Emerging field of metabolomics: big promise for cancer biomarker identification and drug discovery

Refining microbial community metabolic models derived from metagenomics using reference-based taxonomic profiling

Characterization of microbial community metabolic output is crucial to understanding their functions. Construction of genome-scale metabolic models from metagenome-assembled genomes (MAG) has enabled prediction of metabolite production by microbial communities, yet little is known about their accuracy. Here, we examined the performance of two approaches for metabolite prediction from metagenomes, one that is MAG-guided and another that is taxonomic reference-guided. We applied both on shotgun metagenomics data from human and environmental samples, and validated findings in the human samples using untargeted metabolomics. We found that in human samples, where taxonomic profiling is optimized and reference genomes are readily available, when number of input taxa was normalized, the reference-guided approach predicted more metabolites than the MAG-guided approach. The two approaches showed significant overlap but each identified metabolites not predicted in the other. Pathway enrichment analyses identified significant differences in inferences derived from data based on the approach, highlighting the need for caution in interpretation. In environmental samples, when the number of input taxa was normalized, the reference-guided approach predicted more metabolites than the MAG-guided approach for total metabolites in both sample types and non-redundant metabolites in seawater samples. Nonetheless, as was observed for the human samples, the approaches overlapped substantially but also predicted metabolites not observed in the other. Our findings report on utility of a complementary input to genome-scale metabolic model construction that is less computationally intensive forgoing MAG assembly and refinement, and that can be applied on shallow shotgun sequencing where MAGs cannot be generated.IMPORTANCELittle is known about the accuracy of genome-scale metabolic models (GEMs) of microbial communities despite their influence on inferring community metabolic outputs and culture conditions. The performance of GEMs for metabolite prediction from metagenomes was assessed by applying two approaches on shotgun metagenomics data from human and environmental samples, and validating findings in the human samples using untargeted metabolomics. The performance of the approach was found to be dependent on sample type, but collectively, the reference-guided approach predicted more metabolites than the MAG-guided approach. Despite the differences, the predictions from the approaches overlapped substantially but each identified metabolites not predicted in the other. We found significant differences in biological inferences based on the approach, with some examples of uniquely enriched pathways in one group being invalidated when using the alternative approach, highlighting the need for caution in interpretation of GEMs.

Application of metabolomics in oral squamous cell carcinoma

BACKGROUND

Oral squamous cell carcinoma (OSCC) is a prevalent malignancy affecting the head and neck region. The prognosis for OSCC patients remains unfavorable due to the absence of precise and efficient early diagnostic techniques. Metabolomics offers a promising approach for identifying distinct metabolites, thereby facilitating early detection and treatment of OSCC.

OBJECTIVE

This review aims to provide a comprehensive overview of recent advancements in metabolic marker identification for early OSCC diagnosis. Additionally, the clinical significance and potential applications of metabolic markers for the management of OSCC are discussed.

RESULTS

This review summarizes metabolic changes during the occurrence and development of oral squamous cell carcinoma and reviews prospects for the clinical application of characteristic, differential metabolites in saliva, serum, and OSCC tissue. In this review, the application of metabolomic technology in OSCC research was summarized, and future research directions were proposed.

CONCLUSION

Metabolomics, detection technology that is the closest to phenotype, can efficiently identify differential metabolites. Combined with statistical data analyses and artificial intelligence technology, it can rapidly screen characteristic biomarkers for early diagnosis, treatment, and prognosis evaluations.

An Optimized Method for LC-MS-Based Quantification of Endogenous Organic Acids: Metabolic Perturbations in Pancreatic Cancer

Accurate and reliable quantification of organic acids with carboxylic acid functional groups in complex biological samples remains a major analytical challenge in clinical chemistry. Issues such as spontaneous decarboxylation during ionization, poor chromatographic resolution, and retention on a reverse-phase column hinder sensitivity, specificity, and reproducibility in multiple-reaction monitoring (MRM)-based LC-MS assays. We report a targeted metabolomics method using phenylenediamine derivatization for quantifying carboxylic acid-containing metabolites (CCMs). This method achieves accurate and sensitive quantification in various biological matrices, with recovery rates from 90% to 105% and CVs ≤ 10%. It shows linearity from 0.1 ng/mL to 10 µg/mL with linear regression coefficients of 0.99 and LODs as low as 0.01 ng/mL. The library included a wide variety of structurally variant CCMs such as amino acids/conjugates, short- to medium-chain organic acids, di/tri-carboxylic acids/conjugates, fatty acids, and some ring-containing CCMs. Comparing CCM profiles of pancreatic cancer cells to normal pancreatic cells identified potential biomarkers and their correlation with key metabolic pathways. This method enables sensitive, specific, and high-throughput quantification of CCMs from small samples, supporting a wide range of applications in basic, clinical, and translational research.

Metabolic Fingerprint in Childhood Acute Lymphoblastic Leukemia

INTRODUCTION

Acute lymphoblastic leukemia (ALL) is the most prevalent childhood malignancy. Despite high cure rates, several questions remain regarding predisposition, response to treatment, and prognosis of the disease. The role of intermediary metabolism in the individualized mechanistic pathways of the disease is unclear. We have hypothesized that children with any (sub)type of ALL have a distinct metabolomic fingerprint at diagnosis when compared: (i) to a control group; (ii) to children with a different (sub)type of ALL; (iii) to the end of the induction treatment.

MATERIALS AND METHODS

In this prospective case-control study (NCT03035344), plasma and urinary metabolites were analyzed in 34 children with ALL before the beginning (D0) and at the end of the induction treatment (D33). Their metabolic fingerprint was defined by targeted analysis of 106 metabolites and compared to that of an equal number of matched controls. Multivariate and univariate statistical analyses were performed using SIMCAP and scripts under the R programming language.

RESULTS

Metabolomic analysis showed distinct changes in patients with ALL compared to controls on both D0 and D33. The metabolomic fingerprint within the patient group differed significantly between common B-ALL and pre-B ALL and between D0 and D33, reflecting the effect of treatment. We have further identified the major components of this metabolic dysregulation, indicating shifts in fatty acid synthesis, transfer and oxidation, in amino acid and glycerophospholipid metabolism, and in the glutaminolysis/TCA cycle.

CONCLUSIONS

The disease type and time point-specific metabolic alterations observed in pediatric ALL are of particular interest as they may offer potential for the discovery of new prognostic biomarkers and therapeutic targets.

Machine learning: a powerful tool for identifying key microbial agents associated with specific cancer types

Machine learning (ML) includes a broad class of computer programs that improve with experience and shows unique strengths in performing tasks such as clustering, classification and regression. Over the past decade, microbial communities have been implicated in influencing the onset, progression, metastasis, and therapeutic response of multiple cancers. Host-microbe interaction may be a physiological pathway contributing to cancer development. With the accumulation of a large number of high-throughput data, ML has been successfully applied to the study of human cancer microbiomics in an attempt to reveal the complex mechanism behind cancer. In this review, we begin with a brief overview of the data sources included in cancer microbiomics studies. Then, the characteristics of the ML algorithm are briefly introduced. Secondly, the application progress of ML in cancer microbiomics is also reviewed. Finally, we highlight the challenges and future prospects facing ML in cancer microbiomics. On this basis, we conclude that the development of cancer microbiomics can not be achieved without ML, and that ML can be used to develop tumor-targeting microbial therapies, ultimately contributing to personalized and precision medicine.

Liquid biopsies for detection, surveillance, and prognosis of urothelial cancer: a future standard?

INTRODUCTION

Liquid biopsies are used for the detection of tumor-specific elements in body fluid. Their application in prognosis and diagnosis of muscle/non-muscle invasive bladder cancer (MIBC/NMIBC) or upper tract urothelial cancer (UTUC) remains poorly known and rarely mentioned in clinical guidelines.

AREAS COVERED

Herein, we provide an overview of current data regarding the use of liquid biopsies in urothelial tumors.

EXPERT OPINION

Studies that were included analyzed liquid biopsies using the detection of circulating tumor cells (CTCs), deoxyribonucleic acid (DNA), ribonucleic acid (RNA), exosomes, or metabolomics. The sensitivity of blood CTC detection in patients with localized cancer was 35% and raised to 50% in patients with metastatic cancer. In NMIBC patients, blood CTC was associated with poor prognosis, whereas discrepancies were seen in MIBC patients. Circulating plasma DNA presented a superior sensitivity to urine and was a good indicator for diagnosis, follow-up, and oncological outcome. In urine, specific bladder cancer (BC) microRNA had an overall sensitivity of 85% and a specificity of 86% in the diagnosis of urothelial cancer. These results are in favor of the use of liquid biopsies as biomarkers for in urothelial cancer management.

GC-MS-based untargeted metabolic profiling of malignant mesothelioma plasma

Background

Malignant mesothelioma (MM) is a cancer caused mainly by asbestos exposure, and is aggressive and incurable. This study aimed to identify differential metabolites and metabolic pathways involved in the pathogenesis and diagnosis of malignant mesothelioma.

Methods

By using gas chromatography-mass spectrometry (GC-MS), this study examined the plasma metabolic profile of human malignant mesothelioma. We performed univariate and multivariate analyses and pathway analyses to identify differential metabolites, enriched metabolism pathways, and potential metabolic targets. The area under the receiver-operating curve (AUC) criterion was used to identify possible plasma biomarkers.

Results

Using samples from MM (n = 19) and healthy control (n = 22) participants, 20 metabolites were annotated. Seven metabolic pathways were disrupted, involving alanine, aspartate, and glutamate metabolism; glyoxylate and dicarboxylate metabolism; arginine and proline metabolism; butanoate and histidine metabolism; beta-alanine metabolism; and pentose phosphate metabolic pathway. The AUC was used to identify potential plasma biomarkers. Using a threshold of AUC = 0.9, five metabolites were identified, including xanthurenic acid, (s)-3,4-hydroxybutyric acid, D-arabinose, gluconic acid, and beta-d-glucopyranuronic acid.

Conclusions

To the best of our knowledge, this is the first report of a plasma metabolomics analysis using GC-MS analyses of Asian MM patients. Our identification of these metabolic abnormalities is critical for identifying plasma biomarkers in patients with MM. However, additional research using a larger population is needed to validate our findings.

Integrated microbiome and metabolome analysis reveals the interaction between intestinal flora and serum metabolites as potential biomarkers in hepatocellular carcinoma patients

Globally, liver cancer poses a serious threat to human health and quality of life. Despite numerous studies on the microbial composition of the gut in hepatocellular carcinoma (HCC), little is known about the interactions of the gut microbiota and metabolites and their role in HCC. This study examined the composition of the gut microbiota and serum metabolic profiles in 68 patients with HCC, 33 patients with liver cirrhosis (LC), and 34 healthy individuals (NC) using a combination of metagenome sequencing and liquid chromatography-mass spectrometry (LC-MS). The composition of the serum metabolites and the structure of the intestinal microbiota were found to be significantly altered in HCC patients compared to non-HCC patients. LEfSe and metabolic pathway enrichment analysis were used to identify two key species (Odoribacter splanchnicus and Ruminococcus bicirculans) and five key metabolites (ouabain, taurochenodeoxycholic acid, glycochenodeoxycholate, theophylline, and xanthine) associated with HCC, which then were combined to create panels for HCC diagnosis. The study discovered that the diagnostic performance of the metabolome was superior to that of the microbiome, and a panel comprised of key species and key metabolites outperformed alpha-fetoprotein (AFP) in terms of diagnostic value. Spearman's rank correlation test was used to determine the relationship between the intestinal flora and serum metabolites and their impact on hepatocarcinogenesis and progression. A random forest model was used to assess the diagnostic performance of the different histologies alone and in combination. In summary, this study describes the characteristics of HCC patients' intestinal flora and serum metabolism, demonstrates that HCC is caused by the interaction of intestinal flora and serum metabolites, and suggests that two key species and five key metabolites may be potential markers for the diagnosis of HCC.

Comprehensive Two-Dimensional Gas Chromatography as a Bioanalytical Platform for Drug Discovery and Analysis

Over the last decades, comprehensive two-dimensional gas chromatography (GC×GC) has emerged as a significant separation tool for high-resolution analysis of disease-associated metabolites and pharmaceutically relevant molecules. This review highlights recent advances of GC×GC with different detection modalities for drug discovery and analysis, which ideally improve the screening and identification of disease biomarkers, as well as monitoring of therapeutic responses to treatment in complex biological matrixes. Selected recent GC×GC applications that focus on such biomarkers and metabolite profiling of the effects of drug administration are covered. In particular, the technical overview of recent GC×GC implementation with hyphenation to the key mass spectrometry (MS) technologies that provide the benefit of enhanced separation dimension analysis with MS domain differentiation is discussed. We conclude by highlighting the challenges in GC×GC for drug discovery and development with perspectives on future trends.

Alterations of plasma exosomal proteins and motabolies are associated with the progression of castration-resistant prostate cancer

BACKGROUND

Current diagnosis tools for prostate cancer (PCa) such as serum PSA detection and prostate biopsy cannot distinguish dormant tumors from invasive malignancies, either be used as prognosis marker for castration resistant prostate cancer (CRPC), the lethal stage of PCa patients. Exosomes have been widely investigated as promising biomarkers for various diseases. We aim to characterize the proteomic and metabolomic profile of exosomes and to evaluate their potential value for the diagnosis of PCa, especially CRPC. We also investigate the functions of some specific exosome biomarkers in the progression of CRPC.

METHODS

Integrated proteomics and metabolomics analysis were performed for plasma-derived exosomes collected from tumor-free controls (TFC), PCa and CRPC patients. Expression of specific exosomal proteins were further validated by targeted 4D-parallel reaction monitoring (PRM) mass spectrometry among the three cohorts. Tissue distribution and functional role of exosomal protein LRG1 was studied in clinical PCa tissue samples and cell line models.

RESULTS

Three potential exosomal protein markers were identified. The apolipoprotein E level in PCa samples was 1.7-fold higher than that in TFC (receiver operating characteristic value, 0.74). Similarly, the levels of exosome-derived leucine-rich alpha2-glycoprotein 1 (LRG1) and inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3) in the CRPC group were 1.7 and 2.04 times, respectively, higher than those in the PCa group (ROC values, 0.84 and 0.85, respectively), indicating that LRG1 and ITIH3 could serve as predictive markers for CRPC. For metabolomic evaluation of exosomes, a series of differentially expressed metabolites were identified, and a combined metabolite panel showed ROC value of 0.94 for distinguishing PCa from TFC and 0.97 for distinguishing CRPC from PCa. Immunohistochemistry of tissue microarray showed that LRG1 protein was significantly upregulated in advanced prostate cancer and functional assay revealed that ectopic expression of LRG1 can significantly enhance the malignant phenotype of prostate cancer cells. More importantly, PCa cell derived LRG1-overexpressed exosomes remarkably promoted angiogenesis.

CONCLUSION

Integration of proteomics and metabolomics data generated proteomic and metabolic signatures of plasma exosomes that may facilitate discrimination of CRPC from PCa and TFC patients, suggesting the potential of exosomal proteins and metabolites as CRPC markers. The study also confirmed the important role of exosomal protein LRG1 in PCa malignant progression.

Metabolites as extracellular vesicle cargo in health, cancer, pleural effusion, and cardiovascular diseases: An emerging field of study to diagnostic and therapeutic purposes

Extracellular vesicles (EVs) are highly diverse nanoscale membrane-bound structures released from different cell types into the extracellular environment. They play essential functions in cell signaling by transporting their cargo, such as proteins, RNA, DNA, lipids, metabolites, and small molecules, to recipient cells. It has recently been shown that EVs might modulate carcinogenesis by delivering cargo to recipient cells. Furthermore, recent discoveries revealed that changes in plasma-derived EV levels and cargo in subjects with metabolic diseases were documented by many researchers, suggesting that EVs might be a promising source of disease biomarkers. One of the cargos of EVs that has recently attracted the most attention is metabolites. The metabolome of these vesicles introduces a plethora of disease indicators; hence, examining the metabolomics of EVs detected in human biofluids would be an effective approach. On the other hand, metabolites have various roles in biological systems, including the production of energies, synthesizing macromolecules, and serving as signaling molecules and hormones. Metabolome rewiring in cancer and stromal cells is a characteristic of malignancy, but the current understanding of how this affects the metabolite composition and activity of tumor-derived EVs remains in its infancy. Since new findings and studies in the field of exosome biology and metabolism are constantly being published, it is likely that diagnostic and treatment techniques, including the use of exosome metabolites, will be launched in the coming years. Recent years have seen increased interest in the EV metabolome as a possible source for biomarker development. However, our understanding of the role of these molecules in health and disease is still immature. In this work, we have provided the latest findings regarding the role of metabolites as EV cargoes in the pathophysiology of diseases, including cancer, pleural effusion (PE), and cardiovascular disease (CVD). We also discussed the significance of metabolites as EV cargoes of microbiota and their role in host-microbe interaction. In addition, the latest findings on metabolites in the form of EV cargoes as biomarkers for disease diagnosis and treatment are presented in this study.

Metabolomics by NMR Combined with Machine Learning to Predict Neoadjuvant Chemotherapy Response for Breast Cancer

Simple Summary

Neoadjuvant chemotherapy (NACT) is offered to breast cancer (BC) patients to downstage the disease. However, some patients may not respond to NACT, being resistant. We used the serum metabolic profile by Nuclear Magnetic Resonance (NMR) combined with disease characteristics to differentiate between sensitive and resistant BC patients. We obtained accuracy above 80% for the response prediction and showcased how NMR can substantially enhance the prediction of response to NACT.

Abstract

Neoadjuvant chemotherapy (NACT) is offered to patients with operable or inoperable breast cancer (BC) to downstage the disease. Clinical responses to NACT may vary depending on a few known clinical and biological features, but the diversity of responses to NACT is not fully understood. In this study, 80 women had their metabolite profiles of pre-treatment sera analyzed for potential NACT response biomarker candidates in combination with immunohistochemical parameters using Nuclear Magnetic Resonance (NMR). Sixty-four percent of the patients were resistant to chemotherapy. NMR, hormonal receptors (HR), human epidermal growth factor receptor 2 (HER2), and the nuclear protein Ki67 were combined through machine learning (ML) to predict the response to NACT. Metabolites such as leucine, formate, valine, and proline, along with hormone receptor status, were discriminants of response to NACT. The glyoxylate and dicarboxylate metabolism was found to be involved in the resistance to NACT. We obtained an accuracy in excess of 80% for the prediction of response to NACT combining metabolomic and tumor profile data. Our results suggest that NMR data can substantially enhance the prediction of response to NACT when used in combination with already known response prediction factors.

A Selective and Sensitive LC-MS/MS Method for Quantitation of Indole in Mouse Serum and Tissues

Indole is an endogenous substance currently being evaluated as a biomarker for ulcerative colitis, irritable bowel syndrome, Crohn’s disease and non-alcoholic fatty liver disease. A novel, selective, and sensitive method using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) was developed for quantitation of indole concentrations in mouse plasma and tissues. Samples were prepared by protein precipitation using ice-cold acetonitrile (ACN) followed by injecting the extracted analyte to LC-MS/MS system. Indole was separated using Synergi Fusion C18 (4 µm, 250 × 2.0 mm) column with mobile phase 0.1% aqueous formic acid (A) and methanol (B) using gradient flow with run time 12 min. The mass spectrometer was operated in atmospheric pressure chemical ionization (APCI) positive mode at unit resolution in multiple reaction monitoring (MRM) mode, using precursor ion > product ion combinations of 118.1 > 91.1 m/z for indole and 124.15 > 96.1 m/z for internal standard (IS) indole d7. The MS/MS response was linear over the range of indole concentrations (1−500 ng/mL). The validated method was applied for quantitation of indole concentrations range in mouse lungs (4.3−69.4 ng/g), serum (0.8−38.7 ng/mL) and cecum (1043.8−12,124.4 ng/g). This method would help investigate the role of indole as a biomarker and understand its implications in different disease states.

Biomembrane lipids: When physics and chemistry join to shape biological activity

Biomembranes constitute the first lines of defense of cells. While small molecules can often permeate cell walls in bacteria and plants, they are generally unable to penetrate the barrier constituted by the double layer of phospholipids, unless specific receptors or channels are present. Antimicrobial or cell-penetrating peptides are in fact highly specialized molecules able to bypass this barrier and even discriminate among different cell types. This capacity is made possible by the intrinsic properties of its phospholipids, their distribution between the internal and external leaflet, and their ability to mutually interact, modulating the membrane fluidity and the exposition of key headgroups. Although common phospholipids can be found in the membranes of most organisms, some are characteristic of specific cell types. Here, we review the properties of the most common lipids and describe how they interact with each other in biomembrane. We then discuss how their assembly in bilayers determines some key physical-chemical properties such as permeability, potential and phase status. Finally, we describe how the exposition of specific phospholipids determines the recognition of cell types by membrane-targeting molecules.

New bladder cancer non-invasive surveillance method based on voltammetric electronic tongue measurement of urine

Bladder cancer (BC) is the sixth leading cause of death by cancer. Depending on the invasiveness of tumors, patients with BC will undergo surgery and surveillance lifelong, owing the high rate of recurrence and progression. In this context, the development of strategies to support non-invasive BC diagnosis is focusing attention. Voltammetric electronic tongue (VET) has been demonstrated to be of use in the analysis of biofluids. Here, we present the implementation of a VET to study 207 urines to discriminate BC and non-BC for diagnosis and surveillance to detect recurrences. Special attention has been paid to the experimental setup to improve reproducibility in the measurements. PLSDA analysis together with variable selection provided a model with high sensitivity, specificity, and area under the ROC curve AUC (0.844, 0.882, and 0.917, respectively). These results pave the way for the development of non-invasive low-cost and easy-to-use strategies to support BC diagnosis and follow-up.

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Bladder cancer (BC) and control urines were studied by voltammetric electronic tongue

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A PLSDA model was obtained with high sensitivity, specificity, and accuracy (84/88/86)

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103/122 BC urines and 7⅝5 control urines were predicted correctly

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The electronic tongue has the potential for non-invasive BC diagnostics and follow-up

LC–MS-Based Urine Metabolomics Analysis for the Diagnosis and Monitoring of Medulloblastoma

Medulloblastoma (MB) is the most common type of brain cancer in pediatric patients. Body fluid biomarkers will be helpful for clinical diagnosis and treatment. In this study, liquid chromatography–mass spectrometry (LC–MS)-based metabolomics was used to identify specific urine metabolites of MB in a cohort, including 118 healthy controls, 111 MB patients, 31 patients with malignant brain cancer, 51 patients with benign brain disease, 29 MB patients 1 week postsurgery and 80 MB patients 1 month postsurgery. The results showed an apparent separation for MB vs. healthy controls, MB vs. benign brain diseases, and MB vs. other malignant brain tumors, with AUCs values of 0.947/0.906, 0.900/0.873, and 0.842/0.885, respectively, in the discovery/validation group. Among all differentially identified metabolites, 4 metabolites (tetrahydrocortisone, cortolone, urothion and 20-oxo-leukotriene E4) were specific to MB. The analysis of these 4 metabolites in pre- and postoperative MB urine samples showed that their levels returned to a healthy state after the operation (especially after one month), showing the potential specificity of these metabolites for MB. Finally, the combination of two metabolites, tetrahydrocortisone and cortolone, showed diagnostic accuracy for distinguishing MB from non-MB, with an AUC value of 0.851. Our data showed that urine metabolomics might be used for MB diagnosis and monitoring.

Metabolic changes of the reduction of manganese intake in the hepatic encephalopathy rat: NMR- and MS-based metabolomics study

To investigate the metabolic changes in type C hepatic encephalopathy (CHE) rats after reducing manganese (Mn) intake. A total of 80 Sprague-Dawley rats were divided into control group and CHE groups (induced by intraperitoneal injection of thioacetamide at a dose of 250 mg/kg of body weight twice a week for 6 weeks). CHE rats were subdivided into 1Mn group (fed a standard diet, with 10 mg Mn/kg feed), 0.5Mn group (half-Mn diet), 0.25Mn group (quarter-Mn diet) and 0Mn group (no-Mn diet) for 4 to 8 weeks. Morris water maze (MWM), Y maze and narrow beam test (NBT) were used to evaluate cognitive and motor functions. Blood ammonia, brain Mn content, the number of GS-positive cells, and glutamine synthetase (GS) activity were measured. The metabolic changes of CHE rats were investigated using hydrogen-nuclear magnetic resonance and mass spectrometry. Multivariate statistical analysis was used to analyze the results. Significantly decreased numbers of entries in target area of MWM and Y maze, longer NBT latency and total time, higher blood ammonia, brain Mn content and GS activity were found in CHE rats. After reducing Mn intake, CHE rats had better behavioral performance, significantly lower blood ammonia, brain Mn content and GS activity. The main up-regulated metabolites were Ala, GABA, Glu, Gln, Lac, Tyr, Phe in 1Mn rats. After reducing Mn intake, metabolites returned to normal level at different degrees. Reducing Mn intake could reduce brain Mn content and blood ammonia, regulate GS activity and amino acid metabolism, ultimately improve behavioral performance in CHE rats.

Integrated analysis of the faecal metagenome and serum metabolome reveals the role of gut microbiome-associated metabolites in the detection of colorectal cancer and adenoma

OBJECTIVE

To profile gut microbiome-associated metabolites in serum and investigate whether these metabolites could distinguish individuals with colorectal cancer (CRC) or adenoma from normal healthy individuals.

DESIGN

Integrated analysis of untargeted serum metabolomics by liquid chromatography-mass spectrometry and metagenome sequencing of paired faecal samples was applied to identify gut microbiome-associated metabolites with significantly altered abundance in patients with CRC and adenoma. The ability of these metabolites to discriminate between CRC and colorectal adenoma was tested by targeted metabolomic analysis. A model based on gut microbiome-associated metabolites was established and evaluated in an independent validation cohort.

RESULTS

In total, 885 serum metabolites were significantly altered in both CRC and adenoma, including eight gut microbiome-associated serum metabolites (GMSM panel) that were reproducibly detected by both targeted and untargeted metabolomics analysis and accurately discriminated CRC and adenoma from normal samples. A GMSM panel-based model to predict CRC and colorectal adenoma yielded an area under the curve (AUC) of 0.98 (95% CI 0.94 to 1.00) in the modelling cohort and an AUC of 0.92 (83.5% sensitivity, 84.9% specificity) in the validation cohort. The GMSM model was significantly superior to the clinical marker carcinoembryonic antigen among samples within the validation cohort (AUC 0.92 vs 0.72) and also showed promising diagnostic accuracy for adenomas (AUC=0.84) and early-stage CRC (AUC=0.93).

CONCLUSION

Gut microbiome reprogramming in patients with CRC is associated with alterations of the serum metabolome, and GMSMs have potential applications for CRC and adenoma detection.

Untargeted Metabolomics Approach of Cross-Adaptation in Salmonella Enterica Induced by Major Compounds of Essential Oils

Cross-adaptation phenomena in bacterial populations, induced by sublethal doses of antibacterial solutions, are a major problem in the field of food safety. In this regard, essential oils and their major compounds appear as an effective alternative to common sanitizers in food industry environments. The present study aimed to evaluate the untargeted metabolomics perturbations of Salmonella enterica serovar Enteritidis that has been previously exposed to the sublethal doses of the major components of essential oils: cinnamaldehyde, citral, and linalool (CIN, CIT, and LIN, respectively). Cinnamaldehyde appeared to be the most efficient compound in the assays evaluating the inhibitory effects [0.06% (v/v) as MBC]. Also, preliminary tests exhibited a phenotype of adaptation in planktonic and sessile cells of S. Enteritidis when exposed to sublethal doses of linalool, resulting in tolerance to previously lethal concentrations of citral. A metabolomics approach on S. Enteritidis provided an important insight into the phenomenon of cross-adaptation induced by sublethal doses of major compounds of some essential oils. In addition, according to the results obtained, when single molecules were used, many pathways may be involved in bacterial tolerance, which could be different from the findings revealed in previous studies regarding the use of phytocomplex of essential oils. Orthogonal projection to latent structures (OPLS) proved to be an interesting predictive model to demonstrate the adaptation events in pathogenic bacteria because of the global engagement to prevent and control foodborne outbreaks.

Metabolomics of peripheral artery disease

The science of metabolomics has emerged as a novel tool for studying changes in metabolism that accompany different disease states. Several studies have applied this evolving field to the study of various cardiovascular disease states, which has led to improved understanding of metabolic changes that underlie heart failure and ischemic heart disease. A significant amount of progress has also been made in the identification of novel biomarkers of cardiovascular disease. Another common atherosclerotic disease, peripheral artery disease (PAD) affects arteries of the lower extremities. Although certain aspects of the disease pathophysiology overlap with other cardiovascular diseases in general, PAD patients suffer unique manifestations that lead to significant morbidity and mortality as well as severe functional limitations. Furthermore, because over half of PAD patients are asymptomatic, there is a need for improved diagnostic and screening methods. Identification of metabolites associated with the disease may thus be a promising approach for PAD. However, PAD remains highly understudied. In this chapter, we discuss the application of metabolomics to the study of PAD.

A urine and serum metabolomics study of gastroesophageal reflux disease in TCM syndrome differentiation using UPLC-Q-TOF/MS

Gastroesophageal reflux disease (GERD) is a common, chronic and complex upper gastrointestinal disease. In Traditional Chinese medicine (TCM) theory, GERD is classified into two main types: stagnant heat of liver and stomach (SHLS) and deficient cold of spleen and stomach (DCSS). The discovery and evaluation of potential biomarkers for different syndrome types of GERD may contribute to comprehend specific molecular mechanism and identify new targets for diagnosis and appropriate management. In our study, 60 subjects including 40 GERD patients (20 SHLS and 20 DCSS) and 20 healthy controls were recruited, and the serum and urine metabolic profiles from untargeted liquid chromatography coupled to mass spectrometry (LC-MS) metabolomics approach were obtained. Finally 38 biomarkers associated with disease were identified and 9 metabolic pathways were enriched. The most enriched pathways were amino acid metabolism, steroid hormone biosynthesis, glycerophospholipid metabolism, sphingolipid metabolism and TCA cycle. According to the area under curve (AUC) value, we propose a cohort of three metabolites from urine and serum samples as promising biomarkers for TCM syndrome differentiation of GERD, which are prolylhydroxyproline, glycitein-4'-O-glucuronide, capsianoside I in urine and neuAcalpha2-3Galbeta-Cer (d18:1/16:0), sphinganine, arachidonic acid in serum. The cumulative AUC value of merged biomarkers in urine and serum was 0.979 (95%CI 0.927-1) and 0.842 (95%CI 0.704-0.980), respectively. The results indicated that LC-MS based metabolomic profiling method might be an effective and promising tool on further pathogenesis discovering of GERD. The findings provided new strategy for the diagnosis of GERD TCM syndrome differentiation in clinic.

Metabolic profile changes in serum of migraine patients detected using 1H-NMR spectroscopy

Background

Migraine is a common brain disorder but reliable diagnostic biomarkers in blood are still lacking. Our aim was to identify, using proton nuclear magnetic resonance (¹H-NMR) spectroscopy, metabolites in serum that are associated with lifetime and active migraine by comparing metabolic profiles of patients and controls.

Methods

Fasting serum samples from 313 migraine patients and 1512 controls from the Erasmus Rucphen Family (ERF) study were available for ¹H-NMR spectroscopy. Data was analysed using elastic net regression analysis.

Results

A total of 100 signals representing 49 different metabolites were detected in 289 cases (of which 150 active migraine patients) and 1360 controls. We were able to identify profiles consisting of 6 metabolites predictive for lifetime migraine status and 22 metabolites predictive for active migraine status. We estimated with subsequent regression models that after correction for age, sex, BMI and smoking, the association with the metabolite profile in active migraine remained. Several of the metabolites in this profile are involved in lipid, glucose and amino acid metabolism.

Conclusion

This study indicates that metabolic profiles, based on serum concentrations of several metabolites, including lipids, amino acids and metabolites of glucose metabolism, can distinguish active migraine patients from controls.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10194-021-01357-w.

Unravelling the Anticancer Mechanisms of Traditional Herbal Medicines with Metabolomics

Metabolite profiling of cancer cells presents many opportunities for anticancer drug discovery. The Chinese, Indian, and African flora, in particular, offers a diverse source of anticancer therapeutics as documented in traditional folklores. In-depth scientific information relating to mechanisms of action, quality control, and safety profile will promote their extensive usage in cancer therapy. Metabolomics may be a more holistic strategy to gain valuable insights into the anticancer mechanisms of action of plants but this has remained largely unexplored. This review, therefore, presents the available metabolomics studies on the anticancer effects of herbal medicines commonly used in Africa and Asia. In addition, we present some scientifically understudied ‘candidate plants’ for cancer metabolomics studies and highlight the relevance of metabolomics in addressing other challenges facing the drug development of anticancer herbs. Finally, we discussed the challenges of using metabolomics to uncover the underlying mechanisms of potential anticancer herbs and the progress made in this regard.

Age-related changes in metabolites in young donor livers and old recipient sera after liver transplantation from young to old rats

Liver ageing not only damages liver function but also harms systemic metabolism. To better understand the mechanisms underlying liver ageing, we transplanted the livers of young rats to young and old rats and performed untargeted metabolomics to detect changes in the metabolites in the liver tissues and sera. A total of 153 metabolites in the livers and 83 metabolites in the sera were different between the old and young rats that did not undergo liver transplantation; among these metabolites, 7 different metabolites were observed in both the livers and sera. Five weeks after liver transplantation, the levels of 25 metabolites in the young donor livers were similar to those in the old rats, and this result probably occurred due to the effect of the whole‐body environment of the older recipients on the young livers. The 25 altered metabolites included organic acids and derivatives, lipids and lipid‐like molecules, etc. In the sera, the differences in 78 metabolites, which were significant between the young and old rats, were insignificant in the old recipient rats and made the metabolic profile of the old recipients more similar to that of the young recipients. Finally, combining the above metabolomic data with the transcriptomic data from the GEO, we found that the altered metabolites and genes in the liver were enriched in 9 metabolic pathways, including glycerophospholipid, arachidonic acid, histidine and linoleate. Thus, this study revealed important age‐related metabolites and potential pathways as well as the interaction between the liver and the whole‐body environment.

Epigenomic and Metabolomic Integration Reveals Dynamic Metabolic Regulation in Bladder Cancer

Bladder cancer (BC) represents a clinical, social, and economic challenge due to tumor-intrinsic characteristics, limitations of diagnostic techniques and a lack of personalized treatments. In the last decade, the use of liquid biopsy has grown as a non-invasive approach to characterize tumors. Moreover, the emergence of omics has increased our knowledge of cancer biology and identified critical BC biomarkers. The rewiring between epigenetics and metabolism has been closely linked to tumor phenotype. Chromatin remodelers interact with each other to control gene silencing in BC, but also with stress-inducible factors or oncogenic signaling cascades to regulate metabolic reprogramming towards glycolysis, the pentose phosphate pathway, and lipogenesis. Concurrently, one-carbon metabolism supplies methyl groups to histone and DNA methyltransferases, leading to the hypermethylation and silencing of suppressor genes in BC. Conversely, α-KG and acetyl-CoA enhance the activity of histone demethylases and acetyl transferases, increasing gene expression, while succinate and fumarate have an inhibitory role. This review is the first to analyze the interplay between epigenome, metabolome and cell signaling pathways in BC, and shows how their regulation contributes to tumor development and progression. Moreover, it summarizes non-invasive biomarkers that could be applied in clinical practice to improve diagnosis, monitoring, prognosis and the therapeutic options in BC.

Early Breast Cancer Detection Using Untargeted and Targeted Metabolomics

Breast cancer (BC) is a common cause of morbidity and mortality, particularly in women. Moreover, the discovery of diagnostic biomarkers for early BC remains a challenging task. Previously, we [Jasbi et al. J. Chromatogr. B. 2019, 1105, 26-37] demonstrated a targeted metabolic profiling approach capable of identifying metabolite marker candidates that could enable highly sensitive and specific detection of BC. However, the coverage of this targeted method was limited and exhibited suboptimal classification of early BC (EBC). To expand the metabolome coverage and articulate a better panel of metabolites or mass spectral features for classification of EBC, we evaluated untargeted liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) data, both individually as well as in conjunction with previously published targeted LC-triple quadruple (QQQ)-MS data. Variable importance in projection scores were used to refine the biomarker panel, whereas orthogonal partial least squares-discriminant analysis was used to operationalize the enhanced biomarker panel for early diagnosis. In this approach, 33 altered metabolites/features were detected by LC-QTOF-MS from 124 BC patients and 86 healthy controls. For EBC diagnosis, significance testing and analysis of the area under receiver operating characteristic (AUROC) curve identified six metabolites/features [ethyl (R)-3-hydroxyhexanoate; caprylic acid; hypoxanthine; and m/z 358.0018, 354.0053, and 356.0037] with p < 0.05 and AUROC > 0.7. These metabolites informed the construction of EBC diagnostic models; evaluation of model performance for the prediction of EBC showed an AUROC = 0.938 (95% CI: 0.895-0.975), with sensitivity = 0.90 when specificity = 0.90. Using the combined untargeted and targeted data set, eight metabolic pathways of potential biological relevance were indicated to be significantly altered as a result of EBC. Metabolic pathway analysis showed fatty acid and aminoacyl-tRNA biosynthesis as well as inositol phosphate metabolism to be most impacted in response to the disease. The combination of untargeted and targeted metabolomics platforms has provided a highly predictive and accurate method for BC and EBC diagnosis from plasma samples. Furthermore, such a complementary approach yielded critical information regarding potential pathogenic mechanisms underlying EBC that, although critical to improved prognosis and enhanced survival, are understudied in the current literature. All mass spectrometry data and deidentified subject metadata analyzed in this study have been deposited to Mendeley Data and are publicly available (DOI: 10.17632/kcjg8ybk45.1).

Metabolomics and cardiovascular imaging: a combined approach for cardiovascular ageing

The purpose of this review is to explore how metabolomics can help uncover new biomarkers and mechanisms for cardiovascular ageing. Cardiovascular ageing refers to cardiovascular structural and functional alterations that occur with chronological ageing and that can lead to the development of cardiovascular disease. These alterations, which were previously only detectable on tissue histology or corroborated on blood samples, are now detectable with modern imaging techniques. Despite the emergence of powerful new imaging tools, clinical investigation into cardiovascular ageing is challenging because ageing is a life course phenomenon involving known and unknown risk factors that play out in a dynamic fashion. Metabolomic profiling measures large numbers of metabolites with diverse chemical properties. Metabolomics has the potential to capture changes in biochemistry brought about by pathophysiologic processes as well as by normal ageing. When combined with non-invasive cardiovascular imaging tools, metabolomics can be used to understand pathological consequences of cardiovascular ageing. This review will summarize previous metabolomics and imaging studies in cardiovascular ageing. These methods may be a clinically relevant and novel approach to identify mechanisms of cardiovascular ageing and formulate or personalize treatment strategies.

Discrepancies in metabolomic biomarker identification from patient-derived lung cancer revealed by combined variation in data pre-treatment and imputation methods

INTRODUCTION

The identification of metabolomic biomarkers predictive of cancer patient response to therapy and of disease stage has been pursued as a "holy grail" of modern oncology, relying on the metabolic dysfunction that characterizes cancer progression. In spite of the evaluation of many candidate biomarkers, however, determination of a consistent set with practical clinical utility has proven elusive.

OBJECTIVE

In this study, we systematically examine the combined role of data pre-treatment and imputation methods on the performance of multivariate data analysis methods and their identification of potential biomarkers.

METHODS

Uniquely, we are able to systematically evaluate both unsupervised and supervised methods with a metabolomic data set obtained from patient-derived lung cancer core biopsies with true missing values. Eight pre-treatment methods, ten imputation methods, and two data analysis methods were applied in combination.

RESULTS

The combined choice of pre-treatment and imputation methods is critical in the definition of candidate biomarkers, with deficient or inappropriate selection of these methods leading to inconsistent results, and with important biomarkers either being overlooked or reported as a false positive. The log transformation appeared to normalize the original tumor data most effectively, but the performance of the imputation applied after the transformation was highly dependent on the characteristics of the data set.

CONCLUSION

The combined choice of pre-treatment and imputation methods may need careful evaluation prior to metabolomic data analysis of human tumors, in order to enable consistent identification of potential biomarkers predictive of response to therapy and of disease stage.

Molecular Research on Stress Responses in Quercus spp.: From Classical Biochemistry to Systems Biology through Omics Analysis

The genus Quercus (oak), family Fagaceae, comprises around 500 species, being one of the most important and dominant woody angiosperms in the Northern Hemisphere. Nowadays, it is threatened by environmental cues, which are either of biotic or abiotic origin. This causes tree decline, dieback, and deforestation, which can worsen in a climate change scenario. In the 21st century, biotechnology should take a pivotal role in facing this problem and proposing sustainable management and conservation strategies for forests. As a non-domesticated, long-lived species, the only plausible approach for tree breeding is exploiting the natural diversity present in this species and the selection of elite, more resilient genotypes, based on molecular markers. In this direction, it is important to investigate the molecular mechanisms of the tolerance or resistance to stresses, and the identification of genes, gene products, and metabolites related to this phenotype. This research is being performed by using classical biochemistry or the most recent omics (genomics, epigenomics, transcriptomics, proteomics, and metabolomics) approaches, which should be integrated with other physiological and morphological techniques in the Systems Biology direction. This review is focused on the current state-of-the-art of such approaches for describing and integrating the latest knowledge on biotic and abiotic stress responses in Quercus spp., with special reference to Quercus ilex, the system on which the authors have been working for the last 15 years. While biotic stress factors mainly include fungi and insects such as Phytophthora cinnamomi, Cerambyx welensii, and Operophtera brumata, abiotic stress factors include salinity, drought, waterlogging, soil pollutants, cold, heat, carbon dioxide, ozone, and ultraviolet radiation. The review is structured following the Central Dogma of Molecular Biology and the omic cascade, from DNA (genomics, epigenomics, and DNA-based markers) to metabolites (metabolomics), through mRNA (transcriptomics) and proteins (proteomics). An integrated view of the different approaches, challenges, and future directions is critically discussed.

Serum Metabolomic Profiling to Reveal Potential Biomarkers for the Diagnosis of Fatty Liver Hemorrhagic Syndrome in Laying Hens

Fatty liver hemorrhage syndrome (FLHS), a nutritional and metabolic disease that frequently occurs in laying hens, causes serious losses to the poultry industry. Nowadays, the traditional clinical diagnosis of FLHS still has its limitations. Therefore, searching for some metabolic biomarkers and elucidating the metabolic pathway in vivo are useful for the diagnosis and prevention of FLHS. In the present study, a model of FLHS in laying hens induced by feeding a high-energy, low-protein diet was established. Gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) was used to analyze the metabolites in serum at days 40 and 80. The result showed that, in total, 40 differential metabolites closely related to the occurrence and development of FLHS were screened and identified, which were mainly associated with lipid metabolism, amino acid metabolism, and energy metabolism pathway disorders. Further investigation of differential metabolites showed 10 potential biomarkers such as 3-hydroxybutyric acid, oleic acid, palmitoleic acid, and glutamate were possessed of high diagnostic values by analyzing receiver operating characteristic (ROC) curves. In conclusion, this study showed that the metabolomic method based on GC-TOF-MS can be used in the clinical diagnosis of FLHS in laying hens and provide potential biomarkers for early risk evaluation of FLHS and further insights into FLHS development.

Novel Metabolic Signatures of Prostate Cancer Revealed by 1H-NMR Metabolomics of Urine

Prostate cancer (PC) is one of the most common male cancers worldwide. Until now, there is no consensus about using urinary metabolomic profiling as novel biomarkers to identify PC. In this study, urine samples from 50 PC patients and 50 non-cancerous individuals (control group) were collected. Based on 1H nuclear magnetic resonance (1H-NMR) analysis, 20 metabolites were identified. Subsequently, principal component analysis (PCA), partial least squares-differential analysis (PLS-DA) and ortho-PLS-DA (OPLS-DA) were applied to find metabolites to distinguish PC from the control group. Furthermore, Wilcoxon test was used to find significant differences between the two groups in metabolite urine levels. Guanidinoacetate, phenylacetylglycine, and glycine were significantly increased in PC, while L-lactate and L-alanine were significantly decreased. The receiver operating characteristics (ROC) analysis revealed that the combination of guanidinoacetate, phenylacetylglycine, and glycine was able to accurately differentiate 77% of the PC patients with sensitivity = 80% and a specificity = 64%. In addition, those three metabolites showed significant differences in patients stratified for Gleason score 6 and Gleason score ≥7, indicating potential use to detect significant prostate cancer. Pathway enrichment analysis using the KEGG (Kyoto Encyclopedia of Genes and Genomes) and the SMPDB (The Small Molecule Pathway Database) revealed potential involvement of KEGG "Glycine, Serine, and Threonine metabolism" in PC. The present study highlights that guanidinoacetate, phenylacetylglycine, and glycine are potential candidate biomarkers of PC. To the best knowledge of the authors, this is the first study identifying guanidinoacetate, and phenylacetylglycine as potential novel biomarkers in PC.

Investigating metabolism by mass spectrometry: From steady state to dynamic view

Metabolism is the set of life-sustaining reactions in organisms. These biochemical reactions are organized in metabolic pathways, in which one metabolite is converted through a series of steps catalyzed by enzymes in another chemical compound. Metabolic reactions are categorized as catabolic, the breaking down of metabolites to produce energy, and/or anabolic, the synthesis of compounds that consume energy. The balance between catabolism of the preferential fuel substrate and anabolism defines the overall metabolism of a cell or tissue. Metabolomics is a powerful tool to gain new insights contributing to the identification of complex molecular mechanisms in the field of biomedical research, both basic and translational. The enormous potential of this kind of analyses consists of two key aspects: (i) the possibility of performing so-called targeted and untargeted experiments through which it is feasible to verify or formulate a hypothesis, respectively, and (ii) the opportunity to run either steady-state analyses to have snapshots of the metabolome at a given time under different experimental conditions or dynamic analyses through the use of labeled tracers. In this review, we will highlight the most important practical (e.g., different sample extraction approaches) and conceptual steps to consider for metabolomic analysis, describing also the main application contexts in which it is used. In addition, we will provide some insights into the most innovative approaches and progress in the field of data analysis and processing, highlighting how this part is essential for the proper extrapolation and interpretation of data.

Untargeted GC-MS investigation of serum metabolomics of coronary artery disease patients

Recent advances in metabolomics provide tools to investigate human metabolome in order to establish new parameters to study different approaches towards diagnostics, diseases and their treatment. The present study focused on the untargeted identification of metabolites in serum of patients with coronary artery disease who were under treatment at the time of sample collection. AUCs (Area Under the Curves) from different peaks were considered for the analysis and comparison purposes. The metabolome was studied using GC–MS (Gas Chromatography Mass Spectrometry) and the metabolites were identified with NIST (The National Institute of Standards and Technology) and Wiley library matches. A total of 17 metabolites were identified and focused on to compare with the metabolome of healthy individuals. T test analysis found significant differences in alanine, malonic acid, ribitol, D-glucose, mannose (P < 0.001), acetohydroxamic acid, N-carboxyglycine, and aminobutyrate (P < 0.05). Principal Component Analysis of serum metabolites data found three components out of 17 metabolites; RC1 (Acetohydroxamic acid, alanine, D-glucose, malonic acid, mannose, N-carboxy glycine and ribitol), RC2 (Heptadecanoic acid, hexadecanoic acid, octadecanoic acid and Trans-9-octadecanoic acid), RC3 (Aminobutyrate, D-sorbit, gamma lactone, valine, benzene propanoic acid and lactic acid). No correlation was found among the components.

A Comparative Evaluation of Tools to Predict Metabolite Profiles From Microbiome Sequencing Data

Metabolomic analyses of human gut microbiome samples can unveil the metabolic potential of host tissues and the numerous microorganisms they support, concurrently. As such, metabolomic information bears immense potential to improve disease diagnosis and therapeutic drug discovery. Unfortunately, as cohort sizes increase, comprehensive metabolomic profiling becomes costly and logistically difficult to perform at a large scale. To address these difficulties, we tested the feasibility of predicting the metabolites of a microbial community based solely on microbiome sequencing data. Paired microbiome sequencing (16S rRNA gene amplicons, shotgun metagenomics, and metatranscriptomics) and metabolome (mass spectrometry and nuclear magnetic resonance spectroscopy) datasets were collected from six independent studies spanning multiple diseases. We used these datasets to evaluate two reference-based gene-to-metabolite prediction pipelines and a machine-learning (ML) based metabolic profile prediction approach. With the pre-trained model on over 900 microbiome-metabolome paired samples, the ML approach yielded the most accurate predictions (i.e., highest F1 scores) of metabolite occurrences in the human gut and outperformed reference-based pipelines in predicting differential metabolites between case and control subjects. Our findings demonstrate the possibility of predicting metabolites from microbiome sequencing data, while highlighting certain limitations in detecting differential metabolites, and provide a framework to evaluate metabolite prediction pipelines, which will ultimately facilitate future investigations on microbial metabolites and human health.

Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

A discovery-based lipid profiling study of serum samples from a cohort that included patients with clear cell renal cell carcinoma (ccRCC) stages I, II, III, and IV (n = 112) and controls (n = 52) was performed using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry and machine learning techniques. Multivariate models based on support vector machines and the LASSO variable selection method yielded two discriminant lipid panels for ccRCC detection and early diagnosis. A 16-lipid panel allowed discriminating ccRCC patients from controls with 95.7% accuracy in a training set under cross-validation and 77.1% accuracy in an independent test set. A second model trained to discriminate early (I and II) from late (III and IV) stage ccRCC yielded a panel of 26 compounds that classified stage I patients from an independent test set with 82.1% accuracy. Thirteen species, including cholic acid, undecylenic acid, lauric acid, LPC(16:0/0:0), and PC(18:2/18:2), identified with level 1 exhibited significantly lower levels in samples from ccRCC patients compared to controls. Moreover, 3α-hydroxy-5α-androstan-17-one 3-sulfate, cis-5-dodecenoic acid, arachidonic acid, cis-13-docosenoic acid, PI(16:0/18:1), PC(16:0/18:2), and PC(O-16:0/20:4) contributed to discriminate early from late ccRCC stage patients. The results are auspicious for early ccRCC diagnosis after validation of the panels in larger and different cohorts.

Metabolic Profiles of Whole Serum and Serum-Derived Exosomes Are Different in Head and Neck Cancer Patients Treated by Radiotherapy

BACKGROUND

In general, the serum metabolome reflects the patient's body response to both disease state and implemented treatment. Though serum-derived exosomes are an emerging type of liquid biopsy, the metabolite content of these vesicles remains under researched. The aim of this pilot study was to compare the metabolite profiles of the whole serum and serum-derived exosomes in the context of differences between cancer patients and healthy controls as well as patients' response to radiotherapy (RT).

METHODS

Serum samples were collected from 10 healthy volunteers and 10 patients with head and neck cancer before and after RT. Metabolites extracted from serum and exosomes were analyzed by the gas chromatography-mass spectrometry (GC-MS).

RESULTS

An untargeted GC-MS-based approach identified 182 and 46 metabolites in serum and exosomes, respectively. Metabolites that differentiated cancer and control samples, either serum or exosomes, were associated with energy metabolism. Serum metabolites affected by RT were associated with the metabolism of amino acids, sugars, lipids, and nucleotides.

CONCLUSIONS

cancer-related features of energy metabolism could be detected in both types of specimens. On the other hand, in contrast to RT-induced changes observed in serum metabolome, this pilot study did not reveal a specific radiation-related pattern of exosome metabolites.

Comprehensive Metabolomics Analysis of Nontargeted LC-HRMS Data Provides Valuable Insights Regarding the Origin of Veterinary Drug Residues

Liquid chromatography coupled with high resolution mass spectrometry (LC-HRMS) complements standard triple-quadrupole mass spectrometry in veterinary drug residue control. LC-HRMS offers the opportunity for nontargeted screening for metabolites and biomarkers representing metabolic changes. In this work, the feasibility of a nontargeted metabolomics approach based on LC-HRMS data (LC-Q-Orbitrap and LC-Q-TOF) to distinguish between porcine muscle tissue from infected animals and from healthy animals is demonstrated. The differences arise from various compounds associated with metabolic changes in infected animals. Two new biomarker candidates have been identified: tripeptide prolyphenylalanylglycine and a lysophosphatidylcholine derivative. For the first time, a bivariate data analysis procedure is described that may be used to evaluate whether the presence of antibiotic residues points to a therapeutic application or may be the result of a contamination during sampling and/or analysis.

Analysis of urinary VOCs using mass spectrometric methods to diagnose cancer: A review

The development of non-invasive screening techniques for early cancer detection is one of the greatest scientific challenges of the 21st century. One promising emerging method is the analysis of volatile organic compounds (VOCs). VOCs are low molecular weight substances generated as final products of cellular metabolism and emitted through a variety of biological matrices, such as breath, blood, saliva and urine. Urine stands out for its non-invasive nature, availability in large volumes, and the high concentration of VOCs in the kidneys. This review provides an overview of the available data on urinary VOCs that have been investigated in cancer-focused clinical studies using mass spectrometric (MS) techniques. A literature search was conducted in ScienceDirect, Pubmed and Web of Science, using the keywords "Urinary VOCs", "VOCs biomarkers" and "Volatile cancer biomarkers" in combination with the term "Mass spectrometry". Only studies in English published between January 2011 and May 2020 were selected. The three most evaluated types of cancers in the reviewed studies were lung, breast and prostate, and the most frequently identified urinary VOC biomarkers were hexanal, dimethyl disulfide and phenol; with the latter seeming to be closely related to breast cancer. Additionally, the challenges of analyzing urinary VOCs using MS-based techniques and translation to clinical utility are discussed. The outcome of this review may provide valuable information to future studies regarding cancer urinary VOCs.

Metabolite Profiling and Network Analysis Reveal Coordinated Changes in Low-N Tolerant and Low-N Sensitive Maize Genotypes under Nitrogen Deficiency and Restoration Conditions

Nitrogen (N), applied in the form of a nitrogenous fertilizer, is one of the main inputs for agricultural production. Food production is closely associated with the application of N. However, the application of nitrogenous fertilizers to agricultural fields is associated with heavy production of nitrous oxide because agricultural crops can only utilize 30-40% of applied N, leaving behind unused 60-70% N in the environment. The global warming effect of this greenhouse gas is approximately 300 times more than of carbon dioxide. Under the present scenario of climate change, it is critical to maintain the natural balance between food production and environmental sustainability by targeting traits responsible for improving nitrogen-use-efficiency (NUE). Understanding of the molecular mechanisms behind the metabolic alterations due to nitrogen status needs to be addressed. Additionally, mineral nutrient deficiencies and their associated metabolic networks have not yet been studied well. Given this, the alterations in core metabolic pathways of low-N tolerant (LNT) and low-N sensitive (LNS) genotypes of maize under N-deficiency and their efficiency of recovering the changes upon resupplying N were investigated by us, using the GC-MS and LC-MS based metabolomic approach. Significant genotype-specific changes were noted in response to low-N. The N limitation affected the whole plant metabolism, most significantly the precursors of primary metabolic pathways. These precursors may act as important targets for improving the NUE. Limited availability of N reduced the levels of N-containing metabolites, organic acids and amino acids, but soluble sugars increased. Major variations were encountered in LNS, as compared to LNT. This study has revealed potential metabolic targets in response to the N status, which are indeed the prospective targets for crop improvement.

Impact of Pre-blood Collection Factors on Plasma Metabolomic Profiles

Demographic, lifestyle and biospecimen-related factors at the time of blood collection can influence metabolite levels in epidemiological studies. Identifying the major influences on metabolite concentrations is critical to designing appropriate sample collection protocols and considering covariate adjustment in metabolomics analyses. We examined the association of age, sex, and other short-term pre-blood collection factors (time of day, season, fasting duration, physical activity, NSAID use, smoking and alcohol consumption in the days prior to collection) with 133 targeted plasma metabolites (acylcarnitines, amino acids, biogenic amines, sphingolipids, glycerophospholipids, and hexoses) among 108 individuals that reported exposures within 48 h before collection. The differences in mean metabolite concentrations were assessed between groups based on pre-collection factors using two-sided t-tests and ANOVA with FDR correction. Percent differences in metabolite concentrations were negligible across season, time of day of collection, fasting status or lifestyle behaviors at the time of collection, including physical activity or the use of tobacco, alcohol or NSAIDs. The metabolites differed in concentration between the age and sex categories for 21.8% and 14.3% metabolites, respectively. In conclusion, extrinsic factors in the short period prior to collection were not meaningfully associated with concentrations of selected endogenous metabolites in a cross-sectional sample, though metabolite concentrations differed by age and sex. Larger studies with more coverage of the human metabolome are warranted.

1H-NMR based metabolomic profiling of cord blood in gestational hypothyroidism

Background

Gestational hypothyroidism (GHT) is a common pregnancy-related thyroid disfunction. The adverse outcomes by GHT has been increasingly recognized, leading to more public awareness of the disease. However, comprehensive understanding of the prognosis of GHT has not yet achieved. Metabolomics is a powerful tool in evaluation of disease outcomes, and cord blood represents an excellent candidate for the investigation of gestational outcomes.

Methods

In the present study, we performed 1H-NMR based metabolomics on cord blood of 18 pregnant women with GHT and 18 non hypothyroidism (NHT) control.

Results

The metabolomic profile of GHT was separated with the NHT control. A total of 8 metabolites with altered abundances were observed, among which Creatinine and O-Phosphocholine were elevated and the others were downregulated in GHT. Spearman rank correlation suggested that the eight differential metabolites were correlated with the GHT related thyroid hormones. Pathway analysis of the differential metabolites indicated that two metabolic pathways were significantly altered in GHT (adjusted P<0.05), including tyrosine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis. Enrichment analysis of the differential metabolites against disease-associated metabolite sets suggested that GHT was associated with disease risks of non-insulin dependent diabetes mellitus, isovaleric acidemia, and methylmalonic aciduria.

Conclusions

The results of this study revealed GHT associated metabolic changes in cord blood, providing insights into the metabolic intermediates between GHT and its related disease risks.

Breath Metabolomics Provides an Accurate and Noninvasive Approach for Screening Cirrhosis, Primary, and Secondary Liver Tumors

Hepatocellular carcinoma (HCC) and secondary liver tumors, such as colorectal cancer liver metastases are significant contributors to the overall burden of cancer‐related morality. Current biomarkers, such as alpha‐fetoprotein (AFP) for HCC, result in too many false negatives, necessitating noninvasive approaches with improved sensitivity. Volatile organic compounds (VOCs) detected in the breath of patients can provide valuable insight into disease processes and can differentiate patients by disease status. Here, we investigate whether 22 VOCs from the breath of 296 patients can distinguish those with no liver disease (n = 54), cirrhosis (n = 30), HCC (n = 112), pulmonary hypertension (n = 49), or colorectal cancer liver metastases (n = 51). This work extends previous studies by evaluating the ability for VOC signatures to differentiate multiple diseases in a large cohort of patients. Pairwise disease comparisons demonstrated that most of the VOCs tested are present in significantly different relative abundances (false discovery rate P < 0.1), indicating broad impacts on the breath metabolome across diseases. A predictive model developed using random forest machine learning and cross validation classified patients with 85% classification accuracy and 75% balanced accuracy. Importantly, the model detected HCC with 73% sensitivity compared with 53% for AFP in the same cohort. An added value of this approach is that influential VOCs in the predictive model may provide insight into disease etiology. Acetaldehyde and acetone, both of which have roles in tumor promotion, were considered important VOCs for differentiating disease groups in the predictive model and were increased in patients with cirrhosis and HCC compared to patients with no liver disease (false discovery rate P < 0.1). Conclusion: The use of machine learning and breath VOCs shows promise as an approach to develop improved, noninvasive screening tools for chronic liver disease and primary and secondary liver tumors.

Untargeted Metabolomics Analysis of Esophageal Squamous Cell Carcinoma Discovers Dysregulated Metabolic Pathways and Potential Diagnostic Biomarkers

Background: Esophageal squamous cell carcinoma (ESCC) is one of the most fatal diseases worldwide. Because early diagnosis is difficult, ESCC is mostly diagnosed at an advanced stage, leading to a poor overall prognosis. The purpose of this study was to explore the differences between plasma metabolic profiles in ESCC patients and healthy controls and to establish a diagnostic model of ESCC.

Methods: In this study, a cohort of 310 subjects, containing 140 ESCC patients and 170 healthy controls (HC), was recruited. Participants were randomly separated into a training set (80 ESCCs, 80 HCs) and a validation set (60 ESCCs, 90 HCs) and their plasma metabolomics profiles were analyzed by ultra-performance liquid chromatography-tandem quadruple time-of-flight mass spectrometry (UPLC-QTOF/MS) technique. Univariate statistical analysis and multivariate analysis (MVA) methods were used to identify differential metabolites. Finally, the dysregulated pathways associated with ESCC were further explored and the diagnostic performance of the biomarker panel was evaluated.

Results: Metabolic analyses identified 34 significant metabolites involved in the metabolism of amino acids, phospholipids, fatty acids, purine, and choline. Farthermore, an effective diagnostic model for ESCC was constructed based on eight metabolites. This panel of biomarkers consisted of hypoxanthine, proline betaine, indoleacrylic acid, inosine, 9-decenoylcarnitine, tetracosahexaenoic acid, LPE (20:4), and LPC (20:5). The model was verified and evaluated in the validation set. The AUC value of the ROC curve was 0.991(95% CI: 0.981-1.000, CI, Confidence interval), with a sensitivity (SE) of 98.8% and a specificity (SP) of 94.9% for the training set and 0.965(95% CI: 0.936-0.993), with a SE of 88.3% and a SP of 88.9% for the validation set. Among them, three biomarkers, indoleacrylic acid, LPC (20:5), and LPE (20:4), exhibited a trend associated with the ESCC progression.

Conclusions: Our study identified a novel plasma biomarker panel, which clearly distinguishes ESCC patients and provides insight into the mechanisms of ESCC. This finding may form the basis for the development of a minimally invasive method for ESCC detection.

Update on urine as a biomarker in cancer: a necessary review of an old story

Introduction: Cancer causes thousands of deaths worldwide each year. Therefore, monitoring of health status and the early diagnosis of cancer using noninvasive assays, such as the analysis of molecular biomarkers in urine, is essential. However, effective biomarkers for early diagnosis of cancer have not been established in many types of cancer.Areas covered: In this review, we discuss recent findings with regard to the use of urine composition as a biomarker in eleven types of cancer. We also highlight the use of urine biomarkers for improving early diagnosis.Expert opinion: Urinary biomarkers have been applied for clinical application of early diagnosis. The main limitation is a lack of integrated approaches for identification of new biomarkers in most cancer. The utilization of urinary biomarker detection will be promoted by improved detection methods and new data from different types of cancers. With the development of precision medicine, urinary biomarkers will play an increasingly important clinical role. Future early diagnosis would benefit from changes in the utilization of urinary biomarkers.