Metabolomics in pharmaceutical research and development

Metabolomic profiling and biomarker identification for early detection and therapeutic targeting of doxorubicin-induced cardiotoxicity

Background

Doxorubicin (DOX) is a widely used chemotherapeutic agent known for its efficacy against various cancers, but its clinical application is often limited by its cardiotoxic effects. The exact mechanisms of DOX-induced cardiotoxicity remain unclear, requiring further investigation. Early diagnosis is essential to enhance the quality of life and prognosis for patients with malignancies. This study aims to identify biomarkers and therapeutic targets for DOX cardiotoxicity.

Methods

Heart tissue samples from 20 DOX-treated cardiotoxic mice and 19 normal controls were analyzed using liquid chromatography-mass spectrometry (LC-MS). Multivariate statistical analysis identified differential metabolites. Key metabolites were assessed using a random forest algorithm, and ROC curves evaluated diagnostic value. H9C2 rat cardiomyoblast cells were cultured to investigate the protective effects of these metabolites.

Results

Among 291 metabolites, significant differences emerged between cardiotoxic and normal mice. Five metabolites-4-hydroxy-valeric acid, 2-methylbutanoic acid, traumatic acid, PI (18:2 (9Z, 12Z)/0:0), and MIPC (t18:0/24:0 (2OH))-showed diagnostic potential. ROC analysis indicated excellent value for 4-hydroxy-valeric acid and PI (18:2 (9Z, 12Z)/0:0) and high discriminatory power for 2-methylbutanoic acid (AUC = 0. 99). Pathway analysis highlighted glycosylphosphatidylinositol-anchor biosynthesis, unsaturated fatty acids biosynthesis, pantothenate and CoA pathways, among others, associated with DOX-induced cardiotoxicity. In addition, we found that the differential metabolite Cer (d18:0/12:0) can improve DOX-induced myocardial cell damage and inhibit apoptosis-related protein expression at the cellular level.

Conclusion

Heart tissue metabolomics with LC-MS identified critical metabolites and pathways associated with DOX cardiotoxicity, suggesting biomarkers for early diagnosis and potential therapeutic targets to mitigate DOX-related cardiotoxicity and improve clinical outcomes.

Decoding Drug Discovery: Exploring A-to-Z In Silico Methods for Beginners

The drug development process is a critical challenge in the pharmaceutical industry due to its time-consuming nature and the need to discover new drug potentials to address various ailments. The initial step in drug development, drug target identification, often consumes considerable time. While valid, traditional methods such as in vivo and in vitro approaches are limited in their ability to analyze vast amounts of data efficiently, leading to wasteful outcomes. To expedite and streamline drug development, an increasing reliance on computer-aided drug design (CADD) approaches has merged. These sophisticated in silico methods offer a promising avenue for efficiently identifying viable drug candidates, thus providing pharmaceutical firms with significant opportunities to uncover new prospective drug targets. The main goal of this work is to review in silico methods used in the drug development process with a focus on identifying therapeutic targets linked to specific diseases at the genetic or protein level. This article thoroughly discusses A-to-Z in silico techniques, which are essential for identifying the targets of bioactive compounds and their potential therapeutic effects. This review intends to improve drug discovery processes by illuminating the state of these cutting-edge approaches, thereby maximizing the effectiveness and duration of clinical trials for novel drug target investigation.

Combining Metabolomics and Machine Learning to Identify Diagnostic and Prognostic Biomarkers in Patients with Non-Small Cell Lung Cancer Pre- and Post-Radiation Therapy

Lung cancer is the leading cause of cancer-related deaths globally, with non-small cell lung cancer (NSCLC) accounting for over 85% of cases and poor prognosis in advanced stages. This study explored shifts in circulating metabolite levels in NSCLC patients versus healthy controls and examined the effects of conventionally fractionated radiation therapy (CFRT) and stereotactic body radiation therapy (SBRT). We enrolled 91 NSCLC patients (38 CFRT and 53 SBRT) and 40 healthy controls. Plasma metabolite levels were assessed using semi-targeted metabolomics, revealing 32 elevated and 18 reduced metabolites in patients. Key discriminatory metabolites included ethylmalonic acid, maltose, 3-phosphoglyceric acid, taurine, glutamic acid, glycocolic acid, and d-arabinose, with a combined Receiver Operating Characteristics curve indicating perfect discrimination between patients and controls. CFRT and SBRT affected different metabolites, but both changes suggested a partial normalization of energy and amino acid metabolism pathways. In conclusion, metabolomics identified distinct metabolic signatures in NSCLC patients with potential as diagnostic biomarkers. The differing metabolic responses to CFRT and SBRT reflect their unique therapeutic impacts, underscoring the utility of this technique in enhancing NSCLC diagnosis and treatment monitoring.

Exploring machine learning for untargeted metabolomics using molecular fingerprints

BACKGROUND

Metabolomics, the study of substrates and products of cellular metabolism, offers valuable insights into an organism's state under specific conditions and has the potential to revolutionise preventive healthcare and pharmaceutical research. However, analysing large metabolomics datasets remains challenging, with available methods relying on limited and incompletely annotated metabolic pathways.

METHODS

This study, inspired by well-established methods in drug discovery, employs machine learning on metabolite fingerprints to explore the relationship of their structure with responses in experimental conditions beyond known pathways, shedding light on metabolic processes. It evaluates fingerprinting effectiveness in representing metabolites, addressing challenges like class imbalance, data sparsity, high dimensionality, duplicate structural encoding, and interpretable features. Feature importance analysis is then applied to reveal key chemical configurations affecting classification, identifying related metabolite groups.

RESULTS

The approach is tested on two datasets: one on Ataxia Telangiectasia and another on endothelial cells under low oxygen. Machine learning on molecular fingerprints predicts metabolite responses effectively, and feature importance analysis aligns with known metabolic pathways, unveiling new affected metabolite groups for further study.

CONCLUSION

In conclusion, the presented approach leverages the strengths of drug discovery to address critical issues in metabolomics research and aims to bridge the gap between these two disciplines. This work lays the foundation for future research in this direction, possibly exploring alternative structural encodings and machine learning models.

The Therapeutic Potential of Hemp Seed Oil in D-Galactose-Induced Aging Rat Model Was Determined through the Combined Assessment of 1H NMR Metabolomics and 16S rRNA Gene Sequencing

Aging is an irreversible process of natural degradation of bodily function. The increase in the aging population, as well as the rise in the incidence of aging-related diseases, poses one of the most pressing global challenges. Hemp seed oil, extracted from the seeds of hemp (Cannabis sativa L.), possesses significant nutritional and biological properties attributed to its unique composition of polyunsaturated fatty acids and various antioxidant compounds. However, there is limited knowledge regarding the anti-aging mechanism of hemp seed oil. This study aimed to evaluate the beneficial effects and potential mechanisms of hemp seed oil in a D-galactose (D-gal)-induced aging rat model through a combined analysis of metabolomics and 16S rRNA gene sequencing. Using nuclear magnetic resonance (NMR)-based metabolomics, significant alterations in serum and urine metabolic phenotypes were observed between the D-gal-induced aging rat model and the healthy control group. Eight and thirteen differentially expressed metabolites related to aging were identified in serum and urine, respectively. Treatment with hemp seed oil significantly restored four and ten potential biomarkers in serum and urine, respectively. The proposed pathways primarily included energy metabolism, amino acid metabolism, one-carbon metabolism, and lipid metabolism. Furthermore, 16S rRNA gene sequencing analysis revealed significant changes in the gut microbiota of aged rats. Compared to the model group, the hemp seed oil group exhibited significant alterations in the abundance of 21 bacterial taxa at the genus level. The results indicated that hemp seed oil suppressed the prevalence of pathogenic bacterial genera such as Streptococcus, Rothia, and Parabacteroides. Additionally, it facilitated the proliferation of the genera Lachnospirace_NK4B4_group and Lachnospirace_UCG_001, while also enhancing the relative abundance of the genus Butyricoccus; a producer of short-chain fatty acids (SCFAs). These findings provided new insights into the pathogenesis of aging and further supported the potential utility of hemp seed oil as an anti-aging therapeutic agent.

Decoding active compounds and molecular targets of herbal medicine by high-throughput metabolomics technology: A systematic review

Clinical experiences of herbal medicine (HM) have been used to treat a variety of human intractable diseases. As the treatment of diseases using HM is characterized by multi-components and multi-targets, it is difficult to determine the bio-active components, explore the molecular targets and reveal the mechanisms of action. Metabolomics is frequently used to characterize the effect of external disturbances on organisms because of its unique advantages on detecting changes in endogenous small-molecule metabolites. Its systematicity and integrity are consistent with the effective characteristics of HM. After HM intervention, metabolomics can accurately capture and describe the behavior of endogenous metabolites under the disturbance of functional compounds, which will be used to decode the bioactive ingredients of HM and expound the molecular targets. Metabolomics can provide an approach for explaining HM, addressing unclear clinical efficacy and undefined mechanisms of action. In this review, the metabolomics strategy and its applications in HM are systematically introduced, which offers valuable insights for metabolomics methods to characterizing the pharmacological effects and molecular targets of HM.

Huangqi Jianzhong Tang treats chronic atrophic gastritis rats by regulating intestinal flora and conjugated bile acid metabolism

Huangqi Jianzhong Tang (HQJZ) is effective for treating chronic atrophic gastritis (CAG). The present study was carried out to reveal the mechanism of HQJZ in CAG rats. The metabolism and microbial composition of the cecal contents in CAG rats were analyzed through the integration of an untargeted metabolomic approach using ultra-high-performance liquid chromatography coupled with the quadrupole-time of flight mass spectrometry (UHPLC-QTOF-MS) and 16S rRNA gene sequencing, respectively. Finally, MetOrigin analyses were performed to explore the relationship between differential metabolites and intestinal flora. The results showed that HQJZ could significantly regulate metabolic disorders, especially conjugated acid metabolites. 16S rRNA gene sequencing analysis illustrated that HQJZ decreased the abundance of Acetobacter, Desulfovibrio, Escherichia, and Shigella. MetOrigin metabolite traceability analysis showed that the six bile acids associated with HQJZ efficacy included three bacteria-host cometabolites, which were involved in the primary bile acid biosynthesis pathway. Research presented here confirmed that conjugated bile acid metabolism was key to the treatment of CAG by HQJZ and correlates strongly with Bacteroides acidifaciens and Prevotella copri. These findings provide new insights into the mechanisms to explain the efficacy of HQJZ.

Plasma metabolomics by nuclear magnetic resonance reveals biomarkers and metabolic pathways associated with the control of HIV-1 infection/progression

How the human body reacts to the exposure of HIV-1 is an important research goal. Frequently, HIV exposure leads to infection, but some individuals show natural resistance to this infection; they are known as HIV-1-exposed but seronegative (HESN). Others, although infected but without antiretroviral therapy, control HIV-1 replication and progression to AIDS; they are named controllers, maintaining low viral levels and an adequate count of CD4+ T lymphocytes. Biological mechanisms explaining these phenomena are not precise. In this context, metabolomics emerges as a method to find metabolites in response to pathophysiological stimuli, which can help to establish mechanisms of natural resistance to HIV-1 infection and its progression. We conducted a cross-sectional study including 30 HESN, 14 HIV-1 progressors, 14 controllers and 30 healthy controls. Plasma samples (directly and deproteinized) were analyzed through Nuclear Magnetic Resonance (NMR) metabolomics to find biomarkers and altered metabolic pathways. The metabolic profile analysis of progressors, controllers and HESN demonstrated significant differences with healthy controls when a discriminant analysis (PLS-DA) was applied. In the discriminant models, 13 metabolites associated with HESN, 14 with progressors and 12 with controllers were identified, which presented statistically significant mean differences with healthy controls. In progressors, the metabolites were related to high energy expenditure (creatinine), mood disorders (tyrosine) and immune activation (lipoproteins), phenomena typical of the natural course of the infection. In controllers, they were related to an inflammation-modulating profile (glutamate and pyruvate) and a better adaptive immune system response (acetate) associated with resistance to progression. In the HESN group, with anti-inflammatory (lactate and phosphocholine) and virucidal (lactate) effects which constitute a protective profile in the sexual transmission of HIV. Concerning the significant metabolites of each group, we identified 24 genes involved in HIV-1 replication or virus proteins that were all altered in progressors but only partially in controllers and HESN. In summary, our results indicate that exposure to HIV-1 in HESN, as well as infection in progressors and controllers, affects the metabolism of individuals and that this affectation can be determined using NMR metabolomics.

The Crosstalk between Gut Microbiota and Nervous System: A Bidirectional Interaction between Microorganisms and Metabolome

Several studies have shown that the gut microbiota influences behavior and, in turn, changes in the immune system associated with symptoms of depression or anxiety disorder may be mirrored by corresponding changes in the gut microbiota. Although the composition/function of the intestinal microbiota appears to affect the central nervous system (CNS) activities through multiple mechanisms, accurate epidemiological evidence that clearly explains the connection between the CNS pathology and the intestinal dysbiosis is not yet available. The enteric nervous system (ENS) is a separate branch of the autonomic nervous system (ANS) and the largest part of the peripheral nervous system (PNS). It is composed of a vast and complex network of neurons which communicate via several neuromodulators and neurotransmitters, like those found in the CNS. Interestingly, despite its tight connections to both the PNS and ANS, the ENS is also capable of some independent activities. This concept, together with the suggested role played by intestinal microorganisms and the metabolome in the onset and progression of CNS neurological (neurodegenerative, autoimmune) and psychopathological (depression, anxiety disorders, autism) diseases, explains the large number of investigations exploring the functional role and the physiopathological implications of the gut microbiota/brain axis.

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.

Understanding ayahuasca effects in major depressive disorder treatment through in vitro metabolomics and bioinformatics

Emerging insights from metabolomic-based studies of major depression disorder (MDD) are mainly related to biochemical processes such as energy or oxidative stress, in addition to neurotransmission linked to specific metabolite intermediates. Hub metabolites represent nodes in the biochemical network playing a critical role in integrating the information flow in cells between metabolism and signaling pathways. Limited technical-scientific studies have been conducted to understand the effects of ayahuasca (Aya) administration in the metabolism considering MDD molecular context. Therefore, this work aims to investigate an in vitro primary astrocyte model by untargeted metabolomics of two cellular subfractions: secretome and intracellular content after pre-defined Aya treatments, based on DMT concentration. Mass spectrometry (MS)-based metabolomics data revealed significant hub metabolites, which were used to predict biochemical pathway alterations. Branched-chain amino acid (BCAA) metabolism, and vitamin B6 and B3 metabolism were associated to Aya treatment, as "housekeeping" pathways. Dopamine synthesis was overrepresented in the network results when considering the lowest tested DMT concentration (1 µmol L^-1). Building reaction networks containing significant and differential metabolites, such as nicotinamide, L-DOPA, and L-leucine, is a useful approach to guide on dose decision and pathway selection in further analytical and molecular studies.

Natural Products Drug Discovery: On Silica or In-Silico?

Natural products have been the most important source for drug development throughout the human history. Over time, the formulation of drugs has evolved from crude drugs to refined chemicals. In modern drug discovery, conventional natural products lead-finding usually uses a top-down approach, namely bio-guided fractionation. In this approach, the crude extracts are separated by chromatography and resulting fractions are tested for activity. Subsequently, active fractions are further refined until a single active compound is obtained. However, this is a painstakingly slow and expensive process. Among the alternatives that have been developed to improve this situation, metabolomics has proved to yield interesting results having been applied successfully to drug discovery in the last two decades. The metabolomics-based approach in lead-finding comprises two steps: (1) in-depth chemical profiling of target samples, e.g. plant extracts, and bioactivity assessment, (2) correlation of the chemical and biological data by chemometrics. In the first step of this approach, the target samples are chemically profiled in an untargeted manner to detect as many compounds as possible. So far, NMR spectroscopy, LC-MS, GC-MS, and MS/MS spectrometry are the most common profiling tools. The profile data are correlated with the biological activity with the help of various chemometric methods such as multivariate data analysis. This in-silico analysis has a high potential to replace or complement conventional on-silica bioassay-guided fractionation as it will greatly reduce the number of bioassays, and thus time and costs. Moreover, it may reveal synergistic mechanisms, when present, something for which the classical top-down approach is clearly not suited. This chapter aims to give an overview of successful approaches based on the application of chemical profiling with chemometrics in natural products drug discovery.

Lysophosphatidylcholine acyltransferase 1 alleviates silica-induced pulmonary fibrosis by modulating lipid metabolism

Silicosis is an incurable lung disease that can progress even when exposure to silica dust has ended. Lipid metabolism plays an important role in the occurrence and development of silicosis. However, the mechanistic details have not been fully elucidated. This was investigated in the current study by high-performance liquid chromatography-mass spectrometry-based lipidomic analysis of lung tissue in a mouse model of silicosis. Lipid profiles and key metabolic enzymes were compared between silica and control groups. The lipidomic analysis revealed differentially-expressed lipids in the lungs of silicosis mice compared with controls. Among the identified lipid metabolism-related enzymes, the expression of lysophosphatidylcholine acyltransferase 1 (LPCAT1) was significantly down-regulated at the transcript and protein levels. LPCAT1 overexpression in vivo using adeno-associated virus altered the balance between phosphatidylcholine and lysophosphatidylcholine and inhibited the development of silicosis in mice. These results indicate that LPCAT1 dysregulation leads to abnormal lipid metabolism and silicosis, and is a potential therapeutic target for the treatment of silica-induced pulmonary fibrosis.

Dose-Related Urinary Metabolic Alterations of a Combination of Quercetin and Resveratrol-Treated High-Fat Diet Fed Rats

Most herbal polyphenols and flavonoids reveals multiple ameliorative benefits for obesity caused by chronic metabolic disorders. Accumulated studies have revealed that preferable therapeutic effects can be obtained through clinical combination of these two kinds of natural compounds for obesity improvement. The typical representative research was the combination of quercetin and resveratrol (CQR), in which the ratio of quercetin and resveratrol is 2:1, demonstrating a synergistic effect in anti-obesity process. Although there exists reports clarifying the mechanism of the combination of two to improve obesity from the perspective of improving adipose tissue inflammation or modulating the composition of intestinal flora, there are few further studies on the mechanism of drug action from the perspective of metabolites transformation. In this research, we mainly focused on the alterations of endogenous metabolites in rats, and analyzed the urine metabolites of obese and intervention model. Therefore, a gas chromatography-mass spectrometry (GC-MS) based metabolomics approach was applied to assess the potential effects and mechanisms of CQR at different dosages (45, 90, and 180 mg/kg) in high fat diet (HFD)-induced obesity rats. Body weight gain and visceral fat weight were reduced by CQR, as well as blood lipid and inflammatory factor levels were increased by CQR in a dose-related manner. Urinary metabolomics revealed 22 differential metabolites related to the HFD-induced obesity, which were reversed in a dose-dependent manner by CQR, of which 8 were reversed in the 45 mg/kg CQR group, 15 were reversed in the 90 mg/kg CQR group, and 18 were reversed in the 180 mg/kg CQR group. Combined with bioinformatics and pattern recognition, the results demonstrated that the key differential metabolites were basically involved in amino acid metabolism, galactose metabolism, pantothenate and CoA biosynthesis, pyruvate metabolism and lysine degradation. In summary, our results showed significant therapeutic action by CQR administration and remarkable metabolomic changes after HFD feeding and CQR intervention. Urinary metabolomic analysis was highlighted on account of providing holistic and comprehensive insights into the pathophysiological mechanisms of the HFD-induced obesity, which also supplied clues for the future mechanism studies of CQR's anti-obesity effects.

Hyperuricemia induces lipid disturbances mediated by LPCAT3 upregulation in the liver

Potential underlying molecular mechanisms for uric acid-induced lipid metabolic disturbances had not been elucidated clearly. This study investigated the effects and underlying mechanisms of uric acid on the development of lipid metabolic disorders. We collected blood samples from 100 healthy people and 100 patients with hyperuricemia for whom serum lipid analysis was performed. Meanwhile, a mouse model of hyperuricemia was generated, and lipidomics was performed on liver tissues, comparing control and hyperuricemia groups, to analyze lipid profiles and key metabolic enzymes. Uric acid directly induced serum lipid metabolic disorders in both humans and mice based on triglycerides, total cholesterol, and low-density lipoprotein cholesterol. Through lipidomic analysis, 46 lipids were differentially expressed in hyperuricemic mouse livers, and the phosphatidylcholine composition was altered, which was mediated by LPCAT3 upregulation. High-uric acid levels-induced p-STAT3 inhibition and SREBP-1c activation in vivo and in vitro. Moreover, LPCAT3-knockdown significantly attenuated uric acid-induced p-STAT3 inhibition, SREBP-1c activation, and lipid metabolic disorders in L02 cells. In conclusion, uric acid induces lipid metabolic disturbances through LPCAT3-mediated p-STAT3 inhibition and SREBP-1c activation. LPCAT3 could be a key regulatory factor linking hyperuricemia and lipid metabolic disorders. These results might provide novel insights into the clinical treatment of hyperuricemia.

Activation of SREBP-1c alters lipogenesis and promotes tumor growth and metastasis in gastric cancer

PURPOSE

Aggressively growing tumors are characterized by significant variations in metabolites, including lipids, and can involve the elevated synthesis ofde novo fatty acids.

METHODS

Ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS)-based metabolomics and lipidomics were performed to compare human gastric cancer tissues and adjacent normal tissues from clinical patients. A series of cellular and molecular biological methods were applied to validate the lipidomics results.

RESULTS

Palmitic acid (PA) was found to be significantly downregulated in gastric cancer tissues, and it was found that a high concentration of PA specifically inhibited cell proliferation and impaired cell invasiveness and migrationin vitro in AGS, SGC-7901, and MGC-803 gastric cancer cell lines. Moreover, sterol regulatory element-binding protein 1 (SREBP-1c) was activated in human gastric cancer tissues, and it promoted the expression of a series of genes associated with the synthesis of fatty acids, such as SCD1 and FASN. SREBP-1c knockdown rescued the migration and invasion defects in AGS and SGC-7901 gastric cancer cells.

CONCLUSION

Taken together, our findings confirmed the variation in fatty acid synthesis in gastric cancer and identified SREBP-1c as a promising target for gastric cancer treatment.

Metabolomic profiling in neuroblastoma

BACKGROUND AND OBJECTIVES

Previous studies on several cancer types show that metabolomics provides a potentially useful noninvasive screening approach for outcome prediction and accurate response to treatment assessment. Neuroblastoma (NB) accounts for at least 15% of cancer-related deaths in children. Although current risk-based treatment approaches in NB have resulted in improved outcome, survival for high-risk patients remains poor. This study aims to evaluate the use of metabolomics for improving patients' risk-group stratification and outcome prediction in NB.

DESIGN AND METHODS

Plasma samples from 110 patients with NB were collected at diagnosis prior to starting therapy and at the end of treatment if available. Metabolomic analysis of samples was carried out by ultra-performance liquid chromatography-time of flight mass spectrometry (UPLC-MS).

RESULTS

The metabolomic analysis was able to identify different plasma metabolic profiles in high-risk and low-risk NB patients at diagnosis. The metabolic model correctly classified 16 high-risk and 15 low-risk samples in an external validation set providing 84.2% sensitivity (60.4-96.6, 95% CI) and 93.7% specificity (69.8-99.8, 95% CI). Metabolomic profiling could also discriminate high-risk patients with active disease from those in remission. Notably, a plasma metabolomic signature at diagnosis identified a subset of high-risk NB patients who progressed during treatment.

CONCLUSIONS

To the best of our knowledge, this is the largest NB study investigating the prognostic power of plasma metabolomics. Our results support the potential of metabolomic profiling for improving NB risk-group stratification and outcome prediction. Additional validating studies with a large cohort are needed.

Baicalein attenuates the neuroinflammation in LPS-activated BV-2 microglial cells through suppression of pro-inflammatory cytokines, COX2/NF-κB expressions and regulation of metabolic abnormality

Baicalein (5,6,7-trihydroxyflavone), isolated from the root of traditional Chinese herb Scutellaria baicalensis Georgi, has anti-inflammatory and anti-oxidative activities. This study explored the protective and modulatory mechanisms of baicalein on neuroinflammation, oxidative stress and metabolic abnormality in lipopolysaccharide (LPS)-activated BV-2 cells. Our results demonstrated that treatment with baicalein remarkably restrained the production of pro-inflammatory factors including nitric oxide (NO), interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) in LPS-activated BV-2 cells. Moreover, baicalein significantly inhibited reactive oxygen species (ROS) production, decreased cyclooxygenase-2 (COX-2) and nuclear factor-b (NF-κB)/p65 expression. ¹H NMR metabolomics analysis revealed that 12 differential metabolites were regulated by baicalein, implicated in alanine, aspartate and glutamate metabolism, glutathione metabolism, arginine and proline metabolism, D-glutamine and D-glutamate metabolism. In conclusion, these results indicated that baicalein has protective and modulatory effects on neuroinflammation and oxidative stress in LPS-activated BV-2 cells.

Development of a new quantification method for organic acids in urine as potential biomarkers for respiratory illness

Asthma and chronic obstructive pulmonary disease (COPD) are common respiratory disorders that have similar clinical presentation and misdiagnosis may lead to improper treatment. There is a need for a better, non-invasive test for the differentiation of asthma and COPD. In this study, we developed a new validated LC-MS/MS method for 17 urinary organic acids that could serve as potential biomarkers. Human urine samples were collected from adults with asthma or COPD. LC-MS/MS was performed using the differential isotope labeling approach. 4-(Dimethylamino) phenacyl bromide (DmPA) was used for derivatization using two different carbon isotopes, allowing for the formation of internal standard for each metabolite. Gradient elution was employed on a C₁₈ column while the LC-MS/MS operated in the multiple reaction monitoring mode (MRM). Regulatory guidelines were used for method validation. Partial Least Squares Discriminative Analysis (PLS-DA) was applied to the log-transformed values of metabolites in each group of asthma and COPD subjects. Full validation in targeted metabolomics is scarce with usually limited number of metabolites, unlike fit-for-purpose approach. Due to the endogenous nature of the metabolites, numerous challenges were encountered during method development and validation, such as the lactic acid interference from the surrounding environment. The required specificity, accuracy and precision was successfully achieved. The method was fully validated, ensuring robustness and reproducibility when analyzing patient samples. The method was applied to analyze human urine samples and PLS-DA analysis showed differentiation of asthma and COPD subjects (R2 0.89, Q2 0.68). As targeted metabolomics is expanding to the clinical sphere, more validated methods/strategies are needed. Our work will expand the current knowledge-base regarding targeted metabolomics.

Global urinary metabolic profiling of the osteonecrosis of the femoral head based on UPLC-QTOF/MS

INTRODUCTION

Osteonecrosis of the femoral head (ONFH), one of the widespread orthopedic diseases with a decrease in bloodstream to the femoral head, is frequently accompanied by cellular death, trabecula fracture, and collapse of the articular surface. The exactly pathological mechanism of ONFH remains to explore and further identify.

OBJECTIVES

The aim was to identify the global urinary metabolic profiling of ONFH and to detect biomarkers of ONFH.

METHODS

Urine samples were collected from 26 ONFH patients and 26 healthy people. Ultra-performance liquid chromatography-quadrupole time of flight tandem mass spectrometry (UPLC-QTOF/MS) in combination with multivariate statistical analysis was developed and performed to identify the global urinary metabolic profiling of ONFH.

RESULTS

The urinary metabolic profiling of ONFH group was significantly separated from the control group by multivariate statistical analysis. 33 distinctly differential metabolites were detected between the ONFH patients and healthy people. Sulfate, urea, Deoxycholic acid and PE(14:0/14:1(9Z)) were screened as the potential biomarkers of ONFH. In addition, the up/down-regulation of sulfur metabolism, cysteine and methionine metabolism, glycerophospholipid metabolism, and histidine metabolism were clearly be associated with the ONFH pathogenic progress.

CONCLUSION

Our results suggested that metabolomics could serve as a promising approach for identifying the diagnostic biomarkers and elucidating the pathological mechanism of ONFH.

El-MAVEN: A Fast, Robust, and User-Friendly Mass Spectrometry Data Processing Engine for Metabolomics

Analysis of large metabolomic datasets is becoming commonplace with the increased realization of the role that metabolites play in biology and pathophysiology. While there are many open-source analysis tools to extract peaks from liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and tandem mass spectrometry (LC-MS/MS) data, these tools are not very interactive and are suboptimal when a large number of samples are to be analyzed. El-MAVEN is an open-source analysis platform that extends MAVEN and provides fast, powerful, and interactive analysis capabilities especially for datasets containing over 100 samples. The El-MAVEN workflow is easy to use with just four steps from loading data to exporting of the results. Advanced analysis and software techniques such as multiprocessing, machine learning, and reduction of memory leaks are implemented so as to provide a seamless and interactive user experience. Results from El-MAVEN can be exported in a range of formats allowing continued analysis on other platforms. Additionally, El-MAVEN is also fully integrated with Polly™, a cloud-based analysis platform that provides a range of tools for flux analysis and integrative-omics analysis. El-MAVEN is a powerful tool that enables fast and efficient analysis of large metabolomic datasets to accelerate the process of gaining insight from raw data.

GC/MS-based urine metabolomics analysis of renal allograft recipients with acute rejection

BACKGROUND

Acute renal allograft rejection is a common complication after renal transplantation that often leads to chronic rejection and ultimate graft loss. While renal allograft biopsy remains the gold standard for diagnosis of acute rejection, the possibility of biopsy-associated complications cannot be overlooked. The development of noninvasive methods for accurate detection of acute renal allograft rejection is thus of significant clinical importance.

METHODS

Gas chromatography-mass spectrometry (GC/MS) was employed for analysis of urine metabolites in 15 renal allograft recipients with acute rejection and 15 stable renal transplant recipients. Partial least squares (PLS) regression and leave-one-out analyses were performed to ascertain whether the metabolites identified could be exploited to distinguish acute rejection from stable groups as well as their sensitivity and specificity.

RESULTS

Overall, 14 metabolites were significantly altered in the acute rejection group (11 and 3 metabolites displayed higher and lower levels, respectively) relative to the stable transplant group. Data from PLS and leave-one-out analyses revealed that the differential metabolites identified not only distinguished acute rejection from stable transplant recipients but also showed high sensitivity and specificity for diagnosis of renal allograft recipients with acute rejection.

CONCLUSION

Urine metabolites identified with GC/MS can effectively distinguish acute rejection from stable transplant recipients, supporting the potential utility of metabolome analysis in non-invasive diagnosis of acute rejection.

Metabolomic Analysis by Nuclear Magnetic Resonance Spectroscopy as a New Approach to Understanding Inflammation and Monitoring of Pharmacological Therapy in Children and Young Adults With Cystic Fibrosis

15-F2t-Isoprostane, a reliable biomarker of oxidative stress, has been found elevated in exhaled breath condensate (EBC), a non-invasive technique for sampling of airway secretions, in patients with cystic fibrosis (CF). Azithromycin has antioxidant properties in experimental models of CF, but its effects on oxidative stress in CF patients are largely unknown. Primary objective of this pilot, proof-of-concept, prospective, parallel group, pharmacological study, was investigating the potential antioxidant effects of azithromycin in CF patients as reflected by EBC 15-F2t-isoprostane. Secondary objectives included studying the effect of azithromycin on EBC and serum metabolic profiles, and on serum 15-F2t-isoprostane. In CF patients who were on maintenance treatment with oral vitamin E (200 UI once daily), treatment with oral azithromycin (250 or 500 mg depending on body weight) plus vitamin E (400 UI once daily) (group A) (n = 24) or oral vitamin E alone (400 UI once daily) (group B) (n = 21) was not associated with changes in EBC 15-F2t-isoprostane concentrations compared with baseline values after 8-weeks treatment or 2 weeks after treatment suspension. There was no between-group difference in post-treatment EBC 15-F2t-isoprostane. Likewise, no within- or between-group differences in serum 15-F2t-isoprostane concentrations were observed in either study group. NMR spectroscopy-based metabolomics of EBC shows that suspension of both azithromycin plus vitamin E and vitamin E alone has a striking effect on metabolic profiles in EBC. Between-group comparisons show that EBC metabolite distribution after treatment and 2 weeks after treatment suspension is different. Quantitative differences in ethanol, saturated fatty acids, acetate, acetoin/acetone, and methanol are responsible for these differences. Our study was unable to show antioxidant effect of azithromycin as add-on treatment with doubling the dose of oral vitamin E as reflected by 15-F2t-isoprostane concentrations in EBC. Add-on therapy with azithromycin itself does not induce EBC metabolite changes, but its suspension is associated with EBC metabolic profiles that are different from those observed after vitamin E suspension. The pathophysiological and therapeutic implications of these findings in patients with stable CF are unknown and require further research. Preliminary data suggest that EBC NMR-based metabolomics might be used for assessing the effects of pharmacological treatment suspension in stable CF patients.

Impact of Prolonged Blood Incubation and Extended Serum Storage at Room Temperature on the Human Serum Metabolome

Metabolomics is a powerful technology with broad applications in life science that, like other -omics approaches, requires high-quality samples to achieve reliable results and ensure reproducibility. Therefore, along with quality assurance, methods to assess sample quality regarding pre-analytical confounders are urgently needed. In this study, we analyzed the response of the human serum metabolome to pre-analytical variations comprising prolonged blood incubation and extended serum storage at room temperature by using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) -based metabolomics. We found that the prolonged incubation of blood results in a statistically significant 20% increase and 4% decrease of 225 tested serum metabolites. Extended serum storage affected 21% of the analyzed metabolites (14% increased, 7% decreased). Amino acids and nucleobases showed the highest percentage of changed metabolites in both confounding conditions, whereas lipids were remarkably stable. Interestingly, the amounts of taurine and O-phosphoethanolamine, which have both been discussed as biomarkers for various diseases, were 1.8- and 2.9-fold increased after 6 h of blood incubation. Since we found that both are more stable in ethylenediaminetetraacetic acid (EDTA) blood, EDTA plasma should be the preferred metabolomics matrix.

Designing and interpreting 'multi-omic' experiments that may change our understanding of biology

Most biological mechanisms involve more than one type of biomolecule, and hence operate not solely at the level of either genome, transcriptome, proteome, metabolome or ionome. Datasets resulting from single-omic analysis are rapidly increasing in throughput and quality, rendering multi-omic studies feasible. These should offer a comprehensive, structured and interactive overview of a biological mechanism. However, combining single-omic datasets in a meaningful manner has so far proved challenging, and the discovery of new biological information lags behind expectation. One reason is that experiments conducted in different laboratories can typically not to be combined without restriction. Second, the interpretation of multi-omic datasets represents a significant challenge by nature, as the biological datasets are heterogeneous not only for technical, but also for biological, chemical, and physical reasons. Here, multi-layer network theory and methods of artificial intelligence might contribute to solve these problems. For the efficient application of machine learning however, biological datasets need to become more systematic, more precise - and much larger. We conclude our review with basic guidelines for the successful set-up of a multi-omic experiment.

Baicalein Exerts Beneficial Effects in d-Galactose-Induced Aging Rats Through Attenuation of Inflammation and Metabolic Dysfunction

Baicalein is a flavonoid isolated from the roots of Scutellaria baicalensis Georgi. This study aimed to ascertain the effects and potential underlying mechanisms of baicalein in d-galactose (d-gal)-induced aging rat model by integration of behavior examination, biochemical detection, and ¹H nuclear magnetic resonance (NMR)-based metabolomic approach. Our findings suggest that baicalein significantly attenuated memory decline in d-gal-induced aging model, as manifested by increasing recognition index in novel object recognition test, shortening latency time, and increasing platform crossings in Morris water maze test. Baicalein significantly inhibited the releases of inflammatory mediators such as nitric oxide, interleukin-6, interleukin-1 beta, and tumor necrosis factor-α in d-gal-induced aging model. Metabolomic study revealed that 10 endogenous metabolites in cerebral cortex were considered as potential biomarkers of baicalein for its protective effect. Further metabolic pathway analysis showed that the metabolic alterations were associated with alanine, aspartate and glutamate metabolism, glycine, serine and threonine metabolism, inositol phosphate metabolism, and energy metabolism. These data indicate that baicalein improves learning and memory dysfunction in d-gal-induced aging rats. This might be achieved through attenuation of inflammation and metabolic dysfunction.

1H-NMR Based Serum Metabolomics Study to Investigate Hepatoprotective Effect of Qin-Jiao on Carbon Tetrachloride-Induced Acute Hepatotoxicity in Rats

Gentiana macrophylla Radix, commonly known as Qin-Jiao (QJ), was recorded alone to treat jaundice in Compendium of Materia Medica and has been frequently prescribed for treatment of liver disease in China. However, the underlying mechanism remains unknown. In the present work, QJ of 1,2 g/kg or silybin of 40 mg/kg (positive control) was orally given to rats for 7 days to verify the protective effect on acute liver damage induced by tetrachloride (CCl₄). Together with serum biochemistry and histopathological examination, ¹H-NMR based metabolomics work was carried out to investigate the efficacy. It turned out that QJ of 2 g/kg exerted comparable protective effect with positive control and partially recovered disturbed metabolism by CCl₄. Multivariate analysis was conducted and metabolites altered significantly among groups were assigned and discussed, including betaine, glucose, lactate, creatine, and LDL/VLDL. Metabolic regulations involved in QJ or silybin treatment were as follows: tricarboxylic acid (TCA) cycle, synthesis of LDL/VLDL, and gluconeogenesis were enhanced, while betaine metabolism, glycolysis, creatine metabolism, synthesis of ketone bodies, amino acids metabolism, and β-oxidation of fatty acids were suppressed. For the first time hepatoprotective effect of QJ on acute liver damage was revealed by ¹H-NMR based metabolomics, prompting understanding of the underlying mechanism.

A layer-by-layer assembled MoS2 thin film as an efficient platform for laser desorption/ionization mass spectrometry analysis of small molecules

A chip-based platform for laser desorption/ionization mass spectrometry (LDI-MS) analysis of small molecules was developed by utilizing layer-by-layer (LBL) assembly of MoS2 nanoflakes and polyallylamine on an arbitrary substrate. The LDI-MS efficiency of small molecules on MoS2 films increased as a function of LBL assembly cycles until reaching a saturation point. The optimized MoS2 nanoflake film exhibits high LDI-MS efficiency, salt tolerance, reusability and uniform ionic signal distribution, and its performance was further enhanced by surface modification with perfluoroalkanes mimicking a clathrate nanostructure.

Studies on effect of Ginkgo biloba L. leaves in acute gout with hyperuricemia model rats by using UPLC-ESI-Q-TOF/MS metabolomic approach

The therapeutic effects of GBE on acute gout with hyperuricemia rats were investigated by using a metabolomic approach.

Molecular Mechanisms and Metabolomics of Natural Polyphenols Interfering with Breast Cancer Metastasis

Metastatic cancers are the main cause of cancer-related death. In breast primary cancer, the five-year survival rate is close to 100%; however, for metastatic breast cancer, that rate drops to a mere 25%, due in part to the paucity of effective therapeutic options for treating metastases. Several in vitro and in vivo studies have indicated that consumption of natural polyphenols significantly reduces the risk of cancer metastasis. Therefore, this review summarizes the research findings involving the molecular mechanisms and metabolomics of natural polyphenols and how they may be blocking breast cancer metastasis. Most natural polyphenols are thought to impair breast cancer metastasis through downregulation of MMPs expression, interference with the VEGF signaling pathway, modulation of EMT regulator, inhibition of NF-κB and mTOR expression, and other related mechanisms. Intake of natural polyphenols has been shown to impact endogenous metabolites and complex biological metabolic pathways in vivo. Breast cancer metastasis is a complicated process in which each step is modulated by a complex network of signaling pathways. We hope that by detailing the reported interactions between breast cancer metastasis and natural polyphenols, more attention will be directed to these promising candidates as effective adjunct therapies against metastatic breast cancer in the clinic.

UPLC-MS for metabolomics: a giant step forward in support of pharmaceutical research

Metabolomics is a relatively new and rapidly growing area of post-genomic biological research. As use of metabolomics technology grows throughout the spectrum of drug discovery and development, and its applications broaden, its impact is expanding dramatically. This review seeks to provide the reader with a brief history of the development of metabolomics, its significance and strategies for conducting metabolomics studies. The most widely used analytical tools for metabolomics: NMR, LC-MS and GC-MS, are discussed along with considerations for their use. Herein, we will show how metabolomics can assist in pharmaceutical research studies, such as pharmacology and toxicology, and discuss some examples of the importance of metabolomics analysis in research and development.

Metabolic effects of basic fibroblast growth factor in streptozotocin-induced diabetic rats: A 1H NMR-based metabolomics investigation

The fibroblast growth factors (FGFs) family shows a great potential in the treatment of diabetes, but little attention is paid to basic FGF (bFGF). In this study, to explore the metabolic effects of bFGF on diabetes, metabolic changes in serum and feces were analyzed in the normal rats, the streptozocin (STZ)-induced diabetic rats and the bFGF-treated diabetic rats using a ¹H nuclear magnetic resonance (NMR)-based metabolomic approach. Interestingly, bFGF treatment significantly decreased glucose, lipid and low density lipoprotein/very low density lipoprotein (LDL/VLDL) levels in serum of diabetic rats. Moreover, bFGF treatment corrected diabetes-induced reductions in citrate, lactate, choline, glycine, creatine, histidine, phenylalanine, tyrosine and glutamine in serum. Fecal propionate was significantly increased after bFGF treatment. Correlation analysis shows that glucose, lipid and LDL/VLDL were significantly negatively correlated with energy metabolites (citrate, creatine and lactate) and amino acids (alanine, glycine, histidine, phenylalanine, tyrosine and glutamine). In addition, a weak but significant correlation was observed between fecal propionate and serum lipid (R = −0.35, P = 0.046). Based on metabolic correlation and pathway analysis, therefore, we suggest that the glucose and lipid lowering effects of bFGF in the STZ-induced diabetic rats may be achieved by activating microbial metabolism, increasing energy metabolism and correcting amino acid metabolism.

Discrimination of Citrus reticulata Blanco and Citrus reticulata 'Chachi' by gas chromatograph-mass spectrometry based metabolomics approach

Citri Reticulatae Pericarpium, mainly including the pericarp of Citrus reticulata Blanco and the pericarp of Citrus reticulata 'Chachi', has been consumed daily as food and dietary supplement for centuries. In this study, GC-MS based metabolomics was employed to compare comprehensively the volatile constituents in Citrus reticulata Blanco and Citrus reticulata 'Chachi'. Principal component analysis and orthogonal partial least squares discrimination analysis indicated that samples could be distinguished effectively from one another. Fifteen metabolites were finally identified for use as chemical markers in discrimination of Citri Reticulatae Pericarpium samples. The antimicrobial activity against Gram-negative and Gram-positive bacteria of the volatile oil from Citrus reticulata Blanco and Citrus reticulata 'Chachi' was investigated preliminarily.

Metabolic Profiling Regarding Pathogenesis of Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive, eventually fatal disease characterized by fibrosis of the lung parenchyma and loss of lung function. IPF is believed to be caused by repetitive alveolar epithelial cell injury and dysregulated repair process including uncontrolled proliferation of lung (myo) fibroblasts and excessive deposition of extracellular matrix proteins in the interstitial space; however, the pathogenic pathways involved in IPF have not been fully elucidated. In this study, we attempted to characterize metabolic changes of lung tissues involved in the pathogenesis of IPF using gas chromatography-mass spectrometry-based metabolic profiling. Partial least-squares discriminant analysis (PLS-DA) model generated from metabolite data was able to discriminate between the control subjects and IPF patients (R(2)X = 0.37, R(2)Y = 0.613 and Q(2) (cumulative) = 0.54, receiver operator characteristic AUC > 0.9). We discovered 25 metabolite signatures of IPF using both univariate and multivariate statistical analyses (FDR < 0.05 and VIP score of PLS-DA > 1). These metabolite signatures indicated alteration in metabolic pathways: adenosine triphosphate degradation pathway, glycolysis pathway, glutathione biosynthesis pathway, and ornithine aminotransferase pathway. The results could provide additional insight into understanding the disease and potential for developing biomarkers.

Metabolic analysis of osteoarthritis subchondral bone based on UPLC/Q-TOF-MS

Osteoarthritis (OA), one of the most widespread musculoskeletal joint diseases among the aged, is characterized by the progressive loss of articular cartilage and continuous changes in subchondral bone. The exact pathogenesis of osteoarthritis is not completely clear. In this work, ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UPLC/Q-TOF-MS) in combination with multivariate statistical analysis was applied to analyze the metabolic profiling of subchondral bone from 42 primary osteoarthritis patients. This paper described a modified two-step method for extracting the metabolites of subchondral bone from primary osteoarthritis patients. Finally, 68 metabolites were identified to be significantly changed in the sclerotic subchondral bone compared with the non-sclerotic subchondral bone. Taurine and hypotaurine metabolism and beta-alanine metabolism were probably relevant to the sclerosis of subchondral bone. Taurine, L-carnitine, and glycerophospholipids played a vital regulation role in the pathological process of sclerotic subchondral bone. In the sclerotic process, beta-alanine and L-carnitine might be related to the increase of energy consumption. In addition, our findings suggested that the intra-cellular environment of sclerotic subchondral bone might be more acidotic and hypoxic compared with the non-sclerotic subchondral bone. In conclusion, this study provided a new insight into the pathogenesis of subchondral bone sclerosis. Our results indicated that metabolomics could serve as a promising approach for elucidating the pathogenesis of subchondral bone sclerosis in primary osteoarthritis. Graphical Abstract Metabolic analysis of osteoarthritis subchondral bone.

Serum metabolomic profiling facilitates the non-invasive identification of metabolic biomarkers associated with the onset and progression of non-small cell lung cancer

Lung cancer (LC) is responsible for most cancer deaths. One of the main factors contributing to the lethality of this disease is the fact that a large proportion of patients are diagnosed at advanced stages when a clinical intervention is unlikely to succeed. In this study, we evaluated the potential of metabolomics by 1H-NMR to facilitate the identification of accurate and reliable biomarkers to support the early diagnosis and prognosis of non-small cell lung cancer (NSCLC).We found that the metabolic profile of NSCLC patients, compared with healthy individuals, is characterized by statistically significant changes in the concentration of 18 metabolites representing different amino acids, organic acids and alcohols, as well as different lipids and molecules involved in lipid metabolism. Furthermore, the analysis of the differences between the metabolic profiles of NSCLC patients at different stages of the disease revealed the existence of 17 metabolites involved in metabolic changes associated with disease progression.Our results underscore the potential of metabolomics profiling to uncover pathophysiological mechanisms that could be useful to objectively discriminate NSCLC patients from healthy individuals, as well as between different stages of the disease.

Types, principle, and characteristics of tandem high-resolution mass spectrometry and its applications

We review the principle and functional characteristics of different types or models for THRMS and provide a brief description of its applications in medical research, food safety, and environmental protection fields.