Next-generation metabolic screening: targeted and untargeted metabolomics for the diagnosis of inborn errors of metabolism in individual patients

Global Metabolomics Using LC-MS for Clinical Applications

Metabolomics can be used for a multitude of purposes, including monitoring of treatment effects and for increasing the knowledge of the pathophysiology of a wide range of diseases. Global (commonly referred to as "untargeted") metabolomics is hypothesis-generating and provides the opportunity to discover new biomarkers. Being versatile and having a high degree of selectivity and sensitivity, liquid chromatography-mass spectrometry (LC-MS) is the most common technique applied for metabolomics. We here present our global metabolomics LC-electrospray ionization-MS/MS method. The sample preparation procedures for plasma, serum, dried blood spots, urine, and cerebrospinal fluid are simple and nonspecific to reduce the risk of analyte loss. The method is based on reversed-phase chromatography using a diphenyl column. The high-resolution Q Exactive Orbitrap MS with data-dependent acquisition provides MS/MS spectra of a wide range of analytes. Our method covers a large part of the metabolome regarding hydrophobicity and compound class.

Application of a Computational Metabolomics Workflow for the Diagnosis of Inborn Errors of Metabolism in a Laboratory Setting

Inborn errors of metabolism constitute a set of hereditary diseases that impose severe medical and physical challenges in the affected individual, in particular, for the pediatric patient population. Timely diagnosis is crucial for these patients, as any delay could result in irreversible health damage, underscoring the importance of early initiation of personalized treatment. Current routine diagnostic screening for inborn errors of metabolism relies on various targeted analyses of established biomarkers. However, this approach is time-consuming, focuses on a limited number of tests (based on clinical information) with a relatively small number of biomarkers, and does not facilitate the identification of new markers. In contrast, untargeted metabolomics-based screening offers a more efficient diagnostic solution, by assessing thousands of metabolites across multiple metabolic pathways in a single test. This not only saves time but also conserves resources for clinicians, the diagnostic laboratory, and for patients.This chapter describes the computational workflow of our "Next Generation Metabolic Screening" approach, which is a metabolomics-based method that is currently applied at the Translational Metabolic Laboratory of the Radboud University Medical Center (the Netherlands) for the diagnosis of inborn errors of metabolism.

Untargeted Metabolomics for Inborn Errors of Metabolism: Development and Evaluation of a Sustainable Reference Material for Correcting Inter-Batch Variability

BACKGROUND

Untargeted metabolomics has shown promise in expanding screening and diagnostic capabilities for inborn errors of metabolism (IEMs). However, inter-batch variability remains a major barrier to its implementation in the clinical laboratory, despite attempts to address this through normalization techniques. We have developed a sustainable, matrix-matched reference material (RM) using the iterative batch averaging method (IBAT) to correct inter-batch variability in liquid chromatography-high-resolution mass spectrometry-based untargeted metabolomics for IEM screening.

METHODS

The RM was created using pooled batches of remnant plasma specimens. The batch size, number of batch iterations per RM, and stability compared to a conventional pool of specimens were determined. The effectiveness of the RM for correcting inter-batch variability in routine screening was evaluated using plasma collected from a cohort of phenylketonuria (PKU) patients.

RESULTS

The RM exhibited lower metabolite variability between iterations over time compared to metabolites from individual batches or individual specimens used for its creation. In addition, the mean variation across amino acid (n = 19) concentrations over 12 weeks was lower for the RM (CVtotal = 8.8%; range 4.7%-25.3%) compared to the specimen pool (CVtotal = 24.6%; range 9.0%-108.3%). When utilized in IEM screening, RM normalization minimized unwanted inter-batch variation and enabled the correct classification of 30 PKU patients analyzed 1 month apart from 146 non-PKU controls.

CONCLUSIONS

Our RM minimizes inter-batch variability in untargeted metabolomics and demonstrated its potential for routine IEM screening in a cohort of PKU patients. It provides a practical and sustainable solution for data normalization in untargeted metabolomics for clinical laboratories.

Huppke-Brendel syndrome: Novel cases and a therapeutic trial with ketogenic diet and N-acetylcysteine

Huppke-Brendel syndrome (HBS) is an autosomal recessive disorder caused by SLC33A1 mutations, a gene coding for the acetyl-CoA transporter-1 (AT-1). So far it has been described in nine pediatric and one adult patient. Therapeutic trials with copper histidinate failed to achieve any clinical improvement. Here, we describe the clinical characteristics of two novel patients, one of them diagnosed by gene analysis and his sib postmortally based on clinical characteristics. We demonstrate a therapeutic trial with acetylation therapy, consisting of N-acetylcysteine and ketogenic diet, in one of them. We provide biochemical data on N-acetylated amino acids in cerebrospinal fluid (CSF) and plasma before and after starting this treatment regimen. Our results indicate that ketogenic diet and N-acetylcysteine do not seem to normalize the concentrations of N-acetylated amino acids in CSF or plasma. The overall metabolic pattern shows a trend toward lowered levels of N-acetylated amino acids in CSF and to a lesser extent in plasma. Although there are some assumptions, the function of AT-1 is still not clear and further studies are needed to better understand mechanisms underlying this complex disorder.

Targeted ultra performance liquid chromatography tandem mass spectrometry procedures for the diagnosis of inborn errors of metabolism: validation through ERNDIM external quality assessment schemes

OBJECTIVES

Early diagnosis of inborn errors of metabolism (IEM) is crucial to ensure early detection of conditions which are treatable. This study reports on targeted metabolomic procedures for the diagnosis of IEM of amino acids, acylcarnitines, creatine/guanidinoacetate, purines/pyrimidines and oligosaccharides, and describes its validation through external quality assessment schemes (EQA).

METHODS

Analysis was performed on a Waters ACQUITY UPLC H-class system coupled to a Waters Xevo triple-quadrupole (TQD) mass spectrometer, operating in both positive and negative electrospray ionization mode. Chromatographic separation was performed on a CORTECS C18 column (2.1 × 150, 1.6 µm). Data were collected by multiple reaction monitoring.

RESULTS

The internal and EQA results were generally adequate, with a few exceptions. We calculated the relative measurement error (RME) and only a few metabolites displayed a RME higher than 30 % (asparagine and some acylcarnitine species). For oligosaccharides, semi-quantitative analysis of an educational panel clearly identified the 8 different diseases included.

CONCLUSIONS

Overall, we have validated our analytical system through an external quality control assessment. This validation will contribute to harmonization between laboratories, thus improving identification and management of patients with IEM.

Impact of Phenylketonuria on the Serum Metabolome and Plasma Lipidome: A Study in Early-Treated Patients

BACKGROUND

Data suggest that metabolites, other than blood phenylalanine (Phe), better and independently predict clinical outcomes in patients with phenylketonuria (PKU).

METHODS

To find new biomarkers, we compared the results of untargeted lipidomics and metabolomics in treated adult PKU patients to those of matched controls. Samples (lipidomics in EDTA-plasma (22 PKU and 22 controls) and metabolomics in serum (35 PKU and 20 controls)) were analyzed using ultra-high-performance liquid chromatography and high-resolution mass spectrometry. Data were subjected to multivariate (PCA, OPLS-DA) and univariate (Mann-Whitney U test, p < 0.05) analyses.

RESULTS

Levels of 33 (of 20,443) lipid features and 56 (of 5885) metabolite features differed statistically between PKU patients and controls. For lipidomics, findings include higher glycerolipids, glycerophospholipids, and sphingolipids species. Significantly lower values were found for sterols and glycerophospholipids species. Seven features had unknown identities. Total triglyceride content was higher. Higher Phe and Phe catabolites, tryptophan derivatives, pantothenic acid, and dipeptides were observed for metabolomics. Ornithine levels were lower. Twenty-six metabolite features were not annotated.

CONCLUSIONS

This study provides insight into the metabolic phenotype of PKU patients. Additional studies are required to establish whether the observed changes result from PKU itself, diet, and/or an unknown reason.

Inborn errors of metabolism: Historical perspectives to contemporary management

There are many different genetic diseases called inborn errors of metabolism (IEM) which result from defective enzymes in the metabolic pathway. As a result, these defects either cause a harmful accumulation of substances or lead to a lack of certain types of molecule. The present review traces the origin and development of IEMs from Sir Archibald Garrod's theory in the early 20th century to current diagnostic and therapeutic approaches. It also involves a systematic literature review complying with PRISMA which included studies sourced from PubMed, Scopus, Web of Science and Google Scholar. It points out that high rates of consanguinity are associated with high prevalence rates for IEMs especially in the Eastern Mediterranean area. IEMS are classified as energy deficiency disorders, intoxication disorders, and storage disorders. Each category has a variety of clinical manifestations. This study incorporates different diagnostic methods ranging from simple biochemical tests to tandem mass spectrometry and next generation sequencing; while management approaches such as dietary modifications, enzyme replacement therapy and gene therapy were assessed for their efficacy. Specific attention is paid to Pakistan where there exists considerable consanguinity among people coupled with inadequate health care services which have seriously affected delivery of health care services thereby leading to numerous challenges for the country healthcare system during service provision.

Fungicide Metabolite MS2 Spectral Libraries for Comprehensive Human Biomonitoring

Fungicides undergo rapid metabolism and are excreted in the urine. There are few methods for screening these ubiquitous compounds, which have a high potential for human exposure. High-resolution mass spectrometry (HRMS) is a suitable technique to assess fungicide exposures; however, there is a lack of spectral libraries for fungicide annotation and in particular for downstream metabolites. We created spectral libraries for 32 fungicides for suspect screening. Fungicide standards were administered to mice, and 24-h urine was analyzed using hydrophilic interaction and reversed-phase chromatography coupled to hybrid quadrupole-orbitrap mass spectrometry. Suspect metabolite MS2 spectra for library creation were selected based on the ratio of exposed-to-control mouse urine. MS2 libraries were applied to urine collected from female university students (n = 73). Several tetraconazole and tebuconazole metabolites were detected in 3% (2/73) of the samples. The creation of comprehensive suspect screening MS2 libraries is a useful tool to detect fungicide exposure for human biomonitoring.

Deciphering metabolic crosstalk in context: lessons from inflammatory diseases

Metabolism plays a crucial role in regulating the function of immune cells in both health and disease, with altered metabolism contributing to the pathogenesis of cancer and many inflammatory diseases. The local microenvironment has a profound impact on the metabolism of immune cells. Therefore, immunological and metabolic heterogeneity as well as the spatial organization of cells in tissues should be taken into account when studying immunometabolism. Here, we highlight challenges of investigating metabolic communication. Additionally, we review the capabilities and limitations of current technologies for studying metabolism in inflamed microenvironments, including single-cell omics techniques, flow cytometry-based methods (Met-Flow, single-cell energetic metabolism by profiling translation inhibition (SCENITH)), cytometry by time of flight (CyTOF), cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq), and mass spectrometry imaging. Considering the importance of metabolism in regulating immune cells in diseased states, we also discuss the applications of metabolomics in clinical research, as well as some hurdles to overcome to implement these techniques in standard clinical practice. Finally, we provide a flowchart to assist scientists in designing effective strategies to unravel immunometabolism in disease-relevant contexts.

The clinical relevance of novel biomarkers as outcome parameter in adults with phenylketonuria

Recent studies in PKU patients identified alternative biomarkers in blood using untargeted metabolomics. To test the added clinical value of these novel biomarkers, targeted metabolomics of 11 PKU biomarkers (phenylalanine, glutamyl-phenylalanine, glutamyl-glutamyl-phenylalanine, N-lactoyl-phenylalanine, N-acetyl-phenylalanine, the dipeptides phenylalanyl-phenylalanine and phenylalanyl-leucine, phenylalanine-hexose conjugate, phenyllactate, phenylpyruvate, and phenylacetate) was performed in stored serum samples of the well-defined PKU patient-COBESO cohort and a healthy control group. Serum samples of 35 PKU adults and 20 healthy age- and sex-matched controls were analyzed using ultra-high performance liquid chromatography quadrupole time-of-flight mass spectrometry. Group differences were tested using the Mann-Whitney U test. Multiple linear regression analyses were performed with these biomarkers as predictors of (neuro-)cognitive functions working memory, sustained attention, inhibitory control, and mental health. Compared to healthy controls, phenylalanine, glutamyl-phenylalanine, N-lactoyl-phenylalanine, N-acetyl-phenylalanine, phenylalanine-hexose conjugate, phenyllactate, phenylpyruvate, and phenylacetate were significant elevated in PKU adults (p < 0.001). The remaining three were below limit of detection in PKU and controls. Both phenylalanine and N-lactoyl-phenylalanine were associated with DSM-VI Attention deficit/hyperactivity (R2 = 0.195, p = 0.039 and R2 = 0.335, p = 0.002, respectively) of the ASR questionnaire. In addition, N-lactoyl-phenylalanine showed significant associations with ASR DSM-VI avoidant personality (R2 = 0.265, p = 0.010), internalizing (R2 = 0.192, p = 0.046) and externalizing problems (R2 = 0.217, p = 0.029) of the ASR questionnaire and multiple aspects of the MS2D and FI tests, reflecting working memory with R2 between 0.178 (p = 0.048) and 0.204 (p = 0.033). Even though the strength of the models was not considered strong, N-lactoyl-phenylalanine outperformed phenylalanine in its association with working memory and mental health outcomes.

The Multi-Omic Approach to Newborn Screening: Opportunities and Challenges

Newborn screening programs have seen significant evolution since their initial implementation more than 60 years ago, with the primary goal of detecting treatable conditions within the earliest possible timeframe to ensure the optimal treatment and outcomes for the newborn. New technologies have driven the expansion of screening programs to cover additional conditions. In the current era, the breadth of screened conditions could be further expanded by integrating omic technologies such as untargeted metabolomics and genomics. Genomic screening could offer opportunities for lifelong care beyond the newborn period. For genomic newborn screening to be effective and ready for routine adoption, it must overcome barriers such as implementation cost, public acceptability, and scalability. Metabolomics approaches, on the other hand, can offer insight into disease phenotypes and could be used to identify known and novel biomarkers of disease. Given recent advances in metabolomic technologies, alongside advances in genomics including whole-genome sequencing, the combination of complementary multi-omic approaches may provide an exciting opportunity to leverage the best of both approaches and overcome their respective limitations. These techniques are described, along with the current outlook on multi-omic-based NBS research.

Emerging Multi-omic Approaches to the Molecular Diagnosis of Mitochondrial Disease and Available Strategies for Treatment and Prevention

Mitochondria are semi-autonomous organelles present in several copies within most cells in the human body that are controlled by the precise collaboration of mitochondrial DNA (mtDNA) and nuclear DNA (nDNA) encoding mitochondrial proteins. They play important roles in numerous metabolic pathways, such as the synthesis of adenosine triphosphate (ATP), the predominant energy substrate of the cell generated through oxidative phosphorylation (OXPHOS), intracellular calcium homeostasis, metabolite biosynthesis, aging, cell cycles, and so forth. Previous studies revealed that dysfunction of these multi-functional organelles, which may arise due to mutations in either the nuclear or mitochondrial genome, leads to a diverse group of clinically and genetically heterogeneous disorders. These diseases include neurodegenerative and metabolic disorders as well as cardiac and skeletal myopathies in both adults and newborns. The plethora of phenotypes and defects displayed leads to challenges in the diagnosis and treatment of mitochondrial diseases. In this regard, the related literature proposed several diagnostic options, such as high throughput mitochondrial genomics and omics technologies, as well as numerous therapeutic options, such as pharmacological approaches, manipulating the mitochondrial genome, increasing the mitochondria content of the affected cells, and recently mitochondrial diseases transmission prevention. Therefore, the present article attempted to review the latest advances and challenges in diagnostic and therapeutic options for mitochondrial diseases.

Metabolic blood profile and response to treatment with the pyruvate kinase activator mitapivat in patients with sickle cell disease

Mitapivat is an investigational, oral, small-molecule allosteric activator of pyruvate kinase (PK). PK is a regulatory glycolytic enzyme that is key in providing the red blood cell (RBC) with sufficient amounts of adenosine triphosphate (ATP). In sickle cell disease (SCD), decreased 2,3-DPG levels increase the oxygen affinity of hemoglobin, thereby preventing deoxygenation and polymerization of sickle hemoglobin. The PK activator mitapivat has been shown to decrease levels of 2,3-DPG and increase levels of ATP in RBCs in patients with SCD. In this phase 2, investigator-initiated, open-label study (https://www.clinicaltrialsregister.eu/ NL8517; EudraCT 2019-003438-18), untargeted metabolomics was used to explore the overall metabolic effects of 8-week treatment with mitapivat in the dose-finding period. In total, 1773 unique metabolites were identified in dried blood spots of whole blood from ten patients with SCD and 42 healthy controls (HCs). The metabolic phenotype of patients with SCD revealed alterations in 139/1773 (7.8%) metabolites at baseline when compared to HCs (false discovery rate-adjusted p < 0.05), including increases of (derivatives of) polyamines, purines, and acyl carnitines. Eight-week treatment with mitapivat in nine patients with SCD altered 85/1773 (4.8%) of the total metabolites and 18/139 (12.9%) of the previously identified altered metabolites in SCD (unadjusted p < 0.05). Effects were observed on a broad spectrum of metabolites and were not limited to glycolytic intermediates. Our results show the relevance of metabolic profiling in SCD, not only to unravel potential pathophysiological pathways and biomarkers in multisystem diseases but also to determine the effect of treatment.

Exploratory Untargeted Metabolomics of Dried Blood Spot Samples from Newborns with Maple Syrup Urine Disease

Currently, tandem mass spectrometry-based newborn screening (NBS), which examines targeted biomarkers, is the first approach used for the early detection of maple syrup urine disease (MSUD) in newborns, followed by confirmatory genetic mutation tests. However, these diagnostic approaches have limitations, demanding the development of additional tools for the diagnosis/screening of MUSD. Recently, untargeted metabolomics has been used to explore metabolic profiling and discover the potential biomarkers/pathways of inherited metabolic diseases. Thus, we aimed to discover a distinctive metabolic profile and biomarkers/pathways for MSUD newborns using untargeted metabolomics. Herein, untargeted metabolomics was used to analyze dried blood spot (DBS) samples from 22 MSUD and 22 healthy control newborns. Our data identified 210 altered endogenous metabolites in MSUD newborns and new potential MSUD biomarkers, particularly L-alloisoleucine, methionine, and lysoPI. In addition, the most impacted pathways in MSUD newborns were the ascorbate and aldarate pathways and pentose and glucuronate interconversions, suggesting that oxidative and detoxification events may occur in early life. Our approach leads to the identification of new potential biomarkers/pathways that could be used for the early diagnosis/screening of MSUD newborns but require further validation studies. Our untargeted metabolomics findings have undoubtedly added new insights to our understanding of the pathogenicity of MSUD, which helps us select the appropriate early treatments for better health outcomes.

Succinic semialdehyde dehydrogenase deficiency in mice and in humans: An untargeted metabolomics perspective

Succinic semialdehyde dehydrogenase deficiency (SSADHD) is a rare neurometabolic disorder caused by disruption of the gamma-aminobutyric acid (GABA) pathway. A more detailed understanding of its pathophysiology, beyond the accumulation of GABA and gamma-hydroxybutyric acid (GHB), will increase our understanding of the disease and may support novel therapy development. To this end, we compared biochemical body fluid profiles from SSADHD patients with controls using next-generation metabolic screening (NGMS). Targeted analysis of NGMS data from cerebrospinal fluid (CSF) showed a moderate increase of aspartic acid, glutaric acid, glycolic acid, 4-guanidinobutanoic acid, and 2-hydroxyglutaric acid, and prominent elevations of GHB and 4,5-dihydroxyhexanoic acid (4,5-DHHA) in SSADHD samples. Remarkably, the intensities of 4,5-DHHA and GHB showed a significant positive correlation in control CSF, but not in patient CSF. In an established zebrafish epilepsy model, 4,5-DHHA showed increased mobility that may reflect limited epileptogenesis. Using untargeted metabolomics, we identified 12 features in CSF with high biomarker potential. These had comparable increased fold changes as GHB and 4,5-DHHA. For 10 of these features, a similar increase was found in plasma, urine and/or mouse brain tissue for SSADHD compared to controls. One of these was identified as the novel biomarker 4,5-dihydroxyheptanoic acid. The intensities of selected features in plasma and urine of SSADHD patients positively correlated with the clinical severity score of epilepsy and psychiatric symptoms of those patients, and also showed a high mutual correlation. Our findings provide new insights into the (neuro)metabolic disturbances in SSADHD and give leads for further research concerning SSADHD pathophysiology.

Comprehensive metabolomics analysis reveals novel biomarkers and pathways in falsely suspected glutaric aciduria Type-1 newborns

BACKGROUND

Glutaric aciduria type-1 (GA-1) is a rare metabolic disorder due to glutaryl coenzyme A dehydrogenase deficiency, causing elevated levels of glutaryl-CoA and its derivatives. GA-1 exhibits symptoms like macrocephaly, developmental delays, and movement disorders. Timely diagnosis through genetic testing and newborn screening is crucial. However, in some cases, transiently elevated level of glutarylcarnitine (C5DC) challenges accurate diagnosis, highlighting the need for alternative diagnostic methods, like mass spectrometry-based untargeted metabolomics, to identify additional biomarkers for distinguishing falsely suspected GA-1 from healthy newborns.

METHODOLOGY

DBS samples from falsely suspected GA-1 newborns (n = 47) and matched control were collected through the NBS program. Untargeted metabolomics using liquid chromatography-high-resolution mass spectrometry (LC-HRMS) was performed to enable biomarker and pathway investigations for significantly altered metabolites.

RESULTS

582 and 546 were up- and down-regulated metabolites in transient GA-1. 155 endogenous metabolites displayed significant variations compared to the control group. Furthermore, our data identified novel altered metabolic biomarkers, such as N-palmitoylcysteine, heptacarboxyporphyrin, 3-hydroxylinoleoylcarnitine, and monoacylglyceride (MG) (0:0/20:1/0:0), along with perturbed metabolic pathways like sphingolipid and thiamine metabolism associated with the transient elevated C5DC levels in DBS samples.

CONCLUSIONS

A distinct metabolic pattern linked to the transient C5DC elevation in newborns was reported to enhance the prediction of the falsely positive cases, which could help avoiding unnecessary medical treatments and minimizing the financial burdens in the health sector.

Fast Label-Free Metabolic Profile Recognition Identifies Phenylketonuria and Subtypes

Phenylketonuria (PKU) is the most common inherited metabolic disease in humans. Clinical screening of newborn heel blood samples for PKU is costly and time-consuming because it requires multiple procedures, like isotope labeling and derivatization, and PKU subtype identification requires an additional urine sample. Delayed diagnosis of PKU, or subtype identification can result in mental disability. Here, plasmonic silver nanoshells are used for laser desorption/ionization mass spectrometry (MS) detection of PKU with label-free assay by recognizing metabolic profile in dried blood spot (DBS) samples. A total of 1100 subjects are recruited and each DBS sample can be processed in seconds. This platform achieves PKU screening with a sensitivity of 0.985 and specificity of 0.995, which is comparable to existing clinical liquid chromatography MS (LC-MS) methods. This method can process 360 samples per hour, compared with the LC-MS method which processes only 30 samples per hour. Moreover, this assay enables precise identification of PKU subtypes without the need for a urine sample. It is demonstrated that this platform enables high-performance and fast, low-cost PKU screening and subtype identification. This approach might be suitable for the detection of other clinically relevant biomarkers in blood or other clinical samples.

A systematic review of metabolomic findings in adult and pediatric renal disease

Chronic kidney disease (CKD) affects over 0.5 billion people worldwide across their lifetimes. Despite a growingly ageing world population, an increase in all-age prevalence of kidney disease persists. Adult-onset forms of kidney disease often result from lifestyle-modifiable metabolic illnesses such as type 2 diabetes. Pediatric and adolescent forms of renal disease are primarily caused by morphological abnormalities of the kidney, as well as immunological, infectious and inherited metabolic disorders. Alterations in energy metabolism are observed in CKD of varying causes, albeit the molecular mechanisms underlying pathology are unclear. A systematic indexing of metabolites identified in plasma and urine of patients with kidney disease alongside disease enrichment analysis uncovered inborn errors of metabolism as a framework that links features of adult and pediatric kidney disease. The relationship of genetics and metabolism in kidney disease could be classified into three distinct landscapes: (i) Normal genotypes that develop renal damage because of lifestyle and / or comorbidities; (ii) Heterozygous genetic variants and polymorphisms that result in unique metabotypes that may predispose to the development of kidney disease via synergistic heterozygosity, and (iii) Homozygous genetic variants that cause renal impairment by perturbing metabolism, as found in children with monogenic inborn errors of metabolism. Interest in the identification of early biomarkers of onset and progression of CKD has grown steadily in the last years, though it has not translated into clinical routine yet. This systematic review indexes findings of differential concentration of metabolites and energy pathway dysregulation in kidney disease and appraises their potential use as biomarkers.

Challenges and opportunities to bridge translational to clinical research for personalized mitochondrial medicine

Mitochondrial disorders are a group of rare and heterogeneous genetic diseases characterized by dysfunctional mitochondria leading to deficient adenosine triphosphate synthesis and chronic energy deficit in patients. The majority of these patients exhibit a wide range of phenotypic manifestations targeting several organ systems, making their clinical diagnosis and management challenging. Bridging translational to clinical research is crucial for improving the early diagnosis and prognosis of these intractable mitochondrial disorders and for discovering novel therapeutic drug candidates and modalities. This review provides the current state of clinical testing in mitochondrial disorders, discusses the challenges and opportunities for converting basic discoveries into clinical settings, explores the most suited patient-centric approaches to harness the extraordinary heterogeneity among patients affected by the same primary mitochondrial disorder, and describes the current outlook of clinical trials.

An evaluation of untargeted metabolomics methods to characterize inborn errors of metabolism

Inborn errors of metabolism (IEMs) encompass a diverse group of disorders that can be difficult to classify due to heterogenous clinical, molecular, and biochemical manifestations. Untargeted metabolomics platforms have become a popular approach to analyze IEM patient samples because of their ability to detect many metabolites at once, accelerating discovery of novel biomarkers, and metabolic mechanisms of disease. However, there are concerns about the reproducibility of untargeted metabolomics research due to the absence of uniform reporting practices, data analyses, and experimental design guidelines. Therefore, we critically evaluated published untargeted metabolomic platforms used to characterize IEMs to summarize the strengths and areas for improvement of this technology as it progresses towards the clinical laboratory. A total of 96 distinct IEMs were collectively evaluated by the included studies. However, most of these IEMs were evaluated by a single untargeted metabolomic method, in a single study, with a limited cohort size (55/96, 57%). The goals of the included studies generally fell into two, often overlapping, categories: detecting known biomarkers from many biochemically distinct IEMs using a single platform, and detecting novel metabolites or metabolic pathways. There was notable diversity in the design of the untargeted metabolomic platforms. Importantly, the majority of studies reported adherence to quality metrics, including the use of quality control samples and internal standards in their experiments, as well as confirmation of at least some of their feature annotations with commercial reference standards. Future applications of untargeted metabolomics platforms to the study of IEMs should move beyond single-subject analyses, and evaluate reproducibility using a prospective, or validation cohort.

Generalized metabolic flux analysis framework provides mechanism-based predictions of ophthalmic complications in type 2 diabetes patients

Chronic metabolic diseases arise from changes in metabolic fluxes through biomolecular pathways and gene networks accumulated over the lifetime of an individual. While clinical and biochemical profiles present just real-time snapshots of the patients' health, efficient computation models of the pathological disturbance of biomolecular processes are required to achieve individualized mechanistic insights into disease progression. Here, we describe the Generalized metabolic flux analysis (GMFA) for addressing this gap. Suitably grouping individual metabolites/fluxes into pools simplifies the analysis of the resulting more coarse-grain network. We also map non-metabolic clinical modalities onto the network with additional edges. Instead of using the time coordinate, the system status (metabolite concentrations and fluxes) is quantified as function of a generalized extent variable (a coordinate in the space of generalized metabolites) that represents the system's coordinate along its evolution path and evaluates the degree of change between any two states on that path. We applied GMFA to analyze Type 2 Diabetes Mellitus (T2DM) patients from two cohorts: EVAS (289 patients from Singapore) and NHANES (517) from the USA. Personalized systems biology models (digital twins) were constructed. We deduced disease dynamics from the individually parameterized metabolic network and predicted the evolution path of the metabolic health state. For each patient, we obtained an individual description of disease dynamics and predict an evolution path of the metabolic health state. Our predictive models achieve an ROC-AUC in the range 0.79-0.95 (sensitivity 80-92%, specificity 62-94%) in identifying phenotypes at the baseline and predicting future development of diabetic retinopathy and cataract progression among T2DM patients within 3 years from the baseline. The GMFA method is a step towards realizing the ultimate goal to develop practical predictive computational models for diagnostics based on systems biology. This tool has potential use in chronic disease management in medical practice.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13755-023-00218-x.

A one-year pilot study comparing direct-infusion high resolution mass spectrometry based untargeted metabolomics to targeted diagnostic screening for inherited metabolic diseases

Background: Early diagnosis of inherited metabolic diseases (IMDs) is important because treatment may lead to reduced mortality and improved prognosis. Due to their diversity, it is a challenge to diagnose IMDs in time, effecting an emerging need for a comprehensive test to acquire an overview of metabolite status. Untargeted metabolomics has proven its clinical potential in diagnosing IMDs, but is not yet widely used in genetic metabolic laboratories. Methods: We assessed the potential role of plasma untargeted metabolomics in a clinical diagnostic setting by using direct infusion high resolution mass spectrometry (DI-HRMS) in parallel with traditional targeted metabolite assays. We compared quantitative data and qualitative performance of targeted versus untargeted metabolomics in patients suspected of an IMD (n = 793 samples) referred to our laboratory for 1 year. To compare results of both approaches, the untargeted data was limited to polar metabolites that were analyzed in targeted plasma assays. These include amino acid, (acyl)carnitine and creatine metabolites and are suitable for diagnosing IMDs across many of the disease groups described in the international classification of inherited metabolic disorders (ICIMD). Results: For the majority of metabolites, the concentrations as measured in targeted assays correlated strongly with the semi quantitative Z-scores determined with DI-HRMS. For 64/793 patients, targeted assays showed an abnormal metabolite profile possibly indicative of an IMD. In 55 of these patients, similar aberrations were found with DI-HRMS. The remaining 9 patients showed only marginally increased or decreased metabolite concentrations that, in retrospect, were most likely to be clinically irrelevant. Illustrating its potential, DI-HRMS detected additional patients with aberrant metabolites that were indicative of an IMD not detected by targeted plasma analysis, such as purine and pyrimidine disorders and a carnitine synthesis disorder. Conclusion: This one-year pilot study showed that DI-HRMS untargeted metabolomics can be used as a first-tier approach replacing targeted assays of amino acid, acylcarnitine and creatine metabolites with ample opportunities to expand. Using DI-HRMS untargeted metabolomics as a first-tier will open up possibilities to look for new biomarkers.

Three cases of xanthinuria identified by gas chromatography/mass spectrometry-based urine metabolomics

Introduction

Early diagnosis of patients with urolithiasis or hypouricemia owing to inborn errors of hypoxanthine metabolism is important in preventing renal failure or drug-induced toxicity.

Case presentation

We identified three patients with xanthinuria using gas chromatography/mass spectrometry-based urine metabolomics: a 72-year-old male with bladder stone, a severe hypouricemic 59-year-old female with type 2 diabetes mellitus, and an 8-year and 9-month-old female who was first discovered to harbor a mutation in the xanthine dehydrogenase gene using whole-exome sequencing, but had a normal molybdenum cofactor sulfurase gene. Hydantoin-5-propionate was detected in the first and third patients but not in the second, suggesting that the first and second patients had type I and II xanthinuria, respectively.

Conclusion

Gas chromatography/mass spectrometry-based metabolomics can be used for undiagnosed patients with xanthinuria, identification of the type of xanthinuria without allopurinol loading, and the quick functional evaluation of mutations in the xanthinuria-related genes.

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.

Cerebral Palsy - Early Diagnosis and Intervention Trial: protocol for the prospective multicentre CP-EDIT study with focus on diagnosis, prognostic factors, and intervention

BACKGROUND

Early diagnosis of cerebral palsy (CP) is important to enable intervention at a time when neuroplasticity is at its highest. Current mean age at diagnosis is 13 months in Denmark. Recent research has documented that an early-diagnosis set-up can lower diagnostic age in high-risk infants. The aim of the current study is to lower diagnostic age of CP regardless of neonatal risk factors. Additionally, we want to investigate if an early intervention program added to standard care is superior to standard care alone.

METHODS

The current multicentre study CP-EDIT (Early Diagnosis and Intervention Trial) with the GO-PLAY intervention included (Goal Oriented ParentaL supported home ActivitY program), aims at testing the feasibility of an early diagnosis set-up and the GO-PLAY early intervention. CP-EDIT is a prospective cohort study, consecutively assessing approximately 500 infants at risk of CP. We will systematically collect data at inclusion (age 3-11 months) and follow a subset of participants (n = 300) with CP or at high risk of CP until the age of two years. The GO-PLAY early intervention will be tested in 80 infants with CP or high risk of CP. Focus is on eight areas related to implementation and perspectives of the families: early cerebral magnetic resonance imaging (MRI), early genetic testing, implementation of the General Movements Assessment method, analysis of the GO-PLAY early intervention, parental perspective of early intervention and early diagnosis, early prediction of CP, and comparative analysis of the Hand Assessment for Infants, Hammersmith Infant Neurological Examination, MRI, and the General Movements method.

DISCUSSION

Early screening for CP is increasingly possible and an interim diagnosis of "high risk of CP" is recommended but not currently used in clinical care in Denmark. Additionally, there is a need to accelerate identification in mild or ambiguous cases to facilitate appropriate therapy early. Most studies on early diagnosis focus on identifying CP in infants below five months corrected age. Little is known about early diagnosis in the 50% of all CP cases that are discernible later in infancy. The current study aims at improving care of patients with CP even before they have an established diagnosis.

TRIAL REGISTRATION

ClinicalTrials.gov ID 22013292 (reg. date 31/MAR/2023) for the CP-EDIT cohort and ID 22041835 (reg. date 31/MAR/2023) for the GO-PLAY trial.

Rapid and efficient LC-MS/MS diagnosis of inherited metabolic disorders: a semi-automated workflow for analysis of organic acids, acylglycines, and acylcarnitines in urine

OBJECTIVES

The analysis of organic acids in urine is an important part of the diagnosis of inherited metabolic disorders (IMDs), for which gas chromatography coupled with mass spectrometry is still predominantly used.

METHODS

Ultra-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for urinary organic acids, acylcarnitines and acylglycines was developed and validated. Sample preparation consists only of dilution and the addition of internal standards. Raw data processing is quick and easy using selective scheduled multiple reaction monitoring mode. A robust standardised value calculation as a data transformation together with advanced automatic visualisation tools are applied for easy evaluation of complex data.

RESULTS

The developed method covers 146 biomarkers consisting of organic acids (n=99), acylglycines (n=15) and acylcarnitines (n=32) including all clinically important isomeric compounds present. Linearity with r2>0.98 for 118 analytes, inter-day accuracy between 80 and 120 % and imprecision under 15 % for 120 analytes were achieved. Over 2 years, more than 800 urine samples from children tested for IMDs were analysed. The workflow was evaluated on 93 patient samples and ERNDIM External Quality Assurance samples involving a total of 34 different IMDs.

CONCLUSIONS

The established LC-MS/MS workflow offers a comprehensive analysis of a wide range of organic acids, acylcarnitines and acylglycines in urine to perform effective, rapid and sensitive semi-automated diagnosis of more than 80 IMDs.

Synthesis of Metabolites and Metabolite-like Compounds Using Biocatalytic Systems

Methodologies for the synthesis and purification of metabolites, which have been developed following their discovery, analysis, and structural identification, have been involved in numerous life science milestones. The renewed focus on the small molecule domain of biological cells has also created an increasing awareness of the rising gap between the metabolites identified and the metabolites which have been prepared as pure compounds. The design and engineering of resource-efficient and straightforward synthetic methodologies for the production of the diverse and numerous metabolites and metabolite-like compounds have attracted much interest. The variety of metabolic pathways in biological cells provides a wonderful blueprint for designing simplified and resource-efficient synthetic routes to desired metabolites. Therefore, biocatalytic systems have become key enabling tools for the synthesis of an increasing number of metabolites, which can then be utilized as standards, enzyme substrates, inhibitors, or other products, or for the discovery of novel biological functions.

Catching the Culprit: How Chorea May Signal an Inborn Error of Metabolism

Background

Movement disorders, particularly chorea, are uncommon in inborn errors of metabolism, but their identification is essential for improved clinical outcomes. In this context, comprehensive descriptions of movement disorders are limited and primarily derived from single cases or small patient series, highlighting the need for increased awareness and additional research in this field.

Methods

A systematic review was conducted using the MEDLINE database and GeneReviews. The search included studies on inborn errors of metabolism associated with chorea, athetosis, or ballismus. The review adhered to PRISMA guidelines.

Results

The systematic review analyzed 76 studies out of 2350 records, encompassing the period from 1964 to 2022. Chorea was observed in 90.1% of the 173 patients, followed by athetosis in 5.7%. Various inborn errors of metabolism showed an association with chorea, with trace elements and metals being the most frequent. Cognitive and developmental abnormalities were common in the cohort. Frequent neurological features included seizures, dysarthria, and optic atrophy, whereas non-neurological features included, among others, facial dysmorphia and failure to thrive. Neuroimaging and biochemical testing played crucial roles in aiding diagnosis, revealing abnormal findings in 34.1% and 47.9% of patients, respectively. However, symptomatic treatment efficacy for movement disorders was limited.

Discussion

This study emphasizes the complexities of chorea in inborn errors of metabolism. A systematic approach with red flags, biochemical testing, and neuroimaging is required for diagnosis. Collaboration between neurologists, geneticists, and metabolic specialists is crucial for improving early detection and individualized treatment. Utilizing genetic testing technologies and potential therapeutic avenues can aid in the improvement of patient outcomes.

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.

Inherited Metabolic Diseases from Past to Present: A Bibliometric Analysis (1968-2023)

Bibliometric studies on inherited metabolic diseases (IMDs) do not exist in the literature. Therefore, our research aims to conduct a bibliometric study to determine the current status, trending topics, and missing points of publications on IMDs. Between 1968 and 2023, we conducted a literature search with the keyword "inherited metabolic disease" in the SCOPUS database. We included research articles in medicine written in English and published in the final section. We created our data pool using VOSviewer, SciMAT, and Rstudio software programs for the bibliometric parameters of the articles that met the inclusion criteria. We performed a bibliometric analysis of the data with the R package "bibliometrix" and BibExcel programs. We included 2702 research articles published on IMDs. The top three countries that have written the most articles in this field are the USA (n = 501), the United Kingdom (n = 182), and China (n = 172). The most preferred keywords by the authors were: newborn screening (n = 54), mutation (n = 43), phenylketonuria (n = 42), children (n = 35), genetics (n = 34), and maple syrup urine disease (n = 32). Trending topics were osteoporosis, computed tomography, bone marrow transplantation in the early years of the study, chronic kidney disease, urea cycle disorders, next-generation sequencing, newborn screening, and familial hypercholesterolemia in the final years of the study. This study provides clinicians with a new perspective, showing that molecular and genetic studies of inherited metabolic diseases will play an essential role in diagnosis and treatment in the future.

A Distinctive Metabolomics Profile and Potential Biomarkers for Very Long Acylcarnitine Dehydrogenase Deficiency (VLCADD) Diagnosis in Newborns

Very long-chain acylcarnitine dehydrogenase deficiency (VLCADD) is a rare inherited metabolic disorder associated with fatty acid β-oxidation and characterized by genetic mutations in the ACADVL gene and accumulations of acylcarnitines. VLCADD, developed in neonates or later adults, can be diagnosed using newborn bloodspot screening (NBS) or genetic sequencing. These techniques have limitations, such as a high false discovery rate and variants of uncertain significance (VUS). As a result, an extra diagnostic tool is needed to deliver improved performance and health outcomes. As VLCADD is linked with metabolic disturbance, we postulated that newborn patients with VLCADD could display a distinct metabolomics pattern compared to healthy newborns and other disorders. Herein, we applied an untargeted metabolomics approach using liquid chromatography-high resolution mass spectrometry (LC-HRMS) to measure the global metabolites in dried blood spot (DBS) cards collected from VLCADD newborns (n = 15) and healthy controls (n = 15). Two hundred and six significantly dysregulated endogenous metabolites were identified in VLCADD, in contrast to healthy newborns. Fifty-eight and one hundred and eight up- and down-regulated endogenous metabolites were involved in several pathways such as tryptophan biosynthesis, aminoacyl-tRNA biosynthesis, amino sugar and nucleotide sugar metabolism, pyrimidine metabolism and pantothenate, and CoA biosynthesis. Furthermore, biomarker analyses identified 3,4-Dihydroxytetradecanoylcarnitine (AUC = 1), PIP (20:1)/PGF1alpha) (AUC = 0.982), and PIP2 (16:0/22:3) (AUC = 0.978) as potential metabolic biomarkers for VLCADD diagnosis. Our findings showed that compared to healthy newborns, VLCAADD newborns exhibit a distinctive metabolic profile, and identified potential biomarkers that can be used for early diagnosis, which improves the identification of the affected patients earlier. This allows for the timely administration of proper treatments, leading to improved health. However, further studies with large independent cohorts of VLCADD patients with different ages and phenotypes need to be studied to validate our potential diagnostic biomarkers and their specificity and accuracy during early life.

Untargeted Metabolomics Identifies Biomarkers for MCADD Neonates in Dried Blood Spots

Medium-chain acyl-CoA dehydrogenase deficiency (MCADD) is the most common inherited mitochondrial metabolic disease of fatty acid β-oxidation, especially in newborns. MCADD is clinically diagnosed using Newborn Bloodspot Screening (NBS) and genetic testing. Still, these methods have limitations, such as false negatives or positives in NBS and the variants of uncertain significance in genetic testing. Thus, complementary diagnostic approaches for MCADD are needed. Recently, untargeted metabolomics has been proposed as a diagnostic approach for inherited metabolic diseases (IMDs) due to its ability to detect a wide range of metabolic alterations. We performed an untargeted metabolic profiling of dried blood spots (DBS) from MCADD newborns (n = 14) and healthy controls (n = 14) to discover potential metabolic biomarkers/pathways associated with MCADD. Extracted metabolites from DBS samples were analyzed using UPLC-QToF-MS for untargeted metabolomics analyses. Multivariate and univariate analyses were used to analyze the metabolomics data, and pathway and biomarker analyses were also performed on the significantly identified endogenous metabolites. The MCADD newborns had 1034 significantly dysregulated metabolites compared to healthy newborns (moderated t-test, no correction, p-value ≤ 0.05, FC 1.5). A total of 23 endogenous metabolites were up-regulated, while 84 endogenous metabolites were down-regulated. Pathway analyses showed phenylalanine, tyrosine, and tryptophan biosynthesis as the most affected pathways. Potential metabolic biomarkers for MCADD were PGP (a21:0/PG/F1alpha) and glutathione, with an area under the curve (AUC) of 0.949 and 0.898, respectively. PGP (a21:0/PG/F1alpha) was the first oxidized lipid in the top 15 biomarker list affected by MCADD. Additionally, glutathione was chosen to indicate oxidative stress events that could happen during fatty acid oxidation defects. Our findings suggest that MCADD newborns may have oxidative stress events as signs of the disease. However, further validations of these biomarkers are needed in future studies to ensure their accuracy and reliability as complementary markers with established MCADD markers for clinical diagnosis.

Untargeted Metabolomic Analysis of Sjögren-Larsson Syndrome Reveals a Distinctive Pattern of Multiple Disrupted Biochemical Pathways

Sjögren-Larsson syndrome (SLS) is a rare inherited neurocutaneous disease characterized by ichthyosis, spastic diplegia or tetraplegia, intellectual disability and a distinctive retinopathy. SLS is caused by bi-allelic mutations in ALDH3A2, which codes for fatty aldehyde dehydrogenase (FALDH) and results in abnormal lipid metabolism. The biochemical abnormalities in SLS are not completely known, and the pathogenic mechanisms leading to symptoms are still unclear. To search for pathways that are perturbed in SLS, we performed untargeted metabolomic screening in 20 SLS subjects along with age- and sex-matched controls. Of 823 identified metabolites in plasma, 121 (14.7%) quantitatively differed in the overall SLS cohort from controls; 77 metabolites were decreased and 44 increased. Pathway analysis pointed to disrupted metabolism of sphingolipids, sterols, bile acids, glycogen, purines and certain amino acids such as tryptophan, aspartate and phenylalanine. Random forest analysis identified a unique metabolomic profile that had a predictive accuracy of 100% for discriminating SLS from controls. These results provide new insight into the abnormal biochemical pathways that likely contribute to disease in SLS and may constitute a biomarker panel for diagnosis and future therapeutic studies.

Simultaneous Quantitation and Discovery (SQUAD) Analysis: Combining the Best of Targeted and Untargeted Mass Spectrometry-Based Metabolomics

Untargeted and targeted approaches are the traditional metabolomics workflows acquired for a wider understanding of the metabolome under focus. Both approaches have their strengths and weaknesses. The untargeted, for example, is maximizing the detection and accurate identification of thousands of metabolites, while the targeted is maximizing the linear dynamic range and quantification sensitivity. These workflows, however, are acquired separately, so researchers compromise either a low-accuracy overview of total molecular changes (i.e., untargeted analysis) or a detailed yet blinkered snapshot of a selected group of metabolites (i.e., targeted analysis) by selecting one of the workflows over the other. In this review, we present a novel single injection simultaneous quantitation and discovery (SQUAD) metabolomics that combines targeted and untargeted workflows. It is used to identify and accurately quantify a targeted set of metabolites. It also allows data retro-mining to look for global metabolic changes that were not part of the original focus. This offers a way to strike the balance between targeted and untargeted approaches in one single experiment and address the two approaches' limitations. This simultaneous acquisition of hypothesis-led and discovery-led datasets allows scientists to gain more knowledge about biological systems in a single experiment.

Extending inherited metabolic disorder diagnostics with biomarker interaction visualizations

BACKGROUND

Inherited Metabolic Disorders (IMDs) are rare diseases where one impaired protein leads to a cascade of changes in the adjacent chemical conversions. IMDs often present with non-specific symptoms, a lack of a clear genotype-phenotype correlation, and de novo mutations, complicating diagnosis. Furthermore, products of one metabolic conversion can be the substrate of another pathway obscuring biomarker identification and causing overlapping biomarkers for different disorders. Visualization of the connections between metabolic biomarkers and the enzymes involved might aid in the diagnostic process. The goal of this study was to provide a proof-of-concept framework for integrating knowledge of metabolic interactions with real-life patient data before scaling up this approach. This framework was tested on two groups of well-studied and related metabolic pathways (the urea cycle and pyrimidine de-novo synthesis). The lessons learned from our approach will help to scale up the framework and support the diagnosis of other less-understood IMDs.

METHODS

Our framework integrates literature and expert knowledge into machine-readable pathway models, including relevant urine biomarkers and their interactions. The clinical data of 16 previously diagnosed patients with various pyrimidine and urea cycle disorders were visualized on the top 3 relevant pathways. Two expert laboratory scientists evaluated the resulting visualizations to derive a diagnosis.

RESULTS

The proof-of-concept platform resulted in varying numbers of relevant biomarkers (five to 48), pathways, and pathway interactions for each patient. The two experts reached the same conclusions for all samples with our proposed framework as with the current metabolic diagnostic pipeline. For nine patient samples, the diagnosis was made without knowledge about clinical symptoms or sex. For the remaining seven cases, four interpretations pointed in the direction of a subset of disorders, while three cases were found to be undiagnosable with the available data. Diagnosing these patients would require additional testing besides biochemical analysis.

CONCLUSION

The presented framework shows how metabolic interaction knowledge can be integrated with clinical data in one visualization, which can be relevant for future analysis of difficult patient cases and untargeted metabolomics data. Several challenges were identified during the development of this framework, which should be resolved before this approach can be scaled up and implemented to support the diagnosis of other (less understood) IMDs. The framework could be extended with other OMICS data (e.g. genomics, transcriptomics), and phenotypic data, as well as linked to other knowledge captured as Linked Open Data.

Challenges in pediatric inherited/metabolic liver disease: Focus on the disease spectrum, diagnosis and management of relatively common disorders

The clinical scenario of pediatric liver disease is becoming more intricate due to changes in the disease spectrum, in which an increasing number of inherited/ metabolic liver diseases are reported, while infectious diseases show a decreasing trend. The similar clinical manifestations caused by inherited/metabolic diseases might be under-recognized or misdiagnosed due to nonspecific characteristics. A delayed visit to a doctor due to a lack of symptoms or mild symptoms at an early stage will result in late diagnosis and treatment. Moreover, limited diagnostic approaches, especially liver biopsy, are not easily accepted by pediatric patients, leading to challenges in etiological diagnosis. Liver dysfunction due to inherited/metabolic diseases is often caused by a variety of metabolites, so precision treatment is difficult; symptomatic treatment is a compelling option for inherited disorders.

Combined targeted and untargeted high-resolution mass spectrometry analyses to investigate metabolic alterations in pompe disease

INTRODUCTION

Pompe disease is a rare, lysosomal disorder, characterized by intra-lysosomal glycogen accumulation due to an impaired function of α-glucosidase enzyme. The laboratory testing for Pompe is usually performed by enzyme activity, genetic test, or urine glucose tetrasaccharide (Glc4) screening by HPLC. Despite being a good preliminary marker, the Glc4 is not specific for Pompe.

OBJECTIVE

The purpose of the present study was to develop a simple methodology using liquid chromatography-high resolution mass spectrometry (LC-HRMS) for targeted quantitative analysis of Glc₄ combined with untargeted metabolic profiling in a single analytical run to search for complementary biomarkers in Pompe disease.

METHODS

We collected 21 urine specimens from 13 Pompe disease patients and compared their metabolic signatures with 21 control specimens.

RESULTS

Multivariate statistical analyses on the untargeted profiling data revealed Glc₄, creatine, sorbitol/mannitol, L-phenylalanine, N-acetyl-4-aminobutanal, N-acetyl-L-aspartic acid, and 2-aminobenzoic acid as significantly altered in Pompe disease. This panel of metabolites increased sample class prediction (Pompe disease versus control) compared with a single biomarker.

CONCLUSION

This study has demonstrated the potential of combined acquisition methods in LC-HRMS for Pompe disease investigation, allowing for routine determination of an established biomarker and discovery of complementary candidate biomarkers that may increase diagnostic accuracy, or improve the risk stratification of patients with disparate clinical phenotypes.

Metabolomic Studies in Inborn Errors of Metabolism: Last Years and Future Perspectives

The inborn errors of metabolism (IEMs or Inherited Metabolic Disorders) are a heterogeneous group of diseases caused by a deficit of some specific metabolic pathways. IEMs may present with multiple overlapping symptoms, sometimes difficult delayed diagnosis and postponed therapies. Additionally, many IEMs are not covered in newborn screening and the diagnostic profiling in the metabolic laboratory is indispensable to reach a correct diagnosis. In recent years, Metabolomics helped to obtain a better understanding of pathogenesis and pathophysiology of IEMs, by validating diagnostic biomarkers, discovering new specific metabolic patterns and new IEMs itself. The expansion of Metabolomics in clinical biochemistry and laboratory medicine has brought these approaches in clinical practice as part of newborn screenings, as an exam for differential diagnosis between IEMs, and evaluation of metabolites in follow up as markers of severity or therapies efficacy. Lastly, several research groups are trying to profile metabolomics data in platforms to have a holistic vision of the metabolic, proteomic and genomic pathways of every single patient. In 2018 this team has made a review of literature to understand the value of Metabolomics in IEMs. Our review offers an update on use and perspectives of metabolomics in IEMs, with an overview of the studies available from 2018 to 2022.

Combinatorial batching of DNA for ultralow-cost detection of pathogenic variants

BACKGROUND

Next-generation sequencing (NGS) based population screening holds great promise for disease prevention and earlier diagnosis, but the costs associated with screening millions of humans remain prohibitive. New methods for population genetic testing that lower the costs of NGS without compromising diagnostic power are needed.

METHODS

We developed double batched sequencing where DNA samples are batch-sequenced twice - directly pinpointing individuals with rare variants. We sequenced batches of at-birth blood spot DNA using a commercial 113-gene panel in an explorative (n = 100) and a validation (n = 100) cohort of children who went on to develop pediatric cancers. All results were benchmarked against individual whole genome sequencing data.

RESULTS

We demonstrated fully replicable detection of cancer-causing germline variants, with positive and negative predictive values of 100% (95% CI, 0.91-1.00 and 95% CI, 0.98-1.00, respectively). Pathogenic and clinically actionable variants were detected in RB1, TP53, BRCA2, APC, and 19 other genes. Analyses of larger batches indicated that our approach is highly scalable, yielding more than 95% cost reduction or less than 3 cents per gene screened for rare disease-causing mutations. We also show that double batched sequencing could cost-effectively prevent childhood cancer deaths through broad genomic testing.

CONCLUSIONS

Our ultracheap genetic diagnostic method, which uses existing sequencing hardware and standard newborn blood spots, should readily open up opportunities for population-wide risk stratification using genetic screening across many fields of clinical genetics and genomics.

Metabolomics meets systems immunology

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

Targeted Small-Molecule Identification Using Heartcutting Liquid Chromatography-Infrared Ion Spectroscopy

Infrared ion spectroscopy (IRIS) can be used to identify molecular structures detected in mass spectrometry (MS) experiments and has potential applications in a wide range of analytical fields. However, MS-based approaches are often combined with orthogonal separation techniques, in many cases liquid chromatography (LC). The direct coupling of LC and IRIS is challenging due to the mismatching timescales of the two technologies: an IRIS experiment typically takes several minutes, whereas an LC fraction typically elutes in several seconds. To resolve this discrepancy, we present a heartcutting LC-IRIS approach using a setup consisting of two switching valves and two sample loops as an alternative to direct online LC-IRIS coupling. We show that this automated setup enables us to record multiple IR spectra for two LC-features from a single injection without degrading the LC-separation performance. We demonstrate the setup for application in drug metabolism research by recording six m/z-selective IR spectra for two drug metabolites from a single 2 μL sample of cell incubation extract. Additionally, we measure the IR spectra of two closely eluting diastereomeric biomarkers for the inborn error of metabolism pyridoxine-dependent epilepsy (PDE-ALDH7A1), which shows that the heartcutting LC-IRIS setup has good sensitivity (requiring ∼μL injections of ∼μM samples) and that the separation between closely eluting isomers is maintained. We envision applications in a range of research fields, where the identification of molecular structures detected by LC-MS is required.

Benchmarking Outlier Detection Methods for Detecting IEM Patients in Untargeted Metabolomics Data

Untargeted metabolomics (UM) is increasingly being deployed as a strategy for screening patients that are suspected of having an inborn error of metabolism (IEM). In this study, we examined the potential of existing outlier detection methods to detect IEM patient profiles. We benchmarked 30 different outlier detection methods when applied to three untargeted metabolomics datasets. Our results show great differences in IEM detection performances across the various methods. The methods DeepSVDD and R-graph performed most consistently across the three metabolomics datasets. For datasets with a more balanced number of samples-to-features ratio, we found that AE reconstruction error, Mahalanobis and PCA reconstruction error also performed well. Furthermore, we demonstrated the importance of a PCA transform prior to applying an outlier detection method since we observed that this increases the performance of several outlier detection methods. For only one of the three metabolomics datasets, we observed clinically satisfying performances for some outlier detection methods, where we were able to detect 90% of the IEM patient samples while detecting no false positives. These results suggest that outlier detection methods have the potential to aid the clinical investigator in routine screening for IEM using untargeted metabolomics data, but also show that further improvements are needed to ensure clinically satisfying performances.

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.

Novel cerebrospinal fluid biomarkers of glucose transporter type 1 deficiency syndrome: Implications beyond the brain's energy deficit

We used next-generation metabolic screening to identify new biomarkers for improved diagnosis and pathophysiological understanding of glucose transporter type 1 deficiency syndrome (GLUT1DS), comparing metabolic cerebrospinal fluid (CSF) profiles from 12 patients to those of 116 controls. This confirmed decreased CSF glucose and lactate levels in patients with GLUT1DS and increased glutamine at group level. We identified three novel biomarkers significantly decreased in patients, namely gluconic + galactonic acid, xylose-α1-3-glucose, and xylose-α1-3-xylose-α1-3-glucose, of which the latter two have not previously been identified in body fluids. CSF concentrations of gluconic + galactonic acid may be reduced as these metabolites could serve as alternative substrates for the pentose phosphate pathway. Xylose-α1-3-glucose and xylose-α1-3-xylose-α1-3-glucose may originate from glycosylated proteins; their decreased levels are hypothetically the consequence of insufficient glucose, one of two substrates for O-glucosylation. Since many proteins are O-glucosylated, this deficiency may affect cellular processes and thus contribute to GLUT1DS pathophysiology. The novel CSF biomarkers have the potential to improve the biochemical diagnosis of GLUT1DS. Our findings imply that brain glucose deficiency in GLUT1DS may cause disruptions at the cellular level that go beyond energy metabolism, underlining the importance of developing treatment strategies that directly target cerebral glucose uptake.

Abnormal concentrations of acetylated amino acids in cerebrospinal fluid in acetyl-CoA transporter deficiency

Acetyl-CoA transporter 1 (AT-1) is a transmembrane protein which regulates influx of acetyl-CoA from the cytosol to the lumen of the endoplasmic reticulum and is therefore important for the posttranslational modification of numerous proteins. Pathological variants in the SLC33A1 gene coding for AT-1 have been linked to a disorder called Huppke-Brendel syndrome, which is characterized by congenital cataracts, hearing loss, severe developmental delay and early death. It has been described in eight patients so far, who all had the abovementioned symptoms together with low serum copper and ceruloplasmin concentrations. The link between AT-1 and low ceruloplasmin concentrations is not clear, nor is the complex pathogenesis of the disease. Here we describe a further case of Huppke-Brendel syndrome with a novel and truncating homozygous gene variant and provide novel biochemical data on N-acetylated amino acids in cerebrospinal fluid (CSF) and plasma. Our results indicate that decreased levels of many N-acetylated amino acids in CSF are a typical metabolic fingerprint for AT-1 deficiency and are potential biomarkers for the defect. As acetyl-CoA is an important substrate for protein acetylation, we performed N-terminal proteomics, but found only minor effects on this particular protein modification. The acetyl-CoA content in patient's fibroblasts was insignificantly decreased. Our data may help to better understand the mechanisms underlying the metabolic disturbances, the pathophysiology and the clinical phenotype of the disease.

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.

Identification of Δ-1-pyrroline-5-carboxylate derived biomarkers for hyperprolinemia type II

Hyperprolinemia type II (HPII) is an inborn error of metabolism due to genetic variants in ALDH4A1, leading to a deficiency in Δ-1-pyrroline-5-carboxylate (P5C) dehydrogenase. This leads to an accumulation of toxic levels of P5C, an intermediate in proline catabolism. The accumulating P5C spontaneously reacts with, and inactivates, pyridoxal 5’-phosphate, a crucial cofactor for many enzymatic processes, which is thought to be the pathophysiological mechanism for HPII. Here, we describe the use of a combination of LC-QTOF untargeted metabolomics, NMR spectroscopy and infrared ion spectroscopy (IRIS) to identify and characterize biomarkers for HPII that result of the spontaneous reaction of P5C with malonic acid and acetoacetic acid. We show that these biomarkers can differentiate between HPI, caused by a deficiency of proline oxidase activity, and HPII. The elucidation of their molecular structures yields insights into the disease pathophysiology of HPII.

Combined metabolomics, NMR, and, IRIS identify biomarkers of hyperprolinemia type II (HPII) distinct from HPI and similar metabolic signatures as in patients with pyridoxine-dependent epilepsy.

A Direct Infusion Probe for Rapid Metabolomics of Low-Volume Samples

Targeted and nontargeted metabolomics has the potential to evaluate and detect global metabolite changes in biological systems. Direct infusion mass spectrometric analysis enables detection of all ionizable small molecules, thus simultaneously providing information on both metabolites and lipids in chemically complex samples. However, to unravel the heterogeneity of the metabolic status of cells in culture and tissue a low number of cells per sample should be analyzed with high sensitivity, which requires low sample volumes. Here, we present the design and characterization of the direct infusion probe, DIP. The DIP is simple to build and position directly in front of a mass spectrometer for rapid metabolomics of chemically complex biological samples using pneumatically assisted electrospray ionization at 1 μL/min flow rate. The resulting data is acquired in a square wave profile with minimal carryover between samples that enhances throughput and enables several minutes of uniform MS signal from 5 μL sample volumes. The DIP was applied to study the intracellular metabolism of insulin secreting INS-1 cells and the results show that exposure to 20 mM glucose for 15 min significantly alters the abundance of several small metabolites, amino acids, and lipids.

Metabolomics and Chemoinformatics in Agricultural Biotechnology Research: Complementary Probes in Unravelling New Metabolites for Crop Improvement

The United Nations (UN) estimate that the global population will reach 10 billion people by 2050. These projections have placed the agroeconomic industry under immense pressure to meet the growing demand for food and maintain global food security. However, factors associated with climate variability and the emergence of virulent plant pathogens and pests pose a considerable threat to meeting these demands. Advanced crop improvement strategies are required to circumvent the deleterious effects of biotic and abiotic stress and improve yields. Metabolomics is an emerging field in the omics pipeline and systems biology concerned with the quantitative and qualitative analysis of metabolites from a biological specimen under specified conditions. In the past few decades, metabolomics techniques have been extensively used to decipher and describe the metabolic networks associated with plant growth and development and the response and adaptation to biotic and abiotic stress. In recent years, metabolomics technologies, particularly plant metabolomics, have expanded to screening metabolic biomarkers for enhanced performance in yield and stress tolerance for metabolomics-assisted breeding. This review explores the recent advances in the application of metabolomics in agricultural biotechnology for biomarker discovery and the identification of new metabolites for crop improvement. We describe the basic plant metabolomics workflow, the essential analytical techniques, and the power of these combined analytical techniques with chemometrics and chemoinformatics tools. Furthermore, there are mentions of integrated omics systems for metabolomics-assisted breeding and of current applications.