The future perspective: metabolomics in laboratory medicine for inborn errors of metabolism

Metabolic profiling of peri-implant crevicular fluid in peri-implantitis

OBJECTS

This study aims to explore the etiology of peri-implantitis by comparing the metabolic profiles in peri-implant crevicular fluid (PICF) from patients with healthy implants (PH) and those with peri-implantitis (PI).

MATERIALS AND METHODS

Fifty-six patients were enrolled in this cross-sectional study. PICF samples were collected and analyzed using both non-targeted and targeted metabolomics approaches. The relationship between metabolites and clinical indices including probing depth (PD), bleeding on probing (BOP), and marginal bone loss (MBL) was examined. Additionally, submucosal microbiota was collected and analyzed using 16S rRNA gene sequencing to elucidate the association between the metabolites and microbial communities.

RESULTS

Significant differences in metabolic profiles were observed between the PH and PI groups, with 179 distinct metabolites identified. In the PI group, specific amino acids and fatty acids were significantly elevated compared to the PH group. Organic acids including succinic acid, fructose-6-phosphate, and glucose-6-phosphate were markedly higher in the PI group, showing positive correlations with mean PD, BOP, and MBL. Metabolites that increased in the PI group positively correlated with the presence of Porphyromonas and Treponema and negatively with Streptococcus and Haemophilus.

CONCLUSIONS

This study establishes a clear association between metabolic compositions and peri-implant condition, highlighting enhanced metabolite activity in peri-implantitis. These findings open avenues for further research into metabolic mechanisms of peri-implantitis and their potential therapeutic implications.

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.

Development of a fast and robust liquid chromatography-mass spectrometry-based metabolomics analysis method for neonatal dried blood spots

Dried blood spot (DBS) samples have been widely used in many fields including newborn screening, with the advantages in transportation, storage and non-invasiveness. The DBS metabolomics research of neonatal congenital diseases will greatly expand the understanding of the disease. In this study, we developed a liquid chromatography-mass spectrometry-based method for neonatal metabolomics analysis of DBS. The influences of blood volume and chromatographic effects on the filter paper on metabolite levels were studied. The levels of 11.11 % metabolites were different between 75 μL and 35 μL of blood volumes used for DBS preparation. Chromatographic effects on the filter paper occurred in DBS prepared with 75 μL whole blood and 6.67 % metabolites had different MS responses when central disks were compared with outer disks. The DBS storage stability study showed that compared with - 80 °C storage, storing at 4 °C for 1 year had obvious influences on more than half metabolites. Storing at 4 °C and - 20 °C for short term (< 14 days) and - 20 °C for longer term (1 year) had less influences on amino acids, acyl-carnitines and sphingomyelins, but greater influences on partial phospholipids. Method validation showed that this method has a good repeatability, intra-day and inter-day precision and linearity. Finally, this method was applied to investigate metabolic disruptions of congenital hypothyroidism (CH), metabolic changes of CH newborns were mainly involved in amino acid metabolism and lipid metabolism.

Discovery and Validation of Potential Serum Biomarkers with Pro-Inflammatory and DNA Damage Activities in Ulcerative Colitis: A Comprehensive Untargeted Metabolomic Study

Ulcerative colitis is a type of non-specific inflammatory bowel disease with unclear etiology. It is considered a progressive disease with risks of bowel motility disorders, anorectal dysfunction, and even colorectal cancer. Commonly used diagnostic markers have poor specificity and cannot predict the development of ulcerative colitis. In this study, 77 serum samples (31 patients, 46 healthy controls) were analyzed using high performance liquid chromatography-quadrupole time-of-flight mass spectrometry and 31 metabolites with significant level changes were found, revealing the relationship of ulcerative colitis to disturbed glutathione metabolism and caffeine metabolism. In addition, pyroglutamic acid, a biomarker of cervical cancer and gastric cancer, was identified with elevated levels in the serum of ulcerative colitis patients. The role of pyroglutamic acid was further analyzed, and the results indicated its positive correlation with the upregulation of inflammatory factors and increased levels of phosphorylated histone H2AX (γH2AX) in IEC-6 cells, which are related to DNA damage. All these results suggest that pyroglutamic acid is not only a biomarker for distinguishing ulcerative colitis status, but that it is also a potential effective metabolite that promotes the transformation of ulcerative colitis to colorectal cancer.

Untargeted Metabolome Analysis Reveals Reductions in Maternal Hepatic Glucose and Amino Acid Content That Correlate with Fetal Organ Weights in a Mouse Model of Fetal Alcohol Spectrum Disorders

Prenatal alcohol exposure (PAE) causes fetal growth restrictions. A major driver of fetal growth deficits is maternal metabolic disruption; this is under-investigated following PAE. Untargeted metabolomics on the dam and fetus exposed to alcohol (ALC) revealed that the hepatic metabolome of ALC and control (CON) dams were distinct, whereas that of ALC and CON fetuses were similar. Alcohol reduced maternal hepatic glucose content and enriched essential amino acid (AA) catabolites, N-acetylated AA products, urea content, and free fatty acids. These alterations suggest an attempt to minimize the glucose gap by increasing gluconeogenesis using AA and glycerol. In contrast, ALC fetuses had unchanged glucose and AA levels, suggesting an adequate draw of maternal nutrients, despite intensified stress on ALC dams. Maternal metabolites including glycolytic intermediates, AA catabolites, urea, and one-carbon-related metabolites correlated with fetal liver and brain weights, whereas lipid metabolites correlated with fetal body weight, indicating they may be drivers of fetal weight outcomes. Together, these data suggest that ALC alters maternal hepatic metabolic activity to limit glucose availability, thereby switching to alternate energy sources to meet the high-energy demands of pregnancy. Their correlation with fetal phenotypic outcomes indicates the influence of maternal metabolism on fetal growth and development.

The Metabolomic Profiles of Sera of Mice Infected with Plasmodium berghei and Treated by Effective Fraction of Naja naja oxiana Using 1H Nuclear Magnetic Resonance Spectroscopy

BACKGROUND

The use of venom fractions from the Iranian cobra could be useful adjunct treatments of malaria with chloroquine. A metabolomic investigation with ¹HNMR spectroscopy was conducted on an effective fraction tested earlier using Plasmodium berghei as an experimental murine model.

PURPOSE

We sought to ascertain both safety and anti-parasitic effects of experimental therapies.

METHODS

After purification of the venom fractions, 25 mice were infected, then treated for 4 days with 0.2 ml of 5 mg/kg, 2.5 mg/kg and 1 mg/kg of the effective fraction, chloroquine, and a drug vehicle. An ED50 was obtained using Giemsa staining and real-time PCR analysis. The toxicity tests inspecting both liver and kidney tissues were performed.

RESULTS

A clear inhibitory effect on parasitaemia was observed (with 75% inhibition with 5 mg/kg and 50% reduction when 2.5 mg/kg dosage used). ED50 obtained 2.5 mg/kg. The metabolomics were identified as differentiation of aminoacyl-t-RNA biosynthesis, valine, leucine, isoleucine biosynthesis and degradation pathways were observed.

CONCLUSION

Upon therapeutic effects of cobra venom fraction, further optimization of dose-dependent response of pharmacokinetics would be worthwhile for further exploration in adjunct experimental venom therapies.

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.

Laboratory practice of organic acid analysis based on gas chromatography–mass spectrometry in China

Objectives

The aim of this study is to investigate the status of laboratory practice of organic acid (OA) analysis using gas chromatography–mass spectrometry in China.

Methods

A survey, investigating details of laboratory practice of OA analysis, was issued on the website of the National Center for Clinical Laboratories of China. Nationwide external quality assessment participating laboratories of OA assay were informed to participate in this survey.

Results

A total of 36 laboratories completed this survey. Most laboratories started OA analysis during 2016–2020. Most (100%) labs reported semi-quantitative results, in which 79.4% of labs adopted the form of the ratio of peak area of OA and quantitative internal standard. Rare labs reported quantitative results. Few labs released reports in three days, most in 5–7 days. The source of control materials varied, 64.5% of labs adapted self-made materials. A total of 43.8% of laboratories directly used reference intervals (RIs) from published literature, 43.8% of laboratories established RIs themselves, but 21.2% of laboratories reported they didn’t verify RIs.

Conclusions

Appropriate supervision for the organic acid assay is needed in the aspect of the turnaround time of reporting results, the establishment validation and verification of reference ranges, and the quantification of results.

Global metabolomic profiling reveals hepatic biosignatures that reflect the unique metabolic needs of late-term mother and fetus

OBJECTIVE

Gestational disorders including preeclampsia, growth restriction and diabetes are characterized, in part, by altered metabolic interactions between mother and fetus. Understanding their functional relevance requires metabolic characterization under normotypic conditions.

METHODS

We performed untargeted metabolomics on livers of pregnant, late-term C57Bl/6J mice (N = 9 dams) and their fetuses (pooling 4 fetuses/litter), using UPLC-MS/MS.

RESULTS

Multivariate analysis of 730 hepatic metabolites revealed that maternal and fetal metabolite profiles were highly compartmentalized, and were significantly more similar within fetuses (ρ_average = 0.81), or within dams (ρ_average = 0.79), than within each maternal-fetal dyad (ρ_average = - 0.76), suggesting that fetal hepatic metabolism is under distinct and equally tight metabolic control compared with its respective dam. The metabolite profiles were consistent with known differences in maternal-fetal metabolism. The reduced fetal glucose reflected its limited capacity for gluconeogenesis and dependence upon maternal plasma glucose pools. The fetal decreases in essential amino acids and elevations in their alpha-keto acid carnitine conjugates reflects their importance as secondary fuel sources to meet fetal energy demands. Whereas, contrasting elevations in fetal serine, glycine, aspartate, and glutamate reflects their contributions to endogenous nucleotide synthesis and fetal growth. Finally, the elevated maternal hepatic lipids and glycerol were consistent with a catabolic state that spares glucose to meet competing maternal-fetal energy demands.

CONCLUSIONS

The metabolite profile of the late-term mouse dam and fetus is consistent with prior, non-rodent analyses utilizing plasma and urine. These data position mouse as a suitable model for mechanistic investigation into how maternal-fetal metabolism adapts (or not) to gestational stressors.

Evaluation of salivary VOC profile composition directed towards oral cancer and oral lesion assessment

OBJECTIVES

Endogenous substances have been analyzed in biological samples in order to be related with metabolic dysfunctions and diseases. The study aimed to investigate profiles of volatile organic compounds (VOCs) from fresh and incubated saliva donated by healthy controls, individuals with oral tissue lesions and with oral cancer, in order to assess case-specific biomarkers of oxidative stress.

MATERIALS AND METHODS

VOCs were pre-concentrated using headspace-solid phase microextraction and analyzed using gas chromatography-mass spectrometry. Then, VOCs positively modulated by incubation process were subtracted, yielding profiles with selected features. Principal component analysis and hierarchical cluster analysis were used to inspect data distribution, while univariate statistics was applied to indicate potential markers of oral cancer. Machine learning algorithm was implemented, aiming multiclass prediction.

RESULTS

The removal of bacterial contribution to VOC profiles allowed the obtaining of more specific case-related patterns. Artificial neural network model included 9 most relevant compounds (1-octen-3-ol, hexanoic acid, E-2-octenal, heptanoic acid, octanoic acid, E-2-nonenal, nonanoic acid, 2,4-decadienal and 9-undecenoic acid). Model performance was assessed using 10-fold cross validation and receiver operating characteristic curves. Obtained overall accuracy was 90%. Oral cancer cases were predicted with 100% of sensitivity and specificity.

CONCLUSIONS

The selected VOCs were ascribed to lipid oxidation mechanism and presented potential to differentiate oral cancer from other inflammatory conditions.

CLINICAL RELEVANCE

These results highlight the importance of interpretation of saliva composition and the clinical value of salivary VOCs. Elucidated metabolic alterations have the potential to aid the early detection of oral cancer and the monitoring of oral lesions.

Metabolomics in chronic pain research

BACKGROUND AND OBJECTIVE

Metabolomics deals with the identification and quantification of small molecules (metabolites) in biological samples. As metabolite levels can reflect normal or altered metabolic pathways, their measurement provides information to improve the understanding, diagnosis and management of diseases. Despite its immense potential, metabolomics applications to pain research have been sparse. This paper describes current metabolomics techniques, reviews published human metabolomics pain research and compares successful metabolomics research in other areas of medicine with the goal of highlighting opportunities offered by metabolomics to advance pain medicine.

DATABASES AND DATA TREATMENT

Non-systematic review.

RESULTS

Our search identified 19 studies that adopted a metabolomics approach in: fibromyalgia (7), chronic widespread pain (4), other musculoskeletal pain conditions (5), neuropathic pain (1), complex regional pain syndrome (1) and pelvic pain (1). The studies used either mass spectrometry or nuclear magnetic resonance. Most are characterized by small sample sizes. Some consistency has been found for alterations in glutamate and testosterone metabolism, and metabolic imbalances caused by the gut microbiome.

CONCLUSIONS

Metabolomics research in chronic pain is in its infancy. Most studies are at the pilot stage. Metabolomics research has been successful in other areas of medicine. These achievements should motivate investigators to expand metabolomics research to improve the understanding of the basic mechanisms of human pain, as well as provide tools to diagnose, predict and monitor chronic pain conditions. Metabolomics research can lead to the identification of biomarkers to support the development and testing of treatments, thereby facilitating personalized pain medicine.

Integration of quantitative proteomics and metabolomics reveals tissue hypoxia mechanisms in an ischemic-hypoxic rat model

Tissues hypoxia caused by hemorrhage is a common complication in many clinical diseases. However, its pathological mechanism remains largely unknown. To partly address this issue, an ischemic-hypoxic rat model was established and the plasma proteomic and metabolic profiles were quantified and analyzed using TMT-based quantitative proteomics and metabolomics. The analysis revealed a total of 177 differentially expressed proteins and 32 metabolites that were uniquely altered in the hypoxic rat plasma, compared to the control. Bioinformatics analysis showed that these altered proteins and metabolites were involved in a wide range of biological processes. Twelve of the 177 differentially expressed proteins were involved in PI3K-Akt signaling, a pathway that has been reported to be strongly associated with tissue hypoxia. Other signaling pathways such as complement and coagulation cascades, GnRH signaling, relaxin signaling, protein processing in endoplasmic reticulum, as well as AGE-RAGE signaling were markedly altered in the ischemic-hypoxic response, implying their potential roles in tissue hypoxia. A joint analysis of proteome and metabolome showed that the significantly altered metabolites such as guanine, tryptamine, dopamine, hexadecenoic, l-methionine, and fumarate may have participated in the pathogenesis of tissue hypoxia. Further, we found that changes in the levels of metabolites matched the changes in protein abundance within the same pathway. Overall, this study presents an overview of the molecular networks in ischemic-hypoxic pathology and offers biochemical basis for further study on the mechanism of tissue hypoxia. SIGNIFICANCE: We employed an integrated metabonomic-proteomic method to systematically analyze the profiles of metabolites and proteins in an ischemic-hypoxic rat model. Bioinformatics and enrichment analysis showed that the differentially expressed proteins were mainly involved in complement and coagulation cascades, PI3K-Akt signaling, GnRH signaling, relaxin signaling, protein processing in endoplasmic reticulum, and AGE-RAGE signaling. Moreover, a panel of 12 candidate proteins involved in PI3K-Akt signaling (i.e., Vtn, Hsp90b1, Ywhae, Tnc, Ywhaz, Thbs4, Lamc1, Col1a1, Il2rg, Egfr, Newgene 621,351, and Tfrc) may serve as the potential biomarkers to predict tissue hypoxia.

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.

Novel biomarkers for lysosomal storage disorders: Metabolomic and proteomic approaches

Lysosomal storage disorders (LSDs) are characterized by the accumulation of specific disease substrates inside the lysosomes of various cells, eventually leading to the deterioration of cellular function and multisystem organ damage. With the continuous discovery and validation of novel and advanced therapies for most LSDs, there is an urgent need to discover more versatile and clinically relevant biomarkers. The utility of these biomarkers should ideally extend beyond the screening and diagnosis of LSDs to the evaluation of disease severity and monitoring of therapy. Metabolomic and proteomic approaches provide the means to the discovery and validation of such novel biomarkers. This is achieved mainly through the application of various mass spectrometric techniques to common and easily accessible biological samples, such as plasma, urine and dried blood spots. In this review, we tried to summarize the complexity of the lysosomal disorders phenotypes, their current diagnostic and therapeutic approaches, the various techniques supporting metabolomic and proteomic studies and finally we tried to explore the newly discovered biomarkers for most LSDs and their reported clinical values.

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.

Metabolomic Studies of Lipid Storage Disorders, with Special Reference to Niemann-Pick Type C Disease: A Critical Review with Future Perspectives

Lysosomal storage disorders (LSDs) are predominantly very rare recessive autosomal neurodegenerative diseases.Sphingolipidoses, a sub-group of LSDs, result from defects in lysosomal enzymes involved in sphingolipid catabolism, and feature disrupted storage systems which trigger complex pathogenic cascades with other organelles collaterally affected. This process leads to cell dysfunction and death, particularly in the central nervous system. One valuable approach to gaining insights into the global impact of lysosomal dysfunction is through metabolomics, which represents a discovery tool for investigating disease-induced modifications in the patterns of large numbers of simultaneously-analysed metabolites, which also features the identification of biomarkers Here, the scope and applications of metabolomics strategies to the investigation of sphingolipidoses is explored in order to facilitate our understanding of the biomolecular basis of these conditions. This review therefore surveys the benefits of applying ’state-of-the-art’ metabolomics strategies, both univariate and multivariate, to sphingolipidoses, particularly Niemann-Pick type C disease. Relevant limitations of these techniques are also discussed, along with the latest advances and developments. We conclude that metabolomics strategies are highly valuable, distinctive bioanalytical techniques for probing LSDs, most especially for the detection and validation of potential biomarkers. They also show much promise for monitoring disease progression and the evaluation of therapeutic strategies and targets.

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

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

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

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

Drug Metabolite Cluster-Based Data-Mining Method for Comprehensive Metabolism Study of 5-hydroxy-6,7,3',4'-tetramethoxyflavone in Rats

The screening of drug metabolites in biological matrixes and structural characterization based on product ion spectra is among the most important, but also the most challenging due to the significant interferences from endogenous species. Traditionally, metabolite detection is accomplished primarily on the basis of predicted molecular masses or fragmentation patterns of prototype drug metabolites using ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS). Although classical techniques are well-suited for achieving the partial characterization of prototype drug metabolites, there is a pressing need for a strategy to enable comprehensive drug metabolism depiction. Therefore, we present drug metabolite clusters (DMCs), different from, but complementary to, traditional approaches for mining the information regarding drugs and their metabolites on the basis of raw, processed, or identified tandem mass spectrometry (MS/MS) data. In this paper, we describe a DMC-based data-mining method for the metabolite identification of 5-hydroxy-6,7,3′,4′-tetramethoxyflavone (HTF), a typical hydroxylated-polymethoxyflavonoid (OH-PMF), which addressed the challenge of creating a thorough metabolic profile. Consequently, eight primary metabolism clusters, sixteen secondary metabolism clusters, and five tertiary metabolism clusters were proposed and 106 metabolites (19 potential metabolites included) were detected and identified positively and tentatively. These metabolites were presumed to generate through oxidation (mono-oxidation, di-oxidation), methylation, demethylation, methoxylation, glucuronidation, sulfation, ring cleavage, and their composite reactions. In conclusion, our study expounded drug metabolites in rats and provided a reference for further research on therapeutic material basis and the mechanism of drugs.

A comprehensive multiplex PCR based exome-sequencing assay for rapid bloodspot confirmation of inborn errors of metabolism

BACKGROUND

Tandem mass spectrometry (MS MS) and simple fluorometric assays are currently used in newborn screening programs to detect inborn errors of metabolism (IEM). The aim of the study was to evaluate the clinical utility of exome sequencing as a second tier screening method to assist clinical diagnosis of the newborn.

METHODS

A novel PCR-exome amplification and re-sequencing (PEARS) assay was designed and used to detect mutations in 122 genes associated with 101 IEM. Newborn bloodspots positive by biochemical testing were analysed by PEARS assay to detect pathogenic mutations relevant to the IEM.

RESULTS

In initial validation studies of genomic DNA samples, PEARS assay correctly detected 25 known mutations associated with 17 different IEM. Retrospective gene analysis of newborns with clinical phenylketonuria (PKU), identified compound heterozygote phenylalanine hydroxylase (PAH) gene mutations in eight of nine samples (89%). Prospective analysis of 211 bloodspots correctly identified the two true PKU samples, yielding positive and negative predictive values of 100%. Testing of 8 true positive MS MS samples correctly identified potentially pathogenic compound heterozygote genotypes in 2 cases of citrullinemia type 1 and one case each of methylmalonic acidemia, isobutyryl-CoA dehydrogenase deficiency, short chain acyl-CoA dehydrogenase deficiency and glutaric acid type II and heterozygous genotypes in 2 cases of autosomal dominant methioninemia. Analysis of 11 of 12 false positive MS MS samples for other IEM identified heterozygous carriers in 8 cases for the relevant genes associated with the suspected IEM. In the remaining 3 cases, the test revealed compound heterozygote mutations in other metabolic genes not associated with the suspected IEM, indicating a misinterpretation of the original MS MS data.

CONCLUSIONS

The PEARS assay has clinical utility as a rapid and cost effective second-tier test to assist the clinician to accurately diagnose newborns with a suspected IEM.

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

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

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

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

Application of Metabolomic Tools for Studying Low Molecular-Weight Fraction of Animal Venoms and Poisons

Both venoms and poisonous secretions are complex mixtures that assist in defense, predation, communication, and competition in the animal world. They consist of variable bioactive molecules, such as proteins, peptides, salts and also metabolites. Metabolomics opens up new perspectives for the study of venoms and poisons as it gives an opportunity to investigate their previously unexplored low molecular-weight components. The aim of this article is to summarize the available literature where metabolomic technologies were used for examining the composition of animal venoms and poisons. The paper discusses only the low molecular-weight components of venoms and poisons collected from snakes, spiders, scorpions, toads, frogs, and ants. An overview is given of the analytical strategies used in the analysis of the metabolic content of the samples. We paid special attention to the classes of compounds identified in various venoms and poisons and potential applications of the small molecules (especially bufadienolides) discovered. The issues that should be more effectively addressed in the studies of animal venoms and poisons include challenges related to sample collection and preparation, species-related chemical diversity of compounds building the metabolome and a need of an online database that would enhance identification of small molecule components of these secretions.

Molecular Signatures of High-Grade Cervical Lesions

Cervical cancer is the fourth most common neoplasia in women and the infection with human papilloma virus (HPV) is its necessary cause. Screening methods, currently based on cytology and HPV DNA tests, display low specificity/sensitivity, reducing the efficacy of cervical cancer screening programs. Herein, molecular signatures of cervical cytologic specimens revealed by liquid chromatography-mass spectrometry (LC-MS), were tested in their ability to provide a metabolomic screening for cervical cancer. These molecules were tested whether they could clinically differentiate insignificant HPV infections from precancerous lesions. For that, high-grade squamous intraepithelial lesions (HSIL)-related metabolites were compared to those of no cervical lesions in women with and without HPV infection. Samples were collected from women diagnosed with normal cervix (N = 40) and from those detected with HSIL from cytology and colposcopy (N = 40). Liquid-based cytology diagnosis, DNA HPV-detection test, and LC-MS analysis were carried out for all the samples. The same sample, in a customized collection medium, could be used for all the diagnostic techniques employed here. The metabolomic profile of cervical cancer provided by LC-MS was found to indicate unique molecular signatures for HSIL, being two ceramides and a sphingosine metabolite. These molecules occurred independently of women’s HPV status and could be related to the pre-neoplastic phenotype. Statistical models based on such findings could correctly discriminate and classify HSIL and no cervical lesion women. The results showcase the potential of LC-MS as an emerging technology for clinical use in cervical cancer screening, although further validation with a larger sample set is still necessary.

Extended and Fully Automated Newborn Screening Method for Mass Spectrometry Detection

A new and fully automated newborn screening method for mass spectrometry was introduced in this paper. Pathological relevant amino acids, acylcarnitines, and certain steroids are detected within 4 min per sample. Each sample is treated in an automated and standardized workflow, where a mixture of deuterated internal standards is sprayed onto the sample before extraction. All compounds showed good linearity, and intra- and inter-day variation lies within the acceptance criteria (except for aspartic acid). The described workflow decreases analysis cost and labor while improving the sample traceability towards good laboratory practice.