Biallelic variants in SLC35B2 cause a novel chondrodysplasia with hypomyelinating leukodystrophy

Sulfate: a neglected (but potentially highly relevant) anion

Sulfate is an important anion as sulfonation is essential in modulation of several compounds, such as exogens, polysaccharide chains of proteoglycans, cholesterol or cholesterol derivatives and tyrosine residues of several proteins. Sulfonation requires the presence of both the sulfate donor 3'-phosphoadenosine-5'-phosphosulfate (PAPS) and a sulfotransferase. Genetic disorders affecting sulfonation, associated with skeletal abnormalities, impaired neurological development and endocrinopathies, demonstrate the importance of sulfate. Yet sulfate is not measured in clinical practice. This review addresses sulfate metabolism and consequences of sulfonation defects, how to measure sulfate and why we should measure sulfate more often.

Biochemical diagnosis of congenital disorders of glycosylation

Congenital disorders of glycosylation (CDG) are one of the fastest growing groups of inborn errors of metabolism, comprising over 160 described diseases to this day. CDG are characterized by a dysfunctional glycosylation process, with molecular defects localized in the cytosol, the endoplasmic reticulum, or the Golgi apparatus. Depending on the CDG, N-glycosylation, O-glycosylation and/or glycosaminoglycan synthesis can be affected. Various proteins, lipids, and glycosylphosphatidylinositol anchors bear glycan chains, with potential impacts on their folding, targeting, secretion, stability, and thus, functionality. Therefore, glycosylation defects can have diverse and serious clinical consequences. CDG patients often present with a non-specific, multisystemic syndrome including neurological involvement, growth delay, hepatopathy and coagulopathy. As CDG are rare diseases, and typically lack distinctive clinical signs, biochemical and genetic testing bear particularly important and complementary diagnostic roles. Here, after a brief introduction on glycosylation and CDG, we review historical and recent findings on CDG biomarkers and associated analytical techniques, with a particular emphasis on those with relevant use in the specialized clinical chemistry laboratory. We provide the reader with insights and methods which may help them properly assist the clinician in navigating the maze of glycosylation disorders.

Identification of potential non-invasive biomarkers in diastrophic dysplasia

Diastrophic dysplasia (DTD) is a recessive chondrodysplasia caused by pathogenic variants in the SLC26A2 gene encoding for a cell membrane sulfate/chloride antiporter crucial for sulfate uptake and glycosaminoglycan (GAG) sulfation. Research on a DTD animal model has suggested possible pharmacological treatment approaches. In view of future clinical trials, the identification of non-invasive biomarkers is crucial to assess the efficacy of treatments. Urinary GAG composition has been analyzed in several metabolic disorders including mucopolysaccharidoses. Moreover, the N-terminal fragment of collagen X, known as collagen X marker (CXM), is considered a real-time marker of endochondral ossification and growth velocity and was studied in individuals with achondroplasia and osteogenesis imperfecta. In this work, urinary GAG sulfation and blood CXM levels were investigated as potential biomarkers for individuals affected by DTD. Chondroitin sulfate disaccharide analysis was performed on GAGs isolated from urine by HPLC after GAG digestion with chondroitinase ABC and ACII, while CXM was assessed in dried blood spots. Results from DTD patients were compared with an age-matched control population. Undersulfation of urinary GAGs was observed in DTD patients with some relationship to the clinical severity and underlying SLC26A2 variants. Lower than normal CXM levels were observed in most patients, even if the marker did not show a clear pattern in our small patient cohort because CXM values are highly dependent on age, gender and growth velocity. In summary, both non-invasive biomarkers are promising assays targeting various aspects of the disorder including overall metabolism of sulfated GAGs and endochondral ossification.

ClinPrior: an algorithm for diagnosis and novel gene discovery by network-based prioritization

BACKGROUND

Whole-exome sequencing (WES) and whole-genome sequencing (WGS) have become indispensable tools to solve rare Mendelian genetic conditions. Nevertheless, there is still an urgent need for sensitive, fast algorithms to maximise WES/WGS diagnostic yield in rare disease patients. Most tools devoted to this aim take advantage of patient phenotype information for prioritization of genomic data, although are often limited by incomplete gene-phenotype knowledge stored in biomedical databases and a lack of proper benchmarking on real-world patient cohorts.

METHODS

We developed ClinPrior, a novel method for the analysis of WES/WGS data that ranks candidate causal variants based on the patient's standardized phenotypic features (in Human Phenotype Ontology (HPO) terms). The algorithm propagates the data through an interactome network-based prioritization approach. This algorithm was thoroughly benchmarked using a synthetic patient cohort and was subsequently tested on a heterogeneous prospective, real-world series of 135 families affected by hereditary spastic paraplegia (HSP) and/or cerebellar ataxia (CA).

RESULTS

ClinPrior successfully identified causative variants achieving a final positive diagnostic yield of 70% in our real-world cohort. This includes 10 novel candidate genes not previously associated with disease, 7 of which were functionally validated within this project. We used the knowledge generated by ClinPrior to create a specific interactome for HSP/CA disorders thus enabling future diagnoses as well as the discovery of novel disease genes.

CONCLUSIONS

ClinPrior is an algorithm that uses standardized phenotype information and interactome data to improve clinical genomic diagnosis. It helps in identifying atypical cases and efficiently predicts novel disease-causing genes. This leads to increasing diagnostic yield, shortening of the diagnostic Odysseys and advancing our understanding of human illnesses.

Emerging cellular themes in leukodystrophies

Leukodystrophies are a broad spectrum of neurological disorders that are characterized primarily by deficiencies in myelin formation. Clinical manifestations of leukodystrophies usually appear during childhood and common symptoms include lack of motor coordination, difficulty with or loss of ambulation, issues with vision and/or hearing, cognitive decline, regression in speech skills, and even seizures. Many cases of leukodystrophy can be attributed to genetic mutations, but they have diverse inheritance patterns (e.g., autosomal recessive, autosomal dominant, or X-linked) and some arise from de novo mutations. In this review, we provide an updated overview of 35 types of leukodystrophies and focus on cellular mechanisms that may underlie these disorders. We find common themes in specialized functions in oligodendrocytes, which are specialized producers of membranes and myelin lipids. These mechanisms include myelin protein defects, lipid processing and peroxisome dysfunction, transcriptional and translational dysregulation, disruptions in cytoskeletal organization, and cell junction defects. In addition, non-cell-autonomous factors in astrocytes and microglia, such as autoimmune reactivity, and intercellular communication, may also play a role in leukodystrophy onset. We hope that highlighting these themes in cellular dysfunction in leukodystrophies may yield conceptual insights on future therapeutic approaches.

Systematic Collaborative Reanalysis of Genomic Data Improves Diagnostic Yield in Neurologic Rare Diseases

Many patients experiencing a rare disease remain undiagnosed even after genomic testing. Reanalysis of existing genomic data has shown to increase diagnostic yield, although there are few systematic and comprehensive reanalysis efforts that enable collaborative interpretation and future reinterpretation. The Undiagnosed Rare Disease Program of Catalonia project collated previously inconclusive good quality genomic data (panels, exomes, and genomes) and standardized phenotypic profiles from 323 families (543 individuals) with a neurologic rare disease. The data were reanalyzed systematically to identify relatedness, runs of homozygosity, consanguinity, single-nucleotide variants, insertions and deletions, and copy number variants. Data were shared and collaboratively interpreted within the consortium through a customized Genome-Phenome Analysis Platform, which also enables future data reinterpretation. Reanalysis of existing genomic data provided a diagnosis for 20.7% of the patients, including 1.8% diagnosed after the generation of additional genomic data to identify a second pathogenic heterozygous variant. Diagnostic rate was significantly higher for family-based exome/genome reanalysis compared with singleton panels. Most new diagnoses were attributable to recent gene-disease associations (50.8%), additional or improved bioinformatic analysis (19.7%), and standardized phenotyping data integrated within the Undiagnosed Rare Disease Program of Catalonia Genome-Phenome Analysis Platform functionalities (18%).