IMPORTANCE Recent advances in newborn screening (NBS) have improved the diagnosis of inborn errors of metabolism (IEMs); however, many potentially treatable IEMs are not included on NBS panels, nor are they covered in standard, first-line biochemical testing.OBJECTIVE To examine the utility of untargeted metabolomics as a primary screening tool for IEMs by comparing the diagnostic rate of clinical metabolomics with the recommended traditional metabolic screening approach.

Purpose: A primary barrier to improving exome sequencing diagnostic rates is the interpretation of variants of uncertain clinical significance. We aimed to determine the contribution of integrated untargeted metabolomics in the analysis of exome sequencing data by retrospective analysis of patients evaluated by both whole exome sequencing and untargeted metabolomics within the same clinical laboratory. Methods: Exome sequencing and untargeted metabolomic data were collected and analyzed for 170 patients. Pathogenic variants, likely pathogenic variants, and variants of uncertain significance in genes associated with a biochemical phenotype were extracted. Metabolomic data were evaluated to determine if these variants resulted in biochemical abnormalities which could be used to support their interpretation using current ACMG guidelines. Results: Metabolomic data contributed to the interpretation variants in 74 individuals (43.5%) over 73 different genes. The data allowed for the re-classification of 9 variants as likely benign, 15 variants as likely pathogenic, and 3 variants as pathogenic. Metabolomic data confirmed a clinical diagnosis in 21 cases, for a diagnostic rate of 12.3% in this population. Conclusion: Untargeted metabolomics can serve as a useful adjunct to exome sequencing by providing valuable functional data that may not otherwise be clinically available, resulting in improved variant classification.

Smith–Magenis syndrome (SMS) is a complex neurobehavioural disorder caused by haploinsufficiency of the RAI1 gene on chromosome 17p11.2. Key clinical features include intellectual disability, self‐injurious behaviours, sleep disturbance and craniofacial and skeletal anomalies. Diagnostic strategies are focused towards identification of a 17p11.2 microdeletion encompassing RAI1 or a mutation of RAI1 . G‐banding and fluorescent in situ hybridization are classical methods used to detect the SMS deletions, whereas multiplex ligation‐dependent probe amplification, comparative genomic hybridization and real‐time quantitative PCR (polymerase chain reaction) are the newer technologies. Most SMS features are due to RAI1 haploinsufficiency, whereas variability and severity are modified by other genes in the 17p11.2 region. The functional role for RAI1 is not completely understood, but it is likely involved in transcription and functions in several different biological pathways. Management of SMS is a multidisciplinary approach and involves treatment for sleep disturbance, speech and occupational therapies, minor medical interventions and management of behaviours. Synonyms: SMS, del(17)(p11.2), del(17)(p11.2p11.2), RAI1 mutation Key concepts Smith–Magenis syndrome (SMS) is a multiple congenital anomalies disorder caused by an interstitial deletion of chromosome 17p11.2 containing the retinoic acid induced 1 ( RAI1 ) gene or by mutation of RAI1 . Typically a sporadic genomic disorder with an estimated prevalence of 1:15 000–25 000. Individuals with SMS have intellectual disability, distinctive behavioural features, craniofacial and skeletal anomalies, speech and developmental delay and sleep disturbance. Hypotonia, hearing loss and chronic ear infections, eye abnormalities, cardiac and renal defects, and occasionally, cleft lip and/or palates are also observed. Approximately 90% of SMS cases have a FISH detectable 17p11.2 microdeletion (ranging from 650 kb to 9 Mb), whereas the remaining 10% have a mutation in RAI1 . Haploinsufficiency of RAI1 results in most features of SMS, but variabliity and severity are modified by other genes in the 17p11.2 deletion region. RAI1 is a putative transcription factor functioning in multiple biological pathways resulting in the pleiotropic effects seen in SMS. Management includes therapy for sleep disturbance, early childhood intervention programmes, special education and vocational training, and multidisciplinary evaluation for behavioural and systemic manifestations. Recurrence risk for sibs of the proband, if the parental chromosome/gene analyses are normal, is less than 1%. Risk increases if a parent of the proband carries a balanced chromosomal rearrangement or if mosaicism for either a deletion or RAI1 mutation is present in either parent. Mosaicism in a parent of an affected child is estimated at 3–5%.

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The solute carrier (SLC) superfamily encompasses >400 transmembrane transporters involved in the exchange of amino acids, nutrients, ions, metals, neurotransmitters and metabolites across biological membranes. SLCs are highly expressed in the mammalian brain; defects in nearly 100 unique SLC-encoding genes (OMIM: https://www.omim.org) are associated with rare Mendelian disorders including developmental and epileptic encephalopathy (DEE) and severe neurodevelopmental disorders (NDDs). Exome sequencing and family-based rare variant analyses on a cohort with NDD identified two siblings with DEE and a shared deleterious homozygous splicing variant in SLC38A3. The gene encodes SNAT3, a sodium-coupled neutral amino acid transporter and a principal transporter of the amino acids asparagine, histidine, and glutamine, the latter being the precursor for the neurotransmitters GABA and glutamate. Additional subjects with a similar DEE phenotype and biallelic predicted-damaging SLC38A3 variants were ascertained through GeneMatcher and collaborations with research and clinical molecular diagnostic laboratories. Untargeted metabolomic analysis was performed to identify novel metabolic biomarkers. Ten individuals from seven unrelated families from six different countries with deleterious biallelic variants in SLC38A3 were identified. Global developmental delay, intellectual disability, hypotonia, and absent speech were common features while microcephaly, epilepsy, and visual impairment were present in the majority. Epilepsy was drug-resistant in half. Metabolomic analysis revealed perturbations of glutamate, histidine, and nitrogen metabolism in plasma, urine, and cerebrospinal fluid of selected subjects, potentially representing biomarkers of disease. Our data support the contention that SLC38A3 is a novel disease gene for DEE and illuminate the likely pathophysiology of the disease as perturbations in glutamine homeostasis.

Untargeted metabolomics is a global molecular profiling technology that can be used to screen for inborn errors of metabolism (IEMs). Metabolite perturbations are evaluated based on current knowledge of specific metabolic pathway deficiencies, a manual diagnostic process that is qualitative, has limited scalability, and is not equipped to learn from accumulating clinical data. Our purpose was to improve upon manual diagnosis of IEMs in the clinic by developing novel computational methods for analyzing untargeted metabolomics data. We employed CTD, an automated computational diagnostic method that “connects the dots” between metabolite perturbations observed in individual metabolomics profiling data and modules identified in disease­specific metabolite co-perturbation networks learned from prior profiling data. We also extended CTD to calculate distances between any two individuals (CTDncd) and between an individual and a disease state (CTDdm), to provide additional network-quantified predictors for use in diagnosis. We show that across 539 plasma samples, CTD-based network-quantified measures can reproduce accurate diagnosis of 16 different IEMs, including adenylosuccinase deficiency, argininemia, argininosuccinic aciduria, aromatic l-amino acid decarboxylase deficiency, cerebral creatine deficiency syndrome type 2, citrullinemia, cobalamin biosynthesis defect, GABA-transaminase deficiency, glutaric acidemia type 1, maple syrup urine disease, methylmalonic aciduria, ornithine transcarbamylase deficiency, phenylketonuria, propionic acidemia, rhizomelic chondrodysplasia punctata, and the Zellweger spectrum disorders. Our approach can be used to supplement information from biochemical pathways and has the potential to significantly enhance the interpretation of variants of uncertain significance uncovered by exome sequencing. CTD, CTDdm, and CTDncd can serve as an essential toolset for biological interpretation of untargeted metabolomics data that overcomes limitations associated with manual diagnosis to assist diagnosticians in clinical decision-making. By automating and quantifying the interpretation of perturbation patterns, CTD can improve the speed and confidence by which clinical laboratory directors make diagnostic and treatment decisions, while automatically improving performance with new case data.

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