Untargeted metabolomics identifies unique though benign biochemical changes in patients with pathogenic variants in UROC1

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.

Histidine ameliorates elastase- and lipopolysaccharide-induced lung inflammation by inhibiting the activation of the NLRP3 inflammasome

Histidine treatment has anti-inflammatory effects on several diseases such as colitis and obesity. We revealed that histidine levels were decreased in the serum of patients with chronic obstructive pulmonary disease (COPD) in our previous study. However, whether histidine confers protection against COPD is unclear. In the present study, we evaluated the protective effects of histidine in a porcine pancreatic elastase- and lipopolysaccharide-induced COPD mouse model. We found that the serum histidine concentration was decreased in COPD mice. Histidine supplementation improved the COPD mouse lung function and reduced the inflammatory cell counts and production of cytokines in bronchoalveolar lavage fluid. In addition, histidine treatment ameliorated lung inflammation by inhibiting the nucleotide-binding oligomerization domain-like receptor (NLR) family pyrin domain-containing 3 inflammasome activation both in vivo and in vitro. Furthermore, we found that the potential anti-inflammatory mechanism involved the upregulation of silent information regulator factor 2-related enzyme 1. These results suggest that histidine may be a valuable therapeutic target for COPD.

Comparison of Untargeted Metabolomic Profiling vs Traditional Metabolic Screening to Identify Inborn Errors of Metabolism

IMPORTANCE

Recent advances in newborn screening (NBS) have improved the diagnosis of inborn errors of metabolism (IEMs); however, many potentially treatable IEMs are not included on NBS panels, nor are they covered in standard, first-line biochemical testing.

OBJECTIVE

To examine the utility of untargeted metabolomics as a primary screening tool for IEMs by comparing the diagnostic rate of clinical metabolomics with the recommended traditional metabolic screening approach.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study compares data from 4464 clinical samples received from 1483 unrelated families referred for trio testing of plasma amino acids, plasma acylcarnitine profiling, and urine organic acids (June 2014 to October 2018) and 2000 consecutive plasma samples from 1807 unrelated families (July 2014 to February 2019) received for clinical metabolomic screening at a College of American Pathologists and Clinical Laboratory Improvement Amendments-certified biochemical genetics laboratory. Data analysis was performed from September 2019 to August 2020.

EXPOSURES

Metabolic and molecular tests performed at a genetic testing reference laboratory in the US and available clinical information for each patient were assessed to determine diagnostic rate.

MAIN OUTCOMES AND MEASURES

The diagnostic rate of traditional metabolic screening compared with clinical metabolomic profiling was assessed in the context of expanded NBS.

RESULTS

Of 1483 cases screened by the traditional approach, 912 patients (61.5%) were male and 1465 (98.8%) were pediatric (mean [SD] age, 4.1 [6.0] years; range, 0-65 years). A total of 19 families were identified with IEMs, resulting in a 1.3% diagnostic rate. A total of 14 IEMs were detected, including 3 conditions not included in the Recommended Uniform Screening Panel for NBS. Of the 1807 unrelated families undergoing plasma metabolomic profiling, 1059 patients (58.6%) were male, and 1665 (92.1%) were pediatric (mean [SD] age, 8.1 [10.4] years; range, 0-80 years). Screening identified 128 unique cases with IEMs, giving an overall diagnostic rate of 7.1%. In total, 70 different metabolic conditions were identified, including 49 conditions not presently included on the Recommended Uniform Screening Panel for NBS.

CONCLUSIONS AND RELEVANCE

These findings suggest that untargeted metabolomics provided a 6-fold higher diagnostic yield compared with the conventional screening approach and identified a broader spectrum of IEMs. Notably, with the expansion of NBS programs, traditional metabolic testing approaches identify few disorders beyond those covered on the NBS. These data support the capability of clinical untargeted metabolomics in screening for IEMs and suggest that broader screening approaches should be considered in the initial evaluation for metabolic disorders.

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.

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.

Assessment of the effects of repeated freeze thawing and extended bench top processing of plasma samples using untargeted metabolomics

INTRODUCTION

Clinical metabolomics has utility as a screen for inborn errors of metabolism (IEM) and variant classification in patients with rare disease. It is important to understand and characterize preanalytical factors that influence assay performance during patient sample testing.

OBJECTIVES

To evaluate the impact of extended thawing of human EDTA plasma samples on ice prior to extraction as well as repeated freeze-thaw cycling of samples to identify compounds that are unstable prior to metabolomic analysis.

METHODS

Twenty-four (24) donor EDTA plasma samples were collected and immediately frozen at - 80 °C. Twelve samples were thawed on ice and extracted for analysis at time 0, 2, 4, and 6 h. Twelve other donor samples were repeatedly thawed and frozen up to four times and analyzed at each cycle. Compound levels at each time point/freeze-thaw cycle were compared to the control samples using matched-paired t tests to identify analytes affected by each condition.

RESULTS

We identified 1026 biochemicals across all samples. Incubation of thawed EDTA plasma samples on ice for up to 6 h resulted in < 1% of biochemicals changing significantly. Freeze-thaw cycles affected a greater percentage of the metabolome; ~ 2% of biochemicals changed after 3 freeze-thaw cycles.

CONCLUSIONS

Our study highlights that the number and magnitude of these changes are not as widespread as other aspects of improper sample handling. In total, < 3% of the metabolome detected on our clinical metabolomics platform should be disqualified when multiple freeze-thaw cycles or extended thawing at 4 °C are performed on a given sample.

Metabolomic profiling reveals a differential role for hippocampal glutathione reductase in infantile memory formation

The metabolic mechanisms underlying the formation of early-life episodic memories remain poorly characterized. Here, we assessed the metabolomic profile of the rat hippocampus at different developmental ages both at baseline and following episodic learning. We report that the hippocampal metabolome significantly changes over developmental ages and that learning regulates differential arrays of metabolites according to age. The infant hippocampus had the largest number of significant changes following learning, with downregulation of 54 metabolites. Of those, a large proportion was associated with the glutathione-mediated cellular defenses against oxidative stress. Further biochemical, molecular, and behavioral assessments revealed that infantile learning evokes a rapid and persistent increase in the activity of neuronal glutathione reductase, the enzyme that regenerates reduced glutathione from its oxidized form. Inhibition of glutathione reductase selectively impaired long-term memory formation in infant but not in juvenile and adult rats, confirming its age-specific role. Thus, metabolomic profiling revealed that the hippocampal glutathione-mediated antioxidant pathway is differentially required for the formation of infantile memory.

Using Out-of-Batch Reference Populations to Improve Untargeted Metabolomics for Screening Inborn Errors of Metabolism

Untargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). Analysis of a large number of reference samples is crucial for correcting variations in metabolite concentrations that result from factors, such as diet, age, and gender in order to judge whether metabolite levels are abnormal. However, a large number of reference samples requires the use of out-of-batch samples, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.e., technical variations between batches. Methods to merge and accurately normalize data from multiple batches are urgently needed. Based on six metrics, we compared the existing normalization methods on their ability to reduce the batch effects from nine independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method that uses 10 stable isotope-labeled internal standards and a mixed effect model. In addition, we propose a regression model with age and sex as covariates fitted on reference samples that were obtained from all nine batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal, as well as in the detection of 195 known biomarkers across 49 IEM patient samples and performed at least similar to an approach utilizing 15 within-batch reference samples. Furthermore, our regression model indicates that 6.5-37% of the considered features showed significant age-dependent variations. Our comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch reference samples to establish clinically-relevant reference values for metabolite concentrations. These findings open the possibilities to use large scale out-of-batch reference samples in a clinical setting, increasing the throughput and detection accuracy.

Potential Benefits and Pitfalls of Histidine Supplementation for Cancer Therapy Enhancement

Dietary supplementation of the amino acid histidine has demonstrable benefits in various clinical conditions. Recent work in a pediatric leukemia mouse model exposed a surprising potential application of histidine supplementation for cancer therapy enhancement. These findings demand a deeper reassessment of the physiological effects and potential drawbacks of histidine supplementation. As pertinent to this question, we discuss the safety of high doses of histidine and its relevant metabolic fates in the human body. We refrain from recommendations or final conclusions because comprehensive preclinical evidence for safety and efficacy of histidine supplementation is still lacking. However, we emphasize the incentive to study the safety of histidine supplementation and its potential to improve the clinical outcome of pediatric blood cancers through a simple dietary supplementation. The need for comprehensive preclinical testing of histidine supplementation in healthy and tumor-bearing mice is fundamental, and we hope that this review will facilitate such studies.

Histidine Metabolism and Function

Histidine is a dietary essential amino acid because it cannot be synthesized in humans. The WHO/FAO requirement for adults for histidine is 10 mg · kg body weight-1 · d-1. Histidine is required for synthesis of proteins. It plays particularly important roles in the active site of enzymes, such as serine proteases (e.g., trypsin) where it is a member of the catalytic triad. Excess histidine may be converted to trans-urocanate by histidine ammonia lyase (histidase) in liver and skin. UV light in skin converts the trans form to cis-urocanate which plays an important protective role in skin. Liver is capable of complete catabolism of histidine by a pathway which requires folic acid for the last step, in which glutamate formiminotransferase converts the intermediate N-formiminoglutamate to glutamate, 5,10 methenyl-tetrahydrofolate, and ammonia. Inborn errors have been recognized in all of the catabolic enzymes of histidine. Histidine is required as a precursor of carnosine in human muscle and parts of the brain where carnosine appears to play an important role as a buffer and antioxidant. It is synthesized in the tissue by carnosine synthase from histidine and β-alanine, at the expense of ATP hydrolysis. Histidine can be decarboxylated to histamine by histidine decarboxylase. This reaction occurs in the enterochromaffin-like cells of the stomach, in the mast cells of the immune system, and in various regions of the brain where histamine may serve as a neurotransmitter.