A Next Generation Multiscale View of Inborn Errors of Metabolism

PCYT2 deficiency in Saarlooswolfdogs with progressive retinal, central, and peripheral neurodegeneration

We investigated a syndromic disease comprising blindness and neurodegeneration in 11 Saarlooswolfdogs. Clinical signs involved early adult onset retinal degeneration and adult-onset neurological deficits including gait abnormalities, hind limb weakness, tremors, ataxia, cognitive decline and behavioral changes such as aggression towards the owner. Histopathology in one affected dog demonstrated cataract, retinal degeneration, central and peripheral axonal degeneration, and severe astroglial hypertrophy and hyperplasia in the central nervous system. Pedigrees indicated autosomal recessive inheritance. We mapped the suspected genetic defect to a 15 Mb critical interval by combined linkage and autozygosity analysis. Whole genome sequencing revealed a private homozygous missense variant, PCYT2:c.4A>G, predicted to change the second amino acid of the encoded ethanolamine-phosphate cytidylyltransferase 2, XP_038402224.1:(p.Ile2Val). Genotyping of additional Saarlooswolfdogs confirmed the homozygous genotype in all eleven affected dogs and demonstrated an allele frequency of 9.9% in the population. This experiment also identified three additional homozygous mutant young dogs without overt clinical signs. Subsequent examination of one of these dogs revealed early-stage progressive retinal atrophy (PRA) and expansion of subarachnoid CSF spaces in MRI. Dogs homozygous for the pathogenic variant showed ether lipid accumulation, confirming a functional PCYT2 deficiency. The clinical and metabolic phenotype in affected dogs shows some parallels with human patients, in whom PCYT2 variants lead to a rare form of spastic paraplegia or axonal motor and sensory polyneuropathy. Our results demonstrate that PCYT2:c.4A>G in dogs cause PCYT2 deficiency. This canine model with histopathologically documented retinal, central, and peripheral neurodegeneration further deepens the knowledge of PCYT2 deficiency.

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.

Flux Balance Analysis of Mammalian Cell Systems

Flux balance analysis (FBA) is a computational methodology to model and analyze the metabolic behavior of cells. In this chapter, we break down the key steps for formulating an FBA model and other FBA-derived methodologies in the context of mammalian cell biology, including strain design, developing cell line-specific models, and conducting flux sampling. We provide annotated COBRApy code for each step to show how it would work in practice.

Systems Biology and Inborn Error of Metabolism: Analytical Strategy in Investigating Different Biochemical/Genetic Parameters

Inborn errors of metabolism (IEM) are a group of about 500 rare genetic diseases with large diversity and complexity due to number of metabolic pathways involved in. Establishing a correct diagnosis and identifying the specific clinical phenotype is consequently a difficult task. However, an inclusive diagnosis able in capturing the different clinical phenotypes is mandatory for successful treatment. However, in contrast with Garrod's basic assumption "one-gene one-disease," no "simple" correlation between genotype-phenotype can be vindicated in IEMs. An illustrative example of IEM is Phenylketonuria (PKU), an autosomal recessive inborn error of L-phenylalanine (Phe) metabolism, ascribed to variants of the phenylalanine hydroxylase (PAH) gene encoding for the enzyme complex phenylalanine-hydroxylase. Blood values of Phe allow classifying PKU into different clinical phenotypes, albeit the participation of other genetic/biochemical pathways in the pathogenetic mechanisms remains elusive. Indeed, it has been shown that the most serious complications, such as cognitive impairment, are not only related to the gene dysfunction but also to the patient's background and the participation of several nongenetic factors.Therefore, a Systems Biology-based strategy is required in addressing IEM complexity, and in identifying the interplay between different pathways in shaping the clinical phenotype. Such an approach should entail the concerted investigation of genomic, transcriptomics, proteomics, metabolomics profiles altogether with phenylalanine and amino acids metabolism. Noticeably, this "omic" perspective could be instrumental in planning personalized treatment, tailored accordingly to the disease profile and prognosis.

Personalised modelling of clinical heterogeneity between medium-chain acyl-CoA dehydrogenase patients

BACKGROUND

Monogenetic inborn errors of metabolism cause a wide phenotypic heterogeneity that may even differ between family members carrying the same genetic variant. Computational modelling of metabolic networks may identify putative sources of this inter-patient heterogeneity. Here, we mainly focus on medium-chain acyl-CoA dehydrogenase deficiency (MCADD), the most common inborn error of the mitochondrial fatty acid oxidation (mFAO). It is an enigma why some MCADD patients-if untreated-are at risk to develop severe metabolic decompensations, whereas others remain asymptomatic throughout life. We hypothesised that an ability to maintain an increased free mitochondrial CoA (CoASH) and pathway flux might distinguish asymptomatic from symptomatic patients.

RESULTS

We built and experimentally validated, for the first time, a kinetic model of the human liver mFAO. Metabolites were partitioned according to their water solubility between the bulk aqueous matrix and the inner membrane. Enzymes are also either membrane-bound or in the matrix. This metabolite partitioning is a novel model attribute and improved predictions. MCADD substantially reduced pathway flux and CoASH, the latter due to the sequestration of CoA as medium-chain acyl-CoA esters. Analysis of urine from MCADD patients obtained during a metabolic decompensation showed an accumulation of medium- and short-chain acylcarnitines, just like the acyl-CoA pool in the MCADD model. The model suggested some rescues that increased flux and CoASH, notably increasing short-chain acyl-CoA dehydrogenase (SCAD) levels. Proteome analysis of MCADD patient-derived fibroblasts indeed revealed elevated levels of SCAD in a patient with a clinically asymptomatic state. This is a rescue for MCADD that has not been explored before. Personalised models based on these proteomics data confirmed an increased pathway flux and CoASH in the model of an asymptomatic patient compared to those of symptomatic MCADD patients.

CONCLUSIONS

We present a detailed, validated kinetic model of mFAO in human liver, with solubility-dependent metabolite partitioning. Personalised modelling of individual patients provides a novel explanation for phenotypic heterogeneity among MCADD patients. Further development of personalised metabolic models is a promising direction to improve individualised risk assessment, management and monitoring for inborn errors of metabolism.

Metabolic systems approaches update molecular insights of clinical phenotypes and cardiovascular risk in patients with homozygous familial hypercholesterolemia

BACKGROUND

Homozygous familial hypercholesterolemia (HoFH) is an orphan metabolic disease characterized by extremely elevated low-density lipoprotein cholesterol (LDL-C), xanthomas, aortic stenosis, and premature atherosclerotic cardiovascular disease (ASCVD). In addition to LDL-C, studies in experimental models and small clinical populations have suggested that other types of metabolic molecules might also be risk factors responsible for cardiovascular complications in HoFH, but definitive evidence from large-scale human studies is still lacking. Herein, we aimed to comprehensively characterize the metabolic features and risk factors of human HoFH by using metabolic systems strategies.

METHODS

Two independent multi-center cohorts with a total of 868 individuals were included in the cross-sectional study. First, comprehensive serum metabolome/lipidome-wide analyses were employed to identify the metabolomic patterns for differentiating HoFH patients (n = 184) from heterozygous FH (HeFH, n = 376) and non-FH (n = 100) subjects in the discovery cohort. Then, the metabolomic patterns were verified in the validation cohort with 48 HoFH patients, 110 HeFH patients, and 50 non-FH individuals. Subsequently, correlation/regression analyses were performed to investigate the associations of clinical/metabolic alterations with typical phenotypes of HoFH. In the prospective study, a total of 84 HoFH patients with available follow-up were enrolled from the discovery cohort. Targeted metabolomics, deep proteomics, and random forest approaches were performed to investigate the ASCVD-associated biomarkers in HoFH patients.

RESULTS

Beyond LDL-C, various bioactive metabolites in multiple pathways were discovered and validated for differentiating HoFH from HoFH and non-FH. Our results demonstrated that the inflammation and oxidative stress-related metabolites in the pathways of arachidonic acid and lipoprotein(a) metabolism were independently associated with the prevalence of corneal arcus, xanthomas, and supravalvular/valvular aortic stenosis in HoFH patients. Our results also identified a small marker panel consisting of high-density lipoprotein cholesterol, lipoprotein(a), apolipoprotein A1, and eight proinflammatory and proatherogenic metabolites in the pathways of arachidonic acid, phospholipid, carnitine, and sphingolipid metabolism that exhibited significant performances on predicting first ASCVD events in HoFH patients.

CONCLUSIONS

Our findings demonstrate that human HoFH is associated with a variety of metabolic abnormalities and is more complex than previously known. Furthermore, this study provides additional metabolic alterations that hold promise as residual risk factors in HoFH population.

Metabolomic Approach to Screening Homozygotes in Chinese Patients with Severe Familial Hypercholesterolemia

Homozygous familial hypercholesterolemia (HoFH) is a rare inborn-errors-of-metabolism disorder characterized by devastatingly elevated low-density lipoprotein cholesterol (LDL-C) and premature cardiovascular disease. The gold standard for screening and diagnosing HoFH is genetic testing. In China, it is expensive and is always recommended for the most likely HoFH subjects with aggressive LDL-C phenotype. However, the LDL-C levels of HoFH patients and a substantial proportion of heterozygous FH (HeFH) patients overlapped considerably. Here, we performed a cost-effective metabolomic profiling on genetically diagnosed HoFH (n = 69) and HeFH patients (n = 101) with overlapping LDL-C levels, aiming to discovery a unique metabolic pattern for screening homozygotes in patients with severe FH. We demonstrated a differential serum metabolome profile in HoFH patients compared to HeFH patients. Twenty-one metabolomic alterations showed independent capability in differentiating HoFH from severe HeFH. The combined model based on seven identified metabolites yielded a corrected diagnosis in 91.3% of HoFH cases with an area under the curve value of 0.939. Collectively, this study demonstrated that metabolomic profiling serves as a useful and economical approach to preselecting homozygotes in FH patients with severe hypercholesterolemia and may help clinicians to conduct selective genetic confirmation testing and familial cascade screening.

Integrated multi-omics reveals anaplerotic rewiring in methylmalonyl-CoA mutase deficiency

Methylmalonic aciduria (MMA) is an inborn error of metabolism with multiple monogenic causes and a poorly understood pathogenesis, leading to the absence of effective causal treatments. Here we employ multi-layered omics profiling combined with biochemical and clinical features of individuals with MMA to reveal a molecular diagnosis for 177 out of 210 (84%) cases, the majority (148) of whom display pathogenic variants in methylmalonyl-CoA mutase (MMUT). Stratification of these data layers by disease severity shows dysregulation of the tricarboxylic acid cycle and its replenishment (anaplerosis) by glutamine. The relevance of these disturbances is evidenced by multi-organ metabolomics of a hemizygous Mmut mouse model as well as through identification of physical interactions between MMUT and glutamine anaplerotic enzymes. Using stable-isotope tracing, we find that treatment with dimethyl-oxoglutarate restores deficient tricarboxylic acid cycling. Our work highlights glutamine anaplerosis as a potential therapeutic intervention point in MMA.

[The promise of omics in the precision medicine era]

The rise of omics technologies that simultaneously measure thousands of molecules in a complex biological sample represents the core of systems biology. These technologies have profoundly impacted biomarkers and therapeutic targets discovery in the precision medicine era. Systems biology aims to perform a systematic probing of complex interactions in biological systems. Powered by high-throughput omics technologies and high-performance computing, systems biology provides relevant, resolving, and multi-scale overviews from cells to populations. Precision medicine takes advantage of these conceptual and technological developments and is based on two main pillars: the generation of multimodal data and their subsequent modeling. High-throughput omics technologies enable the comprehensive and holistic extraction of biological information, while computational capabilities enable multidimensional modeling and, as a result, offer an intuitive and intelligible visualization. Despite their promise, translating these technologies into clinically actionable tools has been slow. In this contribution, we present the most recent multi-omics data generation and analysis strategies and their clinical deployment in the post-genomic era. Furthermore, medical application challenges of omics-based biomarkers are discussed.

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

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

OMICS Approaches Evaluating Keloid and Hypertrophic Scars

Abnormal scar formation during wound healing can result in keloid and hypertrophic scars, which is a major global health challenge. Such abnormal scars can cause significant physiological pain and psychological distress and become a financial burden. Due to the biological complexity of scar formation, the pathogenesis of such scars and how to prevent them from forming remains elusive. In this review paper, we delve into the world of "omics" approaches to study abnormal scars and provide examples of genomics, transcriptomics, proteomics, epigenomics, and metabolomics. The benefits of "omics" approaches are that they allow for high-throughput studies and the analysis of 100s to 1000s of genes and proteins with the accumulation of large quantities of data. Currently in the field, there is a lack of "omics" review articles describing pathological scars. In this review, we summarize genome-wide linkage analysis, genome-wide association studies, and microarray data to name a few omics technologies. Such data can provide novel insights into different molecular pathways and identify novel factors which may not be captured through small-scale laboratory techniques.

Disruption of morphogenic and growth pathways in lysosomal storage diseases

The lysosome achieved a new protagonism that highlights its multiple cellular functions, such as in the catabolism of complex substrates, nutrient sensing, and signaling pathways implicated in cell metabolism and growth. Lysosomal storage diseases (LSDs) cause lysosomal accumulation of substrates and deficiency in trafficking of macromolecules. The substrate accumulation can impact one or several pathways which contribute to cell damage. Autophagy impairment and immune response are widely studied, but less attention is paid to morphogenic and growth pathways and its impact on the pathophysiology of LSDs. Hedgehog pathway is affected with abnormal expression and changes in distribution of protein levels, and a reduced number and length of primary cilia. Moreover, growth pathways are identified with delay in reactivation of mTOR that deregulate termination of autophagy and reformation of lysosomes. Insulin resistance caused by changes in lipids rafts has been described in different LSDs. While the genetic and biochemical bases of deficient proteins in LSDs are well understood, the secondary molecular mechanisms that disrupt wider biological processes associated with LSDs are only now becoming clearer. Therefore, we explored how specific signaling pathways can be related to specific LSDs, showing that a system medicine approach could be a valuable tool for the better understanding of LSD pathogenesis. This article is categorized under: Metabolic Diseases > Molecular and Cellular Physiology.

Explaining the Black-White Disparity in Preterm Birth: A Consensus Statement From a Multi-Disciplinary Scientific Work Group Convened by the March of Dimes

In 2017–2019, the March of Dimes convened a workgroup with biomedical, clinical, and epidemiologic expertise to review knowledge of the causes of the persistent Black-White disparity in preterm birth (PTB). Multiple databases were searched to identify hypothesized causes examined in peer-reviewed literature, 33 hypothesized causes were reviewed for whether they plausibly affect PTB and either occur more/less frequently and/or have a larger/smaller effect size among Black women vs. White women. While definitive proof is lacking for most potential causes, most are biologically plausible. No single downstream or midstream factor explains the disparity or its social patterning, however, many likely play limited roles, e.g., while genetic factors likely contribute to PTB, they explain at most a small fraction of the disparity. Research links most hypothesized midstream causes, including socioeconomic factors and stress, with the disparity through their influence on the hypothesized downstream factors. Socioeconomic factors alone cannot explain the disparity's social patterning. Chronic stress could affect PTB through neuroendocrine and immune mechanisms leading to inflammation and immune dysfunction, stress could alter a woman's microbiota, immune response to infection, chronic disease risks, and behaviors, and trigger epigenetic changes influencing PTB risk. As an upstream factor, racism in multiple forms has repeatedly been linked with the plausible midstream/downstream factors, including socioeconomic disadvantage, stress, and toxic exposures. Racism is the only factor identified that directly or indirectly could explain the racial disparities in the plausible midstream/downstream causes and the observed social patterning. Historical and contemporary systemic racism can explain the racial disparities in socioeconomic opportunities that differentially expose African Americans to lifelong financial stress and associated health-harming conditions. Segregation places Black women in stressful surroundings and exposes them to environmental hazards. Race-based discriminatory treatment is a pervasive stressor for Black women of all socioeconomic levels, considering both incidents and the constant vigilance needed to prepare oneself for potential incidents. Racism is a highly plausible, major upstream contributor to the Black-White disparity in PTB through multiple pathways and biological mechanisms. While much is unknown, existing knowledge and core values (equity, justice) support addressing racism in efforts to eliminate the racial disparity in PTB.

Utility of Gene Panels for the Diagnosis of Inborn Errors of Metabolism in a Metabolic Reference Center

Next-generation sequencing (NGS) technologies have been proposed as a first-line test for the diagnosis of inborn errors of metabolism (IEM), a group of genetically heterogeneous disorders with overlapping or nonspecific phenotypes. Over a 3-year period, we prospectively analyzed 311 pediatric patients with a suspected IEM using four targeted gene panels. The rate of positive diagnosis was 61.86% for intermediary metabolism defects, 32.84% for complex molecular defects, 19% for hypoglycemic/hyperglycemic events, and 17% for mitochondrial diseases, and a conclusive molecular diagnosis was established in 2-4 weeks. Forty-one patients for whom negative results were obtained with the mitochondrial diseases panel underwent subsequent analyses using the NeuroSeq panel, which groups all genes from the individual panels together with genes associated with neurological disorders (1870 genes in total). This achieved a diagnostic rate of 32%. We next evaluated the utility of a tool, Phenomizer, for differential diagnosis, and established a correlation between phenotype and molecular findings in 39.3% of patients. Finally, we evaluated the mutational architecture of the genes analyzed by determining z-scores, loss-of-function observed/expected upper bound fraction (LOEUF), and haploinsufficiency (HI) scores. In summary, targeted gene panels for specific groups of IEMs enabled rapid and effective diagnosis, which is critical for the therapeutic management of IEM patients.

Next-Generation Molecular Investigations in Lysosomal Diseases: Clinical Integration of a Comprehensive Targeted Panel

Diagnosis of lysosomal disorders (LDs) may be hampered by their clinical heterogeneity, phenotypic overlap, and variable age at onset. Conventional biological diagnostic procedures are based on a series of sequential investigations and require multiple sampling. Early diagnosis may allow for timely treatment and prevent clinical complications. In order to improve LDs diagnosis, we developed a capture-based next generation sequencing (NGS) panel allowing the detection of single nucleotide variants (SNVs), small insertions and deletions, and copy number variants (CNVs) in 51 genes related to LDs. The design of the LD panel covered at least coding regions, promoter region, and flanking intronic sequences for 51 genes. The validation of this panel consisted in testing 21 well-characterized samples and evaluating analytical and diagnostic performance metrics. Bioinformatics pipelines have been validated for SNVs, indels and CNVs. The clinical output of this panel was tested in five novel cases. This capture-based NGS panel provides an average coverage depth of 474× which allows the detection of SNVs and CNVs in one comprehensive assay. All the targeted regions were covered above the minimum required depth of 30×. To illustrate the clinical utility, five novel cases have been sequenced using this panel and the identified variants have been confirmed using Sanger sequencing or quantitative multiplex PCR of short fluorescent fragments (QMPSF). The application of NGS as first-line approach to analyze suspected LD cases may speed up the identification of alterations in LD-associated genes. NGS approaches combined with bioinformatics analyses, are a useful and cost-effective tool for identifying the causative variations in LDs.

A Siamese neural network model for the prioritization of metabolic disorders by integrating real and simulated data

MOTIVATION

Untargeted metabolomic approaches hold a great promise as a diagnostic tool for inborn errors of metabolisms (IEMs) in the near future. However, the complexity of the involved data makes its application difficult and time consuming. Computational approaches, such as metabolic network simulations and machine learning, could significantly help to exploit metabolomic data to aid the diagnostic process. While the former suffers from limited predictive accuracy, the latter is normally able to generalize only to IEMs for which sufficient data are available. Here, we propose a hybrid approach that exploits the best of both worlds by building a mapping between simulated and real metabolic data through a novel method based on Siamese neural networks (SNN).

RESULTS

The proposed SNN model is able to perform disease prioritization for the metabolic profiles of IEM patients even for diseases that it was not trained to identify. To the best of our knowledge, this has not been attempted before. The developed model is able to significantly outperform a baseline model that relies on metabolic simulations only. The prioritization performances demonstrate the feasibility of the method, suggesting that the integration of metabolic models and data could significantly aid the IEM diagnosis process in the near future.

AVAILABILITY AND IMPLEMENTATION

Metabolic datasets used in this study are publicly available from the cited sources. The original data produced in this study, including the trained models and the simulated metabolic profiles, are also publicly available (Messa et al., 2020).

Natural variation in a glucuronosyltransferase modulates propionate sensitivity in a C. elegans propionic acidemia model

Mutations in human metabolic genes can lead to rare diseases known as inborn errors of human metabolism. For instance, patients with loss-of-function mutations in either subunit of propionyl-CoA carboxylase suffer from propionic acidemia because they cannot catabolize propionate, leading to its harmful accumulation. Both the penetrance and expressivity of metabolic disorders can be modulated by genetic background. However, modifiers of these diseases are difficult to identify because of the lack of statistical power for rare diseases in human genetics. Here, we use a model of propionic acidemia in the nematode Caenorhabditis elegans to identify genetic modifiers of propionate sensitivity. Using genome-wide association (GWA) mapping across wild strains, we identify several genomic regions correlated with reduced propionate sensitivity. We find that natural variation in the putative glucuronosyltransferase GLCT-3, a homolog of human B3GAT, partly explains differences in propionate sensitivity in one of these genomic intervals. We demonstrate that loss-of-function alleles in glct-3 render the animals less sensitive to propionate. Additionally, we find that C. elegans has an expansion of the glct gene family, suggesting that the number of members of this family could influence sensitivity to excess propionate. Our findings demonstrate that natural variation in genes that are not directly associated with propionate breakdown can modulate propionate sensitivity. Our study provides a framework for using C. elegans to characterize the contributions of genetic background in models of human inborn errors in metabolism.

Multi-Omic Approach to Identify Phenotypic Modifiers Underlying Cerebral Demyelination in X-Linked Adrenoleukodystrophy

X-linked adrenoleukodystrophy (ALD) is a peroxisomal metabolic disorder with a highly complex clinical presentation. ALD is caused by mutations in the ABCD1 gene, and is characterized by the accumulation of very long-chain fatty acids in plasma and tissues. Disease-causing mutations are 'loss of function' mutations, with no prognostic value with respect to the clinical outcome of an individual. All male patients with ALD develop spinal cord disease and a peripheral neuropathy in adulthood, although age of onset is highly variable. However, the lifetime prevalence to develop progressive white matter lesions, termed cerebral ALD (CALD), is only about 60%. Early identification of transition to CALD is critical since it can be halted by allogeneic hematopoietic stem cell therapy only in an early stage. The primary goal of this study is to identify molecular markers which may be prognostic of cerebral demyelination from a simple blood sample, with the hope that blood-based assays can replace the current protocols for diagnosis. We collected six well-characterized brother pairs affected by ALD and discordant for the presence of CALD and performed multi-omic profiling of blood samples including genome, epigenome, transcriptome, metabolome/lipidome, and proteome profiling. In our analysis we identify discordant genomic alleles present across all families as well as differentially abundant molecular features across the omics technologies. The analysis was focused on univariate modeling to discriminate the two phenotypic groups, but was unable to identify statistically significant candidate molecular markers. Our study highlights the issues caused by a large amount of inter-individual variation, and supports the emerging hypothesis that cerebral demyelination is a complex mix of environmental factors and/or heterogeneous genomic alleles. We confirm previous observations about the role of immune response, specifically auto-immunity and the potential role of PFN1 protein overabundance in CALD in a subset of the families. We envision our methodology as well as dataset has utility to the field for reproducing previous or enabling future modifier investigations.

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.

Flux, toxicity, and expression costs generate complex genetic interactions in a metabolic pathway

Our ability to predict the impact of mutations on traits relevant for disease and evolution remains severely limited by the dependence of their effects on the genetic background and environment. Even when molecular interactions between genes are known, it is unclear how these translate to organism-level interactions between alleles. We therefore characterized the interplay of genetic and environmental dependencies in determining fitness by quantifying ~4000 fitness interactions between expression variants of two metabolic genes, starting from various environmentally modulated expression levels. We detect a remarkable variety of interactions dependent on initial expression levels and demonstrate that they can be quantitatively explained by a mechanistic model accounting for catabolic flux, metabolite toxicity, and expression costs. Complex fitness interactions between mutations can therefore be predicted simply from their simultaneous impact on a few connected molecular phenotypes.

Fibroblast-specific genome-scale modelling predicts an imbalance in amino acid metabolism in Refsum disease

Refsum disease (RD) is an inborn error of metabolism that is characterised by a defect in peroxisomal α‐oxidation of the branched‐chain fatty acid phytanic acid. The disorder presents with late‐onset progressive retinitis pigmentosa and polyneuropathy and can be diagnosed biochemically by elevated levels of phytanate in plasma and tissues of patients. To date, no cure exists for RD, but phytanate levels in patients can be reduced by plasmapheresis and a strict diet. In this study, we reconstructed a fibroblast‐specific genome‐scale model based on the recently published, FAD‐curated model, based on Recon3D reconstruction. We used transcriptomics (available via GEO database with identifier GSE138379), metabolomics and proteomics (available via ProteomeXchange with identifier PXD015518) data, which we obtained from healthy controls and RD patient fibroblasts incubated with phytol, a precursor of phytanic acid. Our model correctly represents the metabolism of phytanate and displays fibroblast‐specific metabolic functions. Using this model, we investigated the metabolic phenotype of RD at the genome scale, and we studied the effect of phytanate on cell metabolism. We identified 53 metabolites that were predicted to discriminate between healthy and RD patients, several of which with a link to amino acid metabolism. Ultimately, these insights in metabolic changes may provide leads for pathophysiology and therapy.

Databases

Transcriptomics data are available via GEO database with identifier GSE138379, and proteomics data are available via ProteomeXchange with identifier PXD015518.

Animal Model Contributions to Congenital Metabolic Disease

Genetic model systems allow researchers to probe and decipher aspects of human disease, and animal models of disease are frequently specifically engineered and have been identified serendipitously as well. Animal models are useful for probing the etiology and pathophysiology of disease and are critical for effective discovery and development of novel therapeutics for rare diseases. Here we review the impact of animal model organism research in three examples of congenital metabolic disorders to highlight distinct advantages of model system research. First, we discuss phenylketonuria research where a wide variety of research fields and models came together to make impressive progress and where a nearly ideal mouse model has been central to therapeutic advancements. Second, we review advancements in Lesch-Nyhan syndrome research to illustrate the role of models that do not perfectly recapitulate human disease as well as the need for multiple models of the same disease to fully investigate human disease aspects. Finally, we highlight research on the GM2 gangliosidoses Tay-Sachs and Sandhoff disease to illustrate the important role of both engineered traditional laboratory animal models and serendipitously identified atypical models in congenital metabolic disorder research. We close with perspectives for the future for animal model research in congenital metabolic disorders.

Inborn Errors of Metabolism in the Era of Untargeted Metabolomics and Lipidomics

Inborn errors of metabolism (IEMs) are a group of inherited diseases with variable incidences. IEMs are caused by disrupting enzyme activities in specific metabolic pathways by genetic mutations, either directly or indirectly by cofactor deficiencies, causing altered levels of compounds associated with these pathways. While IEMs may present with multiple overlapping symptoms and metabolites, early and accurate diagnosis of IEMs is critical for the long-term health of affected subjects. The prevalence of IEMs differs between countries, likely because different IEM classifications and IEM screening methods are used. Currently, newborn screening programs exclusively use targeted metabolic assays that focus on limited panels of compounds for selected IEM diseases. Such targeted approaches face the problem of false negative and false positive diagnoses that could be overcome if metabolic screening adopted analyses of a broader range of analytes. Hence, we here review the prospects of using untargeted metabolomics for IEM screening. Untargeted metabolomics and lipidomics do not rely on predefined target lists and can detect as many metabolites as possible in a sample, allowing to screen for many metabolic pathways simultaneously. Examples are given for nontargeted analyses of IEMs, and prospects and limitations of different metabolomics methods are discussed. We conclude that dedicated studies are needed to compare accuracy and robustness of targeted and untargeted methods with respect to widening the scope of IEM diagnostics.

Systems Biology Meets Functional Medicine

Through experiences gained over the past 3 decades, the development and application of the Functional Medicine model has demonstrated its ability to improve clinical decision making in the treatment of patients with complex chronic disease. The Functional Medicine model provides a system that effectively translates the emerging understanding of the gene-environment regulation of the structure and function of the individual into clinical practice.

Untargeted Metabolomics for Autism Spectrum Disorders: Current Status and Future Directions

Autism spectrum disorders (ASDs) are a group of neurodevelopment disorders characterized by childhood onset deficits in social communication and interaction. Although the exact etiology of most cases of ASDs is unknown, a portion has been proposed to be associated with various metabolic abnormalities including mitochondrial dysfunction, disorders of cholesterol metabolism, and folate abnormalities. Targeted biochemical testing like plasma amino acid and acylcarnitine profiles have demonstrated limited utility in helping to diagnose and manage such patients. Untargeted metabolomics has emerged, however, as a promising tool in screening for underlying biochemical abnormalities and managing treatment and as a means of investigating possible novel biomarkers for the disorder. Here, we review the principles and methodology behind untargeted metabolomics, recent pilot studies utilizing this technology, and areas in which it may be integrated into the care of children with this disorder in the future.

Metabolite flux: A dynamic concept for inherited metabolic disorders as complex traits

In 2000 and 2001, we described factors that lead to the phenotypes of individuals with "simple," "single" gene disorders, like inherited metabolic disorders, being complex, multi-genic traits. These factors include functional thresholds, genetic and environmental modifiers, and systems dynamics, encompassing metabolic control analysis and scale-free, hub-and-spoke networks. This mini-review will consider topics influencing complexity developed in the ensuing nearly two decades, such as "synergistic heterozygosity" and "moonlighting proteins." There will be a focus on the value of the measurement of flux in evaluating the metabolome to ascertain phenotypic variability and the potential role of the gut microbiome in metabolomic flux. A point-of-care metabolomics tool, under development to improve the real time, inter-operative ascertainment of tumor margins and similar devices, will provide opportunities to improve acute care and ongoing management of individuals with inherited metabolic disorders.

Protocol for a scoping review of multi-omic analysis for rare diseases

INTRODUCTION

The development of next generation sequencing technology has enabled cost-efficient, large scale, multiple 'omic' analysis, including epigenomic, genomic, metabolomic, phenomic, proteomic and transcriptomic research. These integrated approaches hold significant promise for rare disease research, with the potential to aid biomarker discovery, improve our understanding of disease pathogenesis and identify novel therapeutic targets. In this paper we outline a systematic approach for a scoping review designed to evaluate what primary research has been performed to date on multi-omics and rare disease.

METHODS AND ANALYSIS

This protocol was designed using the Joanna Briggs Institute methodology for scoping reviews. Databases to be searched will include: MEDLINE, EMBASE, PubMed, Web of Science, Scopus and Google Scholar for primary studies relevant to the key terms 'multi-omics' and 'rare disease', published prior to 30th December 2018. Grey literature databases GreyLit and OpenGrey will also be searched, as well as reverse citation screening of relevant articles and forward citation searching using Web of Science Cited Reference Search Tool. Data extraction will be performed using customised forms and a narrative synthesis of the results will be presented.

ETHICS AND DISSEMINATION

As a secondary analysis study with no primary data generated, this scoping review does not require ethical approval. We anticipate this review will highlight a gap in rare disease research and provide direction for novel research. The completed review will be submitted for publication in peer-reviewed journals and presented at relevant conferences discussing rare disease research and/or molecular strategies.

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

The laboratory diagnosis of inborn errors of metabolism has been revolutionized in recent years, thanks to the amazing developments in the field of DNA sequencing including whole exome and whole genome sequencing (WES and WGS). Interpretation of the results coming from WES and/or WGS analysis is definitely not trivial especially since the biological relevance of many of the variants identified by WES and/or WGS, have not been tested experimentally and prediction programs like POLYPHEN-2 and SIFT are far from perfect. Correct interpretation of WES and/or WGS results can only be achieved by performing functional studies at multiple levels (different metabolomics platforms, enzymology, in vitro and in vivo flux analysis), often requires studies in model organisms like zebra fish, Caenorhabditis elegans, Saccharomyces cerevisiae, mutant mice and others, and also requires the input of many different disciplines to make this Translational Metabolism approach effective.

Systems Biology Approaches Toward Understanding Primary Mitochondrial Diseases

Primary mitochondrial diseases form one of the most common and severe groups of genetic disease, with a birth prevalence of at least 1 in 5000. These disorders are multi-genic and multi-phenotypic (even within the same gene defect) and span the entire age range from prenatal to late adult onset. Mitochondrial disease typically affects one or multiple high-energy demanding organs, and is frequently fatal in early life. Unfortunately, to date there are no known curative therapies, mostly owing to the rarity and heterogeneity of individual mitochondrial diseases, leading to diagnostic odysseys and difficulties in clinical trial design. This review aims to discuss recent advances and challenges of systems approaches for the study of primary mitochondrial diseases. Although there has been an explosion in the generation of omics data, few studies have progressed toward the integration of multiple levels of omics. It is evident that the integration of different types of data to create a more complete representation of biology remains challenging, perhaps due to the scarcity of available integrative tools and the complexity inherent in their use. In addition, "bottom-up" systems approaches have been adopted for use in the iterative cycle of systems biology: from data generation to model prediction and validation. Primary mitochondrial diseases, owing to their complex nature, will most likely benefit from a multidisciplinary approach encompassing clinical, molecular and computational studies integrated together by systems biology to elucidate underlying pathomechanisms for better diagnostics and therapeutic discovery. Just as next generation sequencing has rapidly increased diagnostic rates from approximately 5% up to 60% over two decades, more recent advancing technologies are encouraging; the generation of multi-omics, the integration of multiple types of data, and the ability to predict perturbations will, ultimately, be translated into improved patient care.

Dissecting metabolism using zebrafish models of disease

Zebrafish (Danio rerio) are becoming an increasingly powerful model organism to study the role of metabolism in disease. Since its inception, the zebrafish model has relied on unique attributes such as the transparency of embryos, high fecundity and conservation with higher vertebrates, to perform phenotype-driven chemical and genetic screens. In this review, we describe how zebrafish have been used to reveal novel mechanisms by which metabolism regulates embryonic development, obesity, fatty liver disease and cancer. In addition, we will highlight how new approaches in advanced microscopy, transcriptomics and metabolomics using zebrafish as a model system have yielded fundamental insights into the mechanistic underpinnings of disease.

Novel strategies for clinical investigation and biomarker discovery: a guide to applied metabolomics

Metabolomics is an emerging technology that is increasing both in basic science and in human applications, providing a physiological snapshot. It has been highlighted as one of the most wide ranging and reliable tools for the investigation of physiological status, the discovery of new biomarkers and the analysis of metabolic pathways. Metabolomics uses innovative mass spectrometry (MS) allied to chromatography or nuclear magnetic resonance (NMR). The recent advances in bioinformatics, databases and statistics, have provided a unique perception of metabolites interaction and the dynamics of metabolic pathways at a system level. In this context, several studies have applied metabolomics in physiology- and disease-related works. The application of metabolomics includes, physiological and metabolic evaluation/monitoring, individual response to different exercise, nutritional interventions, pathological processes, responses to pharmacological interventions, biomarker discovery and monitoring for distinct aspects, such as: physiological capacity, fatigue/recovery and aging among other applications. For metabolomic analyses, despite huge improvements in the field, several complex methodological steps must be taken into consideration. In this regard, the present article aims to summarize the novel aspects of metabolomics and provide a guide for metabolomics for professionals related to physiologist and medical applications.

Cofactors revisited - Predicting the impact of flavoprotein-related diseases on a genome scale

Flavin adenine dinucleotide (FAD) and its precursor flavin mononucleotide (FMN) are redox cofactors that are required for the activity of more than hundred human enzymes. Mutations in the genes encoding these proteins cause severe phenotypes, including a lack of energy supply and accumulation of toxic intermediates. Ideally, patients should be diagnosed before they show symptoms so that treatment and/or preventive care can start immediately. This can be achieved by standardized newborn screening tests. However, many of the flavin-related diseases lack appropriate biomarker profiles. Genome-scale metabolic models can aid in biomarker research by predicting altered profiles of potential biomarkers. Unfortunately, current models, including the most recent human metabolic reconstructions Recon and HMR, typically treat enzyme-bound flavins incorrectly as free metabolites. This in turn leads to artificial degrees of freedom in pathways that are strictly coupled. Here, we present a reconstruction of human metabolism with a curated and extended flavoproteome. To illustrate the functional consequences, we show that simulations with the curated model - unlike simulations with earlier Recon versions - correctly predict the metabolic impact of multiple-acyl-CoA-dehydrogenase deficiency as well as of systemic flavin-depletion. Moreover, simulations with the new model allowed us to identify a larger number of biomarkers in flavoproteome-related diseases, without loss of accuracy. We conclude that adequate inclusion of cofactors in constraint-based modelling contributes to higher precision in computational predictions.

The microbiome and inborn errors of metabolism: Why we should look carefully at their interplay?

Research into the influence of the microbiome on the human body has been shedding new light on diseases long known to be multifactorial, such as obesity, mood disorders, autism, and inflammatory bowel disease. Although inborn errors of metabolism (IEMs) are monogenic diseases, genotype alone is not enough to explain the wide phenotypic variability observed in patients with these conditions. Genetics and diet exert a strong influence on the microbiome, and diet is used (alone or as an adjuvant) in the treatment of many IEMs. This review will describe how the effects of the microbiome on the host can interfere with IEM phenotypes through interactions with organs such as the liver and brain, two of the structures most commonly affected by IEMs. The relationships between treatment strategies for some IEMs and the microbiome will also be addressed. Studies on the microbiome and its influence in individuals with IEMs are still incipient, but are of the utmost importance to elucidating the phenotypic variety observed in these conditions.

Oxidized phospholipids regulate amino acid metabolism through MTHFD2 to facilitate nucleotide release in endothelial cells

Oxidized phospholipids (oxPAPC) induce endothelial dysfunction and atherosclerosis. Here we show that oxPAPC induce a gene network regulating serine-glycine metabolism with the mitochondrial methylenetetrahydrofolate dehydrogenase/cyclohydrolase (MTHFD2) as a causal regulator using integrative network modeling and Bayesian network analysis in human aortic endothelial cells. The cluster is activated in human plaque material and by atherogenic lipoproteins isolated from plasma of patients with coronary artery disease (CAD). Single nucleotide polymorphisms (SNPs) within the MTHFD2-controlled cluster associate with CAD. The MTHFD2-controlled cluster redirects metabolism to glycine synthesis to replenish purine nucleotides. Since endothelial cells secrete purines in response to oxPAPC, the MTHFD2-controlled response maintains endothelial ATP. Accordingly, MTHFD2-dependent glycine synthesis is a prerequisite for angiogenesis. Thus, we propose that endothelial cells undergo MTHFD2-mediated reprogramming toward serine-glycine and mitochondrial one-carbon metabolism to compensate for the loss of ATP in response to oxPAPC during atherosclerosis.

Advances in metabolome information retrieval: turning chemistry into biology. Part II: biological information recovery

This work reports the second part of a review intending to give the state of the art of major metabolic phenotyping strategies. It particularly deals with inherent advantages and limits regarding data analysis issues and biological information retrieval tools along with translational challenges. This Part starts with introducing the main data preprocessing strategies of the different metabolomics data. Then, it describes the main data analysis techniques including univariate and multivariate aspects. It also addresses the challenges related to metabolite annotation and characterization. Finally, functional analysis including pathway and network strategies are discussed. The last section of this review is devoted to practical considerations and current challenges and pathways to bring metabolomics into clinical environments.

Inborn errors of metabolism and the human interactome: a systems medicine approach

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.

Flux analysis of inborn errors of metabolism

Patients with an inborn error of metabolism (IEM) are deficient of an enzyme involved in metabolism, and as a consequence metabolism reprograms itself to reach a new steady state. This new steady state underlies the clinical phenotype associated with the deficiency. Hence, we need to know the flux of metabolites through the different metabolic pathways in this new steady state of the reprogrammed metabolism. Stable isotope technology is best suited to study this. In this review the progress made in characterizing the altered metabolism will be presented. Studies done in patients to estimate the residual flux through the metabolic pathway affected by enzyme deficiencies will be discussed. After this, studies done in model systems will be reviewed. The focus will be on glycogen storage disease type I, medium-chain acyl-CoA dehydrogenase deficiency, propionic and methylmalonic aciduria, urea cycle defects, phenylketonuria, and combined D,L-2-hydroxyglutaric aciduria. Finally, new developments are discussed, which allow the tracing of metabolic reprogramming in IEM on a genome-wide scale. In conclusion, the outlook for flux analysis of metabolic derangement in IEMs looks promising.

Simultaneous detection of multiple inherited metabolic diseases using GC-MS urinary metabolomics by chemometrics multi-class classification strategies

Metabonomics has been widely used in disease diagnosis and clinically practical methods often require the detection of multi-class bio-samples. In this work, multi-class classification methods were investigated to simultaneously discriminate among 6 inherited metabolic diseases (IMDs) and the normal instances using gas chromatography-mass spectrometry (GC-MS) of urine samples. Two common multi-class classification strategies, one-against-all (OAA) and one-against-one (OAO) were compared and enhanced using a novel ensemble classification strategy (ECS), which developed a set of sequential sub-classifiers by fusion of OAA and OAO and made the final classification decisions using softmax function. GC-MS data of 240 instances of 6 IMDs and healthy controls were classified by different strategies based on orthogonal partial least squares discriminant analysis (OPLS-DA) and particle swarm optimization (PSO) algorithm was performed for feature selection. By OAA and OAO, the classification accuracies were 70.00% and 82.86%, respectively. Using the two methods based on ECS, the total classification accuracies were 0.9143 and 0.9429. The newly proposed ECS will provide a useful multi-class classification tool for simultaneous detection of clinically similar IMDs and promote practical and reliable diagnosis of IMDs using metabonomics data.

Systematic analysis of complex genetic interactions

To systematically explore complex genetic interactions, we constructed ~200,000 yeast triple mutants and scored negative trigenic interactions. We selected double-mutant query genes across a broad spectrum of biological processes, spanning a range of quantitative features of the global digenic interaction network and tested for a genetic interaction with a third mutation. Trigenic interactions often occurred among functionally related genes, and essential genes were hubs on the trigenic network. Despite their functional enrichment, trigenic interactions tended to link genes in distant bioprocesses and displayed a weaker magnitude than digenic interactions. We estimate that the global trigenic interaction network is ~100 times as large as the global digenic network, highlighting the potential for complex genetic interactions to affect the biology of inheritance, including the genotype-to-phenotype relationship.

Recon3D enables a three-dimensional view of gene variation in human metabolism

Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.