Metabolomic Laboratory-Developed Tests: Current Status and Perspectives

Challenges in the Metabolomics-Based Biomarker Validation Pipeline

As end-products of the intersection between the genome and environmental influences, metabolites represent a promising approach to the discovery of novel biomarkers for diseases. However, many potential biomarker candidates identified by metabolomics studies fail to progress beyond analytical validation for routine implementation in clinics. Awareness of the challenges present can facilitate the development and advancement of innovative strategies that allow improved and more efficient applications of metabolite-based markers in clinical settings. This minireview provides a comprehensive summary of the pre-analytical factors, required analytical validation studies, and kit development challenges that must be resolved before the successful translation of novel metabolite biomarkers originating from research. We discuss the necessity for strict protocols for sample collection, storage, and the regulatory requirements to be fulfilled for a bioanalytical method to be considered as analytically validated. We focus especially on the blood as a biological matrix and liquid chromatography coupled with tandem mass spectrometry as the analytical platform for biomarker validation. Furthermore, we examine the challenges of developing a commercially viable metabolomics kit for distribution. To bridge the gap between the research lab and clinical implementation and utility of relevant metabolites, the understanding of the translational challenges for a biomarker panel is crucial for more efficient development of metabolomics-based precision medicine.

Nutritional metabolomics: Recent developments and future needs

Metabolomics has rapidly been adopted as one of the key methods in nutrition research. This review focuses on the recent developments and updates in the field, including the analytical methodologies that encompass improved instrument sensitivity, sampling techniques and data integration (multiomics). Metabolomics has advanced the discovery and validation of dietary biomarkers and their implementation in health research. Metabolomics has come to play an important role in the understanding of the role of small molecules resulting from the diet-microbiota interactions when gut microbiota research has shifted towards improving the understanding of the activity and functionality of gut microbiota rather than composition alone. Currently, metabolomics plays an emerging role in precision nutrition and the recent developments therein are discussed.

The Key Role of GSH in Keeping the Redox Balance in Mammalian Cells: Mechanisms and Significance of GSH in Detoxification via Formation of Conjugates

Glutathione (GSH) is a ubiquitous tripeptide that is biosynthesized in situ at high concentrations (1-5 mM) and involved in the regulation of cellular homeostasis via multiple mechanisms. The main known action of GSH is its antioxidant capacity, which aids in maintaining the redox cycle of cells. To this end, GSH peroxidases contribute to the scavenging of various forms of ROS and RNS. A generally underestimated mechanism of action of GSH is its direct nucleophilic interaction with electrophilic compounds yielding thioether GSH S-conjugates. Many compounds, including xenobiotics (such as NAPQI, simvastatin, cisplatin, and barbital) and intrinsic compounds (such as menadione, leukotrienes, prostaglandins, and dopamine), form covalent adducts with GSH leading mainly to their detoxification. In the present article, we wish to present the key role and significance of GSH in cellular redox biology. This includes an update on the formation of GSH-S conjugates or GSH adducts with emphasis given to the mechanism of reaction, the dependence on GST (GSH S-transferase), where this conjugation occurs in tissues, and its significance. The uncovering of the GSH adducts' formation enhances our knowledge of the human metabolome. GSH-hematin adducts were recently shown to have been formed spontaneously in multiples isomers at hemolysates, leading to structural destabilization of the endogenous toxin, hematin (free heme), which is derived from the released hemoglobin. Moreover, hemin (the form of oxidized heme) has been found to act through the Kelch-like ECH associated protein 1 (Keap1)-nuclear factor erythroid 2-related factor-2 (Nrf2) signaling pathway as an epigenetic modulator of GSH metabolism. Last but not least, the implications of the genetic defects in GSH metabolism, recorded in hemolytic syndromes, cancer and other pathologies, are presented and discussed under the framework of conceptualizing that GSH S-conjugates could be regarded as signatures of the cellular metabolism in the diseased state.

Linking Clinical Blood Metabogram and Gut Microbiota

Recently, a clinical blood metabogram was developed as a fast, low-cost and reproducible test that allows the implementation of metabolomics in clinical practice. The components of the metabogram are functionally related groups of blood metabolites associated with humoral regulation, the metabolism of lipids, carbohydrates and amines, lipid intake into the organism, and liver function, thereby providing clinically relevant information. It is known that the gut microbiota affects the blood metabolome, and the components of the blood metabolome may affect the composition of the gut microbiota. Therefore, before using the metabogram in the clinic, the link between the metabogram components and the level of gut microorganisms should be established. For this purpose, the metabogram and microbiota data were obtained in this work for the same individuals. Metabograms of blood plasma were obtained by direct mass spectrometry of blood plasma, and the gut microbiome was determined by a culture-based method and real-time polymerase chain reaction (PCR). This study involved healthy volunteers and individuals with varying degrees of deviation in body weight (n = 44). A correlation analysis determined which metabogram components are linked to which gut microorganisms and the strength of this link. Moreover, diagnostic parameters (sensitivity, specificity and accuracy) confirmed the capacity of metabogram components to be used for diagnosing gut microbiota alterations. Therefore, the obtained results allow the use of the metabogram in a clinical setting, taking into account its relationship with gut microbiota.

The Future of Laboratory-Developed Tests in Hemostasis Laboratories

Laboratory-developed tests (LDTs) are widely used in clinical hemostasis laboratories. The extent to which LDTs are regulated varies greatly around the world, and proposed changes to regulations have raised concerns about the future of LDTs in clinical laboratories. It is increasingly difficult to justify the use of an LDT where a commercially available method with regulatory approval is available. Conversely, where there is no suitable test with regulatory approval and there is a compelling clinical need, using an LDT outweighs the risk associated with not performing the test. We argue that LDTs are still required in specialist clinical laboratories to fulfill unmet clinical needs, and in lower middle-income countries where they are a vital resource.

The Expectation and Reality of the HepG2 Core Metabolic Profile

To represent the composition of small molecules circulating in HepG2 cells and the formation of the "core" of characteristic metabolites that often attract researchers' attention, we conducted a meta-analysis of 56 datasets obtained through metabolomic profiling via mass spectrometry and NMR. We highlighted the 288 most commonly studied compounds of diverse chemical nature and analyzed metabolic processes involving these small molecules. Building a complete map of the metabolome of a cell, which encompasses the diversity of possible impacts on it, is a severe challenge for the scientific community, which is faced not only with natural limitations of experimental technologies, but also with the absence of transparent and widely accepted standards for processing and presenting the obtained metabolomic data. Formulating our research design, we aimed to reveal metabolites crucial to the Hepg2 cell line, regardless of all chemical and/or physical impact factors. Unfortunately, the existing paradigm of data policy leads to a streetlight effect. When analyzing and reporting only target metabolites of interest, the community ignores the changes in the metabolomic landscape that hide many molecular secrets.

Serum metabolic profile and metabolome genome-wide association study in chicken

BACKGROUND

Chickens provide globally important livestock products. Understanding the genetic and molecular mechanisms underpinning chicken economic traits is crucial for improving their selective breeding. Influenced by a combination of genetic and environmental factors, metabolites are the ultimate expression of physiological processes and can provide key insights into livestock economic traits. However, the serum metabolite profile and genetic architecture of the metabolome in chickens have not been well studied.

RESULTS

Here, comprehensive metabolome detection was performed using non-targeted LC-MS/MS on serum from a chicken advanced intercross line (AIL). In total, 7,191 metabolites were used to construct a chicken serum metabolomics dataset and to comprehensively characterize the serum metabolism of the chicken AIL population. Regulatory loci affecting metabolites were identified in a metabolome genome-wide association study (mGWAS). There were 10,061 significant SNPs associated with 253 metabolites that were widely distributed across the entire chicken genome. Many functional genes affect metabolite synthesis, metabolism, and regulation. We highlight the key roles of TDH and AASS in amino acids, and ABCB1 and CD36 in lipids.

CONCLUSIONS

We constructed a chicken serum metabolite dataset containing 7,191 metabolites to provide a reference for future chicken metabolome characterization work. Meanwhile, we used mGWAS to analyze the genetic basis of chicken metabolic traits and metabolites and to improve chicken breeding.

Mass Spectrometric Blood Metabogram: Acquisition, Characterization, and Prospects for Application

In metabolomics, many metabolites are measured simultaneously in a single run. Such analytical performance opens up prospects for clinical laboratory diagnostics. In this work, a mass spectrometric metabogram was developed as a simplified and clinically applicable way of measuring the blood plasma metabolome. To develop the metabogram, blood plasma samples from healthy male volunteers (n = 48) of approximately the same age, direct infusion mass spectrometry (DIMS) of the low molecular fraction of samples, and principal component analysis (PCA) of the mass spectra were used. The seven components of the metabogram defined by PCA, which cover ~70% of blood plasma metabolome variability, were characterized using a metabolite set enrichment analysis (MSEA) and clinical test results of participating volunteers. It has been established that the components of the metabogram are functionally related groups of the blood metabolome associated with regulation, lipid-carbohydrate, and lipid-amine blood components, eicosanoids, lipid intake into the organism, and liver function thereby providing a lot of clinically relevant information. Therefore, metabogram provides the possibility to apply the metabolomics performance in the clinic. The features of the metabogram are also discussed in comparison with the thin-layer chromatography and with the analysis of blood metabolome by liquid chromatography combined with mass spectrometry.

Hemoglobin normalization outperforms other methods for standardizing dried blood spot metabolomics: A comparative study

Dried blood spot (DBS) metabolomics has numerous applications in newborn health screening, exposomics, and biomonitoring of environmental chemicals in pregnant women and the elderly. However, accurate metabolite quantification is hindered by several challenges: notably the "hematocrit effect" and unknown blood-spotting volumes. Different techniques have been employed to overcome these issues but there is no consensus on the optimal normalization method for DBS metabolomics, and in some cases no normalization is used. We compared five normalization methods (hemoglobin (Hb), specific gravity (SG), protein, spot weight, potassium (K+)) to unnormalized data, and assessed sex-related differences in the DBS metabolome in 21 adults (group 1, n = 10 males, n = 11 females). The performance of each normalization method was evaluated using multiple criteria: (a) reduction of intragroup variation (pooled median absolute deviation, pooled estimate of variance, pooled coefficient of variation, NMDS and principal component analysis), (b) effect on differential metabolic analysis (dendrogram, heatmap, p-value distribution), and (c) influence on classification accuracy (partial least squares discriminant analysis, sparse partial least squares discriminant analysis error rates, receiver operating curve, random forest out of bag error rate). Our results revealed that Hb normalization outperformed all the other methods based on the three criteria and 13 different parameters; the performance of Hb was further demonstrated in an independent group of DBS from 18 neonates (group 2, n = 9 males, n = 9 females). Furthermore, we showed that SG and Hb are correlated in adults (rs = 0.86, p < 0.001), and validated this relationship in an independent group of 18 neonates and infants (group 3) (rs = 0.84, p < 0.001). Using the equation, SG = -0.4814Hb2 + 2.44Hb + 0.005, SG can be used as a surrogate for normalization by Hb. This is the first comparative study to concurrently evaluate multiple normalization methods for DBS metabolomics which will serve as a robust methodological platform for future environmental epidemiological studies.

Current State and Future Perspectives on Personalized Metabolomics

Metabolomics is one of the most promising 'omics' sciences for the implementation in medicine by developing new diagnostic tests and optimizing drug therapy. Since in metabolomics, the end products of the biochemical processes in an organism are studied, which are under the influence of both genetic and environmental factors, the metabolomics analysis can detect any changes associated with both lifestyle and pathological processes. Almost every case-controlled metabolomics study shows a high diagnostic accuracy. Taking into account that metabolomics processes are already described for most nosologies, there are prerequisites that a high-speed and comprehensive metabolite analysis will replace, in near future, the narrow range of chemical analyses used today, by the medical community. However, despite the promising perspectives of personalized metabolomics, there are currently no FDA-approved metabolomics tests. The well-known problem of complexity of personalized metabolomics data analysis and their interpretation for the end-users, in addition to a traditional need for analytical methods to address the quality control, standardization, and data treatment are reported in the review. Possible ways to solve the problems and change the situation with the introduction of metabolomics tests into clinical practice, are also discussed.

An Open-Source Pipeline for Processing Direct Infusion Mass Spectrometry Data of the Human Plasma Metabolome

Direct infusion mass spectrometry (DIMS) is growing in popularity as an effective method for the screening of biological samples in clinical metabolomics. Being quick to execute, DIMS generally requires special skills when interpreting the results of measurements. By inspecting the similarities between two-dimensional electrospray ionization with quadrupole time-of-flight (ESI-QTOF) and matrix-assisted laser desorption/ionization (MALDI) mass spectra, the pipeline for processing QTOF mass spectra using open-source packages (MALDIquant, MSnbase and MetaboAnalystR) was tested. Previously, all algorithmic workflows have relied on the application of software either provided by a vendor or privately developed by enthusiasts. Here, we computationally examined two ways of interpreting the DIMS results of human blood metabolomic profiling. The studied spectra were acquired using ESI-QTOF maXis Impact II (Bruker Daltonics, Billerica, MA, USA), then pre-processed using COMPASS/DataAnalysis commercial software and mapped onto the metabolites using in-lab-developed MatLab scripts. Alternatively, in this work we used the open-source packages MALDIquant, for spectrum pre-processing, and MetaboAnalystR, for data interpretation, instead of the low-availability commercial and home-made tools. Using a set of 100 plasma samples (20 from volunteers with normal body mass index and 80 from patients at different stages of obesity), we observed a high degree of concordance in annotated metabolic pathways between the proprietary DataAnalysis/MatLab pipeline and our freely available solution.

Multi-Omics Integration in Mice With Parkinson’s Disease and the Intervention Effect of Cyanidin-3-O-Glucoside

Background

Parkinson’s disease (PD) is a multifactorial degenerative disease of the central nervous system, which affects mostly older adults. To date, research has focused on the progression of PD. Simultaneously, it was confirmed that the imbalances in gut microbiota are associated with the onset and progression of PD. Accurate diagnosis and precise treatment of PD are currently deficient due to the absence of effective biomarkers.

Methods

In this study, the pharmacodynamic study of cyanidin-3-O-glucoside in PD mice was used. It intends to use the “imbalance” and “balance” of intestinal microecology as the starting point to investigate the “gut-to-brain” hypothesis using metabolomic-combined 16S rRNA gene sequencing methods. Simultaneously, metabolomic analysis was implemented to acquire differential metabolites, and microbiome analysis was performed to analyze the composition and filter the remarkably altered gut microbiota at the phylum/genera level. Afterward, metabolic pathway and functional prediction analysis of the screened differential metabolites and gut microbiota were applied using the MetaboAnalyst database. In addition, Pearson’s correlation analysis was used for the differential metabolites and gut microbiota. We found that cyanidin-3-O-glucoside could protect 1-methyl-4-phenyl-1,2,3,6− tetrahydropy ridine (MPTP)-induced PD mice.

Results

Metabolomic analysis showed that MPTP-induced dysbiosis of the gut microbiota significantly altered sixty-seven metabolites. The present studies have also shown that MPTP-induced PD is related to lipid metabolism, amino acid metabolism, and so on. The 16S rRNA sequencing analysis indicated that 5 phyla and 22 genera were significantly altered. Furthermore, the differential gut microbiota was interrelated with amino acid metabolism, and so on. The metabolites and gut microbiota network diagram revealed significant correlations between 11 genera and 8 differential metabolites.

Conclusion

In combination, this study offers potential molecular biomarkers that should be validated for future translation into clinical applications for more accurately diagnosing PD. Simultaneously, the results of this study lay a basis for further study of the association between host metabolisms, gut microbiota, and PD.

Special Issue on “The Application of Metabolomics in Clinical Practice: Challenges and Opportunities”

This Special Issue aimed to collect studies based on clinical applications of metabolomics in human disease [...]

Personal Metabolomics: A Global Challenge

Today, the introduction of metabolomics, like other omics sciences, into clinical practice as a personal omics test that realizes the perfect analytical capabilities of this science has become an important subject. The assembled data show that the metabolome of biosamples is a collection of highly informative and accurate signatures of virtually all diseases that are widespread in the population. However, we have not seen the emergence of personalized metabolomics in clinical practice. This article analyzes the causes of this problem. The complexity of personal metabolic data analysis and its incompatibility with widely accepted data treatment in metabolomics are shown. As a result, the impossibility of translating metabolic signatures accumulated in databases into a personal test is revealed. Problem-solving strategies that may radically change the situation and realize the analytical capabilities of metabolomics in medical laboratory practice are discussed.