William Allen Gahl

Free sialic acid storage disorder (FSASD) is a rare, neurodegenerative, lysosomal storage disease caused by biallelic variants in SLC17A5, which encodes the lysosomal sialic acid exporter, sialin. While clinical severity varies, all affected individuals exhibit progressive neurological decline and characteristic lysosomal and urinary accumulation of free sialic acid. No FDA-approved therapies currently exist, and the molecular underpinnings of central nervous system (CNS) dysfunction remain poorly defined. To model the most prevalent FSASD-associated allele, we generated a Slc17a5R39C mouse model carrying the pathogenic p.Arg39Cys variant using CRISPR/Cpf1. Homozygous Slc17a5R39C/R39C mice recapitulated key features of FSASD, including biochemical accumulation of free sialic acid, ataxia, tremors, seizures, and shortened lifespan. Neuropathological analyses revealed widespread CNS hypomyelination, with relatively preserved peripheral nervous system myelination, while transcriptomic profiling showed broad suppression of myelin-associated genes and a cerebellum-specific inflammatory signature. This was accompanied by pronounced astrogliosis, progressive Purkinje cell degeneration, and elevated neurofilament proteins, including serum NfL. Altogether, the Slc17a5R39C/R39C model of FSASD provides critical insights into disease pathogenesis, highlights region-specific neurodegenerative vulnerabilities, and establishes a robust preclinical platform for mechanistic investigation and therapeutic development.

Objective: Benign adult familial myoclonic epilepsy (BAFME) is a rare autosomal dominant disorder characterized by tremor and myoclonic seizures. To date, seven genetic subtypes have been identified, all caused by noncoding pentanucleotide repeat expansions—typically TTTTA expansions with additional TTTCA insertions—across distinct loci. Although aberrant repeat expansions are a shared hallmark, it remains unclear whether different BAFME subtypes exhibit similar patterns of repeat length variability and inheritance. We aimed to characterize pentanucleotide repeat structure, repeat length variability across reads, and intergenerational transmission patterns across multiple BAFME subtypes, and to determine whether these mutational dynamics differ among BAFME1, BAFME4, and BAFME8. Methods: We performed whole‐genome long‐read sequencing and targeted adaptive sampling long‐read sequencing on 18 affected individuals from three multigenerational families with BAFME1 ( SAMD12 ), BAFME4 ( YEATS2 ), and BAFME8 ( RAI1 ). Repeat structure, repeat length variability across reads, and intergenerational transmission dynamics were analyzed. Results: Across patients, 7–33 long reads containing expanded repeats were identified. Several differences in repeat length distributions across reads were observed among the families studied. The BAFME1 family showed comparatively narrower distributions, whereas broader variability was observed in the BAFME4 and BAFME8 families. In BAFME1 and BAFME8, TTTCA repeat length showed a positive correlation with age, consistent with age‐associated increases in repeat length variability across reads. Parent‐to‐offspring transmissions were often associated with increases in repeat length, most prominently in BAFME8. Sibling comparisons further indicated higher variability in TTTCA than in TTTTA repeats. Significance: Our study demonstrates distinct patterns of repeat length variability and inheritance across the families analyzed. These findings suggest that the genomic context of pentanucleotide expansions may influence repeat behavior and could contribute to phenotypic variability in BAFME.

Genetic variation in ATP Binding Cassette Subfamily C Member 6 (ABCC6) can cause both pseudoxanthoma elasticum (PXE) and generalized arterial calcification of infancy (GACI). There are 930 distinct missense variants in ABCC6 reported, 87% of which are of uncertain clinical significance (VUS). New approaches are needed to mechanistically interpret and classify these VUS. We developed 3D protein models of ABCC6 in three functionally relevant conformations to calculate the structural effects of variants. We also used three-dimensional (3D) hotspot detection and developed a mechanistic ontology for critical structure-based functions of ABCC6, enabling us to categorize genomic variants. We identified two 3D hotspots and six specific functions of ABCC6 which variants impact. From this, we propose a mechanism for pathogenicity for 41% of VUS according to their impacted function. We propose that 33 of these variants could be reclassified as Likely Pathogenic with the addition of these structure-based evidence. The mechanistic information we present will guide future research to better address calcification disorders and understand genetic variants. This work emphasizes the benefits of thorough, holistic, and protein-based approaches to genetic interpretation. Further, our VUS reclassification approach will improve the diagnosis of ABCC6-driven diseases, shortening diagnostic odysseys. We believe that computational structural genomics approaches will soon take prominence in genomics data interpretation and variant classification.

Seventy-five unique variants in the KCNMA1 gene have been identified from individuals with neurological disorders. However, variant pathogenicity and evidence for disease causality are lacking in most cases. In this study, the KCNMA1 variants N999S and E656A (rs886039469 and rs149000684, respectively) were investigated from two individuals presenting with neurological disorders. N999S was previously shown to produce strong gain-of-function (GOF) changes in homomeric BK channel properties in vitro and is found as a heterozygous allele associated with epilepsy and paroxysmal dyskinesia in humans. Although its pathogenicity has been demonstrated in heterozygous animal models, the GOF classification for N999S has not been validated in a heterozygous patient-derived tissue. Conversely, the GOF pathogenicity for E656A is based solely on homomeric channels expressed in vitro and is inconclusive. For either variant, the properties of single heterozygous channels and allele expression is unknown. In this study, we profiled the wild-type and mutant KCNMA1 transcripts from primary human skin fibroblasts of heterozygous patients and unaffected controls and performed patch-clamp electrophysiology to characterize endogenous BK channel current properties. GOF gating was observed in single BK channel recordings from both channel types. Fibroblasts from the individual harboring the E656A variant showed decreases in the number of BK channels detected and E656A-containing transcripts compared to controls. These results show that single BK channels can be reliably detected in primary fibroblasts obtained from human skin biopsies, suggesting their utility for establishing variant pathogenicity, and reveal the BK channel expression and functional changes associated with two heterozygous patient genotypes.

Background: Diffuse low-grade gliomas (dLGG) are rare slow growing brain tumors. Symptoms associated with dLGG typically include seizures, hemiparesis, ataxia, behavioral changes, headaches, and tremors. In this study, we present the case of a thirteen-year-old male admitted to the National Institutes of Health (NIH) Undiagnosed Diseases Program (UDP). To the best of our knowledge, this is one of the only documented cases of an adult-type dLGG diagnosed in a pediatric patient with monitoring of tumor progression for nearly a decade prior to diagnosis. Case presentation: The patient presented with a history of progressive signal abnormalities on brain magnetic resonance imaging (MRI), refractory to treatment attention deficit hyperactivity disorder (ADHD) and Oppositional Defiant Disorder (ODD), headaches, irritability, and difficulties sleeping. His detailed neurological exam was normal. Following six years of repeated MRI demonstrating increasing infiltrative tumor mass effect throughout gray and white matter, a brain biopsy was performed. The brain biopsy showed white and gray matter with mildly hypercellular areas, and tumor DNA sequencing showed the presence of a canonical IDH1 mutation. A “watch and wait” approach was adopted resulting from discussions between the patient and his family alongside the medical team with repeated quarterly brain MRI to monitor symptoms and tumor growth. Conclusion: While behavioral and psychiatric changes are common in brain tumor patients, they typically present alongside neurological symptoms which emphasizes the difficulty in diagnosing cases like this patient’s. Low-grade malignancies should be part of the differential diagnosis in cases with progressive multifocal white matter lesions, despite the absence of the typical neurological focal signs.

GM1 gangliosidosis is an inherited, progressive, and fatal neurodegenerative lysosomal storage disorder with no approved treatment. We calculated a predicted brain ages and Brain Structures Age Gap Estimation (BSAGE) for 81 MRI scans from 41 Type II GM1 gangliosidosis patients and 897 MRI scans from 556 neurotypical controls (NC) utilizing BrainStructuresAges , a machine learning MRI analysis pipeline. NC showed whole brain aging at a rate of 0.83 per chronological year compared with 1.57 in juvenile GM1 patients and 12.25 in late-infantile GM1 patients, accurately reflecting the clinical trajectories of the two disease subtypes. Accelerated and distinct brain aging was also observed throughout midbrain structures including the thalamus and caudate nucleus, hindbrain structures including the cerebellum and brainstem, and the ventricles in juvenile and late-infantile GM1 patients compared to NC. Predicted brain age and BSAGE both correlated with cross-sectional and longitudinal clinical assessments, indicating their importance as a surrogate neuroimaging outcome measures for clinical trials in GM1 gangliosidosis.

Background: Volumetric analysis and segmentation of magnetic resonance imaging (MRI) data is an important tool for evaluating neurological disease progression and neurodevelopment. Fully automated segmentation pipelines offer faster and more reproducible results. However, since these analysis pipelines were trained on or run based on atlases consisting of neurotypical controls, it is important to evaluate how accurate these methods are for neurodegenerative diseases. In this study, we compared five fully automated segmentation pipelines, including FSL, Freesurfer, volBrain, SPM12, and SimNIBS, with a manual segmentation process in GM1 gangliosidosis patients and neurotypical controls. Methods: We analyzed 45 MRI scans from 16 juvenile GM1 gangliosidosis patients, 11 MRI scans from 8 late-infantile GM1 gangliosidosis patients, and 19 MRI scans from 11 neurotypical controls. We compared the results for seven brain structures, including volumes of the total brain, bilateral thalamus, ventricles, bilateral caudate nucleus, bilateral lentiform nucleus, corpus callosum, and cerebellum. Results: We found volBrain’s vol2Brain pipeline to have the strongest correlations with the manual segmentation process for the whole brain, ventricles, and thalamus. We also found Freesurfer’s recon-all pipeline to have the strongest correlations with the manual segmentation process for the caudate nucleus. For the cerebellum, we found a combination of volBrain’s vol2Brain and SimNIBS’ headreco to have the strongest correlations, depending on the cohort. For the lentiform nucleus, we found a combination of recon-all and FSL’s FIRST to give the strongest correlations, depending on the cohort. Lastly, we found segmentation of the corpus callosum to be highly variable. Conclusions: Previous studies have considered automated segmentation techniques to be unreliable, particularly in neurodegenerative diseases. However, in our study, we produced results comparable to those obtained with a manual segmentation process. While manual segmentation processes conducted by neuroradiologists remain the gold standard, we present evidence to the capabilities and advantages of using an automated process that includes the ability to segment white matter throughout the brain or analyze large datasets, which pose feasibility issues to fully manual processes. Future investigations should consider the use of artificial intelligence-based segmentation pipelines to determine their accuracy in GM1 gangliosidosis, lysosomal storage disorders, and other neurodegenerative diseases.

Background: Clinical trials for rare diseases pose unique challenges warranting alternative approaches in demonstrating treatment efficacy. Such trials face challenges including small patient populations, variable onset of symptoms and rate of disease progression, and ethical considerations, particularly in neurodegenerative diseases. In this study, we present the retrospective clinical global impression (RCGI) severity and change (RCGI-S/C) scale on 27 patients with GM1 gangliosidosis, a post hoc clinician-rated outcome measure to evaluate natural history study participants as historical controls for comparisons with treated patients in a clinical trial. Methods: We conducted a systematic chart review of 27 GM1 gangliosidosis natural history participants across 95 total visits. RCGI-S was assessed at the first visit and rated 1 (normal) to 7 (among the most extremely ill). Each subsequent follow-up was rated on the RCGI-C scale from 1 (very much improved) to 7 (very much worse). We demonstrate scoring guidelines of both scales with examples and justifications for this pilot in GM1 gangliosidosis natural history participants. The convergent validity of the RCGI scales was explored through correlations with magnetic resonance imaging (MRI) and the Vineland Adaptive Behavioral Scales. Results: We found strong association between the RCGI-S scores with gray matter volume (r(14) = −0.81; 95% CI [−0.93, −0.51], p < 0.001), and RCGI-C scores significantly correlated with increases in ventricular volume (χ²(1) = 18.6, p < 0.001). Baseline RCGI-S scores also strongly correlated with Vineland adaptive behavioral composite scores taken at the same visit (r(14) = −0.72; 95% CI [−0.93, −0.17], p = 0.02). Conclusion: RCGI-S/C scales, which use the clinical evaluation to assess the severity of disease of each patient visit over time, were consolidated into a single quantitative metric in this study. Longitudinal RCGI-C scores allowed us to quantify disease progression in our late-infantile and juvenile GM1 patients. We suggest that the retrospective CGI may be an important tool in evaluating historical data for comparison with changes in disease progression/mitigation following therapeutic interventions.

Purpose: Erdheim–Chester disease (ECD) is an L Group Langerhans histiocytosis associated with pathogenic variants within the MAPK pathways, most commonly the BRAF gene. We analyzed prevalence, genetic, biochemical, and pituitary imaging features associated with arginine vasopressin deficiency (AVP-D), one of the most common endocrinopathies in ECD. Methods: A cross-sectional descriptive study of 61 subjects with ECD was conducted at a clinical research center from January 2011 to December 2018, with molecular genetics, baseline biochemical and pituitary endocrine function studies, and dedicated pituitary MRI (or CT) studies. AVP-D and anterior pituitary endocrinopathies (hypothyroidism, hypogonadism, adrenal insufficiency and panhypopituitarism) were assessed. Students’ t-tests, nonparametric tests, Fisher’s exact tests, and logistic regression were employed for analysis. Results: In total, 22 out of 61 subjects (36%; 19 males and 3 females) had AVP-D; 18 subjects with AVP-D were in active treatment with desmopressin. Those with versus without AVP-D were younger [mean (±SD): 50.00 (±10.45) vs. 56.72 (±10.45) years], had higher prevalence of BRAF V600E pathogenic variants [68% vs. 43%], lower IGF-1 [mean (±SD): 137.05 (±67.97) vs. 175.92 (±61.89) ng/mL], lower urine osmolality [416.00 (250.00–690.00) vs. 644.50 (538.75–757.75)) mOsm/kg], and a higher burden of central hypogonadism [81.82% vs. 36.00%], central hypothyroidism [23% vs. 2.5%], panhypopituitarism [41% vs. 0%], anterior pituitary endocrine deficits, absent posterior pituitary bright spots [63.64% vs. 20.51%], and abnormal pituitary imaging. In adjusted models, [OR (95%CI)] BRAF V600E mutation [7.38 (1.84–39.01)], central hypogonadism [6.193 (1.44–34.80)], primary hypothyroidism [13.89 (1.401–406.5)], absent posterior pituitary bright spot [12.84 (3.275–65.04)], and abnormal pituitary imaging [10.60 (2.844–48.29)] were associated with higher odds of having AVP-D. Conclusions: AVP-D is common in ECD and accompanied by a higher burden of pituitary endocrinopathies, BRAF V600E pathogenic variants, abnormal pituitary imaging, and absent posterior pituitary bright spots.

Background: Volumetric analysis and segmentation of magnetic resonance imaging (MRI) data is an important tool for evaluating neurological disease progression and neurodevelopment. Fully automated segmentation pipelines offer faster and more reproducible results. However, since these analysis pipelines were trained on or run based on atlases consisting of neurotypical controls, it is important to evaluate how accurate these methods are for neurodegenerative diseases. In this study, we compared 5 fully automated segmentation pipelines including FSL, Freesurfer, volBrain, SPM12, and SimNIBS with a manual segmentation process in GM1 gangliosidosis patients and neurotypical controls. Methods: We analyzed 45 MRI scans from 16 juvenile GM1 gangliosidosis patients, 11 MRI scans from 8 late-infantile GM1 gangliosidosis patients, and 19 MRI scans from 11 neurotypical controls. We compared results for 7 brain structures including volumes of the total brain, bilateral thalamus, ventricles, bilateral caudate nucleus, bilateral lentiform nucleus, corpus callosum, and cerebellum. Results: We found volBrain’s vol2Brain pipeline to have the strongest correlations with the manual segmentation process for the whole brain, ventricles, and thalamus. We also found Freesurfer’s recon-all pipeline to have the strongest correlations with the manual segmentation process for the caudate nucleus. For the cerebellum, we found a combination of volBrain’s vol2Brain and SimNIBS’ headreco to have the strongest correlations depending on the cohort. For the lentiform nucleus, we found a combination of recon-all and FSL’s FIRST to give the strongest correlations depending on the cohort. Lastly, we found segmentation of the corpus callosum to be highly variable. Conclusion: Previous studies have considered automated segmentation techniques to be unreliable, particularly in neurodegenerative diseases. However, in our study we produced results comparable to those obtained with a manual segmentation process. While manual segmentation processes conducted by neuroradiologists remain the gold standard, we present evidence to the capabilities and advantages of using an automated process including the ability to segment white matter throughout the brain or analyze large datasets, which pose feasibility issues to fully manual processes. Future investigations should consider the use of artificial intelligence-based segmentation pipelines to determine their accuracy in GM1 gangliosidosis, lysosomal storage disorders, and other neurodegenerative diseases.