Translational Metabolism: A multidisciplinary approach towards precision diagnosis of inborn errors of metabolism in the omics era

Towards personalized genome-scale modeling of inborn errors of metabolism for systems medicine applications

Inborn errors of metabolism (IEMs) are a group of more than 1000 inherited diseases that are individually rare but have a cumulative global prevalence of 50 per 100,000 births. Recently, it has been recognized that like common diseases, patients with rare diseases can greatly vary in the manifestation and severity of symptoms. Here, we review omics-driven approaches that enable an integrated, holistic view of metabolic phenotypes in IEM patients. We focus on applications of Constraint-based Reconstruction and Analysis (COBRA), a widely used mechanistic systems biology approach, to model the effects of inherited diseases. Moreover, we review evidence that the gut microbiome is also altered in rare diseases. Finally, we outline an approach using personalized metabolic models of IEM patients for the prediction of biomarkers and tailored therapeutic or dietary interventions. Such applications could pave the way towards personalized medicine not just for common, but also for rare diseases.

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets

Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.

The Autism Spectrum: Behavioral, Psychiatric and Genetic Associations

Autism spectrum disorder (ASD) consists of a group of heterogeneous genetic neurobehavioral disorders associated with developmental impairments in social communication skills and stereotypic, rigid or repetitive behaviors. We review common behavioral, psychiatric and genetic associations related to ASD. Autism affects about 2% of children with 4:1 male-to-female ratio and a heritability estimate between 70 and 90%. The etiology of ASD involves a complex interplay between inheritance and environmental factors influenced by epigenetics. Over 800 genes and dozens of genetic syndromes are associated with ASD. Novel gene–protein interactions with pathway and molecular function analyses have identified at least three functional pathways including chromatin modeling, Wnt, Notch and other signaling pathways and metabolic disturbances involving neuronal growth and dendritic spine profiles. An estimated 50% of individuals with ASD are diagnosed with chromosome deletions or duplications (e.g., 15q11.2, BP1-BP2, 16p11.2 and 15q13.3), identified syndromes (e.g., Williams, Phelan-McDermid and Shprintzen velocardiofacial) or single gene disorders. Behavioral and psychiatric conditions in autism impacted by genetics influence clinical evaluations, counseling, diagnoses, therapeutic interventions and treatment approaches. Pharmacogenetics testing is now possible to help guide the selection of psychotropic medications to treat challenging behaviors or co-occurring psychiatric conditions commonly seen in ASD. In this review of the autism spectrum disorder, behavioral, psychiatric and genetic observations and associations relevant to the evaluation and treatment of individuals with ASD are discussed.

Metabolic individuality: Limitations, challenges, and potential for clinical utility

In Nature Medicine, Surendran and colleagues recently reported the analysis of human plasma metabolomic data for 913 metabolites in ∼20,000 individuals, identifying 2,599 metabolite-genetic variant associations and >400 metabolite signatures comprised of jointly regulated metabolites. This extensive atlas of variant-metabolite relationships reveals novel genomic mechanisms driving metabolic phenotypes.

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

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

Understanding Inborn Errors of Metabolism through Metabolomics

Inborn errors of metabolism (IEMs) are rare diseases caused by a defect in a single enzyme, co-factor, or transport protein. For most IEMs, no effective treatment is available and the exact disease mechanism is unknown. The application of metabolomics and, more specifically, tracer metabolomics in IEM research can help to elucidate these disease mechanisms and hence direct novel therapeutic interventions. In this review, we will describe the different approaches to metabolomics in IEM research. We will discuss the strengths and weaknesses of the different sample types that can be used (biofluids, tissues or cells from model organisms; modified cell lines; and patient fibroblasts) and when each of them is appropriate to use.

2022 Overview of Metabolic Epilepsies

Understanding the genetic architecture of metabolic epilepsies is of paramount importance, both to current clinical practice and for the identification of further research directions. The main goals of our study were to identify the scope of metabolic epilepsies and to investigate their clinical presentation, diagnostic approaches and treatments. The International Classification of Inherited Metabolic Disorders and IEMbase were used as a basis for the identification and classification of metabolic epilepsies. Six hundred metabolic epilepsies have been identified, accounting for as much as 37% of all currently described inherited metabolic diseases (IMD). Epilepsy is a particularly common symptom in disorders of energy metabolism, congenital disorders of glycosylation, neurotransmitter disorders, disorders of the synaptic vesicle cycle and some other IMDs. Seizures in metabolic epilepsies may present variably, and most of these disorders are complex and multisystem. Abnormalities in routine laboratory tests and/or metabolic testing may be identified in 70% of all metabolic epilepsies, but in many cases they are non-specific. In total, 111 metabolic epilepsies (18% of all) have specific treatments that may significantly change health outcomes if diagnosed in time. Although metabolic epilepsies comprise an important and significant group of disorders, their real scope and frequency may have been underestimated.

An improved functional assay in blood spot to diagnose Barth syndrome using the monolysocardiolipin/cardiolipin ratio

Barth syndrome is an X-linked disorder characterized by cardiomyopathy, skeletal myopathy, and neutropenia, caused by deleterious variants in TAFAZZIN. This gene encodes a phospholipid-lysophospholipid transacylase that is required for the remodeling of the mitochondrial phospholipid cardiolipin (CL). Biochemically, individuals with Barth syndrome have a deficiency of mature CL and accumulation of the remodeling intermediate monolysocardiolipin (MLCL). Diagnosis typically relies on mass spectrometric measurement of CL and MLCL in cells or tissues, and we previously described a method in blood spot that uses a specific MLCL/CL ratio as diagnostic biomarker. Here, we describe the evolution of our blood spot assay that is based on the implementation of reversed phase-UHPLC separation followed by full scan high resolution mass spectrometry. In addition to the MLCL/CL ratio, our improved method also generates a complete CL spectrum allowing the interrogation of the CL fatty acid composition, which considerably enhances the diagnostic reliability. This addition negates the need for a confirmatory test in lymphocytes thereby providing a shorter turn-around-time while achieving a more certain test result. As one of the few laboratories that offer this assay, we also evaluated the diagnostic yield and performance from 2006 to 2021 encompassing the use of both the original and improved assay. In this period, we performed 796 diagnostic analyses of which 117 (15%) were characteristic of Barth syndrome. In total, we diagnosed 93 unique individuals with Barth syndrome, including three females, which together amounts to about 40% of all reported individuals with Barth syndrome in the world.

Multidisciplinary interaction and MCD gene discovery. The perspective of the clinical geneticist

The increasing pace of gene discovery in the last decade has brought a major change in the way the genetic causes of brain malformations are being diagnosed. Unbiased genomic screening has gained the first place in the diagnostic protocol of a child with congenital (brain) anomalies and the detected variants are matched with the phenotypic presentation afterwards. This process is defined as "reverse phenotyping". Screening of DNA, through copy number variant analysis of microarrays and analysis of exome data on different platforms, obtained from the index patient and both parents has become a routine approach in many centers worldwide. Clinicians are used to multidisciplinary team interaction in patient care and disease management and this explains why the majority of research that has led to the discovery of new genetic disorders nowadays proceeds from clinical observations to genomic analysis and to data exchange facilitated by open access sharing databases. However, the relevance of multidisciplinary team interaction has not been object of systematic research in the field of brain malformations. This review will illustrate some examples of how diagnostically driven questions through multidisciplinary interaction, among clinical and preclinical disciplines, can be successful in the discovery of new genes related to brain malformations. The first example illustrates the setting of interaction among neurologists, geneticists and neuro-radiologists. The second illustrates the importance of interaction among clinical dysmorphologists for pattern recognition of syndromes with multiple congenital anomalies. The third example shows how fruitful it can be to step out of the "clinical comfort zone", and interact with basic scientists in applying emerging technologies to solve the diagnostic puzzles.

Treatable inherited metabolic disorders causing intellectual disability: 2021 review and digital app

BACKGROUND

The Treatable ID App was created in 2012 as digital tool to improve early recognition and intervention for treatable inherited metabolic disorders (IMDs) presenting with global developmental delay and intellectual disability (collectively 'treatable IDs'). Our aim is to update the 2012 review on treatable IDs and App to capture the advances made in the identification of new IMDs along with increased pathophysiological insights catalyzing therapeutic development and implementation.

METHODS

Two independent reviewers queried PubMed, OMIM and Orphanet databases to reassess all previously included disorders and therapies and to identify all reports on Treatable IDs published between 2012 and 2021. These were included if listed in the International Classification of IMDs (ICIMD) and presenting with ID as a major feature, and if published evidence for a therapeutic intervention improving ID primary and/or secondary outcomes is available. Data on clinical symptoms, diagnostic testing, treatment strategies, effects on outcomes, and evidence levels were extracted and evaluated by the reviewers and external experts. The generated knowledge was translated into a diagnostic algorithm and updated version of the App with novel features.

RESULTS

Our review identified 116 treatable IDs (139 genes), of which 44 newly identified, belonging to 17 ICIMD categories. The most frequent therapeutic interventions were nutritional, pharmacological and vitamin and trace element supplementation. Evidence level varied from 1 to 3 (trials, cohort studies, case-control studies) for 19% and 4-5 (case-report, expert opinion) for 81% of treatments. Reported effects included improvement of clinical deterioration in 62%, neurological manifestations in 47% and development in 37%.

CONCLUSION

The number of treatable IDs identified by our literature review increased by more than one-third in eight years. Although there has been much attention to gene-based and enzyme replacement therapy, the majority of effective treatments are nutritional, which are relatively affordable, widely available and (often) surprisingly effective. We present a diagnostic algorithm (adjustable to local resources and expertise) and the updated App to facilitate a swift and accurate workup, prioritizing treatable IDs. Our digital tool is freely available as Native and Web App (www.treatable-id.org) with several novel features. Our Treatable ID endeavor contributes to the Treatabolome and International Rare Diseases Research Consortium goals, enabling clinicians to deliver rapid evidence-based interventions to our rare disease patients.

A Great Catch for Investigating Inborn Errors of Metabolism-Insights Obtained from Zebrafish

Inborn errors of metabolism cause abnormal synthesis, recycling, or breakdown of amino acids, neurotransmitters, and other various metabolites. This aberrant homeostasis commonly causes the accumulation of toxic compounds or depletion of vital metabolites, which has detrimental consequences for the patients. Efficient and rapid intervention is often key to survival. Therefore, it requires useful animal models to understand the pathomechanisms and identify promising therapeutic drug targets. Zebrafish are an effective tool to investigate developmental mechanisms and understanding the pathophysiology of disorders. In the past decades, zebrafish have proven their efficiency for studying genetic disorders owing to the high degree of conservation between human and zebrafish genes. Subsequently, several rare inherited metabolic disorders have been successfully investigated in zebrafish revealing underlying mechanisms and identifying novel therapeutic targets, including methylmalonic acidemia, Gaucher’s disease, maple urine disorder, hyperammonemia, TRAPPC11-CDGs, and others. This review summarizes the recent impact zebrafish have made in the field of inborn errors of metabolism.

Clinical Validation of Targeted and Untargeted Metabolomics Testing for Genetic Disorders: A 3 Year Comparative Study

Global untargeted metabolomics (GUM) has entered clinical diagnostics for genetic disorders. We compared the clinical utility of GUM with traditional targeted metabolomics (TM) as a screening tool in patients with established genetic disorders and determined the scope of GUM as a discovery tool in patients with no diagnosis under investigation. We compared TM and GUM data in 226 patients. The first cohort (n = 87) included patients with confirmed inborn errors of metabolism (IEM) and genetic syndromes; the second cohort (n = 139) included patients without diagnosis who were undergoing evaluation for a genetic disorder. In patients with known disorders (n = 87), GUM performed with a sensitivity of 86% (95% CI: 78–91) compared with TM for the detection of 51 diagnostic metabolites. The diagnostic yield of GUM in patients under evaluation with no established diagnosis (n = 139) was 0.7%. GUM successfully detected the majority of diagnostic compounds associated with known IEMs. The diagnostic yield of both targeted and untargeted metabolomics studies is low when assessing patients with non-specific, neurological phenotypes. GUM shows promise as a validation tool for variants of unknown significance in candidate genes in patients with non-specific phenotypes.

Genetics of mitochondrial diseases: Identifying mutations to help diagnosis

Mitochondrial diseases are amongst the most genetically and phenotypically diverse groups of inherited diseases. The vast phenotypic overlap with other disease entities together with the absence of reliable biomarkers act as driving forces for the integration of unbiased methodologies early in the diagnostic algorithm, such as whole exome sequencing (WES) and whole genome sequencing (WGS). Such approaches are used in variant discovery and in combination with high-throughput functional assays such as transcriptomics in simultaneous variant discovery and validation. By capturing all genes, they not only increase the diagnostic rate in heterogenous mitochondrial disease patients, but accelerate novel disease gene discovery, and are valuable in side-stepping the risk of overlooking unexpected or even treatable genetic disease diagnoses.

The diagnosis of inborn errors of metabolism by an integrative "multi-omics" approach: A perspective encompassing genomics, transcriptomics, and proteomics

Given the rapidly decreasing cost and increasing speed and accessibility of massively parallel technologies, the integration of comprehensive genomic, transcriptomic, and proteomic data into a "multi-omics" diagnostic pipeline is within reach. Even though genomic analysis has the capability to reveal all possible perturbations in our genetic code, analysis typically reaches a diagnosis in just 35% of cases, with a diagnostic gap arising due to limitations in prioritization and interpretation of detected variants. Here we review the utility of complementing genetic data with transcriptomic data and give a perspective for the introduction of proteomics into the diagnostic pipeline. Together these methodologies enable comprehensive capture of the functional consequence of variants, unobtainable by the analysis of each methodology in isolation. This facilitates functional annotation and reprioritization of candidate genes and variants-a promising approach to shed light on the underlying molecular cause of a patient's disease, increasing diagnostic rate, and allowing actionability in clinical practice.

Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community

The use of multiple omics techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics) is becoming increasingly popular in all facets of life science. Omics techniques provide a more holistic molecular perspective of studied biological systems compared to traditional approaches. However, due to their inherent data differences, integrating multiple omics platforms remains an ongoing challenge for many researchers. As metabolites represent the downstream products of multiple interactions between genes, transcripts, and proteins, metabolomics, the tools and approaches routinely used in this field could assist with the integration of these complex multi-omics data sets. The question is, how? Here we provide some answers (in terms of methods, software tools and databases) along with a variety of recommendations and a list of continuing challenges as identified during a peer session on multi-omics integration that was held at the recent 'Australian and New Zealand Metabolomics Conference' (ANZMET 2018) in Auckland, New Zealand (Sept. 2018). We envisage that this document will serve as a guide to metabolomics researchers and other members of the community wishing to perform multi-omics studies. We also believe that these ideas may allow the full promise of integrated multi-omics research and, ultimately, of systems biology to be realized.