Metabolic fingerprinting as a diagnostic tool

The role of salivary metabolomics in chronic periodontitis: bridging oral and systemic diseases

BACKGROUND

Chronic periodontitis is a condition impacting approximately 50% of the world's population. As chronic periodontitis progresses, the bacteria in the oral cavity change resulting in new microbial interactions which in turn influence metabolite production. Chronic periodontitis manifests with inflammation of the periodontal tissues, which is progressively developed due to bacterial infection and prolonged bacterial interaction with the host immune response. The bi-directional relationship between periodontitis and systemic diseases has been reported in many previous studies. Traditional diagnostic methods for chronic periodontitis and systemic diseases such as chronic kidney diseases (CKD) have limitations due to their invasiveness, requiring practised individuals for sample collection, frequent blood collection, and long waiting times for the results. More rapid methods are required to detect such systemic diseases, however, the metabolic profiles of the oral cavity first need to be determined.

AIM OF REVIEW

In this review, we explored metabolomics studies that have investigated salivary metabolic profiles associated with chronic periodontitis and systemic illnesses including CKD, oral cancer, Alzheimer's disease, Parkinsons's disease, and diabetes to highlight the most recent methodologies that have been applied in this field.

KEY SCIENTIFIC CONCEPTS OF THE REVIEW

Of the rapid, high throughput techniques for metabolite profiling, Nuclear magnetic resonance (NMR) spectroscopy was the most applied technique, followed by liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS). Furthermore, Raman spectroscopy was the most used vibrational spectroscopic technique for comparison of the saliva from periodontitis patients to healthy individuals, whilst Fourier Transform Infra-Red Spectroscopy (FT-IR) was not utilised as much in this field. A recommendation for cultivating periodontal bacteria in a synthetic medium designed to replicate the conditions and composition of saliva in the oral environment is suggested to facilitate the identification of their metabolites. This approach is instrumental in assessing the potential of these metabolites as biomarkers for systemic illnesses.

Integration of FTIR Spectroscopy and Machine Learning for Kidney Allograft Rejection: A Complementary Diagnostic Tool

Background: Kidney transplantation is a life-saving treatment for end-stage kidney disease, but allograft rejection remains a critical challenge, requiring accurate and timely diagnosis. The study aims to evaluate the integration of Fourier Transform Infrared (FTIR) spectroscopy and machine learning algorithms as a minimally invasive method to detect kidney allograft rejection and differentiate between T Cell-Mediated Rejection (TCMR) and Antibody-Mediated Rejection (AMR). Additionally, the goal is to discriminate these rejection types aiming to develop a reliable decision-making support tool. Methods: This retrospective study included 41 kidney transplant recipients and analyzed 81 serum samples matched to corresponding allograft biopsies. FTIR spectroscopy was applied to pre-biopsy serum samples, and Naïve Bayes classification models were developed to distinguish rejection from non-rejection and classify rejection types. Data preprocessing involved, e.g., atmospheric compensation, second derivative, and feature selection using Fast Correlation-Based Filter for spectral regions 600-1900 cm-1 and 2800-3400 cm-1. Model performance was assessed via area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and accuracy. Results: The Naïve Bayes model achieved an AUC-ROC of 0.945 in classifying rejection versus non-rejection and AUC-ROC of 0.989 in distinguishing TCMR from AMR. Feature selection significantly improved model performance, identifying key spectral wavenumbers associated with rejection mechanisms. This approach demonstrated high sensitivity and specificity for both classification tasks. Conclusions: The integration of FTIR spectroscopy with machine learning may provide a promising, minimally invasive method for early detection and precise classification of kidney allograft rejection. Further validation in larger, more diverse populations is needed to confirm these findings' reliability.

Extracting Knowledge from MS Clinical Metabolomic Data: Processing and Analysis Strategies

Assessing potential alterations of metabolic pathways using large-scale approaches plays today a central role in clinical research. Because several thousands of mass features can be measured for each sample with separation techniques hyphenated to mass spectrometry (MS) detection, adapted strategies have to be implemented to detect altered pathways and help to elucidate the mechanisms of pathologies. These procedures include peak detection, sample alignment, normalization, statistical analysis, and metabolite annotation. Interestingly, considerable advances have been made over the last years in terms of analytics, bioinformatics, and chemometrics to help massive and complex metabolomic data to be more adequately handled with automated processing and data analysis workflows. Recent developments and remaining challenges related to MS signal processing, metabolite annotation, and biomarker discovery based on statistical models are illustrated in this chapter in light of their application to clinical research.

Combined urine proteomics and metabolomics analysis for the diagnosis of pulmonary tuberculosis

BACKGROUND

Tuberculosis (TB) diagnostic monitoring is paramount to clinical decision-making and the host biomarkers appears to play a significant role. The currently available diagnostic technology for TB detection is inadequate. In the present study, we aimed to identify biomarkers for diagnosis of pulmonary tuberculosis (PTB) using urinary metabolomic and proteomic analysis.

METHODS

In the study, urine from 40 PTB, 40 lung cancer (LCA), 40 community-acquired pneumonia (CAP) patients and 40 healthy controls (HC) was collected. Biomarker panels were selected based on random forest (RF) analysis.

RESULTS

A total of 3,868 proteins and 1,272 annotated metabolic features were detected using pairwise comparisons. Using AUC ≥ 0.80 as a cutoff value, we picked up five protein biomarkers for PTB diagnosis. The five-protein panel yielded an AUC for PTB/HC, PTB/CAP and PTB/LCA of 0.9840, 0.9680 and 0.9310, respectively. Additionally, five metabolism biomarkers were selected for differential diagnosis purpose. By employment of the five-metabolism panel, we could differentiate PTB/HC at an AUC of 0.9940, PTB/CAP of 0.8920, and PTB/LCA of 0.8570.

CONCLUSION

Our data demonstrate that metabolomic and proteomic analysis can identify a novel urine biomarker panel to diagnose PTB with high sensitivity and specificity. The receiver operating characteristic curve analysis showed that it is possible to perform non-invasive clinical diagnoses of PTB through these urine biomarkers.

NMR-based metabolomics for investigating urinary profiles of metal carpentry workers exposed to welding fumes and volatile organic compounds

Introduction

Metal carpentry includes a wide range of work activities such as welding and cutting metallic components, use of solvents and paints. Therefore, the employees in these types of activities are mainly exposed to welding fumes and volatile organic solvents. Here, we present an NMR-based metabolomic approach for assessing urinary profiles of workers in the same company that are exposed to two different risk factors.

Methods

The study enrolled 40 male subjects exposed to welding fumes, 13 male subjects exposed to volatile organic compounds of a metal carpentry company, and 24 healthy volunteers. All samples were collected, in the middle of the working week at fast. Thirty-five urinary metabolites belonging to different chemical classes such as amino acids, organic acids and amines were identified and quantified. Results were processed by multivariate statistical analysis for identifying significant metabolites for each working group examined, compared to controls.

Results

Workers exposed to welding fumes displayed urinary increase in glutamine, tyrosine, taurine, creatine, methylguanidine and pseudouridine associated to oxidative impairment, while workers exposed to volatile organic compounds showed higher urinary levels of branched chain aminoacids.

Conclusion

Our work identified specific urinary profile related to each occupational exposure, even if it is below the threshold limit values.

Unraveling Protein-Metabolite Interactions in Precision Nutrition: A Case Study of Blueberry-Derived Metabolites Using Advanced Computational Methods

Metabolomics, the study of small-molecule metabolites within biological systems, has become a potent instrument for understanding cellular processes. Despite its profound insights into health, disease, and drug development, identifying the protein partners for metabolites, especially dietary phytochemicals, remains challenging. In the present study, we introduced an innovative in silico, structure-based target prediction approach to efficiently predict protein targets for metabolites. We analyzed 27 blood serum metabolites from nutrition intervention studies' blueberry-rich diets, known for their health benefits, yet with elusive mechanisms of action. Our findings reveal that blueberry-derived metabolites predominantly interact with Carbonic Anhydrase (CA) family proteins, which are crucial in acid-base regulation, respiration, fluid balance, bone metabolism, neurotransmission, and specific aspects of cellular metabolism. Molecular docking showed that these metabolites bind to a common pocket on CA proteins, with binding energies ranging from -5.0 kcal/mol to -9.0 kcal/mol. Further molecular dynamics (MD) simulations confirmed the stable binding of metabolites near the Zn binding site, consistent with known compound interactions. These results highlight the potential health benefits of blueberry metabolites through interaction with CA proteins.

Understanding seminal plasma in male infertility: emerging markers and their implications

Infertility affects a significant proportion of the reproductive-aged population, with male-associated factors contributing to over half of the cases. However, current diagnostic tools have limitations, leading to an underestimation of the true prevalence of male infertility. While traditional semen parameters provide some insights, they fail to determine the true fertility potential in a substantial number of instances. Therefore, it is crucial to investigate additional molecular targets responsible for male infertility to improve understanding and identification of such cases. Seminal plasma, the main carrier of molecules derived from male reproductive glands, plays a crucial role in reproduction. Amongst its multifarious functions, it regulates processes such as sperm capacitation, sperm protection and maturation, and even interaction with the egg's zona pellucida. Seminal plasma offers a non-invasive sample for urogenital diagnostics and has shown promise in identifying biomarkers associated with male reproductive disorders. This review aims to provide an updated and comprehensive overview of seminal plasma in the diagnosis of male infertility, exploring its composition, function, methods used for analysis, and the application of emerging markers. Apart from the application, the potential challenges of seminal plasma analysis such as standardisation, marker interpretation and confounding factors have also been addressed. Moreover, we have also explored future avenues for enhancing its utility and its role in improving diagnostic strategies. Through comprehensive exploration of seminal plasma's diagnostic potential, the present analysis seeks to advance the understanding of male infertility and its effective management.

Biomolecular Actions by Intestinal Endotoxemia in Metabolic Syndrome

Metabolic syndrome (MetS) is a combination of metabolic disorders that concurrently act as factors promoting systemic pathologies such as atherosclerosis or diabetes mellitus. It is now believed to encompass six main interacting conditions: visceral fat, imbalance of lipids (dyslipidemia), hypertension, insulin resistance (with or without impairing both glucose tolerance and fasting blood sugar), and inflammation. In the last 10 years, there has been a progressive interest through scientific research investigations conducted in the field of metabolomics, confirming a trend to evaluate the role of the metabolome, particularly the intestinal one. The intestinal microbiota (IM) is crucial due to the diversity of microorganisms and their abundance. Consequently, IM dysbiosis and its derivate toxic metabolites have been correlated with MetS. By intervening in these two factors (dysbiosis and consequently the metabolome), we can potentially prevent or slow down the clinical effects of the MetS process. This, in turn, may mitigate dysregulations of intestinal microbiota axes, such as the lung axis, thereby potentially alleviating the negative impact on respiratory pathology, such as the chronic obstructive pulmonary disease. However, the biomolecular mechanisms through which the IM influences the host's metabolism via a dysbiosis metabolome in both normal and pathological conditions are still unclear. In this study, we seek to provide a description of the knowledge to date of the IM and its metabolome and the factors that influence it. Furthermore, we analyze the interactions between the functions of the IM and the pathophysiology of major metabolic diseases via local and systemic metabolome's relate endotoxemia.

Surface-enhanced Raman database of 24 metabolites: Stable measurement of spectra, extraction and analysis of the main features

Surface-enhanced Raman spectroscopy (SERS) has been used in Raman-based metabolomics to provide abundant molecular fingerprint information in situ with extremely high sensitivity, without damaging the sample. However, poor reproducibility, caused by the randomness of the adsorption sites, and the short-range effect of SERS have hindered the development of SERS in metabolomics, resulting in very few SERS reference databases for small-molecule metabolites. In this work, our previously proposed large laser spot-swift mapping SERS method was adopted for the measurement of 24 commercially available metabolite standards, to provide reproducible and reliable references for Raman-based metabolomics study. Among these 24 metabolites, 22 contained no Raman data in PubChem. Other than the SERS spectra data, we extracted and explained the molecular vibration information of these metabolites, and combined with the density functional theory (DFT) calculations, we provided a new possibility for the fast Raman recognition of small-molecule metabolites. Accordingly, a large laser spot-swift mapping SERS database of metabolites in human serum was initially established, which contained not only the original spectral data but also other detailed feature information regarding the Raman peaks. With continuous accumulation, this database could play a promising role in Raman-based metabolomics and other Raman-related research.

Study on the acute toxicity of trichlorfon and its breakdown product dichlorvos to goldfish (Carassius auratus) based on 1H NMR metabonomics

Trichlorfon, one of the most widely used organophosphate insecticides, is commonly employed in aquaculture and agriculture to combat parasitic infestations. However, its inherent instability leads to rapid decomposition into dichlorvos (DDVP), increasing its toxicity by eightfold. Therefore, the environmental effects of trichlorfon in real-world scenarios involve the combined effects of trichlorfon and its degradation product, DDVP. In this study, we systematically investigated the degradation of trichlorfon in tap water over time using HPLC and LC-MS/MS analysis. Subsequently, an experiment was conducted to assess the acute toxicity of trichlorfon and DDVP on goldfish (Carassius auratus), employing a 1H NMR-based metabolic approach in conjunction with serum biochemistry, histopathological inspection, and correlation network analysis. Exposure to trichlorfon and its degradation product DDVP leads to increased lipid peroxidation, reduced antioxidant activity, and severe hepatotoxicity and nephrotoxicity in goldfish. Based on the observed pathological changes and metabolite alterations, short-term exposure to trichlorfon significantly affected the liver and kidney functions of goldfish, while exerting minimal influence on the brain, potentially due to the presence of the blood-brain barrier. The changes in the metabolic profile indicated that trichlorfon and DDVP influenced several pathways, including oxidative stress, protein synthesis, energy metabolism, and nucleic acid metabolism. This study demonstrated the applicability and potential of 1H NMR-based metabonomics in pesticide environmental risk assessment, providing a feasible method for the comprehensive study of pesticide toxicity in water environments.

The effects of a hydrolyzed protein diet on the plasma, fecal and urine metabolome in cats with chronic enteropathy

Hydrolyzed protein diets are extensively used to treat chronic enteropathy (CE) in cats. However, the biochemical effects of such a diet on feline CE have not been characterized. In this study an untargeted 1H nuclear magnetic resonance spectroscopy-based metabolomic approach was used to compare the urinary, plasma, and fecal metabolic phenotypes of cats with CE to control cats with no gastrointestinal signs recruited at the Royal Veterinary College (RVC). In addition, the biomolecular consequences of a hydrolyzed protein diet in cats with CE was also separately determined in cats recruited from the RVC (n = 16) and the University of Bristol (n = 24) and whether these responses differed between dietary responders and non-responders. Here, plasma metabolites related to energy and amino acid metabolism significantly varied between CE and control cats in the RVC cohort. The hydrolyzed protein diet modulated the urinary metabolome of cats with CE (p = 0.005) in both the RVC and Bristol cohort. In the RVC cohort, the urinary excretion of phenylacetylglutamine, p-cresyl-sulfate, creatinine and taurine at diagnosis was predictive of dietary response (p = 0.025) although this was not observed in the Bristol cohort. Conversely, in the Bristol cohort plasma betaine, glycerol, glutamine and alanine at diagnosis was predictive of outcome (p = 0.001), but these same results were not observed in the RVC cohort. The biochemical signature of feline CE in the RVC cohort was consistent with that identified in human and animal models of inflammatory bowel disease. The hydrolyzed protein diet had the same effect on the urinary metabolome of cats with CE at both sites. However, biomarkers that were predictive of dietary response at diagnosis differed between the 2 sites. This may be due to differences in disease severity, disease heterogeneity, factors unrelated to the disease or small sample size at both sites. As such, further studies utilizing larger number of cats are needed to corroborate these findings.

Urinary Metabolic Distinction of Niemann-Pick Class 1 Disease through the Use of Subgroup Discovery

In this investigation, we outline the applications of a data mining technique known as Subgroup Discovery (SD) to the analysis of a sample size-limited metabolomics-based dataset. The SD technique utilized a supervised learning strategy, which lies midway between classificational and descriptive criteria, in which given the descriptive property of a dataset (i.e., the response target variable of interest), the primary objective was to discover subgroups with behaviours that are distinguishable from those of the complete set (albeit with a differential statistical distribution). These approaches have, for the first time, been successfully employed for the analysis of aromatic metabolite patterns within an NMR-based urinary dataset collected from a small cohort of patients with the lysosomal storage disorder Niemann-Pick class 1 (NPC1) disease (n = 12) and utilized to distinguish these from a larger number of heterozygous (parental) control participants. These subgroup discovery strategies discovered two different NPC1 disease-specific metabolically sequential rules which permitted the reliable identification of NPC1 patients; the first of these involved 'normal' (intermediate) urinary concentrations of xanthurenate, 4-aminobenzoate, hippurate and quinaldate, and disease-downregulated levels of nicotinate and trigonelline, whereas the second comprised 'normal' 4-aminobenzoate, indoxyl sulphate, hippurate, 3-methylhistidine and quinaldate concentrations, and again downregulated nicotinate and trigonelline levels. Correspondingly, a series of five subgroup rules were generated for the heterozygous carrier control group, and 'biomarkers' featured in these included low histidine, 1-methylnicotinamide and 4-aminobenzoate concentrations, together with 'normal' levels of hippurate, hypoxanthine, quinolinate and hypoxanthine. These significant disease group-specific rules were consistent with imbalances in the combined tryptophan-nicotinamide, tryptophan, kynurenine and tyrosine metabolic pathways, along with dysregulations in those featuring histidine, 3-methylhistidine and 4-hydroxybenzoate. In principle, the novel subgroup discovery approach employed here should also be readily applicable to solving metabolomics-type problems of this nature which feature rare disease classification groupings with only limited patient participant and sample sizes available.

Untargeted Metabolomics of Feces Reveals Diagnostic and Prognostic Biomarkers for Active Tuberculosis and Latent Tuberculosis Infection: Potential Application for Precise and Non-Invasive Identification

Purpose

Distinguishing latent tuberculosis infection (LTBI) from active tuberculosis (ATB) is important to control the prevalence of tuberculosis; however, there is currently no effective method. The aim of this study was to discover specific metabolites through fecal untargeted metabolomics to discriminate ATB, individuals with LTBI, and healthy controls (HC) and to probe the metabolic perturbation associated with the progression of tuberculosis.

Patients and Methods

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was performed to comprehensively detect compounds in fecal samples from HC, LTBI, and ATB patients. Differential metabolites between the two groups were screened, and their underlying biological functions were explored. Candidate metabolites were selected and enrolled in LASSO regression analysis to construct diagnostic signatures for discriminating between HC, LTBI, and ATB. A receiver operating characteristic (ROC) curve was applied to evaluate diagnostic value. A nomogram was constructed to predict the risk of progression of LTBI.

Results

A total of 35 metabolites were found to exist differentially in HC, LTBI, and ATB, and eight biomarkers were selected. Three diagnostic signatures based on the eight biomarkers were constructed to distinguish between HC, LTBI, and ATB, demonstrating excellent discrimination performance in ROC analysis. A nomogram was successfully constructed to evaluate the risk of progression of LTBI to ATB. Moreover, 3,4-dimethylbenzoic acid has been shown to distinguish ATB patients with different responses to etiological tests.

Conclusion

This study constructed diagnostic signatures based on fecal metabolic biomarkers that effectively discriminated HC, LTBI, and ATB, and established a predictive model to evaluate the risk of progression of LTBI to ATB. The results provide scientific evidence for establishing an accurate, sensitive, and noninvasive differential diagnosis scheme for tuberculosis.

Metabolomic Profiling of Recombinant Erythropoietin (rHuEpo) in Trained Caucasian Athletes

OBJECTIVE

Recombinant human erythropoietin (rHuEpo) is prohibited by the World Anti-Doping Agency but remains the drug of choice for many cheating athletes wishing to evade detection using current methods. The aim of this study was to identify a robust metabolomics signature of rHuEpo using an untargeted approach in blood (plasma and serum) and urine.

DESIGN

Longitudinal study.

SETTING

University of Glasgow.

PARTICIPANTS

Eighteen male participants regularly engaged in predominantly endurance-based activities, such as running, cycling, swimming, triathlon, and team sports, were recruited.

INTERVENTIONS

Each participant received 50 IU·kg -1 body mass of rHuEpo subcutaneously every 2 days for 4 weeks. Samples were collected at baseline, during rHuEpo administration (over 4 weeks) and after rHuEpo administration (week 7-10). The samples were analyzed using hydrophilic interaction liquid chromatography mass spectrometry.

MAIN OUTCOME MEASURES

Significant metabolic signatures of rHuEpo administration were identified in all biofluids tested in this study.

RESULTS

Regarding metabolomics data, 488 plasma metabolites, 694 serum metabolites, and 1628 urinary metabolites were identified. Reproducible signatures of rHuEpo administration across all biofluids included alterations of pyrimidine metabolism (orotate and dihydroorotate) and acyl-carnitines (palmitoyl-carnitine and elaidic carnitine), metabolic pathways that are associated with erythropoiesis or erythrocyte membrane function, respectively.

CONCLUSIONS

Preliminary metabolic signatures of rHuEpo administration were identified. Future studies will be required to validate these encouraging results in independent cohorts and with orthogonal techniques, such as integration of our data with signatures derived from other "omics" analyses of rHuEpo administration (eg, transcriptomics).

Mass spectrometry-based metabolomic analysis as a tool for quality control of natural complex products

Metabolomics is an area of intriguing and growing interest. Since the late 1990s, when the first Omic applications appeared to study metabolite's pool ("metabolome"), to understand new aspects of the global regulation of cellular metabolism in biology, there have been many evolutions. Currently, there are many applications in different fields such as clinical, medical, agricultural, and food. In our opinion, it is clear that developments in metabolomics analysis have also been driven by advances in mass spectrometry (MS) technology. As natural complex products (NCPs) are increasingly used around the world as medicines, food supplements, and substance-based medical devices, their analysis using metabolomic approaches will help to bring more and more rigor to scientific studies and industrial production monitoring. This review is intended to emphasize the importance of metabolomics as a powerful tool for studying NCPs, by which significant advantages can be obtained in terms of elucidation of their composition, biological effects, and quality control. The different approaches of metabolomic analysis, the main and basic techniques of multivariate statistical analysis are also briefly illustrated, to allow an overview of the workflow associated with the metabolomic studies of NCPs. Therefore, various articles and reviews are illustrated and commented as examples of the application of MS-based metabolomics to NCPs.

The Impact of the Serum Extraction Protocol on Metabolomic Profiling Using UPLC-MS/MS and FTIR Spectroscopy

Biofluid metabolomics is a very appealing tool to increase the knowledge associated with pathophysiological mechanisms leading to better and new therapies and biomarkers for disease diagnosis and prognosis. However, due to the complex process of metabolome analysis, including the metabolome isolation method and the platform used to analyze it, there are diverse factors that affect metabolomics output. In the present work, the impact of two protocols to extract the serum metabolome, one using methanol and another using a mixture of methanol, acetonitrile, and water, was evaluated. The metabolome was analyzed by ultraperformance liquid chromatography associated with tandem mass spectrometry (UPLC-MS/MS), based on reverse-phase and hydrophobic chromatographic separations, and Fourier transform infrared (FTIR) spectroscopy. The two extraction protocols of the metabolome were compared over the analytical platforms (UPLC-MS/MS and FTIR spectroscopy) concerning the number of features, the type of features, common features, and the reproducibility of extraction replicas and analytical replicas. The ability of the extraction protocols to predict the survivability of critically ill patients hospitalized at an intensive care unit was also evaluated. The FTIR spectroscopy platform was compared to the UPLC-MS/MS platform and, despite not identifying metabolites and consequently not contributing as much as UPLC-MS/MS in terms of information concerning metabolic information, it enabled the comparison of the two extraction protocols as well as the development of very good predictive models of patient's survivability, such as the UPLC-MS/MS platform. Furthermore, FTIR spectroscopy is based on much simpler procedures and is rapid, economic, and applicable in the high-throughput mode, i.e., enabling the simultaneous analysis of hundreds of samples in the microliter range in a couple of hours. Therefore, FTIR spectroscopy represents a very interesting complementary technique not only to optimize processes as the metabolome isolation but also for obtaining biomarkers such as those for disease prognosis.

Surface-enhanced Raman spectroscopy of centrifuged blood serum samples of diabetic type II patients by using 50KDa filter devices

Blood serum contains essential biochemical information which are used for early disease diagnosis. Blood serum consisted of higher molecular weight fractions (HMWF) and lower molecular weight fractions (LMWF). The disease biomarkers are lower molecular weight fraction proteins, and their contribution to disease diagnosis is suppressed due to higher molecular weight fraction proteins. To diagnose diabetes in early stages are difficult because of the presence of huge amount of these HMWF. In the current study, surface-enhanced Raman spectroscopy (SERS) are employed to diagnose diabetes after centrifugation of serum samples using Amicon ultra filter devices of 50 kDa which produced two fractions of whole blood serum of filtrate, low molecular weight fraction, and residue, high molecular weight fraction. Furthermore SERS is employed to study the LMW fractions of healthy and diseased samples. Some prominent SERS bands are observed at 725 cm^-1, 842 cm^-1, 1025 cm^-1, 959 cm^-1, and 1447 cm^-1 due to small molecular weight proteins, and these biomarkers helped to diagnose the disease early stage. Moreover, chemometric techniques such as principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are employed to check the potential of surface-enhanced Raman spectroscopy for the differentiation and classifications of the blood serum samples. SERS can be employed for the early diagnosis and screening of biochemical changes during type II diabetes.

Optical photothermal infrared spectroscopy: A novel solution for rapid identification of antimicrobial resistance at the single-cell level via deuterium isotope labeling

The rise and extensive spread of antimicrobial resistance (AMR) has become a growing concern, and a threat to the environment and human health globally. The majority of current AMR identification methods used in clinical setting are based on traditional microbiology culture-dependent techniques which are time-consuming or expensive to be implemented, thus appropriate antibiotic stewardship is provided retrospectively which means the first line of treatment is to hope that a broad-spectrum antibiotic works. Hence, culture-independent and single-cell technologies are needed to allow for rapid detection and identification of antimicrobial-resistant bacteria and to support a more targeted and effective antibiotic therapy preventing further development and spread of AMR. In this study, for the first time, a non-destructive phenotyping method of optical photothermal infrared (O-PTIR) spectroscopy, coupled with deuterium isotope probing (DIP) and multivariate statistical analysis was employed as a metabolic fingerprinting approach to detect AMR in Uropathogenic Escherichia coli (UPEC) at both single-cell and population levels. Principal component-discriminant function analysis (PC-DFA) of FT-IR and O-PTIR spectral data showed clear clustering patterns as a result of distinctive spectral shifts (C-D signature peaks) originating from deuterium incorporation into bacterial cells, allowing for rapid detection and classification of sensitive and resistant isolates at the single-cell level. Furthermore, the single-frequency images obtained using the C-D signature peak at 2,163 cm-1 clearly displayed the reduced ability of the trimethoprim-sensitive strain for incorporating deuterium when exposed to this antibiotic, compared to the untreated condition. Hence, the results of this study indicated that O-PTIR can be employed as an efficient tool for the rapid detection of AMR at the single-cell level.

Effect of environmental conditions on bloodstain metabolite analysis

Establishing a correlation between environmental variables and chemical change can significantly improve the quality of research in multiple fields. Among various environmental variables, temperature and humidity are closely related to the rate of chemical reactions. This study aimed to confirm changes in metabolite markers that were previously discovered in other temperature and humidity environment conditions and to confirm the possibility that they could act as markers. After blood collection from the subjects and bloodstain preparation, the quantitative values of the bloodstain metabolites were confirmed (when the age of the bloodstain was within a month) under eight environmental conditions (4 °C/30%, 4 °C/60%, 25 °C/30%, 25 °C/60%, 25 °C/90%, 40 °C/30%, 40 °C/60%, and 40 °C/90%). Age-of-bloodstain estimation models were constructed to confirm the applicability of bloodstain metabolites as markers for bloodstain age in various environments. The average concentration of metabolite markers exhibited a decreasing trend with the age of the bloodstain, which transformed into an increasing trend from day 7 onwards. In terms of temperature and humidity, 25 °C and 90%, respectively, showed the most dissimilar metabolite change pattern compared to other conditions. The age-of-bloodstain estimation models developed here have an R-square value of up to 0.92 for each condition and an R-square value of 0.71 when all environmental conditions were combined. The findings herein highlight the immense potential of blood metabolites for field application, confirming the possibility of predicting metabolite changes from the rates of their chemical reactions and validating the importance of metabolites as age-of-bloodstain markers under various environmental conditions.

Machine Learning Using Neural Networks for Metabolomic Pathway Analyses

Elucidating the mechanisms of metabolic pathways helps us understand the cascade of enzyme-catalyzed reactions that lead to the conversion of substances into final products. This has implications for predicting how newly synthesized compounds will affect a person's metabolism and, hence, the development of novel treatments to improve one's health. The study of metabolomic pathways, together with protein engineering, may also aid in the extraction, at a scale, of natural products to be used as drugs and drug precursors. Several approaches have been used to correlate protein annotations to metabolic pathways in order to derive pathways directly related to specific organisms. These could range from association rule-mining techniques to machine learning methods such as decision trees, naïve Bayes, logistic regression, and ensemble methods.In this chapter, we will be reviewing the use of machine learning for metabolic pathway analyses, with a step-by-step focus on the use of deep learning to predict the association of compounds (metabolites) to their respective metabolomic pathway classes. This prediction could help explain interactions of small molecules in organisms. Inspired by the work of Baranwal et al. (2019), we demonstrate how to build and train a deep learning neural network model to perform a multi-label prediction. We considered two different types of fingerprints as features (inputs to the model). The output of the model is the set of metabolic pathway classes (from the KEGG dataset) in which the input molecule participates. We will walk through the various steps of this process, including data collection, feature engineering, model selection, training, and evaluation. This model-building and evaluation process may be easily transferred to other domains of interest. All the source code used in this chapter is made publicly available at https://github.com/jp-um/machine_learning_for_metabolomic_pathway_analyses .

Decoding the role of cytochrome c in metabolism of human spermatozoa by Raman imaging

The normal functioning of sperm cells requires cytochrome c in the redox balanced forms: reduced and oxidized. The oxidized form of cytochrome c is localized in the mitochondrial intermembrane space and is a part of the electron transport chain. This ensures that electron shuttling between the complex III, cytochrome c, and complex IV can occur leading to controlled effective oxidative phosphorylation (respiration) and ATP production needed for most steps in spermatozoal maturation, motility, hyperactivation and fertilization. We studied the biochemical composition of specific organelles in sperm cells by Raman imaging. The structures of the head consisting of the nucleus and acrosome, the midpiece representing mitochondria, and the tail characterized by the sperm axoneme surrounded by outer dense fiber and covered by the membrane were measured. Metabolic biochemical analysis of mitochondria, head and tail of sperm cells, and seminal plasma by using Raman imaging combined with chemometric classification method of Cluster Analysis has been obtained. Our results show that cytochrome c, which is a key protein that is needed to maintain life (respiration) and cell death (apoptosis), is located in sperm mitochondria in the oxidized or reduced form of the heme group. This work demonstrated that an application of Raman micro-spectroscopy can be extended to monitoring the redox state of mitochondrial cytochrome c in sperm cells.

NMR-Based Metabolomics of Rat Hippocampus, Serum, and Urine in Two Models of Autism

Autism spectrum disorders (ASDs) are increasingly diagnosed as developmental disabilities of unclear etiology related to genetic, epigenetic, or environmental factors. The diagnosis of ASD in children is based on the recognition of typical behavioral symptoms, while no reliable biomarkers are available. Rats in whom ASD-like symptoms are due to maternal administration of the teratogenic drugs valproate or thalidomide on critical day 11 of pregnancy are widely used models in autism research. The present studies, aimed at detecting changes in the levels of hydrophilic and hydrophobic metabolites, were carried out on 1-month-old rats belonging to the abovementioned two ASD models and on a control group. Analysis of both hydrophilic and hydrophobic metabolite levels gives a broader view of possible mechanisms involved in the pathogenesis of autism. Hippocampal proton magnetic resonance (MRS) spectroscopy and ex vivo nuclear magnetic resonance (NMR) analysis of serum and urine samples were used. The results were analyzed using advanced statistical tests. Both the results of our present MRS studies of the hippocampus and of the NMR studies of body fluids in both ASD models, particularly from the THAL model, appeared to be consistent with previously published NMR results of hippocampal homogenates and data from the literature on autistic children. We detected symptoms of disturbances in neurotransmitter metabolism, energy deficit, and oxidative stress, as well as intestinal malfunction, which shed light on the pathogenesis of ASD and could be used for diagnostic purposes. These results confirm the usefulness of the noninvasive techniques used in ASD studies.

Lipidomic Analysis Reveals the Protection Mechanism of GLP-1 Analogue Dulaglutide on High-Fat Diet-Induced Chronic Kidney Disease in Mice

Many clinical studies have suggested that glucagon-like peptide-1 receptor agonists (GLP-1RAs) have renoprotective properties by ameliorating albuminuria and increasing glomerular filtration rate in patients with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD) by lowering ectopic lipid accumulation in the kidney. However, the mechanism of GLP-1RAs was hitherto unknown. Here, we conducted an unbiased lipidomic analysis using ultra-high-performance liquid chromatography/electrospray ionization-quadrupole time-of-flight mass spectrometry (UHPLC/ESI-Q-TOF-MS) and matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to reveal the changes of lipid composition and distribution in the kidneys of high-fat diet-fed mice after treatment with a long-acting GLP-1RA dulaglutide for 4 weeks. Treatment of dulaglutide dramatically improved hyperglycemia and albuminuria, but there was no substantial improvement in dyslipidemia and ectopic lipid accumulation in the kidney as compared with controls. Intriguingly, treatment of dulaglutide increases the level of an essential phospholipid constituent of inner mitochondrial membrane cardiolipin at the cortex region of the kidneys by inducing the expression of key cardiolipin biosynthesis enzymes. Previous studies demonstrated that lowered renal cardiolipin level impairs kidney function via mitochondrial damage. Our untargeted lipidomic analysis presents evidence for a new mechanism of how GLP-1RAs stimulate mitochondrial bioenergetics via increasing cardiolipin level and provides new insights into the therapeutic potential of GLP-1RAs in mitochondrial-related diseases.

Study on degranulation of mast cells under C48/80 treatment by electroporation-assisted and ultrasound-assisted surface-enhanced Raman spectrascopy

Both electroporation-assisted and ultrasound-assisted delivery methods can rapidly deliver nanoparticles into living cells for surface-enhanced Raman scattering (SERS) detection, but these two methods have never been compared. In this study, electroporation-assisted SERS and ultrasound-assisted SERS were employed to detect the biochemical changes of degranulated mast cells induced by mast cell stimulator (C48/80). The results showed that the cell damage of electroporation based on controllable electric pulse was smaller than that of ultrasound based on cavitation. Transmission electron microscope images of cells indicated that the nanoparticles delivered by electroporation were mainly distributed in the cytoplasm, while ultrasound could transport nanoparticles to the cytoplasm and nucleus. Therefore, electroporation-assisted SERS mainly detects the biochemical information of cytoplasm, while ultrasound-assisted SERS gets more spectral signals of nucleic acid. Both methods can obtain high quality SERS signal of cells. With drug treatment, the SERS peak intensity of 733 cm^-1 attributed to phosphatidylserine decreased significantly, which may be due to the activation of mast cell degranulation pathway stimulated by C48/80 agonist, resulting in a large amount of intracellular serine being used to synthesize tryptase, while the production of phosphatidylserine decreased. Further, based on principal component analysis and linear discriminant analysis (PCA-LDA approach), ultrasound-assisted SERS could achieve better sensitivity, specificity and accuracy in the discrimination and identification of drug-treated degranulated mast cells than electroporation assisted SERS. This exploratory work is helpful to realize the real-time dynamic SERS detection of intracellular biochemical components, and it also has great potential in intracellular SERS analysis, such as the cytotoxicity assay of anti-tumor drugs or cancer cell screening.

A systematic review: metabolomics-based identification of altered metabolites and pathways in the skin caused by internal and external factors

The skin's ability to function optimally is affected by many diverse factors. Metabolomics has a great potential to improve our understanding of the underlying metabolic changes and the affected pathways. Therefore, the objective of this study was to review the current state of the literature and to perform further metabolic pathway analysis on the obtained data. The aim was to gain an overview of the metabolic changes under altered conditions and to identify common and different patterns as a function of the investigated factors. A cross-study comparison of the extracted studies from different databases identified 364 metabolites, whose concentrations were considerably altered by the following factor groups: irradiation, xenobiotics, aging, and skin diseases (mainly psoriasis). Using metabolic databases and pathway analysis tools the individual metabolites were assigned to the corresponding metabolic pathways and the most strongly affected signaling pathways were identified. All factors induced oxidative stress. Thus, antioxidant defense systems, especially coenzyme Q₁₀  (aging) and the glutathione system (irradiation, aging, xenobiotics) were impacted. Lipid metabolism was also impacted by all factors studied. The carnitine shuttle as part of β-oxidation was activated by all factor groups except aging. Glycolysis, Krebs (TCA) cycle and purine metabolism were mainly affected by irradiation and xenobiotics. The pentose phosphate pathway was activated and Krebs cycle was downregulated in response to oxidative stress. In summary, it can be ascertained that mainly energy metabolism, lipid metabolism, antioxidative defense and DNA repair systems were impacted by the factors studied.

Metabolic Profile Characterization of Different Thyroid Nodules Using FTIR Spectroscopy: A Review

Thyroid cancer's incidence has increased in the last decades, and its diagnosis can be a challenge. Further and complementary testing based in biochemical alterations may be important to correctly identify thyroid cancer and prevent unnecessary surgery. Fourier-transform infrared (FTIR) spectroscopy is a metabolomic technique that has already shown promising results in cancer metabolome analysis of neoplastic thyroid tissue, in the identification and classification of prostate tumor tissues and of breast carcinoma, among others. This work aims to gather and discuss published information on the ability of FTIR spectroscopy to be used in metabolomic studies of the thyroid, including discriminating between benign and malignant thyroid samples and grading and classifying different types of thyroid tumors.

A conversation between hyphenated spectroscopic techniques and phytometabolites from medicinal plants

Medicinal plant metabolomics has emerged as a goldmine for the natural product chemists. It provides a pool of bioactive phytoconstituents leading to accelerated novel discoveries and the elucidation of a variety of biosynthetic pathways. Further, it also acts as an innovative tool for herbal medicine's scientific validation and quality assurance. This review highlights different strategies and analytical techniques employed in the practice of metabolomics. Further, it also discusses several other applications and advantages of metabolomics in the area of natural product chemistry. Additional examples of integrating metabolomics with multivariate data analysis techniques for some Indian medicinal plants are also reviewed. Recent technical advances in mass spectrometry-based hyphenated techniques, nuclear magnetic resonance-based techniques, and comprehensive hyphenated technologies for phytometabolite profiling studies have also been reviewed. Mass Spectral Imaging (MSI) has been presented as a highly promising method for high precision in situ spatiotemporal monitoring of phytometabolites. We conclude by introducing GNPS (Global Natural Products Social Molecular Networking) as an emerging platform to make social networks of related molecules, to explore data and to annotate more metabolites, and expand the networks to novel "predictive" metabolites that can be validated.

The Application of Metabolomics in Forensic Science with Focus on Forensic Toxicology and Time-of-Death Estimation

Recently, the diagnostic methods used by scientists in forensic examinations have enormously expanded. Metabolomics provides an important contribution to analytical method development. The main purpose of this review was to investigate and summarize the most recent applications of metabolomics in forensic science. The primary research method was an extensive review of available international literature in PubMed. The keywords “forensic” and “metabolomics” were used as search criteria for the PubMed database scan. Most authors emphasized the analysis of different biological sample types using chromatography methods. The presented review is a summary of recently published implementations of metabolomics in forensic science and types of biological material used and techniques applied. Possible opportunities for valuable metabolomics’ applications are discussed to emphasize the essential necessities resulting in numerous nontargeted metabolomics’ assays.

Metabolomics in Exercise and Sports: A Systematic Review

BACKGROUND

Metabolomics is a field of omics science that involves the comprehensive measurement of small metabolites in biological samples. It is increasingly being used to study exercise physiology and exercise-associated metabolism. However, the field of exercise metabolomics has not been extensively reviewed or assessed.

OBJECTIVE

This review on exercise metabolomics has three aims: (1) to provide an introduction to the general workflow and the different metabolomics technologies used to conduct exercise metabolomics studies; (2) to provide a systematic overview of published exercise metabolomics studies and their findings; and (3) to discuss future perspectives in the field of exercise metabolomics.

METHODS

We searched electronic databases including Google Scholar, Science Direct, PubMed, Scopus, Web of Science, and the SpringerLink academic journal database between January 1st 2000 and September 30th 2020.

RESULTS

Based on our detailed analysis of the field, exercise metabolomics studies fall into five major categories: (1) exercise nutrition metabolism; (2) exercise metabolism; (3) sport metabolism; (4) clinical exercise metabolism; and (5) metabolome comparisons. Exercise metabolism is the most popular category. The most common biological samples used in exercise metabolomics studies are blood and urine. Only a small minority of exercise metabolomics studies employ targeted or quantitative techniques, while most studies used untargeted metabolomics techniques. In addition, mass spectrometry was the most commonly used platform in exercise metabolomics studies, identified in approximately 54% of all published studies. Our data indicate that biomarkers or biomarker panels were identified in 34% of published exercise metabolomics studies.

CONCLUSION

Overall, there is an increasing trend towards better designed, more clinical, mass spectrometry-based metabolomics studies involving larger numbers of participants/patients and larger numbers of metabolites being identified.

Metabolomic Profile of Young Adults Born Preterm

Prematurity is a risk factor for the development of chronic adult diseases. Metabolomics can correlate the biochemical changes to a determined phenotype, obtaining real information about the state of health of a subject at that precise moment. Significative differences in the metabolomic profile of preterm newborns compared to those born at term have been already identified at birth. An observational case–control study was performed at the University Hospital of Siena. The aim was to evaluate and compare the metabolomic profiles of young adults born preterm to those born at term. Urinary samples were collected from 67 young adults (18–23 years old) born preterm (mean gestational age of 30 weeks, n = 49), and at term of pregnancy (mean gestational age of 38 weeks, n = 18). The urinary spectra of young adults born preterm was different from those born at term and resembled what was previously described at birth. The Random Forest algorithm gave the best classification (accuracy 82%) and indicated the following metabolites as responsible for the classification: citrate, CH2 creatinine, fumarate and hippurate. Urine spectra are promising tools for the early identification of neonates at risk of disease in adulthood and may provide insight into the pathogenesis and effects of fetal programming and infants’ outcomes.

A robust metabolomics approach for the evaluation of human embryos from in vitro fertilization

The identification of the most competent embryos for transfer to the uterus constitutes the main challenge of in vitro fertilization (IVF). We established a metabolomic-based approach by applying Fourier transform infrared (FTIR) spectroscopy on 130 samples of 3-day embryo culture supernatants from 26 embryos that implanted and 104 embryos that failed. On examining the internal structure of the data by unsupervised multivariate analysis, we found that the supernatant spectra of nonimplanted embryos constituted a highly heterogeneous group. Whereas ∼40% of these supernatants were spectroscopically indistinguishable from those of successfully implanted embryos, ∼60% exhibited diverse, heterogeneous metabolic fingerprints. This observation proved to be the direct result of pregnancy's multifactorial nature, involving both intrinsic embryonic traits and external characteristics. Our data analysis strategy thus involved one-class modelling techniques employing soft independent modelling of class analogy that identified deviant fingerprints as unsuitable for implantation. From these findings, we could develop a noninvasive Fourier-transform-infrared-spectroscopy-based approach that represents a shift in the fundamental paradigm for data modelling applied in assisted-fertilization technologies.

Metabolomics Analysis of Amniotic Fluid in Euploid Foetuses with Thickened Nuchal Translucency by Gas Chromatography-Mass Spectrometry

Persistence of a fetal thickened nuchal translucency (NT), one of the most sensitive and specific individual markers of fetal disorders, is strongly correlated with the possibility of a genetic syndrome, congenital infections, or other malformations. Thickened NT can also be found in normal pregnancies. Several of its pathophysiological aspects still remain unexplained. Metabolomics could offer a fresh opportunity to explore maternal-foetal metabolism in an effort to explain its physiological and pathological mechanisms. For this prospective case-control pilot study, thirty-nine samples of amniotic fluids were collected, divisible into 12 euploid foetuses with an enlarged nuchal translucency (>NT) and 27 controls (C). Samples were analyzed using gas chromatography mass spectrometry. Multivariate and univariate statistical analyses were performed to find a specific metabolic pattern of >NT class. The correlation between the metabolic profile and clinical parameters was evaluated (NT showed an R2 = 0.75, foetal crown-rump length showed R2 = 0.65, pregnancy associated plasma protein-A showed R2 = 0.60). Nine metabolites significantly differing between >NT foetuses and C were detected: 2-hydroxybutyric acid, 3-hydroxybutyric, 1,5 Anydro-Sorbitol, cholesterol, erythronic acid, fructose, malic acid, threitol, and threonine, which were linked to altered pathways involved in altered energetic pathways. Through the metabolomics approach, it was possible to identify a specific metabolic fingerprint of the fetuses with >NT.

Metabolomics in Parkinson's disease

Parkinson's disease (PD) is a multifactorial neurodegenerative disorder in which environmental (lifestyle, dietary, infectious disease) factors as well as genetic make-up play a role. Metabolomics, an evolving research field combining biomarker discovery and pathogenetics, is particularly useful in studying complex pathophysiology in general and Parkinson's disease (PD) specifically. PD, the second most frequent neurodegenerative disorder, is characterized by the loss of dopaminergic neurons in the substantia nigra and the presence of intraneural inclusions of α-synuclein aggregates. Although considered a predominantly movement disorder, PD is also associated with number of non-motor features. Metabolomics has provided useful information regarding this neurodegenerative process with the aim of identifying a disease-specific fingerprint. Unfortunately, many disease variables such as clinical presentation, motor system involvement, disease stage and duration substantially affect biomarker relevance. As such, metabolomics provides a unique approach to studying this multifactorial neurodegenerative disorder.

The Role of Raman Spectroscopy Within Quantitative Metabolomics

Ninety-four years have passed since the discovery of the Raman effect, and there are currently more than 25 different types of Raman-based techniques. The past two decades have witnessed the blossoming of Raman spectroscopy as a powerful physicochemical technique with broad applications within the life sciences. In this review, we critique the use of Raman spectroscopy as a tool for quantitative metabolomics. We overview recent developments of Raman spectroscopy for identification and quantification of disease biomarkers in liquid biopsies, with a focus on the recent advances within surface-enhanced Raman scattering-based methods. Ultimately, we discuss the applications of imaging modalities based on Raman scattering as label-free methods to study the abundance and distribution of biomolecules in cells and tissues, including mammalian, algal, and bacterial cells.

Serum Metabolomic Analysis Suggests Impairment of Myocardial Energy Production in Takotsubo Syndrome

INTRODUCTION

Takotsubo syndrome is a complex entity that, although it usually has a good prognosis, can be life threatening. While recent advances have improved the knowledge of takotsubo syndrome, many aspects of its etiology still remain uncertain. Metabolomics, a hypothesis generating approach, could provide novel pathophysiology information about this disease.

METHODS AND RESULTS

Serum samples were obtained from takotsubo (n = 19) and acute myocardial infarction patients (n = 8) at the cath lab and, in the case of takotsubo, again once the patient had recovered, 3 months after the main event. ¹H NMR spectra of the serum were acquired at 9.4T using a CPMG pulse sequence (32 ms effective delay). Supervised and unsupervised pattern recognition approaches where applied to the data. Pattern recognition was able to differentiate between takotsubo and acute myocardial infarction during the acute phase with 95% accuracy. Myocardial infarction patients showed an increase in lipid signals, a known risk factor for the disease while takotsubo patients showed a relative increase in acetate that could suggest a reduced turnover of the Krebs cycle. When comparing acute and recovered phases, we could detect an increase in alanine and creatine once patients recovered.

CONCLUSIONS

Our results demonstrate that takotsubo syndrome is metabolically different than AMI, showing limited myocardial energy production capacity during the acute phase. We achieved high classification success against AMI; however, this study should be considered as a proof of concept regarding clinical application of metabolic profiling in takotsubo cardiomyopathy.

Metabolomic Laboratory-Developed Tests: Current Status and Perspectives

Laboratory-developed tests (LDTs) are a subset of in vitro diagnostic devices, which the US Food and Drug Administration defines as "tests that are manufactured by and used within a single laboratory". The review describes the emergence and history of LDTs. The current state and development prospects of LDTs based on metabolomics are analyzed. By comparing LDTs with the scientific metabolomics study of human bio samples, the characteristic features of metabolomic LDT are shown, revealing its essence, strengths, and limitations. The possibilities for further developments and scaling of metabolomic LDTs and their potential significance for healthcare are discussed. The legal aspects of LDT regulation in the United States, European Union, and Singapore, demonstrating different approaches to this issue, are also provided. Based on the data presented in the review, recommendations were made on the feasibility and ways of further introducing metabolomic LDTs into practice.

Urine NMR-based TB metabolic fingerprinting for the diagnosis of TB in children

Tuberculosis (TB) is a major cause of morbidity and mortality in children, and early diagnosis and treatment are crucial to reduce long-term morbidity and mortality. In this study, we explore whether urine nuclear magnetic resonance (NMR)-based metabolomics could be used to identify differences in the metabolic response of children with different diagnostic certainty of TB. We included 62 children with signs and symptoms of TB and 55 apparently healthy children. Six of the children with presumptive TB had bacteriologically confirmed TB, 52 children with unconfirmed TB, and 4 children with unlikely TB. Urine metabolic fingerprints were identified using high- and low-field proton NMR platforms and assessed with pattern recognition techniques such as principal components analysis and partial least squares discriminant analysis. We observed differences in the metabolic fingerprint of children with bacteriologically confirmed and unconfirmed TB compared to children with unlikely TB (p = 0.041 and p = 0.013, respectively). Moreover, children with unconfirmed TB with X-rays compatible with TB showed differences in the metabolic fingerprint compared to children with non-pathological X-rays (p = 0.009). Differences in the metabolic fingerprint in children with different diagnostic certainty of TB could contribute to a more accurate characterisation of TB in the paediatric population. The use of metabolomics could be useful to improve the prediction of TB progression and diagnosis in children.

Plasma Metabolomics of Acute Coronary Syndrome Patients Based on Untargeted Liquid Chromatography-Mass Spectrometry

Background: Acute coronary syndrome (ACS) is the main cause of death and morbidity worldwide. The present study aims to investigate the altered metabolites in plasma from patients with ACS and sought to identify metabolic biomarkers for ACS. Methods: The plasma metabolomics profiles of 284 ACS patients and 130 controls were carried out based on an untargeted liquid chromatography coupled with tandem mass spectrometry (LC-MS) approach. Multivariate statistical methods, pathway enrichment analysis, and univariate receiver operating characteristic (ROC) curve analysis were performed. Results: A total of 328 and 194 features were determined in positive and negative electrospray ionization mode in the LC-MS analysis, respectively. Twenty-eight metabolites were found to be differentially expressed, in ACS patients relative to controls (p < 0.05). Pathway analysis revealed that these metabolites are mainly involved in synthesis and degradation of ketone bodies, phenylalanine metabolism, and arginine and proline metabolism. Furthermore, a diagnostic model was constructed based on the metabolites identified and the areas under the curve (AUC) for 5-oxo-D-proline, creatinine, phosphatidylethanolamine lyso 16:0, and LPC (20:4) range from 0.764 to 0.844. The higher AUC value of 0.905 was obtained for the combined detection of phosphatidylethanolamine lyso 16:0 and LPC (20:4). Conclusions: Differential metabolic profiles may be useful for the effective diagnosis of ACS and may provide additional insight into the molecular mechanisms underlying ACS.

Research Progress of Urine Biomarkers in the Diagnosis, Treatment, and Prognosis of Bladder Cancer

Bladder cancer (BC) is one of the most common tumor with high incidence. Relative to other cancers, BC has a high rate of recurrence, which results in increased mortality. As a result, early diagnosis and life-long monitoring are clinically significant for improving the long-term survival rate of BC patients. At present, the main methods of BC detection are cystoscopy and biopsy; however, these procedures can be invasive and expensive. This can lead to patient refusal and reluctance for monitoring. There are several BC biomarkers that have been approved by the FDA, but their sensitivity, specificity, and diagnostic accuracy are not ideal. More research is needed to identify suitable biomarkers that can be used for early detection, evaluation, and observation. There has been heavy research in the proteomics and genomics of BC and many potential biomarkers have been found. Although the advent of metabonomics came late, with the recent development of advanced analytical technology and bioinformatics, metabonomics has become a widely used diagnostic tool in clinical and biomedical research. It should be emphasized that despite progress in new biomarkers for BC diagnosis, there remains challenges and limitations in metabonomics research that affects its translation into clinical practice. In this chapter, the latest literature on BC biomarkers was reviewed.

Metabolomic Profiling of Aqueous Humor and Plasma in Primary Open Angle Glaucoma Patients Points Towards Novel Diagnostic and Therapeutic Strategy

Glaucoma is the second leading cause of blindness globally characterized by progressive loss of retinal ganglion cells (RGCs) and irreversible visual deficiency. As the most common type of glaucoma, primary open angle glaucoma (POAG) is currently an unmet medical need with limited therapy by lowering intraocular pressure (IOP). However, some patients continue to progress even though their IOP are controlled. Although early diagnosis and prompt treatment are crucial in preventing irreversible visual impairment, there are currently no biomarkers for screening POAG. Metabolomics has the advantages of illustrating the final downstream products of the genome and establishing the closest link to the phenotype. So far, there is no study investigating the metabolomic profiles in both aqueous humor and plasma of POAG patients. Therefore, to explore diagnostic biomarkers, unveil underlying pathophysiology and potential therapeutic strategies, a widely targeted metabolomic approach was applied using ultrahigh-resolution mass spectrometry with C18 liquid chromatography to characterize the metabolomic profiles in both aqueous humor and plasma of 28 POAG patients and 25 controls in our study. Partial least squares-discriminant analysis (PLS-DA) was performed to determine differentially expressed metabolites (DEMs) between POAG and age-matched controls. The area under the receiver operating characteristic curve (AUC) was calculated to assess the prediction accuracy of the DEMs. The correlation of DEMs with the clinical parameters was determined by Pearson correlation, and the metabolic pathways were analyzed using MetaboAnalyst 4.0. PLS-DA significantly separated POAG from controls with 22 DEMs in the aqueous humor and 11 DEMs in the plasma. Additionally, univariate ROC analysis and correlation analysis with clinical parameters revealed cyclic AMP (AUC = 0.87), 2-methylbenzoic acid (AUC = 0.75), 3'-sialyllactose (AUC = 0.73) in the aqueous humor and N-lac-phe (AUC = 0.76) in the plasma as potential biomarkers for POAG. Moreover, the metabolic profiles pointed towards the alteration in the purine metabolism pathway. In conclusion, the study identified potential and novel biomarkers for POAG by crosslinking the metabolomic profiles in aqueous humor and plasma and correlating with the clinical parameters. These findings have important clinical implications given that no biomarkers are currently available for glaucoma in the clinic, and the study provided new insights in exploring diagnostic biomarkers and potential therapeutic strategies of POAG by targeting metabolic pathways.

Fourier-Transform Infrared Spectroscopy as a Discriminatory Tool for Myotonic Dystrophy Type 1 Metabolism: A Pilot Study

Myotonic dystrophy type 1 (DM1) is a hereditary disease characterized by progressive distal muscle weakness and myotonia. Patients with DM1 have abnormal lipid metabolism and a high propensity to develop a metabolic syndrome in comparison to the general population. It follows that metabolome evaluation in these patients is crucial and may contribute to a better characterization and discrimination between DM1 disease phenotypes and severities. Several experimental approaches are possible to carry out such an analysis; among them is Fourier-transform infrared spectroscopy (FTIR) which evaluates metabolic profiles by categorizing samples through their biochemical composition. In this study, FTIR spectra were acquired and analyzed using multivariate analysis (Principal Component Analysis) using skin DM1 patient-derived fibroblasts and controls. The results obtained showed a clear discrimination between both DM1-derived fibroblasts with different CTG repeat length and with the age of disease onset; this was evident given the distinct metabolic profiles obtained for the two groups. Discrimination could be attributed mainly to the altered lipid metabolism and proteins in the 1800–1500 cm⁻¹ region. These results suggest that FTIR spectroscopy is a valuable tool to discriminate both DM1-derived fibroblasts with different CTG length and age of onset and to study the metabolomic profile of patients with DM1.

Detecting the effects of predator-induced stress on the global metabolism of an ungulate prey using fecal metabolomic fingerprinting

Few field tests have assessed the effects of predator-induced stress on prey fitness, particularly in large carnivore-ungulate systems. Because traditional measures of stress present limitations when applied to free-ranging animals, new strategies and systemic methodologies are needed. Recent studies have shown that stress and anxiety related behaviors can influence the metabolic activity of the gut microbiome in mammal hosts, and these metabolic alterations may aid in identification of stress. In this study, we used NMR-based fecal metabolomic fingerprinting to compare the fecal metabolome, a functional readout of the gut microbiome, of cattle herds grazing in low vs. high wolf-impacted areas within three wolf pack territories. Additionally, we evaluated if other factors (e.g., cattle nutritional state, climate, landscape) besides wolf presence were related to the variation in cattle metabolism. By collecting longitudinal fecal samples from GPS-collared cattle, we found relevant metabolic differences between cattle herds in areas where the probability of wolf pack interaction was higher. Moreover, cattle distance to GPS-collared wolves was the factor most correlated with this difference in cattle metabolism, potentially reflecting the variation in wolf predation risk. We further validated our results through a regression model that reconstructed cattle distances to GPS-collared wolves based on the metabolic difference between cattle herds. Although further research is needed to explore if similar patterns also hold at a finer scale, our results suggests that fecal metabolomic fingerprinting is a promising tool for assessing the physiological responses of prey to predation risk. This novel approach will help improve our knowledge of the consequences of predators beyond the direct effect of predation.

The effects of exercise training on lipid profile in patients with sarcoidosis

This study aimed to determine the use of lipid profiling to assess the effects of moderate intensity exercise training (ET) on patients with sarcoidosis. Fourteen patients with sarcoidosis (mean age, 46.0 ± 9.6 years) were examined before and after 3-week of ET programme in hospital settings. Symptoms (fatigue: FAS, dyspnoea: MRC), lung function tests and physical function tests (6 MWT, muscle force) were measured before and after ET. Proton nuclear magnetic resonance (NMR) spectroscopy combined with orthogonal partial least squares-discriminant analysis (OPLS-DA) was used to determine lipid profile before and after ET. Twenty-five NMR signals from lipid compounds were selected for further analysis as well as serum lipid and inflammatory markers. Three weeks of ET results in improvement of symptoms (FAS: 27.5 vs. 21.0; p < 0.001, MRC: 0.86 vs. 0.14; p = 0.002) and physical function (6MWT: 508.43 vs. 547.29; p = 0.039). OPLS-DA analysis of the lipid profiles of patients with sarcoidosis revealed differences among the samples before and after ET, including decreases in fatty acids (p < 0.017), triglycerides (p < 0.022) and total cholesterol (p < 0.020). Other changes included shifts in fatty acids oxidation products and triacylglycerol esters. A short-time, in-hospital exercise training benefits patients with sarcoidosis by enhancing their physical function. Additionally, positive effect on lipid profile was observed also in this study. It is suggested that lipid profiling could become a new prognostic method to assess effects of pulmonary rehabilitation in patients with sarcoidosis.

Mass spectrometry-based metabolomics diagnostics - myth or reality?

ABSTACTIntroduction: Metabolomics, one of the most high-promising technologies, is the most recently developed post-genomics discipline for developing new diagnostic tests for future implementation in medicine. More than 2,000 scientific papers, using mass spectrometry-based (MS-based) metabolomics analysis for human disease diagnostics, have been published during the past two decades, and almost every metabolomics study shows high diagnostic accuracy. However, despite the great results and promising perspectives, there are currently no diagnostic tests based on metabolomics that have been approved and introduced into clinics.Areas covered: In this report, the advantages and challenges of MS-based metabolomics are discussed with a focus on its developing role in diagnostics, and the current trends in implementing metabolomics diagnostics in the clinic.Expert opinion: In the development of new clinical diagnostics tests, MS-based metabolomics has potential as both a preliminary discovery base for routine testing and a multi-test prototype, which is hoped to be introduced into clinical practice in the near future. A laboratory-developed test (LDT) is one possible way that multi-testing could be developed.

Fast diagnosis of men's fertility using Raman spectroscopy combined with chemometric methods: An experimental study

Background

Idiopathic infertile men suffer from unexplained male infertility; they are infertile despite having a normal semen analysis, a normal history, and physical examination, and when female infertility factor has been ruled out.

Objective

The present study aimed to develop a metabolic fingerprinting methodology using Raman spectroscopy combined with Chemometrics to detect idiopathic infertile men vs. fertile ones by seminal plasma.

Materials and Methods

In this experimental study, the seminal plasma of 26 men including 13 fertile and 13 with unexplained infertility who reffered to, Avicenna Infertility Clinic, 2018, Tehran, Iran, have been investigated. The seminal metabolomic fingerprinting was evaluated using Raman spectrometer from 100 to 4250 cm-1. The principal component analysis and discriminate analysis methods were used.

Results

The total of 26 samples were divided into 20 training and 6 test sets. The Principal component analysis score plot of the training set showed that the data were perfectly divided into two sides of the plot, which statistically approves the direct effect of semen metabolome changes on the Raman spectra. A classification model was constructed by linear discriminant analysis using the training set and evaluated by the test group which resulted in completely correct classification. While three of the six test samples appeared in the fertile group, the rest appeared in the infertile as expected.

Conclusion

Metabolic fingerprinting of seminal plasma using Raman spectroscopy combined with chemometric classification methods accurately discriminated between the idiopathic infertile men and the fertile ones and predicted their fertility type.

Metabolomics: small molecules that matter more

Metabolomics, an analytical study with high-throughput profiling, helps to understand interactions within a biological system. Small molecules, called metabolites or metabolomes with the size of <1500 Da, depict the status of a biological system in a different manner. Currently, we are in need to globally analyze the metabolome and the pathways involved in healthy, as well as diseased conditions, for possible therapeutic applications. Metabolome analysis has revealed high-abundance molecules during different conditions such as diet, environmental stress, microbiota, and disease and treatment states. As a result, it is hard to understand the complete and stable network of metabolites of a biological system. This review helps readers know the available techniques to study metabolomics in addition to other major omics such as genomics, transcriptomics, and proteomics. This review also discusses the metabolomics in various pathological conditions and the importance of metabolomics in therapeutic applications.

Metabolic Alterations in Myotonic Dystrophy Type 1 and Their Correlation with Lipin

Myotonic dystrophy type 1 (DM1) is an autosomal dominant hereditary and multisystemic disease, characterized by progressive distal muscle weakness and myotonia. Despite huge efforts, the pathophysiological mechanisms underlying DM1 remain elusive. In this review, the metabolic alterations observed in patients with DM1 and their connection with lipin proteins are discussed. We start by briefly describing the epidemiology, the physiopathological and systemic features of DM1. The molecular mechanisms proposed for DM1 are explored and summarized. An overview of metabolic syndrome, dyslipidemia, and the summary of metabolic alterations observed in patients with DM1 are presented. Patients with DM1 present clinical evidence of metabolic alterations, namely increased levels of triacylglycerol and low-density lipoprotein, increased insulin and glucose levels, increased abdominal obesity, and low levels of high-density lipoprotein. These metabolic alterations may be associated with lipins, which are phosphatidate phosphatase enzymes that regulates the triacylglycerol levels, phospholipids, lipid signaling pathways, and are transcriptional co-activators. Furthermore, lipins are also important for autophagy, inflammasome activation and lipoproteins synthesis. We demonstrate the association of lipin with the metabolic alterations in patients with DM1, which supports further clinical studies and a proper exploration of lipin proteins as therapeutic targets for metabolic syndrome, which is important for controlling many diseases including DM1.

Discrimination of bacteria using whole organism fingerprinting: the utility of modern physicochemical techniques for bacterial typing

This review compares and contrasts MALDI-MS, FT-IR spectroscopy and Raman spectroscopy for whole organism fingerprinting and bacterial typing.

Comprehensive Two-Dimensional Gas Chromatography Mass Spectrometry-Based Metabolomics

Compared to one-dimensional gas chromatography with mass spectrometry (GC-MS), GC × GC-MS provides significantly increased peak capacity, resolution, and sensitivity for analysis of complex biological samples. In the last decade, GC × GC-MS has been increasingly applied to the discovery of metabolite biomarkers and elucidation of metabolic mechanisms in human diseases. The recent development of coupling GC × GC with a high-resolution mass spectrometer further accelerates these metabolomic applications. In this chapter, we will briefly review the instrumentation, sample preparation, data analysis, and applications of GC × GC-MS-based metabolomic analysis.

Discovery and validation of an NMR-based metabolomic profile in urine as TB biomarker

Despite efforts to improve tuberculosis (TB) detection, limitations in access, quality and timeliness of diagnostic services in low- and middle-income countries are challenging for current TB diagnostics. This study aimed to identify and characterise a metabolic profile of TB in urine by high-field nuclear magnetic resonance (NMR) spectrometry and assess whether the TB metabolic profile is also detected by a low-field benchtop NMR spectrometer. We included 189 patients with tuberculosis, 42 patients with pneumococcal pneumonia, 61 individuals infected with latent tuberculosis and 40 uninfected individuals. We acquired the urine spectra from high and low-field NMR. We characterised a TB metabolic fingerprint from the Principal Component Analysis. We developed a classification model from the Partial Least Squares-Discriminant Analysis and evaluated its performance. We identified a metabolic fingerprint of 31 chemical shift regions assigned to eight metabolites (aminoadipic acid, citrate, creatine, creatinine, glucose, mannitol, phenylalanine, and hippurate). The model developed using low-field NMR urine spectra correctly classified 87.32%, 85.21% and 100% of the TB patients compared to pneumococcal pneumonia patients, LTBI and uninfected individuals, respectively. The model validation correctly classified 84.10% of the TB patients. We have identified and characterised a metabolic profile of TB in urine from a high-field NMR spectrometer and have also detected it using a low-field benchtop NMR spectrometer. The models developed from the metabolic profile of TB identified by both NMR technologies were able to discriminate TB patients from the rest of the study groups and the results were not influenced by anti-TB treatment or TB location. This provides a new approach in the search for possible biomarkers for the diagnosis of TB.