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. 2021 Jul 20;54(4):5574–5585. doi: 10.1111/ejn.15382

Timing and localization of myasthenia gravis‐related gene expression

Dana L E Vergoossen 1, Arlin Keo 2,3, Ahmed Mahfouz 1,2,3, Maartje G Huijbers 1,4,✉
PMCID: PMC8457065  PMID: 34228850

Abstract

Myasthenia gravis (MG) is an acquired autoimmune disorder caused by autoantibodies binding acetylcholine receptors (AChR), muscle‐specific kinase (MuSK), agrin or low‐density lipoprotein receptor‐related protein 4 (Lrp4). These autoantibodies inhibit neuromuscular transmission by blocking the function of these proteins and thereby cause fluctuating skeletal muscle weakness. Several reports suggest that these autoantibodies might also affect the central nervous system (CNS) in MG patients. A comprehensive overview of the timing and localization of the expression of MG‐related antigens in other organs is currently lacking. To investigate the spatio‐temporal expression of MG‐related genes outside skeletal muscle, we used in silico tools to assess public expression databases. Acetylcholine esterase, nicotinic AChR α1 subunit, agrin, collagen Q, downstream of kinase‐7, Lrp4, MuSK and rapsyn were included as MG‐related genes because of their well‐known involvement in either congenital or autoimmune MG. We investigated expression of MG‐related genes in (1) all human tissues using GTEx data, (2) specific brain regions, (3) neurodevelopmental stages, and (4) cell types using datasets from the Allen Institute for Brain Sciences. MG‐related genes show heterogenous spatio‐temporal expression patterns in the human body as well as in the CNS. For each of these genes, several (new) tissues, brain areas and cortical cell types with (relatively) high expression were identified suggesting a potential role for these genes outside skeletal muscle. The possible presence of MG‐related antigens outside skeletal muscle suggests that autoimmune MG, congenital MG or treatments targeting the same proteins may affect MG‐related protein function in other organs.

Keywords: brain, Dok7, Lrp4, MuSK, myasthenia gravis, neuromuscular junction

Many of the proteins involved in organizing and maintaining neuromuscular signalling cause (congenital) myasthenia gravis (MG) when their function is impaired. However, little is known on the presence and function of these proteins outside skeletal muscle. We generated a comprehensive overview of spatio‐temporal MG‐related gene expression from public human‐based databases, with a focus on the brain. These data provide a guide on tissues at risk for non‐motor symptoms in MG patients and upon MG‐related genes targeted therapy.

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Abbreviations

ACh

acetylcholine

AChR

acetylcholine receptor

AHBA

Allen Human Brain Atlas

CMS

congenital myasthenic syndrome

CNS

central nervous system

CPM

counts per million reads mapped

CT

corticothalamic

CTX

isocortex

ENTI/ENT

entorhinal area

ET

extratelencephalic

GTEx

genotype‐tissue expression consortium

IT

intratelencephalic

MG

myasthenia gravis

Micro‐PVM

microglia—perivascular macrophages

MuSK

muscle‐specific kinase

NMJ

neuromuscular junction

NP

near projecting

OPC

oligodendrocyte progenitor cells

PT

pyramidal tract

SMC‐Peri

smooth muscle cell ‐ pericyte

snRNA‐seq

single nucleus RNA sequencing

TPE

temporal, perirhinal and ectorhinal

TPM

transcripts per million

VLMC

vascular leptomeningeal cells

1. INTRODUCTION

Myasthenia gravis (MG) is an antibody‐mediated autoimmune disorder hallmarked by fatigable skeletal muscle weakness. This muscle weakness results from autoantibodies targeting essential proteins at the neuromuscular junction (NMJ). Till date, four antigens have been described: acetylcholine receptors (AChR), muscle‐specific kinase (MuSK), low‐density lipoprotein receptor‐related protein 4 (Lrp4) and agrin (Gilhus & Verschuuren, 2015). These proteins converge on a single pathway essential for establishing and maintaining NMJs and facilitating neuromuscular transmission (Burden et al., 2018). Consequently, binding of autoantibodies interferes with the function of these proteins, resulting in failure of neuromuscular transmission and subsequent muscle fatigue and paralysis.

Even though most knowledge on these proteins relates to their function in the NMJ, they are also expressed in tissues other than the skeletal muscle, like the retina, lung and brain (Bezakova & Ruegg, 2003; Burden et al., 2018; Carlisle et al., 2004; Tian et al., 2006). Insight in the localization of expression of these genes/proteins and their roles in other organs is important because: (1) Other tissues may also be affected by the autoantibodies in MG patients, and (2) new therapeutic strategies targeting these MG‐related genes/proteins are emerging; therefore, knowledge on their localization may identify potential off‐target effects. A comprehensive overview of MG‐related gene expression in different human tissues however is lacking.

Evidence of other organs being affected by MG autoantibodies is mostly focused on the central nervous system (CNS). The non‐motor symptoms include pain, cognitive dysfunction, fatigue and sleep disturbances (Bhagavati et al., 2007; Mao et al., 2015; Paul et al., 2000; Ruiter et al., 2020; Tong et al., 2018). Although AChR MG serum antibodies were reported not to bind neuronal AChR (Whiting et al., 1987) and immunostaining with MuSK autoantibodies seems challenging on brain sections (personal observation), passive transfer of patient‐derived MuSK and AChR antibodies resulted in behavioural deficits and EEG abnormalities in mice (Fulpius et al., 1977; Sabre et al., 2019). Moreover, AChR autoantibodies have been detected in cerebrospinal fluid (Keesey et al., 1978; Lefvert & Pirskanen, 1977; Müller et al., 1987). Mutations in NMJ genes may furthermore result in congenital myasthenic syndrome (CMS) and sometimes give CNS abnormalities (Engel, 2018). Although CNS defects are not at the foreground of clinical symptoms in MG patients, these observations suggest that autoantibodies may have detrimental effects in the CNS when they are able to cross the blood–brain barrier.

If indeed MG‐related autoantibodies are able to affect their antigens in other organs several important questions arise, for example, (1) do these proteins, like in the NMJ, converge on a similar pathway in these organs and (2) which cells are responsible for this expression. We therefore investigated the spatio‐temporal expression patterns of MG‐related genes in healthy human tissues, with a focus on the CNS, using a range of publicly‐available expression databases. We selected the four NMJ genes which encode known antigens for autoantibodies in MG (AGRN, CHRNA1, LRP4, MUSK) and four other NMJ genes involved in maintaining NMJ neurotransmission and where mutations can cause CMS (acetylcholine esterase (ACHE), collagen Q (COLQ), downstream of kinase‐7 (DOK7), and rapsyn (RAPSN)) (Table 1) (Engel, 2018). We will refer to these eight genes and their gene products as MG‐related genes.

TABLE 1.

The MG‐related genes, their function at the NMJ and association with autoimmune MG or CMS (Burden et al., 2018; Engel, 2018; Gilhus & Verschuuren, 2015; Zisimopoulou et al., 2014)

Gene symbol Encoded protein Function at NMJ Role in CMS or autoimmune MG
ACHE Acetylcholine esterase Breaks down the neurotransmitter acetylcholine (ACh), thereby halting neuromuscular transmission and muscle contraction. 12.53% of CMS patients have a AChE deficiency. AChE is the main target for symptomatic treatment of autoimmune MG patients.
AGRN Agrin Trophic signalling molecule released by the presynaptic motor nerve terminal to induce and maintain postsynaptic differentiation. Agrin lingers in the basal lamina, can bind Lrp4 and thereby activates MuSK and AChR clustering. Mutations in AGRN are a rare cause of CMS (0.58%). Autoantibodies to agrin are thought to cause autoimmune MG in a small subset of patients.
CHRNA1 Nicotinic acetylcholine receptor α subunit Muscle‐specific subunit of the AChR required for neuromuscular transmission and location for ACh binding. Upon binding of ACh to the AChR, its ion channel opens which depolarizes the muscle end plate, and may induce an action potential and muscle contraction. ~50% of CMS patients have an AChR deficiency caused by mutations in one of the AChR subunits leading to kinetic defects and myasthenic symptoms. ~80% of autoimmune MG patients have autoantibodies to the AChR. The α subunit contains the main immunogenic region.
COLQ Collagen Q Collagenic subunit anchoring AChE to the basal lamina and thereby responsible for ACh breakdown. Also interacts with MuSK. The pathomechanism of AChE and MuSK CMS as well as autoimmune MG are known to affect ColQ function.
DOK7 Downstream of kinase 7 Cytoplasmic adaptor of MuSK required for regulating the kinase activity of MuSK, subsequent AChR clustering and NMJ formation. 9.75% of CMS patients have a mutation in DOK7. DOK7 gene therapy rescued the phenotype of a Dok7 CMS mouse model.
LRP4 Low‐density lipoprotein receptor‐related protein 4 The agrin receptor that directly interacts with MuSK and further activates AChR clustering and postsynaptic differentiation. Lrp4 is also critical for presynaptic differentiation. 0.56% of CMS patients have mutations in LRP4. 1–2% of MG patients have Lrp4 autoantibodies. MuSK autoantibodies inhibit MuSK‐Lrp4 interaction, obstructing normal trophic signalling at the NMJ and induce myasthenia.
MUSK Muscle‐specific kinase Orchestrates anterograde trophic signalling at the NMJ, resulting in AChR clustering. It is also required for retrograde signalling towards presynaptic differentiation of the motor neuron end plate. 0.28% of CMS patients have a mutation in MUSK. Autoantibodies to MuSK cause autoimmune MG in 5–8% of patients.
RAPSN Rapsyn Cytoplasmic anchor that facilitates AChR clustering. 14.21% of CMS patients have a rapsyn deficiency.
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2. MATERIALS AND METHODS

2.1. Genotype‐tissue expression consortium (GTEx)

The expression of MG‐related genes was analysed across 54 nondiseased human tissues using RNA sequencing data of the nearly 1,000 individuals from the open access genotype‐tissue expression consortium (GTEx) version 8. For ACHE (ENSG00000087085) and AGRN (ENSG00000188157), all isoforms were annotated using the Ensembl database. The median gene‐level transcripts per million (TPM) for all MG‐related genes were downloaded per tissue. Because it is known that ACHE and AGRN produce different splice variants, of which only one is relevant for NMJs, ACHE‐207 (ENST00000428317.5) and AGRN‐208 (ENST00000620552.4) isoform data were downloaded separately. The anterior cingulate cortex, frontal cortex and cerebellar hemisphere were removed because they were covered in the cortex or cerebellum respectively. EBV‐transformed lymphocytes and cultured fibroblasts were removed, because they do not naturally occur in healthy humans. Tissues (rows) and genes (columns) were hierarchically clustered using hclust function in R with Eucledian distance and average linkage. Qualitative expression levels are used as follows: low (Log10 < 1), moderate (Log10 1–1.5), high (Log10 > 1.5).

2.2. Allen Human Brain Atlas

The Allen Human Brain Atlas (AHBA) includes anatomically mapped Oligo‐dT primed microarray data of 3,702 samples from six neurotypical individuals (5 males and 1 female, mean age 42, range 24–57 years) (Hawrylycz et al., 2012). The expression of the MG‐related genes was mapped across regions of the human brain using BrainScope (Huisman et al., 2017). The MG‐related gene‐probes used are marked in Table S1. All probes were confirmed to align to their respective MG‐related gene using the Ensembl database. AGRN probe A_24_P358462 (#) detected the NMJ‐specific AGRN‐208 isoform, able to induce AChR clustering and clusters separately from the probe used in the other analyses (*) (Figure S3a) (McMahan et al., 1992). For ACHE, the available probe did not selectively bind to ACHE‐207. The expression of MG‐related genes was downloaded from http://human.brain-map.org/. Log2‐transformed expression values were converted into z‐scores normalized per donor. The median z‐score of each gene was taken across the set of 22 nonoverlapping brain regions. Finally, the median z‐score of every MG gene across the six donors was taken. Hierarchical clustering was done as described above. The nature of these data only allowed comparison of z‐scores across anatomical brain areas within one gene, which is why the term relative expression is used. Qualitative expression levels are used as follows: low (Z < 0), moderate (Z 0–1), high (Z > 1).

2.3. BrainSpan atlas of the developing human brain

The BrainSpan atlas of the developing human brain includes 42 healthy human brains, ranging in age from 8 weeks postconception to 40 years old, from which a total of 524 anatomically annotated samples were taken (Miller et al., 2014). Gene expression was determined using RNA sequencing and visualized using BrainScope (Huisman et al., 2017). ACHE‐207 and AGRN‐208 isoforms could not be distinguished in this dataset. MG‐related genes CHRNA1, DOK7, MUSK and RAPSN were not present in BrainScope, since genes with RPKM‐value above 1 in less than 20% of all samples were removed. For those genes, we used the brain‐map.org portal to plot their expression (Figure S4).

2.4. Single‐cell analyses

Single‐nucleus RNA sequencing (snRNA‐seq) data from multiple human cortical regions and single‐cell RNA sequencing data from the whole mouse cortex were downloaded from the Allen Institute (https://portal.brain-map.org/atlases-and-data/rnaseq). For the human dataset, the donors included 4 males and 4 females (age 24–66) without a history of neuropsychiatric or neurological conditions (Hodge et al., 2019). From 4 donors, multiple cortical areas were sampled postmortem and from 4 donors the medial temporal gyrus was removed during neurosurgery. For the mouse dataset, 538 animals were used from multiple transgenic lines to enrich for rare cell types, all on a C57BL/6 J background (Yao et al., 2021).

Sample processing and analysis methods have been described previously (Hodge et al., 2019; Yao et al., 2021). Briefly, the SMART‐seq method yielded transcriptome profiles for 10,708 glutamatergic neurons, 4,297 GABAergic neurons and 923 nonneuronal cells for the human dataset and 40,276 glutamatergic neurons, 22,573 GABAergic neurons and 1,958 nonneuronal cells for the mouse dataset. The trimmed‐mean gene expression (counts per million reads mapped [CPM]) data per cluster was downloaded for 8 MG‐related genes and grouped in the cell type subclasses defined in the sampling strategy. In the mouse dataset, we selected the analogous cell type subclasses that are present in the human dataset (Hodge et al., 2019).

3. RESULTS

3.1. Expression of MG‐related genes in muscle and brain regions

Expression of MG‐related genes was assessed in human tissues using GTEx data. For ACHE and AGRN, specific splice variants (ACHE‐207 and AGRN‐208) are known to be (NMJ) synapse‐specific (McMahan et al., 1992; Zimmermann, 2013). ACHE‐207 and AGRN‐208 were indeed specifically expressed in skeletal muscle and/or brain regions, in contrast to other isoforms (Figure S1). Because of their relevance for the NMJ and association with autoimmune MG, ACHE‐207 and AGRN‐208 were selected for further analysis (Figure 1). For completeness, the analysis was also performed using gene counts (i.e., including all isoforms) in Figure S2.

FIGURE 1.

FIGURE 1

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Overview of tissue‐specific expression of MG‐related genes reveals most co‐expression in skeletal muscle, the CNS and parts of the male and female reproductive system. The heatmap shows the median expression of each MG‐related gene (column) in TPM across human tissues (rows) using data from GTEx V8. Average expression is reported as Log10(TPM + 1)

Hierarchical clustering revealed that skeletal muscle segregates from all other tissues based on expression of MG‐related genes, supporting the unique combined role of these genes in the NMJ (Figure 1). Agrin is secreted by motor neurons, explaining the absence of AGRN‐208 expression in skeletal muscle. The absence of AGRN‐208 in the tibial nerve may be explained by localization of mRNA to subcellular compartments in long axons (Jung et al., 2012). Such transcripts are missed when sampling the nerve nucleus.

Cerebral brain areas clustered together close to skeletal muscle with expression of nearly all MG‐related genes, confirming expression of these genes in healthy adult human brain. All MG‐related genes could be detected in areas such as hippocampus and basal ganglia, although the level of expression differed compared to skeletal muscle. The cerebellum clustered separately from the cerebrum with moderate expression of DOK7, LRP4 and COLQ. Transcriptomic separation of cerebellum and cerebrum is observed for many other genes (Huisman et al., 2017). Outside the brain, almost all MG‐related genes are found in the testis and the ectocervix. Other components of the male and female reproductive systems cluster together with moderate expression of LRP4 and COLQ and low expression of DOK7, MUSK and/or RAPSN. Varying expression for subsets of MG‐related genes could be detected in the remaining tissues.

We observed three clusters of the MG‐related genes (Figure 1). LRP4, COLQ and DOK7 have relatively ubiquitous expression across all tissues. Notably, LRP4 was most expressed in the brain and the skin, COLQ in the cerebellum, pituitary gland, testis and heart, and DOK7 in the heart and pituitary gland. In contrast, MUSK, RAPSN and CHRNA1 showed low expression in a more limited subset of tissues. MUSK expression is highest in the small intestine, bladder and testis. RAPSN and CHRNA1 expression was largely restricted to skeletal muscle, with some additional expression in the tibial nerve, and testis and pituitary gland respectively. Finally, the ACHE‐207 and AGRN‐208 isoforms clustered together based on their expression in the brain and ectocervix. Taken together, the expression of MG‐related genes is prominent in skeletal muscle and the brain, but individual genes are also expressed in other tissues of the human body.

3.2. NMJ genes do not share anatomical expression patterns in the brain

Compared to the 10 brain regions in the GTEx database, the AHBA provides a high‐resolution map of relative gene expression across the adult human brain. Strong correlation between spatial expression patterns would become apparent when genes cluster in the same region of the t‐SNE plot (Figure 2a). However, MG‐related genes are scattered across the t‐SNE plot, suggesting they do not share spatial expression patterns across the brain.

FIGURE 2.

FIGURE 2

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Spatial expression of MG‐related genes in the adult human brain. Co‐expression analysis of MG‐related genes, based on their distribution in a t‐SNE embedding, reveals that they do not share spatial coexpression (a). The t‐SNE from BrainScope shows an embedding of all genes (dots) based on their expression pattern across the whole brain. (b) A heatmap showing the average expression of MG genes across distinct (non‐overlapping) brain regions. Expression values represent the average z‐score normalized expression per gene (i.e., relative expression across brain regions)

For hierarchical clustering analysis of distinct brain regions, the NMJ‐specific isoform of agrin (AGRN‐208) was included (Figure 2b) while a heatmap including all AGRN isoforms can be found in Figure S3b. The hippocampus clustered separately from all other brain regions with the unique pattern of high relative expression of CHRNA1, RAPSN and MUSK (Figure 2b). The remaining anatomical brain regions separated in two large clusters enriched in the developmentally early and late regions respectively. The early developmental cluster with hindbrain, midbrain and diencephalon was characterized by high relative expression of DOK7 and ACHE and predominant low relative expression of COLQ and AGRN‐208. In contrast, the late developmental cluster, with the basal ganglia, limbic system and cortical regions, was characterized by moderate relative expression of COLQ and AGRN‐208 and low relative expression of DOK7 and ACHE. Furthermore, expression is highest in a unique structure for most genes. DOK7 in cerebellum, CHRNA1 in hippocampus, MUSK in epithalamus, LRP4 in globus pallidus and RAPSN in the hypothalamus. Taken together, this suggests that MG‐related genes are likely not active in similar pathways in the CNS as they do not share spatial expression patterns. However, subsets of these genes may be involved in signalling in similar structures.

3.3. NMJ genes do not co‐segregate in developmental time in the human brain

To investigate whether expression of these genes follows a temporal pattern in brain development, we used the BrainSpan atlas visualized in the BrainScope browser (Huisman et al., 2017; Miller et al., 2014). The expression of ACHE, AGRN, COLQ and LRP4 did not follow the same pattern across human brain development (Figure 3). AGRN was particularly expressed until early childhood while LRP4 was expressed from the 3rd prenatal stage into adulthood. ACHE expression was predominantly restricted to the cerebellum, thalamus, amygdala and striatum from birth until adulthood. COLQ is highest across the entire brain in adult life, although various brain regions also show high expression in the last prenatal stage. CHRNA1, DOK7, MUSK and RAPSN could not be studied with BrainScope, but the brain‐map.org portal confirms the low and unique expression patterns of these genes (Figure S4). In sum, spatio‐temporal expression of MG‐related genes is heterogenous in the human brain.

FIGURE 3.

FIGURE 3

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Overview of the expression patterns of four MG‐related genes throughout human brain development. ACHE, AGRN, COLQ and LRP4 are expressed during different phases of human brain development

3.4. MG‐related genes do not coexpress in the same cell types in the cortex

To understand which cell types are responsible for the regional expression of MG‐related genes in the CNS, we explored a snRNAseq dataset (Hodge et al., 2019). The expression of CHRNA1 and RAPSN could not be detected in cortical cell types (Figure 4a). This is in line with the very low to absent expression in the bulk RNAseq datasets of the cortical regions (Figures 1 and S4). Interestingly, MUSK and LRP4 were uniquely expressed in non‐neuronal cell types, with MUSK solely in vascular leptomeningeal cells (VLMC) and oligodendrocytes and LRP4 predominantly in astrocytes and oligodendrocyte progenitor cells (Figure 4a). ACHE, AGRN, COLQ and DOK7 were predominantly expressed in glutamatergic and/or GABAergic neurons; although, AGRN was also expressed in pericytes. Overall, a variety of cell types seem to be responsible for expression of MG‐related genes in the cortex.

FIGURE 4.

FIGURE 4

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Cortical cell type expression of MG‐related genes. Heatmap of adult human cortical cell types (a). Heatmap of adult mouse cortical cell types (b). CT, corticothalamic; CTX, isocortex; ENTI/ENT, entorhinal area; ET, extratelencephalic; IT, intratelencephalic; micro‐PVM, microglia—perivascular macrophages; NP, near projecting; OPC, oligodendrocyte progenitor cells, PT, pyramidal tract; SMC‐Peri, smooth muscle cell—pericyte; TPE, temporal, perirhinal and ectorhinal; VLMC, vascular leptomeningeal cells. Average expression is reported as Log10(TPM + 1)

As many preclinical studies related to MG are done in rodents, it is relevant to know how the expression of MG‐related genes compares between humans and rodents. In mouse cortical regions, similar cell types expressed MUSK, LRP4, CHRNA1 and RAPSN, suggesting adequate translatability of results (Figure 4b). In contrast, ACHE and AGRN expression was ubiquitously high in mouse neuronal cell types, but much more restricted to GABAergic or glutamatergic cell types respectively in the human cortex. For COLQ, the expression is ubiquitously high in human neuronal cell types, but very much restricted to a single glutamatergic subclass in the mouse. For these genes, translatability of functional studies to humans may thus be limited.

4. DISCUSSION

This study confirms that many of the MG‐related genes are widely expressed outside skeletal muscle, with the exact pattern of involved organs varying per gene. Within the CNS, these genes furthermore show heterogenous spatial, temporal and cellular expression patterns. This suggests that these genes do not converge on a single pathway in the brain or other human tissues. It furthermore provides valuable insights for new research questions and hypotheses, explaining possible non‐motor symptoms in myasthenic syndromes or off‐target effects of MG‐related targeted treatments.

Validity of these expression data is supported by studies confirming the presence of these MG‐related proteins outside skeletal muscle. Congruent with the observed expression in testis, agrin, MuSK and rapsyn are present in human sperm, (Kumar et al., 2006; Kumar & Meizel, 2005). Dok7 protein was detected in heart and brain, and MuSK in bladder, heart, lung and liver (Garcia‐Osta et al., 2006; Okada et al., 2006). Agrin is present in glutamatergic neurons and increasingly surrounding brain microvasculature during development of the blood–brain barrier, validating the expression in pericytes and during prenatal development (Barber & Lieth, 1997; Cohen et al., 1997). Lrp4 is indeed abundantly present in the brain, most prominently in astrocytes (Karakatsani et al., 2017; Tian et al., 2006). Not all observations however match with previous studies. Dok7 was not detected in liver and spleen before (Garcia‐Osta et al., 2006; Okada et al., 2006) and MuSK protein was detected in spleen but is not present in our dataset. MuSK and agrin were found in glutamatergic neurons in the mouse cortex, while MUSK is only expressed in VLMCs and AGRN also in GABAergic neurons in the cell‐type analysis (Ksiazek et al., 2007). It is well established that RNA levels do not always translate to protein; however, discrepancies may also be explained by sensitivity and type of detection method, timing or location of sampling, or differences between rodents and humans. The Allen Institute databases furthermore cover a limited number of individuals, making it difficult to translate these observations to a larger heterogenous population. Cell type‐specific expression is currently only available for the cortex, covering a fraction of the cellular complexity in the CNS. More single cell data is anticipated as this field is rapidly expanding. Keeping these limitations in mind, our gene expression analysis method provides biological rationale to further investigate a possible role for candidate genes in newly identified target tissues. This is particularly important for MG‐related genes like Musk, Agrin, Dok7 and Lrp4, for which the role in other organs may be overlooked, because null mice die at birth of impaired breathing due to dysfunctional NMJs (Dechiara et al., 1996; Gautam et al., 1996; Okada et al., 2006; Weatherbee et al., 2006).

Surprisingly, many of the MG‐related genes were found in parts of the male and female reproductive system. Their role there remains to be uncovered. However, fertility issues have not been described in MG patients nor in mouse models lacking these genes. Interestingly, quite a number of MG‐related genes have prominent expression in non‐neuronal cell types in the cortex, suggesting a role for these genes outside a synaptic context. Expression of MUSK in VLMCs has not been described before and provides an important clue for further research into the role of MuSK in the brain. Till date, for a limited number of MG‐related genes the function was studied in the CNS. Reduced agrin, Lrp4 or MuSK levels impaired cortical synaptogenesis and/or hippocampal functioning in mice (Garcia‐Osta et al., 2006; Gomez et al., 2014; Karakatsani et al., 2017; Ksiazek et al., 2007; Sun et al., 2016; Zhang et al., 2019). This fits the observation that all three genes are expressed in the hippocampus, although the prominent effects of reducing MuSK or agrin were surprising considering the relatively low expression of MUSK and AGRN‐208. Whether these three genes converge on the same pathway in the hippocampus is uncertain. Agrin was shown to induce MuSK signalling through Lrp4 in hippocampal astrocytes (Sun et al., 2016). However, the role of agrin and Lrp4 in hippocampal neurogenesis seems to be mediated by Ror2, instead of MuSK, supporting that subsets of MG‐related genes may also work together with other proteins (Zhang et al., 2019). Taken together, MG‐related genes may participate in similar pathways under certain condition in the hippocampus, but little is known about their role in the rest of the brain. The spatial, temporal and cellular resolution of our analysis can guide future studies to specific anatomical regions, developmental phase and cell types in the brain.

The expression of MG‐related genes outside skeletal muscle suggests that other organs may be at risk for impairment by autoantibodies. In autoimmune MG and CMS patients, skeletal muscle weakness is clearly at the foreground of symptoms. The presence of these genes in the CNS suggests that the reported CNS‐related symptoms in subsets of MG patients may be due to autoantibodies binding their target if they cross the blood–brain barrier, although this is not expected in patients with an intact blood–brain barrier (Bhagavati et al., 2007; Mao et al., 2015; Paul et al., 2000; Ruiter et al., 2020; Tong et al., 2018). In CMS patients, a mutational bias may occur as observed genetic defects in these genes are likely to result in a sufficiently mild phenotype to allow development. Absence of other symptoms may be explained by MG‐related gene products (1) having low levels of expression, (2) being inaccessible to autoantibodies, (3) not fulfilling an essential function, (4) having alternative splice variants or (5) being differentially posttranslationally modified masking relevant epitopes. Furthermore, observed non‐motor symptoms or comorbidities may also be due to underlying immune dysfunction, thymoma's or treatment side‐effects. Future studies are needed to investigate whether CNS‐related or other non‐motor symptoms in MG patients are due to autoantibody binding or gene dysfunction not yet recognized.

MG‐related genes or proteins are interesting targets for treatment of neuromuscular diseases through strengthening NMJs and improving or maintaining muscle function (Ohno et al., 2017). DOK7 gene therapy, agonistic MuSK antibodies and agrin biologicals have proven beneficial in several mouse models for ALS, Dok7 CMS, Emery Dreifuss muscular dystrophy, spinal muscular atrophy or sarcopenia (Arimura et al., 2014; Boido et al., 2018; Cantor et al., 2018; Hettwer et al., 2014; Miyoshi et al., 2017; Sengupta‐Ghosh et al., 2019). Off‐target effects in other organs have not been reported, but also not specifically investigated. Our data furthermore identified some differences between mouse and human MG‐related gene expression which emphasizes where caution is needed for accurate interpretation and translatability of studies using mouse models. For further clinical development, our data provides clear guidance as to which organs may be at risk for off‐target effects. Since many of the MG‐related genes are expressed in the brain, a treatment strategy that does not cross the blood–brain barrier is recommended.

This hypothesis‐free approach to study timing and localization of MG‐related gene expression suggests wide‐spread expression of these genes outside skeletal muscle. These insights can guide future studies to uncover their role in other organs and guide pre‐clinical development of related novel therapeutics.

CONFLICT OF INTEREST

LUMC receives royalties from IBL for a MuSK diagnostic assay. LUMC and MGH receive royalties from licensed patent applications on MuSK‐related research. The other authors report no conflict of interests.

AUTHOR CONTRIBUTIONS

DV, AM and MH designed the study, analysed the data and wrote the paper. AK analysed the data.

PEER REVIEW

The peer review history for this article is available at https://publons.com/publon/10.1111/ejn.15382.

Supporting information

Table S1. Genes and probes used for MG‐related genes from the AHBA. * used for analysis, unless otherwise specified. # detects the AGRN‐208 isoform able to induce AChR clustering at the NMJ.

Figure S1. Expression of ACHE isoforms (A) and AGRN isoforms (B) extracted from the GTEx database. Expression of synapse‐specific ACHE‐207 isoform (red) is limited to brain areas and skeletal muscle. Expression of NMJ‐specific AGRN‐208 isoform (red) is limited to brain areas. Grey isoforms are non‐protein coding according to Ensembl (GRCh38). * isoforms identified in earlier assemblies of the human genome.

Figure S2. Hierarchical clustering of gene expression in the GTEx database using all isoforms of ACHE and AGRN.

Figure S3. Heatmaps from Allen human brain atlas. Relative expression of different agrin probes across distinct brain areas (A). # probe that selectively detects NMJ‐relevant isoform of agrin. * probe for AGRN used in the BrainScope tool detecting all AGRN isoforms. Hierarchical clustering using probe for AGRN that does not discriminate between isoforms (*) (B).

Figure S4. Gene expression of MG‐related genes during human development do not show the same pattern across development. CHRNA1 expression is increased across development restricted to the hippocampus. DOK7 shows a pattern of increasing expression across development, with highest expression in the cerebellum and thalamus. MUSK is lowly expressed during prenatal development in the hippocampus, amygdala and cerebellum. RAPSN shows very limited expression across development, restricted to some subcortical regions of the brain. Visualized using the BrainSpan portal.

Click here for additional data file. (1.1MB, pdf)

ACKNOWLEDGEMENTS

Thanks to Mink Schinkelshoek for our fruitful joint discussions on the methodology and scientific relevance and Prof. Jan Verschuuren and Prof. Silvère van der Maarel for their support and advice on the manuscript. Graphical abstract was created with BioRender.com. MH receives financial support from the Leids Universitair Medisch Centrum (OIO, 2017), Top Sector Life Sciences & Health to Samenwerkende Gezondheidsfondsen (SGF) (LSHM18055‐SGF and LSHM19130), Prinses Beatrix Spierfonds (W.OR‐17.13 and W.OR‐19.13) and the Dutch Organization for Scientific research (NWO) (VENI 0915016181 0040). AM receives financial support from the Dutch Organization for Scientific Research (NWO) Gravitation: BRAINSCAPES: A Roadmap from Neurogenetics to Neurobiology (024.004.012). MH is a member of the European Reference Network for Rare Neuromuscular Diseases [ERN EURO‐NMD] and The Netherlands Neuromuscular Center (NL‐NMD).

Vergoossen, D. L. E., Keo, A., Mahfouz, A., & Huijbers, M. G. (2021). Timing and localization of myasthenia gravis‐related gene expression. European Journal of Neuroscience, 54(4), 5574–5585. 10.1111/ejn.15382

Ahmed Mahfouz and Maartje G. Huijbers contributed equally to this work.

Edited by: Francisco Alvarez.

Funding information LUMC; Top Sector Life Sciences & Health to Samenwerkende Gezondheidsfondsen (SGF), Grant/Award Number: LSHM18055‐SGF, LSHM19130; Prinses Beatrix Spierfonds, Grant/Award Number: W.OR‐17.13, W.OR‐19.13; Dutch Science Organization NWO, Grant/Award Number: VENI 0915016181 0040; Dutch Organization for Scientific Research (NWO) Gravitation: BRAINSCAPES: A Roadmap from Neurogenetics to Neurobiology, Grant/Award Number: 024.004.012

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from GTEx version 8 and the Allen Institute as described under the relevant subheadings of the methods section. Scripts to generate all the results presented in this manuscript can be found online at: https://github.com/ahmedmahfouz/MG-analysis.

REFERENCES

  1. Arimura, S., Okada, T., Tezuka, T., Chiyo, T., Kasahara, Y., Yoshimura, T., Motomura, M., Yoshida, N., Beeson, D., Takeda, S., & Yamanashi, Y. (2014). DOK7 gene therapy benefits mouse models of diseases characterized by defects in the neuromuscular junction. Science, 345, 1505–1508. 10.1126/science.1250744 [DOI] [PubMed] [Google Scholar]
  2. Barber, A. J., & Lieth, E. (1997). Agrin accumulates in the brain microvascular basal lamina during development of the blood‐brain barrier. Developmental Dynamics, 208, 62–74. [DOI] [PubMed] [Google Scholar]
  3. Bezakova, G., & Ruegg, M. A. (2003). New insights into the roles of agrin. Nature Reviews Molecular Cell Biology, 4, 295–309. 10.1038/nrm1074 [DOI] [PubMed] [Google Scholar]
  4. Bhagavati, S., Maccabee, P. J., & Chari, G. (2007). Is cerebral involvement an occasional feature of muscle‐specific kinase antibody‐positive syndrome? European Journal of Neurology, 14, e21–e22. 10.1111/j.1468-1331.2007.01873.x [DOI] [PubMed] [Google Scholar]
  5. Boido, M., De Amicis, E., Valsecchi, V., Trevisan, M., Ala, U., Ruegg, M. A., Hettwer, S., & Vercelli, A. (2018). Increasing agrin function antagonizes muscle atrophy and motor impairment in spinal muscular atrophy. Frontiers in Cellular Neuroscience, 12, 17. 10.3389/fncel.2018.00017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Burden, S. J., Huijbers, M. G., & Remedio, L. (2018). Fundamental molecules and mechanisms for forming and maintaining neuromuscular synapses. International Journal of Molecular Sciences, 19(2), 490. 10.3390/ijms19020490 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Cantor, S., Zhang, W., Delestree, N., Remedio, L., Mentis, G. Z., & Burden, S. J. (2018). Preserving neuromuscular synapses in ALS by stimulating MuSK with a therapeutic agonist antibody. eLife, 7, e34375. 10.7554/eLife.34375 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Carlisle, D. L., Hopkins, T. M., Gaither‐Davis, A., Silhanek, M. J., Luketich, J. D., Christie, N. A., & Siegfried, J. M. (2004). Nicotine signals through muscle‐type and neuronal nicotinic acetylcholine receptors in both human bronchial epithelial cells and airway fibroblasts. Respiratory Research, 5, 27. 10.1186/1465-9921-5-27 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cohen, N. A., Kaufmann, W. E., Worley, P. F., & Rupp, F. (1997). Expression of agrin in the developing and adult rat brain. Neuroscience, 76, 581–596. 10.1016/S0306-4522(96)00345-4 [DOI] [PubMed] [Google Scholar]
  10. Dechiara, T. M., Bowen, D. C., Valenzuela, D. M., Simmons, M. V., Poueymirou, W. T., Thomas, S., Kinetz, E., Compton, D. L., Rojas, E., Park, J. S., Smith, C., DiStefano, P. S., Glass, D. J., Burden, S. J., & Yancopoulos, G. D. (1996). The receptor tyrosine kinase MuSK is required for neuromuscular junction formation in vivo. Cell, 85, 501–512. 10.1016/S0092-8674(00)81251-9 [DOI] [PubMed] [Google Scholar]
  11. Engel, A. G. (2018). Congenital myasthenic syndromes in 2018. Current Neurology and Neuroscience Reports, 18, 46. 10.1007/s11910-018-0852-4 [DOI] [PubMed] [Google Scholar]
  12. Fulpius, B. W., Fontana, A., & Cuénoud, S. (1977). Central nervous system involvement in experimental autoimmune myasthenia gravis. The Lancet, 310, 350–351. 10.1016/S0140-6736(77)91505-7 [DOI] [PubMed] [Google Scholar]
  13. Garcia‐Osta, A., Tsokas, P., Pollonini, G., Landau, E. M., Blitzer, R., & Alberini, C. M. (2006). MuSK expressed in the brain mediates cholinergic responses, synaptic plasticity, and memory formation. The Journal of Neuroscience, 26, 7919–7932. 10.1523/JNEUROSCI.1674-06.2006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Gautam, M., Noakes, P. G., Moscoso, L., Rupp, F., Scheller, R. H., Merlie, J. P., & Sanes, J. R. (1996). Defective neuromuscular synaptogenesis in agrin‐deficient mutant mice. Cell, 85, 525–535. 10.1016/S0092-8674(00)81253-2 [DOI] [PubMed] [Google Scholar]
  15. Gilhus, N. E., & Verschuuren, J. J. (2015). Myasthenia gravis: Subgroup classification and therapeutic strategies. The Lancet Neurology, 14, 1023–1036. 10.1016/S1474-4422(15)00145-3 [DOI] [PubMed] [Google Scholar]
  16. Gomez, A. M., Froemke, R. C., & Burden, S. J. (2014). Synaptic plasticity and cognitive function are disrupted in the absence of Lrp4. eLife, 3, e04287. 10.7554/elife.04287 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Hawrylycz, M. J., Lein, E. S., Guillozet‐Bongaarts, A. L., Shen, E. H., Ng, L., Miller, J. A., Van De Lagemaat, L. N., Smith, K. A., Ebbert, A., Riley, Z. L., Abajian, C., Beckmann, C. F., Bernard, A., Bertagnolli, D., Boe, A. F., Cartagena, P. M., Chakravarty, M. M., Chapin, M., Chong, J., … Jones, A. R. (2012). An anatomically comprehensive atlas of the adult human brain transcriptome. Nature, 489, 391–399. 10.1038/nature11405 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Hettwer, S., Lin, S., Kucsera, S., Haubitz, M., Oliveri, F., Fariello, R. G., Ruegg, M. A., & Vrijbloed, J. W. (2014). Injection of a soluble fragment of neural agrin (NT‐1654) considerably improves the muscle pathology caused by the disassembly of the neuromuscular junction. PLoS ONE, 9, e88739. 10.1371/journal.pone.0088739 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Hodge, R. D., Bakken, T. E., Miller, J. A., Smith, K. A., Barkan, E. R., Graybuck, L. T., Close, J. L., Long, B., Johansen, N., Penn, O., Yao, Z., Eggermont, J., Hollt, T., Levi, B. P., Shehata, S. I., Aevermann, B., Beller, A., Bertagnolli, D., Brouner, K., … Lein, E. S. (2019). Conserved cell types with divergent features in human versus mouse cortex. Nature, 573, 61–68. 10.1038/s41586-019-1506-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Huisman, S. M. H., Van Lew, B., Mahfouz, A., Pezzotti, N., Höllt, T., Michielsen, L., Vilanova, A., Reinders, M. J. T., & Lelieveldt, B. P. F. (2017). BrainScope: interactive visual exploration of the spatial and temporal human brain transcriptome. Nucleic Acids Research, 45(10), e83. 10.1093/nar/gkx046 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Jung, H., Yoon, B. C., & Holt, C. E. (2012). Axonal mRNA localization and local protein synthesis in nervous system assembly, maintenance and repair. Nature Reviews Neuroscience, 13, 308–324. 10.1038/nrn3210 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Karakatsani, A., Marichal, N., Urban, S., Kalamakis, G., Ghanem, A., Schick, A., Zhang, Y., Conzelmann, K.‐K., Rüegg, M. A., Berninger, B., Ruiz De Almodovar, C., Gascón, S., & Kröger, S. (2017). Neuronal LRP4 regulates synapse formation in the developing CNS. Development, 144, 4604–4615. 10.1242/dev.150110 [DOI] [PubMed] [Google Scholar]
  23. Keesey, J. C., Tourtellotte, W. W., Herrmann, C., Andrews, J. M., & Lindstrom, J. (1978). Acetylcholine‐receptor antibody in cerebrospinal fluid. The Lancet, 311, 777. [DOI] [PubMed] [Google Scholar]
  24. Ksiazek, I., Burkhardt, C., Lin, S., Seddik, R., Maj, M., Bezakova, G., Jucker, M., Arber, S., Caroni, P., Sanes, J. R., Bettler, B., & Ruegg, M. A. (2007). Synapse loss in cortex of agrin‐deficient mice after genetic rescue of perinatal death. Journal of Neuroscience, 27, 7183–7195. 10.1523/JNEUROSCI.1609-07.2007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Kumar, P., Ferns, M. J., & Meizel, S. (2006). Identification of agrinSN isoform and muscle‐specific receptor tyrosine kinase in sperm. BBRC, 342, 522–528. 10.1016/j.bbrc.2006.01.161 [DOI] [PubMed] [Google Scholar]
  26. Kumar, P., & Meizel, S. (2005). Nicotinic acetylcholine receptor subunits and associated proteins in human sperm. Journal of Biological Chemistry, 280, 25928–25935. 10.1074/jbc.M502435200 [DOI] [PubMed] [Google Scholar]
  27. Lefvert, A. K., & Pirskanen, R. (1977). Acetylcholine‐receptor antibodies in cerebrospinal fluids of patients with myasthenia gravis. The Lancet, 310, 351–352. 10.1016/S0140-6736(77)91506-9 [DOI] [PubMed] [Google Scholar]
  28. Mao, Z., Yin, J., Lu, Z., & Hu, X. (2015). Association between myasthenia gravis and cognitive function: A systematic review and meta‐analysis. Annals of Indian Academy of Neurology, 18, 131–137. 10.4103/0972-2327.156560 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. McMahan, U. J., Horton, S. E., Werle, M. J., Honig, L. S., Kröger, S., Ruegg, M. A., & Escher, G. (1992). Agrin isoforms and their role in synaptogenesis. Current Opinion in Cell Biology, 4, 869–874. 10.1016/0955-0674(92)90113-Q [DOI] [PubMed] [Google Scholar]
  30. Miller, J. A., Ding, S.‐L., Sunkin, S. M., Smith, K. A., Ng, L., Szafer, A., Ebbert, A., Riley, Z. L., Royall, J. J., Aiona, K., Arnold, J. M., Bennet, C., Bertagnolli, D., Brouner, K., Butler, S., Caldejon, S., Carey, A., Cuhaciyan, C., Dalley, R. A., … Lein, E. S. (2014). Transcriptional landscape of the prenatal human brain. Nature, 508, 199–206. 10.1038/nature13185 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Miyoshi, S., Tezuka, T., Arimura, S., Tomono, T., Okada, T., & Yamanashi, Y. (2017). DOK 7 gene therapy enhances motor activity and life span in ALS model mice. EMBO Molecular Medicine, 9, 880–889. 10.15252/emmm.201607298 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Müller, K. M. I., Taskinen, E., Lefvert, A. K., Pirskanen, R., & Iivanainen, M. (1987). Immunoactivation in the central nervous system in myasthenia gravis. Journal of the Neurological Sciences, 80, 13–23. 10.1016/0022-510X(87)90217-6 [DOI] [PubMed] [Google Scholar]
  33. Ohno, K., Ohkawara, B., & Ito, M. (2017). Agrin‐LRP4‐MuSK signaling as a therapeutic target for myasthenia gravis and other neuromuscular disorders. Expert Opinion on Therapeutic Targets, 21, 949–958. 10.1080/14728222.2017.1369960 [DOI] [PubMed] [Google Scholar]
  34. Okada, K., Inoue, A., Okada, M., Murata, Y., Kakuta, S., Jigami, T., Kubo, S., Shiraishi, H., Eguchi, K., Motomura, M., Akiyama, T., Iwakura, Y., Higuchi, O., & Yamanashi, Y. (2006). The muscle protein Dok‐7 is essential for neuromuscular synaptogenesis. Science, 312, 1802–1805. 10.1126/science.1127142 [DOI] [PubMed] [Google Scholar]
  35. Paul, R. H., Cohen, R. A., Gilchrist, J. M., Aloia, M. S., & Goldstein, J. M. (2000). Cognitive dysfunction in individuals with myasthenia gravis. Journal of the Neurological Sciences, 179, 59–64. 10.1016/S0022-510X(00)00367-1 [DOI] [PubMed] [Google Scholar]
  36. Ruiter, A. M., Verschuuren, J. J. G. M., & Tannemaat, M. R. (2020). Fatigue in patients with myasthenia gravis. A systematic review of the literature. Neuromuscular Disorders, 30, 631–639. 10.1016/j.nmd.2020.06.010 [DOI] [PubMed] [Google Scholar]
  37. Sabre, L., Evoli, A., & Punga, A. R. (2019). Cognitive dysfunction in mice with passively induced MuSK antibody seropositive myasthenia gravis. Journal of the Neurological Sciences, 399, 15–21. 10.1016/j.jns.2019.02.001 [DOI] [PubMed] [Google Scholar]
  38. Sengupta‐Ghosh, A., Dominguez, S. L., Xie, L., Barck, K. H., Jiang, Z., Earr, T., Imperio, J., Phu, L., Budayeva, H. G., Kirkpatrick, D. S., Cai, H., Eastham‐Anderson, J., Ngu, H., Foreman, O., Hedehus, M., Reichelt, M., Hotzel, I., Shang, Y., Carano, R. A. D., … Easton, A. (2019). Muscle specific kinase (MuSK) activation preserves neuromuscular junctions in the diaphragm but is not sufficient to provide a functional benefit in the SOD1(G93A) mouse model of ALS. Neurobiology of Disease, 124, 340–352. 10.1016/j.nbd.2018.12.002 [DOI] [PubMed] [Google Scholar]
  39. Sun, X.‐D., Li, L., Liu, F., Huang, Z.‐H., Bean, J. C., Jiao, H.‐F., Barik, A., Kim, S.‐M., Wu, H., Shen, C., Tian, Y., Lin, T. W., Bates, R., Sathyamurthy, A., Chen, Y.‐J., Yin, D.‐M., Xiong, L., Lin, H.‐P., Hu, J.‐X., … Mei, L. (2016). Lrp4 in astrocytes modulates glutamatergic transmission. Nature Neuroscience, 19, 1010–1018. 10.1038/nn.4326 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Tian, Q. B., Suzuki, T., Yamauchi, T., Sakagami, H., Yoshimura, Y., Miyazawa, S., Nakayama, K., Saitoh, F., Zhang, J. P., Lu, Y., Kondo, H., & Endo, S. (2006). Interaction of LDL receptor‐related protein 4 (LRP4) with postsynaptic scaffold proteins via its C‐terminal PDZ domain‐binding motif, and its regulation by Ca/calmodulin‐dependent protein kinase II. The European Journal of Neuroscience, 23, 2864–2876. 10.1111/j.1460-9568.2006.04846.x [DOI] [PubMed] [Google Scholar]
  41. Tong, O., Delfiner, L., & Herskovitz, S. (2018). Pain, headache, and other non‐motor symptoms in myasthenia gravis. Current Pain and Headache Reports, 22, 39. 10.1007/s11916-018-0687-3 [DOI] [PubMed] [Google Scholar]
  42. Weatherbee, S. D., Anderson, K. V., & Niswander, L. A. (2006). LDL‐receptor‐related protein 4 is crucial for formation of the neuromuscular junction. Development, 133, 4993–5000. 10.1242/dev.02696 [DOI] [PubMed] [Google Scholar]
  43. Whiting, P., Cooper, J., & Lindstrom, J. (1987). Antibodies in sera from patients with myasthenia gravis do not bind to nicotinic acetylcholine receptors from human brain. Journal of Neuroimmunology, 16, 205–213. 10.1016/0165-5728(87)90075-0 [DOI] [PubMed] [Google Scholar]
  44. Yao, Z., Nguyen, T. N., van Velthoven, C. T. J., Goldy, J., Sedeno‐Cortes, A. E., Baftizadeh, F., Bertagnolli, D., Casper, T., Crichton, K., Ding, S.‐L., Fong, O., Garren, E., Glandon, A., Gray, J., Graybuck, L. T., Hirschstein, D., Kroll, M., Lathia, K., Levi, B., … Zeng, H. (2021). A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell, 184(12), 3083–3085. 10.1016/j.cell.2021.04.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Zhang, H., Sathyamurthy, A., Liu, F., Li, L., Zhang, L., Dong, Z., Cui, W., Sun, X., Zhao, K., Wang, H., Ho, H.‐Y. H., Xiong, W.‐C., & Mei, L. (2019). Agrin‐Lrp4‐Ror2 signaling regulates adult hippocampal neurogenesis in mice. eLife, 8, e45303. 10.7554/eLife.45303 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Zimmermann, M. (2013). Neuronal AChE splice variants and their non‐hydrolytic functions: Redefining a target of AChE inhibitors? British Journal of Pharmacology, 170, 953–967. 10.1111/bph.12359 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Zisimopoulou, P., Evangelakou, P., Tzartos, J., Lazaridis, K., Zouvelou, V., Mantegazza, R., Antozzi, C., Andreetta, F., Evoli, A., Deymeer, F., Saruhan‐Direskeneli, G., Durmus, H., Brenner, T., Vaknin, A., Berrih‐Aknin, S., Frenkian Cuvelier, M., Stojkovic, T., Debaets, M., Losen, M., … Tzartos, S. J. (2014). A comprehensive analysis of the epidemiology and clinical characteristics of anti‐LRP4 in myasthenia gravis. Journal of Autoimmunity, 52, 139–145. 10.1016/j.jaut.2013.12.004 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1. Genes and probes used for MG‐related genes from the AHBA. * used for analysis, unless otherwise specified. # detects the AGRN‐208 isoform able to induce AChR clustering at the NMJ.

Figure S1. Expression of ACHE isoforms (A) and AGRN isoforms (B) extracted from the GTEx database. Expression of synapse‐specific ACHE‐207 isoform (red) is limited to brain areas and skeletal muscle. Expression of NMJ‐specific AGRN‐208 isoform (red) is limited to brain areas. Grey isoforms are non‐protein coding according to Ensembl (GRCh38). * isoforms identified in earlier assemblies of the human genome.

Figure S2. Hierarchical clustering of gene expression in the GTEx database using all isoforms of ACHE and AGRN.

Figure S3. Heatmaps from Allen human brain atlas. Relative expression of different agrin probes across distinct brain areas (A). # probe that selectively detects NMJ‐relevant isoform of agrin. * probe for AGRN used in the BrainScope tool detecting all AGRN isoforms. Hierarchical clustering using probe for AGRN that does not discriminate between isoforms (*) (B).

Figure S4. Gene expression of MG‐related genes during human development do not show the same pattern across development. CHRNA1 expression is increased across development restricted to the hippocampus. DOK7 shows a pattern of increasing expression across development, with highest expression in the cerebellum and thalamus. MUSK is lowly expressed during prenatal development in the hippocampus, amygdala and cerebellum. RAPSN shows very limited expression across development, restricted to some subcortical regions of the brain. Visualized using the BrainSpan portal.

Click here for additional data file. (1.1MB, pdf)

Data Availability Statement

The data that support the findings of this study are available from GTEx version 8 and the Allen Institute as described under the relevant subheadings of the methods section. Scripts to generate all the results presented in this manuscript can be found online at: https://github.com/ahmedmahfouz/MG-analysis.

Articles from The European Journal of Neuroscience are provided here courtesy of Wiley

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