Argininosuccinate lyase deficiency causes blood-brain barrier disruption via nitric oxide-mediated dysregulation of claudin expression.

Nitric oxide (NO) is a critical signaling molecule that has been implicated in the pathogenesis of neurocognitive diseases. Both excessive and insufficient NO production have been linked to pathology. Previously, we have shown that argininosuccinate lyase deficiency (ASLD) is a novel model system to investigate cell-autonomous, nitric oxide synthase-dependent NO deficiency. Humans with ASLD are at increased risk for developing hyperammonemia due to a block in ureagenesis. However, natural history studies have shown that individuals with ASLD have multisystem disease including neurocognitive deficits that can be independent of ammonia. Here, using ASLD as a model of NO deficiency, we investigated the effects of NO on brain endothelial cells in vitro and the blood-brain barrier (BBB) in vivo. Knockdown of ASL in human brain microvascular endothelial cells (HBMECs) led to decreased transendothelial electrical resistance, indicative of increased cell permeability. Mechanistically, treatment with an NO donor or inhibition of Claudin-1 improved barrier integrity in ASL-deficient HBMECs. Furthermore, in vivo assessment of a hypomorphic mouse model of ASLD showed increased BBB leakage, which was partially rescued by NO supplementation. Our results suggest that ASL-mediated NO synthesis is required for proper maintenance of brain microvascular endothelial cell functions as well as BBB integrity.

Mucopolysaccharidoses and the blood-brain barrier.

Mucopolysaccharidoses comprise a set of genetic diseases marked by an enzymatic dysfunction in the degradation of glycosaminoglycans in lysosomes. There are eight clinically distinct types of mucopolysaccharidosis, some with various subtypes, based on which lysosomal enzyme is deficient and symptom severity. Patients with mucopolysaccharidosis can present with a variety of symptoms, including cognitive dysfunction, hepatosplenomegaly, skeletal abnormalities, and cardiopulmonary issues. Additionally, the onset and severity of symptoms can vary depending on the specific disorder, with symptoms typically arising during early childhood. While there is currently no cure for mucopolysaccharidosis, there are clinically approved therapies for the management of clinical symptoms, such as enzyme replacement therapy. Enzyme replacement therapy is typically administered intravenously, which allows for the systemic delivery of the deficient enzymes to peripheral organ sites. However, crossing the blood-brain barrier (BBB) to ameliorate the neurological symptoms of mucopolysaccharidosis continues to remain a challenge for these large macromolecules. In this review, we discuss the transport mechanisms for the delivery of lysosomal enzymes across the BBB. Additionally, we discuss the several therapeutic approaches, both preclinical and clinical, for the treatment of mucopolysaccharidoses.

Machine learning may predict individual hand motor activation from resting-state fMRI in patients with brain tumors in perirolandic cortex.

OBJECTIVE: The study aimed to evaluate the predictive validity of the neural network (NN) method for presurgical mapping of motor areas using resting-state functional MRI (rs-fMRI) data of patients with brain tumor located in the perirolandic cortex (PRC). METHODS: A total of 109 patients with brain tumors occupying PRC underwent rs-fMRI and hand movement task-based fMRI (tb-fMRI) scans. Using a NN model trained on fMRI data of 47 healthy controls, individual task activation maps were predicted from their rs-fMRI data. NN-predicted maps were compared with task activation and independent component analysis (ICA)-derived maps. Spatial Pearson's correlation coefficients (CC) matrices and Dice coefficients (DC) between task activation and predicted activation using NN (DCNN_Act) and ICA (DCICA_Act) were calculated and compared using non-parametric tests. The effects of tumor types and head motion on predicted maps were demonstrated. RESULTS: The CC matrix of NN-predicted maps showed higher diagonal values compared with ICA-derived maps (p < 0.001). DCNN_Act were higher than DCICA_Act (p < 0.001) for patients with or without motor deficits. Lower DCs were found in subjects with head motion greater than one voxel. DCs were higher on the nontumor side than on the tumor side (p < 0.001), especially in the glioma group compared with meningioma and metastatic groups. CONCLUSIONS: This study indicated that the NN approach could predict individual motor activation using rs-fMRI data and could have promising clinical applications in brain tumor patients with anatomical and functional reorganizations. KEY POINTS: • The neural network machine learning approach successfully predicted hand motor activation in patients with a tumor in the perirolandic cortex, despite space-occupying effects and possible functional reorganization. • Compared to the conventional independent component analysis, the neural network approach utilizing resting-state fMRI data yielded a higher correlation to the active task hand activation data. • The Dice coefficient of machine learning-predicted activation vs. task fMRI activation was different between tumor and nontumor side, also between tumor types, which might indicate different effects of possible neurovascular uncoupling on resting-state and task fMRI.

Fetal Sex and RHD Genotyping with Digital PCR Demonstrates Greater Sensitivity than Real-time PCR.

BACKGROUND: Noninvasive genotyping of fetal RHD (Rh blood group, D antigen) can prevent the unnecessary administration of prophylactic anti-D to women carrying RHD-negative fetuses. We evaluated laboratory methods for such genotyping. METHODS: Blood samples were collected in EDTA tubes and Streck® Cell-Free DNA™ blood collection tubes (Streck BCTs) from RHD-negative women (n = 46). Using Y-specific and RHD-specific targets, we investigated variation in the cell-free fetal DNA (cffDNA) fraction and determined the sensitivity achieved for optimal and suboptimal samples with a novel Droplet Digital™ PCR (ddPCR) platform compared with real-time quantitative PCR (qPCR). RESULTS: The cffDNA fraction was significantly larger for samples collected in Streck BCTs compared with samples collected in EDTA tubes (P < 0.001). In samples expressing optimal cffDNA fractions (≥4%), both qPCR and digital PCR (dPCR) showed 100% sensitivity for the TSPY1 (testis-specific protein, Y-linked 1) and RHD7 (RHD exon 7) assays. Although dPCR also had 100% sensitivity for RHD5 (RHD exon 5), qPCR had reduced sensitivity (83%) for this target. For samples expressing suboptimal cffDNA fractions (<2%), dPCR achieved 100% sensitivity for all assays, whereas qPCR achieved 100% sensitivity only for the TSPY1 (multicopy target) assay. CONCLUSIONS: qPCR was not found to be an effective tool for RHD genotyping in suboptimal samples (<2% cffDNA). However, when testing the same suboptimal samples on the same day by dPCR, 100% sensitivity was achieved for both fetal sex determination and RHD genotyping. Use of dPCR for identification of fetal specific markers can reduce the occurrence of false-negative and inconclusive results, particularly when samples express high levels of background maternal cell-free DNA.