Metabolomics to Improve the Diagnostic Efficiency of Inborn Errors of Metabolism

Single-cell metabolomics in rare disease: From technology to disease

With the development of clinical experience and technology, rare diseases (RDs) are gradually coming into the limelight. As they often lead to poor prognosis, it is urgent to promote the accuracy and rapidity of diagnosis and promote the development of therapeutic drugs. In recent years, with the rapid improvement of single-cell sequencing technology, the advantages of multi-omics combined application in diseases have been continuously explored. Single-cell metabolomics represents a powerful tool for advancing our understanding of rare diseases, particularly metabolic RDs, and transforming clinical practice. By unraveling the intricacies of cellular metabolism at a single-cell resolution, this innovative approach holds the potential to revolutionize diagnosis, treatment, and management strategies, ultimately improving outcomes for RDs patients. Continued research and technological advancements in single-cell metabolomics are essential for realizing its full potential in the field of RDs diagnosis and therapeutics. It is expected that single-cell metabolomics can be better applied to RDs research in the future, for the benefit of patients and society.

Exploring disease-specific metabolite signatures in hereditary angioedema patients

Introduction

Hereditary angioedema (HAE) is a rare, life-threatening autosomal dominant genetic disorder caused by a deficient and/or dysfunctional C1 esterase inhibitor (C1-INH) (type 1 and type 2) leading to recurrent episodes of edema. This study aims to explore HAE patients' metabolomic profiles and identify novel potential diagnostic biomarkers for HAE. The study also examined distinguishing HAE from idiopathic angioedema (AE).

Methods

Blood plasma samples from 10 HAE (types 1/2) patients, 15 patients with idiopathic AE, and 20 healthy controls were collected in Latvia and analyzed using LC-MS based targeted metabolomics workflow. T-test and fold change calculation were used to identify metabolites with significant differences between diseases and control groups. ROC analysis was performed to evaluate metabolite based classification model.

Results

A total of 33 metabolites were detected and quantified. The results showed that isovalerylcarnitine, cystine, and hydroxyproline were the most significantly altered metabolites between the disease and control groups. Aspartic acid was identified as a significant metabolite that could differentiate between HAE and idiopathic AE. The mathematical combination of metabolites (hydroxyproline * cystine)/(creatinine * isovalerylcarnitine) was identified as the diagnosis signature for HAE. Furthermore, glycine/asparagine ratio could differentiate between HAE and idiopathic AE.

Conclusion

Our study identified isovalerylcarnitine, cystine, and hydroxyproline as potential biomarkers for HAE diagnosis. Identifying new biomarkers may offer enhanced prospects for accurate, timely, and economical diagnosis of HAE, as well as tailored treatment selection for optimal patient care.

Improving newborn screening in India: Disease gaps and quality control

In India, newborn screening (NBS) is essential for detecting health problems in infants. Despite significant progress, significant gaps and challenges persist. India has made great strides in genomics dueto the existence of the National Institute of Biomedical Genomics in West Bengal. The work emphasizes the challenges NBS programs confront with technology, budgetary constraints, insufficient counseling, inequality in illness panels, and a lack of awareness. Advancements in technology, such as genetic testing and next-generation sequencing, are expected to significantly transform the process. The integration of analytical tools, artificial intelligence, and machine learning algorithms could improve the efficiency of newborn screening programs, offering a personalized healthcare approach. It is critical to address gaps in information, inequities in illness incidence, budgetary restrictions, and inadequate counseling. Strengthening national NBS programs requires increased public awareness and coordinated efforts between state and central agencies. Quality control procedures must be used at every level for implementation to be successful. Additional studies endeavor to enhance NBS in India through public education, illness screening expansion, enhanced quality control, government incentive implementation, partnership promotion, and expert training. Improved neonatal health outcomes and the viability of the program across the country will depend heavily on new technology and counseling techniques.

Newborn Screening for Inborn Errors of Metabolism by Next-Generation Sequencing Combined with Tandem Mass Spectrometry

The aim of this study was to observe the outcomes of newborn screening (NBS) in a certain population by using next-generation sequencing (NGS) as a first-tier screening test combined with tandem mass spectrometry (MS/MS). We performed a multicenter study of 29,601 newborns from eight screening centers with NBS via NGS combined with MS/MS. A custom-designed panel targeting the coding region of the 142 genes of 128 inborn errors of metabolism (IEMs) was applied as a first-tier screening test, and expanded NBS using MS/MS was executed simultaneously. In total, 52 genes associated with the 38 IEMs screened by MS/MS were analyzed. The NBS performance of these two methods was analyzed and compared respectively. A total of 23 IEMs were diagnosed via NGS combined with MS/MS. The incidence of IEMs was approximately 1 in 1287. Within separate statistical analyses, the positive predictive value (PPV) for MS/MS was 5.29%, and the sensitivity was 91.3%. However, for genetic screening alone, the PPV for NGS was 70.83%, with 73.91% sensitivity. The three most common IEMs were methylmalonic academia (MMA), primary carnitine deficiency (PCD) and phenylketonuria (PKU). The five genes with the most common carrier frequencies were PAH (1:42), PRODH (1:51), MMACHC (1:52), SLC25A13 (1:55) and SLC22A5 (1:63). Our study showed that NBS combined with NGS and MS/MS improves the performance of screening methods, optimizes the process, and provides accurate diagnoses.

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

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

Supplementary Information

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

Phenylketonuria (PKU) Urinary Metabolomic Phenotype Is Defined by Genotype and Metabolite Imbalance: Results in 51 Early Treated Patients Using Ex Vivo 1H-NMR Analysis

Phenylketonuria (PKU) is a rare metabolic disorder caused by mutations in the phenylalanine hydroxylase gene. Depending on the severity of the genetic mutation, medical treatment, and patient dietary management, elevated phenylalanine (Phe) may occur in blood and brain tissues. Research has recently shown that high Phe not only impacts the central nervous system, but also other organ systems (e.g., heart and microbiome). This study used ex vivo proton nuclear magnetic resonance (¹H-NMR) analysis of urine samples from PKU patients (mean 14.9 ± 9.2 years, n = 51) to identify the impact of elevated blood Phe and PKU treatment on metabolic profiles. Our results found that 24 out of 98 urinary metabolites showed a significant difference (p < 0.05) for PKU patients compared to age-matched healthy controls (n = 51) based on an analysis of urinary metabolome. These altered urinary metabolites were related to Phe metabolism, dysbiosis, creatine synthesis or intake, the tricarboxylic acid (TCA) cycle, end products of nicotinamide-adenine dinucleotide degradation, and metabolites associated with a low Phe diet. There was an excellent correlation between the metabolome and genotype of PKU patients and healthy controls of 96.7% in a confusion matrix model. Metabolomic investigations may contribute to a better understanding of PKU pathophysiology.

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

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

Ex vivo proton spectroscopy (1 H-NMR) analysis of inborn errors of metabolism: Automatic and computer-assisted analyses

There are about 1500 genetic metabolic diseases. A small number of treatable diseases are diagnosed by newborn screening programs, which are continually being developed. However, most diseases can only be diagnosed based on clinical symptoms or metabolic findings. The main biological fluids used are urine, plasma and, in special situations, cerebrospinal fluid. In contrast to commonly used methods such as gas chromatography and high performance liquid chromatography mass spectrometry, ex vivo proton spectroscopy (¹ H-NMR) is not yet used in routine clinical practice, although it has been recommended for more than 30 years. Automatic analysis and improved NMR technology have also expanded the applications used for the diagnosis of inborn errors of metabolism. We provide a mini-overview of typical applications, especially in urine but also in plasma, used to diagnose common but also rare genetic metabolic diseases with ¹ H-NMR. The use of computer-assisted diagnostic suggestions can facilitate interpretation of the profiles. In a proof of principle, to date, 182 reports of 59 different diseases and 500 reports of healthy children are stored. The percentage of correct automatic diagnoses was 74%. Using the same ¹ H-NMR profile-targeted analysis, it is possible to apply an untargeted approach that distinguishes profile differences from healthy individuals. Thus, additional conditions such as lysosomal storage diseases or drug interferences are detectable. Furthermore, because ¹ H-NMR is highly reproducible and can detect a variety of different substance categories, the metabolomic approach is suitable for monitoring patient treatment and revealing additional factors such as nutrition and microbiome metabolism. Besides the progress in analytical techniques, a multiomics approach is most effective to combine metabolomics with, for example, whole exome sequencing, to also diagnose patients with nondetectable metabolic abnormalities in biological fluids. In this mini review we also provide our own data to demonstrate the role of NMR in a multiomics platform in the field of inborn errors of metabolism.

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

INTRODUCTION

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

A retrospective analysis of MS/MS screening for IEM in high-risk areas

Inborn errors of metabolism (IEM) can lead to severe motor and neurological developmental disorders and even disability and death in children due to untimely treatment. In this study, we used tandem mass spectrometry (MS/MS) for primary screening and recall of those with positive primary screening for rescreening. Further diagnosis was based on biochemical tests, imaging and clinical presentation as well as accurate genetic testing using multi-gene panel with high-throughput sequencing of 130 IEM-related genes. The screening population was 16,207 newborns born between July 1, 2019, and December 31, 2021. Based on the results, 8 newborns were diagnosed with IEM, constituting a detection rate of 1:2,026. Phenylketonuria was the most common form of IEM. In addition, seven genes associated with IEM were detected in these eight patients. All eight patients received standardized treatment starting in the neonatal period, and the follow-up results showed good growth and development. Therefore, our study suggests that MS/MS rescreening for IEM pathogenic variants in high-risk areas, combined with a sequencing validation strategy, can be highly effective in the early detection of affected children. This strategy, combined with early intervention, can be effective in preventing neonatal morbidity and improving population quality.

Incidence and genetic variants of inborn errors of metabolism identified through newborn screening: A 7-year study in eastern coastal areas of China

BACKGROUND

The incidence of inborn errors of metabolism (IEM) varies across countries and areas. Currently, there are no studies on IEM using newborn screening (NBS) in eastern coastal areas of China. We aimed to estimate the incidence and genetic variants of IEM and understand the spectrum of diseases caused by IEM and variants among them in this area.

METHODS

The NBS performed by tandem mass spectrometry (MS/MS) from 2016 to 2021 was retrospectively reviewed. Heel blood was collected from all newborns 72 h after birth. Targeted massively parallel sequencing was performed for genetic analysis.

RESULTS

Among 245,194 newborns, 95 were diagnosed with IEM, the overall incidence observed was-IEM: 1/2581; amino acid metabolism disorder: 1/4715; organic acid metabolism disorder: 1/11676; and fatty acid metabolism disorder: 1/11145. The incidence of different IEM was in the range of 1/245194 to 1/6452. Phenylketonuria (PKU, 1/7211) was the most common IEM, followed by methylmalonic acidemia (MMA, 1/27244), short-chain acyl-CoA dehydrogenase deficiency (SCADD, 1/30649), and citrin deficiency (CD, 1/35028). For genetic variants, the common hotspot variants found were-PAH gene for PKU: c.728G > A, c.442-1G > A, c.611A > G, c.721C > T; PTS gene for non-classical PKU: c.259C > T; MMACHC gene for MMA: c.658_660delAAG, c.609G > A; MMUT gene for MMA: c.1663G > A; ACADS gene for SCADD: c.1031A > G and c.1130C > T; and SLC25A13 gene for CD: c.1638_1660dup, c.852_855del.

CONCLUSION

This study displayed the diseases and varied spectrum of IEM in eastern coastal areas of China. Implementing NBS for IEM by MS/MS combined with massively parallel sequencing can offer an improved plan for NBS to detect IEM.

The landscape of CRISPR/Cas9 for inborn errors of metabolism

Since its discovery as a genome editing tool, the clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR/Cas9) system has opened new horizons in the diagnosis, research, and treatment of genetic diseases. CRISPR/Cas9 can rewrite the genome at any region with outstanding precision to modify it and further instructions for gene expression. Inborn Errors of Metabolism (IEM) are a group of more than 1500 diseases produced by mutations in genes encoding for proteins that participate in metabolic pathways. IEM involves small molecules, energetic deficits, or complex molecules diseases, which may be susceptible to be treated with this novel tool. In recent years, potential therapeutic approaches have been attempted, and new models have been developed using CRISPR/Cas9. In this review, we summarize the most relevant findings in the scientific literature about the implementation of CRISPR/Cas9 in IEM and discuss the future use of CRISPR/Cas9 to modify epigenetic markers, which seem to play a critical role in the context of IEM. The current delivery strategies of CRISPR/Cas9 are also discussed.

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.

Imaging of Inherited Metabolic and Endocrine Disorders

"Inherited metabolic disorders represent a large group of disorders of which approximately 25% present in neonatal period with acute metabolic decompensation, rapid clinical deterioration, and often nonspecific imaging findings. Neonatal onset signifies the profound severity of the metabolic abnormality compared with cases with later presentation and necessitates rapid diagnosis and urgent therapeutic measures in an attempt to decrease the extent of brain injury and prevent grave neurologic sequela or death. Here, the authors discuss classification and clinical and imaging findings in a spectrum of metabolic and endocrine disorders with neonatal presentation."

Understanding Inborn Errors of Metabolism through Metabolomics

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

Diagnostic approach in adult-onset neurometabolic diseases

Neurometabolic diseases are a group of individually rare but numerous and heterogeneous genetic diseases best known to paediatricians. The more recently reported adult forms may present with phenotypes strikingly different from paediatric ones and may mimic other more common neurological disorders in adults. Furthermore, unlike most neurogenetic diseases, many neurometabolic diseases are treatable, with both conservative and more recent innovative therapeutics. However, the phenotypical complexity of this group of diseases and the growing number of specialised biochemical tools account for a significant diagnostic delay and underdiagnosis. We reviewed all series and case reports of patients with a confirmed neurometabolic disease and a neurological onset after the age of 10 years, with a focus on the 36 treatable ones, and classified these diseases according to their most relevant clinical manifestations. The biochemical diagnostic approach of neurometabolic diseases lays on the use of numerous tests studying a set of metabolites, an enzymatic activity or the function of a given pathway; and therapeutic options aim to restore the enzyme activity or metabolic function, limit the accumulation of toxic substrates or substitute the deficient products. A quick diagnosis of a treatable neurometabolic disease can have a major impact on patients, leading to the stabilisation of the disease and cease of repeated diagnostic investigations, and allowing for familial screening. For the aforementioned, in addition to an exhaustive and clinically meaningful review of these diseases, we propose a simplified diagnostic approach for the neurologist with the aim to help determine when to suspect a neurometabolic disease and how to proceed in a rational manner. We also discuss the place of next-generation sequencing technologies in the diagnostic process, for which deep phenotyping of patients (both clinical and biochemical) is necessary for improving their diagnostic yield.

Altered Plasma Mitochondrial Metabolites in Persistently Symptomatic Individuals after a GBCA-Assisted MRI

Despite the impressive safety of gadolinium (Gd)-based contrast agents (GBCAs), a small number of patients report the onset of new, severe, ongoing symptoms after even a single exposure—a syndrome termed Gadolinium Deposition Disease (GDD). Mitochondrial dysfunction and oxidative stress have been repeatedly implicated by animal and in vitro studies as mechanisms of Gd/GBCA-related toxicity, and as pathogenic in other diseases with similarities in presentation. Here, we aimed to molecularly characterize and explore potential metabolic associations with GDD symptoms. Detailed clinical phenotypes were systematically obtained for a small cohort of individuals (n = 15) with persistent symptoms attributed to a GBCA-enhanced MRI and consistent with provisional diagnostic criteria for GDD. Global untargeted mass spectroscopy-based metabolomics analyses were performed on plasma samples and examined for relevance with both single marker and pathways approaches. In addition to GDD criteria, frequently reported symptoms resembled those of patients with known mitochondrial-related diseases. Plasma differences compared to a healthy, asymptomatic reference cohort were suggested for 45 of 813 biochemicals. A notable proportion of these are associated with mitochondrial function and related disorders, including nucleotide and energy superpathways, which were over-represented. Although early evidence, coincident clinical and biochemical indications of potential mitochondrial involvement in GDD are remarkable in light of preclinical models showing adverse Gd/GBCA effects on multiple aspects of mitochondrial function. Further research on the potential contributory role of these markers and pathways in persistent symptoms attributed to GBCA exposure is recommended.

How paediatric drug development and use could benefit from OMICs: a c4c expert group white paper

The safety and efficacy of pharmacotherapy in children, particularly preterms, neonates, and infants, is limited by a paucity of good quality data from prospective clinical drug trials. A specific challenge is the establishment of valid biomarkers. OMICs technologies may support these efforts, by complementary information about targeted and non-targeted molecules through systematic characterization and quantitation of biological samples. OMICs technologies comprise at least genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics in addition to the patient's phenotype. OMICs technologies are in part hypothesis-generating allowing an in depth understanding of disease pathophysiology and pharmacological mechanisms. Application of OMICs technologies in paediatrics faces major challenges before routine adoption. First, developmental processes need to be considered, including a sub-division into specific age groups as developmental changes clearly impact OMICs data. Second, compared to the adult population, the number of patients is limited as well as type and amount of necessary biomaterial, especially in neonates and preterms. Thus, advanced trial designs and biostatistical methods, non-invasive biomarkers, innovative biobanking concepts including data and samples from healthy children, as well as analytical approaches (e.g. liquid biopsies) should be addressed to overcome these obstacles. The ultimate goal is to link OMICs technologies with innovative analysis tools, like artificial intelligence at an early stage. The use of OMICs data based on a feasible approach will contribute to identify complex phenotypes and subpopulations of patients to improve development of medicines for children with potential economic advantages.

Factors associated with bovine respiratory disease case fatality in feedlot cattle

Bovine respiratory disease (BRD) is the primary cause of morbidity and mortality in cattle feedlots. There is a need to understand what animal health and production factors are associated with increased mortality risk due to BRD. The aim of the present study was to explore factors associated with BRD case fatality in feedlot cattle. Four pens totaling 898 steers were monitored daily for visual signs of BRD such as difficult breathing and coughing, and animals exhibiting signs of BRD were taken to the hospital shed for further examination and clinical measures. Blood samples were obtained at feedlot entry and at time of first BRD pull from animals diagnosed with BRD (n = 121) and those that died due to BRD confirmed by postmortem examination (n = 16; 13.2% case fatality rate). Mixed-effects linear regression models were used to estimate differences in animal health and production factors and the relative concentrations of 34 identified blood metabolites between animals that survived versus those that died. Generalized linear mixed-effects models were used to obtain the odds of being seronegative (at both feedlot entry and first BRD pull) to 5 BRD viruses and having a positive nasal swab result at the time of first pull in died and survived animals. Animals that died from BRD had lower average daily gain (ADG), reduced weight at first BRD pull, higher visual BRD scores and received more treatments for BRD compared with animals that survived BRD (P < 0.05). The odds of being seronegative for bovine viral diarrhea virus 1 (BVDV-1) were 5.66 times higher for animals that died compared with those that survived (P = 0.013). The odds of having a positive bovine coronavirus nasal swab result were 13.73 times higher in animals that died versus those that survived (P = 0.007). Animals that died from BRD had higher blood concentrations of α glucose chain, β-hydroxybutyrate, leucine, phenylalanine, and pyruvate compared with those that survived (P < 0.05). Animals that died from BRD had lower concentrations of acetate, citrate, and glycine compared with animals that survived (P < 0.05). The results of the current study suggest that ADG to first BRD pull, weight at first BRD pull, visual BRD score, the number of BRD treatments, seronegativity to BVDV-1, virus positive to BCoV nasal swab, and that certain blood metabolites are associated with BRD case fatality risk. The ability of these measures to predict the risk of death due to BRD needs further research.

Current Scenario of Clinical Diagnosis to Identify Inborn Errors of Metabolism with Precision Profiling for Expanded Screening in Infancy in a Resource-limited Setting

Inborn errors of metabolism (IEM) are a diverse collection of abnormalities that cause a variety of morbidities and mortality in children and are classified as uncommon genetic diseases. Early and accurate detection of the condition can save a patient's life. By aiding families as they navigate the experience of having a child with an IEM, healthcare practitioners have the chance to reduce the burden of negative emotional consequences. New therapeutic techniques, such as enzyme replacement and small chemical therapies, organ transplantation, and cellular and gene-based therapies using whole-genome sequencing, have become available in addition to traditional medical intake and cofactor treatments. In the realm of metabolic medicine and metabolomics, the twentyfirst century is an exciting time to be alive. The availability of metabolomics and genomic analysis has led to the identification of a slew of novel diseases. Due to the rarity of individual illnesses, obtaining high-quality data for these treatments in clinical trials and real-world settings has proven difficult. Guidelines produced using standardized techniques have helped enhance treatment delivery and clinical outcomes over time. This article gives a comprehensive description of IEM and how to diagnose it in patients who have developed clinical signs early or late. The appropriate use of standard laboratory outcomes in the preliminary patient assessment is also emphasized that can aid in the ordering of specific laboratory tests to confirm a suspected diagnosis, in addition, to begin treatment as soon as possible in a resource limiting setting where genomic analysis or newborn screening facility is not available.

Inborn Errors of Metabolism Screening in Neonates: Current Perspective with Diagnosis and Therapy

Inborn errors of metabolism (IEMs) are rare hereditary or acquired disorders resulting from an enzymatic deformity in biochemical and metabolic pathways influencing proteins, fats, carbohydrate metabolism, or hampered some organelle function. Even though individual IEMs are uncommon, together, they represent a diverse class of genetic diseases, with new issues and disease mechanisms being portrayed consistently. IEM includes the extraordinary multifaceted nature of the fundamental pathophysiology, biochemical diagnosis, molecular level investigation, and complex therapeutic choices. However, due to the molecular, biochemical, and clinical heterogeneity of IEM, screening alone will not detect and diagnose all illnesses included in newborn screening programs. Early diagnosis prevents the emergence of severe clinical symptoms in the majority of IEM cases, lowering morbidity and death. The appearance of IEM disease can vary from neonates to adult people, with the more serious conditions showing up in juvenile stages along with significant morbidity as well as mortality. Advances in understanding the physiological, biochemical, and molecular etiologies of numerous IEMs by means of modalities, for instance, the latest molecular-genetic technologies, genome engineering knowledge, entire exome sequencing, and metabolomics, have prompted remarkable advancement in detection and treatment in modern times. In this review, we analyze the biochemical basis of IEMs, clinical manifestations, the present status of screening, ongoing advances, and efficiency of diagnosis in treatment for IEMs, along with prospects for further exploration as well as innovation.

Metabolomics in Clinical Practice: Improving Diagnosis and Informing Management

BACKGROUND

Metabolomics is the study of small molecules to simultaneously identify multiple low molecular weight molecules in a system. Broadly speaking, metabolomics can be subdivided into targeted and untargeted types of analysis, each type having advantages and drawbacks. Targeted metabolomics can quantify analytes but only looks for known or expected analytes related to particular disease(s), whereas untargeted metabolomics is typically nonquantitative but can detect thousands of analytes from an agnostic or nonhypothesis driven perspective, allowing for novel discoveries.

CONTENT

One application of metabolomics is the study of inborn errors of metabolism (IEM). The biochemical hallmark of IEMs is decreased concentrations of analytes distal to the enzymatic defect and buildup of analytes proximal to the defect. Metabolomics can detect these changes with one test and is effective in screening for and diagnosis of IEMs. Metabolomics has also been used to study many nonmetabolic diseases such as autism spectrum disorder, various cancers, and multiple congenital anomalies syndromes. Metabolomics has led to the discovery of many novel biomarkers of disease. Recent publications demonstrate how metabolomics can be useful clinically in the diagnosis and management of patients, as well as for research and clinical discovery.

SUMMARY

Metabolomics has proved to be a useful tool clinically for screening and diagnostic purposes and from a research perspective for the detection of novel biomarkers. In the future, metabolomics will likely become a routine part of the evaluation for many diseases as either a supplementary test or it may simply replace historical analyses that require several individual tests and sample types.

Liquid Chromatography-Mass Spectrometry for Clinical Metabolomics: An Overview

Metabolomics is a discipline that offers a comprehensive analysis of metabolites in biological samples. In the last decades, the notable evolution in liquid chromatography and mass spectrometry technologies has driven an exponential progress in LC-MS-based metabolomics. Targeted and untargeted metabolomics strategies are important tools in health and medical science, especially in the study of disease-related biomarkers, drug discovery and development, toxicology, diet, physical exercise, and precision medicine. Clinical and biological problems can now be understood in terms of metabolic phenotyping. This overview highlights the current approaches to LC-MS-based metabolomics analysis and its applications in the clinical research.

Untargeted metabolomic analysis of urine samples for diagnosis of inherited metabolic disorders

Metabolomics has become an important tool for clinical research, especially for analyzing inherited metabolic disorders (IMDs). The purpose of this study was to explore the performance of metabolomics in diagnosing IMDs using an untargeted metabolomic approach. A total of 40 urine samples were collected: 20 samples from healthy children and 20 from pediatric patients, of whom 13 had confirmed IMDs and seven had suspected IMDs. Samples were analyzed by Orbitrap mass spectrometry in positive and negative mode alternately, coupled with ultra-high liquid chromatography. Raw data were processed using Compound Discovery 2.0 ™ and then exported for partial least squares discriminant analysis (PLS-DA) by SIMCA-P 14.1. After comparing with m/zCloud and chemSpider libraries, compounds with similarity above 80% were selected and normalized for subsequent relative quantification analysis. The uncommon compounds discovered were analyzed based on the Kyoto Encyclopedia of Genes and Genomes to explore their possible metabolic pathways. All IMDs patients were successfully distinguished from controls in the PLS-DA. Untargeted metabolomics revealed a broader metabolic spectrum in patients than what is observed using routine chromatographic methods for detecting IMDs. Higher levels of certain compounds were found in all 13 confirmed IMD patients and 5 of 7 suspected IMD patients. Several potential novel markers emerged after relative quantification. Untargeted metabolomics may be able to diagnose IMDs from urine and may deepen insights into the disease by revealing changes in various compounds such as amino acids, acylcarnitines, organic acids, and nucleosides. Such analyses may identify biomarkers to improve the study and treatment of IMDs.

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.

Multi-Organ Dysfunction in Cerebral Palsy

Cerebral Palsy (CP) describes a heterogenous group of non-progressive disorders of posture or movement, causing activity limitation, due to a lesion in the developing brain. CP is an umbrella term for a heterogenous condition and is, therefore, descriptive rather than a diagnosis. Each case requires detailed consideration of etiology. Our understanding of the underlying cause of CP has developed significantly, with areas such as inflammation, epigenetics and genetic susceptibility to subsequent insults providing new insights. Alongside this, there has been increasing recognition of the multi-organ dysfunction (MOD) associated with CP, in particular in children with higher levels of motor impairment. Therefore, CP should not be seen as an unchanging disorder caused by a solitary insult but rather, as a condition which evolves over time. Assessment of multi-organ function may help to prevent complications in later childhood or adulthood. It may also contribute to an improved understanding of the etiology and thus may have an implication in prevention, interventional methods and therapies. MOD in CP has not yet been quantified and a scoring system may prove useful in allowing advanced clinical planning and follow-up of children with CP. Additionally, several biomarkers hold promise in assisting with long-term monitoring. Clinicians should be aware of the multi-system complications that are associated with CP and which may present significant diagnostic challenges given that many children with CP communicate non-verbally. A step-wise, logical, multi-system approach is required to ensure that the best care is provided to these children. This review summarizes multi-organ dysfunction in children with CP whilst highlighting emerging research and gaps in our knowledge. We identify some potential organ-specific biomarkers which may prove useful in developing guidelines for follow-up and management of these children throughout their lifespan.

Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics

Metabolomics has been widely used for investigating the biological functions of disease expression and has the potential to discover biomarkers in circulating biofluids or tissue extracts that reflect in phenotypic changes. Metabolic profiling has advantages because of the use of unbiased techniques, including multivariate analysis, and has been applied in pharmacological studies to predict therapeutic and adverse reactions of drugs, which is called pharmacometabolomics (PMx). Nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based metabolomics has contributed to the discovery of recent disease biomarkers; however, the optimal strategy for the study purpose must be selected from many established protocols, methodologies and analytical platforms. Additionally, information on molecular localization in tissue is essential for further functional analyses related to therapeutic and adverse effects of drugs in the process of drug development. MS imaging (MSI) is a promising technology that can visualize molecules on tissue surfaces without labeling and thus provide localized information. This review summarizes recent uses of MS-based global and wide-targeted metabolomics technologies and the advantages of the MSI approach for PMx and highlights the PMx technique for the biomarker discovery of adverse drug effects.

Urinary Metabolomic Profiling Reveals Biological Pathways and Predictive Signatures Associated with Childhood Asthma

Background

Despite considerable efforts, the pathogenic mechanisms of asthma are still incompletely understood, due to its heterogeneous nature. However, metabolomics can offer a global view of a biological system, making it a valuable tool for further elucidation of mechanisms and biomarker discovery in asthma.

Methods

GC-MS-based metabolomic analysis was conducted for comparison of urine metabolic profiles between asthmatic children (n=30) and healthy controls (n=30).

Results

An orthogonal projections to latent structures discriminant-analysis model revealed a clear separation of the asthma and control groups (R ² _x =0.137, R ² _y =0.947, Q ²=0.82). A total of 20 differential metabolites were identified as discriminant factors, of which eleven were significantly increased and nine decreased in the asthma group compared to the control group. Pathway-enrichment analysis based on these differential metabolites indicated that sphingolipid metabolism, protein biosynthesis, and citric acid cycle were strongly associated with asthma. Among the identified metabolites, 2-hydroxybutanoic acid showed excellent discriminatory performance for distinguishing asthma from healthy controls, with an AUC of 0.969.

Conclusion

Our study revealed significant changes in the urine metabolome of asthma patients. Several perturbed pathways (eg, sphingolipid metabolism and citric acid cycle) may be related to asthma pathogenesis, and 2-hydroxybutanoic acid could serve as a potential biomarker for asthma diagnosis.