and axon disintegration [23]. Most often, the levels of phosphorylated neurofilament heavy chain (pNfH) and neurofilament light chain (NfL) were measured in SMA patients as being increased or very high, both in pre-symptomatic SMA patients with 2 SMN2 copies and symptomatic SMA patients [24]. In recent years, NfH and NfL have emerged as promising biomarkers for monitoring SMA progression and as a response to nusinersen therapy in neonates and infants [21]. NfL is substantially elevated in pathological conditions such as multiple sclerosis, Alzheimer’s disease, and ALS [25][26][27].
Creatinine. As a waste product of the creatinine kinase system, creatinine is an indicator of muscle mass [28]. A correlation between serum creatinine and motor functions has been demonstrated [29][30], as well as the former’s potential as a prognostic biomarker of SMA [31].
In order to identify new diagnostic and prognostic biomarkers for SMA, studies have analyzed the whole proteome, transcriptome, metabolome, and microRNAome (miRNAome) in the biological fluids of SMA patients.
Whole proteome. Several studies have compared the whole proteome between SMA patients and HCs and correlated significant proteins with scores on scales for the assessment of motor functions. Bianchi et al. analyzed the whole proteome in the CSF of 10 SMA 1 patients and 7 healthy controls (HCs) and observed 39 differentially expressed proteins between SMA patients and HCs (with APOA1, hemoglobin subunit β, hemoglobin subunit α, and transthyretin being the most significant) [32]. In extracellular vesicles released from fibroblasts of one SMA 1, two SMA 2, and three HC subjects, Roberto et al. observed 116 differentially expressed proteins (with IGFBP3, Plastin 3, PTK7, TCP1, FETUA, and FXA being the most significant) [33]. Kobayashi et al. analyzed nearly 1000 plasma proteins in 266 SMA patients and 22 HCs. They even developed a commercial SMA-MAP biomarker panel, including 27 proteins [34]. Another study that analyzed the plasma proteome observed 97 plasma proteins in correlation with the MHFMS score, with TNXB, CILP2, COMP, CLEC3B, ADAMTSL4, THBS4, OMD, LUM, DPP4, PEPD, and CDH13 being the most significantly differentially expressed between SMA patients and HCs [18].
Whole miRNAome. Zaharieva et al. detected 42 differentially expressed miRNAs in the serum of SMA patients compared to HCs [35]. On the other hand, Abiusi et al. detected only an increase in serum miR-181a-5p, miR324-5p, and miR-451a in SMA patients compared to HCs [36].
Whole transcriptome. In a study that conducted a whole blood transcriptomic screen, seven downregulated and three upregulated KEGG pathways were observed, with the most significantly downregulated pathway being “Regulation of Actin Cytoskeleton” [37]. A study that analyzed the transcriptome from fibroblasts using the Gene Expression Plate “Neurodegeneration” observed a decrease in the expression of SMN1, SNCA, SV2A, and SYN2 mRNA in SMA patients compared to HCs [38].
Whole metabolome. Urinary metabolic profiles successfully differentiated SMA patients from HCs with 81% sensitivity and 98% specificity [39]. Another study showed that even 59 plasma metabolites and 44 urine metabolites correlated with the MHFMS scores [18].
Non-molecular biomarkers. In contrast to the previously mentioned molecular biological markers, electrophysiological methods such as compound muscle action potential (CMAP) amplitude, motor unit number estimation (MUNE), and electrical impedance myography (EIM) provide insight into disease severity and progression, enabling the detection of symptomatic patients among pre-symptomatic patients. The aforementioned methods are crucial for detecting symptomatic patients during the so-called pre-symptomatic phases and play an important role as predictive factors in the treatment response [21]. In addition, magnetic resonance imaging (MRI), muscle ultrasound, and, more recently, multispectral optoacoustic tomography, a laser method for determining tissue composition, are used as imaging biological indicators of the symptomatic phase of the disease [21].

3. Monitoring of Therapeutic Response in SMA Using Molecular Biomarkers

Therapy with nusinersen is a modifying therapy that alters the natural course of the disease: it delays premature mortality, postpones the need for permanent invasive mechanical ventilation, halts progression, stabilizes the disease, and improves the patient’s clinical condition. Measurable molecular markers may contribute to the objectification of the SMA prognosis as well as the prediction and surveillance of the therapeutic effect in SMA [23]. 
Tau protein. Total tau (t-tau), an additional marker of neurodegeneration, was evaluated as a potential pharmacodynamic biomarker. Tau proteins stabilize microtubules, and an increase in t-tau levels in CSF is observed when neuronal death occurs [40]. Most studies observed a decrease in t-tau levels in response to nusinersen treatment [41][42][43], whereas others observe no change in the levels of this biomarker [29][44][45]. Studies that analyzed phosphorylated tau isoforms after nusinersen treatment observed no change [29] or decrease [44] in phosphorylated tau levels.
S100 calcium-binding protein B (S100B), chitotriosidase 1 (CHIT1), neuron-specific enolase (NSE), and amyloid-β were also investigated as potential pharmacodynamic biomarkers in SMA; however, due to the limited number of studies involving these biomarkers, it is difficult to draw a conclusion regarding the utility of these biomarkers for monitoring the response to nusinersen therapy.
S100B. S100B protein, which is expressed mainly by astrocytes, belongs to a small dimeric and multigenic calcium-binding family of proteins [46]. An increase in S100B levels was observed in many diseases of the central nervous system. Only two studies analyzed S100B as a pharmacodynamic biomarker, and both of these studies observed no change in S100B levels upon nusinersen treatment [43][45].
CHIT1. CHIT1, which is mainly expressed by activated macrophages in both inflammation and normal conditions [47], was observed to be increased in SMA patients compared to HCs [48]. Few studies have analyzed the pharmacodynamic potential of CHIT1, giving conflicting results. Either an increase [48] or a decrease in CHIT1 levels [49] was observed after the treatment with nusinersen.
Amyloid β. Amyloid β peptide alterations were observed in many neurological disorders. It was also studied as a potential pharmacodynamic biomarker in SMA. Upon nusinersen treatment, there was an increase [50] or no change [44] in amyloid β levels. Other proteins involved in amyloid β metabolism (such as amyloid precursor protein (APP) and BACE-1) were also studied as potential biomarkers in SMA [42][51].
GFAP. Glial fibrillary acidic protein (GFAP) is a type III intermediate filament protein that is expressed mainly by astrocytes [52]. Only two studies have examined GFAP as a pharmacodynamic biomarker in SMA, and both of these studies observed a decrease in GFAP levels following treatment with nusinersen [41][53].
Cytokine profile. Several recent studies have examined various inflammatory markers for monitoring therapeutic response in SMA [49][54][55]. Probably due to the analysis of different cytokines and the use of different methodologies, these studies did not yield unifying results. After nusinersen treatment, an increase in G-CSF, IL-8, MCP-1, MIP-1α, and MIP-1β levels and a decrease in IL-1ra, IL-2, IL-4, IL-7, IL-9, IL-12, IL-17, VEGF, eotaxin, and TNF-α levels [54], as well as an increase in IL-10, MCP-1/CCL2 [55], and fractalkine levels [51] and a decrease in IL-8 and IP-10 levels [51], were observed.
Routine CSF parameters. To determine the safety of nusinersen, several studies analyzed the changes in routine CSF parameters following treatment with nusinersen. These studies mostly observed an increase in total proteins and the CSF/serum quotient of albumin (Qalb) after the treatment with nusinersen [29][48][56][57][58][59][60].
Whole proteome, miRNAome, and metabolome. Lastly, some studies analyzed the effects of nusinersen administration on the proteome, miRNAome, and metabolome. Magen et al. observed an increase in CSF miR-103b and a decrease in CSF miR-1-3p, miR-133a/b, and miR-206 in nusinersen responders [61], while Welby et al. observed an increase in CSF miR-132, miR-218, miR-9, miR-23a, and miR-146a after treatment with nusinersen [62]. Nusinersen treatment led to an increase in serum miR-335-5p, miR-328-3p, miR-423-3p, miR-142-5p, and a decrease in serum miR-26b-5p [35]. Muscle-specific miRNAs miR-133a, miR-133b, and miR-1 decreased after nusinersen treatment [63]. An analysis of the whole proteome revealed a decrease in CSF Cathepsin D, pNfH and pNfL [64], haptoglobin, and hemoglobin sub. β levels [32], as well as an increase in APOA1 and transthyretin levels [32] and two protein clusters that were identified after nusinersen treatment which differed from the protein clusters at baseline [59].
During nusinersen treatment, various biomarkers were correlated with motor function assessment scale values. There was an inverse correlation between CSF NfL levels and the HFMSE score [42], as well as between CSF [41] and serum [23] NfL levels and the CHOP-INTEND scores. Plasma pNfH levels are negatively correlated with the CHOP-INTEND score [65], serum pNfH levels are negatively correlated with the RULM score [66], and CSF pNfH levels are negatively correlated with the HFMSE and RULM scores [42]. CSF t-tau levels correlated negatively with the CHOP-INTEND score [41][42], whereas serum creatinine levels correlated positively with the HFMSE and RULM scores [29].

4. Limitations and Future Perspectives

The main limitations for the usage of molecular biomarkers in clinical practice are: (1) a lack of general agreement and recommendations between different countries for the best molecular biomarker choice; (2) a lack of agreement and recommendations for body fluids in which biomarkers should be analyzed (CSF vs. serum or plasma); (3) inconsistencies in the results between different studies due to the usage of different analytical methods or analysis of different bodily fluids; (4) high cost of molecular biomarker analysis. The debate is still ongoing regarding whether molecular biomarkers should be prioritized between imaging and electrophysiological biomarkers when monitoring disease progression and therapeutic response (since genetic biomarkers cannot be used to monitor therapeutic response). With the main purpose of addressing these issues, the recently formed SMA Multidisciplinary Biomarkers Working Group consists of 11 experts in the field of SMA research [67]. The main goal of this Working Group is to provide recommendations for the usage of prognostic, predictive, and pharmacodynamic biomarkers of SMA in clinical practice [67]. Several biomarkers have been considered: (1) biomolecular biomarkers (Nf, SMN protein, and muscle indicators [creatinine, creatine kinase, and markers of muscle damage]); (2) genetic biomarkers (copy number or polymorphisms of the SMN2 gene and the expression of modifier genes); (3) gene transcription and splicing regulators (miRNAs, long non-coding RNAs, methylation factors); (4) imaging biomarkers (EIM and muscle imaging using MRI); and (5) electrophysiological biomarkers (repetitive nerve stimulation (RNS), CMAP, and MUNE). The top biomarker among the aforementioned biomarkers was Nf, and the Working Group recommended Nf for further research and development since it showed prognostic, predictive, and pharmacodynamic potential [67].
Studies like the one performed by Glascock et al. [67] are very important given the approval of three disease-modifying therapies for SMA and an increase in newborn screening for SMA.

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Subjects: Clinical Neurology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Marija Babić
,
Maria Banović
,
Ivana Berečić
,
Tea Banić
,
Mirjana Babić Leko
,
Monika Ulamec
,
Alisa Junaković
,
Janja Kopić
,
Jadranka Sertić
,
Nina Barišić
,
Goran Šimić
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Revisions: 2 times (View History)
Update Date: 14 Aug 2023
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