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Cited by 3Lysosomal Storage Disorders ResearchMetabolism and Genetic DisordersGenomics and Rare Diseases

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A 37-Year-Old Man With Intellectual Disability Discovered to Have Aspartylglucosaminuria: Implications for the Diagnosis of Genetic Causes

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Kouhashi, Mutsuo, Yukawa, Kayoko, Yano, Naoko, Hagemeijer, Marne C., Hirata, Shinya, Kambe, Daisuke, Yokoyama, Atsushi, Yoshida, Akira, Kora, Kengo, de Ronde, Corline J., Vrieswijk, Sandrien, van der Meijden, Eric, Yoshida, Takeshi, Yamashita, Hirofumi·2024·Neurology Genetics
DOI 10.1212/nxg.0000000000200161PMC PMC11145746
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Abstract

Objectives: The causes of intellectual disability (ID) are varied, with as many as 1,400 causative genes. We attempted to identify the causative gene in a patient with long-standing undiagnosed ID. Methods: Although this was an isolated case with no family history, we searched for the causative gene using trio-based whole-exome sequencing (trio-WES), because severe ID is often caused by genetic variations, and inherited metabolic disorders (IMDs) are assumed to be the cause when regression and epilepsy occur. Results: gene (aspartylglucosaminidase; NM_000027.4) Chr4(GRCh38):g. 177436275C>A, c.698+1G>T. This gene is implicated in aspartylglucosaminuria (AGU; OMIM #208400) and originated from both of the patient's parents. We confirmed the pathogenicity of the variant by detecting the splicing defect in cDNA from the patient's blood and accumulation of aberrant metabolites in the patient's urine. Discussion: We discuss how to more readily achieve an accurate diagnosis for patients with undiagnosed intellectual disabilities. Medical practitioners' awareness of the characteristics of the disease leading to clinical suspicion in patients with matching presentations, and the performance of newborn screening when possible, is important for the diagnosis of ID. In addition, the characteristic symptoms and course of the disease give rise to suspicion of IMDs. Given our results, we consider trio-WES to be a powerful method for identifying the causative genes in cases of ID with genetic causes.

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Figure 1 Clinical Features, Pedigree, and Genetic Analysis (A) The patient's facial appearance showing coarse facial features with a high forehead, thick eyebrows, hypertelorism, and a broad nasal bridge. (B.a to d) Brain MRI showing mild atrophy of the bilateral frontal lobes, mild T2 hypointensities in the pulvinar nuclei of the thalami (a, arrows), and mild T2/FLAIR hyperintensity in the periventricular white matter (b, d). (C) Family pedigree. The filled symbol indicates the patient, and the arrow indicates the proband. Blood samples were obtained from the mother (I-1), the father (I-2), and the patient (II-3), and analyzed by trio-WES. (D) Sanger sequencing of AGA.
Figure 1 Clinical Features, Pedigree, and Genetic Analysis (A) The patient's facial appearance showing coarse facial features with a high forehead, thick eyebrows, hypertelorism, and a broad nasal bridge. (B.a to d) Brain MRI showing mild atrophy of the bilateral frontal lobes, mild T2 hypointensities in the pulvinar nuclei of the thalami (a, arrows), and mild T2/FLAIR hyperintensity in the periventricular white matter (b, d). (C) Family pedigree. The filled symbol indicates the patient, and the arrow indicates the proband. Blood samples were obtained from the mother (I-1), the father (I-2), and the patient (II-3), and analyzed by trio-WES. (D) Sanger sequencing of AGA.CC BY-NC-ND · PubMed Central
Figure 2 cDNA Analysis of AGA (A) Position of the primers for cDNA PCR. (B) Electrophoresis image of the cDNA PCR product in the patient, the parents, and healthy control samples. (C) Schematic depiction of structural variations in each splicing product. A1 to A4 correspond to those in Figure 2B. (D) In silico prediction of aberrant splicing by the variant with SpliceAI. The higher the Δ-score, which is calculated by the REF score minus the ALT score, the more likely the splice change is to be validated. The column marked with Position indicates base-pair distance from the variant position (c.698 + 1) to the newly predicted splice site toward the 5′direction. The column marked with Confirmed transcripts indicates cDNA PCR products that were validated for the newly predicted splice site.
Figure 2 cDNA Analysis of AGA (A) Position of the primers for cDNA PCR. (B) Electrophoresis image of the cDNA PCR product in the patient, the parents, and healthy control samples. (C) Schematic depiction of structural variations in each splicing product. A1 to A4 correspond to those in Figure 2B. (D) In silico prediction of aberrant splicing by the variant with SpliceAI. The higher the Δ-score, which is calculated by the REF score minus the ALT score, the more likely the splice change is to be validated. The column marked with Position indicates base-pair distance from the variant position (c.698 + 1) to the newly predicted splice site toward the 5′direction. The column marked with Confirmed transcripts indicates cDNA PCR products that were validated for the newly predicted splice site.CC BY-NC-ND · PubMed Central
Figure 3 Z-Score Plots of the Patient's and Control Urine Samples Elevated AGU-specific biomarkers (squares) are observed in the urine sample of the patient (upper table), but they are not detectable in the control urine sample (lower table). Pathologic AGU-specific reference values are indicated as dots. Semiquantitative concentrations of each AGU biomarker are indicated next to the Z-score plot (for additional details, see Hagemeijer et al., 20234). 334_aspartylglucos = Asn-GlcNAc, 496_aspartylglucos = Asn-GlcNAc-Gal, and 787_aspartylglucos = Asn-GlcNAc-Gal-NANA.
Figure 3 Z-Score Plots of the Patient's and Control Urine Samples Elevated AGU-specific biomarkers (squares) are observed in the urine sample of the patient (upper table), but they are not detectable in the control urine sample (lower table). Pathologic AGU-specific reference values are indicated as dots. Semiquantitative concentrations of each AGU biomarker are indicated next to the Z-score plot (for additional details, see Hagemeijer et al., 20234). 334_aspartylglucos = Asn-GlcNAc, 496_aspartylglucos = Asn-GlcNAc-Gal, and 787_aspartylglucos = Asn-GlcNAc-Gal-NANA.CC BY-NC-ND · PubMed Central

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Metadata

Authors
Kouhashi, M., Yukawa, K., Yano, N., Hagemeijer, M. C., Hirata, S., Kambe, D., Yokoyama, A., Yoshida, A., Kora, K., de Ronde, C. J., Vrieswijk, S., van der Meijden, E., Yoshida, T., & Yamashita, H.
Affiliations
Kyoto University (JP)
Corresponding author
Yamashita, Hirofumi
ORCID
Venue
Neurology Genetics
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2024-06
Volume
10
Issue
3
Pages
e200161-e200161
ISSN
2376-7839 (ISSN-L)
DOI
10.1212/nxg.0000000000200161
PMC
PMC11145746
OpenAlex
W4399117454
Cited by
3
References
1
Citations by year
2026: 2 · 2025: 1
FWCI
0.85
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Neurology Genetics (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
Erasmus Universitair Medisch Centrum Rotterdam
Topics
  • Health Sciences › Medicine › Genetics (clinical) › Lysosomal Storage Disorders Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › Metabolism and Genetic Disorders
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
Keywords
aspartylglucosaminuria, intellectual disability, aspartylglucosaminidase, aberrant splicing, inborn errors of metabolism, causal gene identification, newborn screening

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