Integration of genomics and metabolomics for prioritization of rare disease variants: a 2018 literature review

Metabolomic biomarkers for benign conditions and malignant ovarian cancer: Advancing early diagnosis

BACKGROUND

Ovarian cancer (OC) is a major global cause of death among gynecological cancers, with a high mortality rate. Early diagnosis, distinguishing between benign conditions and early malignant OC forms, is vital for successful treatment. This research investigates serum metabolites to find diagnostic biomarkers for early OC identification.

METHODS

Metabolomic profiles derived from the serum of 60 patients with benign conditions and 60 patients with malignant OC were examined using ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS). Comparative analysis revealed differential metabolites linked to OC, aiding biomarker identification for early-diagnosis of OC via machine learning features. The predictive ability of these biomarkers was evaluated against the traditional biomarker, cancer antigen 125 (CA125).

RESULTS

84 differential metabolites were identified, including 2-Thiothiazolidine-4-carboxylic acid (TTCA), Methionyl-Cysteine, and Citrulline that could serve as potential biomarkers to identify benign conditions and malignant OC. In the diagnosis of early-stage OC, the area under the curve (AUC) for Citrulline was 0.847 (95 % Confidence Interval (CI): 0.719-0.974), compared to 0.770 (95 % CI: 0.596-0.944) for TTCA, and 0.754 for Methionine-Cysteine (95 % CI: 0.589-0.919). These metabolites demonstrate a superior diagnostic capability relative to CA125, which has an AUC of 0.689 (95 % CI: 0.448-0.931). Among these biomarkers, Citrulline stands out as the most promising. Additionally, in the diagnosis of benign conditions and malignant OC, using logistic regression to combine potential biomarkers with CA125 has an AUC of 0.987 (95 % CI: 0.9708-1) has been proven to be more effective than relying solely on the traditional biomarker CA125 with an AUC of 0.933 (95 % CI: 0.870-0.996). Furthermore, among all the differential metabolites, lipid metabolites dominate, significantly impacting glycerophospholipid metabolism pathway.

CONCLUSION

The discovered serum metabolite biomarkers demonstrate excellent diagnostic performance for distinguishing between benign conditions and malignant OC and for early diagnosis of malignant OC.

Inherited metabolic disorders in Cyprus

Selective screening for inherited metabolic disorders (IMD) began in Cyprus in 1990. Over the last thirty-three years 7388 patients were investigated for IMD and 200 diagnoses were made (diagnostic yield 2.7%). The existence of a single laboratory of Biochemical Genetics for the whole island facilitated the creation of a national registry for IMD. The minimal prevalence of IMD in Cyprus is 53.3 cases per 100,000 live births. The most common group are disorders of amino acid metabolism (41.0%), followed by disorders of carbohydrate metabolism (16.5%), disorders of complex molecule degradation (16.5%), mitochondrial disorders (10.5%) and disorders of vitamin and co-factor metabolism (5.5%). Hyperphenylalaninaemia is the most common IMD (14.0%) followed by galactosaemia (7.0%), glutaric aciduria type I (5.5%) and MSUD (4.0%). Some disorders were found to have a relatively high incidence in specific communities, for example Sandhoff disease among the Cypriot Maronites and GM1 gangliosidosis in one particular area of the island. Other disorders were found to have a relatively higher overall incidence, compared to other Caucasian populations, for example galactosaemia, glutaric aciduria type I and MSUD, while fatty acid oxidation defects, Gaucher disease and classic PKU were found to have a relatively lower incidence. Molecular characterization of selected disorders revealed many novel genetic variants, specific to the Cypriot population.

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Integrative omics approaches to advance rare disease diagnostics

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

Diagnosis, treatment, and research status of rare diseases related to birth defects

Rare diseases are diseases that occur at low prevalence, and most of them are chronic and serious diseases that are often life-threatening. Currently, there is no unified definition for rare diseases. The diagnosis, treatment, and research of rare diseases have become the focus of medicine and biopharmacology, as well as the breakthrough point of clinical and basic research. Birth defects are the hard-hit area of rare diseases and the frontiers of its research. Since most of these defects have a genetic basis, early screening and diagnosis have important scientific value and social significance for the prevention and control of such diseases. At present, there is no effective treatment for most rare diseases, but progress in prenatal diagnosis and screening can prevent the occurrence of diseases and help prevent and treat rare diseases. This article discusses the progress in genetic-related birth defects and rare diseases.

[Expert recommendations on homogenization management of the diagnosis and treatment of rare diseases in children]

Rare diseases in children are characterized by low prevalence, complex pathogenesis, variety, and difficulty in the diagnosis and treatment. With the development of medical services, progress has been made in the diagnosis and treatment of rare diseases. However, due to asymmetric allocation of medical resources at different levels, there are still many shortcomings in the establishment and promotion of the homogenized management system of rare disease diagnosis and treatment. In order to further standardize the homogenized management of rare diseases in children, achieve early and accurate diagnosis and treatment, and improve the quality of life of the children, the Rare Disease Diagnosis and Treatment Center of Tianjin Children's Hospital (Tianjin University Children's Hospital) invited relevant experts in the field to develop recommendations for the management model of homogenized diagnosis and treatment of rare diseases in children from the aspects of information construction, hierarchical diagnosis and treatment, personnel training, scientific popularization, and multi-participation. The recommendations provide reference for the regional homogenization of clinical diagnosis and treatment management system for children with rare diseases.

Applications of machine learning in metabolomics: Disease modeling and classification

Metabolomics research has recently gained popularity because it enables the study of biological traits at the biochemical level and, as a result, can directly reveal what occurs in a cell or a tissue based on health or disease status, complementing other omics such as genomics and transcriptomics. Like other high-throughput biological experiments, metabolomics produces vast volumes of complex data. The application of machine learning (ML) to analyze data, recognize patterns, and build models is expanding across multiple fields. In the same way, ML methods are utilized for the classification, regression, or clustering of highly complex metabolomic data. This review discusses how disease modeling and diagnosis can be enhanced via deep and comprehensive metabolomic profiling using ML. We discuss the general layout of a metabolic workflow and the fundamental ML techniques used to analyze metabolomic data, including support vector machines (SVM), decision trees, random forests (RF), neural networks (NN), and deep learning (DL). Finally, we present the advantages and disadvantages of various ML methods and provide suggestions for different metabolic data analysis scenarios.

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

BACKGROUND AND OBJECTIVE

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

METHODS

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

KEY CONTENT AND FINDINGS

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

CONCLUSIONS

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

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.

Identification of three novel pathogenic mutations in cystathionine beta-synthase gene of Pakistani intellectually disabled patients

BACKGROUND

Classical homocystinuria (HCU) is an autosomal recessive inborn error of metabolism, which is caused by the cystathionine-β-synthase (CBS: encoded by CBS) deficiency. Symptoms of untreated classical HCU patients include intellectual disability (ID), ectopia lentis and long limbs, along with elevated plasma methionine, and homocysteine.

METHODS

A total of 429 ID patients (age range: 1.6-23 years) were sampled from Northern areas of Punjab, Pakistan. Biochemical and genetic analyses were performed to find classical HCU disease in ID patients.

RESULTS

Biochemically, nine patients from seven unrelated families were identified with high levels of plasma methionine and homocysteine. Targeted exonic analysis of CBS confirmed seven causative homozygous mutations; of which three were novel missense mutations (c.451G>T; p.Gly151Trp, c.975G>C; p.Lys325Asn and c.1039 + 1G>T splicing), and four were recurrent variants (c.451 + 1G>A; IVS4 + 1 splicing, c.770C>T; p.Thr257Met, c.808_810del GAG; p.Glu270del and c.752T>C; p.Leu251Pro). Treatment of patients was initiated without further delay with pyridoxine, folic acid, cobalamin, and betaine as well as dietary protein restriction. The immediate impact was noticed in behavioral improvement, decreased irritability, improved black hair color, and socialization. Overall, health outcomes in this disorder depend on the age and symptomatology at the time of treatment initiation.

CONCLUSIONS

With personalized treatment and care, such patients can reach their full potential of living as healthy a life as possible. This screening study is one of the pioneering initiatives in Pakistan which would help to minimize the burden of such treatable inborn errors of metabolism in the intellectually disabled patients.

Phenotype-tissue expression and exploration (PTEE) resource facilitates the choice of tissue for RNA-seq-based clinical genetics studies

BACKGROUND

RNA-seq emerges as a valuable method for clinical genetics. The transcriptome is "dynamic" and tissue-specific, but typically the probed tissues to analyze (TA) are different from the tissue of interest (TI) based on pathophysiology.

RESULTS

We developed Phenotype-Tissue Expression and Exploration (PTEE), a tool to facilitate the decision about the most suitable TA for RNA-seq. We integrated phenotype-annotated genes, used 54 tissues from GTEx to perform correlation analyses and identify expressed genes and transcripts between TAs and TIs. We identified skeletal muscle as the most appropriate TA to inquire for cardiac arrhythmia genes and skin as a good proxy to study neurodevelopmental disorders. We also explored RNA-seq limitations and show that on-off switching of gene expression during ontogenesis or circadian rhythm can cause blind spots for RNA-seq-based analyses.

CONCLUSIONS

PTEE aids the identification of tissues suitable for RNA-seq for a given pathology to increase the success rate of diagnosis and gene discovery. PTEE is freely available at https://bioinf.eva.mpg.de/PTEE/.

The diagnostic rate of inherited metabolic disorders by exome sequencing in a cohort of 547 individuals with developmental disorders

Considering that some Inherited Metabolic Disorders (IMDs) can be diagnosed in patients with no distinctive clinical features of IMDs, we aimed to evaluate the power of exome sequencing (ES) to diagnose IMDs within a cohort of 547 patients with unspecific developmental disorders (DD). IMDs were diagnosed in 12% of individuals with causative diagnosis (177/547). There are clear benefits of using ES in DD to diagnose IMD, particularly in cases where biochemical studies are unavailable.

Synopsis

Exome sequencing and diagnostic rate of Inherited Metabolic Disorders in individuals with developmental disorders.

Strategies to Uplift Novel Mendelian Gene Discovery for Improved Clinical Outcomes

Rare genetic disorders, while individually rare, are collectively common. They represent some of the most severe disorders affecting patients worldwide with significant morbidity and mortality. Over the last decade, advances in genomic methods have significantly uplifted diagnostic rates for patients and facilitated novel and targeted therapies. However, many patients with rare genetic disorders still remain undiagnosed as the genetic etiology of only a proportion of Mendelian conditions has been discovered to date. This article explores existing strategies to identify novel Mendelian genes and how these discoveries impact clinical care and therapeutics. We discuss the importance of data sharing, phenotype-driven approaches, patient-led approaches, utilization of large-scale genomic sequencing projects, constraint-based methods, integration of multi-omics data, and gene-to-patient methods. We further consider the health economic advantages of novel gene discovery and speculate on potential future methods for improved clinical outcomes.

The History of Gene Hunting in Hereditary Spinocerebellar Degeneration: Lessons From the Past and Future Perspectives

Hereditary spinocerebellar degeneration (SCD) encompasses an expanding list of rare diseases with a broad clinical and genetic heterogeneity, complicating their diagnosis and management in daily clinical practice. Correct diagnosis is a pillar for precision medicine, a branch of medicine that promises to flourish with the progressive improvements in studying the human genome. Discovering the genes causing novel Mendelian phenotypes contributes to precision medicine by diagnosing subsets of patients with previously undiagnosed conditions, guiding the management of these patients and their families, and enabling the discovery of more causes of Mendelian diseases. This new knowledge provides insight into the biological processes involved in health and disease, including the more common complex disorders. This review discusses the evolution of the clinical and genetic approaches used to diagnose hereditary SCD and the potential of new tools for future discoveries.

High levels of blood glutamic acid and ornithine in children with intellectual disability

Objectives: Aminoacidopathies are inborn errors of metabolism (IEMs) that cause intellectual disability in children. Luckily, aminoacidopathies are potentially treatable, if diagnosed earlier in life. The focus of this study was the screening of aminoacidopathies in a cohort of patients suspected for IEMs. Methods: Blood samples from healthy (IQ > 90; n = 391) and intellectually disabled (IQ < 70; n = 409) children (suspected for IEMs) were collected from different areas of Northern Punjab, Pakistan. An analytical HPLC assay was used for the screening of plasma amino acids. Results: All the samples (n = 800) were analyzed on HPLC and forty-three out of 409 patient samples showed abnormal amino acid profiles mainly in the levels of glutamic acid, ornithine and methionine. Plasma concentration (Mean ± SD ng/mL) were significantly high in 40 patients for glutamic acid (patients: 165 ± 38 vs. controls: 57 ± 8, p < 0.00001) and ornithine (patients: 3177 ± 937 vs. controls: 1361 ± 91, p < 0.0001). Moreover, 3 patients showed abnormally high (53.3 ± 8.6 ng/mL) plasma levels of methionine. Conclusion: In conclusion, biochemical analysis of samples from such patients at the metabolites level could reveal the underlying diseases which could be confirmed through advanced biochemical and genetic analyses. Thus, treatment to some of such patients could be offered. Thus burden of intellectual disability caused by such rare metabolic diseases could be reduced from the target populations.

A pipeline-friendly software tool for genome diagnostics to prioritize genes by matching patient symptoms to literature

Despite an explosive growth of next-generation sequencing data, genome diagnostics only provides a molecular diagnosis to a minority of patients. Software tools that prioritize genes based on patient symptoms using known gene-disease associations may complement variant filtering and interpretation to increase chances of success. However, many of these tools cannot be used in practice because they are embedded within variant prioritization algorithms, or exist as remote services that cannot be relied upon or are unacceptable because of legal/ethical barriers. In addition, many tools are not designed for command-line usage, closed-source, abandoned, or unavailable. We present Variant Interpretation using Biomedical literature Evidence (VIBE), a tool to prioritize disease genes based on Human Phenotype Ontology codes. VIBE is a locally installed executable that ensures operational availability and is built upon DisGeNET-RDF, a comprehensive knowledge platform containing gene-disease associations mostly from literature and variant-disease associations mostly from curated source databases. VIBE's command-line interface and output are designed for easy incorporation into bioinformatic pipelines that annotate and prioritize variants for further clinical interpretation. We evaluate VIBE in a benchmark based on 305 patient cases alongside seven other tools. Our results demonstrate that VIBE offers consistent performance with few cases missed, but we also find high complementarity among all tested tools. VIBE is a powerful, free, open source and locally installable solution for prioritizing genes based on patient symptoms. Project source code, documentation, benchmark and executables are available at https://github.com/molgenis/vibe.

New Diagnostic Approaches for Undiagnosed Rare Genetic Diseases

Accurate diagnosis is the cornerstone of medicine; it is essential for informed care and promoting patient and family well-being. However, families with a rare genetic disease (RGD) often spend more than five years on a diagnostic odyssey of specialist visits and invasive testing that is lengthy, costly, and often futile, as 50% of patients do not receive a molecular diagnosis. The current diagnostic paradigm is not well designed for RGDs, especially for patients who remain undiagnosed after the initial set of investigations, and thus requires an expansion of approaches in the clinic. Leveraging opportunities to participate in research programs that utilize new technologies to understand RGDs is an important path forward for patients seeking a diagnosis. Given recent advancements in such technologies and international initiatives, the prospect of identifying a molecular diagnosis for all patients with RGDs has never been so attainable, but achieving this goal will require global cooperation at an unprecedented scale.

Analysis of transcript-deleterious variants in Mendelian disorders: implications for RNA-based diagnostics

Background

At least 50% of patients with suspected Mendelian disorders remain undiagnosed after whole-exome sequencing (WES), and the extent to which non-coding variants that are not captured by WES contribute to this fraction is unclear. Whole transcriptome sequencing is a promising supplement to WES, although empirical data on the contribution of RNA analysis to the diagnosis of Mendelian diseases on a large scale are scarce.

Results

Here, we describe our experience with transcript-deleterious variants (TDVs) based on a cohort of 5647 families with suspected Mendelian diseases. We first interrogate all families for which the respective Mendelian phenotype could be mapped to a single locus to obtain an unbiased estimate of the contribution of TDVs at 18.9%. We examine the entire cohort and find that TDVs account for 15% of all “solved” cases. We compare the results of RT-PCR to in silico prediction. Definitive results from RT-PCR are obtained from blood-derived RNA for the overwhelming majority of variants (84.1%), and only a small minority (2.6%) fail analysis on all available RNA sources (blood-, skin fibroblast-, and urine renal epithelial cells-derived), which has important implications for the clinical application of RNA-seq. We also show that RNA analysis can establish the diagnosis in 13.5% of 155 patients who had received “negative” clinical WES reports. Finally, our data suggest a role for TDVs in modulating penetrance even in otherwise highly penetrant Mendelian disorders.

Conclusions

Our results provide much needed empirical data for the impending implementation of diagnostic RNA-seq in conjunction with genome sequencing.

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.

Cross-Omics: Integrating Genomics with Metabolomics in Clinical Diagnostics

Next-generation sequencing and next-generation metabolic screening are, independently, increasingly applied in clinical diagnostics of inborn errors of metabolism (IEM). Integrated into a single bioinformatic method, these two –omics technologies can potentially further improve the diagnostic yield for IEM. Here, we present cross-omics: a method that uses untargeted metabolomics results of patient’s dried blood spots (DBSs), indicated by Z-scores and mapped onto human metabolic pathways, to prioritize potentially affected genes. We demonstrate the optimization of three parameters: (1) maximum distance to the primary reaction of the affected protein, (2) an extension stringency threshold reflecting in how many reactions a metabolite can participate, to be able to extend the metabolite set associated with a certain gene, and (3) a biochemical stringency threshold reflecting paired Z-score thresholds for untargeted metabolomics results. Patients with known IEMs were included. We performed untargeted metabolomics on 168 DBSs of 97 patients with 46 different disease-causing genes, and we simulated their whole-exome sequencing results in silico. We showed that for accurate prioritization of disease-causing genes in IEM, it is essential to take into account not only the primary reaction of the affected protein but a larger network of potentially affected metabolites, multiple steps away from the primary reaction.

Statistically Driven Metabolite and Lipid Profiling of Patients from the Undiagnosed Diseases Network

Advancements in molecular separations coupled with mass spectrometry have enabled metabolome analyses for clinical cohorts. A population of interest for metabolome profiling is patients with rare disease for which abnormal metabolic signatures may yield clues into the genetic basis, as well as mechanistic drivers of the disease and possible treatment options. We undertook the metabolome profiling of a large cohort of patients with mysterious conditions characterized through the Undiagnosed Diseases Network (UDN). Due to the size and enrollment procedures, collection of the metabolomes for UDN patients took place over 2 years. We describe the study designed to adjust for measurements collected over a long time scale and how this enabled statistical analyses to summarize the metabolome of individual patients. We demonstrate the removal of time-based batch effects, overall statistical characteristics of the UDN population, and two case studies of interest that demonstrate the utility of metabolome profiling for rare diseases.

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.

Untargeted Metabolomics-Based Screening Method for Inborn Errors of Metabolism using Semi-Automatic Sample Preparation with an UHPLC- Orbitrap-MS Platform

Routine diagnostic screening of inborn errors of metabolism (IEM) is currently performed by different targeted analyses of known biomarkers. This approach is time-consuming, targets a limited number of biomarkers and will not identify new biomarkers. Untargeted metabolomics generates a global metabolic phenotype and has the potential to overcome these issues. We describe a novel, single platform, untargeted metabolomics method for screening IEM, combining semi-automatic sample preparation with pentafluorophenylpropyl phase (PFPP)-based UHPLC- Orbitrap-MS. We evaluated analytical performance and diagnostic capability of the method by analysing plasma samples of 260 controls and 53 patients with 33 distinct IEM. Analytical reproducibility was excellent, with peak area variation coefficients below 20% for the majority of the metabolites. We illustrate that PFPP-based chromatography enhances identification of isomeric compounds. Ranked z-score plots of metabolites annotated in IEM samples were reviewed by two laboratory specialists experienced in biochemical genetics, resulting in the correct diagnosis in 90% of cases. Thus, our untargeted metabolomics platform is robust and differentiates metabolite patterns of different IEMs from those of controls. We envision that the current approach to diagnose IEM, using numerous tests, will eventually be replaced by untargeted metabolomics methods, which also have the potential to discover novel biomarkers and assist in interpretation of genetic data.

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.

Biochemical Screening of Intellectually Disabled Patients: A Stepping Stone to Initiate a Newborn Screening Program in Pakistan

Inborn errors of metabolism (IEMs) are rare group of genetic disorders comprising of more than 1,000 different types. Around 200 of IEMs are potentially treatable through diet, pharmacological and other therapies, if diagnosed earlier in life. IEMs can be diagnosed early through newborn screening (NBS) programs, which are in place in most of the developed countries. However, establishing a NBS in a developing country is a challenging task due to scarcity of disease related data, large population size, poor economy, and burden of other common disorders. Since, not enough data is available for the prevalence of IEMs in Pakistan; therefore, in this study, we set out to find the prevalence of various treatable IEMs in a cohort of intellectually disabled patients suspected for IEMs, which will help us to initiate a NBS program for the most frequent IEMs in Pakistan. Therefore, a total of 429 intellectually disabled (IQ <70) patient samples were collected from Pakistan. A subset of 113 patient samples was selected based on the clinical information for the detailed biochemical screening. Advance analytical techniques like, Amino Acid Analyzer, GC-MS, UHPLC-MS, and MS/MS were used to screen for different treatable IEMs like aminoacidopathies, fatty acid β-oxidation disorders and mucopolysaccharidoses (MPS) etc. A total of 14 patients were diagnosed with an IEM i.e., 9 with homocystinuria, 2 with MPS, 2 with Guanidinoacetate methyltransferase (GAMT) deficiency and 1 with sitosterolemia. These IEMs are found frequent in the collected patient samples from Pakistan. Thus, present study can help to take an initiative step to start a NBS program in Pakistan, especially for the homocystinuria having highest incidence among aminoacidopathies in the studied patients, and which is amenable to treatment. This endeavor will pave the way for a healthier life of affected patients and will lessen the burden on their families and society.

Metabolomics for Animal Models of Rare Human Diseases: An Expert Review and Lessons Learned

Rare diseases occur with a frequency ≤1:1500-1:2500 depending on the location and applicable definitions across countries. Although individually rare, they collectively affect as much as 4-8% of population imposing a large burden on public health. Rarity in prevalence means prolonged path to accurate diagnosis, lack of treatment options, and also limited chances for preclinical studies of pathogenesis. I discuss in this expert review (1) what metabolomics, as a high throughput systems sciences technology, offers for rare disease studies, (2) why animal models are important for the study of rare human diseases and what should be kept in mind while using animal models, and finally, (3) provide examples of recent research to highlight how metabolomics on animal models of rare diseases perform, and how these results can lead to the knowhow, which raises genome, metabolome, and phenotype integration to a whole new level. In sum, metabolomics has been for years in clinical use for diagnosis of certain types of rare diseases. Determination of pathogenesis of more complex diseases and testing of treatment strategies is where animal models and systems biology analytical approaches are necessary. From gathered data, it is possible to go back to diagnostic and prognostic markers for rare diseases, which so far lack reliable and robust diagnosis and therapeutic options. In the future, a major challenge is to reveal the links between genotype, metabolism, and phenotype. Rare diseases could be the key in that process.

A Multi-Trait Approach Identified Genetic Variants Including a Rare Mutation in RGS3 with Impact on Abnormalities of Cardiac Structure/Function

Heart failure is a major cause for premature death. Given the heterogeneity of the heart failure syndrome, identifying genetic determinants of cardiac function and structure may provide greater insights into heart failure. Despite progress in understanding the genetic basis of heart failure through genome wide association studies, the heritability of heart failure is not well understood. Gaining further insights into mechanisms that contribute to heart failure requires systematic approaches that go beyond single trait analysis. We integrated a Bayesian multi-trait approach and a Bayesian networks for the analysis of 10 correlated traits of cardiac structure and function measured across 3387 individuals with whole exome sequence data. While using single-trait based approaches did not find any significant genetic variant, applying the integrative Bayesian multi-trait approach, we identified 3 novel variants located in genes, RGS3, CHD3, and MRPL38 with significant impact on the cardiac traits such as left ventricular volume index, parasternal long axis interventricular septum thickness, and mean left ventricular wall thickness. Among these, the rare variant NC_000009.11:g.116346115C > A (rs144636307) in RGS3 showed pleiotropic effect on left ventricular mass index, left ventricular volume index and maximal left atrial anterior-posterior diameter while RGS3 can inhibit TGF-beta signaling associated with left ventricle dilation and systolic dysfunction.

Biochemical screening of intellectually disabled and healthy children in Punjab, Pakistan: differences in liver function test and lipid profiles

Objectives: Inborn errors of metabolism (IEMs) are rare genetic disorders. Generally, IEMs are untreatable; however, some IEMs causing intellectual disability are potentially treatable if diagnosed earlier. In this study, levels of some clinically important biochemical parameters in intellectually disabled children suspected for IEMs were tested to see their association with intellectual disability, which could be helpful in preliminary screening. Methods: This comparative cross-sectional observational study was carried out from 2014 to 2017. Blood samples from 800 boys and girls (aged 4-24 years) were collected, of which 391 were healthy (IQ >90) and 409 were intellectually disabled (IQ <70) children with unknown cause. Clinically important (Liver and kidney enzymes etc.) biochemical parameters were analyzed in sera samples using commercial kits on semi-automated clinical chemistry analyzer. Results: Serum analysis showed the levels of ALP (p < 0.00001), ASAT (p = 0.001), ALAT (p = 0.016), albumin (p < 0.001), uric acid (p < 0.001), cholesterol (p < 0.001), triglycerides (p < 0.001), and hemoglobin (p = 0.005) were significantly different between healthy and intellectually disabled children. Conclusion: Changes in the liver function test and lipid profile parameters were significantly different in children with intellectual disability; however, it requires further detailed analysis for complete characterization of these diseases.

Representing glycophenotypes: semantic unification of glycobiology resources for disease discovery

While abnormalities related to carbohydrates (glycans) are frequent for patients with rare and undiagnosed diseases as well as in many common diseases, these glycan-related phenotypes (glycophenotypes) are not well represented in knowledge bases (KBs). If glycan-related diseases were more robustly represented and curated with glycophenotypes, these could be used for molecular phenotyping to help to realize the goals of precision medicine. Diagnosis of rare diseases by computational cross-species comparison of genotype-phenotype data has been facilitated by leveraging ontological representations of clinical phenotypes, using Human Phenotype Ontology (HPO), and model organism ontologies such as Mammalian Phenotype Ontology (MP) in the context of the Monarch Initiative. In this article, we discuss the importance and complexity of glycobiology and review the structure of glycan-related content from existing KBs and biological ontologies. We show how semantically structuring knowledge about the annotation of glycophenotypes could enhance disease diagnosis, and propose a solution to integrate glycophenotypes and related diseases into the Unified Phenotype Ontology (uPheno), HPO, Monarch and other KBs. We encourage the community to practice good identifier hygiene for glycans in support of semantic analysis, and clinicians to add glycomics to their diagnostic analyses of rare diseases.

Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing

Metabolomics is the untargeted measurement of the metabolome, which is composed of the complement of small molecules detected in a biological sample. As such, metabolomic analysis produces a global biochemical phenotype. It is a technology that has been utilized in the research setting for over a decade. The metabolome is directly linked to and is influenced by genetics, epigenetics, environmental factors, and the microbiome-all of which affect health. Metabolomics can be applied to human clinical diagnostics and to other fields such as veterinary medicine, nutrition, exercise, physiology, agriculture/plant biochemistry, and toxicology. Applications of metabolomics in clinical testing are emerging, but several aspects of its use as a clinical test differ from applications focused on research or biomarker discovery and need to be considered for metabolomics clinical test data to have optimum impact, be meaningful, and be used responsibly. In this review, we deconstruct aspects and challenges of metabolomics for clinical testing by illustrating the significance of test design, accurate and precise data acquisition, quality control, data processing, n-of-1 comparison to a reference population, and biochemical pathway analysis. We describe how metabolomics technology is integral to defining individual biochemical phenotypes, elaborates on human health and disease, and fits within the precision medicine landscape. Finally, we conclude by outlining some future steps needed to bring metabolomics into the clinical space and to be recognized by the broader medical and regulatory fields.

Metabolomics: a challenge for detecting and monitoring inborn errors of metabolism

Timely newborn screening and genetic profiling are crucial in early recognition and treatment of inborn errors of metabolism (IEMs). A proposed nosology of IEMs has inserted 1,015 well-characterized IEMs causing alterations in specific metabolic pathways. With the increasing expansion of metabolomics in clinical biochemistry and laboratory medicine communities, several research groups have focused their interest on the analysis of metabolites and their interconnections in IEMs. Metabolomics has the potential to extend metabolic information, thus allowing to achieve an accurate diagnosis for the individual patient and to discover novel IEMs. Structural and functional information on 247 metabolites associated with 147 IEMs and 202 metabolic pathways involved in various IEMs have been reported in the human metabolome data base (HMDB). For each metabolic gene, a new computational approach can be developed for predicting a set of metabolites, whose concentration is predicted to change after gene knockout in urine, blood and other biological fluids. Both targeted and untargeted mass spectrometry (MS)-based metabolomic approaches have been used to expand the range of disease-associate metabolites. The quantitative targeted approach, in conjunction with chemometrics, can be considered a basic tool for validating known diagnostic biomarkers in various metabolic disorders. The untargeted approach broadens the identification of new biomarkers in known IEMs and allows pathways analysis. Urine is an ideal biological fluid for metabolomics in neonatology; however, the lack of standardization of preanalytical phase may generate potential interferences in metabolomic studies. The integration of genomic and metabolomic data represents the current challenge for improving diagnosis and prognostication of IEMs. The goals consist in identifying both metabolically active loci and genes relevant to a disease phenotype, which means deriving disease-specific biological insights.