Emerging applications of metabolomics in drug discovery and precision medicine

Tryptophan-5-HT pathway disorder was uncovered in the olfactory bulb of a depression mice model by metabolomic analysis

Major depression (MD) is a severe mental illness that creates a heavy social burden, and the potential molecular mechanisms remain largely unknown. Lots of research demonstrate that the olfactory bulb is associated with MD. Recently, gas chromatography-mass spectrometry-based metabolomic studies on depressive rats indicated that metabolisms of purine and lipids were disordered in the olfactory bulb. With various physicochemical properties and extensive concentration ranges, a single analytical technique could not completely cover all metabolites, hence it is necessary to adopt another metabolomic technique to seek new biomarkers or molecular mechanisms for depression. Therefore, we adopted a liquid chromatography-mass spectrometry metabonomic technique in the chronic mild stress (CMS) model to investigate significant metabolic changes in the olfactory bulb of the mice. We discovered and identified 16 differential metabolites in the olfactory bulb of the CMS treatments. Metabolic pathway analysis by MetaboAnalyst 5.0 was generated according to the differential metabolites, which indicated that the tryptophan metabolism pathway was the core pathogenesis in the olfactory bulb of the CMS depression model. Further, the expressions of tryptophan hydroxylase (TpH) and aromatic amino acid decarboxylase (AAAD) were detected by western blotting and immunofluorescence staining. The expression of TpH was increased after CMS treatment, and the level of AAAD was unaltered. These results revealed that abnormal metabolism of the tryptophan pathway in the olfactory bulb mediated the occurrence of MD.

Metabolomics detects clinically silent neuroinflammatory lesions earlier than neurofilament-light chain in a focal multiple sclerosis animal model

Background

Despite widespread searches, there are currently no validated biofluid markers for the detection of subclinical neuroinflammation in multiple sclerosis (MS). The dynamic nature of human metabolism in response to changes in homeostasis, as measured by metabolomics, may allow early identification of clinically silent neuroinflammation. Using the delayed-type hypersensitivity (DTH) MS rat model, we investigated the serum and cerebrospinal fluid (CSF) metabolomics profiles and neurofilament-light chain (NfL) levels, as a putative marker of neuroaxonal damage, arising from focal, clinically silent neuroinflammatory brain lesions and their discriminatory abilities to distinguish DTH animals from controls.

Methods

¹H nuclear magnetic resonance (NMR) spectroscopy metabolomics and NfL measurements were performed on serum and CSF at days 12, 28 and 60 after DTH lesion initiation. Supervised multivariate analyses were used to determine metabolomics differences between DTH animals and controls. Immunohistochemistry was used to assess the extent of neuroinflammation and tissue damage.

Results

Serum and CSF metabolomics perturbations were detectable in DTH animals (vs. controls) at all time points, with the greatest change occurring at the earliest time point (day 12) when the neuroinflammatory response was most intense (mean predictive accuracy [SD]—serum: 80.6 [10.7]%, p < 0.0001; CSF: 69.3 [13.5]%, p < 0.0001). The top discriminatory metabolites at day 12 (serum: allantoin, cytidine; CSF: glutamine, glucose) were all reduced in DTH animals compared to controls, and correlated with histological markers of neuroinflammation, particularly astrogliosis (Pearson coefficient, r—allantoin: r = − 0.562, p = 0.004; glutamine: r = − 0.528, p = 0.008). Serum and CSF NfL levels did not distinguish DTH animals from controls at day 12, rather, significant differences were observed at day 28 (mean [SEM]—serum: 38.5 [4.8] vs. 17.4 [2.6] pg/mL, p = 0.002; CSF: 1312.0 [379.1] vs. 475.8 [74.7] pg/mL, p = 0.027). Neither serum nor CSF NfL levels correlated with markers of neuroinflammation; serum NfL did, however, correlate strongly with axonal loss (r = 0.641, p = 0.001), but CSF NfL did not (p = 0.137).

Conclusions

While NfL levels were elevated later in the pathogenesis of the DTH lesion, serum and CSF metabolomics were able to detect early, clinically silent neuroinflammation and are likely to present sensitive biomarkers for the assessment of subclinical disease activity in patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12974-022-02614-8.

Metabolic, genetic, and pharmacokinetic parameters for the prediction of olanzapine efficacy

Clinical use of the a olanzapine has significantly different individual-to-individual outcomes. Accordingly, this study aimed to develop a means of predicting response to olanzapine using a combined approach based on pharmacokinetics, pharmacometabonomics, and genetic polymorphism. The olanzapine pharmacokinetics of 19 healthy volunteers treated with orally disintegrating tablets were determined using high-performance liquid chromatography-tandem mass spectrometry. Metabolic profiling and phenotyping were performed on the blood samples that remained after pharmacokinetic analysis using ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry. Uridine diphosphate-glucuronosyltransferase (UGT), tyrosine hydroxylase (TH), γ-aminobutyric acid transaminase (GABA-T), and succinic semialdehyde dehydrogenase (SSADH) were identified as key genes. The single nucleotide polymorphism genotypes most related to drug metabolism were investigated by polymerase chain reaction and Sanger sequencing. Forty-one metabolites (p < 0.05) are increased or decreased after treatment with olanzapine. Tryptophan metabolism, norepinephrine metabolism, and γ-aminobutyric acid metabolism were identified as being related to the effects of olanzapine. Subjects carrying rs1641031 AC and CC exhibited a 59.2% increase in the mean peak concentration (C_max) value and a 25.33% decrease in the mean oral clearance rate (CL/F) value, compared to that in subjects with the GABA-T rs1641031 AA genotype (p < 0.05). Moreover, polymorphism of the GABA-T gene has an impact on the metabolism of 5-hydroxytryptamine. Lysophosphatidylethanolamine (0:0/18:3), lysophosphatidylethanolamine (0:0/22:5), and octadecatrienoic acid distinguish subjects with high and low olanzapine drug oral clearance and are thus identified as biomarkers for predicting its efficacy.

Pharmacometabolomics-Based Translational Biomarkers: How to Navigate the Data Ocean

Metabolome is the end point of the genome-environment interplay, and enables an important holistic overview of individual adaptability and host responses to environmental, ecological, as well as endogenous changes such as disease. Pharmacometabolomics is the application of metabolome knowledge to decipher the mechanisms of interindividual and intraindividual variations in drug efficacy and safety. Pharmacometabolomics also contributes to prediction of drug treatment outcomes on the basis of baseline (predose) and postdose metabotypes through mathematical modeling. Thus, pharmacometabolomics is a strong asset for a diverse community of stakeholders interested in theory and practice of evidence-based and precision/personalized medicine: academic researchers, public health scholars, health professionals, pharmaceutical, diagnostics, and biotechnology industries, among others. In this expert review, we discuss pharmacometabolomics in four contexts: (1) an interdisciplinary omics tool and field to map the mechanisms and scale of interindividual variability in drug effects, (2) discovery and development of translational biomarkers, (3) advance digital biomarkers, and (4) empower drug repurposing, a field that is increasingly proving useful in the current era of Covid-19. As the applications of pharmacometabolomics are growing rapidly in the current postgenome era, next-generation proteomics and metabolomics follow the example of next-generation sequencing analyses. Pharmacometabolomics can also empower data reliability and reproducibility through multiomics integration strategies, which use each data layer to correct, connect with, and inform each other. Finally, we underscore here that contextual data remain crucial for precision medicine and drug development that stand the test of time and clinical relevance.

Advances in multi-omics study of biomarkers of glycolipid metabolism disorder

Glycolipid metabolism disorder are major threats to human health and life. Genetic, environmental, psychological, cellular, and molecular factors contribute to their pathogenesis. Several studies demonstrated that neuroendocrine axis dysfunction, insulin resistance, oxidative stress, chronic inflammatory response, and gut microbiota dysbiosis are core pathological links associated with it. However, the underlying molecular mechanisms and therapeutic targets of glycolipid metabolism disorder remain to be elucidated. Progress in high-throughput technologies has helped clarify the pathophysiology of glycolipid metabolism disorder. In the present review, we explored the ways and means by which genomics, transcriptomics, proteomics, metabolomics, and gut microbiomics could help identify novel candidate biomarkers for the clinical management of glycolipid metabolism disorder. We also discuss the limitations and recommended future research directions of multi-omics studies on these diseases.

massDatabase: utilities for the operation of the public compound and pathway database

SUMMARY

One of the major challenges in liquid chromatography coupled to mass spectrometry data is converting many metabolic feature entries to biological function information, such as metabolite annotation and pathway enrichment, which are based on the compound and pathway databases. Multiple online databases have been developed. However, no tool has been developed for operating all these databases for biological analysis. Therefore, we developed massDatabase, an R package that operates the online public databases and combines with other tools for streamlined compound annotation and pathway enrichment. massDatabase is a flexible, simple and powerful tool that can be installed on all platforms, allowing the users to leverage all the online public databases for biological function mining. A detailed tutorial and a case study are provided in the Supplementary Material.

AVAILABILITY AND IMPLEMENTATION

https://massdatabase.tidymass.org/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The berberine-enriched gut commensal Blautia producta ameliorates high-fat diet (HFD)-induced hyperlipidemia and stimulates liver LDLR expression

Berberine (BBR) is an effective cholesterol-lowering drug. Although gut microbiota has been implicated in the pharmacological activities of BBR, little evidence exists on the specific species of gut microbiota involved in its therapeutic effects, nor on linking gut bacteria to its recognized hypercholesterolemia-alleviating mechanism-upregulation of the low-density lipoprotein receptor (LDLR) in the liver. The present study was performed to identify the specific species of gut microbiota involved in the anti-hyperlipdemic effect of BBR, and interpret its mechanism through linking the gut microbiota and LDLR. The BBR-enriched gut bacterial species were identified by whole genome shotgun sequencing. Pure cultured B. producta was orally administered to C57BL/6 mice to evaluate its anti-hyperlipdemic effect. The LDLR-upregulating effect of B. producta was evaluated both in vitro and in vivo. Orally administration of BBR (200 mg/kg) decreased serum and liver lipid levels in HFD-induced hyperlipidemic mice. Microbiome analysis indicated that Blautia was closely associated with BBR's lipid-modulating activities. Further analysis revealed that BBR selectively promoted the growth of Blautia producta. Orally treatment of HFD mice with live B. producta reduced obesity and alleviated hyperlipidemia. Notably, the B. producta significantly increased LDLR expression in the liver, and its spent culture supernatant upregulated the LDLR level and promoted LDL uptake by HepG2 cells. Simultaneously, B. producta also linked butyrate-producing and bile salt hydrolase (BSH)-inhibiting effect of BBR. The gut microbiota, especially B. producta, may confers the hypercholesterolemia-alleviating effects of berberine. B. producta represents a novel probiotic that may be used for the treatment of dyslipidemia.

Urine NMR Metabolomics for Precision Oncology in Colorectal Cancer

Metabolomics is a fundamental approach to discovering novel biomarkers and their potential use for precision medicine. When applied for population screening, NMR-based metabolomics can become a powerful clinical tool in precision oncology. Urine tests can be more widely accepted due to their intrinsic non-invasiveness. Our review provides the first exhaustive evaluation of NMR metabolomics for the determination of colorectal cancer (CRC) in urine. A specific search in PubMed, Web of Science, and Scopus was performed, and 10 studies met the required criteria. There were no restrictions on the query for study type, leading to not only colorectal cancer samples versus control comparisons, but also prospective studies of surgical effects. With this review, all compounds in the included studies were merged into a database. In doing so, we identified up to 100 compounds in urine samples, and 11 were found in at least three articles. Results were analyzed in three groups: case (CRC and adenomas)/control, pre-/post-surgery, and combining both groups. When combining the case-control and the pre-/post-surgery groups, up to twelve compounds were found to be relevant. Seven down-regulated metabolites in CRC were identified, creatinine, 4-hydroxybenzoic acid, acetone, carnitine, d-glucose, hippuric acid, l-lysine, l-threonine, and pyruvic acid, and three up-regulated compounds in CRC were identified, acetic acid, phenylacetylglutamine, and urea. The pathways and enrichment analysis returned only two pathways significantly expressed: the pyruvate metabolism and the glycolysis/gluconeogenesis pathway. In both cases, only the pyruvic acid (down-regulated in urine of CRC patients, with cancer cell proliferation effect in the tissue) and acetic acid (up-regulated in urine of CRC patients, with chemoprotective effect) were present.

Discovery of potential hypoglycemic metabolites in Cassiae Semen by coupling UHPLC-QTOF-MS/MS combined plant metabolomics and spectrum-effect relationship analyses

Cassiae Semen (CS) is consumed as fried tea or medicinal food in Asian areas. Its two commercial forms, namely raw and fried CS, exert different clinical applications, in which fried CS is commonly applied as a functional tea for losing weight. To prevent confusion in the use of the two forms of CS, a comprehensive strategy by combining plant metabolomics and spectrum-effect relationship analyses was developed for the fast and efficient discrimination of raw and fried CS, and further for the discovery of the potential hypoglycemic metabolites of CS to control its quality. First, the plant metabolic profiling of raw and processed samples was performed by UHPLC-QTOF-MS/MS. A total of 1111 differential metabolites were found to well distinguish the raw and fried CS after analyzing by MPP software. Subsequently, α-glucosidase inhibition of raw and fried CS was investigated. As a result, fried CS demonstrated much stronger α-glucosidase inhibition activity than the raw sample. By analyzing the spectrum-effect relationship with GRA, BCA, and PLSR, 14 potential hypoglycemic-related compounds were discovered. As anticipated, they were also found as the differential metabolites of the raw and fried samples with a potential hypoglycemic effect, which might be beneficial for the quality control of CS tea. Additionally, molecular docking analysis was conducted to reveal the underlying inhibition mechanisms of the four most critical constituents, including physcion, chrysoobtusin, aurantio-obtusin, and obtusifolin. This study provides a powerful tool for the discrimination of processed samples and fast screening of the active constituents in complex natural products on a high-throughput basis.

Exploring Metabolic Signatures of Ex Vivo Tumor Tissue Cultures for Prediction of Chemosensitivity in Ovarian Cancer

Simple Summary

Women diagnosed with ovarian cancer have 5-year survival rates below 45%. Prediction of patient’s outcome and the onset of drug resistance are still major challenges. The patient’s drug response is influenced by the environment that surrounds the tumor cells. We previously showed that patient-derived tumor tissue can be kept in the lab, alive and retaining aspects of that environment. In this study, we exposed tumor tissue derived from ovarian cancer patients to the chemotherapy patients receive and identified metabolites released by the tumor tissue after treatment (metabolic footprint). Using machine learning, we uncovered metabolic signatures that discriminate tumor tissues with higher vs. lower drug sensitivity. We propose potential biomarkers involved in the production of specific building blocks of cells and energy generation processes. Overall, we established a platform to explore metabolic features of the complex environment of each patient’s tumor that can underpin the discovery of biomarkers of drug response.

Abstract

Predicting patient response to treatment and the onset of chemoresistance are still major challenges in oncology. Chemoresistance is deeply influenced by the complex cellular interactions occurring within the tumor microenvironment (TME), including metabolic crosstalk. We have previously shown that ex vivo tumor tissue cultures derived from ovarian carcinoma (OvC) resections retain the TME components for at least four weeks of culture and implemented assays for assessment of drug response. Here, we explored ex vivo patient-derived tumor tissue cultures to uncover metabolic signatures of chemosensitivity and/or resistance. Tissue cultures derived from nine OvC cases were challenged with carboplatin and paclitaxel, the standard-of-care chemotherapeutics, and the metabolic footprints were characterized by LC-MS. Partial least-squares discriminant analysis (PLS-DA) revealed metabolic signatures that discriminated high-responder from low-responder tissue cultures to ex vivo drug exposure. As a proof-of-concept, a set of potential metabolic biomarkers of drug response was identified based on the receiver operating characteristics (ROC) curve, comprising amino acids, fatty acids, pyrimidine, glutathione, and TCA cycle pathways. Overall, this work establishes an analytical and computational platform to explore metabolic features of the TME associated with response to treatment, which can leverage the discovery of biomarkers of drug response and resistance in OvC.

Integrated pyroptosis measurement and metabolomics to elucidate the effect and mechanism of tangzhiqing on atherosclerosis

Tangzhiqing formula (TZQ) is a traditional Chinese medicine prescribed to treat glucose and lipid metabolism disorders. A significant effect of TZQ on diabetes and hyperlipidemia has been demonstrated, but its effect on atherosclerosis (AS) remains unknown. This study combines pyroptosis with metabolomics to elucidate the effect and mechanism of TZQ on AS. A model of AS was developed using ApoE^−/− mice fed a high-fat diet for 8 weeks. After 6 weeks of atorvastatin (Ator) or TZQ treatment, aortic lumen diameter, aortic lesion size, serum lipid profile, cytokines, and Nod-like receptor protein 3 (NLRP3) inflammasome-mediated pyroptosis were analyzed. Serum metabolomics profiles were obtained to examine the effect of TZQ on AS and the correlation between pyroptosis and metabolites was further analyzed. As a result, TZQ significantly reduced the diameter of the common carotid artery during diastole and the blood flow velocity in the aorta during systole; reduced blood lipid levels, arterial vascular plaques, and the release of inflammatory cytokines; and inhibited the NLRP3 inflammasome-mediated pyroptosis. According to metabolomics profiling, TZQ is engaged in the treatment of AS via altering arachidonic acid metabolism, glycerophospholipid metabolism, steroid hormone production, and unsaturated fatty acid biosynthesis. The cytochrome P450 enzyme family and cyclooxygenase 2 (COX-2) are two major metabolic enzymes associated with pyroptosis.

New Sulfamethoxazole Derivatives as Selective Carbonic Anhydrase IX and XII Inhibitors: Design, Synthesis, Cytotoxic Activity and Molecular Modeling

In this study new sulphamethoxazole derivatives (S1-S4, S6-S12, and S14-S22) were designed and synthesized and their structures were fully characterized and validated using NMR, mass, and IR spectroscopy, as well as elemental analyses. All new derivatives (S1-S22) were assayed against human carbonic anhydrase (hCAs IX and XII) for their inhibitory activities. hCAs IX and XII were chosen due to the fact that CAIX expression is recognized as a hypoxia marker with a poor prognosis in breast cancer. When compared to Dorzolamide HCl as a standard reference, derivatives S2, S3, S8, S9, and S15 had the most effective inhibition with low IC₅₀ values. The active compounds were further evaluated against hCAs I and II inhibitory activity and compounds S8, S9 and S15 showed the least inhibitory effect compared to the reference standard, acetazolamide, indicating that their effect in normal cells is the lowest. Cell viability tests for the selected compounds were carried out on MCF7 (normoxia and hypoxia) and on the normal breast cell line (MCF10a) with Staurosporine as a standard. The results showed that compound S15 had a highly potent cytotoxic effect. Furthermore, cell cycle analysis results showed that compound S15 triggered cell cycle arrest and apoptosis in G1/S of MCF7 cancer cells. Finally, molecular docking was performed to point out the possible explanation for the vital structural features and key-interactions exerted by our ligands with hCAs IX and XII that might share additional designs and highlight possible leads for a hopeful anticancer agent. Consequently, sulphamethoxazole Derivative S15 could be the potential lead for emerging selective cytotoxic compounds directing h CAs IX and XII.

Characterization of lipomatous tumors with high-resolution 1H MRS at 17.6T: Do benign lipomas, atypical lipomatous tumors and liposarcomas have a distinct metabolic signature?

Background

Distinguishing between some benign lipomas (BLs), atypical lipomatous tumors (ALTs), and dedifferentiated liposarcomas (DDLs) can be challenging due to overlapping magnetic resonance imaging characteristics, and poorly understood molecular mechanisms underlying the malignant transformation of liposarcomas.

Purpose

To identify metabolic biomarkers of the lipomatous tumor spectrum by examining human tissue specimens using high-resolution ¹H magnetic resonance spectroscopy (MRS).

Materials and methods

In this prospective study, human tissue specimens were obtained from participants who underwent surgical resection for radiologically-indeterminate lipomatous tumors between November 2016 and May 2019. Tissue specimens were obtained from normal subcutaneous fat (n=9), BLs (n=10), ALTs (n=7) and DDLs (n=8). Extracts from specimens were examined with high-resolution MRS at 17.6T. Computational modeling of pattern recognition-based cluster analysis was utilized to identify significant differences in metabolic signatures between the lipomatous tumor types.

Results

Significant differences between BLs and ALTs were observed for multiple metabolites, including leucine, valine, branched chain amino acids, alanine, acetate, glutamine, and formate. DDLs were distinguished from ALTs by increased glucose and lactate, and increased phosphatidylcholine. Multivariate principal component analysis showed clear clustering identifying distinct metabolic signatures of the tissue types.

Conclusion

Metabolic signatures identified in ¹H MR spectra of lipomatous tumors provide new insights into malignant progression and metabolic targeting. The metabolic patterns identified provide the foundation of developing noninvasive MRS or PET imaging biomarkers to distinguish between BLs, ALTs, and DDLs.

Optic Neuritis in Multiple Sclerosis—A Review of Molecular Mechanisms Involved in the Degenerative Process

Multiple sclerosis is a central nervous system inflammatory demyelinating disease with a wide range of clinical symptoms, ocular involvement being frequently marked by the presence of optic neuritis (ON). The emergence and progression of ON in multiple sclerosis is based on various pathophysiological mechanisms, disease progression being secondary to inflammation, demyelination, or axonal degeneration. Early identification of changes associated with axonal degeneration or further investigation of the molecular processes underlying remyelination are current concerns of researchers in the field in view of the associated therapeutic potential. This article aims to review and summarize the scientific literature related to the main molecular mechanisms involved in defining ON as well as to analyze existing data in the literature on remyelination strategies in ON and their impact on long-term prognosis.

Metabolomic analysis in spondyloarthritis: A systematic review

Spondyloarthritis (SpA) is a group of rheumatic diseases that cause joint inflammation. Accumulating studies have focused on the metabolomic profiling of SpA in recent years. We conducted a systematic review to provide a collective summary of previous findings on metabolomic profiling associated with SpA. We systematically searched PubMed, Medline, Embase and Web of Science for studies on comparisons of the metabolomic analysis of SpA patients and non-SpA controls. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of the included articles. From 482 records identified, 31 studies were included in the analysis. A number of metabolites were differentially distributed between SpA and non-SpA cases. SpA patients showed higher levels of glucose, succinic acid, malic acid and lactate in carbohydrate metabolism, higher glycerol levels and lower fatty acid (especially unsaturated fatty acid) levels in lipid metabolism, and lower levels of tryptophan and glutamine in amino acid metabolism than healthy controls. Both conventional and biological therapy of SpA can insufficiently reverse the aberrant metabolism state toward that of the controls. However, the differences in the results of metabolic profiling between patients with SpA and other inflammatory diseases as well as among patients with several subtypes of SpA are inconsistent across studies. Studies on metabolomics have provided insights into etiological factors and biomarkers for SpA. Supplementation with the metabolites that exhibit decreased levels, such as short-chain fatty acids (SCFAs), has good treatment prospects for modulating immunity. Further studies are needed to elucidate the role of disordered metabolic molecules in the pathogenesis of SpA.

The Metabolomic Approach for the Screening of Endometrial Cancer: Validation from a Large Cohort of Women Scheduled for Gynecological Surgery

Endometrial cancer (EC) is the most common gynecological neoplasm in high-income countries. Five-year survival rates are related to stage at diagnosis, but currently, no validated screening tests are available in clinical practice. The metabolome offers an unprecedented overview of the molecules underlying EC. In this study, we aimed to validate a metabolomics signature as a screening test for EC on a large study population of symptomatic women. Serum samples collected from women scheduled for gynecological surgery (n = 691) were separated into training (n = 90), test (n = 38), and validation (n = 563) sets. The training set was used to train seven classification models. The best classification performance during the training phase was the PLS-DA model (96% accuracy). The subsequent screening test was based on an ensemble machine learning algorithm that summed all the voting results of the seven classification models, statistically weighted by each models’ classification accuracy and confidence. The efficiency and accuracy of these models were evaluated using serum samples taken from 871 women who underwent endometrial biopsies. The EC serum metabolomes were characterized by lower levels of serine, glutamic acid, phenylalanine, and glyceraldehyde 3-phosphate. Our results illustrate that the serum metabolome can be an inexpensive, non-invasive, and accurate EC screening test.

Acupuncture at Taichong and Zusanli points exerts hypotensive effect in spontaneously hypertensive rats by metabolomic analysis

BACKGROUND

The development of hypertension affects several target organs, the kidneys being one of them. Acupuncture has been used to treat hypertension for a long time. Several mechanisms of acupuncture on hypotensive effect have been reveled, while the effects of acupuncture on the alterations in renal cortex from a metabolomic perspective are still unclear.

METHODS

Twelve male Wistar rats served as the control group (Wistar Group). Twenty-four male spontaneously hypertensive rats (SHR) were randomly divided into two groups: the model group (SHR Group) and the acupuncture group (AC Group). In the AC Group, milli-needle acupuncture was used to puncture the bilateral Taichong (LR3) and Zusanli (ST36) points. Blood pressure values were measured weekly and the rats were euthanized after three weeks. Renal cortical tissues were collected for non-targeted and targeted metabolomic analyses.

RESULTS

Acupuncture reduced blood pressure values in rats (Compared with the SHR Group, P < 0.001). Thirteen metabolites with significant differences and three metabolic pathways were screened by untargeted metabolomics. The SHR Group was compared with the Wistar Group and AC Group both involving metabolites and pathways related to bile acid metabolism. Furthermore, targeted metabolomics quantification of four bile acids, Cholic acid (CA), Allocholic acid (ACA), Deoxycholic acid (DCA) and Chenodeoxycholic acid (CDCA), revealed that all bile acid concentrations were relatively high in the SHR Group, except for ACA.

CONCLUSION

This study indicate that abnormal bile acid metabolism may be an independent risk factor the development of hypertension. Acupuncture at Taichong and at Zusanli points effectively modulated bile acids metabolism in SHR renal cortex tissues to exert a hypotensive effect, and CA may be able to be a new target for the treatment of hypertension.

Postinfection Metabolic Reprogramming of the Murine Trigeminal Ganglion Limits Herpes Simplex Virus-1 Replication

Herpes simplex virus type-1 (HSV-1) infections are known to alter the host metabolism for efficient propagation in vitro. However, in vivo metabolic perturbations upon prolonged HSV-1 infection remain poorly understood. We used high-resolution liquid chromatography coupled with mass spectrometry (LC-MS) and functional assays to determine the state of the trigeminal ganglion (TG) tissue metabolism upon prolonged corneal HSV-1 infection in a murine model. The metabolomics data indicated significant alterations in the host metabolic profile. After HSV-1 infection, the TG microenvironment assumed downregulation of central carbon metabolism and nucleotide synthesis pathways. We validated our observations using in vitro and ex vivo models through targeted inhibition of crucial metabolic polyamine pathways identified in our metabolomics screen. Our findings collectively suggested that HSV-1 infection altered the host metabolic product regulations that limit the energy and macromolecular precursors required for viral replication.

Dysregulated Metabolic Pathways in Subjects with Obesity and Metabolic Syndrome

Background: Obesity coexists with variable features of metabolic syndrome, which is associated with dysregulated metabolic pathways. We assessed potential associations between serum metabolites and features of metabolic syndrome in Arabic subjects with obesity. Methods: We analyzed a dataset of 39 subjects with obesity only (OBO, n = 18) age-matched to subjects with obesity and metabolic syndrome (OBM, n = 21). We measured 1069 serum metabolites and correlated them to clinical features. Results: A total of 83 metabolites, mostly lipids, were significantly different (p < 0.05) between the two groups. Among lipids, 22 sphingomyelins were decreased in OBM compared to OBO. Among non-lipids, quinolinate, kynurenine, and tryptophan were also decreased in OBM compared to OBO. Sphingomyelin is negatively correlated with glucose, HbA1C, insulin, and triglycerides but positively correlated with HDL, LDL, and cholesterol. Differentially enriched pathways include lysine degradation, amino sugar and nucleotide sugar metabolism, arginine and proline metabolism, fructose and mannose metabolism, and galactose metabolism. Conclusions: Metabolites and pathways associated with chronic inflammation are differentially expressed in subjects with obesity and metabolic syndrome compared to subjects with obesity but without the clinical features of metabolic syndrome.

Comprehensive plasma metabolomics and lipidomics of benign and malignant solitary pulmonary nodules

INTRODUCTION

Solitary pulmonary nodules (SPNs) are commonly found in imaging technologies, but are plagued by high false-positive rates.

OBJECTIVE

We aimed to identify metabolic alterations in SPN etiology and diagnosis using less invasive plasma metabolomics and lipidomics.

METHODS

In total, 1160 plasma samples were obtained from healthy volunteers (n = 280), benign SPNs (n = 157) and malignant SPNs (stage I, n = 723) patients enrolled from 5 independent centers. Gas chromatography-triple quadrupole mass spectrometry (GC‒MS) and liquid chromatography-Q Exactive Hybrid Quadrupole-Orbitrap mass spectrometry (LC‒MS) were used to analyze the samples for untargeted metabolomics and lipidomics.

RESULTS AND CONCLUSION

GC‒MS-based metabolomics revealed 1336 metabolic features, while LC‒MS-based lipidomics revealed 6088 and 2542 lipid features in the positive and negative ion modes, respectively. The metabolic and lipidic characteristics of healthy vs. benign or malignant SPNs exhibited substantial pattern differences. Of note, benign and malignant SPNs had no significant variations in circulating metabolic and lipidic markers and were validated in four other centers. This study demonstrates evidence of early metabolic alterations that can possibly distinguish SPNs from healthy controls, but not between benign and malignant SPNs.

The metabolic fate of oxaliplatin in the biological milieu investigated during in vivo lung perfusion using a unique miniaturized sampling approach based on solid-phase microextraction coupled with liquid chromatography-mass spectrometry

Adjuvant chemotherapy after pulmonary metastasectomy for colorectal cancer may reduce recurrence and improve survival rates; however, the benefits of this treatment are limited by the significant side effects that accompany it. The development of a novel in vivo lung perfusion (IVLP) platform would permit the localized delivery of high doses of chemotherapeutic drugs to target residual micrometastatic disease. Nonetheless, it is critical to continuously monitor the levels of such drugs during IVLP administration, as lung injury can occur if tissue concentrations are not maintained within the therapeutic window. This paper presents a simple chemical-biopsy approach based on sampling with a small nitinol wire coated with a sorbent of biocompatible morphology and evaluates its applicability for the near-real-time in vivo determination of oxaliplatin (OxPt) in a 72-h porcine IVLP survival model. To this end, the pigs underwent a 3-h left lung IVLP with 3 doses of the tested drug (5, 7.5, and 40 mg/L), which were administered to the perfusion circuit reservoir as a bolus after a full perfusion flow had been established. Along with OxPt levels, the biocompatible solid-phase microextraction (SPME) probes were employed to profile other low-molecular-weight compounds to provide spatial and temporal information about the toxicity of chemotherapy or lung injury. The resultant measurements revealed a rather heterogeneous distribution of OxPt (over the course of IVLP) in the two sampled sections of the lung. In most cases, the OxPt concentration in the lung tissue peaked during the second hour of IVLP, with this trend being more evident in the upper section. In turn, OxPt in supernatant samples represented ∼25% of the entire drug after the first hour of perfusion, which may be attributable to the binding of OxPt to albumin, its sequestration into erythrocytes, or its rapid nonenzymatic biotransformation. Additionally, the Bio-SPME probes also facilitated the extraction of various endogenous molecules for the purpose of screening biochemical pathways affected during IVLP (i.e., lipid and amino acid metabolism, steroidogenesis, or purine metabolism). Overall, the results of this study demonstrate that the minimally invasive SPME-based sampling approach presented in this work can serve as (pre)clinical and precise bedside medical tool.

Metabolic profiling of induced acute pancreatitis and pancreatic cancer progression in a mutant Kras mouse model

Untargeted Nuclear Magnetic Resonance (NMR) metabolomics of polar extracts from the pancreata of a caerulin-induced mouse model of pancreatitis (Pt) and of a transgenic mouse model of pancreatic cancer (PCa) were used to find metabolic markers of Pt and to characterize the metabolic changes accompanying PCa progression. Using multivariate analysis a 10-metabolite metabolic signature specific to Pt tissue was found to distinguish the benign condition from both normal tissue and precancerous tissue (low grade pancreatic intraepithelial neoplasia, PanIN, lesions). The mice pancreata showed significant changes in the progression from normal tissue, through low-grade and high-grade PanIN lesions to pancreatic ductal adenocarcinoma (PDA). These included increased lactate production, amino acid changes consistent with enhanced anaplerosis, decreased concentrations of intermediates in membrane biosynthesis (phosphocholine and phosphoethanolamine) and decreased glycosylated uridine phosphates, reflecting activation of the hexosamine biosynthesis pathway and protein glycosylation.

Molecular network strategy in multi-omics and mass spectrometry imaging

Human physiological activities and pathological changes arise from the coordinated interactions of multiple molecules. Mass spectrometry (MS)-based multi-omics and MS imaging (MSI)-based spatial omics are powerful methods used to investigate molecular information related to the phenotype of interest from homogenated or sliced samples, including the qualitative, relative quantitative and spatial distributions. Molecular network strategy provides efficient methods to help us understand and mine the biological patterns behind the phenotypic data. It illustrates and combines various relationships between molecules, and further performs the molecule identification and biological interpretation. Here, we describe the recent advances of network-based analysis and its applications for different biological processes, such as, obesity, central nervous system diseases, and environmental toxicology.

Metabolic-Dysregulation-Based iEESI-MS Reveals Potential Biomarkers Associated with Early-Stage and Progressive Colorectal Cancer

The application of rapid and accurate diagnostic methods can improve colorectal cancer (CRC) survival rates dramatically. Here, we used a non-targeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry (iEESI-MS) to detect metabolite ions associated with the progression of CRC from 172 tissues (45 stage I/II CRC, 41 stage III/IV CRC, and 86 well-matched normal tissues). A support vector machine (SVM) model based on 10 differential metabolite ions for differentiating early-stage CRC from normal tissues was built with a good prediction accuracy of 92.6%. The biomarker panel consisting of lysophosphatidylcholine (LPC) (18:0) has good diagnostic potential in differentiating early-stage CRC from advanced-stage CRC. We showed that the down-regulation of LPC (18:0) in tumor tissues is associated with CRC progression and related to the regulation of the epidermal growth factor receptor. Pathway analysis showed that metabolic pathways in CRC are related to glycerophospholipid metabolism and purine metabolism. In conclusion, we built an SVM model with good performance to distinguish between early-stage CRC and normal groups based on iEESI-MS and found that LPC (18:0) is associated with the progression of CRC.

Deep learning-based pseudo-mass spectrometry imaging analysis for precision medicine

Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics provides systematic profiling of metabolic. Yet, its applications in precision medicine (disease diagnosis) have been limited by several challenges, including metabolite identification, information loss and low reproducibility. Here, we present the deep-learning-based Pseudo-Mass Spectrometry Imaging (deepPseudoMSI) project (https://www.deeppseudomsi.org/), which converts LC-MS raw data to pseudo-MS images and then processes them by deep learning for precision medicine, such as disease diagnosis. Extensive tests based on real data demonstrated the superiority of deepPseudoMSI over traditional approaches and the capacity of our method to achieve an accurate individualized diagnosis. Our framework lays the foundation for future metabolic-based precision medicine.

Application of plasma metabolome for monitoring the effect of rivaroxaban in patients with nonvalvular atrial fibrillation

Rivaroxaban, an oral factor Xa inhibitor, has been used to treating a series of thromboembolic disorders in clinical practice. Measurement of the anticoagulant effect of rivaroxaban is important to avoid serious bleeding events, thus ensuring the safety and efficacy of drug administration. Metabolomics could help to predict differences in the responses among patients by profiling metabolites in biosamples. In this study, plasma metabolomes before and 3 hours after rivaroxaban intake in 150 nonvalvular atrial fibrillation (NVAF) patients and 100 age/gender-matched controls were analyzed by liquid chromatography coupled with mass spectrometry (LC-MS/MS). When compared with controls, a total of thirteen plasma metabolites were differentially expressed in the NVAF patients. Pathway analysis revealed that purine and lipid metabolism were dysregulated. A panel of three metabolites (17a-ethynylestradiol, tryptophyl-glutamate and adenosine) showed good predictive ability to distinguish nonvalvular atrial fibrillation with an area under the receiver operating characteristic curve (AUC) of 1 for the discovery phase and 1 for validation. Under rivaroxaban treatment, a total of seven metabolites changed, the lipid and glycosylphosphatidylinositol biosynthesis pathways were altered and the panel consisting of avocadene, prenyl glucoside and phosphatidylethanolamine showed predictive ability with an AUC of 0.86 for the discovery dataset and 0.82 for the validation. The study showed that plasma metabolomic analyses hold the potential to differentiate nonvalvular atrial fibrillation and can help to monitor the effect of rivaroxaban anticoagulation.

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

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

Integrated metabolomics revealed the fibromyalgia-alleviation effect of Mo2C nanozyme through regulated homeostasis of oxidative stress and energy metabolism

Fibromyalgia (FM), the most common cause of chronic musculoskeletal pain in the general public, lacks advanced therapeutic methodology and detailed bioinformation. However, acting as a newly developed and important transition metal carbide or carbonitride, the Mo2C nanozyme has provided a novel iatrotechnique with excellent bioactivity in a cell/animal model, which also exhibits potential prospects for future clinical applications. In addition, high-content and high-throughput integrated metabolomics (including aqueous metabolomics, lipidomics, and desorption electrospray ionization-mass spectrometry imaging) also specializes in qualitative and quantitative analysis of metabolic shifts at the molecular level. In this work, the FM-alleviation effect of Mo2C nanozyme was investigated through integrated metabolomics in a mouse model. An advanced platform combining gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry and bioinformatics was utilized to study the variation in the mouse metabolome and lipidome. The results revealed that Mo2C treatment could effectively enhance energy metabolism-related biological events impaired by FM, leading to homeostasis of oxidative stress and energy metabolism toward the control levels. During this process, Mo2C facilitated the elimination of ROS in plasma and cells and the rehabilitation of mice from oxidative stress and mitochondrial dysfunction. It was believed that such an integrated metabolomics study on the FM-alleviation effect of Mo2C nanozyme could provide another excellent alternative to traditional Mo2C-based research with numerous pieces of bioinformation, further supporting research area innovation, material modification, and clinical application.

Metabolic Molecular Diagnosis of Inflammatory Bowel Disease by Synergistical Promotion of Layered Titania Nanosheets with Graphitized Carbon

Due to inefficient diagnostic methods, inflammatory bowel disease (IBD) normally progresses into severe complications including cancer. Highly efficient extraction and identification of metabolic fingerprints are of significance for disease surveillance. In this work, we synthesized a layered titania nanosheet doped with graphitized carbon (2D-GC-mTNS) through a simple one-step assembly process for assisting laser desorption ionization mass spectrometry (LDI-MS) for metabolite analysis. Based on the synergistic effect of graphitized carbon and mesoporous titania, 2D-GC-mTNS exhibits good extraction ability including high sensitivity (< 1 fmol µL^-1) and great repeatability toward metabolites. A total of 996 fingerprint spectra were collected from hundreds of native urine samples (including IBD patients and healthy controls), each of which contained 1220 m/z metabolite features. Diagnostic model was further established for precise discrimination of patients from healthy controls, with high area under the curve value of 0.972 and 0.981 toward discovery cohort and validation cohort, respectively. The 2D-GC-mTNS promotes LDI-MS to be close to clinical application, with rapid speed, minimum sample consumption and free of sample pretreatment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00055-0.

TidyMass an object-oriented reproducible analysis framework for LC-MS data

Reproducibility, traceability, and transparency have been long-standing issues for metabolomics data analysis. Multiple tools have been developed, but limitations still exist. Here, we present the tidyMass project ( https://www.tidymass.org/ ), a comprehensive R-based computational framework that can achieve the traceable, shareable, and reproducible workflow needs of data processing and analysis for LC-MS-based untargeted metabolomics. TidyMass is an ecosystem of R packages that share an underlying design philosophy, grammar, and data structure, which provides a comprehensive, reproducible, and object-oriented computational framework. The modular architecture makes tidyMass a highly flexible and extensible tool, which other users can improve and integrate with other tools to customize their own pipeline.

Addition of grapes to both a standard and a high-fat Western pattern diet modifies hepatic and urinary metabolite profiles in the mouse

The benefits of fruit and vegetable dietary consumption are largely defined in epidemiological terms. Relatively little is known about the discrete effects on metabolic pathways elicited by individual dietary fruits and vegetables. To address this, grape powder was added to both a standard and a high-fat Western pattern diet given to 10-week-old female C57BL/6J mice for a period of 91 days, whereupon 24 h urines were collected and the mice euthanized after a 12 h fast for the collection of liver tissue. Alterations in hepatic and urinary metabolite patterns were determined by gas chromatography-mass spectrometry-based metabolomics. Urinary excretion of the gut microbiota metabolites 4-hydroxyphenylacetic acid, 5-hydroxyindole, glyceric acid, gluconic acid and myo-inositol was attenuated when grape was added to the standard diet but the gut microbiota metabolites gluconic acid, scyllo-inositol, mannitol, xylitol, 5-hydroxyindole and 2-deoxyribonic acid were increased in urine when grape was added to the high-fat diet. Increased hepatic ascorbic acid and 5-oxoproline levels indicated the anti-oxidant effect of grape powder on the liver. Pathway enrichment analysis demonstrated that for both standard and high-fat diets, grape addition significantly upregulated the malate-aspartate shuttle indicating enhanced hepatic utilization of glucose via cytosolic glycolysis for mitochondrial ATP production. It is concluded that a grape diet reprogrammes gut microbiota metabolism, attenuates the hepatic oxidative stress of a high-fat diet and increases the efficiency of glucose utilization by the liver for energy production.

Pharmacokinetics and Metabolomic Profiling of Metformin and Andrographis paniculata: A Protocol for a Crossover Randomised Controlled Trial

This protocol aims to profile the pharmacokinetics of metformin and Andrographis paniculata (AP) and continue with untargeted pharmacometabolomics analysis on pre-dose and post-dose samples to characterise the metabolomics profiling associated with the human metabolic pathways. This is a single-centre, open-labelled, three periods, crossover, randomised-controlled, single-dose oral administration pharmacokinetics and metabolomics trial of metformin 1000 mg (n = 18), AP 1000 mg (n = 18), or AP 2000 mg (n = 18) in healthy volunteers under the fasting condition. Subjects will be screened according to a list of inclusion and exclusion criteria. Investigational products will be administered according to the scheduled timeline. Vital signs and adverse events will be monitor periodically, and standardized meals will be provided to the subjects. Fifteen blood samples will be collected over 24 h, and four urine samples will be collected within a 12 h period. Onsite safety monitoring throughout the study and seven-day phone call safety follow-up will be compiled after the last dose of administration. The plasma samples will be analysed for the pharmacokinetics parameters to estimate the drug maximum plasma concentration. Untargeted metabolomic analysis between pre-dose and maximum plasma concentration (Cmax) samples will be performed for metabolomic profiling to identify the dysregulation of human metabolic pathways that link to the pharmacodynamics effects. The metformin arm will focus on the individualised Cmax plasma concentration for metabolomics study and used as a model drug. After this, an investigation of the dose-dependent effects will be performed between pre-dose samples and median Cmax concentration samples in the AP 1000 mg and AP 2000 mg arms for metabolomics study. The study protocol utilises a crossover study design to incorporate a metabolomics-based study into pharmacokinetics trial in the drug development program. The combination analyses will complement the interpretation of pharmacological effects according to the bioavailability of the drug.

In situ identification of environmental microorganisms with Raman spectroscopy

Microorganisms in natural environments are crucial in maintaining the material and energy cycle and the ecological balance of the environment. However, it is challenging to delineate environmental microbes' actual metabolic pathways and intraspecific heterogeneity because most microorganisms cannot be cultivated. Raman spectroscopy is a culture-independent technique that can collect molecular vibration profiles from cells. It can reveal the physiological and biochemical information at the single-cell level rapidly and non-destructively in situ. The first part of this review introduces the principles, advantages, progress, and analytical methods of Raman spectroscopy applied in environmental microbiology. The second part summarizes the applications of Raman spectroscopy combined with stable isotope probing (SIP), fluorescence in situ hybridization (FISH), Raman-activated cell sorting and genomic sequencing, and machine learning in microbiological studies. Finally, this review discusses expectations of Raman spectroscopy and future advances to be made in identifying microorganisms, especially for uncultured microorganisms.

Development and validation of a sensitive and reliable targeted metabolomics method for the quantification of cardiovascular disease-related biomarkers in plasma using ultrahigh-performance liquid chromatography-tandem mass spectrometry

RATIONALE

Cardiovascular disease, as a multifactorial disease caused by genetics and environment, has emerged as a leading cause of mortality. The discovery of metabolic biomarkers for the clinical diagnosis, early warning and elucidation of the molecular pathogenesis of cardiovascular disease, using metabolomics, has attracted broad interest. Therefore, this work aimed to develop a sensitive and reliable targeted metabolomics method for the quantification of cardiovascular disease-related biomarkers in plasma.

METHODS

The method was developed and validated using ultrahigh-performance liquid chromatography augmented with tandem mass spectrometry (UHPLC/MS/MS). The LC conditions and MS parameters were optimized using selected reaction monitoring scanning mode to high-throughput and sensitive separation, and could detect 20 metabolic biomarkers in a single experiment. And the linearity, selectivity, accuracy, precision, stability and recovery of the developed method were assessed according to the Bioanalytical Method Validation guidelines of the United States Food and Drug Administration.

RESULTS

These quantified metabolic biomarkers are involved in pathways such as aromatic amino acid catabolism (e.g. phenylalanine, tryptophan, tyrosine), trimethylamine N-oxide (TMAO) biosynthesis (e.g. TMAO, choline, carnitine, betaine) and histidine metabolism (e.g. histidine), among others. All analytes exhibited excellent linearities with coefficients of determination greater than 0.99. Accuracies deviated by less than 15% for medium- and high-concentration samples and less than 20% for low-concentration samples, with intra- and inter-day precisions of 1.12-14.12% and 0.30-13.74%, respectively. Recoveries and stabilities also met the analysis requirements of biological samples.

CONCLUSIONS

The targeted metabolomics method was shown to have a powerful ability to accurately analyze metabolic biomarkers, thereby providing valuable information for large-scale biomarker validation and clarifying the potential material basis of cardiovascular disease for clinical diagnosis or early warning.

Medicinal Plants for the Treatment of Gastrointestinal Cancers From the Metabolomics Perspective

Gastrointestinal cancer (GIC), primarily including colorectal cancer, gastric cancer, liver cancer, pancreatic cancer, and esophageal cancer, is one of the most common causes of cancer-related deaths with increasing prevalence and poor prognosis. Medicinal plants have been shown to be a great resource for the treatment of GIC. Due to their complex manifestations of multi-component and multi-target, the underlying mechanisms how they function against GIC remain to be completely deciphered. Cell metabolism is of primary importance in the initialization and development of GIC, which is reported to be a potential target. As an essential supplement to the newest “omics” sciences, metabolomics focuses on the systematic study of the small exogenous and endogenous metabolites involved in extensive biochemical metabolic pathways of living system. In good agreement with the systemic perspective of medicinal plants, metabolomics offers a new insight into the efficacy assessment and action mechanism investigation of medicinal plants as adjuvant therapeutics for GIC therapy. In this review, the metabolomics investigations on metabolism-targeting therapies for GIC in the recent 10 years were systematically reviewed from five aspects of carbohydrate, lipid, amino acid, and nucleotide metabolisms, as well as other altered metabolisms (microbial metabolism, inflammation, and oxidation), with particular attention to the potential of active compounds, extracts, and formulae from medicinal plants. Meanwhile, the current perspectives and future challenges of metabolism-targeting therapies of medicinal plants for GIC were also discussed. In conclusion, the understanding of the action mechanisms of medicinal plants in GIC from the metabolomics perspective will contribute to the clinical application of potential candidates from the resourceful medicinal plants as novel and efficient adjuvant therapeutics for GIC therapy.

Metabolomics-based mass spectrometry methods to analyze the chemical content of 3D organoid models

Metabolomics, the study of metabolites present in biological samples, can provide a global view of sample state as well as insights into biological changes caused by disease or environmental interactions. Mass spectrometry (MS) is commonly used for metabolomics analysis given its high-throughput capabilities, high sensitivity, and capacity to identify multiple compounds in complex samples simultaneously. MS can be coupled to separation methods that can handle small volumes, making it well suited for analyzing the metabolome of organoids, miniaturized three-dimensional aggregates of stem cells that model in vivo organs. Organoids are being used in research efforts to study human disease and development, and in the design of personalized drug treatments. For organoid models to be useful, they need to recapitulate morphological and chemical aspects, such as the metabolome, of the parent tissue. This review highlights the separation- and imaging-based MS-based metabolomics methods that have been used to analyze the chemical contents of organoids. Future perspectives on how MS techniques can be optimized to determine the accuracy of organoid models and expand the field of organoid research are also discussed.

COLMARq: A Web Server for 2D NMR Peak Picking and Quantitative Comparative Analysis of Cohorts of Metabolomics Samples

Highly quantitative metabolomics studies of complex biological mixtures are facilitated by the resolution enhancement afforded by 2D NMR spectra such as 2D ¹³C-¹H HSQC spectra. Here, we describe a new public web server, COLMARq, for the semi-automated analysis of sets of 2D HSQC spectra of cohorts of samples. The workflow of COLMARq includes automated peak picking using the deep neural network DEEP Picker, quantitative cross-peak volume extraction by numerical fitting using Voigt Fitter, the matching of corresponding cross-peaks across cohorts of spectra, peak volume normalization between different spectra, database query for metabolite identification, and basic univariate and multivariate statistical analyses of the results. COLMARq allows the analysis of cross-peaks that belong to both known and unknown metabolites. After a user has uploaded cohorts of 2D ¹³C-¹H HSQC and optionally 2D ¹H-¹H TOCSY spectra in their preferred format, all subsequent steps on the web server can be performed fully automatically, allowing manual editing if needed and the sessions can be saved for later use. The accuracy, versatility, and interactive nature of COLMARq enables quantitative metabolomics analysis, including biomarker identification, of a broad range of complex biological mixtures as is illustrated for cohorts of samples from bacterial cultures of Pseudomonas aeruginosa in both its biofilm and planktonic states.

Serum untargeted metabolomics analysis of the mechanisms of evodiamine on type 2 diabetes mellitus model rats

Evodiamine (EVO) is an alkaloid extracted from Evodia rutaecarpa and has various pharmacological activities, including hypolipidemic, anti-inflammatory, anti-infective, and antitumor effects. However, the therapeutic effects of EVO on type 2 diabetes mellitus (T2DM) and the possible mechanisms remain unknown. In this study, we used a T2DM rat model using a high-fat diet (HFD) combined with streptozotocin (STZ) injections followed by treatment with EVO. First, we evaluated the therapeutic effects of EVO on T2DM rats, following which we evaluated the anti-inflammatory and anti-oxidative effects of EVO on T2DM rats. Finally, we analyzed the metabolic regulatory mechanism of EVO in T2DM rats using an untargeted metabolomics approach. The results showed that EVO treatment alleviated the hyperglycemia, hyperlipidemia, insulin resistance (IR), and pathological changes of the liver, pancreas and kidneys in T2DM rats. Moreover, EVO treatment ameliorated the oxidative stress and decreased the serum levels of pro-inflammatory cytokines in T2DM model rats. Serum untargeted metabolomics analysis indicated that the EVO treatment affected the levels of 26 metabolites, such as methionine, citric acid, cholesterol, taurocholic acid, pilocarpine, adrenic acid, and other metabolites. These metabolites were mainly related to the amino sugar and nucleotide sugar metabolism, arginine biosynthesis, arginine and proline metabolism, glutathione metabolism, and tryptophan metabolism pathways. In conclusion, EVO can reduce blood glucose and improve oxidative stress and inflammatory response in T2DM rats. These functions are related to the regulation of amino sugar and nucleotide sugar metabolism, arginine biosynthesis, arginine and proline metabolism, glutathione metabolism, and tryptophan metabolism pathways.

Organelle Interaction and Drug Discovery: Towards Correlative Nanoscopy and Molecular Dynamics Simulation

The inter-organelle interactions, including the cytomembrane, endoplasmic reticulum, mitochondrion, lysosome, dictyosome, and nucleus, play the important roles in maintaining the normal function and homeostasis of cells. Organelle dysfunction can lead to a range of diseases (e.g., Alzheimer’s disease (AD), Parkinson’s disease (PD), and cancer), and provide a new perspective for drug discovery. With the development of imaging techniques and functional fluorescent probes, a variety of algorithms and strategies have been developed for the ever-improving estimation of subcellular structures, organelle interaction, and organelle-related drug discovery with accounting for the dynamic structures of organelles, such as the nanoscopy technology and molecular dynamics (MD) simulations. Accordingly, this work summarizes a series of state-of-the-art examples of the recent progress in this rapidly changing field and uncovering the drug screening based on the structures and interactions of organelles. Finally, we propose the future outlook for exciting applications of organelle-related drug discovery, with the cooperation of nanoscopy and MD simulations.

Serum and Soleus Metabolomics Signature of Klf10 Knockout Mice to Identify Potential Biomarkers

The transcription factor Krüppel-like factor 10 (Klf10), also known as Tieg1 for TGFβ (Inducible Early Gene-1) is known to control numerous genes in many cell types that are involved in various key biological processes (differentiation, proliferation, apoptosis, inflammation), including cell metabolism and human disease. In skeletal muscle, particularly in the soleus, deletion of the Klf10 gene (Klf10 KO) resulted in ultrastructure fiber disorganization and mitochondrial metabolism deficiencies, characterized by muscular hypertrophy. To determine the metabolic profile related to loss of Klf10 expression, we analyzed blood and soleus tissue using UHPLC-Mass Spectrometry. Metabolomics analyses on both serum and soleus revealed profound differences between wild-type (WT) and KO animals. Klf10 deficient mice exhibited alterations in metabolites associated with energetic metabolism. Additionally, chemical classes of aromatic and amino-acid compounds were disrupted, together with Krebs cycle intermediates, lipids and phospholipids. From variable importance in projection (VIP) analyses, the Warburg effect, citric acid cycle, gluconeogenesis and transfer of acetyl groups into mitochondria appeared to be possible pathways involved in the metabolic alterations observed in Klf10 KO mice. These studies have revealed essential roles for Klf10 in regulating multiple metabolic pathways whose alterations may underlie the observed skeletal muscle defects as well as other diseases.

Circulating Metabolic Biomarkers Are Consistently Associated With Type 2 Diabetes Risk in Asian and European Populations

CONTEXT

While Asians have a higher risk of type 2 diabetes (T2D) than Europeans for a given body mass index (BMI), it remains unclear whether the same markers of metabolic pathways are associated with diabetes.

OBJECTIVE

We evaluated associations between metabolic biomarkers and incidence of T2D in 3 major Asian ethnic groups (Chinese, Malay, and Indian) and a European population.

METHODS

We analyzed data from adult males and females of 2 cohorts from Singapore (n = 6393) consisting of Chinese, Malays, and Indians and 3 cohorts of European-origin participants from Finland (n = 14 558). We used nuclear magnetic resonance to quantify 154 circulating metabolic biomarkers at baseline and performed logistic regression to assess associations with T2D risk adjusted for age, sex, BMI and glycemic markers.

RESULTS

Of the 154 metabolic biomarkers, 59 were associated with higher risk of T2D in both Asians and Europeans (P < 0.0003, Bonferroni-corrected). These included branched chain and aromatic amino acids, the inflammatory marker glycoprotein acetyls, total fatty acids, monounsaturated fatty acids, apolipoprotein B, larger very low-density lipoprotein particle sizes, and triglycerides. In addition, 13 metabolites were associated with a lower T2D risk in both populations, including omega-6 polyunsaturated fatty acids and larger high-density lipoprotein particle sizes. Associations were consistent within the Asian ethnic groups (all Phet ≥ 0.05) and largely consistent for the Asian and European populations (Phet ≥ 0.05 for 128 of 154 metabolic biomarkers).

CONCLUSION

Metabolic biomarkers across several biological pathways were consistently associated with T2D risk in Asians and Europeans.

Microbiome Changes in Connective Tissue Diseases and Vasculitis: Focus on Metabolism and Inflammation

The microbial community acts as an active player in maintaining homeostasis and immune functions through a continuous and changeable cross-talk with the host immune system. Emerging evidence suggests that altered microbial composition, known as dysbiosis, might perturb the delicate balance between the microbiota and the immune system, triggering inflammation and potentially contributing to the pathogenesis and development of chronic inflammatory diseases. This review will summarize the current evidence about the microbiome-immunity cross-talk, especially focusing on the microbiota alterations described in patients with rheumatic diseases and on the recent findings concerning the interaction between microbiota, metabolic function, and the immune system.

SERS optical accumulators as unified nanoplatforms for tear sampling and sensing in soft contact lenses

Tear analysis has become an invaluable asset in clinical research in order to identify and quantify novel biomarkers for a wide array of conditions. The present work is intended to take this area of study one step further by implementing an innovative sensing platform through which exploration of low-molecular-weight compounds is conducted outperforming traditional analytical technologies. With this aim, carefully engineered plasmonic nanoassemblies have been synergistically combined with molecular-sieving materials giving rise to size-selective samplers with SERS detection capabilities. These architectures have been then integrated onto hydrogel-based contact lenses and tested in simulated tear fluids in order to evidence their operational features. Through this approach, a prolonged analyte accumulation can be realized, thus providing a competitive advantage in those scenarios where concentration of biomarkers is typically low or minimum sample volumes are not met. Additionally, quenching of metabolic flux and analyte extraction protocols can be circumvented, hence preventing the intrinsic physical and chemical interferences stemming from these procedures. The obtained results render these sensing platforms as promising medical devices, and constitute a great opportunity in order to expand the clinical toolkit in tear analysis.

Metabolomic Analysis Points to Bioactive Lipid Species and Acireductone Dioxygenase 1 (ADI1) as Potential Therapeutic Targets in Poor Prognosis Endometrial Cancer

Simple Summary

Uterine serous carcinoma is considered a rare and aggressive variant of endometrial cancer that accounts for 10% of all endometrial cancers diagnosed but is responsible for 40% of endometrial cancer-related deaths. Unfortunately, current treatments for serous endometrial carcinoma are ineffective. Therefore, there is a need to find new therapeutic targets. The aim of this study was to analyse the metabolic profile of serous cancer in order to identify new molecules and thereby define potential therapeutic targets. We observed that most of the differential metabolites are lipid species (suggesting the important role of the lipid metabolism). In addition, we found an increase in 2-Oxo-4-methylthiobutanoic acid (synthesised by the ADI1 enzyme) in serous carcinomas. Using public database analysis and immunohistochemistry, we established a correlation between elevated ADI1 levels and serous carcinoma. Furthermore, the ectopic modification of ADI1 expression in vitro revealed the ability of ADI1 to induce pathological cell migration and invasion capabilities.

Abstract

Metabolomic profiling analysis has the potential to highlight new molecules and cellular pathways that may serve as potential therapeutic targets for disease treatment. In this study, we used an LC-MS/MS platform to define, for the first time, the specific metabolomic signature of uterine serous carcinoma (SC), a relatively rare and aggressive variant of endometrial cancer (EC) responsible for 40% of all endometrial cancer-related deaths. A metabolomic analysis of 31 ECs (20 endometrial endometrioid carcinomas (EECs) and 11 SCs) was performed. Following multivariate statistical analysis, we identified 232 statistically different metabolites among the SC and EEC patient samples. Notably, most of the metabolites identified (89.2%) were lipid species and showed lower levels in SCs when compared to EECs. In addition to lipids, we also documented metabolites belonging to amino acids and purine nucleotides (such as 2-Oxo-4-methylthiobutanoic acid, synthesised by acireductone dioxygenase 1 (ADI1) enzyme), which showed higher levels in SCs. To further investigate the role of ADI1 in SC, we analysed the expression protein levels of ADI1 in 96 ECs (67 EECs and 29 SCs), proving that the levels of ADI1 were higher in SCs compared to EECs. We also found that ADI1 mRNA levels were higher in p53 abnormal ECs compared to p53 wild type tumours. Furthermore, elevated ADI1 mRNA levels showed a statistically significant negative correlation with overall survival and progression-free survival among EEC patients. Finally, we tested the ability of ADI1 to induce migration and invasion capabilities in EC cell lines. Altogether, these results suggest that ADI1 could be a potential therapeutic target in poor-prognosis SCs and other Ecs with abnormal p53 expression.

Metabolomics and lipidomics to identify biomarkers of effect related to exposure to non-dioxin-like polychlorinated biphenyls in pigs

Recent epidemiological studies show that current levels of exposure to polychlorinated biphenyls (PCBs) remain of great concern, as there is still a link between such exposures and the development of chronic environmental diseases. In this sense, most studies have focused on the health effects caused by exposure to dioxin-like PCBs (DL-PCBs), although chemical exposure to non-dioxin-like PCB (NDL-PCB) congeners is more significant. In addition, adverse effects of PCBs have been documented in humans after accidental and massive exposure, but little is known about the effect of chronic exposure to low-dose PCB mixtures. In this work, exposure to Aroclor 1260 (i.e. a commercially available mixture of PCBs consisting primarily of NDL-PCB congeners) in pigs is investigated as new evidence in the risk assessment of NDL-PCBs. This animal model has been selected due to the similarities with human metabolism and to support previous toxicological studies carried out with more frequently used animal models. Dietary exposure doses in the order of few ng/kg body weight (b.w.) per day were applied. As expected, exposure to Aroclor 1260 led to the bioaccumulation of NDL-PCBs in perirenal fat of pigs. Metabolomics and lipidomics have been applied to reveal biomarkers of effect related to Aroclor 1260 exposure, and by extension to NDL-PCB exposure, for 21 days. In the metabolomics analysis, 33 metabolites have been identified (level 1 and 2) as significantly altered by the Aroclor 1260 administration, while in the lipidomics analysis, 39 metabolites were putatively annotated (level 3) and associated with NDL-PCB exposure. These biomarkers are mainly related to the alteration of fatty acid metabolism, glycerophospholipid metabolism and tryptophan-kynurenine pathway.

Comprehensive pseudotargeted metabolomics analysis based on two-phase liquid extraction-UHPLC-MS/MS for the investigation of depressive rats

Pseudotargeted analysis combines the advantages of untargeted and targeted metabolomics methods. This study proposed a comprehensive pseudotargeted metabolomics method based on two-phase liquid extraction using ultra-high-performance liquid chromatography-tandem mass spectrometry. Two-phase liquid extraction, composed of both aqueous and organic phases, extracted a wide range of metabolites from polar to nonpolar in plasma samples. Besides, the two phases were combined and detected in a single injection to save analytical time. A total of 486 potential metabolites were detected by the developed approach. Compared with the conventional methanol-based protein precipitation method, the two-phase liquid extraction method significantly increased the metabolite coverage by 20.29%. Besides, the proposed pseudotargeted metabolomics method exhibited higher sensitivity and better repeatability than the untargeted method. Finally, we applied the established pseudotargeted method to the metabolomics study of depressive rats and screened 53 differential variables. Sixteen determined differential metabolites were mainly in four metabolic pathways, including glycerophospholipid, arachidonic acid, sphingolipid metabolisms, pentose and glucuronate interconversions. The results indicated that the pseudotargeted method based on two-phase liquid extraction broadened the metabolite coverage with good sensitivity and repeatability, exhibiting significant potential for discovering differential metabolites in metabolomics studies. This article is protected by copyright. All rights reserved.

Precision medicine journey through omics approach

It has been well established that understanding the underlying heterogeneity of numerous complex disease process needs new strategies that present in precision medicine for prediction, prevention and personalized treatment strategies. This approach must be tailored for each individual's unique omics that lead to personalized management of disease. The correlation between different omics data should be considered in precision medicine approach. The interaction provides a hypothesis which is called domino effect in the present minireview. Here we review the various potentials of omics data including genomics, transcriptomics, proteomics, metabolomics, pharmacogenomics. We comprehensively summarize the impact of omics data and its major role in precision medicine and provide a description about the domino effect on the pathophysiology of diseases. Each constituent of the omics data typically provides different information in associated with disease. Current research, although inadequate, clearly indicate that the information of omics data can be applicable in the concept of precision medicine. Integration of different omics data type in domino effect hypothesis can explain the causative changes of disease as it is discussed in the system biology too. While most existing studies investigate the omics data separately, data integration is needed on the horizon of precision medicine by using machine learning.