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Inborn errors of metabolism (IEM) represent a subclass of rare inherited diseases caused by a wide range of defects in metabolic enzymes or their regulation. Of over a thousand characterized IEMs, only about half are understood at the molecular level, and overall the development of treatment and management strategies has proved challenging. An overview of the changing landscape of therapeutic approaches is helpful in assessing strategic patterns in the approach to therapy, but the information is scattered throughout the literature and public data resources.
Inborn errors of metabolism (IEM) are not unlike common diseases. They often present as a spectrum of disease phenotypes that correlates poorly with the severity of the disease-causing mutations. This greatly impacts patient care and reveals fundamental gaps in our knowledge of disease modifying biology. Systems biology approaches that integrate multi-omics data into molecular networks have significantly improved our understanding of complex diseases. Similar approaches to study IEM are rare despite their complex nature. We highlight that existing common disease-derived datasets and networks can be repurposed to generate novel mechanistic insight in IEM and potentially identify candidate modifiers. While understanding disease pathophysiology will advance the IEM field, the ultimate goal should be to understand per individual how their phenotype emerges given their primary mutation on the background of their whole genome, not unlike personalized medicine. We foresee that panomics and network strategies combined with recent experimental innovations will facilitate this.
Next-generation sequencing (NGS) technology has allowed the promotion of genetic diagnosis and are becoming increasingly inexpensive and faster. To evaluate the utility of NGS in the clinical field, a targeted genetic panel approach was designed for the diagnosis of a set of inborn errors of metabolism (IEM). The final aim of the study was to compare the findings for the diagnostic yield of NGS in patients who presented with consistent clinical and biochemical suspicion of IEM with those obtained for patients who did not have specific biomarkers.
Intellectual disability ('developmental delay' at age<5 years) affects 2.5% of population worldwide. Recommendations to investigate genetic causes of intellectual disability are based on frequencies of single conditions and on the yield of diagnostic methods, rather than availability of causal therapy. Inborn errors of metabolism constitute a subgroup of rare genetic conditions for which an increasing number of treatments has become available. To identify all currently treatable inborn errors of metabolism presenting with predominantly intellectual disability, we performed a systematic literature review.
Viruses that are typically benign sometimes invade the brainstem in otherwise healthy children. We report bi-allelic DBR1 mutations in unrelated patients from different ethnicities, each of whom had brainstem infection due to herpes simplex virus 1 (HSV1), influenza virus, or norovirus. DBR1 encodes the only known RNA lariat debranching enzyme. We show that DBR1 expression is ubiquitous, but strongest in the spinal cord and brainstem. We also show that all DBR1 mutant alleles are severely hypomorphic, in terms of expression and function. The fibroblasts of DBR1-mutated patients contain higher RNA lariat levels than control cells, this difference becoming even more marked during HSV1 infection. Finally, we show that the patients' fibroblasts are highly susceptible to HSV1. RNA lariat accumulation and viral susceptibility are rescued by wild-type DBR1. Autosomal recessive, partial DBR1 deficiency underlies viral infection of the brainstem in humans through the disruption of tissue-specific and cell-intrinsic immunity to viruses.
The group of inborn errors of metabolism (IEM) displays a marked heterogeneity and IEM can affect virtually all functions and organs of the human organism; however, IEM share that their associated proteins function in metabolism. Most proteins carry out cellular functions by interacting with other proteins, and thus are organized in biological networks. Therefore, diseases are rarely the consequence of single gene mutations but of the perturbations caused in the related cellular network. Systematic approaches that integrate multi-omics and database information into biological networks have successfully expanded our knowledge of complex disorders but network-based strategies have been rarely applied to study IEM. We analyzed IEM on a proteome scale and found that IEM-associated proteins are organized as a network of linked modules within the human interactome of protein interactions, the IEM interactome. Certain IEM disease groups formed self-contained disease modules, which were highly interlinked. On the other hand, we observed disease modules consisting of proteins from many different disease groups in the IEM interactome. Moreover, we explored the overlap between IEM and non-IEM disease genes and applied network medicine approaches to investigate shared biological pathways, clinical signs and symptoms, and links to drug targets. The provided resources may help to elucidate the molecular mechanisms underlying new IEM, to uncover the significance of disease-associated mutations, to identify new biomarkers, and to develop novel therapeutic strategies.
Inborn errors of metabolism (IEM) constitute a huge group of rare diseases affecting 1 in every 1000 newborns. Next-generation sequencing has transformed the diagnosis of IEM, leading to its proposed use as a second-tier technology for confirming cases detected by clinical/biochemical studies or newborn screening. The diagnosis rate is, however, still not 100%. This paper reports the use of a personalized multi-omics (metabolomic, genomic and transcriptomic) pipeline plus functional genomics to aid in the genetic diagnosis of six unsolved cases, with a clinical and/or biochemical diagnosis of galactosemia, mucopolysaccharidosis type I (MPS I), maple syrup urine disease (MSUD), hyperphenylalaninemia (HPA), citrullinemia, or urea cycle deficiency. Eight novel variants in six genes were identified: six (four of them deep intronic) located in GALE, IDUA, PTS, ASS1 and OTC, all affecting the splicing process, and two located in the promoters of IDUA and PTS, thus affecting these genes' expression. All the new variants were subjected to functional analysis to verify their pathogenic effects. This work underscores how the combination of different omics technologies and functional analysis can solve elusive cases in clinical practice.
We report here the results of treatment in a panel of 65 inborn errors of metabolism, obtained in the 25th year of a longitudinal project, first reported in 1983. The phenotypic impact of these 65 diseases was scored before and after treatment using a consistent set of parameters, which we have retained to measure change in clinical phenotype throughout the project. We observed significant improvements in the response to treatment for the disease set as a whole. The number of conditions for which there is no response to treatment has progressively decreased; from 31 in 1983, to 20 in 1993, to 17 in 2008. Concomitantly, there has been an increase in the number of conditions that fully respond to treatment (from 8 in 1983 and 1993, to 20 in 2008), and in those for which there is a partial response. Reasons for improved treatment responses include new small molecules, new enzyme replacement therapies, more conditions that can be treated by organ and cell transplantation, and new experimental approaches to substrate reduction and chaperone assisted therapy. However, the most important and new development was not found in one or other particular therapeutic modality but in the access to new knowledge surrounding the individual diseases via the Internet and related resources. Our longitudinal analysis of treatment efficacy for this subset of inborn errors of metabolism continues to constitute a robust and representative assessment of our ability to restore more normal homeostasis in the inborn errors of metabolism.
Inborn errors of metabolism (IEMs) have been anecdotally reported in the literature as presenting with features of cerebral palsy (CP) or misdiagnosed as 'atypical CP'. A significant proportion is amenable to treatment either directly targeting the underlying pathophysiology (often with improvement of symptoms) or with the potential to halt disease progression and prevent/minimize further damage.
This paper summarizes key features of the dose-finding strategies used in the development of 11 approved new molecular entities that are first-in-class enzyme replacement therapy (ERT), with a goal to gain insight into the dose exploration approaches to inform efficient dose-finding in future development of biological products for Inborn Errors of Metabolism (IEM). Dose exploration should preferably begin in in vitro studies, followed by testing multiple doses in an appropriate animal disease model, when available, which can provide important information for dose assessment in humans. Performing adequate dose-finding in early phase clinical studies in a well-defined study population, including pediatric subjects, is generally critical to inform dose selection for pivotal trials; alternatively, additional dose exploration can be incorporated as part of a pivotal trial. Two important considerations for successful dose selection include (1) identifying appropriate disease-specific endpoints, including pharmacodynamic (PD) end points and intermediate clinical end points or clinical end points, and (2) designing a study with adequate treatment durations for evaluating these end points. Appropriately selected PD biomarkers is useful for dose selection, and early development of these biomarkers can facilitate the overall clinical development program. Optimization of ERT doses, as well as evaluations of patient intrinsic factors and/or immune tolerance strategies may be necessary to overcome antibody responses or increase efficacy in IEM.
Massively parallel DNA sequencing (MPS) has the potential to revolutionize diagnostics, in particular for monogenic disorders. Inborn errors of metabolism (IEM) constitute a large group of monogenic disorders with highly variable clinical presentation, often with acute, nonspecific initial symptoms. In many cases irreversible damage can be reduced by initiation of specific treatment, provided that a correct molecular diagnosis can be rapidly obtained. MPS thus has the potential to significantly improve both diagnostics and outcome for affected patients in this highly specialized area of medicine.
Significant improvements in automated image analysis have been achieved in recent years and tools are now increasingly being used in computer-assisted syndromology. However, the ability to recognize a syndromic facial gestalt might depend on the syndrome and may also be confounded by severity of phenotype, size of available training sets, ethnicity, age, and sex. Therefore, benchmarking and comparing the performance of deep-learned classification processes is inherently difficult. For a systematic analysis of these influencing factors we chose the lysosomal storage diseases mucolipidosis as well as mucopolysaccharidosis type I and II that are known for their wide and overlapping phenotypic spectra. For a dysmorphic comparison we used Smith-Lemli-Opitz syndrome as another inborn error of metabolism and Nicolaides-Baraitser syndrome as another disorder that is also characterized by coarse facies. A classifier that was trained on these five cohorts, comprising 289 patients in total, achieved a mean accuracy of 62%. We also developed a simulation framework to analyze the effect of potential confounders, such as cohort size, age, sex, or ethnic background on the distinguishability of phenotypes. We found that the true positive rate increases for all analyzed disorders for growing cohorts (n = [10...40]) while ethnicity and sex have no significant influence. The dynamics of the accuracies strongly suggest that the maximum distinguishability is a phenotype-specific value, which has not been reached yet for any of the studied disorders. This should also be a motivation to further intensify data sharing efforts, as computer-assisted syndrome classification can still be improved by enlarging the available training sets.
Expanded newborn screening using tandem mass spectrometry (MS/MS) for inborn errors of metabolism (IEM), such as organic acidemias (OAs), fatty acid oxidation disorders (FAODs), and amino acid disorders (AAs), is increasingly popular but has not yet been introduced in Africa. With this study, we aim to establish the disease spectrum and frequency of inborn errors of OAs, FAODs, and AAs in Morocco.
Inborn errors of metabolism are an individually rare but collectively significant cause of mortality and morbidity in the neonatal period. They are identified by either newborn screening programmes or clinician-initiated targeted biochemical screening. This study examines the relative contribution of these two methods to the identification of inborn errors of metabolism and describes the incidence of these conditions in a large, tertiary, neonatal unit. We also examined which factors could impact the reliability of metabolic testing in this cohort. This is a retrospective, single-site study examining infants in whom a targeted metabolic investigation was performed from January 2018 to December 2020 inclusive. Data was also provided by the national newborn screening laboratory regarding newborn screening diagnoses. Two hundred and four newborns received a clinician-initiated metabolic screen during the time period examined with 5 newborns being diagnosed with an inborn error of metabolism (IEM) (2.4%). Of the 25,240 infants born in the hospital during the period examined, a further 11 newborns had an inborn error of metabolism diagnosed on newborn screening. This produced an incidence in our unit over the time described of 6.34 per 10,000 births. This number reflects a minimum estimate, given that the conditions diagnosed refer to early-onset disorders and distinctive categories of IEM only. Efficiency of the clinician-initiated metabolic screening process was also examined. The only statistically significant variable in requiring repeat metabolic screening was early day of life (z-score = - 2.58, p = 0.0098). A total of 28.4% was missing one of three key metabolic investigation parameters of blood glucose, ammonia or lactate concentration with ammonia the most common investigation missing. While hypoglycemia was the most common clinical rationale for a clinician-initiated metabolic test, it was a poor predictor of inborn error of metabolism with no newborns of 25 screened were diagnosed with a metabolic disorder.
PurposeRecognizing individuals with inherited diseases can be difficult because signs and symptoms often overlap those of common medical conditions. Focusing on inborn errors of metabolism (IEMs), we present a method that brings the knowledge of highly specialized experts to professionals involved in early diagnoses. We introduce IEMbase, an online expert-curated IEM knowledge base combined with a prototype diagnosis support (mini-expert) system.MethodsDisease-characterizing profiles of specific biochemical markers and clinical symptoms were extracted from an expert-compiled IEM database. A mini-expert system algorithm was developed using cosine similarity and semantic similarity. The system was evaluated using 190 retrospective cases with established diagnoses, collected from 15 different metabolic centers.ResultsIEMbase provides 530 well-defined IEM profiles and matches a user-provided phenotypic profile to a list of candidate diagnoses/genes. The mini-expert system matched 62% of the retrospective cases to the exact diagnosis and 86% of the cases to a correct diagnosis within the top five candidates. The use of biochemical features in IEM annotations resulted in 41% more exact phenotype matches than clinical features alone.ConclusionIEMbase offers a central IEM knowledge repository for many genetic diagnostic centers and clinical communities seeking support in the diagnosis of IEMs.
Inborn errors of metabolism (IEMs) comprise a diverse group of individually rare monogenic disorders that affect metabolic pathways. Mutations lead to enzymatic deficiency or dysfunction, which results in intermediate metabolite accumulation or deficit leading to disease phenotypes. Currently, treatment options for many IEMs are insufficient. Rarity of individual IEMs hampers therapy development and phenotypic and genetic heterogeneity suggest beneficial effects of personalized approaches. Recently, cultures of patient-own liver-derived intrahepatic cholangiocyte organoids (ICOs) have been established. Since most metabolic genes are expressed in the liver, patient-derived ICOs represent exciting possibilities for in vitro modeling and personalized drug testing for IEMs. However, the exact application range of ICOs remains unclear. To address this, we examined which metabolic pathways can be studied with ICOs and what the potential and limitations of patient-derived ICOs are to model metabolic functions. We present functional assays in patient ICOs with defects in branched-chain amino acid metabolism (methylmalonic acidemia), copper metabolism (Wilson disease), and transporter defects (cystic fibrosis). We discuss the broad range of functional assays that can be applied to ICOs, but also address the limitations of these patient-specific cell models. In doing so, we aim to guide the selection of the appropriate cell model for studies of a specific disease or metabolic process.
Patients with inborn errors of amino acid metabolism frequently show neuropsychiatric symptoms despite accurate metabolic control. This study aimed to gain insight into the underlying mechanisms of neural dysfunction. Here we analyzed the expression of brain-derived neurotrophic factor (BDNF) and 10 genes required for correct brain functioning in plasma and blood of patients with Urea Cycle Disorders (UCD), Maple Syrup Urine Disease (MSUD) and controls. Receiver-operating characteristic (ROC) analysis was used to evaluate sensitivity and specificity of potential biomarkers. CACNA2D2 (α2δ2 subunit of voltage-gated calcium channels) and MECP2 (methyl-CpG binding protein 2) mRNA and protein showed an excellent neural function biomarker signature (AUC ≥ 0,925) for recognition of MSUD. THBS3 (thrombospondin 3) mRNA and AABA gave a very good biomarker signature (AUC 0,911) for executive-attention deficits. THBS3, LIN28A mRNA, and alanine showed a perfect biomarker signature (AUC 1) for behavioral and mood disorders. Finally, a panel of BDNF protein and at least two large neural AAs showed a perfect biomarker signature (AUC 1) for recognition of psychomotor delay, pointing to excessive protein restriction as central causative of psychomotor delay. To conclude, our study has identified promising biomarker panels for neural function evaluation, providing a base for future studies with larger samples.
The incidence of inborn errors of metabolisms (IEMs) varies dramatically in different countries and regions. Expanded newborn screening for IEMs by tandem mass spectrometry (MS/MS) is an efficient approach for early diagnosis and presymptomatic treatment to prevent severe permanent sequelae and death. To determine the characteristics of IEMs and IEMs-associated mutations in newborns in Jining area, China, 48,297 healthy neonates were recruited for expanded newborn screening by MS/MS. The incidence of IEMs was 1/1178 in Jining, while methylmalonic acidemia, phenylketonuria, and primary carnitine deficiency ranked the top 3 of all detected IEMs. Thirty mutations in nine IEMs-associated genes were identified in 28 confirmed cases. As 19 cases with the mutations in phenylalanine hydroxylase (PAH), solute carrier family 22 member 5 (SLC22A5), and methylmalonic aciduria (cobalamin deficiency) cblC type with homocystinuria (MMACHC) genes, respectively, it suggested that mutations in the PAH, SLC22A5, and MMACHC genes are the predominant causes of IEMs, leading to the high incidence of phenylketonuria, primary carnitine deficiency, and methylmalonic acidemia, respectively. Our work indicated that the overall incidence of IEMs is high and the mutations in PAH, SLC22A5, and MMACHC genes are the leading causes of IEMs in Jining area. Therefore, it is critical to increase the coverage of expanded newborn screening by MS/MS and prenatal genetic consulting in Jining area.
Background: Progressive intellectual and neurological deterioration (PIND) is a rare but severe childhood disorder characterized by loss of intellectual or developmental abilities, and requires quick diagnosis to ensure timely treatment to prevent possible irreversible neurological damage. Inborn errors of metabolism (IEMs) constitute a group of more than 1,000 monogenic conditions in which the impairment of a biochemical pathway is intrinsic to the pathophysiology of the disease, resulting in either accumulation of toxic metabolites and/or shortage of energy and building blocks for the cells. Many IEMs are amenable to treatment with the potential to improve outcomes. With this literature review we aim to create an overview of IEMs presenting with PIND in children, to aid clinicians in accelerating the diagnostic process. Methods: We performed a PubMed search on IEMs presenting with PIND in individuals aged 0-18 years. We applied stringent selection criteria and subsequently derived information on encoding genes, pathways, clinical and biochemical signs and diagnostic tests from IEMbase (www.iembase.org) and other sources. Results: The PubMed search resulted in a total of 2,152 articles and a review of references added another 19 articles. After applying our selection criteria, a total of 85 IEMs presenting with PIND remained, of which 57 IEMs were reported in multiple unrelated cases and 28 in single families. For 44 IEMs (52%) diagnosis can be achieved through generally accessible metabolic blood and urine screening tests; the remainder requires enzymatic and/or genetic testing. Treatment targeting the underlying pathophysiology is available for 35 IEMs (41%). All treatment strategies are reported to achieve stabilization of deterioration, and a subset improved seizure control and/or neurodevelopment. Conclusions: We present the first comprehensive overview of IEMs presenting with PIND, and provide a structured approach to diagnosis and overview of treatability. Clearly IEMs constitute the largest group of genetic PIND conditions and have the advantage of detectable biomarkers as well as amenability to treatment. Thus, the clinician should keep IEMs at the forefront of the diagnostic workup of a child with PIND. With the ongoing discovery of new IEMs, expanded phenotypes, and novel treatment strategies, continuous updates to this work will be required.
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