Integration Of Metabolomics Research Articles

Integrative metabolomic characterisation identifies altered portal vein serum metabolome contributing to human hepatocellular carcinoma

ObjectiveAltered metabolites are important for the tumourigenicity of hepatocellular carcinoma (HCC). We performed integrative metabolomics analysis of the metabolites changes in portal venous blood and in comparison with the metabolites...

An integrative metabolomics and network pharmacology method for exploring bioactive components and preliminary pharmacodynamics in medicinal parts of Harrisonia perforata

In this paper,metabolomics and network pharmacology were used to investigate the bioactive components of Harrisonia perforata and their possible mechanisms of action. Metabolites in the flowers,fruits,branches,leaves and stalks of H. perforata were analyzed by ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry. Meanwhile,multiple statistical analysis methods including principal component analysis( PCA) and orthogonal partial least squares discriminant analysis( OPLS-DA)were applied to screen and identify differential compounds. With metabolomics method,9 differential compounds were preliminarily identified from leaves and other non-traditional medicinal parts. Subsequently,these compounds were explored by using network pharmacology. With gastrointestinal absorption and drug-likeness as limiting conditions,they were imported into the Swiss ADME,from which 7 compounds with potential medicinal activity were obtained. Then,their targets were predicted by PharmMapper,with Human Protein Targets Only and Normalized Fit Score>0. 9 set as limiting conditions,and 60 standardized potential targets were identified with Uniprot. KEGG( Kyoto encyclopedia of genes and genomes) pathway data was obtained using metascape and the " potential active ingredients-target-pathway" network was constructed with Cytoscape 3. 7. 2. The enrichment analysis of KEGG demonstrated that the 60 targets were enriched in 78 signaling pathways( min overlap: 3,P value cutoff: 0. 01,min enrichment: 1. 5),many of which are related to anti-bacteria,anti-inflammation and anti-virus,such as IL-17 signaling pathway,RIG-I-like receptor signaling pathway and NOD-like receptor signaling pathway. Finally,depending on the clinical activity of H. perforata,the relevant signaling pathways were analyzed through experimental data and literature. Dehydroconiferyl alcohol was reported to have the anti-inflammatory effect and perforamone D to possess the antimycobacterial activity. The KEGG pathway enrichment analysis showed that dehydroconiferyl alcohol could act on the Alzheimer's disease( AD) signaling pathway by targeting CDK5 R1 and BACE1. ACh E inhibitor is the most promising drug to treat AD,while dehydroconiferyl alcohol has been proved to inhibit ACh E according to literature. The experimental results revealed that the extract of leaves of H. perforata can effectively inhibit the growth of Staphylococcus aureus. These are consistent with the enrichment analysis results of KEGG. This study explored the bioactive components and pharmacodynamics of the leaves of the H. perforata,laying a theoretical foundation for its in-depth development and rational application.

Integrative metabolomics and transcriptomics identifies itaconate as an adjunct therapy to treat ocular bacterial infection

SummaryThe eye is highly susceptible to inflammation-mediated tissue damage evoked during bacterial infection. However, mechanisms regulating inflammation to protect the eye remain elusive. Here, we used integrated metabolomics and transcriptomics to show that the immunomodulatory metabolite itaconate and immune-responsive gene 1 (Irg1) are induced in bacterial (Staphylococcus aureus)-infected mouse eyes, bone-marrow-derived macrophages (BMDMs), and Müller glia. Itaconate levels are also elevated in the vitreous of patients with bacterial endophthalmitis. Irg1 deficiency in mice led to increased ocular pathology. Conversely, intraocular administration of itaconate protects both Irg1−/− and wild-type mice from bacterial endophthalmitis by reducing inflammation, bacterial burden, and preserving retinal architecture and visual function. Notably, itaconate exerts synergistic effects with antibiotics. The protective, anti-inflammatory effects of itaconate are mediated via activation of NRF2/HO-1 signaling and inhibition of NLRP3 inflammasome. Collectively, our study demonstrates the Irg1/itaconate axis is a regulator of intraocular inflammation and provides evidence for using itaconate, along with antibiotics, to treat bacterial infections.

Integrative Metabolomics to Identify Molecular Signatures of Responses to Vaccines and Infections.

Approaches to the identification of metabolites have progressed from early biochemical pathway evaluation to modern high-dimensional metabolomics, a powerful tool to identify and characterize biomarkers of health and disease. In addition to its relevance to classic metabolic diseases, metabolomics has been key to the emergence of immunometabolism, an important area of study, as leukocytes generate and are impacted by key metabolites important to innate and adaptive immunity. Herein, we discuss the metabolomic signatures and pathways perturbed by the activation of the human immune system during infection and vaccination. For example, infection induces changes in lipid (e.g., free fatty acids, sphingolipids, and lysophosphatidylcholines) and amino acid pathways (e.g., tryptophan, serine, and threonine), while vaccination can trigger changes in carbohydrate and bile acid pathways. Amino acid, carbohydrate, lipid, and nucleotide metabolism is relevant to immunity and is perturbed by both infections and vaccinations. Metabolomics holds substantial promise to provide fresh insight into the molecular mechanisms underlying the host immune response. Its integration with other systems biology platforms will enhance studies of human health and disease.

A Brief Discussion on Chinese Medicine Behaviour and Prescription Compatibility

Chinese medicine (CM) is gaining popularity in recent years, prompting researchers’ and clinicians’ interest and effort into the makings and effects of it, especially after the announcement of World Health Organisation’s incorporation of CM into the mainstream medical compendium, followed by the promising outcome brought about by the implementation of CM in the recent pandemic. Individual herb has complex properties, coming from its pharmacological properties and the Chinese medical principles of organ-directed, taste and dynamic orientational behaviours. The use of individual herb in CM prescription is rare. Formula prescriptions predominantly consist of two ingredients or more. To fully reveal the effects of CM is a great challenge. The compound reaction of various herbs, the absorption and utility rate by the body, uniqueness of individual physique, sub-types of pathological behaviours and time-line progression at the healing process add on to the complexity of understanding the full effect of CM. Various theories such as pathophysiology guidance, pharmacokinetic-pharmacodynamic compatibility method, and Global Systems Biology for Integrative Genomics, Proteomics and Metabolomics, which interactively provide a more comprehensive and greater in-depth understanding, together with the consideration of development timeline, may shed more light to revealing the full picture of the effects of compatibility prescription.

An integrative metabolomics and network pharmacology method for exploring the effect and mechanism of Radix Bupleuri and Radix Paeoniae Alba on anti-depression

Depression is a common mental illness, which is caused by ‘liver qi stagnationin in traditional Chinese medicine (TCM) theory. Thus, relieving “liver qi stagnation” is considered to be effective at treating depression. The Radix Bupleuri and Radix Paeoniae Alba drug pair is the most classic compatible drug pair for mitigating a great variety of depression symptoms. However, its mechanisms remain largely unclear. In this study, metabolomics and network pharmacology methods were used to explore the potential mechanism of antidepressant-like effects of the Radix Bupleuri and Radix Paeoniae Alba drug pair. Analysis of metabolomics results showed that the drug pair can significantly improve CUMS-induced depression. The underlying mechanism of its antidepressant effect involves regulating the expression of brain-derived neurotrophic factors, inhibiting neurotoxicity, and regulating the HPA axis. Network pharmacology showed that drug pairs screened a total of 23 active ingredients and 63 targets, participated in the regulation of protein metabolism, Metabolism, Energy pathways, Cell growth and / or maintenance and other biological processes (BP), and mainly involved multiple signaling pathways and metabolic pathways to jointly exert antidepressant effects. Four related metabolic pathways regulated by the Radix Bupleuri and Radix Paeoniae Alba drug pair were input into the KEGG database to obtain the key genes of the related metabolic pathways. The predicted target of the network pharmacology was compared with the key genes of the metabolic pathway, and the common genes were screened: CYP1A1, CYP1A2; Western blot results showed that the drug pair up-regulated the protein expression of CYP1A1, CYP1A2. The medicine has an antidepressant effect by regulating the action of key enzymes. Metabolomics combined with network pharmacology research strategy revealed that antidepressant-like effects of the Radix Bupleuri and Radix Paeoniae Alba drug pair are characterized by multi-component, multi-target and multi-path mechanism of action.

Global Metabolomics in Allogeneic Hematopoietic Cell Transplantation Recipients Discordant for Chronic Graft-versus-Host Disease.

Chronic graft-versus-host disease (cGVHD) remains a significant late effect issue for allogeneic hematopoietic cell transplantation (allo-HCT) survivors, contributing to morbidity and mortality. The etiology of cGVHD is not well elucidated. Owing to a lack of early diagnostic tests and pathophysiology ambiguity, targeted treatments remain limited. Biomarkers for prediction, control response, or prognostication have not yet been identified. Metabolomics, the quantification of metabolites, is a potential biomarker of cGVHD but has not been evaluated in this population. In this study, we examined global metabolites of stored plasma to identify differentially expressed metabolites of individuals discordant for cGVHD following allo-HCT. A descriptive, comparative, cross-sectional study design was used to examine differentially expressed metabolites of plasma samples obtained from 40 adult allo-HCT recipients (20 with cGVHD and 20 without cGVHD) from 2 parent studies. Metabolomics profiling was conducted at the University of Florida's Southeast Center for Integrative Metabolomics. Full experimental methods followed a previously published method. All statistical analyses were performed by a PhD-prepared, trained bioinformatics statistician. There were 10 differentially expressed metabolites between participants with cGVHD and those without cGVHD. Differential metabolites included those related to energy metabolism (n=3), amino acid metabolism (n=3), lipid metabolism (n=2), caffeine metabolism (n=1), and neurotransmission (n=1). Serotonin had the greatest fold change (21.01). This study suggests that cGVHD may be associated with expanded cellular energy and potentially mitochondrial dysfunction. The differential metabolic profile between patients with and without cGVHD indicates metabolic perturbations that merit further exploration as potential biomarkers of cGVHD. These findings support the need for further examination using a larger, prospective study design to identify metabolomic risk factors that may signal the need for earlier preventive measures and earlier treatment to reduce cGVHD.

Seven-day Green Tea Supplementation Revamps Gut Microbiome and Caecum/Skin Metabolome in Mice from Stress

Green tea supplementation has beneficial health effects. However, its underlying mechanisms, such as effects on modulating the intestinal microbiome and endogenous metabolome, particularly following short-term supplementation, are largely unclear. We conducted an integrative metabolomics study to evaluate the effects of short-term (7-day) supplementation of green tea extract (GTE) or its components, epigallocatechin gallate, caffeine, and theanine, on the caecum microbiota and caecum/skin metabolome in mice. Further, we established an integrative metabolome-microbiome model for correlating gut and skin findings. The effects of short-term supplementation with dietary compounds were evaluated with respect to UV stress response, with GTE showing the most remarkable effects. Biplot analysis revealed that Bifidobacteria and Lactobacillus spp. were considerably influenced by short-term GTE supplementation, while Clostridium butyricum was significantly increased by UV stress without supplementation. GTE supplementation helped the skin metabolome defend against UV stress. Interestingly, a significant positive correlation was observed between caecum bacteria (Bifidobacteria, Lactobacillus spp.) and metabolites including skin barrier function-related skin metabolites, caecal fatty acids, and caecal amino acids. Overall, 7-day GTE supplementation was sufficient to alter the gut microbiota and endogenous caecum/skin metabolome, with positive effects on UV stress response, providing insight into the mechanism of the prebiotic effects of GTE supplementation.

Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis.

MetaboAnalyst (https://www.metaboanalyst.ca) is an easy-to-use web-based tool suite for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since its first release in 2009, MetaboAnalyst has evolved significantly to meet the ever-expanding bioinformatics demands from the rapidly growing metabolomics community. In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst supports a wide array of functions for statistical, functional, as well as data visualization tasks. Some of the most widely used approaches include PCA (principal component analysis), PLS-DA (partial least squares discriminant analysis), clustering analysis and visualization, MSEA (metabolite set enrichment analysis), MetPA (metabolic pathway analysis), biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. The current version of MetaboAnalyst (4.0) features a complete overhaul of the user interface and significantly expanded underlying knowledge bases (compound database, pathway libraries, and metabolite sets). Three new modules have been added to support pathway activity prediction directly from mass peaks, biomarker meta-analysis, and network-based multi-omics data integration. To enable more transparent and reproducible analysis of metabolomic data, we have released a companion R package (MetaboAnalystR) to complement the web-based application. This article provides an overview of the main functional modules and the general workflow of MetaboAnalyst 4.0, followed by 12 detailed protocols: © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Data uploading, processing, and normalization Basic Protocol 2: Identification of significant variables Basic Protocol 3: Multivariate exploratory data analysis Basic Protocol 4: Functional interpretation of metabolomic data Basic Protocol 5: Biomarker analysis based on receiver operating characteristic (ROC) curves Basic Protocol 6: Time-series and two-factor data analysis Basic Protocol 7: Sample size estimation and power analysis Basic Protocol 8: Joint pathway analysis Basic Protocol 9: MS peaks to pathway activities Basic Protocol 10: Biomarker meta-analysis Basic Protocol 11: Knowledge-based network exploration of multi-omics data Basic Protocol 12: MetaboAnalystR introduction.

Integrated metallomic and metabolomic profiling of plasma and tissues provides deep insights into the protective effect of raw and salt-processed Achyranthes bidentata Blume extract in ovariectomia rats

Ethnopharmacological relevanceAchyranthes bidentata Blume (AB) is a well-known traditional Chinese medicine for treating osteoporosis and bone fracture. In the current, researches on pharmacological mechanism of AB mostly focused on molecular pathways, knowledge about its metabolic signatures is largely unclear. This study aims to develop an integrative metabolomics and metallomic approach for deciphering the biochemical basis of anti-osteoporosis effects of raw and salt-processed AB. MethodGas chromatography–mass spectrometry (GC–MS) and inductively coupled plasma mass spectrometry (ICP-MS) were combined for metabolomic and metallomic profiling of rats serum, liver and kidney derived from the sham group, model group, E2, raw and salt-processed AB treated groups. Meanwhile, micro-CT and biomechanical analysis were carried out to ensure the success of the osteoporosis model and to validate the anti-osteoporosis effect of raw and salt-processed AB. Partial least squares discriminant analysis (PLS-DA) was employed to screen potential biomarkers and the MetaboAnalyst and KEGG PATHWAY Database were used to investigate the metabolic pathway. ResultsRaw and salt-processed AB protected the rats against osteoporosis, as evidenced by the restoration of the alkaline phosphatase activity, osteocalcin concentration, urine calcium/creatinine ratio and urine phosphorus/creatinine ratio. The combination of PCA and PLS-DA revealed deviations in ninety-four differential biomarkers between raw AB treated group and model group. The identified biomarkers were primarily engaged in the metabolic pathways including galactose metabolism, urea cycle, arginine and proline metabolism, alanine metabolism, lactose degradation, ammonia recycling and glycine and serine metabolism. The levels of these biomarkers showed significant alterations and a tendency to be restored to normal values in raw and salt-processed AB treated osteoporosis rats. Of note, the levels of trace elements, such as Zn, Se, Mn, Cu and Fe, were elevated after raw and salt-processed AB treatment. Finally, a correlation network diagram was constructed to show the biomarkers perturbed by raw and salt-processed AB. ConclusionOur findings indicate that raw and salt-processed AB has positive effects on osteoporosis rats. Meanwhile, metabolomic and metallomic method coupled with metabolites enrichment analysis and pattern recognition serves as a useful tool for revealing the action mechanism of traditional Chinese medicine.

Integrative metabolomics reveals unique metabolic traits in Guillain-Barr\xe9 Syndrome and its variants

Guillain–Barré syndrome (GBS) is an acute fatal progressive disease caused by autoimmune mechanism mainly affecting peripheral nervous system. Although the syndrome is clinically sub-classified into several variants, specific biomarker and exact pathomechanism of each subtypes are not well elucidated yet. In current study, integrative metabolomic and lipidomic profiles were acquisitioned from cerebrospinal fluid samples of 86 GBS from three variants and 20 disease controls. And the data were systematically compared to our previous result on inflammatory demyelination disorders of central nervous system (IDDs) and healthy controls. Primary metabolite profiles revealed unique metabolic traits in which 9 and 7 compounds were specifically changed in GBS and IDD, respectively. Next, the biomarker panel with 10 primary metabolites showed a fairly good discrimination power among 3 GBS subtypes, healthy controls, and disease controls (AUCs ranged 0.849–0.999). The robustness of the biomarker panel was vigorously validated by multi-step statistical evaluation. Subsequent lipidomics revealed GBS variant-specific alteration where the significant elevations of lyso-phosphatidylcholines and sphingomyelins were unique to AIDP (acute inflammatory demyelinating polyneuropathy) and AMAN (acute motor axonal neuropathy), respectively. And metabolome-wide multivariate correlation analysis identified potential clinical association between GBS disability scale (Hughes score) and CSF lipids (monoacylglycerols, and sphingomyelins). Finally, Bayesian network analysis of covarianced structures of primary metabolites and lipids proposed metabolic hub and potential biochemical linkage associated with the pathology.

Metabolomics profiling of metformin-mediated metabolic reprogramming bypassing AMPKα

BackgroundMetformin is a first-line drug for treating type 2 diabetes and has gained considerable interest as a potential anticancer agent. Increasing evidence suggests that metformin antagonizes diabetes and tumors through disrupting metabolic homeostasis and altering energy state. However, whether AMP activated protein kinase (AMPK) contributes to such effects of metformin remains controversial. MethodsWe performed integrative metabolomics analyses to systematically examine the effects of metformin on metabolic pathways in Prkaa1 wild type (WT) and knock-out (KO) mouse embryonic fibroblast (MEF) cells as well as human cells based on gas chromatography–mass spectrometry and capillary electrophoresis–mass spectrometry (CE–MS). ResultsMetformin treatment induced metabolic reprogramming and reduced the energy state of both Prkaa1 WT and KO MEF cells, as evidenced by suppressed tricarboxylic acid (TCA) cycle, elevated lactate production as well as decreased NAD+/NADH ratio. Additionally, metabolic flux analysis also showed that metformin Ampkα-independently increased metabolic flux from glucose to lactate and decreased metabolic flux from acetyl-CoA to TCA cycle as well as from pyruvate to malate. Moreover, metformin Ampkα-dependently upregulated P-Acc but Ampkα-independently inhibited the levels of P-mTor, P-S6, Lc3, Atgl and P-Erk in MEF cells. Similarly, we demonstrated that a commonly used AMPK agonist 5-Aminoimidazole-4-carboxamide ribonucleotide (AICAR) and fetal bovine serum (FBS) starvation, as a common model for energy stress, both led to Ampkα-independent metabolism alterations in MEF cells. Furthermore, these effects of metformin were also confirmed in human hepatocellular carcinoma (HCC) cells as well as in MCF10A shControl and shPRKAA1 cells. Importantly, we found that metformin could obviously inhibit colony conformation of HCC cells in an Ampkα-independent manner. ConclusionsOur data highlight a comprehensive view of metabolic reprogramming mediated by metformin as well as AICAR. These observations suggest that metformin could affect cellular metabolism largely bypassing Ampkα, and may provide a new insight for its clinical usage.

Unveiling metabolic remodeling in mucopolysaccharidosis type III through integrative metabolomics and pathway analysis

BackgroundMetabolomics represent a valuable tool to recover biological information using body fluids and may help to characterize pathophysiological mechanisms of the studied disease. This approach has not been widely used to explore inherited metabolic diseases. This study investigates mucopolysaccharidosis type III (MPS III). A thorough and holistic understanding of metabolic remodeling in MPS III may allow the development, improvement and personalization of patient care.MethodsWe applied both targeted and untargeted metabolomics to urine samples obtained from a French cohort of 49 patients, consisting of 13 MPS IIIA, 16 MPS IIIB, 13 MPS IIIC, and 7 MPS IIID, along with 66 controls. The analytical strategy is based on ultra-high-performance liquid chromatography combined with ion mobility and high-resolution mass spectrometry. Twenty-four amino acids have been assessed using tandem mass spectrometry combined with liquid chromatography. Multivariate data modeling has been used for discriminant metabolite selection. Pathway analysis has been performed to retrieve metabolic pathways impairments.ResultsData analysis revealed distinct biochemical profiles. These metabolic patterns, particularly those related to the amino acid metabolisms, allowed the different studied groups to be distinguished. Pathway analysis unveiled major amino acid pathways impairments in MPS III mainly arginine–proline metabolism and urea cycle metabolism.ConclusionThis represents one of the first metabolomics-based investigations of MPS III. These results may shed light on MPS III pathophysiology and could help to set more targeted studies to infer the biomarkers of the affected pathways, which is crucial for rare conditions such as MPS III.

Deciphering biochemical basis of Qingkailing injection-induced anaphylaxis in a rat model by time-dependent metabolomic profiling based on metabolite polarity-oriented analysis

Ethnopharmacological relevanceQingkailing injection (QKLI) is prepared from eight traditional Chinese medicinal materials or their extracts, which is widely used in clinical practice to treat the upper respiratory inflammation, pneumonia, high fever and viral encephalitis, nonetheless, suffering from serious anaphylaxis. Aim of studyThis study aims to develop an integrative metabolomics approach for deciphering the biochemical basis of QKLI induced anaphylaxis (QKLI-IA). Materials and methodsThe accuracy of animal modeling, the coverage of detected metabolites and the timeliness of pathological reaction are three key factors for revealing the biochemical basis of disease with untargeted metabolomics. In this study, firstly, the allergic rats (responders) were first screened by passive cutaneous anaphylaxis experiment and then were utilized for modeling. To cover a wider range of metabolites, a large-scale untargeted metabolomics based on metabolites polarity-oriented analysis was performed using ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Then, to evaluate the timeliness of QKLI-IA, a time-dependent metabolomic profiling including the early, mid and late anaphylaxis stages of QKLI-IA, was performed. ResultsCorresponding to early, mid and late anaphylaxis stages of QKLI-IA, 14, 9 and 4 potential biomarkers were identified, respectively. Metabolism pathway analysis revealed that QKLI-IA resulted in dynamic changes in serum amino acid, fatty acid, glycerolipid, and phospholipid metabolisms. Twenty-four metabolites were found with identical fluctuating trends across the three stages of QKLI-IA. The results indicate that the pathogenesis of QKLI-IA is closely related to arachidonic acid metabolism. ConclusionThis research provides a methodology reference for revealing the biochemical basis of disease using metabolomic profiling and offers a new insight to understand the pathogenesis of QKLI-IA.

MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis.

We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca.

Integrative metabolomics and transcriptomics signatures of clinical tolerance to Plasmodium vivax reveal activation of innate cell immunity and T cell signaling

Almost invariably, humans become ill during primary infections with malaria parasites which is a pathology associated with oxidative stress and perturbations in metabolism. Importantly, repetitive exposure to Plasmodium results in asymptomatic infections, which is a condition defined as clinical tolerance. Integration of transcriptomics and metabolomics data provides a powerful way to investigate complex disease processes involving oxidative stress, energy metabolism and immune cell activation. We used metabolomics and transcriptomics to investigate the different clinical outcomes in a P. vivax controlled human malaria infection trial. At baseline, the naïve and semi-immune subjects differed in the expression of interferon related genes, neutrophil and B cell signatures that progressed with distinct kinetics after infection. Metabolomics data indicated differences in amino acid pathways and lipid metabolism between the two groups. Top pathways during the course of infection included methionine and cysteine metabolism, fatty acid metabolism and urea cycle. There is also evidence for the activation of lipoxygenase, cyclooxygenase and non-specific lipid peroxidation products in the semi-immune group. The integration of transcriptomics and metabolomics revealed concerted molecular events triggered by the infection, notably involving platelet activation, innate immunity and T cell signaling. Additional experiment confirmed that the metabolites associated with platelet activation genes were indeed enriched in the platelet metabolome.

Comparative and integrative metabolomics reveal that S-nitrosation inhibits physiologically relevant metabolic enzymes

Cysteine S-nitrosation is a reversible post-translational modification mediated by nitric oxide (•NO)-derived agents. S-Nitrosation participates in cellular signaling and is associated with several diseases such as cancer, cardiovascular diseases, and neuronal disorders. Despite the physiological importance of this nonclassical •NO-signaling pathway, little is understood about how much S-nitrosation affects protein function. Moreover, identifying physiologically relevant targets of S-nitrosation is difficult because of the dynamics of transnitrosation and a limited understanding of the physiological mechanisms leading to selective protein S-nitrosation. To identify proteins whose activities are modulated by S-nitrosation, we performed a metabolomics study comparing WT and endothelial nitric-oxide synthase knockout mice. We integrated our results with those of a previous proteomics study that identified physiologically relevant S-nitrosated cysteines, and we found that the activity of at least 21 metabolic enzymes might be regulated by S-nitrosation. We cloned, expressed, and purified four of these enzymes and observed that S-nitrosation inhibits the metabolic enzymes 6-phosphogluconate dehydrogenase, Δ1-pyrroline-5-carboxylate dehydrogenase, catechol-O-methyltransferase, and d-3-phosphoglycerate dehydrogenase. Furthermore, using site-directed mutagenesis, we identified the predominant cysteine residue influencing the observed activity changes in each enzyme. In summary, using an integrated metabolomics approach, we have identified several physiologically relevant S-nitrosation targets, including metabolic enzymes, which are inhibited by this modification, and we have found the cysteines modified by S-nitrosation in each enzyme.

Compound Fuling Granule Suppresses Ovarian Cancer Development and Progression by disrupting mitochondrial function, galactose and fatty acid metabolism

Our previous studies have demonstrated that the compound fuling granule (CFG), a traditional Chinese medicine, suppresses ovarian cancer cell growth, migration and metastasis. However, the underlying mechanisms remain to be fully elucidated. In this study, we found that CFG could induce mitochondrial fragmentation, mitochondrial membrane potential reduction and cytochrome c release in ovarian SKOV3 cancer cells. In addition, both metabolomics and transcriptomics approaches were applied to illustrate the systemic mechanism of CFG on ovarian cancer formation and progression. To this end, we established two tumor-bearing mice models with subcutaneous injection or tail intravenous injection. Functionally, administration of CFG suppresses in situ tumor growth and distant lung metastasis. Subsequently, gas chromatography-mass spectrometry (GC-MS) was applied to determine the metabolic alterations among the plasma samples from these in vivo models. In the subcutaneous injection model, 26 distinguishable metabolites were identified and 12 metabolic pathways were reprogrammed. Meanwhile, 19 metabolites involved in 7 metabolic pathways showed significant differences in the tail intravenous injection model. Importantly, integrative metabolomics and transcriptomics analysis showed these metabolites were highly associated with galactose metabolism and fatty acid metabolism. This study suggests that CFG may suppress ovarian cancer cell proliferation and metastasis by regulating mitochondrion-related energy metabolisms.

Integrative metabolomics as emerging tool to study autophagy regulation.

Recent technological developments in metabolomics research have enabled in-depth characterization of complex metabolite mixtures in a wide range of biological, biomedical, environmental, agricultural, and nutritional research fields. Nuclear magnetic resonance spectroscopy and mass spectrometry are the two main platforms for performing metabolomics studies. Given their broad applicability and the systemic insight into metabolism that can be obtained it is not surprising that metabolomics becomes increasingly popular in basic biological research. In this review, we provide an overview on key metabolites, recent studies, and future opportunities for metabolomics in studying autophagy regulation. Metabolites play a pivotal role in autophagy regulation and are therefore key targets for autophagy research. Given the recent success of metabolomics, it can be expected that metabolomics approaches will contribute significantly to deciphering the complex regulatory mechanisms involved in autophagy in the near future and promote understanding of autophagy and autophagy-related diseases in living cells and organisms.

MTORC1-dependent AMD1 regulation sustains polyamine metabolism in prostate cancer.

Activation of the PTEN-PI3K-mTORC1 pathway consolidates metabolic programs that sustain cancer cell growth and proliferation1,2. Here we show that mTORC1 regulates polyamine dynamics, a metabolic route that is essential for oncogenicity. Through the use of integrative metabolomics in a mouse model3 and human biopsies4 of prostate cancer, we identified alterations in tumours impacting on the production of decarboxylated S-adenosylmethionine (dcSAM) and polyamine synthesis. Mechanistically, this metabolic rewiring stems from mTORC1-dependent regulation of S-adenosylmethionine decarboxylase 1 (AMD1) stability. This novel molecular regulation was validated in murine and human cancer specimens. AMD1 was upregulated in prostate cancer specimens with activated mTORC1. Conversely, samples from a clinical trial with the mTORC1 inhibitor everolimus5 exhibited a predominant decrease in AMD1 immunoreactivity that was associated to a decrease in proliferation, in line with the requirement of dcSAM production for oncogenicity. These findings provide fundamental information about the complex regulatory landscape controlled by mTORC1 to integrate and translate growth signals into an oncogenic metabolic program.