Integrated analysis of metabolomic profiling and exome data supplements sequence variant interpretation, classification, and diagnosis

The expanding diagnostic toolbox for rare genetic diseases

Genomic technologies, such as targeted, exome and short-read genome sequencing approaches, have revolutionized the care of patients with rare genetic diseases. However, more than half of patients remain without a diagnosis. Emerging approaches from research-based settings such as long-read genome sequencing and optical genome mapping hold promise for improving the identification of disease-causal genetic variants. In addition, new omic technologies that measure the transcriptome, epigenome, proteome or metabolome are showing great potential for variant interpretation. As genetic testing options rapidly expand, the clinical community needs to be mindful of their individual strengths and limitations, as well as remaining challenges, to select the appropriate diagnostic test, correctly interpret results and drive innovation to address insufficiencies. If used effectively - through truly integrative multi-omics approaches and data sharing - the resulting large quantities of data from these established and emerging technologies will greatly improve the interpretative power of genetic and genomic diagnostics for rare diseases.

Variant reclassification and clinical implications

Genomic technologies have transformed clinical genetic testing, underlining the importance of accurate molecular genetic diagnoses. Variant classification, ranging from benign to pathogenic, is fundamental to these tests. However, variant reclassification, the process of reassigning the pathogenicity of variants over time, poses challenges to diagnostic legitimacy. This review explores the medical and scientific literature available on variant reclassification, focusing on its clinical implications.Variant reclassification is driven by accruing evidence from diverse sources, leading to variant reclassification frequency ranging from 3.6% to 58.8%. Recent studies have shown that significant changes can occur when reviewing variant classifications within 1 year after initial classification, illustrating the importance of early, accurate variant assignation for clinical care.Variants of uncertain significance (VUS) are particularly problematic. They lack clear categorisation but have influenced patient treatment despite recommendations against it. Addressing VUS reclassification is essential to enhance the credibility of genetic testing and the clinical impact. Factors affecting reclassification include standardised guidelines, clinical phenotype-genotype correlations through deep phenotyping and ancestry studies, large-scale databases and bioinformatics tools. As genomic databases grow and knowledge advances, reclassification rates are expected to change, reducing discordance in future classifications.Variant reclassification affects patient diagnosis, precision therapy and family screening. The exact patient impact is yet unknown. Understanding influencing factors and adopting standardised guidelines are vital for precise molecular genetic diagnoses, ensuring optimal patient care and minimising clinical risk.

Incorporating functional genomics into the pathology-supported genetic testing framework implemented in South Africa: A future view of precision medicine for breast carcinomas

A pathology-supported genetic testing (PSGT) framework was established in South Africa to improve access to precision medicine for patients with breast carcinomas. Nevertheless, the frequent identification of variants of uncertain significance (VUSs) with the use of genome-scale next-generation sequencing has created a bottleneck in the return of results to patients. This review highlights the importance of incorporating functional genomics into the PSGT framework as a proposed initiative. Here, we explore various model systems and experimental methods available for conducting functional studies in South Africa to enhance both variant classification and clinical interpretation. We emphasize the distinct advantages of using in vitro, in vivo, and translational ex vivo models to improve the effectiveness of precision oncology. Moreover, we highlight the relevance of methodologies such as protein modelling and structural bioinformatics, multi-omics, metabolic activity assays, flow cytometry, cell migration and invasion assays, tube-formation assays, multiplex assays of variant effect, and database mining and machine learning models. The selection of the appropriate experimental approach largely depends on the molecular mechanism of the gene under investigation and the predicted functional effect of the VUS. However, before making final decisions regarding the pathogenicity of VUSs, it is essential to assess the functional evidence and clinical outcomes under current variant interpretation guidelines. The inclusion of a functional genomics infrastructure within the PSGT framework will significantly advance the reclassification of VUSs and enhance the precision medicine pipeline for patients with breast carcinomas in South Africa.

Solving the unsolved genetic epilepsies: Current and future perspectives

Many patients with epilepsy undergo exome or genome sequencing as part of a diagnostic workup; however, many remain genetically unsolved. There are various factors that account for negative results in exome/genome sequencing for patients with epilepsy: (1) the underlying cause is not genetic; (2) there is a complex polygenic explanation; (3) the illness is monogenic but the causative gene remains to be linked to a human disorder; (4) family segregation with reduced penetrance; (5) somatic mosaicism or the complexity of, for example, a structural rearrangement; or (6) limited knowledge or diagnostic tools that hinder the proper classification of a variant, resulting in its designation as a variant of unknown significance. The objective of this review is to outline some of the diagnostic options that lie beyond the exome/genome, and that might become clinically relevant within the foreseeable future. These options include: (1) re-analysis of older exome/genome data as knowledge increases or symptoms change; (2) looking for somatic mosaicism or long-read sequencing to detect low-complexity repeat variants or specific structural variants missed by traditional exome/genome sequencing; (3) exploration of the non-coding genome including disruption of topologically associated domains, long range non-coding RNA, or other regulatory elements; and finally (4) transcriptomics, DNA methylation signatures, and metabolomics as complementary diagnostic methods that may be used in the assessment of variants of unknown significance. Some of these tools are currently not integrated into standard diagnostic workup. However, it is reasonable to expect that they will become increasingly available and improve current diagnostic capabilities, thereby enabling precision diagnosis in patients who are currently undiagnosed.

CRISPR/Cas9-based functional genomics strategy to decipher the pathogenicity of genetic variants in inherited metabolic disorders

The determination of the functional impact of variants of uncertain significance (VUS) is one of the major bottlenecks in the diagnostic workflow of inherited genetic diseases. To face this problem, we set up a CRISPR/Cas9-based strategy for knock-in cellular model generation, focusing on inherited metabolic disorders (IMDs). We selected variants in seven IMD-associated genes, including seven reported disease-causing variants and four benign/likely benign variants. Overall, 11 knock-in cell models were generated via homology-directed repair in HAP1 haploid cells using CRISPR/Cas9. The functional impact of the variants was determined by analyzing the characteristic biochemical alterations of each disorder. Functional studies performed in knock-in cell models showed that our approach accurately distinguished the functional effect of pathogenic from non-pathogenic variants in a reliable manner in a wide range of IMDs. Our study provides a generic approach to assess the functional impact of genetic variants to improve IMD diagnosis and this tool could emerge as a promising alternative to invasive tests, such as muscular or skin biopsies. Although the study has been performed only in IMDs, this strategy is generic and could be applied to other genetic disorders.

Integrative omics approaches to advance rare disease diagnostics

Over the past decade high-throughput DNA sequencing approaches, namely whole exome and whole genome sequencing became a standard procedure in Mendelian disease diagnostics. Implementation of these technologies greatly facilitated diagnostics and shifted the analysis paradigm from variant identification to prioritisation and evaluation. The diagnostic rates vary widely depending on the cohort size, heterogeneity and disease and range from around 30% to 50% leaving the majority of patients undiagnosed. Advances in omics technologies and computational analysis provide an opportunity to increase these unfavourable rates by providing evidence for disease-causing variant validation and prioritisation. This review aims to provide an overview of the current application of several omics technologies including RNA-sequencing, proteomics, metabolomics and DNA-methylation profiling for diagnostics of rare genetic diseases in general and inborn errors of metabolism in particular.

Beyond the exome: What's next in diagnostic testing for Mendelian conditions

Despite advances in clinical genetic testing, including the introduction of exome sequencing (ES), more than 50% of individuals with a suspected Mendelian condition lack a precise molecular diagnosis. Clinical evaluation is increasingly undertaken by specialists outside of clinical genetics, often occurring in a tiered fashion and typically ending after ES. The current diagnostic rate reflects multiple factors, including technical limitations, incomplete understanding of variant pathogenicity, missing genotype-phenotype associations, complex gene-environment interactions, and reporting differences between clinical labs. Maintaining a clear understanding of the rapidly evolving landscape of diagnostic tests beyond ES, and their limitations, presents a challenge for non-genetics professionals. Newer tests, such as short-read genome or RNA sequencing, can be challenging to order, and emerging technologies, such as optical genome mapping and long-read DNA sequencing, are not available clinically. Furthermore, there is no clear guidance on the next best steps after inconclusive evaluation. Here, we review why a clinical genetic evaluation may be negative, discuss questions to be asked in this setting, and provide a framework for further investigation, including the advantages and disadvantages of new approaches that are nascent in the clinical sphere. We present a guide for the next best steps after inconclusive molecular testing based upon phenotype and prior evaluation, including when to consider referral to research consortia focused on elucidating the underlying cause of rare unsolved genetic disorders.

Beyond the exome: what's next in diagnostic testing for Mendelian conditions

Despite advances in clinical genetic testing, including the introduction of exome sequencing (ES), more than 50% of individuals with a suspected Mendelian condition lack a precise molecular diagnosis. Clinical evaluation is increasingly undertaken by specialists outside of clinical genetics, often occurring in a tiered fashion and typically ending after ES. The current diagnostic rate reflects multiple factors, including technical limitations, incomplete understanding of variant pathogenicity, missing genotype-phenotype associations, complex gene-environment interactions, and reporting differences between clinical labs. Maintaining a clear understanding of the rapidly evolving landscape of diagnostic tests beyond ES, and their limitations, presents a challenge for non-genetics professionals. Newer tests, such as short-read genome or RNA sequencing, can be challenging to order and emerging technologies, such as optical genome mapping and long-read DNA or RNA sequencing, are not available clinically. Furthermore, there is no clear guidance on the next best steps after inconclusive evaluation. Here, we review why a clinical genetic evaluation may be negative, discuss questions to be asked in this setting, and provide a framework for further investigation, including the advantages and disadvantages of new approaches that are nascent in the clinical sphere. We present a guide for the next best steps after inconclusive molecular testing based upon phenotype and prior evaluation, including when to consider referral to a consortium such as GREGoR, which is focused on elucidating the underlying cause of rare unsolved genetic disorders.

Diagnosing, discarding, or de-VUSsing: A practical guide to (un)targeted metabolomics as variant-transcending functional tests

PURPOSE

For patients with inherited metabolic disorders (IMDs), any diagnostic delay should be avoided because early initiation of personalized treatment could prevent irreversible health damage. To improve diagnostic interpretation of genetic data, gene function tests can be valuable assets. For IMDs, variant-transcending functional tests are readily available through (un)targeted metabolomics assays. To support the application of metabolomics for this purpose, we developed a gene-based guide to select functional tests to either confirm or exclude an IMD diagnosis.

METHODS

Using information from a diagnostic IMD exome panel, Kyoto Encyclopedia of Genes and Genomes, and Inborn Errors of Metabolism Knowledgebase, we compiled a guide for metabolomics-based gene function tests. From our practical experience with this guide, we retrospectively selected illustrative cases for whom combined metabolomic/genomic testing improved diagnostic success and evaluated the effect hereof on clinical management.

RESULTS

The guide contains 2047 metabolism-associated genes for which a validated or putative variant-transcending gene function test is available. We present 16 patients for whom metabolomic testing either confirmed or ruled out the presence of a second pathogenic variant, validated or ruled out pathogenicity of variants of uncertain significance, or identified a diagnosis initially missed by genetic analysis.

CONCLUSION

Metabolomics-based gene function tests provide additional value in the diagnostic trajectory of patients with suspected IMD by enhancing and accelerating diagnostic success.

Diagnostic potential of the amniotic fluid cells transcriptome in deciphering mendelian disease: a proof-of-concept

RNA sequencing (RNA-seq) is emerging in genetic diagnoses as it provides functional support for the interpretation of variants of uncertain significance. However, the use of amniotic fluid (AF) cells for RNA-seq has not yet been explored. Here, we examined the expression of clinically relevant genes in AF cells (n = 48) compared with whole blood and fibroblasts. The number of well-expressed genes in AF cells was comparable to that in fibroblasts and much higher than that in blood across different disease categories. We found AF cells RNA-seq feasible and beneficial in prenatal diagnosis (n = 4) as transcriptomic data elucidated the molecular consequence leading to the pathogenicity upgrade of variants in CHD7 and COL1A2 and revising the in silico prediction of a variant in MYRF. AF cells RNA-seq could become a reasonable choice for postnatal patients with advantages over fibroblasts and blood as it prevents invasive procedures.

A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism

BACKGROUND AND OBJECTIVE

Traditional targeted metabolomic investigations identify a pre-defined list of analytes in samples and have been widely used for decades in the diagnosis and monitoring of inborn errors of metabolism (IEMs). Recent technological advances have resulted in the development and maturation of untargeted metabolomics: a holistic, unbiased, analytical approach to detecting metabolic disturbances in human disease. We aim to provide a summary of untargeted metabolomics [focusing on tandem mass spectrometry (MS-MS)] and its application in the field of IEMs.

METHODS

Data for this review was identified through a literature search using PubMed, Google Scholar, and personal repositories of articles collected by the authors. Findings are presented within several sections describing the metabolome, the current use of targeted metabolomics in the diagnostic pathway of patients with IEMs, the more recent integration of untargeted metabolomics into clinical care, and the limitations of this newly employed analytical technique.

KEY CONTENT AND FINDINGS

Untargeted metabolomic investigations are increasingly utilized in screening for rare disorders, improving understanding of cellular and subcellular physiology, discovering novel biomarkers, monitoring therapy, and functionally validating genomic variants. Although the untargeted metabolomic approach has some limitations, this "next generation metabolic screening" platform is becoming increasingly affordable and accessible.

CONCLUSIONS

When used in conjunction with genomics and the other promising "-omic" technologies, untargeted metabolomics has the potential to revolutionize the diagnostics of IEMs (and other rare disorders), improving both clinical and health economic outcomes.

Biallelic Variants in the Ectonucleotidase ENTPD1 Cause a Complex Neurodevelopmental Disorder with Intellectual Disability, Distinct White Matter Abnormalities, and Spastic Paraplegia

OBJECTIVE

Human genomics established that pathogenic variation in diverse genes can underlie a single disorder. For example, hereditary spastic paraplegia is associated with >80 genes, with frequently only few affected individuals described for each gene. Herein, we characterize a large cohort of individuals with biallelic variation in ENTPD1, a gene previously linked to spastic paraplegia 64 (Mendelian Inheritance in Man # 615683).

METHODS

Individuals with biallelic ENTPD1 variants were recruited worldwide. Deep phenotyping and molecular characterization were performed.

RESULTS

A total of 27 individuals from 17 unrelated families were studied; additional phenotypic information was collected from published cases. Twelve novel pathogenic ENTPD1 variants are described (NM 001776.6): c.398_399delinsAA; p.(Gly133Glu), c.540del; p.(Thr181Leufs*18), c.640del; p.(Gly216Glufs*75), c.185 T > G; p.(Leu62*), c.1531 T > C; p.(*511Glnext*100), c.967C > T; p.(Gln323*), c.414-2_414-1del, and c.146 A > G; p.(Tyr49Cys) including 4 recurrent variants c.1109 T > A; p.(Leu370*), c.574-6_574-3del, c.770_771del; p.(Gly257Glufs*18), and c.1041del; p.(Ile348Phefs*19). Shared disease traits include childhood onset, progressive spastic paraplegia, intellectual disability (ID), dysarthria, and white matter abnormalities. In vitro assays demonstrate that ENTPD1 expression and function are impaired and that c.574-6_574-3del causes exon skipping. Global metabolomics demonstrate ENTPD1 deficiency leads to impaired nucleotide, lipid, and energy metabolism.

INTERPRETATION

The ENTPD1 locus trait consists of childhood disease onset, ID, progressive spastic paraparesis, dysarthria, dysmorphisms, and white matter abnormalities, with some individuals showing neurocognitive regression. Investigation of an allelic series of ENTPD1 (1) expands previously described features of ENTPD1-related neurological disease, (2) highlights the importance of genotype-driven deep phenotyping, (3) documents the need for global collaborative efforts to characterize rare autosomal recessive disease traits, and (4) provides insights into disease trait neurobiology. ANN NEUROL 2022;92:304-321.

Metabolic analysis as a driver for discovery, diagnosis, and therapy

Metabolic anomalies contribute to tissue dysfunction. Current metabolism research spans from organelles to populations, and new technologies can accommodate investigation across these scales. Here, we review recent advancements in metabolic analysis, including small-scale metabolomics techniques amenable to organelles and rare cell types, functional screening to explore how cells respond to metabolic stress, and imaging approaches to non-invasively assess metabolic perturbations in diseases. We discuss how metabolomics provides an informative phenotypic dimension that complements genomic analysis in Mendelian and non-Mendelian disorders. We also outline pressing challenges and how addressing them may further clarify the biochemical basis of human disease.

How to proceed after "negative" exome: A review on genetic diagnostics, limitations, challenges, and emerging new multiomics techniques

Exome sequencing (ES) in the clinical setting of inborn metabolic diseases (IMDs) has created tremendous improvement in achieving an accurate and timely molecular diagnosis for a greater number of patients, but it still leaves the majority of patients without a diagnosis. In parallel, (personalized) treatment strategies are increasingly available, but this requires the availability of a molecular diagnosis. IMDs comprise an expanding field with the ongoing identification of novel disease genes and the recognition of multiple inheritance patterns, mosaicism, variable penetrance, and expressivity for known disease genes. The analysis of trio ES is preferred over singleton ES as information on the allelic origin (paternal, maternal, "de novo") reduces the number of variants that require interpretation. All ES data and interpretation strategies should be exploited including CNV and mitochondrial DNA analysis. The constant advancements in available techniques and knowledge necessitate the close exchange of clinicians and molecular geneticists about genotypes and phenotypes, as well as knowledge of the challenges and pitfalls of ES to initiate proper further diagnostic steps. Functional analyses (transcriptomics, proteomics, and metabolomics) can be applied to characterize and validate the impact of identified variants, or to guide the genomic search for a diagnosis in unsolved cases. Future diagnostic techniques (genome sequencing [GS], optical genome mapping, long-read sequencing, and epigenetic profiling) will further enhance the diagnostic yield. We provide an overview of the challenges and limitations inherent to ES followed by an outline of solutions and a clinical checklist, focused on establishing a diagnosis to eventually achieve (personalized) treatment.

Integration of metabolomics with genomics: Metabolic gene prioritization using metabolomics data and genomic variant (CADD) scores

The integration of metabolomics data with sequencing data is a key step towards improving the diagnostic process for finding the disease-causing genetic variant(s) in patients suspected of having an inborn error of metabolism (IEM). The measured metabolite levels could provide additional phenotypical evidence to elucidate the degree of pathogenicity for variants found in genes associated with metabolic processes. We present a computational approach, called Reafect, that calculates for each reaction in a metabolic pathway a score indicating whether that reaction is deficient or not. When calculating this score, Reafect takes multiple factors into account: the magnitude and sign of alterations in the metabolite levels, the reaction distances between metabolites and reactions in the pathway, and the biochemical directionality of the reactions. We applied Reafect to untargeted metabolomics data of 72 patient samples with a known IEM and found that in 81% of the cases the correct deficient enzyme was ranked within the top 5% of all considered enzyme deficiencies. Next, we integrated Reafect with Combined Annotation Dependent Depletion (CADD) scores (a measure for gene variant deleteriousness) and ranked the metabolic genes of 27 IEM patients. We observed that this integrated approach significantly improved the prioritization of the genes containing the disease-causing variant when compared with the two approaches individually. For 15/27 IEM patients the correct affected gene was ranked within the top 0.25% of the set of potentially affected genes. Together, our findings suggest that metabolomics data improves the identification of affected genes in patients suffering from IEM.

An integrated multiomic approach as an excellent tool for the diagnosis of metabolic diseases: our first 3720 patients

To present our experience using a multiomic approach, which integrates genetic and biochemical testing as a first-line diagnostic tool for patients with inherited metabolic disorders (IMDs). A cohort of 3720 patients from 62 countries was tested using a panel including 206 genes with single nucleotide and copy number variant (SNV/CNV) detection, followed by semi-automatic variant filtering and reflex biochemical testing (25 assays). In 1389 patients (37%), a genetic diagnosis was achieved. Within this cohort, the highest diagnostic yield was obtained for patients from Asia (57.5%, mainly from Pakistan). Overall, 701 pathogenic/likely pathogenic unique SNVs and 40 CNVs were identified. In 620 patients, the result of the biochemical tests guided variant classification and reporting. Top five diagnosed diseases were: Gaucher disease, Niemann-Pick disease type A/B, phenylketonuria, mucopolysaccharidosis type I, and Wilson disease. We show that integrated genetic and biochemical testing facilitated the decision on clinical relevance of the variants and led to a high diagnostic yield (37%), which is comparable to exome/genome sequencing. More importantly, up to 43% of these patients (n = 610) could benefit from medical treatments (e.g., enzyme replacement therapy). This multiomic approach constitutes a unique and highly effective tool for the genetic diagnosis of IMDs.

Biallelic variants in SLC38A3 encoding a glutamine transporter cause epileptic encephalopathy

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 and severe neurodevelopmental disorders. Exome sequencing and family-based rare variant analyses on a cohort with neurodevelopmental disorders identified two siblings with developmental and epileptic encephalopathy 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 developmental and epileptic encephalopathy 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 CSF of selected subjects, potentially representing biomarkers of disease. Our data support the contention that SLC38A3 is a novel disease gene for developmental and epileptic encephalopathy and illuminate the likely pathophysiology of the disease as perturbations in glutamine homeostasis.

Clinical diagnosis of metabolic disorders using untargeted metabolomic profiling and disease-specific networks learned from profiling data

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 disease-specific 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.

Untargeted Metabolomics of Slc13a5 Deficiency Reveal Critical Liver-Brain Axis for Lipid Homeostasis

Though biallelic variants in SLC13A5 are known to cause severe encephalopathy, the mechanism of this disease is poorly understood. SLC13A5 protein deficiency reduces citrate transport into the cell. Downstream abnormalities in fatty acid synthesis and energy generation have been described, though biochemical signs of these perturbations are inconsistent across SLC13A5 deficiency patients. To investigate SLC13A5-related disorders, we performed untargeted metabolic analyses on the liver, brain, and serum from a Slc13a5-deficient mouse model. Metabolomic data were analyzed using the connect-the-dots (CTD) methodology and were compared to plasma and CSF metabolomics from SLC13A5-deficient patients. Mice homozygous for the Slc13a5^tm1b/tm1b null allele had perturbations in fatty acids, bile acids, and energy metabolites in all tissues examined. Further analyses demonstrated that for several of these molecules, the ratio of their relative tissue concentrations differed widely in the knockout mouse, suggesting that deficiency of Slc13a5 impacts the biosynthesis and flux of metabolites between tissues. Similar findings were observed in patient biofluids, indicating altered transport and/or flux of molecules involved in energy, fatty acid, nucleotide, and bile acid metabolism. Deficiency of SLC13A5 likely causes a broader state of metabolic dysregulation than previously recognized, particularly regarding lipid synthesis, storage, and metabolism, supporting SLC13A5 deficiency as a lipid disorder.

Clinical metabolomics for inborn errors of metabolism

Metabolism is a highly regulated process that provides nutrients to cells and essential building blocks for the synthesis of protein, DNA and other macromolecules. In healthy biological systems, metabolism maintains a steady state in which the concentrations of metabolites are relatively constant yet are subject to metabolic demands and environmental stimuli. Rare genetic disorders, such as inborn errors of metabolism (IEM), cause defects in regulatory enzymes or proteins leading to metabolic pathway disruption and metabolite accumulation or deficiency. Traditionally, the laboratory diagnosis of IEMs has been limited to analytical methods that target specific metabolites such as amino acids and acyl carnitines. This approach is effective as a screening method for the most common IEM disorders but lacks the comprehensive coverage of metabolites that is necessary to identify rare disorders that present with nonspecific clinical symptoms. Fortunately, advancements in technology and data analytics has introduced a new field of study called metabolomics which has allowed scientists to perform comprehensive metabolite profiling of biological systems to provide insight into mechanism of action and gene function. Since metabolomics seeks to measure all small molecule metabolites in a biological specimen, it provides an innovative approach to evaluating disease in patients with rare genetic disorders. In this review we provide insight into the appropriate application of metabolomics in clinical settings. We discuss the advantages and limitations of the method and provide details related to the technology, data analytics and statistical modeling required for metabolomic profiling of patients with IEMs.

Metabolomics in Clinical Practice: Improving Diagnosis and Informing Management

BACKGROUND

Metabolomics is the study of small molecules to simultaneously identify multiple low molecular weight molecules in a system. Broadly speaking, metabolomics can be subdivided into targeted and untargeted types of analysis, each type having advantages and drawbacks. Targeted metabolomics can quantify analytes but only looks for known or expected analytes related to particular disease(s), whereas untargeted metabolomics is typically nonquantitative but can detect thousands of analytes from an agnostic or nonhypothesis driven perspective, allowing for novel discoveries.

CONTENT

One application of metabolomics is the study of inborn errors of metabolism (IEM). The biochemical hallmark of IEMs is decreased concentrations of analytes distal to the enzymatic defect and buildup of analytes proximal to the defect. Metabolomics can detect these changes with one test and is effective in screening for and diagnosis of IEMs. Metabolomics has also been used to study many nonmetabolic diseases such as autism spectrum disorder, various cancers, and multiple congenital anomalies syndromes. Metabolomics has led to the discovery of many novel biomarkers of disease. Recent publications demonstrate how metabolomics can be useful clinically in the diagnosis and management of patients, as well as for research and clinical discovery.

SUMMARY

Metabolomics has proved to be a useful tool clinically for screening and diagnostic purposes and from a research perspective for the detection of novel biomarkers. In the future, metabolomics will likely become a routine part of the evaluation for many diseases as either a supplementary test or it may simply replace historical analyses that require several individual tests and sample types.

Newborn screening system: Safety, technology, advocacy

Newborn screening (NBS) is more than 50 years old and has proven to be a powerful and successful public health system. NBS must be regarded as a system and not simply as a test. We need to work as a community to improve the culture of safety for the NBS system and thereby to reduce the risk of babies being missed by the NBS system. Adding new technologies will not prevent system failures; that will require adherence to the culture of safety. Some have argued that every newborn should have their genome sequenced at birth and this sequencing could be part of NBS. However, NBS has depended on biomarker phenotypes throughout its history and our understanding of the relationships between genotype and phenotype is imperfect. Therefore, we should avoid being seduced by genomic sequencing technology and continue to focus on phenotypic biomarkers in NBS.

Genetic Testing and Counseling and Child Neurology

Advances in genetic technology have decreased the cost and increased the accessibility of genetic testing, and introduced new therapeutic options for many genetic conditions. With new treatments available for previously untreatable neurogenetic conditions, identifying a genetic diagnosis has become of great importance. This article provides a review of basic genetic concepts, ethical and counseling considerations with genetic testing, and genetic testing strategies, and highlights a series of clinical care pearls.

Metabolic impact of pathogenic variants in the mitochondrial glutamyl-tRNA synthetase EARS2

Glutamyl-tRNA synthetase 2 (encoded by EARS2) is a mitochondrial aminoacyl-tRNA synthetase required to translate the 13 subunits of the electron transport chain encoded by the mitochondrial DNA. Pathogenic EARS2 variants cause combined oxidative phosphorylation deficiency, subtype 12 (COXPD12), an autosomal recessive disorder involving lactic acidosis, intellectual disability, and other features of mitochondrial compromise. Patients with EARS2 deficiency present with variable phenotypes ranging from neonatal lethality to a mitigated disease with clinical improvement in early childhood. Here, we report a neonate homozygous for a rare pathogenic variant in EARS2 (c.949G>T; p.G317C). Metabolomics in primary fibroblasts from this patient revealed expected abnormalities in TCA cycle metabolites, as well as numerous changes in purine, pyrimidine, and fatty acid metabolism. To examine genotype-phenotype correlations in COXPD12, we compared the metabolic impact of reconstituting these fibroblasts with wild-type EARS2 versus four additional EARS2 variants from COXPD12 patients with varying clinical severity. Metabolomics identified a group of signature metabolites, mostly from the TCA cycle and amino acid metabolism, that discriminate between EARS2 variants causing relatively mild and severe COXPD12. Taken together, these findings indicate that metabolomics in patient-derived fibroblasts may help establish genotype-phenotype correlations in EARS2 deficiency and likely other mitochondrial disorders.

Comparison of Untargeted Metabolomic Profiling vs Traditional Metabolic Screening to Identify Inborn Errors of Metabolism

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.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study compares data from 4464 clinical samples received from 1483 unrelated families referred for trio testing of plasma amino acids, plasma acylcarnitine profiling, and urine organic acids (June 2014 to October 2018) and 2000 consecutive plasma samples from 1807 unrelated families (July 2014 to February 2019) received for clinical metabolomic screening at a College of American Pathologists and Clinical Laboratory Improvement Amendments-certified biochemical genetics laboratory. Data analysis was performed from September 2019 to August 2020.

EXPOSURES

Metabolic and molecular tests performed at a genetic testing reference laboratory in the US and available clinical information for each patient were assessed to determine diagnostic rate.

MAIN OUTCOMES AND MEASURES

The diagnostic rate of traditional metabolic screening compared with clinical metabolomic profiling was assessed in the context of expanded NBS.

RESULTS

Of 1483 cases screened by the traditional approach, 912 patients (61.5%) were male and 1465 (98.8%) were pediatric (mean [SD] age, 4.1 [6.0] years; range, 0-65 years). A total of 19 families were identified with IEMs, resulting in a 1.3% diagnostic rate. A total of 14 IEMs were detected, including 3 conditions not included in the Recommended Uniform Screening Panel for NBS. Of the 1807 unrelated families undergoing plasma metabolomic profiling, 1059 patients (58.6%) were male, and 1665 (92.1%) were pediatric (mean [SD] age, 8.1 [10.4] years; range, 0-80 years). Screening identified 128 unique cases with IEMs, giving an overall diagnostic rate of 7.1%. In total, 70 different metabolic conditions were identified, including 49 conditions not presently included on the Recommended Uniform Screening Panel for NBS.

CONCLUSIONS AND RELEVANCE

These findings suggest that untargeted metabolomics provided a 6-fold higher diagnostic yield compared with the conventional screening approach and identified a broader spectrum of IEMs. Notably, with the expansion of NBS programs, traditional metabolic testing approaches identify few disorders beyond those covered on the NBS. These data support the capability of clinical untargeted metabolomics in screening for IEMs and suggest that broader screening approaches should be considered in the initial evaluation for metabolic disorders.

Genetic basis of mitochondrial diseases

Mitochondrial disorders are monogenic disorders characterized by a defect in oxidative phosphorylation and caused by pathogenic variants in one of over 340 different genes. The implementation of whole-exome sequencing has led to a revolution in their diagnosis, duplicated the number of associated disease genes, and significantly increased the diagnosed fraction. However, the genetic etiology of a substantial fraction of patients exhibiting mitochondrial disorders remains unknown, highlighting limitations in variant detection and interpretation, which calls for improved computational and DNA sequencing methods, as well as the addition of OMICS tools. More intriguingly, this also suggests that some pathogenic variants lie outside of the protein-coding genes and that the mechanisms beyond the Mendelian inheritance and the mtDNA are of relevance. This review covers the current status of the genetic basis of mitochondrial diseases, discusses current challenges and perspectives, and explores the contribution of factors beyond the protein-coding regions and monogenic inheritance in the expansion of the genetic spectrum of disease.

Assessment of the effects of repeated freeze thawing and extended bench top processing of plasma samples using untargeted metabolomics

INTRODUCTION

Clinical metabolomics has utility as a screen for inborn errors of metabolism (IEM) and variant classification in patients with rare disease. It is important to understand and characterize preanalytical factors that influence assay performance during patient sample testing.

OBJECTIVES

To evaluate the impact of extended thawing of human EDTA plasma samples on ice prior to extraction as well as repeated freeze-thaw cycling of samples to identify compounds that are unstable prior to metabolomic analysis.

METHODS

Twenty-four (24) donor EDTA plasma samples were collected and immediately frozen at - 80 °C. Twelve samples were thawed on ice and extracted for analysis at time 0, 2, 4, and 6 h. Twelve other donor samples were repeatedly thawed and frozen up to four times and analyzed at each cycle. Compound levels at each time point/freeze-thaw cycle were compared to the control samples using matched-paired t tests to identify analytes affected by each condition.

RESULTS

We identified 1026 biochemicals across all samples. Incubation of thawed EDTA plasma samples on ice for up to 6 h resulted in < 1% of biochemicals changing significantly. Freeze-thaw cycles affected a greater percentage of the metabolome; ~ 2% of biochemicals changed after 3 freeze-thaw cycles.

CONCLUSIONS

Our study highlights that the number and magnitude of these changes are not as widespread as other aspects of improper sample handling. In total, < 3% of the metabolome detected on our clinical metabolomics platform should be disqualified when multiple freeze-thaw cycles or extended thawing at 4 °C are performed on a given sample.

Hematologic presentation and the role of untargeted metabolomics analysis in monitoring treatment for riboflavin transporter deficiency

Riboflavin transporter deficiency (RTD) (MIM #614707) is a neurogenetic disorder with its most common manifestations including sensorineural hearing loss, peripheral neuropathy, respiratory insufficiency, and bulbar palsy. Here, we present a 2-year-old boy whose initial presentation was severe macrocytic anemia necessitating multiple blood transfusions and intermittent neutropenia; he subsequently developed ataxia and dysarthria. Trio-exome sequencing detected compound heterozygous variants in SLC52A2 that were classified as pathogenic and a variant of uncertain significance. Bone marrow evaluation demonstrated megaloblastic changes. Notably, his anemia and neutropenia resolved after treatment with oral riboflavin, thus expanding the clinical phenotype of this disorder. We reiterate the importance of starting riboflavin supplementation in a young child who presents with macrocytic anemia and neurological features while awaiting biochemical and genetic work up. We detected multiple biochemical abnormalities with the help of untargeted metabolomics analysis associated with abnormal flavin adenine nucleotide function which normalized after treatment, emphasizing the reversible pathomechanisms involved in this disorder. The utility of untargeted metabolomics analysis to monitor the effects of riboflavin supplementation in RTD has not been previously reported.

Untargeted metabolomics as an unbiased approach to the diagnosis of inborn errors of metabolism of the non-oxidative branch of the pentose phosphate pathway

Inborn errors of metabolism (IEM) involving the non-oxidative pentose phosphate pathway (PPP) include the two relatively rare conditions, transketolase deficiency and transaldolase deficiency, both of which can be difficult to diagnosis given their non-specific clinical presentations. Current biochemical testing approaches require an index of suspicion to consider targeted urine polyol testing. To determine whether a broad-spectrum biochemical test could accurately identify a specific metabolic pattern defining IEMs of the non-oxidative PPP, we employed the use of clinical metabolomic profiling as an unbiased novel approach to diagnosis. Subjects with molecularly confirmed IEMs of the PPP were included in this study. Targeted quantitative analysis of polyols in urine and plasma samples was accomplished with chromatography and mass spectrometry. Semi-quantitative unbiased metabolomic analysis of urine and plasma samples was achieved by assessing small molecules via liquid chromatography and high-resolution mass spectrometry. Results from untargeted and targeted analyses were then compared and analyzed for diagnostic acuity. Two siblings with transketolase (TKT) deficiency and three unrelated individuals with transaldolase (TALDO) deficiency were identified for inclusion in the study. For both IEMs, targeted polyol testing and untargeted metabolomic testing on urine and/or plasma samples identified typical perturbations of the respective disorder. Additionally, untargeted metabolomic testing revealed elevations in other PPP metabolites not typically measured with targeted polyol testing, including ribonate, ribose, and erythronate for TKT deficiency and ribonate, erythronate, and sedoheptulose 7-phosphate in TALDO deficiency. Non-PPP alternations were also noted involving tryptophan, purine, and pyrimidine metabolism for both TKT and TALDO deficient patients. Targeted polyol testing and untargeted metabolomic testing methods were both able to identify specific biochemical patterns indicative of TKT and TALDO deficiency in both plasma and urine samples. In addition, untargeted metabolomics was able to identify novel biomarkers, thereby expanding the current knowledge of both conditions and providing further insight into potential underlying pathophysiological mechanisms. Furthermore, untargeted metabolomic testing offers the advantage of having a single effective biochemical screening test for identification of rare IEMs, like TKT and TALDO deficiencies, that may otherwise go undiagnosed due to their generally non-specific clinical presentations.