Elucidation of the complex metabolic profile of cerebrospinal fluid using an untargeted biochemical profiling assay

Cerebrospinal fluid metabolomes of treatment-resistant depression subtypes and ketamine response: a pilot study

Depression is a disorder with variable presentation. Selecting treatments and dose-finding is, therefore, challenging and time-consuming. In addition, novel antidepressants such as ketamine have sparse optimization evidence. Insights obtained from metabolomics may improve the management of patients. The objective of this study was to determine whether compounds in the cerebrospinal fluid (CSF) metabolome correlate with scores on questionnaires and response to medication. We performed a retrospective pilot study to evaluate phenotypic and metabolomic variability in patients with treatment-resistant depression using multivariate data compression algorithms. Twenty-nine patients with treatment-resistant depression provided fasting CSF samples. Over 300 metabolites were analyzed in these samples with liquid chromatography-mass spectrometry. Chart review provided basic demographic information, clinical status with self-reported questionnaires, and response to medication. Of the 300 metabolites analyzed, 151 were present in all CSF samples and used in the analyses. Hypothesis-free multivariate analysis compressed the resultant data set into two dimensions using Principal Component (PC) analysis, accounting for ~ 32% of the variance. PC1 accounted for 16.9% of the variance and strongly correlated with age in one direction and 5-methyltetrahydrofolate, homocarnosine, and depression and anxiety scores in the opposite direction. PC2 accounted for 15.4% of the variance, with one end strongly correlated with autism scores, male gender, and cognitive fatigue scores, and the other end with bipolar diagnosis, lithium use, and ethylmalonate disturbance. This small pilot study suggests that complex treatment-resistant depression can be mapped onto a 2-dimensional pathophysiological domain. The results may have implications for treatment selection for depression subtypes.

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.

Downstream Assays for Variant Resolution: Epigenetics, RNA Sequnncing, and Metabolomics

As the availability of advanced molecular testing like whole exome and genome sequencing expands, it comes with the added complication of interpreting inconclusive results, including determining the relevance of variants of uncertain significance or failing to find a variant in an otherwise suspected specific genetic disorder. This complication necessitates the use of alternative testing methods to gather more information in support of, or against, a particular genetic diagnosis. Therefore, new genome-wide approaches, including DNA epigenetic testing, RNA sequencing, and metabolomics, are increasingly being used to increase the diagnostic yield when used in conjunction with more conventional genetic tests.

Benchmarking Outlier Detection Methods for Detecting IEM Patients in Untargeted Metabolomics Data

Untargeted metabolomics (UM) is increasingly being deployed as a strategy for screening patients that are suspected of having an inborn error of metabolism (IEM). In this study, we examined the potential of existing outlier detection methods to detect IEM patient profiles. We benchmarked 30 different outlier detection methods when applied to three untargeted metabolomics datasets. Our results show great differences in IEM detection performances across the various methods. The methods DeepSVDD and R-graph performed most consistently across the three metabolomics datasets. For datasets with a more balanced number of samples-to-features ratio, we found that AE reconstruction error, Mahalanobis and PCA reconstruction error also performed well. Furthermore, we demonstrated the importance of a PCA transform prior to applying an outlier detection method since we observed that this increases the performance of several outlier detection methods. For only one of the three metabolomics datasets, we observed clinically satisfying performances for some outlier detection methods, where we were able to detect 90% of the IEM patient samples while detecting no false positives. These results suggest that outlier detection methods have the potential to aid the clinical investigator in routine screening for IEM using untargeted metabolomics data, but also show that further improvements are needed to ensure clinically satisfying performances.

A combination of neuroimaging and plasma metabolomic analysis suggests inflammation is associated with white matter structural connectivity in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a common mental disorder with unknown pathophysiology. The abnormality of white matter structural connectivity and dysregulation of metabolome in MDD had been widely reported previously. Exploration of the relationship between white matter structural connectivity and plasma metabolites would be helpful for explanation of molecular mechanism for the findings from neuroimaging researches in MDD.

METHODS

The diffusion spectrum imaging data were collected for identification of difference of white matter structural connectivity between MDD (n = 49) and HC (n = 68). The plasma metabolite profiles were acquired by liquid chromatography-mass spectrometry analysis and clustered as co-expression modules. The correlation analysis was performed to identify structural connectivity associated metabolite.

RESULTS

We identified two structural connectivity related metabolite modules. One module was correlated with fractional anisotropy (FA) value between left middle temporal gyrus and left inferior temporal gyrus, which were enriched in tryptophan metabolism pathway; another module was correlated with fiber numbers (FN) between right fusiform gyrus and right inferior temporal gyrus, which was enriched in lysophosphatidylcholine (LPC), lysophosphatidylinositol (LPI) and lysophosphatidylglycerol (LPG) lipid sets. l-Kynurenine in tryptophan metabolism pathway was negatively correlated with FN between right fusiform gyrus and right inferior temporal gyrus, and LPC was positively correlated with FA value between left middle temporal gyrus and left inferior temporal gyrus in MDD.

LIMITATIONS

First, the sample size was relatively small. Second, the long-term effects of antidepressants were not excluded.

CONCLUSION

The results suggested inflammation-related mechanism was associated with white matter structural connectivity in MDD.

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.

Biallelic Variants in the Ectonucleotidase ENTPD1 Cause a Complex Neurodevelopmental Disorder with Intellectual Disability, Distinct White Matter Abnormalities, and Spastic Paraplegia

OBJECTIVE

Human genomics established that pathogenic variation in diverse genes can underlie a single disorder. For example, hereditary spastic paraplegia is associated with >80 genes, with frequently only few affected individuals described for each gene. Herein, we characterize a large cohort of individuals with biallelic variation in ENTPD1, a gene previously linked to spastic paraplegia 64 (Mendelian Inheritance in Man # 615683).

METHODS

Individuals with biallelic ENTPD1 variants were recruited worldwide. Deep phenotyping and molecular characterization were performed.

RESULTS

A total of 27 individuals from 17 unrelated families were studied; additional phenotypic information was collected from published cases. Twelve novel pathogenic ENTPD1 variants are described (NM 001776.6): c.398_399delinsAA; p.(Gly133Glu), c.540del; p.(Thr181Leufs*18), c.640del; p.(Gly216Glufs*75), c.185 T > G; p.(Leu62*), c.1531 T > C; p.(*511Glnext*100), c.967C > T; p.(Gln323*), c.414-2_414-1del, and c.146 A > G; p.(Tyr49Cys) including 4 recurrent variants c.1109 T > A; p.(Leu370*), c.574-6_574-3del, c.770_771del; p.(Gly257Glufs*18), and c.1041del; p.(Ile348Phefs*19). Shared disease traits include childhood onset, progressive spastic paraplegia, intellectual disability (ID), dysarthria, and white matter abnormalities. In vitro assays demonstrate that ENTPD1 expression and function are impaired and that c.574-6_574-3del causes exon skipping. Global metabolomics demonstrate ENTPD1 deficiency leads to impaired nucleotide, lipid, and energy metabolism.

INTERPRETATION

The ENTPD1 locus trait consists of childhood disease onset, ID, progressive spastic paraparesis, dysarthria, dysmorphisms, and white matter abnormalities, with some individuals showing neurocognitive regression. Investigation of an allelic series of ENTPD1 (1) expands previously described features of ENTPD1-related neurological disease, (2) highlights the importance of genotype-driven deep phenotyping, (3) documents the need for global collaborative efforts to characterize rare autosomal recessive disease traits, and (4) provides insights into disease trait neurobiology. ANN NEUROL 2022;92:304-321.

Removal of Uremic Solutes from Dialysate by Activated Carbon

BACKGROUND AND OBJECTIVES

Adsorption of uremic solutes to activated carbon provides a potential means to limit dialysate volumes required for new dialysis systems. The ability of activated carbon to take up uremic solutes has, however, not been adequately assessed.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Graded volumes of waste dialysate collected from clinical hemodialysis treatments were passed through activated carbon blocks. Metabolomic analysis assessed the adsorption by activated carbon of a wide range of uremic solutes. Additional experiments tested the ability of the activated carbon to increase the clearance of selected solutes at low dialysate flow rates.

RESULTS

Activated carbon initially adsorbed the majority, but not all, of 264 uremic solutes examined. Solute adsorption fell, however, as increasing volumes of dialysate were processed. Moreover, activated carbon added some uremic solutes to the dialysate, including methylguanidine. Activated carbon was particularly effective in adsorbing uremic solutes that bind to plasma proteins. In vitro dialysis experiments showed that introduction of activated carbon into the dialysate stream increased the clearance of the protein-bound solutes indoxyl sulfate and p-cresol sulfate by 77%±12% (mean±SD) and 73%±12%, respectively, at a dialysate flow rate of 200 ml/min, but had a much lesser effect on the clearance of the unbound solute phenylacetylglutamine.

CONCLUSIONS

Activated carbon adsorbs many but not all uremic solutes. Introduction of activated carbon into the dialysate stream increased the clearance of those solutes that it does adsorb.

Biallelic variants in SLC38A3 encoding a glutamine transporter cause epileptic encephalopathy

The solute carrier (SLC) superfamily encompasses >400 transmembrane transporters involved in the exchange of amino acids, nutrients, ions, metals, neurotransmitters and metabolites across biological membranes. SLCs are highly expressed in the mammalian brain; defects in nearly 100 unique SLC-encoding genes (OMIM: https://www.omim.org) are associated with rare Mendelian disorders including developmental and epileptic encephalopathy and severe neurodevelopmental disorders. Exome sequencing and family-based rare variant analyses on a cohort with neurodevelopmental disorders identified two siblings with developmental and epileptic encephalopathy and a shared deleterious homozygous splicing variant in SLC38A3. The gene encodes SNAT3, a sodium-coupled neutral amino acid transporter and a principal transporter of the amino acids asparagine, histidine, and glutamine, the latter being the precursor for the neurotransmitters GABA and glutamate. Additional subjects with a similar developmental and epileptic encephalopathy phenotype and biallelic predicted-damaging SLC38A3 variants were ascertained through GeneMatcher and collaborations with research and clinical molecular diagnostic laboratories. Untargeted metabolomic analysis was performed to identify novel metabolic biomarkers. Ten individuals from seven unrelated families from six different countries with deleterious biallelic variants in SLC38A3 were identified. Global developmental delay, intellectual disability, hypotonia, and absent speech were common features while microcephaly, epilepsy, and visual impairment were present in the majority. Epilepsy was drug-resistant in half. Metabolomic analysis revealed perturbations of glutamate, histidine, and nitrogen metabolism in plasma, urine, and CSF of selected subjects, potentially representing biomarkers of disease. Our data support the contention that SLC38A3 is a novel disease gene for developmental and epileptic encephalopathy and illuminate the likely pathophysiology of the disease as perturbations in glutamine homeostasis.

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.

Urinary Mass Spectrometry Profiles in Age-Related Macular Degeneration

We and others have shown that patients with different severity stages of age-related macular degeneration (AMD) have distinct plasma metabolomic profiles compared to controls. Urine is a biofluid that can be obtained non-invasively and, in other fields, urine metabolomics has been proposed as a feasible alternative to plasma biomarkers. However, no studies have applied urinary mass spectrometry (MS) metabolomics to AMD. This study aimed to assess urinary metabolomic profiles of patients with different stages of AMD and a control group. We included two prospectively designed, multicenter, cross-sectional study cohorts: Boston, US (n = 185) and Coimbra, Portugal (n = 299). We collected fasting urine samples, which were used for metabolomic profiling (Ultrahigh Performance Liquid chromatography—Mass Spectrometry). Multivariable logistic and ordinal logistic regression models were used for analysis, accounting for gender, age, body mass index and use of AREDS supplementation. Results from both cohorts were then meta-analyzed. No significant differences in urine metabolites were seen when comparing patients with AMD and controls. When disease severity was considered as an outcome, six urinary metabolites differed significantly (p < 0.01). In particular, two of the metabolites identified have been previously shown by our group to also differ in the plasma of patients of AMD compared to controls and across severity stages. While there are fewer urinary metabolites associated with AMD than plasma metabolites, this study identified some differences across stages of disease that support previous work performed with plasma, thus highlighting the potential of these metabolites as future biomarkers for AMD.

Impaired Tubular Secretion of Organic Solutes in Advanced Chronic Kidney Disease

BACKGROUND

The clearance of solutes removed by tubular secretion may be altered out of proportion to the GFR in CKD. Recent studies have described considerable variability in the secretory clearance of waste solutes relative to the GFR in patients with CKD.

METHODS

To test the hypothesis that secretory clearance relative to GFR is reduced in patients approaching dialysis, we used metabolomic analysis to identify solutes in simultaneous urine and plasma samples from 16 patients with CKD and an eGFR of 7±2 ml/min per 1.73 m2 and 16 control participants. Fractional clearances were calculated as the ratios of urine to plasma levels of each solute relative to those of creatinine and urea in patients with CKD and to those of creatinine in controls.

RESULTS

Metabolomic analysis identified 39 secreted solutes with fractional clearance >3.0 in control participants. Fractional clearance values in patients with CKD were reduced on average to 65%±27% of those in controls. These values were significantly lower for 18 of 39 individual solutes and significantly higher for only one. Assays of the secreted anions phenylacetyl glutamine, p-cresol sulfate, indoxyl sulfate, and hippurate confirmed variable impairment of secretory clearances in advanced CKD. Fractional clearances were markedly reduced for phenylacetylglutamine (4.2±0.6 for controls versus 2.3±0.6 for patients with CKD; P<0.001), p-cresol sulfate (8.6±2.6 for controls versus 4.1±1.5 for patients with CKD; P<0.001), and indoxyl sulfate (23.0±7.3 versus 7.5±2.8; P<0.001) but not for hippurate (10.2±3.8 versus 8.4±2.6; P=0.13).

CONCLUSIONS

Secretory clearances for many solutes are reduced more than the GFR in advanced CKD. Impaired secretion of these solutes might contribute to uremic symptoms as patients approach dialysis.

Association of Plasma Uremic Solute Levels with Residual Kidney Function in Children on Peritoneal Dialysis

BACKGROUND AND OBJECTIVES

Residual native kidney function confers health benefits in patients on dialysis. It can facilitate control of extracellular volume and inorganic ion concentrations. Residual kidney function can also limit the accumulation of uremic solutes. This study assessed whether lower plasma concentrations of uremic solutes were associated with residual kidney function in pediatric patients on peritoneal dialysis.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Samples were analyzed from 29 pediatric patients on peritoneal dialysis, including 13 without residual kidney function and ten with residual kidney function. Metabolomic analysis by untargeted mass spectrometry compared plasma solute levels in patients with and without residual kidney function. Dialytic and residual clearances of selected solutes were also measured by assays using chemical standards.

RESULTS

Metabolomic analysis showed that plasma levels of 256 uremic solutes in patients with residual kidney function averaged 64% (interquartile range, 51%-81%) of the values in patients without residual kidney function who had similar total Kt/V_urea. The plasma levels were significantly lower for 59 of the 256 solutes in the patients with residual kidney function and significantly higher for none. Assays using chemical standards showed that residual kidney function provides a higher portion of the total clearance for nonurea solutes than it does for urea.

CONCLUSIONS

Concentrations of many uremic solutes are lower in patients on peritoneal dialysis with residual kidney function than in those without residual kidney function receiving similar treatment as assessed by Kt/V_urea.

Data Processing Thresholds for Abundance and Sparsity and Missed Biological Insights in an Untargeted Chemical Analysis of Blood Specimens for Exposomics

Background: An untargeted chemical analysis of bio-fluids provides semi-quantitative data for thousands of chemicals for expanding our understanding about relationships among metabolic pathways, diseases, phenotypes and exposures. During the processing of mass spectral and chromatography data, various signal thresholds are used to control the number of peaks in the final data matrix that is used for statistical analyses. However, commonly used stringent thresholds generate constrained data matrices which may under-represent the detected chemical space, leading to missed biological insights in the exposome research. Methods: We have re-analyzed a liquid chromatography high resolution mass spectrometry data set for a publicly available epidemiology study (n = 499) of human cord blood samples using the MS-DIAL software with minimally possible thresholds during the data processing steps. Peak list for individual files and the data matrix after alignment and gap-filling steps were summarized for different peak height and detection frequency thresholds. Correlations between birth weight and LC/MS peaks in the newly generated data matrix were computed using the spearman correlation coefficient. Results: MS-DIAL software detected on average 23,156 peaks for individual LC/MS file and 63,393 peaks in the aligned peak table. A combination of peak height and detection frequency thresholds that was used in the original publication at the individual file and the peak alignment levels can reject 90% peaks from the untargeted chemical analysis dataset that was generated by MS-DIAL. Correlation analysis for birth weight data suggested that up to 80% of the significantly associated peaks were rejected by the data processing thresholds that were used in the original publication. The re-analysis with minimum possible thresholds recovered metabolic insights about C19 steroids and hydroxy-acyl-carnitines and their relationships with birth weight. Conclusions: Data processing thresholds for peak height and detection frequencies at individual data file and at the alignment level should be used at minimal possible level or completely avoided for mining untargeted chemical analysis data in the exposome research for discovering new biomarkers and mechanisms.

Precision of a Clinical Metabolomics Profiling Platform for Use in the Identification of Inborn Errors of Metabolism

BACKGROUND

The application of whole-exome sequencing for the diagnosis of genetic disease has paved the way for systems-based approaches in the clinical laboratory. Here, we describe a clinical metabolomics method for the screening of metabolic diseases through the analysis of a multi-pronged mass spectrometry platform. By simultaneously measuring hundreds of metabolites in a single sample, clinical metabolomics offers a comprehensive approach to identify metabolic perturbations across multiple biochemical pathways.

METHODS

We conducted a single- and multi-day precision study on hundreds of metabolites in human plasma on 4, multi-arm, high-throughput metabolomics platforms.

RESULTS

The average laboratory coefficient of variation (CV) on the 4 platforms was between 9.3 and 11.5% (median, 6.5-8.4%), average inter-assay CV on the 4 platforms ranged from 9.9 to 12.6% (median, 7.0-8.3%) and average intra-assay CV on the 4 platforms ranged from 5.7 to 6.9% (median, 3.5-4.4%). In relation to patient sample testing, the precision of multiple biomarkers associated with IEM disorders showed CVs that ranged from 0.2 to 11.0% across 4 analytical batches.

CONCLUSIONS

This evaluation describes single and multi-day precision across 4 identical metabolomics platforms, comprised each of 4 independent method arms, and reproducibility of the method for the measurement of key IEM metabolites in patient samples across multiple analytical batches, providing evidence that the method is robust and reproducible for the screening of patients with inborn errors of metabolism.

A plasma metabolomics study suggests alteration of multiple metabolic pathways in patients with bipolar disorder

Previous omics studies have greatly contributed to our knowledge of bipolar disorder. Metabolomics is a relatively new field of omics science that can provide complementary insight into data obtained from genomics, transcriptomics or proteomics analyses. In this study, we aimed to identify metabolic pathways associated with bipolar disorder. We performed a liquid chromatography-mass spectrometry-based study to identify plasma metabolic profiles in patients with bipolar disorder (N = 91) and healthy controls (N = 92). Multivariate features selection by sparse partial least square-discriminant analysis combined with metabolite set enrichment analysis were used to identify metabolites and biological pathways that discriminate patients with bipolar disorder from healthy controls. The results showed that eighty metabolites in the plasma were identified to discriminate patients with bipolar disorder from healthy controls, and nine metabolic pathways, i.e., (1) glycine and serine metabolism, (2) glutamate metabolism, (3) arginine and proline metabolism, (4) tyrosine metabolism, (5) catecholamine biosynthesis, (6) purine metabolism, (7) amino sugar metabolism, (8) ammonia recycling, and (9) carnitine synthesis, were identified to be altered in bipolar disorder compared to healthy controls. We conclude that the 80 metabolites and nine metabolic pathways identified might serve as biomarkers to distinguish bipolar disorder patients from healthy controls.

Global biochemical analysis of plasma, serum and whole blood collected using various anticoagulant additives

Introduction

Analysis of blood for the evaluation of clinically relevant biomarkers requires precise collection and sample handling by phlebotomists and laboratory staff. An important consideration for the clinical application of metabolomics are the different anticoagulants utilized for sample collection. Most studies that have characterized differences in metabolite levels in various blood collection tubes have focused on single analytes. We define analyte levels on a global metabolomics platform following blood sampling using five different, but commonly used, clinical laboratory blood collection tubes (i.e., plasma anticoagulated with either EDTA, lithium heparin or sodium citrate, along with no additive (serum), and EDTA anticoagulated whole blood).

Methods

Using an untargeted metabolomics platform we analyzed five sample types after all had been collected and stored at -80°C. The biochemical composition was determined and differences between the samples established using matched-pair t-tests.

Results

We identified 1,117 biochemicals across all samples and detected a mean of 1,036 in the sample groups. Compared to the levels of metabolites in EDTA plasma, the number of biochemicals present at statistically significant different levels (p<0.05) ranged from 452 (serum) to 917 (whole blood). Several metabolites linked to screening assays for rare diseases including acylcarnitines, bilirubin and heme metabolites, nucleosides, and redox balance metabolites varied significantly across the sample collection types.

Conclusions

Our study highlights the widespread effects and importance of using consistent additives for assessing small molecule levels in clinical metabolomics. The biochemistry that occurs during the blood collection process creates a reproducible signal that can identify specimens collected with different anticoagulants in metabolomic studies.

Impact statement

In this manuscript, normal/healthy donors had peripheral blood collected using multiple anticoagulants as well as serum during a fasted blood draw. Global metabolomics is a new technology being utilized to draw clinical conclusions and we interrogated the effects of different anticoagulants on the levels of biochemicals from each of the donors. Characterizing the effects of the anticoagulants on biochemical levels will help researchers leverage the information using global metabolomics in order to make conclusions regarding important disease biomarkers.

Optimization of pretreatment methods for cerebrospinal fluid metabolomics based on ultrahigh performance liquid chromatography/mass spectrometry

Sample pretreatment of cerebrospinal fluid (CSF) in metabolomics plays an important role in metabolic profiling study, especially for samples related to central nervous system diseases. However, there is few study about optimization of CSF metabolomics pretreatment. Therefore, it is an urgent need to optimize CSF pretreatment in order to promote the extraction efficiency of metabolites. In this study, CSF samples were separately subjected to nine different protein precipitation solvents and five different reconstitution solvents to establish the most effective pretreatment method before hydrophilic interaction (HILIC) and reverse-phase (RP) ultrahigh performance liquid chromatography mass spectrometry (UPLC/MS) analysis. The optimal conditions for different sample pretreatment methods were analyzed based on coverage (number of detected potential metabolites), stability (the relative standard deviation (RSD) distribution of metabolites) and the reproducibility of the data. Our results suggested that using EtOH or MeOH-EtOH-ACN (1:1:1, v/v/v) as the protein precipitation solvents and H₂O-MeOH-ACN (2:1:1, v/v/v) as the reconstitution solvent were optimal methods for T3 column analysis. For HILIC column analysis, using EtOH to precipitate protein and H₂O-MeOH-ACN (2:1:1, v/v/v) to reconstitute or MeOH to precipitate and 5 %ACN to reconstitute performed best. This developed UPLC/MS pretreatment method could provide better protein precipitation solvents and reconstitution solvents for global CSF metabolic analysis, potentially facilitating the comprehensive understanding of many central nervous system diseases.

Targeted metabolomic profiling of cerebrospinal fluid from patients with progressive multifocal leukoencephalopathy

Progressive multifocal leukoencephalopathy (PML), caused by JC polyomavirus, is a demyelinating disease of the central nervous system that primarily affects oligodendrocytes. It can cause significant morbidity and mortality. An early diagnosis is of high relevance as timely immune reconstitution is essential. However, diagnosis can be challenging if virus detection via cerebrospinal fluid (CSF) PCR remains negative. Hence, identifying CSF biomarkers for this disease is of crucial importance. We applied a targeted metabolomic screen to CSF from 23 PML patients and eight normal pressure hydrocephalus (NPH) patients as controls. Out of 188 potentially detectable metabolites, 48 (13 amino acids, 4 biogenic amines, 1 acylcarnitine, 21 phosphatidylcholines, 8 sphingolipids, and the sum of hexoses) passed the quality screen and were included in the analyses. Even though there was a tendency towards lower concentrations in PML (mostly of phosphatidylcholines and sphingomyelins), none of the differences between PML and controls in individual metabolite concentrations reached statistical significance (lowest p = 0.104) and there were no potential diagnostic biomarkers (highest area under the ROC curve 0.68). Thus, CSF metabolite changes in PML are likely subtle and possibly larger group sizes and broader metabolite screens are needed to identify potential CSF metabolite biomarkers for PML.

Metabolomics Analysis of the Beef Samples with Different Meat Qualities and Tastes

The purpose of this study was to investigate the meat metabolite profiles related to differences in beef quality attributes (i.e., high-marbled and low-marbled groups) using nuclear magnetic resonance (NMR) spectroscopy. The beef of different marbling scores showed significant differences in water content and fat content. High-marbled meat had mainly higher taste compounds than low-marbled meat. Metabolite analysis showed differences between two marbling groups based on partial least square discriminant analysis (PLS-DA). Metabolites identified by PLS-DA, such as N,N-dimethylglycine, creatine, lactate, carnosine, carnitine, sn-glycero-3-phosphocholine, betaine, glycine, glucose, alanine, tryptophan, methionine, taurine, tyrosine, could be directly linked to marbling groups. Metabolites from variable importance in projection plots were identified and estimated high sensitivity as candidate markers for beef quality attributes. These potential markers were involved in beef taste-related pathways including carbohydrate and amino acid metabolism. Among these metabolites, carnosine, creatine, glucose, and lactate had significantly higher in high-marbled meat compared to low-marbled meat (p<0.05). Therefore, these results will provide an important understanding of the roles of taste-related metabolites in beef quality attributes. Our findings suggest that metabolomics analysis of taste compounds and meat quality may be a powerful method for the discovery of novel biomarkers underlying the quality of beef products.

Confirmation of neurometabolic diagnoses using age-dependent cerebrospinal fluid metabolomic profiles

Timely diagnosis is essential for patients with neurometabolic disorders to enable targeted treatment. Next-Generation Metabolic Screening (NGMS) allows for simultaneous screening of multiple diseases and yields a holistic view of disturbed metabolic pathways. We applied this technique to define a cerebrospinal fluid (CSF) reference metabolome and validated our approach with patients with known neurometabolic disorders. Samples were measured using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry followed by (un)targeted analysis. For the reference metabolome, CSF samples from patients with normal general chemistry results and no neurometabolic diagnosis were selected and grouped based on sex and age (0-2/2-5/5-10/10-15 years). We checked the levels of known biomarkers in CSF from seven patients with five different neurometabolic disorders to confirm the suitability of our method for diagnosis. Untargeted analysis of 87 control CSF samples yielded 8036 features for semiquantitative analysis. No sex differences were found, but 1782 features (22%) were different between age groups (q < 0.05). We identified 206 diagnostic metabolites in targeted analysis. In a subset of 20 high-intensity metabolites and 10 biomarkers, 17 (57%) were age-dependent. For each neurometabolic patient, ≥1 specific biomarker(s) could be identified in CSF, thus confirming the diagnosis. In two cases, age-matching was essential for correct interpretation of the metabolomic profile. In conclusion, NGMS in CSF is a powerful tool in defining a diagnosis for neurometabolic disorders. Using our database with many (age-dependent) features in CSF, our untargeted approach will facilitate biomarker discovery and further understanding of mechanisms of neurometabolic disorders.

Untargeted metabolomics as an unbiased approach to the diagnosis of inborn errors of metabolism of the non-oxidative branch of the pentose phosphate pathway

Inborn errors of metabolism (IEM) involving the non-oxidative pentose phosphate pathway (PPP) include the two relatively rare conditions, transketolase deficiency and transaldolase deficiency, both of which can be difficult to diagnosis given their non-specific clinical presentations. Current biochemical testing approaches require an index of suspicion to consider targeted urine polyol testing. To determine whether a broad-spectrum biochemical test could accurately identify a specific metabolic pattern defining IEMs of the non-oxidative PPP, we employed the use of clinical metabolomic profiling as an unbiased novel approach to diagnosis. Subjects with molecularly confirmed IEMs of the PPP were included in this study. Targeted quantitative analysis of polyols in urine and plasma samples was accomplished with chromatography and mass spectrometry. Semi-quantitative unbiased metabolomic analysis of urine and plasma samples was achieved by assessing small molecules via liquid chromatography and high-resolution mass spectrometry. Results from untargeted and targeted analyses were then compared and analyzed for diagnostic acuity. Two siblings with transketolase (TKT) deficiency and three unrelated individuals with transaldolase (TALDO) deficiency were identified for inclusion in the study. For both IEMs, targeted polyol testing and untargeted metabolomic testing on urine and/or plasma samples identified typical perturbations of the respective disorder. Additionally, untargeted metabolomic testing revealed elevations in other PPP metabolites not typically measured with targeted polyol testing, including ribonate, ribose, and erythronate for TKT deficiency and ribonate, erythronate, and sedoheptulose 7-phosphate in TALDO deficiency. Non-PPP alternations were also noted involving tryptophan, purine, and pyrimidine metabolism for both TKT and TALDO deficient patients. Targeted polyol testing and untargeted metabolomic testing methods were both able to identify specific biochemical patterns indicative of TKT and TALDO deficiency in both plasma and urine samples. In addition, untargeted metabolomics was able to identify novel biomarkers, thereby expanding the current knowledge of both conditions and providing further insight into potential underlying pathophysiological mechanisms. Furthermore, untargeted metabolomic testing offers the advantage of having a single effective biochemical screening test for identification of rare IEMs, like TKT and TALDO deficiencies, that may otherwise go undiagnosed due to their generally non-specific clinical presentations.

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.

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.

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

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

Accumulation of uremic solutes in the cerebrospinal fluid in experimental acute renal failure

The accumulation of uremic solutes in kidney failure may impair mental function. The present study profiled the accumulation of uremic solutes in the cerebrospinal fluid (CSF) in acute renal failure. CSF and plasma ultrafiltrate were obtained from rats at 48 h after sham operation (control; n = 10) or bilateral nephrectomy (n = 10) and analyzed using an established metabolomic platform. Two hundred forty-eight solutes were identified as uremic based on their accumulation in the plasma ultrafiltrate of nephrectomized compared with control rats. CSF levels of 124 of these solutes were sufficient to allow calculation of CSF-to-plasma ultrafiltrate concentration ratios. Levels of many of the uremic solutes were normally lower in the CSF than in the plasma ultrafiltrate, indicating exclusion of these solutes from the brain. CSF levels of the great majority of the uremic solutes increased in renal failure. The increase in the CSF was, however, relatively less than in the plasma ultrafiltrate for most solutes. In particular, for the 31 uremic solutes with CSF-to-plasma ultrafiltrate ratios of <0.25 in control rats, the average CSF-to-plasma ultrafiltrate ratio decreased from 0.13 ± 0.07 in control rats to 0.09 ± 0.06 in nephrectomized rats, revealing sustained ability to exclude these solutes from the brain. In summary, levels of many uremic solutes are normally kept lower in the CSF than in the plasma ultrafiltrate by the action of the blood-brain and blood-CSF barriers. These barriers remain functional but cannot prevent accumulation of uremic solutes in the CSF when the kidneys fail.

De novo missense variant in the GTPase effector domain (GED) of DNM1L leads to static encephalopathy and seizures

DNM1L encodes a GTPase of the dynamin superfamily, which plays a crucial role in mitochondrial and peroxisomal fission. Pathogenic variants affecting the middle domain and the GTPase domain of DNM1L have been implicated in encephalopathy because of defective mitochondrial and peroxisomal fission 1 (EMPF1, MIM #614388). Patients show variable phenotypes ranging from severe hypotonia leading to death in the neonatal period to developmental delay/regression, with or without seizures. Familial pathogenic variants in the GTPase domain have also been associated with isolated optic atrophy. We present a 27-yr-old woman with static encephalopathy, a history of seizures, and nystagmus, in whom a novel de novo heterozygous variant was detected in the GTPase effector domain (GED) of DNM1L (c.2072A>G, p.Tyr691Cys). Functional studies in Drosophila demonstrate large, abnormally distributed peroxisomes and mitochondria, an effect very similar to that of middle domain missense alleles observed in pediatric subjects with EMPF1. To our knowledge, not only is this the first report of a disease-causing variant in the GED domain in humans, but this is also the oldest living individual reported with EMPF1. Longitudinal data of this kind helps to expand our knowledge of the natural history of a growing list of DNM1L-related disorders.

2-Pyrrolidinone and Succinimide as Clinical Screening Biomarkers for GABA-Transaminase Deficiency: Anti-seizure Medications Impact Accurate Diagnosis

Broad-scale untargeted biochemical phenotyping is a technology that supplements widely accepted assays, such as organic acid, amino acid, and acylcarnitine analyses typically utilized for the diagnosis of inborn errors of metabolism. In this study, we investigate the analyte changes associated with 4-aminobutyrate aminotransferase (ABAT, GABA transaminase) deficiency and treatments that affect GABA metabolism. GABA-transaminase deficiency is a rare neurodevelopmental and neurometabolic disorder caused by mutations in ABAT and resulting in accumulation of GABA in the cerebrospinal fluid (CSF). For that reason, measurement of GABA in CSF is currently the primary approach to diagnosis. GABA-transaminase deficiency results in severe developmental delay with intellectual disability, seizures, and movement disorder, and is often associated with death in childhood. Using an untargeted metabolomics platform, we analyzed EDTA plasma, urine, and CSF specimens from four individuals with GABA-transaminase deficiency to identify biomarkers by comparing the biochemical profile of individual patient samples to a pediatric-centric population cohort. Metabolomic analyses of over 1,000 clinical plasma samples revealed a rich source of biochemical information. Three out of four patients showed significantly elevated levels of the molecule 2-pyrrolidinone (Z-score ≥2) in plasma, and whole exome sequencing revealed variants of uncertain significance in ABAT. Additionally, these same patients also had elevated levels of succinimide in plasma, urine, and CSF and/or homocarnosine in urine and CSF. In the analysis of clinical EDTA plasma samples, the levels of succinimide and 2-pyrrolidinone showed a high level of correlation (R = 0.73), indicating impairment in GABA metabolism and further supporting the association with GABA-transaminase deficiency and the pathogenicity of the ABAT variants. Further analysis of metabolomic data across our patient population revealed the association of elevated levels of 2-pyrrolidinone with administration of vigabatrin, a commonly used anti-seizure medication and a known inhibitor of GABA-transaminase. These data indicate that anti-seizure medications may alter the biochemical and metabolomic data, potentially impacting the interpretation and diagnosis for the patient. Further, these data demonstrate the power of combining broad scale genotyping and phenotyping technologies to diagnose inherited neurometabolic disorders and support the use of metabolic phenotyping of plasma to screen for GABA-transaminase deficiency.

Direct-infusion based metabolomics unveils biochemical profiles of inborn errors of metabolism in cerebrospinal fluid

BACKGROUND

For inborn errors of metabolism (IEM), metabolomics is performed for three main purposes: 1) development of next generation metabolic screening platforms, 2) identification of new biomarkers in predefined patient cohorts and 3) for identification of new IEM. To date, plasma, urine and dried blood spots are used. We anticipate that cerebrospinal fluid (CSF) holds additional - valuable - information, especially for IEM with neurological involvement. To expand metabolomics to CSF, we here tested whether direct-infusion high-resolution mass spectrometry (DI-HRMS) based non-quantitative metabolomics could correctly capture the biochemical profile of patients with an IEM in CSF.

METHODS

Eleven patient samples, harboring eight different IEM, and thirty control samples were analyzed using DI-HRMS. First we assessed whether the biochemical profile of the control samples represented the expected profile in CSF. Next, each patient sample was assigned a 'most probable diagnosis' by an investigator blinded for the known diagnoses of the patients.

RESULTS

the biochemical profile identified using DI-HRMS in CSF samples resembled the known profile, with - among others - the highest median intensities for mass peaks annotated with glucose, lactic acid, citric acid and glutamine. Subsequent analysis of patient CSF profiles resulted in correct 'most probable diagnoses' for all eleven patients, including non-ketotic hyperglycinaemia, propionic aciduria, purine nucleoside phosphorylase deficiency, argininosuccinic aciduria, tyrosinaemia type I, hyperphenylalaninemia and hypermethioninaemia.

CONCLUSION

We here demonstrate that DI-HRMS based non-quantitative metabolomics accurately captures the biochemical profile of this set of patients in CSF, opening new ways for using metabolomics in CSF in the metabolic diagnostic laboratory.

Allopregnanolone Treatment Improves Plasma Metabolomic Profile Associated with GABA Metabolism in Fragile X-Associated Tremor/Ataxia Syndrome: a Pilot Study

Currently, there is no effective treatment for the fragile X-associated tremor/ataxia syndrome (FXTAS), a late-onset neurodegenerative disorder. In this pilot study, we evaluated whether allopregnanolone, a natural neurosteroid that exerts beneficial effects in neurodegenerative diseases, nervous system injury, and peripheral neuropathies, could improve lymphocytic bioenergetics and plasma pharmacometabolomics in six males with FXTAS (68 ± 3 years old; FMR1 CGG repeats 94 ± 4; FXTAS stages ranging from 3 to 5) enrolled in a 12-week open-label intervention study conducted at the University of California Davis from December 2015 through July 2016. Plasma pharmacometabolomics and lymphocytic mitochondria function were assessed at baseline (on the day of the first infusion) and at follow-up (within 48 h from the last infusion). In parallel, quantitative measurements of tremor and ataxia and neuropsychological evaluations of mental state, executive function, learning, memory, and psychological symptoms were assessed at the same time points. Allopregnanolone treatment impacted significantly GABA metabolism, oxidative stress, and some of the mitochondria-related outcomes. Notably, the magnitude of the individual metabolic response, as well as the correlation with some of the behavioral tests, was overwhelmingly carrier-specific. Based on this pilot study, allopregnanolone treatment has the potential for improving cognitive and GABA metabolism in FXTAS aligned with the concept of precision medicine.

Untargeted metabolomic profiling reveals multiple pathway perturbations and new clinical biomarkers in urea cycle disorders

Purpose:

Untargeted metabolomic analysis is increasingly being used in the screening and management of individuals with inborn errors of metabolism (IEM). We aimed to test whether untargeted metabolomic analysis in plasma might be useful for monitoring the disease course and management of urea cycle disorders (UCDs).

Methods:

Untargeted mass spectrometry-based metabolomic analysis was used to generate z-scores for more than 900 metabolites in plasma from 48 individuals with various UCDs. Pathway analysis was used to identify common pathways that were perturbed in each UCD.

Results:

Our metabolomic analysis in plasma identified multiple potentially neurotoxic metabolites of arginine in arginase deficiency and, thus, may have utility in monitoring the efficacy of treatment in arginase deficiency. In addition, we were also able to detect multiple biochemical perturbations in all UCDs that likely reflect clinical management, including metabolite alterations secondary to dietary and medication management.

Conclusions:

In addition to utility in screening for IEM, our results suggest that untargeted metabolomic analysis in plasma may be beneficial for monitoring efficacy of clinical management and off-target effects of medications in UCDs and potentially other 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.

A metabolomic map of Zellweger spectrum disorders reveals novel disease biomarkers

PURPOSE

Peroxisome biogenesis disorders-Zellweger spectrum disorders (PBD-ZSD) are metabolic diseases with multisystem manifestations. Individuals with PBD-ZSD exhibit impaired peroxisomal biochemical functions and have abnormal levels of peroxisomal metabolites, but the broader metabolic impact of peroxisomal dysfunction and the utility of metabolomic methods is unknown.

METHODS

We studied 19 individuals with clinically and molecularly characterized PBD-ZSD. We performed both quantitative peroxisomal biochemical diagnostic studies in parallel with untargeted small molecule metabolomic profiling in plasma samples with detection of >650 named compounds.

RESULTS

The cohort represented intermediate to mild PBD-ZSD subjects with peroxisomal biochemical alterations on targeted analysis. Untargeted metabolomic profiling of these samples revealed elevations in pipecolic acid and long-chain lysophosphatidylcholines, as well as an unanticipated reduction in multiple sphingomyelin species. These sphingomyelin reductions observed were consistent across the PBD-ZSD samples and were rare in a population of >1,000 clinical samples. Interestingly, the pattern or "PBD-ZSD metabolome" was more pronounced in younger subjects suggesting studies earlier in life reveal larger biochemical changes.

CONCLUSION

Untargeted metabolomics is effective in detecting mild to intermediate cases of PBD-ZSD. Surprisingly, dramatic reductions in plasma sphingomyelin are a consistent feature of the PBD-ZSD metabolome. The use of metabolomics in PBD-ZSD can provide insight into novel biomarkers of disease.

Characteristics of Colon-Derived Uremic Solutes

BACKGROUND AND OBJECTIVES

Colon microbial metabolism produces solutes that are normally excreted in the urine and accumulate in the plasma when the kidneys fail. This study sought to further identify and characterize human colon-derived uremic solutes.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Colon-derived solutes normally excreted in the urine were identified by comparing urine from controls (n=17) and patients with total colectomies (n=12), using an established metabolomic platform. Colon-derived solutes that accumulate in kidney failure were then identified by comparing the plasma of the control patients with that of patients on dialysis (n=14).

RESULTS

Ninety-one urinary solutes were classified as colon-derived on the basis of the finding of a urine excretion rate at least four-fold higher in control patients than in patients with total colectomies. Forty-six were solutes with known chemical structure, 35 of which had not previously been identified as colon-derived. Sixty of the colon-derived solutes accumulated in the plasma of patients with ESKD to a degree greater than urea and were therefore classified as uremic. The estimated urinary clearance for 27 out of the 32 colon-derived solutes for which clearance could be calculated exceeded that of creatinine, consistent with tubular secretion. Sulfatase treatment revealed that 42 out of the 91 colon-derived solutes detected were likely conjugates.

CONCLUSIONS

Metabolomic analysis identified numerous colon-derived solutes that are normally excreted in human urine. Clearance by tubular secretion limits plasma levels of many colon-derived solutes.

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.

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

Many inborn errors of metabolism (IEMs) are amenable to treatment; therefore, early diagnosis and treatment is imperative. Despite recent advances, the genetic basis of many metabolic phenotypes remains unknown. For discovery purposes, whole exome sequencing (WES) variant prioritization coupled with clinical and bioinformatics expertise is the primary method used to identify novel disease-causing variants; however, causation is often difficult to establish due to the number of plausible variants. Integrated analysis of untargeted metabolomics (UM) and WES or whole genome sequencing (WGS) data is a promising systematic approach for identifying disease-causing variants. In this review, we provide a literature-based overview of UM methods utilizing liquid chromatography mass spectrometry (LC-MS), and assess approaches to integrating WES/WGS and LC-MS UM data for the discovery and prioritization of variants causing IEMs. To embed this integrated -omics approach in the clinic, expansion of gene-metabolite annotations and metabolomic feature-to-metabolite mapping methods are needed.

Disturbed phospholipid metabolism in serine biosynthesis defects revealed by metabolomic profiling

Serine biosynthesis defects are autosomal recessive metabolic disorders resulting from the deficiency of any of the three enzymes involved in de novo serine biosynthesis, specifically phosphoglycerate dehydrogenase (PGDH), phosphoserine aminotransferase (PSAT), and phosphoserine phosphatase (PSP). In this study, we performed metabolomic profiling on 4 children with serine biosynthesis defects; 3 with PGDH deficiency and 1 with PSAT deficiency. The evaluations were performed at baseline and with serine and glycine supplementation. Metabolomic profiling performed at baseline showed low phospholipid species, including glycerophosphocholine, glycerophosphoethanolamine, and sphingomyelin. All children had low serine and glycine as expected. Low glycerophosphocholine compounds were found in 4 children, low glycerophosphoethanolamine compounds in 3 children, and low sphingomyelin species in 2 children. Metabolic profiling with serine and glycine supplementation showed normalization of most of the low phospholipid compounds in the 4 children. Phospholipids are the major component of plasma and intracellular membranes, and phosphatidylcholine is the most abundant phospholipid of all mammalian cell types and subcellular organelles. Phosphatidylcholine is of particular importance for the nervous system, where it is essential for neuronal differentiation. The observed low phosphatidylcholine species in children with serine biosynthesis defects that improved after serine supplementation, supports the role of serine as a significant precursor for phosphatidylcholine. The vital role that phosphatidylcholine has during neuronal differentiation and the pronounced neurological manifestations in serine biosynthesis defects suggest that phosphatidylcholine deficiency occurring secondary to serine deficiency may have a significant contribution to the development of the neurological manifestations in individuals with serine biosynthesis defects.

Biologically Consistent Annotation of Metabolomics Data

Annotation of metabolites remains a major challenge in liquid chromatography-mass spectrometry (LC-MS) based untargeted metabolomics. The current gold standard for metabolite identification is to match the detected feature with an authentic standard analyzed on the same equipment and using the same method as the experimental samples. However, there are substantial practical challenges in applying this approach to large data sets. One widely used annotation approach is to search spectral libraries in reference databases for matching metabolites; however, this approach is limited by the incomplete coverage of these libraries. An alternative computational approach is to match the detected features to candidate chemical structures based on their mass and predicted fragmentation pattern. Unfortunately, both of these approaches can match multiple identities with a single feature. Another issue is that annotations from different tools often disagree. This paper presents a novel LC-MS data annotation method, termed Biologically Consistent Annotation (BioCAn), that combines the results from database searches and in silico fragmentation analyses and places these results into a relevant biological context for the sample as captured by a metabolic model. We demonstrate the utility of this approach through an analysis of CHO cell samples. The performance of BioCAn is evaluated against several currently available annotation tools, and the accuracy of BioCAn annotations is verified using high-purity analytical standards.

Analyses of SLC13A5-epilepsy patients reveal perturbations of TCA cycle

OBJECTIVE

To interrogate the metabolic profile of five subjects from three families with rare, nonsense and missense mutations in SLC13A5 and Early Infantile Epileptic Encephalopathies (EIEE) characterized by severe, neonatal onset seizures, psychomotor retardation and global developmental delay.

METHODS

Mass spectrometry of plasma, CSF and urine was used to identify consistently dysregulated analytes in our subjects.

RESULTS

Distinctive elevations of citrate and dysregulation of citric acid cycle intermediates, supporting the hypothesis that loss of SLC13A5 function alters tricarboxylic acid cycle (TCA) metabolism and may disrupt metabolic compartmentation in the brain.

SIGNIFICANCE

Our results indicate that analysis of plasma citrate and other TCA analytes in SLC13A5 deficient patients define a diagnostic metabolic signature that can aid in diagnosing children with this disease.