Clinical Metabolomics: The New Metabolic Window for Inborn Errors of Metabolism Investigations in the Post-Genomic Era

Retrospective analysis of reference intervals for dried blood spot based ms/ms newborn screening programs in Chinese preterm neonates: a nationwide study

OBJECTIVES

Although recent discoveries regarding the biomarkers of newborn screening (NBS) programs by tandem mass spectrometry (MS/MS) highlight the critical need to establish reference intervals (RIs) specifically for preterm infants, no such RIs has been formally published yet. This study addressed the gap by offering a comprehensive set of reference intervals (RIs) for preterm neonates, and illustrating the dynamic changes of each biomarker with age.

DESIGN AND METHODS

The NBS data of 199,693 preterm newborns (< 37 weeks of gestation) who met the inclusion and exclusion criteria from the NNSCP database were included in study analysis. The birth weight stratified dynamic trend of each biomarker were captured by their concentrations over age. Reference partitions were determined by the method of Harris and Boyd. RIs, corresponding to the 2.5th and 97.5th percentiles, as well as the 0.5th, 25th, 50th, 75th and 99.5th percentiles were calculated using a non-parametric rank approach.

RESULTS

Increasing birth weight is associated with an elevation in the levels of arginine, citrulline, glycine, leucine and isobarics, methionine, ornithine, phenylalanine, and valine, whereas the levels of alanine, proline and tyrosine decrease. Additionally, two short-chain acylcarnitines (butyrylcarnitine + isobutyrylcarnitine and isovalerylcarnitine + methylbutyrylcarnitine) and a median-chain acylcarnitine (octenoylcarnitine) decrease, while four long-chain acylcarnitines (tetradecanoylcarnitine, palmitoylcarnitine, palmitoleylcarnitine and oleoylcarnitine) increase with increasing birth weight. Age impacts the levels of all MS/MS NBS biomarkers, while sex only affects the level of malonylcarnitine + 3-hydroxybutyrylcarnitine (C3-DC + C4-OH) in very low birth weight preterm neonates.

CONCLUSION

The current study developed reference intervals (RIs) specific to birth weight, age, and/or sex for 35 MS/MS biomarkers, which can help in the timely evaluation of the health and disease of preterm neonates.

Heavy metal exposure and metabolomics analysis: an emerging frontier in environmental health

Exposure to heavy metals in various populations can lead to extensive damage to different organs, as these metals infiltrate and bioaccumulate in the human body, causing metabolic disruptions in various organs. To comprehensively understand the metal homeostasis, inter-organ "traffic," and extensive metabolic alterations resulting from heavy metal exposure, employing complementary analytical methods is crucial. Metabolomics is pivotal in unraveling the intricacies of disease vulnerability by furnishing thorough understandings of metabolic changes linked to different metabolic diseases. This field offers exciting prospects for enhancing the disease prevention, early detection, and tailoring treatment approaches to individual needs. This article consolidates the existing knowledge on disease-linked metabolic pathways affected by the exposure of diverse heavy metals providing concise overview of the underlying impact mechanisms. The main aim is to investigate the connection between the altered metabolic pathways and long-term complex health conditions induced by heavy metals such as diabetes mellitus, cardiovascular diseases, renal disorders, inflammation, neurodegenerative diseases, reproductive risks, and organ damage. Further exploration of common pathways may unveil the shared targets for treating associated pathological conditions. In this article, the role of metabolomics in disease susceptibility is emphasized that metabolomics is expected to be routinely utilized for the diagnosis and monitoring of diseases and practical value of biomarkers derived from metabolomics, as well as determining their appropriate integration into extensive clinical settings.

Disease spectrum, prevalence, genetic characteristics of inborn errors of metabolism in 21,840 hospitalized infants in Chongqing, China, 2017-2022

Inborn errors of metabolism (IEMs) are uncommon. Although some studies have explored the distribution and characteristics of IEMs in newborns, the impact of these disorders on hospitalized newborns remains unclear. In this study, we gathered data from 21,840 newborn patients admitted for various medical conditions at the Children's Hospital of Chongqing Medical University from January 2017 and December 2022. Liquid chromatography-tandem mass spectrometry (LC-MS/MS), gas chromatography-mass spectrometry (GC-MS/MS), and genetic analysis were used to elucidate the disease spectrum, incidence rate, and genetic characteristics of IEMs in hospitalized newborns. The results revealed that the incidence of IEMs in hospitalized newborns was 1/377 (58/21,840), with a higher incidence in full-term infants (1/428) than in premature infants (1/3,120). Among the diagnosed genetic metabolic diseases, organic acid metabolism disorders (1/662), amino acid metabolism disorders (1/950), and fatty acid oxidation disorders (1/10,920) were the most prevalent. Methylmalonic acidemia (MMA), especially the isolated form, emerged as the most common IEM, while neonatal intrahepatic cholestasis caused by citrin deficiency (NICCD) and ornithine transcarbamylase deficiency (OTCD) were prevalent in premature infants. Of the 58 confirmed cases of IEMs, 72 variants were identified, of which 31.94% (23/72) had not been reported previously. This study contributes to understanding the incidence and clinical features of IEMs in hospitalized newborns, offering more efficient strategies for screening and diagnosing these disorders.

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

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

An evaluation of untargeted metabolomics methods to characterize inborn errors of metabolism

Inborn errors of metabolism (IEMs) encompass a diverse group of disorders that can be difficult to classify due to heterogenous clinical, molecular, and biochemical manifestations. Untargeted metabolomics platforms have become a popular approach to analyze IEM patient samples because of their ability to detect many metabolites at once, accelerating discovery of novel biomarkers, and metabolic mechanisms of disease. However, there are concerns about the reproducibility of untargeted metabolomics research due to the absence of uniform reporting practices, data analyses, and experimental design guidelines. Therefore, we critically evaluated published untargeted metabolomic platforms used to characterize IEMs to summarize the strengths and areas for improvement of this technology as it progresses towards the clinical laboratory. A total of 96 distinct IEMs were collectively evaluated by the included studies. However, most of these IEMs were evaluated by a single untargeted metabolomic method, in a single study, with a limited cohort size (55/96, 57%). The goals of the included studies generally fell into two, often overlapping, categories: detecting known biomarkers from many biochemically distinct IEMs using a single platform, and detecting novel metabolites or metabolic pathways. There was notable diversity in the design of the untargeted metabolomic platforms. Importantly, the majority of studies reported adherence to quality metrics, including the use of quality control samples and internal standards in their experiments, as well as confirmation of at least some of their feature annotations with commercial reference standards. Future applications of untargeted metabolomics platforms to the study of IEMs should move beyond single-subject analyses, and evaluate reproducibility using a prospective, or validation cohort.

An epidemiological introduction to human metabolomic investigations

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

Knowledge and awareness level of healthcare professional candidate students on inherited metabolic diseases: a cross-sectional study

BACKGROUND

Healthcare professionals play a key role in the diagnosis, treatment, and follow-up of inborn metabolic diseases. However, the level of inborn metabolic disease knowledge of prospective healthcare professional students in our country has not yet been determined. Therefore, this study aimed to evaluate the level of knowledge of healthcare professional candidate students about inborn metabolic diseases.

METHODS

The knowledge levels of 761 students enrolled in the Department of Nutrition and Dietetics, Child Development, Midwifery, Occupational Therapy, Audiology, Health Management and Social Work at Gülhane Faculty of Health Sciences, Health Sciences University, were evaluated through a questionnaire using a face-to-face interview technique. Correct answers to the questions measuring the level of knowledge were scored as "1", and incorrect answers were scored as "0".

RESULTS

The mean knowledge-level score of the individuals was 14.23 ± 4.56. A total of 56.0% of individuals had heard about inborn metabolic diseases before, 37.8% had heard of rare disease organizations/platforms before, and 16.8% had encountered an awareness campaign about inborn metabolic diseases. The level of exposure to awareness-raising campaigns, department of education, and grade level were shown to be factors affecting knowledge levels.

CONCLUSION

It is necessary to improve the awareness and knowledge levels of health professional candidates involved in the treatment of inborn metabolic diseases. Education curricula in health sciences faculties should be evaluated with this aspect.

Metabolite interactions between host and microbiota during health and disease: Which feeds the other?

Metabolites produced by the host and microbiota play a crucial role in how human bodies develop and remain healthy. Most of these metabolites are produced by microbiota and hosts in the digestive tract. Metabolites in the gut have important roles in energy metabolism, cellular communication, and host immunity, among other physiological activities. Although numerous host metabolites, such as free fatty acids, amino acids, and vitamins, are found in the intestine, metabolites generated by gut microbiota are equally vital for intestinal homeostasis. Furthermore, microbiota in the gut is the sole source of some metabolites, including short-chain fatty acids (SCFAs). Metabolites produced by microbiota, such as neurotransmitters and hormones, may modulate and significantly affect host metabolism. The gut microbiota is becoming recognized as a second endocrine system. A variety of chronic inflammatory disorders have been linked to aberrant host-microbiota interplays, but the precise mechanisms underpinning these disturbances and how they might lead to diseases remain to be fully elucidated. Microbiome-modulated metabolites are promising targets for new drug discovery due to their endocrine function in various complex disorders. In humans, metabolotherapy for the prevention or treatment of various disorders will be possible if we better understand the metabolic preferences of bacteria and the host in specific tissues and organs. Better disease treatments may be possible with the help of novel complementary therapies that target host or bacterial metabolism. The metabolites, their physiological consequences, and functional mechanisms of the host-microbiota interplays will be highlighted, summarized, and discussed in this overview.

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

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

Mass Spectroscopy as an Analytical Tool to Harness the Production of Secondary Plant Metabolites: The Way Forward for Drug Discovery

The molecular map of diverse biological molecules linked with structure, function, signaling, and regulation within a cell can be elucidated using an analytically demanding omic approach. The latest trend of using "metabolomics" technologies has explained the natural phenomenon of opening a new avenue to understand and enhance bioactive compounds' production. Examination of sequenced plant genomes has revealed that a considerable portion of these encodes genes of secondary metabolism. In addition to genetic and molecular tools developed in the current era, the ever-increasing knowledge about plant metabolism's biochemistry has initiated an approach for wisely designed, more productive genetic engineering of plant secondary metabolism for improved defense systems and enhanced biosynthesis of beneficial metabolites. Secondary plant metabolites are natural products synthesized by plants that are not directly involved with their average growth and development but play a vital role in plant defense mechanisms. Plant secondary metabolites are classified into four major classes: terpenoids, phenolic compounds, alkaloids, and sulfur-containing compounds. More than 200,000 secondary metabolites are synthesized by plants having a unique and complex structure. Secondary plant metabolites are well characterized and quantified by omics approaches and therefore used by humans in different sectors such as agriculture, pharmaceuticals, chemical industries, and biofuel. The aim is to establish metabolomics as a comprehensive and dynamic model of diverse biological molecules for biomarkers and drug discovery. In this chapter, we aim to illustrate the role of metabolomic technology, precisely liquid chromatography-mass spectrometry, capillary electrophoresis mass spectrometry, gas chromatography-mass spectrometry, and nuclear magnetic resonance spectroscopy, specifically as a research tool in the production and identification of novel bioactive compounds for drug discovery and to obtain a unified insight of secondary metabolism in plants.

Challenges of genetic diagnosis of inborn errors of metabolism in a major tertiary care center in Lebanon

Background: Inborn errors of metabolism are rare genetic disorders; however, these are prevalent in countries with high consanguinity rates, like Lebanon. Patients are suspected, based on a combination of clinical and biochemical features; however, the final confirmation relies on genetic testing. Using next generation sequencing, as a new genetic investigational tool, carries several challenges for the physician, the geneticist, and the families. Methods: In this retrospective study, we analyzed the clinical, biochemical, and genetic profile of inborn errors of metabolism suspected patients, seen at a major tertiary care center in Lebanon, between 2015 and 2018. Genetic testing was performed using next generation sequencing. Genotype-phenotype correlation and diagnostic yield of each testing modality were studied. Results: Out of 211 patients genetically tested, 126 were suspected to have an inborn error of metabolism. The diagnostic yield of next generation sequencing reached 64.3%. Single gene testing was requested in 53%, whole exome sequencing in 36% and gene panels in 10%. Aminoacid disorders were mostly diagnosed followed by storage disorders, organic acidemias and mitochondrial diseases. Targeted testing was performed in 77% of aminoacid and organic acid disorders and half of suspected storage disorders. Single gene sequencing was positive in 75%, whereas whole exome sequencing diagnostic yield for complex cases, like mitochondrial disorders, reached 49%. Good clinical and biochemical correlation allowed the interpretation of variants of unknown significance and negative mutations as well as therapeutic management of most patients. Conclusion: Tailoring the choice of test modality, by next generation sequencing, to the category of suspected inborn errors of metabolism may lead to rapid diagnosis, shortcutting the cost of repeated testing. Whole exome sequencing as a first-tier investigation may be considered mainly for suspected mitochondrial diseases, whereas targeted sequencing can be offered upon suspicion of a specific enzyme deficiency. Timing and modality of gene test remain challenging, in view of the cost incurred by families.

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.

Expanded newborn screening for inherited metabolic disorders by tandem mass spectrometry in a northern Chinese population

Tandem mass spectrometry (MS/MS) has been developed as one of the most important diagnostic platforms for the early detection and screening of inherited metabolic disorders (IMDs). To determine the disease spectrum and genetic characteristics of IMDs in Suqian city of Jiangsu province in the northern Chinese population, dried blood spots from 2,04,604 newborns, were assessed for IMDs by MS/MS from January 2016 to November 2020. Suspected positive patients were diagnosed through next-generation sequencing (NGS) and validated by Sanger sequencing. One hundred patients with IMDs were diagnosed, resulting in an overall incidence of 1/2,046, of which 56 (1/3,653), 22 (1/9,300), and 22 (1/9,300) were confirmed amino acids disorders (AAs), organic acids disorders (OAs), fatty acid oxidation disorders (FAODs) positive cases, respectively. The highest incidence of IMDs is phenylalanine hydroxylase deficiency (PAHD) (45 cases), with a total incidence of 1:4,546. Hot spot mutations in phenylalanine hydroxylase (PAH)-related genes are c.158G > A (24.44%), c.728G > A (16.67%), c.611A > G (7.78%), and c.331C>T (7.78%). The related hot spot mutation of the MMACHC gene is c.609G > A (45.45%). Short-chain acyl-CoA dehydrogenase deficiency (SCAD)-related ACADS gene hotspot mutations are c.164C > T (33.33%) and c.1031A > G (33.33%). Our work indicated that the overall incidence of IMDs is high, and the mutations in PAH, ACADS, and MMACHC genes are the leading causes of IMDs in Suqian city. The incidence of AAs in Suqian city is higher than in other Chinese areas. The disease spectrum and genetic backgrounds were elucidated, contributing to the treatment and prenatal genetic counseling of these disorders in this region.

[The promise of omics in the precision medicine era]

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

Cost-effectiveness analysis of newborn screening by tandem mass spectrometry in Shenzhen, China: value and affordability of new screening technology

Background

Newborn screening (NBS) can prevent inborn errors of metabolism (IEMs), which may cause long-term disability and even death in newborns. However, in China, tandem mass spectrometry (MS/MS) screening has just started. This study aimed to assess the cost-effectiveness of NBS using MS/MS in Shenzhen under the nationally recommended program, as well as evaluate the value and affordability of introducing this new screening technology.

Methods

A Markov model was built to estimate the cost and quality-adjusted life-years (QALYs) of different screening programs. We compared PKU screening using traditional immunofluorescence (IF) with the other 11 IEMs not screened and all 12 IEMs screened using MS/MS, and the programs detecting different numbers of IEMs chosen from the national recommended program were also compared. A sensitivity analysis and budget impact analysis (BIA) were performed.

Results

The incremental cost-effectiveness ratio (ICER) of detecting all 12 IEMs in the national program is 277,823 RMB per QALY, below three times per capita GDP in Shenzhen. MS/MS screening in Shenzhen can be cost-effective only if at least three diseases (PKU, PCD and MMA) are covered and when the screening program covers five diseases (PKU, PCD, MMA, MSUD, IVA), the ICER closely approaches its critical threshold. The BIA indicated the implementation cost of the national program to be around 490 million RMB over 10 years and showed no difference in budget between programs detecting different numbers of IEMs.

Conclusions

We conclude that the newborn screening using MS/MS in Shenzhen is cost-effective, and the budget affordable for the Shenzhen government. Two concepts for selecting the IEMs to be detected are also presented. One is to choose the most cost-effective screening programs detecting highest number of IEMs to achieve a minimal ICER. The other considers the curability and affordability of the disease as the basis of healthcare decisions to screen suitable IEMs, achieving an ICER under the threshold and close to the minimum value.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08394-4.

Metabolic Fingerprinting of Fabry Disease: Diagnostic and Prognostic Aspects

Fabry disease (FD) is an X-linked lysosomal disease due to a deficiency in the activity of the lysosomal-galactosidase A (GalA), a key enzyme in the glycosphingolipid degradation pathway. FD is a complex disease with a poor genotype–phenotype correlation. In the early stages, FD could involve the peripheral nervous system (acroparesthesias and dysautonomia) and the ski (angiokeratoma), but later kidney, heart or central nervous system impairment may significantly decrease life expectancy. The advent of omics technologies offers the possibility of a global, integrated and systemic approach well-suited for the exploration of this complex disease. In this narrative review, we will focus on the main metabolomic studies, which have underscored the importance of detecting biomarkers for a diagnostic and prognostic purpose in FD. These investigations are potentially useful to explain the wide clinical, biochemical and molecular heterogeneity found in FD patients. Moreover, the quantitative mass spectrometry methods developed to evaluate concentrations of these biomarkers in urine and plasma will be described. Finally, the complex metabolic biomarker profile depicted in FD patients will be reported, which varies according to gender, types of mutations, and therapeutic treatment.

Biomarkers of Metabolomics in Inflammatory Bowel Disease and Damp-Heat Syndrome: A Preliminary Study

Aims

This study aims to investigate the potential biomarkers of inflammatory bowel disease (IBD) and IBD with damp-heat syndrome (IBD-DH) by metabolomics.

Methods

Plasma and urine samples were collected from 15 healthy controls and 30 IBD patients, including 15 IBD-DH and 15 IBD with spleen deficiency syndrome (IBD-SD), which was coded as SF8G and SF70 according to the International Classification of Diseases Eleventh Revision (ICD-11) issued by World Health Organization. Pseudotargeted metabolomics method was used based on ultra-high-performance liquid chromatography-high-resolution mass spectrometry and triple-quadrupole mass spectrometry.

Results

Under the condition of false discovery rate (FDR) < 0.05, variable importance projection (VIP) > 1.0, and fold change (FC) > 1.5 or < 2/3, we found 57 plasma differential metabolites and 20 urinary differential metabolites in IBD. Then, with area under the curve (AUC) ≥ 0.85 and FC ≥ 2 or ≤ 0.3, 11 potential biomarkers were identified, such as acylcarnitine (ACar 20:4, ACar 18:1, and ACar 20:3), 3-indoleacetic acid, hippuric acid, and dehydroepiandrosterone sulfate, which is related to intestinal microbiota and immune response. However, less obvious differences were observed in IBD-DH when compared with IBD-SD. Under the condition of FDR < 0.2, VIP >1.0, and FC > 1.5 or < 2/3, we identified 16 plasma differential metabolites. In urine samples, IBD-DH and IBD-SD had the same metabolite pattern. With AUC ≥ 0.80, 7 differential plasma metabolites, mainly glycerophospholipids, were identified in IBD-DH. Kyoto Encyclopedia of Genes and Genomes analysis indicated that metabolic pathways, such as citrate cycle and amino acids metabolism, were mainly responsible for the distinction between IBD and healthy controls, whereas glycerophospholipid metabolism perturbation was not only a manifestation of IBD but also an important pathway to distinguish two subtypes defined by traditional medicine, IBD-DH and IBD-SD.

Conclusion

In this study, we found that several metabolites of aromatic acids and lipid derivatives could act as potential biomarkers to discriminate IBD from healthy controls. Glycerophospholipids metabolites might be used to differentiate IBD-DH from IBD-SD.

Application of metabolite set enrichment analysis on untargeted metabolomics data prioritises relevant pathways and detects novel biomarkers for inherited metabolic disorders

Untargeted metabolomics (UM) allows for the simultaneous measurement of hundreds of metabolites in a single analytical run. The sheer amount of data generated in UM hampers its use in patient diagnostics because manual interpretation of all features is not feasible. Here, we describe the application of a pathway-based metabolite set enrichment analysis method to prioritise relevant biological pathways in UM data. We validate our method on a set of 55 patients with a diagnosed inherited metabolic disorder (IMD) and show that it complements feature-based prioritisation of biomarkers by placing the features in a biological context. In addition, we find that by taking enriched pathways shared across different IMDs, we can identify common drugs and compounds that could otherwise obscure genuine disease biomarkers in an enrichment method. Finally, we demonstrate the potential of this method to identify novel candidate biomarkers for known IMDs. Our results show the added value of pathway-based interpretation of UM data in IMD diagnostics context.

Untargeted Metabolomics Reveals Metabolic Stress Alleviation by Prepartum Exercise in Transition Dairy Cows

Prepartum exercise (PA) has been proposed as a strategy for the peripartum management of dairy cows; however, the mechanism by which PA affects metabolism has not been elucidated. Here, we investigated the metabolic changes in transition dairy cows with PA. Holstein transition multiparous dairy cows were assigned to an exercise (n = 12) or a control (n = 12) group; the cows in the exercise group walked for a targeted 45 min at 3.25 km/h, two times a day. Plasma non-esterified fatty acid (NEFA), β-hydroxybutyric acid (BHBA), glucose, and triglyceride levels were measured, and metabolic profiles were analyzed using untargeted mass spectrometry. Compared with those in the control group, the concentrations of NEFA at −7 d, glucose at 0 d, and BHBA at +7 d relative to calving were considerably decreased in the exercise group. Untargeted metabolomics analysis revealed differences in the levels of key metabolites, including kynurenine, tryptophan, homovanillic acid, dopamine, cis-9-palmitoleic acid, and palmitic acid, between the exercise and control group cows. This study suggests that PA may decrease homovanillic acid and cis-9-palmitoleic acid levels and increase tryptophan levels to alleviate the metabolic stress in dairy cows during calving, thereby improving postpartum health.

Clinical metabolomics for inborn errors of metabolism

Metabolism is a highly regulated process that provides nutrients to cells and essential building blocks for the synthesis of protein, DNA and other macromolecules. In healthy biological systems, metabolism maintains a steady state in which the concentrations of metabolites are relatively constant yet are subject to metabolic demands and environmental stimuli. Rare genetic disorders, such as inborn errors of metabolism (IEM), cause defects in regulatory enzymes or proteins leading to metabolic pathway disruption and metabolite accumulation or deficiency. Traditionally, the laboratory diagnosis of IEMs has been limited to analytical methods that target specific metabolites such as amino acids and acyl carnitines. This approach is effective as a screening method for the most common IEM disorders but lacks the comprehensive coverage of metabolites that is necessary to identify rare disorders that present with nonspecific clinical symptoms. Fortunately, advancements in technology and data analytics has introduced a new field of study called metabolomics which has allowed scientists to perform comprehensive metabolite profiling of biological systems to provide insight into mechanism of action and gene function. Since metabolomics seeks to measure all small molecule metabolites in a biological specimen, it provides an innovative approach to evaluating disease in patients with rare genetic disorders. In this review we provide insight into the appropriate application of metabolomics in clinical settings. We discuss the advantages and limitations of the method and provide details related to the technology, data analytics and statistical modeling required for metabolomic profiling of patients with IEMs.

An investigation of different intracellular parameters for Inborn Errors of Metabolism: Cellular stress, antioxidant response and autophagy

Oxidative stress is associated with various disease pathologies including Inborn Errors of Metabolism (IEMs), among the most important causes of childhood morbidity and mortality. At least as much as oxidative stress in cells, reductive stress poses a danger to the disruption of cell homeostasis. p62/SQSTM1, protects cells from stress by activation of Nrf2/Keap1 and autophagy pathways. In this study, we tested the role of cellular stress, mitochondrial dysfunction and autophagy via Nrf2/Keap1/p62 pathway in the pathophysiology of three main groups of IEMs. Our results showed that antioxidant and oxidant capacity alone would not be sufficient to reflect the true clinical picture of these diseases. ATP, ROS and mitochondrial membrane potantial (MMP) measurements demonstrated increased cellular stress and bioenergetic imbalance in methylmalonic acidemia (MMA), indicating mild mitochondrial dysfunction. In isovaleric acidemia (IVA), no major change was detected in ATP, ROS and MMP values. Propionic acidemia (PA), mitochondrial diseases (MIT) and mucopolysaccharidosis IV (MPS IV) might point out mitohormesis to cope with chronic reductive stress. Induction of Nrf2/Keap1/p62 pathway and increased expression of HMOX1 were detected in all IEMs. LC3B-II and p62 expression results indicated an impaired autophagic flux in MIT and MPS IV and an induction of autophagic flux in MMA, PA and IVA, but also partial expression of Beclin1, enables autophagy activation, was detected in all IEMs. We conclude that individual diagnosis and treatments are of great importance in IEMs. In addition, we assume that the application of therapeutic antioxidant or preventive treatments without determining the cellular stress status in IEMs may disrupt the sensitive oxidant-antioxidant balance in the cell, leading to the potential to further disrupt the clinical picture, especially in patients with reductive stress. To the best of our knowledge, this is the first study to simultaneously relate IEMs with cellular stress, mitochondrial dysfunction, and autophagy.

A Checklist for Reproducible Computational Analysis in Clinical Metabolomics Research

Clinical metabolomics emerged as a novel approach for biomarker discovery with the translational potential to guide next-generation therapeutics and precision health interventions. However, reproducibility in clinical research employing metabolomics data is challenging. Checklists are a helpful tool for promoting reproducible research. Existing checklists that promote reproducible metabolomics research primarily focused on metadata and may not be sufficient to ensure reproducible metabolomics data processing. This paper provides a checklist including actions that need to be taken by researchers to make computational steps reproducible for clinical metabolomics studies. We developed an eight-item checklist that includes criteria related to reusable data sharing and reproducible computational workflow development. We also provided recommended tools and resources to complete each item, as well as a GitHub project template to guide the process. The checklist is concise and easy to follow. Studies that follow this checklist and use recommended resources may facilitate other researchers to reproduce metabolomics results easily and efficiently.

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.

Parsing Fabry Disease Metabolic Plasticity Using Metabolomics

Background: Fabry disease (FD) is an X-linked lysosomal disease due to a deficiency in the activity of the lysosomal α-galactosidase A (GalA), a key enzyme in the glycosphingolipid degradation pathway. FD is a complex disease with a poor genotype–phenotype correlation. FD could involve kidney, heart or central nervous system impairment that significantly decreases life expectancy. The advent of omics technologies offers the possibility of a global, integrated and systemic approach well-suited for the exploration of this complex disease. Materials and Methods: Sixty-six plasmas of FD patients from the French Fabry cohort (FFABRY) and 60 control plasmas were analyzed using liquid chromatography and mass spectrometry-based targeted metabolomics (188 metabolites) along with the determination of LysoGb3 concentration and GalA enzymatic activity. Conventional univariate analyses as well as systems biology and machine learning methods were used. Results: The analysis allowed for the identification of discriminating metabolic profiles that unambiguously separate FD patients from control subjects. The analysis identified 86 metabolites that are differentially expressed, including 62 Glycerophospholipids, 8 Acylcarnitines, 6 Sphingomyelins, 5 Aminoacids and 5 Biogenic Amines. Thirteen consensus metabolites were identified through network-based analysis, including 1 biogenic amine, 2 lysophosphatidylcholines and 10 glycerophospholipids. A predictive model using these metabolites showed an AUC-ROC of 0.992 (CI: 0.965–1.000). Conclusion: These results highlight deep metabolic remodeling in FD and confirm the potential of omics-based approaches in lysosomal diseases to reveal clinical and biological associations to generate pathophysiological hypotheses.

Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline

Inborn errors of metabolism (IEM) are inherited conditions caused by genetic defects in enzymes or cofactors. These defects result in a specific metabolic fingerprint in patient body fluids, showing accumulation of substrate or lack of an end-product of the defective enzymatic step. Untargeted metabolomics has evolved as a high throughput methodology offering a comprehensive readout of this metabolic fingerprint. This makes it a promising tool for diagnostic screening of IEM patients. However, the size and complexity of metabolomics data have posed a challenge in translating this avalanche of information into knowledge, particularly for clinical application. We have previously established next-generation metabolic screening (NGMS) as a metabolomics-based diagnostic tool for analyzing plasma of individual IEM-suspected patients. To fully exploit the clinical potential of NGMS, we present a computational pipeline to streamline the analysis of untargeted metabolomics data. This pipeline allows for time-efficient and reproducible data analysis, compatible with ISO:15189 accredited clinical diagnostics. The pipeline implements a combination of tools embedded in a workflow environment for large-scale clinical metabolomics data analysis. The accompanying graphical user interface aids end-users from a diagnostic laboratory for efficient data interpretation and reporting. We also demonstrate the application of this pipeline with a case study and discuss future prospects.

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.

Cerebellar and multi-system metabolic reprogramming associated with trauma exposure and post-traumatic stress disorder (PTSD)-like behavior in mice

Mitochondrial metabolism is increasingly implicated in psychopathologies and mood disorders, including post-traumatic stress disorder (PTSD). We recently reported that mice exposed to a novel paradigm for the induction of PTSD-like behavior displayed reduced mitochondrial electron transport chain (mtETC) complex activity as well as decreased multi-system fatty acid oxidation (FAO) flux. Based on these results, we hypothesized that stressed and PTSD-like animals would display evidence of metabolic reprogramming in both cerebellum and plasma consistent with increased energetic demand, mitochondrial metabolic reprogramming, and increased oxidative stress. We performed targeted metabolomics in both cerebellar tissue and plasma, as well as untargeted nuclear magnetic resonance (NMR) spectroscopy in the cerebellum of 6 PTSD-like and 7 resilient male mice as well as 7 trauma-naïve controls. We identified numerous differences in amino acids and tricarboxylic acid (TCA) cycle metabolite concentrations in the cerebellum and plasma consistent with altered mitochondrial energy metabolism in trauma exposed and PTSD-like animals. Pathway analysis identified metabolic pathways with significant metabolic pathway shifts associated with trauma exposure, including the tricarboxylic acid cycle, pyruvate, and branched-chain amino acid metabolism in both cerebellar tissue and plasma. Altered glutamine and glutamate metabolism, and arginine biosynthesis was evident uniquely in cerebellar tissue, while ketone body levels were modified in plasma. Importantly, we also identified several cerebellar metabolites (e.g. choline, adenosine diphosphate, beta-alanine, taurine, and myo-inositol) that were sufficient to discriminate PTSD-like from resilient animals. This multilevel analysis provides a comprehensive understanding of local and systemic metabolite fingerprints associated with PTSD-like behavior, and subsequently altered brain bioenergetics. Notably, several transformed metabolic pathways observed in the cerebellum were also reflected in plasma, connecting central and peripheral biosignatures of PTSD-like behavior. These preliminary findings could direct further mechanistic studies and offer insights into potential metabolic interventions, either pharmacological or dietary, to improve PTSD resilience.

Serum metabolic profiling of traditional Chinese medicine syndromes in patients with diarrhea-predominant irritable bowel syndrome

OBJECTIVE

The clinical symptoms of diarrhea-predominant irritable bowel syndrome (IBS-D) can be effectively improved by traditional Chinese medicine (TCM) treatment, based on the usage of specific therapies for different TCM syndromes. However, in the stage of diagnosis, the standard criteria for the classification of TCM syndrome were still deficient. Through serum metabolic profiling, this study aimed to explore potential biomarkers in IBS-D patients with different TCM syndromes, which can assist in diagnosis of the disease.

METHODS

Serum samples were collected from healthy controls (30 cases), IBS-D patients with Liver-Stagnation and Spleen-Deficiency syndrome (LSSD, 30 cases), Yang Deficiency of Spleen and Kidney syndrome (YDSK, 11 cases) and Damp Abundance due to Spleen-Deficiency syndrome (DASD, 22 cases). Serum metabolic profiling was conducted by ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. The potential biomarkers were screened by orthogonal partial least square-discriminate analysis, while metabolic pathways undergoing alterations were identified by pathway enrichment analysis in MetaboAnalyst 4.0.

RESULTS

Overall, 34 potential biomarkers were identified in LSSD group, 36 in YDSK group and 31 in DASD group. And the 13 metabolites shared by three groups were determined as the potential biomarkers of IBS-D. Glycerophospholipid metabolism was disturbed significantly in IBS-D patients, which may play a role in IBS-D through inflammation. What's more, three TCM syndromes have the specific potential biomarkers in glycerophospholipid metabolism.

CONCLUSION

The serum metabolomics revealed that different TCM syndrome types in IBS-D may have different metabolic patterns during disease progression and glycerophospholipid metabolism was one of the pathways, whose metabolism was disturbed differently among three TCM syndromes in IBS-D. Therefore, the specific potential biomarkers in glycerophospholipid metabolism of three TCM syndromes in IBS-D can serve as the objective indicators, which can facilitate the TCM-syndrome objective classification of IBS-D.

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

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

Incidence of inherited metabolic disorders in southern Israel: a comparison between consanguinity and non-consanguinity communities

Background

Inherited metabolic disorders (IMDs) are group of rare monogenic diseases, usually derived from reduced or absent activity in a single metabolic pathway. Most of the IMDs are inherited in an autosomal recessive manner. The incidence of IMDs varies from country to country and within different ethnic groups, but data is still scarce. Consanguinity rate among populations is highly contributor factor for IMDs incidence. There are no reports comparing the incidence of IMD in consanguineous and non-consanguineous populations from the same geographic region with the same diagnostic capabilities. Our study objective is to compare the incidence of IMDs between between the relatively low consanguineous Jewish population and the consanguineous Bedouin population, both living in the southern of Israel.

Results

During 1990–2017 there were 393,772 live births in the Negev district, of Southern of Israel. Among them 187,049 were of Jewish origin while 206,723 were of Bedouin-Muslim origin. A total of 223 children were diagnosed in this study period with IMDs. Among those 223 children with IMD, 33 were of Jewish origin while the other 190 children were of Bedouin-Muslim origin. The overall incidence for IMDs of the overall Negev population was 56.6/100,000 live birth. The incidence for IMD's among the Bedouin population was significantly higher than among Jewish population.

Conclusions

IMDs are extremely more common in the consanguineous Bedouin compared with the relatively non-consanguineous Jewish population of Southern Israel. Health policy makers should consider these data and prepare educational and genetic counselling problems accordingly.

Omics and Cardiometabolic Disease Risk Prediction

Risk assessments are integral for the prevention and management of cardiometabolic disease (CMD). However, individuals may develop CMD without traditional risk factors, necessitating the development of novel biomarkers to aid risk prediction. The emergence of omic technologies, including genomics, proteomics, and metabolomics, has allowed for assessment of orthogonal measures of cardiometabolic risk, potentially improving the ability for novel biomarkers to refine disease risk assessments. While omics has shed light on novel mechanisms for the development of CMD, its adoption in clinical practice faces significant challenges. We review select omic technologies and cardiometabolic investigations for risk prediction, while highlighting challenges and opportunities for translating findings to clinical practice.

The diagnosis of inborn errors of metabolism in previously undiagnosed adults referred for medical genetics evaluation

Traditionally thought of as a pediatric diagnostic and therapeutic dilemma, the diagnostic rate and spectrum of inborn errors of metabolism (IEM) in the adult population is largely unknown. A retrospective chart review of patients seen by the Michigan Medicine Adult Medical Genetics Clinic for clinical evaluation from 2014 to 2018 was conducted. Patients referred for a primary indication possibly consistent with an IEM were considered. Variables included age at genetic evaluation, symptom onset age, sex, clinical course, organ systems involved, developmental history, family history and prior genetic testing. Of patients evaluated during the study period, 112 were referred for an indication possibly consistent with an IEM and underwent a complete biochemical workup with an IEM diagnostic rate of 9.8% achieved. An additional 9.8% were diagnosed with a non-IEM genetic diagnosis. Management changes were implemented in all IEM diagnoses. Metabolic disorders in the adult population are under-recognized and under-diagnosed. This report demonstrates the need for clinicians to consider these diagnoses in adults and either refer to a genetics clinic or initiate a biochemical workup. As advances in diagnosis, treatment, and life expectancy of patients with IEMs increases, recognizing and diagnosing these conditions can significantly impact care.

OmicsON - Integration of omics data with molecular networks and statistical procedures

A huge amount of atomized biological data collected in various databases and the need for a description of their relation by theoretical methods causes the development of data integration methods. The omics data analysis by integration of biological knowledge with mathematical procedures implemented in the OmicsON R library is presented in the paper. OmicsON is a tool for the integration of two sets of data: transcriptomics and metabolomics. In the workflow of the library, the functional grouping and statistical analysis are applied. Subgroups among the transcriptomic and metabolomics sets are created based on the biological knowledge stored in Reactome and String databases. It gives the possibility to analyze such sets of data by multivariate statistical procedures like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). The integration of metabolomic and transcriptomic data based on the methodology contained in OmicsON helps to easily obtain information on the connection of data from two different sets. This information can significantly help in assessing the relationship between gene expression and metabolite concentrations, which in turn facilitates the biological interpretation of the analyzed process.

Precision Medicine for Lysosomal Disorders

Precision medicine (PM) is an emerging approach for disease treatment and prevention that accounts for the individual variability in the genes, environment, and lifestyle of each person. Lysosomal diseases (LDs) are a group of genetic metabolic disorders that include approximately 70 monogenic conditions caused by a defect in lysosomal function. LDs may result from primary lysosomal enzyme deficiencies or impairments in membrane-associated proteins, lysosomal enzyme activators, or modifiers that affect lysosomal function. LDs are heterogeneous disorders, and the phenotype of the affected individual depends on the type of substrate and where it accumulates, which may be impacted by the type of genetic change and residual enzymatic activity. LDs are individually rare, with a combined incidence of approximately 1:4000 individuals. Specific therapies are already available for several LDs, and many more are in development. Early identification may enable disease course prediction and a specific intervention, which is very important for clinical outcome. Driven by advances in omics technology, PM aims to provide the most appropriate management for each patient based on the disease susceptibility or treatment response predictions for specific subgroups. In this review, we focused on the emerging diagnostic technologies that may help to optimize the management of each LD patient and the therapeutic options available, as well as in clinical developments that enable customized approaches to be selected for each subject, according to the principles of PM.

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.

A Proteomics-Based Analysis Reveals Predictive Biological Patterns in Fabry Disease

Background: Fabry disease (FD) is an X-linked progressive lysosomal disease (LD) due to glycosphingolipid metabolism impairment. Currently, plasmatic globotriaosylsphingosine (LysoGb3) is used for disease diagnosis and monitoring. However, this biomarker is inconstantly increased in mild forms and in some female patients. Materials and Methods: We applied a targeted proteomic approach to explore disease-related biological patterns that might explain the disease pathophysiology. Forty proteins, involved mainly in inflammatory and angiogenesis processes, were assessed in 69 plasma samples retrieved from the French Fabry cohort (FFABRY) and from 83 healthy subjects. For predictive performance assessment, we also included other LD samples (Gaucher, Pompe and Niemann Pick C). Results: The study yielded four discriminant proteins that include three angiogenesis proteins (fibroblast growth factor 2 (FGF2), vascular endothelial growth factor A (VEGFA), vascular endothelial growth factor C (VEGFC)) and one cytokine interleukin 7 (IL-7). A clear elevation of FGF2 and IL-7 concentrations was observed in FD compared to other LD samples. No correlation was observed between these proteins and globotriaosylsphingosine (LysoGb3). A significant correlation exists between IL-7 and residual enzyme activity in a non-classical phenotype. This highlights the orthogonal biological information yielded by these proteins that might help in stratifying Fabry patients. Conclusion: This work highlights the potential of using proteomics approaches in exploring FD and enhancing FD diagnosis and therapeutic monitoring performances.

Metabolic changes in early neonatal life: NMR analysis of the neonatal metabolic profile to monitor postnatal metabolic adaptations

BACKGROUND

A major challenge from the moment a child is delivered is the adaptation to the extrauterine life, where rapid metabolic changes take place. The study of these changes during the first days of human life may assist in the understanding of the metabolic processes that occur at this critical period, which is likely to provide significant clinical insights. To date, metabolomics has become a powerful field, ideal for the monitoring of such dynamic variations, since it offers the possibility to identify alterations in metabolic profiles, even on daily basis.

METHODS

The study included 253 healthy newborns (GA 35 to 40 weeks) from the region of Western Greece. Urine samples were collected immediately after birth and at the third day of life. NMR-based metabolomics was used to compare the metabolic urinary profiles of newborns from the first and third day of their life, assessing the impact of six perinatal factors; delivery mode, prematurity, maternal smoking, gender, nutrition and neonatal jaundice.

RESULTS

Analysis of urine metabolic fingerprint from the first and third day of life, coupled with multivariate statistics, provides insights into the details of early life metabolic profile differentiation. Αt the third day of life metabolic adaptations are evident, as many differences were noted in urine of healthy neonates within the first 72 h postpartum. Trends in differentiation of metabolites levels between the two groups, late preterm and term newborns, have been also observed.

CONCLUSIONS

Newborn's urine metabolic profiles confirmed the rapid changes in their metabolism after birth. Further, ongoing research will enable us to develop one reference model of urinary metabolomics in healthy newborns during the period of adaptation to the extra-uterine life.

Metabolic correlates of prevalent mild cognitive impairment and Alzheimer's disease in adults with Down syndrome

INTRODUCTION

Disruption of metabolic function is a recognized feature of late onset Alzheimer's disease (LOAD). We sought to determine whether similar metabolic pathways are implicated in adults with Down syndrome (DS) who have increased risk for Alzheimer's disease (AD).

METHODS

We examined peripheral blood from 292 participants with DS who completed baseline assessments in the Alzheimer's Biomarkers Consortium-Down Syndrome (ABC-DS) using untargeted mass spectrometry (MS). Our sample included 38 individuals who met consensus criteria for AD (DS-AD), 43 who met criteria for mild cognitive impairment (DS-MCI), and 211 who were cognitively unaffected and stable (CS).

RESULTS

We measured relative abundance of 8,805 features using MS and 180 putative metabolites were differentially expressed (DE) among the groups at false discovery rate-corrected q< 0.05. From the DE features, a nine-feature classifier model classified the CS and DS-AD groups with receiver operating characteristic area under the curve (ROC AUC) of 0.86 and a two-feature model classified the DS-MCI and DS-AD groups with ROC AUC of 0.88. Metabolite set enrichment analysis across the three groups suggested alterations in fatty acid and carbohydrate metabolism.

DISCUSSION

Our results reveal metabolic alterations in DS-AD that are similar to those seen in LOAD. The pattern of results in this cross-sectional DS cohort suggests a dynamic time course of metabolic dysregulation which evolves with clinical progression from non-demented, to MCI, to AD. Metabolomic markers may be useful for staging progression of DS-AD.

Impact of storage conditions on the human stool metabolome and lipidome: Preserving the most accurate fingerprint

Faecal metabolomics markedly emerged in clinical as well as analytical chemistry through the unveiling of aberrations in metabolic signatures as reflection of variance in gut (patho)physiology and beyond. Logistic hurdles, however, hinder the analysis of stool samples immediately following collection, inferring the need of biobanking. Yet, the optimum way of storing stool material remains to be determined, in order to conserve an accurate snapshot of the metabolome and circumvent artifacts regarding the disease and parameter(s) under observation. To address this problem, this study scrutinised the impact of freeze-thaw cycling, storage duration, temperature and aerobicity, thereby using ultra-high performance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS)-based polar metabolomics and lipidomics methodologies for faecal metabolomics. Both targeted (n > 400) and untargeted approaches were implemented to assess storage effects on individual chemical classes of metabolites as well as the faecal fingerprint. In general, recommendations are that intact stool samples should be divided into aliquots, lyophilised and stored at -80 °C for a period no longer than 18 weeks, and avoiding any freeze-thawing. The first preservation week exerted the most decisive impact regarding storage temperature, i.e. 12.1% and 6.4% of the polar metabolome experienced a shift at -20 °C and at -80 °C, respectively, whereas 8.6% and 7.9% was observed to be changed significantly for the lipidome. In addition, aside from the negligible impact of aerobicity, the polar metabolome appeared to be more dependent on the storage conditions applied compared to the lipidome, which emerged as the more stable fraction when assessing the storage duration for 25 weeks. If the interest would greatly align with particular chemical classes, such as branched-chain amino acids or short-chain fatty acids, specific storage duration recommendations are reported. The provided insights on the stability of the faecal metabolome may contribute to a more reasoned design of experiments in biomarker detection or pathway elucidation within the field of faecal metabolomics.

Current and emerging treatments for neonatal sepsis

Introduction: Mortality due to sepsis is still prevalent, peaking at extreme ages of life including infancy. Despite many efforts, the peculiarity of the infant immune system has limited further advances in its treatment. Indeed, neonates experience a dramatic physiological transition from immune tolerance to the maternal antigens to functional maturity. Such a transition is extremely dynamic, as is the pathophysiology of infant sepsis, which is dependent on many infant, maternal, and environmental factors.Areas covered: In this review, the authors critically update and summarize the current paradigm of immunomodulation in infant sepsis. They confirm how exogenous stimulation of the immune system through intravenous immunoglobulin, colony stimulating factors, and granulocyte transfusion have failed to impact on the prognosis of infant sepsis. They also strongly support the beneficial effects of supplementation/replacement therapies with products naturally contained within maternal milk as well as antioxidant compounds.Expert opinion: Breastfeeding is beneficial against sepsis. Knowledge of the neonatal immune system is indeed too limited to effectively strengthen immune response by exogenous interventions, especially in preterm and low-birth-weight infants. Awareness of this limitation should pave the way for future studies (e.g. gender- and omics-based) aimed at better characterizing the infant immune system and promoting a more tailored approach.

The Untapped Opportunity and Challenge of Immunometabolism: A New Paradigm for Drug Discovery

Here, we explore the manipulation of immune cell metabolism as a strategy in target discovery and drug development for immune-mediated diseases. Comparing exploitation of metabolic pathways to kill tumor cells for cancer treatment with the reprogramming of immune cells to treat autoimmune diseases highlights differences that confer several advantages to the latter (including a more favorable therapeutic index and greater target stability). We discuss technological capabilities and gaps, including the challenge of relating in vitro observations to in vivo biology. Finally, we conclude by identifying future opportunities that will move the field forward and accelerate drug discovery.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

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.

Investigation of the 12-Month Stability of Dried Blood and Urine Spots Applying Untargeted UHPLC-MS Metabolomic Assays

The use of dried blood spot (DBS) and dried urine spot (DUS) samples represents an attractive opportunity for researchers in biomedical metabolomics to collect whole blood and urine samples in the absence of a processing laboratory and so to allow collection in remote areas or in longitudinal studies away from the clinic. The 12-month stability of the thousands of metabolites present in these biofluids and the applicability of DBS and DUS samples for untargeted metabolomics applications has not previously been investigated in detail and compared to blood and urine samples. Here, the 12-month stability of DBS and DUS at different storage temperatures (-20, +4, and +21 °C) have been compared to plasma and urine biofluids stored at the same storage temperatures and time. Samples were analyzed applying complementary HILIC and C₁₈ reversed-phase UHPLC-MS untargeted metabolomic assays. Results show that metabolites demonstrate increased stability in DBS and DUS compared to whole blood and urine at all storage temperatures and times. DBS and DUS stored at +21 °C are stable for up to 4 weeks but are not stable over a 1 year period. DBS and DUS showed good stability when stored at -20 °C for 1 year. We recommend that DBS and DUS samples are collected and transported within 28 days at room temperature and are stored for longer periods of time at -20 or -80 °C. The metabolomes of DUS samples and urine were very similar but the metabolome of DBS included additional metabolites not detected in plasma and therefore proposed to be released from cells in whole blood.

Personalized laboratory medicine: a patient-centered future approach

In contrast to population-based medical decision making, which emphasizes the use of evidence-based treatment strategies for groups of patients, personalized medicine is based on optimizing treatment at the level of the individual patient. The creation of molecular profiles of individual patients was made possible by the advent of "omics" technologies, based on high throughput instrumental techniques in combination with biostatistics tools and artificial intelligence. The goal of personalized laboratory medicine is to use advanced technologies in the process of preventive, curative or palliative patient management. Personalized medicine does not rely on changes in concentration of a single molecular marker to make a therapeutic decision, but rather on changes of a profile of markers characterizing an individual patient's status, taking into account not only the expected response to treatment of the disease but also the expected response of the patient. Such medical approach promises a more effective diagnostics with more effective and safer treatment, as well as faster recovery and restoration of health and improved cost effectiveness. The laboratory medicine profession is aware of its key role in personalized medicine, but to empower the laboratories, at least an enhancement in cooperation between disciplines within laboratory medicine will be necessary.

Advances and challenges in development of precision psychiatry through clinical metabolomics on mood and psychotic disorders

Metabolomics is defined as the study of the global metabolite profile in a system under a given set of conditions. The objective of this review is to comprehensively assess the literature on metabolomics in mood disorders and schizophrenia and provide data for mental health researchers about the challenges and potentials of metabolomics. The majority of studies in metabolomics in Psychiatry uses peripheral blood or urine. The most widely used analytical techniques in metabolomics research are nuclear magnetic resonance (NMR) and mass spectrometry (MS). They are multiparametric and provide extensive structural and conformational information on multiple chemical classes. NMR is useful in untargeted analysis, which focuses on biosignatures or 'metabolic fingerprints' of illnesses. MS targeted metabolomics approach focuses on the identification and quantification of selected metabolites known to be involved in a particular metabolic pathway. The available studies of metabolomics in Schizophrenia, Bipolar Disorder and Major Depressive Disorder suggest a potential in investigating metabolic pathways involved in these diseases' pathophysiology and response to treatment, as well as its potential in biomarkers identification.

Tandem mass spectrometry-based multiplex assays for α-mannosidosis and fucosidosis

Multiplex tandem mass spectrometry (MS/MS)-based enzyme activity assays for newborn screening (NBS) and diagnosis of lysosomal storage diseases (LSDs) in newborns, using dried blood spots (DBS) on newborn screening cards, have garnered much attention due to its sensitivity, high precision, and the capability to screen for an unprecedented number of diseases in a single assay. Herein we report the development of MS/MS-based enzyme assays for the diagnosis of α-mannosidosis and fucosidosis. These new protocols are able to distinguish untreated patients from random newborns, carriers and a post-bone marrow transplant patient. We have successfully multiplexed the α-mannosidosis assay with a multiplex MS/MS assay for the screening and diagnosis of other LSDs, namely Fabry, Pompe, MPS I, Gaucher, Niemann-Pick-A/B, and Krabbe diseases. Additionally, we also multiplexed the fucosidosis NBS assay with a 5-plex assay that tests for MPS-II, MPS-IIIB, MPS-IVA, MPS-VI and MPS-VII.

A new optimization strategy for MALDI FTICR MS tissue analysis for untargeted metabolomics using experimental design and data modeling

Ultra-high-resolution imaging mass spectrometry using matrix-assisted laser desorption ionization (MALDI) MS coupled to a Fourier transform ion cyclotron resonance (FTICR) mass analyzer is a powerful technique for the visualization of small molecule distribution within biological tissues. The FTICR MS provides ultra-high resolving power and mass accuracy that allows large molecular coverage and molecular formula assignments, both essential for untargeted metabolomics analysis. These performances require fine optimizations of the MALDI FTICR parameters. In this context, this study proposes a new strategy, using experimental design, for the optimization of ion transmission voltages and MALDI parameters, for tissue untargeted metabolomics analysis, in both positive and negative ionization modes. These experiments were conducted by assessing the effects of nine factors for ion transmission voltages and four factors for MALDI on the number of peaks, the weighted resolution, and the mean error within m/z 150-1000 mass range. For this purpose, fractional factorial designs were used with multiple linear regression (MLR) to evaluate factor effects and to optimize parameter values. The optimized values of ion transmission voltages (RF amplitude TOF, RF amplitude octopole, frequency transfer optic, RF frequency octopole, deflector plate, funnel 1, skimmer, funnel RF amplitude, time-of-flight, capillary exit), MALDI parameters (laser fluence, number of laser shots), and detection parameters (data size, number of scans) led to an increase of 32% and 18% of the number of peaks, an increase of 8% and 39% of the resolution, and a decrease of 56% and 34% of the mean error in positive and negative ionization modes, respectively. Graphical abstract.