Breath biomarkers for lung cancer detection and assessment of smoking related effects — confounding variables,influence of normalization and statistical algorithms

BackgroundUp to now, none of the breath biomarkers or marker sets proposed for cancer recognition has reached clinical relevance. Possible reasons are the lack of standardized methods of sampling, analysis and data processing and effects of environmental contaminants.MethodsConcentration profiles of endogenous and exogenous breath markers were determined in exhaled breath of 31 lung cancer patients, 31 smokers and 31 healthy controls by means of SPME-GC-MS. Different correcting and normalization algorithms and a principal component analysis were applied to the data.ResultsDifferences of exhalation profiles in cancer and non-cancer patients did not persist if physiology and confounding variables were taken into account. Smoking history, inspired substance concentrations, age and gender were recognized as the most important confounding variables. Normalization onto PCO₂ or BSA or correction for inspired concentrations only partially solved the problem. In contrast, previous smoking behaviour could be recognized unequivocally.ConclusionExhaled substance concentrations may depend on a variety of parameters other than the disease under investigation. Normalization and correcting parameters have to be chosen with care as compensating effects may be different from one substance to the other. Only well-founded biomarker identification, normalization and data processing will provide clinically relevant information from breath analysis.     

Explore potential plasma biomarkers of acute respiratory distress syndrome (ARDS) using GC–MS metabolomics analysis

ObjectivesThe aim of this study was to analyse the metabolomics of patients with acute respiratory distress syndrome (ARDS) for the identification of metabolic markers with potential diagnostic and prognostic value.MethodsThe enrolled subjects included adult patients with ARDS that met the Berlin definition and healthy controls matched based on age, gender, and body mass index (BMI). Plasma samples were collected from 37 patients with ARDS and 28 healthy controls. The plasma metabolites were detected with gas chromatography–mass spectrometry (GC–MS), and the relevant metabolic pathways were predicted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.ResultsA total of 222 metabolites were identified in our study, of which 128 were significantly altered in patients with ARDS compared with healthy controls. Phenylalanine, aspartic acid, and carbamic acid levels were significantly different between all groups of patients with ARDS classified from mild to severe. Furthermore, four metabolites, ornithine, caprylic acid, azetidine, and iminodiacetic acid, could serve as biomarkers to potentially predict the severity of ARDS. We discovered 92 pathways that were significantly different between ARDS and control groups, including 57 pathways linked to metabolism.ConclusionsPlasma metabolomics may improve our understanding of ARDS biology. Specific products related to hypoxia may serve as early biomarkers for ARDS prediction, while the metabolites with significant correlations with partial pressure of arterial oxygen (PaO₂)/percentage of inspired oxygen (FiO₂) may play a role in determining ARDS severity. This study suggests that metabolomic analysis in patients at risk of ARDS or those with early ARDS may provide new insight into disease pathogenesis or prognosis.     

Discovery of potential therapeutic targets for non-small cell lung cancer using high-throughput metabolomics analysis based on liquid chromatography coupled with tandem mass spectrometry

Lung cancer is a severe health problem and threatens a patient''s quality of life. The metabolites present in biological systems are expected to be key mediators and the changes in these metabolites play an important role in promoting health. Metabolomics can unravel the global metabolic changes and identify significant biological pathways involved in disease development. However, the role of metabolites in lung cancer is still largely unknown. In the present study, we developed a liquid chromatography coupled with tandem mass spectrometry method for biomarker discovery and identification of non-small cell lung cancer (NSCLC) from metabolomics data sets and aimed to investigate the metabolic profiles of NSCLC samples to identify potential disease biomarkers and to reveal the pathological mechanism. After cell metabolite extraction, the metabolic changes in NSCLC cells were characterized and targeted metabolite analysis was adopted to offer a novel opportunity to probe into the relationship between differentially regulated cell metabolites and NSCLC. Quantitative analysis of key enzymes in the disturbed pathways by proteomics was employed to verify metabolomic pathway changes. A total of 13 specific biomarkers were identified in NSCLC cells related with metabolic disturbance of NSCLC morbidity, which were involved in 4 vital pathways, namely glycine, serine and threonine metabolism, aminoacyl-tRNA biosynthesis, tyrosine metabolism and sphingolipid metabolism. The proteomics analysis illustrated the obvious fluctuation of the expression of the key enzymes in these pathways, including the downregulation of 3-phosphoglycerate dehydrogenase, phosphoserine phosphatase, tyrosinase and argininosuccinic acid catenase. NSCLC occurrence is mainly related to amino acid and fatty acid metabolic alteration. These findings highlight that the metabolome can provide information on the molecular profiles of cells, which can aid in investigating the metabolite changes to reveal the pathological mechanism.

High-throughput metabolomics can discover potential therapeutic targets for non-small cell lung cancer.     

呼气冷凝液中3种肿瘤标志物联合检测在肺癌诊断中的价值研究

目的对呼气冷凝液（EBC）中3种肿瘤标志物进行联合检测,研究其在肺癌诊断中的临床价值,探讨该方法在临床应用的可能性,为今后的肺癌诊断和治疗效果评估提供可靠的参考依据。方法选择2011年1月15日至2012年12月15日该院肿瘤科收治的肺癌患者30例作为肺癌组,另抽取同期健康体检者30例作为健康对照组,对肺癌组患者治疗前后和健康对照组进行EBC及血清中癌胚抗原（CEA）、细胞角蛋白19的可溶性片段（CYFRA21—1）、血管内皮生长因子（VEGF）水平检测,并对比分析检测结果。采用标准EBC收集器收集EBC,CEA、CYFRA21—1采用化学发光法,VEGF用酶联免疫吸附法测定。结果肺癌组患者EBC及血清中CEA、CY—FRA21-1、VEGF检测水平明显高于对照组（P〈0．05）,化疗后CEA、CYFRA21-1、VEGF水平较治疗前显著降低（P〈O．05）,EBC中cEA、CYFRA21-1、VEGF水平较血清中低（P〈0．05）。结论对EBC中CEA、CYFRA21—1、vEGF水平进行检测对于肺癌的诊断、病理分型和疗效判断均具有重要的参考价值,EBC检测结果与血清结果联合可实现相互补充,提高阳性率,值得关注。     

Prognostic relationship of metabolic profile obtained of melanoma B16F10

Melanoma is a type of cancer that reaches more people in the world, characterized by genetic mutations that trigger the growth of disorganized cells. The diagnosis of skin tumors by invasive techniques has become a risk to the patients, so the search for new non-invasive techniques has been the subject of research in recent years. The objective of this work is to propose a non-invasive method prognosis based on the identification of specific biomarkers of the cancer, known as metabolomics analysis. For this study, we used B16F10 melanoma tumor cells and metabolic profiles were obtained at three time-periods by ¹H NMR and comparison with the cell cycle, apoptosis pathways and proliferation index. The metabolic profiles show the relationship between the metabolites found with energy metabolism, pathways of apoptosis and proliferation, which showed increases in proportion during growth and progression. Were found 29 metabolites, of which the differentially expressed are: lactate, aspartate, glycerol, lipids, alanine, myo-inositol, phosphocholine, choline, acetate, creatine and taurine. Choline and creatine are closely related with tumor progression, and are inversely expressed in later stages of tumor growth, which demonstrates the ability to be markers of tumor progression or monitoring the pharmacological efficacy when combined with other therapies. We conclude that the metabolome appeared as effective non-invasive technique predicts, besides providing possible biomarkers of melanoma.     

Metabolomic analysis of 92 pulmonary embolism patients from a nested case–control study identifies metabolites associated with adverse clinical outcomes

Essentials

• Risk‐stratification often fails to predict clinical deterioration in pulmonary embolism (PE).
• First‐ever high‐throughput metabolomics analysis of risk‐stratified PE patients.
• Changes in circulating metabolites reflect a compromised energy metabolism in PE.
• Metabolites play a key role in the pathophysiology and risk stratification of PE.

Summary

Background

Patients with acute pulmonary embolism (PE) exhibit wide variation in clinical presentation and outcomes. Our understanding of the pathophysiologic mechanisms differentiating low‐risk and high‐risk PE is limited, so current risk‐stratification efforts often fail to predict clinical deterioration and are insufficient to guide management.

Objectives

To improve our understanding of the physiology differentiating low‐risk from high‐risk PE, we conducted the first‐ever high‐throughput metabolomics analysis (843 named metabolites) comparing PE patients across risk strata within a nested case–control study.

Patients/methods

We enrolled 92 patients diagnosed with acute PE and collected plasma within 24 h of PE diagnosis. We used linear regression and pathway analysis to identify metabolites and pathways associated with PE risk‐strata.

Results

When we compared 46 low‐risk with 46 intermediate/high‐risk PEs, 50 metabolites were significantly different after multiple testing correction. These metabolites were enriched in the following pathways: tricarboxylic acid (TCA) cycle, fatty acid metabolism (acyl carnitine) and purine metabolism, (hypo)xanthine/inosine containing. Additionally, energy, nucleotide and amino acid pathways were downregulated in intermediate/high‐risk PE patients. When we compared 28 intermediate‐risk with 18 high‐risk PE patients, 41 metabolites differed at a nominal P‐value level. These metabolites were enriched in fatty acid metabolism (acyl cholines), and hemoglobin and porphyrin metabolism.

Conclusion

Our results suggest that high‐throughput metabolomics can provide insight into the pathophysiology of PE. Specifically, changes in circulating metabolites reflect compromised energy metabolism in intermediate/high‐risk PE patients. These findings demonstrate the important role metabolites play in the pathophysiology of PE and highlight metabolomics as a potential tool for risk stratification of PE.

     

Serum metabolomic alterations in multiple myeloma revealed by targeted and untargeted metabolomics approaches: a pilot study

Multiple myeloma (MM) is the second most prevalent hematological malignancy characterized by rapid proliferation of plasma cells, which leads to overproduction of antibodies. MM affects around 15% of all hemato-oncology cases across the world. The present study involves identification of metabolomic alterations in the serum of an MM cohort compared to healthy controls using both LC-MRM/MS based targeted and GC-MS based untargeted approaches. Several MM specific serum metabolomic signatures were observed in this study. A total of 54 metabolites were identified as being significantly altered in MM cohort, out of which, 26 metabolites were identified from LC-MRM/MS based targeted analysis, whereas 28 metabolites were identified from the GC-MS based untargeted analysis. Receiver operating characteristic (ROC) curve analysis demonstrated that six metabolites each from both the datasets can be projected as marker metabolites to discriminate MM subjects with higher specificity and sensitivity. Moreover, pathway analysis deciphered that several metabolic pathways were altered in MM including pyrimidine metabolism, purine metabolism, amino acid metabolism, nitrogen metabolism, sulfur metabolism, and the citrate cycle. Comprehensively, this study contributes valuable information regarding MM induced serum metabolite alterations and their pathways, which could offer further insights into this cancer.

This study presents the potential of serum metabolomics approach towards the segregation of multiple myeloma cohort from healthy controls.     

Exhaled breath analysis for early cancer detection: principle and progress in direct mass spectrometry techniques

Abstract Volatile biomarker analysis in exhaled breath is becoming one of the desirable strategies for cancer detection because it may offer a relatively inexpensive, rapid, and non-invasive screening method for early diagnosis. Breath analysis has attracted a considerable amount of scientific and clinical interest over the past decade. However, breath is not yet used for routine medical diagnostic purposes. Challenges faced in the development of breath analysis for cancer diagnosis include developing techniques that can measure biomarkers in exhaled breath at ultratrace levels, providing definitive evidence for their presence and for the relationship between the proposed biomarker and the underlying condition. Various analytical methods are used for the detection of breath biomarkers. Gas chromatography-based methods which involve sample collection, analyte preconcentration, desorption, and separation steps are the most popular. However, direct-sampling mass spectrometry techniques have been proven more reliable for air analysis without prior sample pretreatment or chromatographic separation. This review focuses on the most commonly used direct mass spectrometry methods for the direct online analysis of endogenous cancer biomarkers in exhaled breath, with particular attention to principle of detection, method performance, advantages, shortcomings, recent advances, and applications within health-related studies for cancer biomarkers research. The principle behind the science of breath analysis for cancer diagnosis is also discussed.     

Metabolomic profiling of extraesophageal reflux disease in children

Although respiratory symptoms in children are often attributed to gastroesophageal reflux disease, establishing a clear diagnosis of extraesophageal reflux disease (EERD) can be challenging, as there are no sensitive or specific EERD biomarkers. The aim of this study was to evaluate the metabolite profile in bronchoalveolar (BAL) fluid from children with suspected EERD and assess the impact of reflux treatment on these metabolites. In this prospective pilot study, we performed nontargeted global metabolomic profiling on BAL fluid from 43 children undergoing testing with bronchoscopy, upper endoscopy, and multichannel intraluminal impedance with pH (pH‐MII) for evaluation of chronic respiratory symptoms. Twenty‐three (54%) patients had an abnormal pH‐MII study. Seventeen (40%) patients were on proton pump inhibitors (PPIs) for testing. Levels of histamine, malate, adenosine 5′‐monophosphate, and ascorbate were significantly lower in subjects with abnormal pH‐MII studies compared to those normal studies. Furthermore, in children off PPI therapy, those with abnormal pH‐MII studies had robust increases in a number of glycerophospholipids within phospholipid metabolic pathways, including derivatives of glycerophosphorylcholine, glycerophosphoglycerol, and glycerophosphoinositol, compared to those with normal pH‐MII studies. These findings offer insight into the impact of reflux and PPIs on the lungs and provide a foundation for future studies using targeted metabolomic analysis to identify potential biomarkers of EERD.

Study Highlights

• WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?

Establishing an association between gastroesophageal reflux disease and respiratory disease in children is challenging as there are no sensitive or specific biomarkers for extraesophageal reflux disease (EERD). Metabolomic analysis of bronchoalveolar lavage (BAL) fluid has offered promising insight into possible biomarkers for a variety of respiratory diseases but has never been studied in children with suspected EERD.

• WHAT QUESTION DID THIS STUDY ADDRESS?

This study addressed the questions of (1) whether there are differences in BAL metabolites in children with abnormal reflux testing with multichannel intraluminal impedance with pH and (2) whether reflux therapy with proton pump inhibitors was associated with different metabolite profiles.

• WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE?

We found that there were significant increases in a number of glycerophospholipids within phospholipid metabolic pathways in the BAL of children with untreated reflux, suggesting that gastroesophageal reflux may impact the lung metabolome.

• HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE?

These findings may provide mechanistic insight into reflux‐induced lung injury and the impact of acid suppression medications on the lungs, which may help guide future biomarker discovery.     

Deciphering metabonomics biomarkers-targets interactions for psoriasis vulgaris by network pharmacology

Objectives: Psoriasis vulgaris is a chronic inflammatory and immune-mediated skin disease. 44 metabonomics biomarkers were identified by high-throughput liquid chromatography coupled to mass spectrometry in our previous work, but the roles of metabonomics biomarkers in the pathogenesis of psoriasis is unclear.

Methods: The metabonomics biomarker-enzyme network was constructed. The key metabonomics biomarkers and enzymes were screened out by network analysis. The binding affinity between each metabonomics biomarker and target was calculated by molecular docking. A binding energy-weighted polypharmacological index was introduced to evaluate the importance of target-related pathways.

Results: Long-chain fatty acids, phospholipids, Estradiol and NADH were the most important metabonomics biomarkers. Most key enzymes belonged hydrolase, thioesterase and acyltransferase. Six proteins (TNF-alpha, MAPK3, iNOS, eNOS, COX2 and mTOR) were extensively involved in inflammatory reaction, immune response and cell proliferation, and might be drug targets for psoriasis. PI3K-Akt signaling pathway and five other pathways had close correlation with the pathogenesis of psoriasis and could deserve further research.

Conclusions: The inflammatory reaction, immune response and cell proliferation are mainly involved in psoriasis. Network pharmacology provide a new insight into the relationships between metabonomics biomarkers and the pathogenesis of psoriasis.

• KEY MESSAGES

• ??Network pharmacology was adopted to identify key metabonomics biomarkers and enzymes.

• ??Six proteins were screened out as important drug targets for psoriasis.

• ??A binding energy-weighted polypharmacological index was introduced to evaluate the importance of target-related pathways.

     

Using high-throughput metabolomics to discover perturbed metabolic pathways and biomarkers of allergic rhinitis as potential targets to reveal the effects and mechanism of geniposide

In this study, we probed the molecular mechanisms of metabolic biomarkers and pathways affected by the bioactive ingredient geniposide (GP), which was shown to protect against experimental allergic rhinitis in mice. The methods used here involved a metabolomics strategy based on ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-TOF/MS). Using the metabolomics strategy, serum samples of mice in control, model and GP groups were used to explore the differential production of metabolites and pathways related to defense activity of GP towards allergic rhinitis. Allergic symptom, inflammatory factors, and cell populations in the mice spleens were reversed by GP treatment. Seventeen potential biomarkers were discovered in experimental allergic rhinitis mice. GP was shown to have a regulatory effect on 12 of them, which were associated with 8 key metabolic pathways. The ingenuity pathway analysis platform was used to further understand the relationship between metabolic changes and pharmacological activity of GP. The pathways which affected by GP involved cellular growth and proliferation, organismal development, and free radical scavenging. This metabolomics study produced valuable information about potential biomarkers and pathways affected by GP during its effective prevention and therapeutic targeting of allergic rhinitis.

In this study, we probed the molecular mechanisms of metabolic biomarkers and pathways affected by the bioactive ingredient geniposide (GP), which was shown to protect against experimental allergic rhinitis in mice.     

Advanced multimodal diagnostic approaches for detection of lung cancer

Introduction: Lung cancer (LC) emerges as a principle cause of death among smokers and is also one of the most lethal forms of cancer in nonsmokers. LC is mainly classified as non-small cell lung cancer (NSCLC), small cell LC, and lung carcinoid tumor. NSCLC is the most prevalent form of LC and its early stage diagnosis is essential to reduce mortality rate of patients and provide specific therapy. The field of LC diagnostics witnessed a gradual escalation with advancement in technology.

Areas covered: This comprehensive review focuses on classification of LC and advanced diagnostics for LC detection like biosensors, biomarkers, nanotechnology-based diagnostics, wearable devices, mobile health, artificial intelligence (AI), aptamers, and molecularly imprinted polymers (MIPs).

Expert opinion: Liquid biopsy and breath analysis developments are the most promising and advanced technologies for the detection of biomarkers associated with LC. Wearable devices and AI are two niche areas that require development and standardization for commercialization. The upcoming technology based on nanosystems includes robots, fibers, and particles for sensitive detection of LC. In the near future, nanotechnology-based theranostics, aptamers, and MIPs will emerge in early-stage diagnosis of LC.     

Differential expression profiling of microRNAs and their potential involvement in renal cell carcinoma pathogenesis

ObjectiveWe seek to identify the differentially expressed miRNAs in the clear cell subtype (ccRCC) of kidney cancer.Design and methodsWe performed a miRNA microarray analysis to compare the miRNA expression levels between ccRCC tissues and their normal counterpart. The top dysregulated miRNAs were validated by quantitative RT-PCR analysis. Bioinformatics analysis was also performed.ResultsA total of 33 dysregulated miRNAs were identified in ccRCC, including 21 upregulated miRNAs and many of these miRNAs have been reported to be dysregulated in other malignancies and have a potential role in cancer pathogenesis. The miRNAs showed a significant correlation with reported chromosomal aberration sites. We also utilized target prediction algorithms to identify gene targets. Preliminary analyses showed these targets can be directly involved in RCC pathogenesis.ConclusionWe identified miRNAs that are dysregulated in ccRCC and bioinformatics analysis suggests that these miRNAs may be involved in cancer pathogenesis and have the potential to be biomarkers.     

Development of a lipid metabolism-related gene model to predict prognosis in patients with pancreatic cancer

BACKGROUNDPancreatic cancer is a highly heterogeneous disease, making prognosis prediction challenging. Altered energy metabolism to satisfy uncontrolled proliferation and metastasis has become one of the most important markers of tumors. However, the specific regulatory mechanism and its effect on prognosis have not been fully elucidated.AIMTo construct a prognostic polygene signature of differentially expressed genes (DEGs) related to lipid metabolism.METHODSFirst, 9 tissue samples from patients with pancreatic cancer were collected and divided into a cancer group and a para-cancer group. All patient samples were subjected to metabolomics analysis based on liquid tandem chromatography quadrupole time of flight mass spectrometry. Then, mRNA expression profiles and corresponding clinical data of pancreatic cancer were downloaded from a public database. Least absolute shrinkage and selection operator Cox regression analysis was used to construct a multigene model for The Cancer Genome Atlas.RESULTSPrincipal component analysis and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) based on lipid metabolomics analysis showed a clear distribution in different regions. A Euclidean distance matrix was used to calculate the quantitative value of differential metabolites. The permutation test of the OPLS-DA model for tumor tissue and paracancerous tissue indicated that the established model was consistent with the actual condition based on sample data. A bar plot showed significantly higher levels of the lipid metabolites phosphatidylcholine (PC), phosphatidyl ethanolamine (PE), phosphatidylethanol(PEtOH), phosphatidylmethanol (PMeOH), phosphatidylserine (PS) and diacylglyceryl trimethylhomoserine (DGTS) in tumor tissues than in paracancerous tissues. According to bubble plots, PC, PE, PEtOH, PMeOH, PS and DGTS were significantly higher in tumor tissues than in paracancerous tissues. In total, 12.3% (25/197) of genes related to lipid metabolism were differentially expressed between tumor tissues and adjacent paracancerous tissues. Six DEGs correlated with overall survival in univariate Cox regression analysis (P < 0.05), and a 4-gene signature model was developed to divide patients into two risk groups, with patients in the high-risk group having significantly lower overall survival than those in the low-risk group (P < 0.05). ROC curve analysis confirmed the predictive power of the model.CONCLUSIONThis novel model comprising 4 lipid metabolism-related genes might assist clinicians in the prognostic evaluation of patients with pancreatic cancer.     

Untargeted metabolomics using liquid chromatography coupled with mass spectrometry for rapid discovery of metabolite biomarkers to reveal therapeutic effects of Psoralea corylifolia seeds against osteoporosis

Liquid chromatography coupled with mass spectrometry has been used as metabolomics profiling tool to discover and identify the metabolites in metabolic diseases. Osteoporosis (OP) syndrome is a chronic metabolic disease characterized by bone mass reduction and changes in bone microstructure. Psoralea corylifolia Linn. seeds (PCS) have a therapeutic effect on osteoporosis, but their action mechanism and therapeutic target are still unclear. This study aims to explore the metabolic changes of the urine profile in glucocorticoid-induced OP model rats and the therapeutic effect of PCS. High-throughput metabolomics based on the liquid chromatography coupled with mass spectrometry quadrupole time-of-flight mass spectrometry and multivariate data analysis were used to analyze the urine metabolites. The results showed that has an obvious separation between model and control groups. OPLS-DA was used to further analyze and discover substances that contributed to the separation. 42 potential biomarkers and 12 related metabolic pathways were identified in combination with network databases. After the intervention of PCS, 24 biomarkers were significantly regulated, mainly with glycone, serine and threonine metabolism, glutathione metabolism and purine metabolism and other metabolic pathways are related and discovered. This study has proved that PCS has therapeutic effect against OP by regulating that metabolic pathways disturbed in the OP. It provided a basis for the research and future development of new drugs for OP treatment.

Liquid chromatography coupled with mass spectrometry has been used as metabolomics profiling tool to discover and identify the metabolites in metabolic diseases.     

High throughput metabolomics-proteomics investigation on metabolic phenotype changes in rats caused by Radix Scrophulariae using ultra-performance liquid chromatography with mass spectrometry

Radix Scrophulariae, a traditional Chinese herb, is used to treat various diseases, including H₂O₂-induced apoptosis in cardiomyocytes, HaCaT cells, hyperuricaemia, and depression. This study screened metabolites, proteins and common pathways to better understand both the therapeutic effects and side effects of this herb. Methods: Untargeted metabolomics based on UPLC-TOF-MS, coupled with proteomics based on nano-UPLC-Q-Exactive-MS/MS, were used to investigate the effects of R. Scrophulariae in rats. Fifty-one identified metabolites in urine samples and 76 modulated proteins in liver tissue were potential biomarkers for R. Scrophulariae treatment. The biomarkers and common pathways involved were steroid hormone biosynthesis, drug metabolism-cytochrome p450, drug metabolism-other enzymes, pentose and glucuronate interconversions, and starch and sucrose metabolism. Some biomarkers were beneficial for treating diseases such as cancer, tuberculosis and isovaleric acidaemia, while other biomarkers caused side effects. Metabolomic and proteomic analyses of R. Scrophulariae-treated rats provided valuable information on the biological safety and efficacy of using R. Scrophulariae clinically.

Radix Scrophulariae, a traditional Chinese herb, is used to treat various diseases, including H₂O₂-induced apoptosis in cardiomyocytes, HaCaT cells, hyperuricaemia, and depression.     

Metabolomics analysis of the serum from children with urolithiasis using UPLC‐MS

Pediatric urolithiasis is a common urologic disease with high morbidity and recurrence rates. Recent studies have shown that metabolic dysfunction plays a vital role in the pathogenesis of urolithiasis, especially in children, but the specific mechanism is still unclear. Metabolomics is an ideal technology for exploring the mechanism of metabolic disorders in urolithiasis. In the present study, a serum metabolomics based on ultra‐performance liquid chromatography mass spectrometry was performed. A total of 50 children subjects were recruited for the study, including 30 patients with kidney stones and 20 normal controls (NCs). Principal component analysis and orthogonal partial least‐squares determinant analysis were carried, and 40 metabolites were found to be significantly altered in patients with kidney stones, mainly involving retinol metabolism, steroid hormone biosynthesis, and porphyrin and chlorophyll metabolism. The kidney stone group appeared to have a lower serum level of bilirubin, but a relative higher level of retinal, all‐transretinoic acid, progesterone, and prostaglandin E2 compared with those of the NC group. All the findings suggest that patients with urolithiasis have several metabolic characteristics, which are related to stone formation or compensation. These metabolites and pathways are very likely associated with development of kidney stones and should be considered as potential novel targets for treatment and prevention.

Study Highlights

• WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?

Metabolic disorders can be found in most children with kidney stones, suggesting that it plays a vital role in the pathogenesis of pediatric urolithiasis. Metabolomics is an ideal strategy to explore the mechanism of metabolic disorders in kidney stones.

• WHAT QUESTION DID THIS STUDY ADDRESS?

We aimed to identify the changes of serum metabolites in children with urolithiasis compared with normal controls by using ultra‐performance liquid chromatography mass spectrometry.

• WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE?

We found the special metabolic characteristics in patients with pediatric urolithiasis, which are related to stone formation or compensation.

• HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE?

Our findings indicate that the metabolic phenotype of serum in pediatric patients with urolithiasis is significantly different from that in normal controls. These metabolites and metabolic pathways associated with stone formation will help to develop novel therapeutic strategies and preventive interventions.     

High-throughput system metabolomics method reveals new mechanistic insights of chlorogenic acid by using liquid chromatography coupled to high resolution mass spectrometry

It has increasingly been recognized that metabolism is highly interconnected with disease, and system metabolomics studies have aimed to discover metabolic biomarkers and analyze the pathways of metabolome phenotypes. To better understand the metabolic alteration related with disease, a urine metabolic profile using a high-throughput system metabolomics technology approach was applied to probe the underlying molecular mechanisms of alcohol-induced liver injury and the therapeutic effects of chlorogenic acid (CA). In this study, endogenous low-molecular-weight metabolites were characterized using liquid chromatography coupled with mass spectrometry (LC-MS). The acquired data was parsed by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) to identify potential biomarkers. A total of 19 biomarkers were identified in a model of alcohol-induced liver injury rats, and it was found that chlorogenic acid had a regulatory effect on 14 of them, associated with multiple metabolic pathways. Comprehensive pathway analysis suggests that CA has the ability to regulate abnormal metabolic states. In addition, accessory examinations such as biochemical analysis and histopathological observations were also performed that showed similar results. As a natural product agent against ethanol-induced liver injury, CA was validated in the rebalancing of a wide range of metabolic disorders. High-throughput system metabolomics represents a powerful approach for revealing new mechanistic insights of natural products.

It has increasingly been recognized that metabolism is highly interconnected with disease, and system metabolomics studies have aimed to discover metabolic biomarkers and analyze the pathways of metabolome phenotypes.     

Endogenous metabolic markers for predicting the activity of dihydropyrimidine dehydrogenase

Five‐fluorouracil (5‐FU) is a chemotherapeutic agent that is mainly metabolized by the rate‐limiting enzyme dihydropyrimidine dehydrogenase (DPD). The DPD enzyme activity deficiency involves a wide range of severities. Previous studies have demonstrated the effect of a DPYD single nucleotide polymorphism on 5‐FU efficacy and highlighted the importance of studying such genes for cancer treatment. Common polymorphisms of DPYD in European ancestry populations are less frequently present in Koreans. DPD is also responsible for the conversion of endogenous uracil (U) into dihydrouracil (DHU). We quantified U and DHU in plasma samples of healthy male Korean subjects, and samples were classified into two groups based on DHU/U ratio. The calculated DHU/U ratios ranged from 0.52 to 7.12, and the two groups were classified into the 10th percentile and 90th percentile for untargeted metabolomics analysis using liquid chromatography‐quantitative time‐of‐flight‐mass spectrometry. A total of 4440 compounds were detected and filtered out based on a coefficient of variation below 30%. Our results revealed that six metabolites differed significantly between the high activity group and low activity group (false discovery rate q‐value < 0.05). Uridine was significantly higher in the low DPD activity group and is a precursor of U involved in pyrimidine metabolism; therefore, we speculated that DPD deficiency can influence uridine levels in plasma. Furthermore, the cutoff values for detecting DPD deficient patients from previous studies were unsuitable for Koreans. Our metabolomics approach is the first study that reported the DHU/U ratio distribution in healthy Korean subjects and identified a new biomarker of DPD deficiency.

Study Highlights

• WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?

Dihydropyrimidine dehydrogenase (DPD) deficiency can cause severe 5‐fluorouracil toxicity. To predict DPD activity, DPYD genotyping and DPD phenotyping using dihydrouracil and uracil ratio were used.

• WHAT QUESTION DID THIS STUDY ADDRESS?

This study aimed to identify the distribution of DPD activity in a Korean population and explore new biomarkers of DPD activity using untargeted metabolomics.

• WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE?

This study recognized different distributions of DPD activity between a Korean population and populations of other ethnicities. The cutoff values for activity deficiency based on other ethnic groups could not be considered for East Asian populations, including Korean populations.

• HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE?

This study indicated that a new cutoff value should be further validated to overcome the variability in DPD phenotyping in East Asian populations. The new biomarker for DPD deficiency uridine has the potential for more feasible and convenient prediction analysis.     

Metabolomic alteration induced by psychotropic drugs: Short‐term metabolite profile as a predictor of weight gain evolution

Psychotropic drugs can induce strong metabolic adverse effects, potentially increasing morbidity and/or mortality of patients. Metabolomic profiling, by studying the levels of numerous metabolic intermediates and products in the blood, allows a more detailed examination of metabolism dysfunctions. We aimed to identify blood metabolomic markers associated with weight gain in psychiatric patients. Sixty‐two patients starting a treatment known to induce weight gain were recruited. Two hundred and six selected metabolites implicated in various pathways were analyzed in plasma, at baseline and after 1 month of treatment. Additionally, 15 metabolites of the kynurenine pathway were quantified. This latter analysis was repeated in a confirmatory cohort of 24 patients. Among the 206 metabolites, a plasma metabolomic fingerprint after 1 month of treatment embedded 19 compounds from different chemical classes (amino acids, acylcarnitines, carboxylic acids, catecholamines, nucleosides, pyridine, and tetrapyrrole) potentially involved in metabolic disruption and inflammation processes. The predictive potential of such early metabolite changes on 3 months of weight evolution was then explored using a linear mixed‐effects model. Of these 19 metabolites, short‐term modifications of kynurenine, hexanoylcarnitine, and biliverdin, as well as kynurenine/tryptophan ratio at 1 month, were associated with 3 months weight evolution. Alterations of the kynurenine pathway were confirmed by quantification, in both exploratory and confirmatory cohorts. Our metabolomic study suggests a specific metabolic dysregulation after 1 month of treatment with psychotropic drugs known to induce weight gain. The identified metabolomic signature could contribute in the future to the prediction of weight gain in patients treated with psychotropic drugs.

Study Highlights

• WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?

Psychotropic drugs can induce weight gain, potentially increasing morbidity and/or mortality of patients.

• WHAT QUESTION DID THIS STUDY ADDRESS?

Could the use of metabolomics permit the identification of early blood markers associated with weight gain in treated psychiatric patients?

• WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE?

It identified a metabolomic signature related to psychotropic drug exposure. Our results suggest an overall metabolic dysregulation after 1 month of treatment, and a specific dysregulation that is associated with weight gain worsening at 3 months. Understanding the biochemical significance of those dysregulations should provide further mechanistic comprehensions of pathways involved in metabolic effects of psychotropic drugs and possibly also in therapeutic response to treatments.

• HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE?

Metabolomic profiling could be used in a combinatorial model with clinical, exposure, and genetic markers, to improve the early prediction of weight gain during psychotropic treatment. This prediction could be useful to offer personalized treatment to patients and, consequently, improved medical care and patient quality of life.