Metabolomics by Gas Chromatography-Mass Spectrometry: Combined Targeted and Untargeted Profiling

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

Matrix Effects in GC–MS Profiling of Common Metabolites after Trimethylsilyl Derivatization

Metabolite profiling using gas chromatography coupled to mass spectrometry (GC–MS) is one of the most frequently applied and standardized methods in research projects using metabolomics to analyze complex samples. However, more than 20 years after the introduction of non-targeted approaches using GC–MS, there are still unsolved challenges to accurate quantification in such investigations. One particularly difficult aspect in this respect is the occurrence of sample-dependent matrix effects. In this project, we used model compound mixtures of different compositions to simplify the study of the complex interactions between common constituents of biological samples in more detail and subjected those to a frequently applied derivatization protocol for GC–MS analysis, namely trimethylsilylation. We found matrix effects as signal suppression and enhancement of carbohydrates and organic acids not to exceed a factor of ~2, while amino acids can be more affected. Our results suggest that the main reason for our observations may be an incomplete transfer of carbohydrate and organic acid derivatives during the injection process and compound interaction at the start of the separation process. The observed effects were reduced at higher target compound concentrations and by using a more suitable injection-liner geometry.

Medium Roasting and Brewing Methods Differentially Modulate Global Metabolites, Lipids, Biogenic Amines, Minerals, and Antioxidant Capacity of Hawai'i-Grown Coffee (Coffea arabica)

In the United States, besides the US territory Puerto Rico, Hawai'i is the only state that grows commercial coffee. In Hawai'i, coffee is the second most valuable agricultural commodity. Health benefits associated with moderate coffee consumption, including its antioxidant capacity, have been correlated to its bioactive components. Post-harvest techniques, coffee variety, degree of roasting, and brewing methods significantly impact the metabolites, lipids, minerals, and/or antioxidant capacity of brewed coffees. The goal of our study was to understand the impact of roasting and brewing methods on metabolites, lipids, biogenic amines, minerals, and antioxidant capacity of two Hawai'i-grown coffee (Coffea arabica) varieties, "Kona Typica" and "Yellow Catuai". Our results indicated that both roasting and coffee variety significantly modulated several metabolites, lipids, and biogenic amines of the coffee brews. Furthermore, regardless of coffee variety, the antioxidant capacity of roasted coffee brews was higher in cold brews. Similarly, total minerals were higher in "Kona Typica" cold brews followed by "Yellow Catuai" cold brews. Hawai'i-grown coffees are considered "specialty coffees" since they are grown in unique volcanic soils and tropical microclimates with unique flavors. Our studies indicate that both Hawai'i-grown coffees contain several health-promoting components. However, future studies are warranted to compare Hawai'i-grown coffees with other popular brand coffees and their health benefits in vivo.

Differences in uterine and serum metabolome associated with metritis in dairy cows

Objectives were to evaluate differences in the uterine and serum metabolomes associated with metritis in dairy cows. Vaginal discharge was evaluated using a Metricheck device (Simcro) at 5, 7, and 11 d in milk (DIM; herd 1) or 4, 6, 8, 10, and 12 DIM (herd 2). Cows with reddish or brownish, watery, and fetid discharge were diagnosed with metritis (n = 24). Cows with metritis were paired with herdmates without metritis (i.e., clear mucous vaginal discharge or clear lochia with ≤50% of pus) based on DIM and parity (n = 24). Day of metritis diagnosis was considered study d 0. All cows diagnosed with metritis received antimicrobial therapy. The metabolome of uterine lavage collected on d 0 and 5, and serum samples collected on d 0 were evaluated using untargeted gas chromatography time-of-flight mass spectrometry. Normalized data were subjected to multivariate canonical analysis of population using the MultBiplotR and MixOmics packages in R Studio. Univariate analyses including t-test, principal component analyses, partial least squares discriminant analyses, and pathway analyses were conducted using Metaboanalyst. The uterine metabolome differed between cows with and without metritis on d 0. Differences in the uterine metabolome associated with metritis on d 0 were related to the metabolism of butanoate, amino acids (i.e., glycine, serine, threonine, alanine, aspartate, and glutamate), glycolysis and gluconeogenesis, and the tricarboxylic acid cycle. No differences in the serum metabolome were observed between cows diagnosed with metritis and counterparts without metritis on d 0. Similarly, no differences in uterine metabolome were observed between cows with metritis and counterparts not diagnosed with metritis on d 5. These results indicate that the establishment of metritis in dairy cows is associated with local disturbances in amino acid, lipid, and carbohydrate metabolism in the uterus. The lack of differences in the uterine metabolome on d 5 indicates that processes implicated with the disease are reestablished by d 5 after diagnosis and treatment.

Phytochemical Profile of Trigonella caerulea (Blue Fenugreek) Herb and Quantification of Aroma-Determining Constituents

The herb of Trigonella caerulea (Fabaceae), commonly known as blue fenugreek, is used for the production of traditional cheese and bread varieties in the Alpine region. Despite its frequent consumption, only one study so far has focused on the constituent pattern of blue fenugreek, revealing qualitative information on some flavor-determining constituents. However, with regard to the volatile constituents present in the herb, the applied methods were insufficient and did not take relevant terpenoids into account. In the present study, we analyzed the phytochemical composition of T. caerulea herb applying a set of analytical methods, such as headspace-GC, GC-MS, LC-MS, and NMR spectroscopy. We thus determined the most dominant primary and specialized metabolites and assessed the fatty acid profile as well as the amounts of taste-relevant α-keto acids. In addition, eleven volatiles were quantified, of which tiglic aldehyde, phenylacetaldehyde, methyl benzoate, n-hexanal, and trans-menthone were identified as most significantly contributing to the aroma of blue fenugreek. Moreover, pinitol was found accumulated in the herb, whereas preparative works led to the isolation of six flavonol glycosides. Hence, our study shows a detailed analysis of the phytochemical profile of blue fenugreek and provides an explanation for its characteristic aroma and its health-beneficial effects.

Metabolomics: A New Era in the Diagnosis or Prognosis of B-Cell Non-Hodgkin's Lymphoma

A wide range of histological as well as clinical properties are exhibited by B-cell non-Hodgkin's lymphomas. These properties could make the diagnostics process complicated. The diagnosis of lymphomas at an initial stage is essential because early remedial actions taken against destructive subtypes are commonly deliberated as successful and restorative. Therefore, better protective action is needed to improve the condition of those patients who are extensively affected by cancer when diagnosed for the first time. The development of new and efficient methods for early detection of cancer has become crucial nowadays. Biomarkers are urgently needed for diagnosing B-cell non-Hodgkin's lymphoma and assessing the severity of the disease and its prognosis. New possibilities are now open for diagnosing cancer with the help of metabolomics. The study of all the metabolites synthesised in the human body is called "metabolomics." A patient's phenotype is directly linked with metabolomics, which can help in providing some clinically beneficial biomarkers and is applied in the diagnostics of B-cell non-Hodgkin's lymphoma. In cancer research, it can analyse the cancerous metabolome to identify the metabolic biomarkers. This review provides an understanding of B-cell non-Hodgkin's lymphoma metabolism and its applications in medical diagnostics. A description of the workflow based on metabolomics is also provided, along with the benefits and drawbacks of various techniques. The use of predictive metabolic biomarkers for the diagnosis and prognosis of B-cell non-Hodgkin's lymphoma is also explored. Thus, we can say that abnormalities related to metabolic processes can occur in a vast range of B-cell non-Hodgkin's lymphomas. The metabolic biomarkers could only be discovered and identified as innovative therapeutic objects if we explored and researched them. In the near future, the innovations involving metabolomics could prove fruitful for predicting outcomes and bringing out novel remedial approaches.

Towards inferring absolute concentrations from relative abundance in time-course GC-MS metabolomics data

Metabolomics, the large-scale study of metabolites, has significant appeal as a source of information for metabolic modeling and other scientific applications. One common approach for measuring metabolomics data is gas chromatography-mass spectrometry (GC-MS). However, GC-MS metabolomics data are typically reported as relative abundances, precluding their use with approaches and tools where absolute concentrations are necessary. While chemical standards can be used to help provide quantification, their use is time-consuming, expensive, or even impossible due to their limited availability. The ability to infer absolute concentrations from GC-MS metabolomics data without chemical standards would have significant value. We hypothesized that when analyzing time-course metabolomics datasets, the mass balances of metabolism and other biological information could provide sufficient information towards inference of absolute concentrations. To demonstrate this, we developed and characterized MetaboPAC, a computational framework that uses two approaches-one based on kinetic equations and another using biological heuristics-to predict the most likely response factors that allow translation between relative abundances and absolute concentrations. When used to analyze noiseless synthetic data generated from multiple types of kinetic rate laws, MetaboPAC performs significantly better than negative control approaches when 20% of kinetic terms are known a priori. Under conditions of lower sampling frequency and high noise, MetaboPAC is still able to provide significant inference of concentrations in 3 of 4 models studied. This provides a starting point for leveraging biological knowledge to extract concentration information from time-course intracellular GC-MS metabolomics datasets, particularly for systems that are well-studied and have partially known kinetic structures.

Application of Machine Learning to Metabolomic Profile Characterization in Glioblastoma Patients Undergoing Concurrent Chemoradiation

We here characterize changes in metabolite patterns in glioblastoma patients undergoing surgery and concurrent chemoradiation using machine learning (ML) algorithms to characterize metabolic changes during different stages of the treatment protocol. We examined 105 plasma specimens (before surgery, 2 days after surgical resection, before starting concurrent chemoradiation, and immediately after chemoradiation) from 36 patients with isocitrate dehydrogenase (IDH) wildtype glioblastoma. Untargeted GC-TOF mass spectrometry-based metabolomics was used given its superiority in identifying and quantitating small metabolites; this yielded 157 structurally identified metabolites. Using Multinomial Logistic Regression (MLR) and GradientBoostingClassifier (GB Classifier), ML models classified specimens based on metabolic changes. The classification performance of these models was evaluated using performance metrics and area under the curve (AUC) scores. Comparing post-radiation to pre-radiation showed increased levels of 15 metabolites: glycine, serine, threonine, oxoproline, 6-deoxyglucose, gluconic acid, glycerol-alpha-phosphate, ethanolamine, propyleneglycol, triethanolamine, xylitol, succinic acid, arachidonic acid, linoleic acid, and fumaric acid. After chemoradiation, a significant decrease was detected in 3-aminopiperidine 2,6-dione. An MLR classification of the treatment phases was performed with 78% accuracy and 75% precision (AUC = 0.89). The alternative GB Classifier algorithm achieved 75% accuracy and 77% precision (AUC = 0.91). Finally, we investigated specific patterns for metabolite changes in highly correlated metabolites. We identified metabolites with characteristic changing patterns between pre-surgery and post-surgery and post-radiation samples. To the best of our knowledge, this is the first study to describe blood metabolic signatures using ML algorithms during different treatment phases in patients with glioblastoma. A larger study is needed to validate the results and the potential application of this algorithm for the characterization of treatment responses.

How searching against multiple libraries can lead to biased results in GC/MS-based metabolomics

RATIONALE

Databases of electron ionization mass spectra are often used in GC/MS-based untargeted metabolomics analysis. The results of the library search depend on several factors, such as the size and quality of the database, and the library search algorithm. We found out that the list of considered m/z values is another important parameter. Unfortunately, this information is not usually specified by software developers and it is hidden from the end user.

METHODS

We created synthetic data sets and figured out how several popular software products (AMDIS, ChromaTOF, MS Search, and Xcalibur) select the list of m/z values for the library search. Moreover, we considered data sets of real mass spectra (presented in both the NIST and FiehnLib libraries) and compared the library search results obtained within different software products. All programs under consideration use the NIST MS Search binaries to perform the library search using the Identity algorithm.

RESULTS

We found that AMDIS and ChromaTOF can give biased library search results under particular conditions. In untargeted metabolomics, this can happen when NIST and FiehnLib libraries are used simultaneously, the scan range of the instrument is less than 85, and the correct answer is present only in the FiehnLib library.

CONCLUSIONS

The main reason for biased results is that the information about the scan range is not stored in the metadata of library records. As a result, in the case of AMDIS and ChromaTOF software, some unrecorded peaks are considered as missing during the library search, the respective compound is penalized, and the correct answer falls outside the top five or even top 10 hits. At the same time, the default algorithm for selecting the list of considered m/z values implemented in MS Search is free from such unexpected behavior.

Pharmacometabolomics for the Study of Lipid-Lowering Therapies: Opportunities and Challenges

Lipid-lowering therapies are widely used to prevent the development of atherosclerotic cardiovascular disease (ASCVD) and related mortality worldwide. "Omics" technologies have been successfully applied in recent decades to investigate the mechanisms of action of these drugs, their pleiotropic effects, and their side effects, aiming to identify novel targets for future personalized medicine with an improvement of the efficacy and safety associated with the treatment. Pharmacometabolomics is a branch of metabolomics that is focused on the study of drug effects on metabolic pathways that are implicated in the variation of response to the treatment considering also the influences from a specific disease, environment, and concomitant pharmacological therapies. In this review, we summarized the most significant metabolomic studies on the effects of lipid-lowering therapies, including the most commonly used statins and fibrates to novel drugs or nutraceutical approaches. The integration of pharmacometabolomics data with the information obtained from the other "omics" approaches could help in the comprehension of the biological mechanisms underlying the use of lipid-lowering drugs in view of defining a precision medicine to improve the efficacy and reduce the side effects associated with the treatment.

Miniaturized Two-Dimensional Heart Cutting for LC-MS-Based Metabolomics

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics usually combines hydrophilic interaction liquid chromatography (HILIC) and reversed-phase (RP) chromatography to cover a wide range of metabolomes, requiring both significant sample consumption and analysis time for separate workflows. We developed an integrated workflow enabling the coverage of both polar and nonpolar metabolites with only one injection of the sample for each ionization mode using heart-cutting trapping to combine HILIC and RP separations. This approach enables the trapping of some compounds eluted from the first chromatographic dimension for separation later in the second dimension. In our case, we applied heart-cutting to non-retained metabolites in the first dimension. For that purpose, two independent miniaturized one-dimensional HILIC and RP methods were developed by optimizing the chromatographic and ionization conditions using columns with an inner diameter of 1 mm. They were then merged into one two-dimensional micro LC-MS method by optimization of the trapping conditions. Equilibration of the HILIC column during elution on the RP column and vice versa reduced the overall analysis time, and the multidimensionality allows us to avoid signal measurements during the solvent front. To demonstrate the benefits of this approach to metabolomics, it was applied to the analysis of the human plasma standard reference material SRM 1950, enabling the detection of hundreds of metabolites without the significant loss of some of them while requiring an injection volume of only 0.5 μL.

Metabolomics profiling to characterize cerebral ischemia-reperfusion injury in mice

Cerebral ischemia, resulting from compromised blood flow, is one of the leading causes of death worldwide with limited therapeutic options. Potential deleterious injuries resulting from reperfusion therapies remain a clinical challenge for physicians. This study aimed to explore the metabolomic alterations during ischemia-reperfusion injury by employing metabolomic analysis coupled with gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) and ultraperformance liquid chromatography quadrupole (UPLC/Q)-TOF-MS. Metabolomic data from mice subjected to middle cerebral artery occlusion (MCAO) followed by reperfusion (MCAO/R) were compared to those of the sham and MCAO groups. A total of 82 simultaneously differentially expressed metabolites were identified among each group. The top three major classifications of these differentially expressed metabolites were organic acids, lipids, and organooxygen compounds. Metabolomics pathway analysis was conducted to identify the underlying pathways implicated in MCAO/R. Based on impactor scores, the most significant pathways involved in the response to the reperfusion after cerebral ischemia were glycerophospholipid metabolism, linoleic acid metabolism, pyrimidine metabolism, and galactose metabolism. 17 of those 82 metabolites were greatly elevated in the MCAO/Reperfusion group, when compared to those in the sham and MCAO groups. Among those metabolites, glucose-6-phosphate 1, fructose-6-phosphate, cellobiose 2, o-phosphonothreonine 1, and salicin were the top five elevated metabolites in MCAO/R group, compared with the MCAO group. Glycolysis, the pentose phosphate pathway, starch and sucrose metabolism, and fructose and mannose degradation were the top four ranked pathways according to metabolite set enrichment analysis (MSEA). The present study not only advances our understanding of metabolomic changes among animals in the sham and cerebral ischemia groups with or without reperfusion via metabolomic profiling, but also paves the way to explore potential molecular mechanisms underlying metabolic alteration induced by cerebral ischemia-reperfusion.

Characterization of a new chlorimuron-ethyl-degrading strain Cedecea sp. LAM2020 and biodegradation pathway revealed by multiomics analysis

The widespread use of the herbicide chlorimuron-methyl is hazard to rotational crops and causes soil degradation problems. Biodegradation is considered a promising way for removing herbicide residues from the environment. Here, a new isolated strain, Cedecea sp. LAM2020, enabled complete degradation of 100 mg/L chlorimuron-methyl within five days. Transcriptome analysis revealed that ABC transporters, atrazine degradation and purine metabolism were enriched in the KEGG pathway. Integrating GO and KEGG classification with related reports, we predict that carboxylesterases are involved in the biodegradation of chlorimuron-methyl by LAM2020. Heterologous expression of the carboxylesterase gene carH showed 26.67% degradation of 50 mg/L chlorimuron-methyl within 6 h. The intracellular potential biological response and extracellular degradation process of chlorimuron-ethyl were analyzed by the nontarget metabolomic and mass spectrometry respectively, and the biodegradation characteristics and complete mineralization pathway was revealed. The cleavage of the sulfonylurea bridge and the ester bond achieved the first step in the degradation of chlorimuron-methyl. Together, these results reveal the presence of acidolysis and enzymatic degradation of chlorimuron-methyl by strain LAM2020. Hydroponic corn experiment showed that the addition of strain LAM2020 alleviated the toxic effects of chlorimuron-ethyl on the plants. Collectively, strain LAM2020 may be a promising microbial agent for plants chlorimuron-ethyl detoxification and soil biofertilizer.

An automated workflow on data processing (AutoDP) for semiquantitative analysis of urine organic acids with GC-MS to facilitate diagnosis of inborn errors of metabolism

Determination of urine organic acids (UOAs) is essential to understand the disease progress of inborn errors of metabolism (IEM) and often relies on GC-MS analysis. However, the efficiency of analytical reports is sometimes restricted by data processing due to labor-intensive work if no proper tool is employed. Herein, we present a simple and rapid workflow with an R-based script for automated data processing (AutoDP) of GC-MS raw files to quantitatively analyze essential UOAs. AutoDP features automatic quality checks, compound identification and confirmation with specific fragment ions, retention time correction from analytical batches, and visualization of abnormal UOAs with age-matched references on chromatograms. Compared with manual processing, AutoDP greatly reduces analytical time and increases the number of identifications. Speeding up data processing is expected to shorten the waiting time for clinical diagnosis, which could greatly benefit clinicians and patients with IEM. In addition, with quantitative results obtained from AutoDP, it would be more feasible to perform retrospective analysis of specific UOAs in IEM and could provide new perspectives for studying IEM.

In silico anti-SARS-CoV-2, antiplasmodial, antioxidant, and antimicrobial activities of crude extracts and homopterocarpin from heartwood of Pterocarpus macrocarpus Kurz

Natural products play an essential role in new drug discovery. In the present study, we determined the anti-SARS-CoV-2 (severe acute respiratory syndrome-related coronavirus-2), antioxidant, antiplasmodial, and antimicrobial activities of Pterocarpus macrocarpus Kurz. heartwood and structurally characterized the bioactive compounds. P. macrocarpus Kurz. heartwood was macerated with n-hexane, ethyl acetate, and ethanol, respectively, for 7 days, three times. The compounds were isolated by recrystallization with n-hexane and evaluated by thin-layer chromatography (TLC), gas chromatography-mass spectrophotometry (GC-MS), Fourier transform infrared spectroscopy (FITR), and nuclear magnetic resonance (NMR) spectroscopy. Ethyl acetate, ethanol, n-hexane extracts, and homopterocarpin exhibited antiplasmodial activity at 1.78, 2.21, 7.11, and 0.52 μg/ml, respectively, against P. falciparum 3D7 with low toxicity (selectivity index/SI ≥ 28.46). GC-MS identified compound showed in silico anti-SARS-CoV-2 binding affinity with stigmasterol and SARS-CoV-2 helicase of -8.2 kcal/mol. Ethyl acetate extract exhibited the best antioxidant activity against DPPH (0.76 ± 0.92 μg/ml) and ABTS (0.61 ± 0.46 μg/ml). They also demonstrated antimicrobial activity against B. subtilis, ethanol and ethyl acetate extracts against E. coli and C. albicans, and ethanol extract against S. aureus with diameter zone of inhibition of more than 1 cm. The results highlighted antiplasmodial activity of extracts and homopterocarpin from P. macrocarpus Kurz. heartwood and its potent binding in silico to anti-SARS-CoV-2 proteins with low toxicity. This study also confirmed that extracts exhibited antioxidant and antimicrobial activities. Further studies are needed to assess the safety and clinical trial of P. macrocarpus Kurz. for development as new drug candidate.

Divergent Metabolomic Signatures of TGFβ2 and TNFα in the Induction of Retinal Epithelial-Mesenchymal Transition

Epithelial-mesenchymal transition (EMT) is a dedifferentiation program in which polarized, differentiated epithelial cells lose their cell-cell adhesions and transform into matrix-producing mesenchymal cells. EMT of retinal pigment epithelial (RPE) cells plays a crucial role in many retinal diseases, including age-related macular degeneration, proliferative vitreoretinopathy, and diabetic retinopathy. This dynamic process requires complex metabolic reprogramming to accommodate the demands of this dramatic cellular transformation. Both transforming growth factor-beta 2 (TGFβ2) and tumor necrosis factor-alpha (TNFα) have the capacity to induce EMT in RPE cells; however, little is known about their impact on the RPE metabolome. Untargeted metabolomics using high-resolution mass spectrometry was performed to reveal the metabolomic signatures of cellular and secreted metabolites of primary human fetal RPE cells treated with either TGFβ2 or TNFα for 5 days. A total of 638 metabolites were detected in both samples; 188 were annotated as primary metabolites. Metabolomics profiling showed distinct metabolomic signatures associated with TGFβ2 and TNFα treatment. Enrichment pathway network analysis revealed alterations in the pentose phosphate pathway, galactose metabolism, nucleotide and pyrimidine metabolism, purine metabolism, and arginine and proline metabolism in TNFα-treated cells compared to untreated control cells, whereas TGFβ2 treatment induced perturbations in fatty acid biosynthesis metabolism, the linoleic acid pathway, and the Notch signaling pathway. These results provide a broad metabolic understanding of the bioenergetic rewiring processes governing TGFβ2- and TNFα-dependent induction of EMT. Elucidating the contributions of TGFβ2 and TNFα and their mechanistic differences in promoting EMT of RPE will enable the identification of novel biomarkers for diagnosis, management, and tailored drug development for retinal fibrotic diseases.

Exploiting Multi-Omics Profiling and Systems Biology to Investigate Functions of TOMM34

Although modern biology is now in the post-genomic era with vastly increased access to high-quality data, the set of human genes with a known function remains far from complete. This is especially true for hundreds of mitochondria-associated genes, which are under-characterized and lack clear functional annotation. However, with the advent of multi-omics profiling methods coupled with systems biology algorithms, the cellular role of many such genes can be elucidated. Here, we report genes and pathways associated with TOMM34, Translocase of Outer Mitochondrial Membrane, which plays role in the mitochondrial protein import as a part of cytosolic complex together with Hsp70/Hsp90 and is upregulated in various cancers. We identified genes, proteins, and metabolites altered in TOMM34-/- HepG2 cells. To our knowledge, this is the first attempt to study the functional capacity of TOMM34 using a multi-omics strategy. We demonstrate that TOMM34 affects various processes including oxidative phosphorylation, citric acid cycle, metabolism of purine, and several amino acids. Besides the analysis of already known pathways, we utilized de novo network enrichment algorithm to extract novel perturbed subnetworks, thus obtaining evidence that TOMM34 potentially plays role in several other cellular processes, including NOTCH-, MAPK-, and STAT3-signaling. Collectively, our findings provide new insights into TOMM34's cellular functions.

Gas Chromatography-Mass Spectrometry (GC-MS) Metabolites Analysis in Endometriosis Patients: A Prospective Observational Translational Study

BACKGROUND

Endometriosis affects women of reproductive age, and its pathogenesis is still unclear. Typically, it overlaps other similar medical and surgical conditions, determining a delay in early diagnosis. Metabolomics allows studying metabolic changes in different physiological or pathological states to discover new potential biomarkers. We used the gas chromatography-mass spectrometer (GC-MS) to explore metabolic alterations in endometriosis to better understand its pathophysiology and find new biomarkers.

METHODS

Twenty-two serum samples of patients with symptomatic endometriosis and ten without it were collected and subjected to GC-MS analysis. Multivariate and univariate statistical analyses were performed, followed by pathway analysis.

RESULTS

Partial least squares discriminant analysis was performed to determine the differences between the two groups (p = 0.003). Threonic acid, 3-hydroxybutyric acid, and proline increased significantly in endometriosis patients, while alanine and valine decreased. ROC curves were built to test the diagnostic power of metabolites. The pathway analysis identified the synthesis and degradation of ketone bodies and the biosynthesis of phenylalanine, tyrosine, and tryptophan as the most altered pathways.

CONCLUSIONS

The metabolomic approach identifies metabolic alterations in women with endometriosis. These findings may improve our understanding of the pathophysiological mechanisms of disease and the discovery of new biomarkers.

Simultaneous SGLT2 inhibition and caloric restriction improves insulin resistance and kidney function in OLETF rats

SGLT2 inhibitors (SGLT2i) are emerging as a novel therapy for type 2 diabetes due to their effective hypoglycemic and potential cardio- and nephroprotective effects, while caloric restriction (CR) is a common behavioral modification to improve adiposity and insulin resistance. Therefore, both interventions simultaneously may potentially further improve metabolic syndrome by enhancing carbohydrate metabolism. To test this hypothesis, cohorts of 10-week old, male Long Evans Tokushima Otsuka (LETO) and Otsuka Long Evans Tokushima Fatty (OLETF) rats were treated with SGLT2i (10 mg luseoglifozin/kg/day x 4 wks) (OLETF only) and/or 30% CR (2 wks at 12 weeks of age). CR maintained body mass in both strains while SGLT2i alone did not have any effect on body mass. Simultaneous treatments decreased SBP in OLETF vs SGLT2i alone, decreased insulin resistance index (IRI), and increased creatinine clearance vs OLETF ad lib. Conversely, CR decreased albuminuria independent of SGLT2i. In conclusion, SGLT2i treatment by itself did not elicit significant improvements in insulin resistance, kidney function or blood pressure. However, when combined with CR, these changes where more profound than with CR alone without inducing chronic hypoglycemia.

Current Role of Modern Chromatography with Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy in the Investigation of Biomarkers of Endometriosis

Endometriosis has a wide range of clinical manifestations, and the disease course is unpredictable, making the diagnosis a challenging task. Despite significant advances in the pathophysiology of endometriosis and various proposed theories, the exact etiology is not fully understood and is still unknown. The most commonly used biomarker of endometriosis is CA-125, however, it is nonspecific and is applied for cancers diagnosis. Therefore, the development of reliable noninvasive diagnostic tests for the early diagnosis of endometriosis remains one of the top priorities. Omics technologies are very promising approaches for constructing diagnostic models and biomarker discovery. Their use can greatly facilitate the study of such a complex disease as endometriosis. Nowadays, powerful analytical platforms commonly used in omics, such as gas and liquid chromatography with mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, have proven to be a promising tools for biomarker discovery. The aim of this review is to summarize the various features of the analytical approaches, practical challenges and features of gas and liquid chromatography with MS and NMR spectroscopy (including sample processing protocols, technological advancements, and methodology) used for profiling of metabolites, lipids, peptides and proteins in physiological fluids and tissues from patients with endometriosis. In addition, this report devotes special attention to the issue of how comprehensive analyses of these profiles can effectively contribute to the study of endometriosis. The search query included reports published between 2012 and 2022 years in PubMed, Web-of-Science, SCOPUS, Science Direct.

Integrative proteomics and metabolomics profiling to understand the biochemical basis of beef muscle darkening at a slightly elevated pH

Previous studies investigated the biochemical basis of dark-cutting conditions at elevated muscle pH (above 6), but the molecular basis at slightly above normal pH (between 5.6 and 5.8) is still unclear. The objective was to determine protein and metabolite profiles to elucidate postmortem muscle darkening at slightly elevated pH. Loins were selected based on the criteria established in our laboratory before sample collections, such as pH less than 5.8, L* values (muscle lightness) less than 38, and not discounted by the grader (high-pH beef with dark color are discounted and not sold in retail stores). Six bright red loins (longissimus lumborum) at normal-pH (average pH = 5.57) and six dark-colored strip loins at slightly elevated pH (average pH = 5.70) from A maturity carcasses were obtained within 72-h postmortem from a commercial beef purveyor. Surface color, oxygen consumption, metmyoglobin reducing activity, protein, and metabolite profiles were determined on normal-pH and dark-colored steaks at slightly elevated pH. Enzymes related to glycogen metabolism and glycolytic pathways were more differently abundant than metabolites associated with these pathways. The results indicated that oxygen consumption and metmyoglobin reducing activity were greater (P < 0.05) in darker steaks than normal-pH steaks. Enzymes involved with glycogen catabolic pathways and glycogen storage disease showed lower abundance in dark beef. The tricarboxylic acid metabolite, aconitic acid, was overabundant in darker-colored beef than normal-pH beef, but glucose derivative metabolites were less abundant. The majority of glycogenolytic proteins and metabolites reported as overabundant in the previous dark-cutting studies at high pH (>6.4) also did not show significant differences in the current study. Therefore, our data suggest enzymes involved in glycogen metabolism, in part, create a threshold for muscle darkening than metabolites.

Some Lessons Learned on the Impact of the Storage Conditions, Syringe Wash Solvent, and the Way of GC-MS Injection on the Reproducibility of Metabolomic Studies

Metabolomics based on two-dimensional gas chromatography coupled with mass spectrometry is making high demands on accuracy at all stages of sample preparation, up to the storage and injection into the analytical system. In high sample flow conditions, good repeatability in peak areas and a list of detectable metabolites is sometimes challenging to obtain. In this research, we successfully obtained good repeatability for the peak areas of MSFTA-derivatives of 29 core blood plasma metabolites. Six different strategies of storage and injection were investigated and evaluated for the reproducibility of the obtained data. As the essential factors, we considered popular GC-MS syringe washing solvents (methanol and pyridine); storage conditions (freshly prepared samples and stored for 24 h in ambient temperature or in the refrigerator); scheme of injection (one injection per intact vial or three sequential injections per vial). Our GC×GC-MS results demonstrated that the usage of pyridine as a syringe wash solvent and triple injecting the sample from the same vial was the most appropriate for minimizing the coefficient of variation (CV) of the results obtained (in general, <10%). The prolonged storage of samples does not have a noticeable effect on the change in the areas of chromatographic peaks of metabolites, although it reduces CV in some cases. These storage and injection recommendations can be used in future study protocols for the GC×GC-MS analysis of blood plasma.

Metabonomic analysis of tumor microenvironments: a mini-review

Metabolomic analysis is a vital part of studying cancer progression. Metabonomic crosstalk, such as nutrient availability, physicochemical transformation, and intercellular interactions can affect tumor metabolism. Many original studies have demonstrated that metabolomics is important in some aspects of tumor metabolism. In this mini-review, we summarize the definition of metabolomics and how it can help change a tumor microenvironment, especially in pathways of three metabonomic tumors. Just as non-invasive biofluids have been identified as early biomarkers of tumor development, metabolomics can also predict differences in tumor drug response, drug resistance, and efficacy. Therefore, metabolomics is important for tumor metabolism and how it can affect oncology drugs in cancer therapy.

Untargeted GC-MS metabolomics to identify and classify bioactive compounds in Combretum platypetalum subsp. oatesii (Rolfe) Exell (Combretaceae)

INTRODUCTION

Combretum platypetalum is used in traditional African healing practices against different infections. Unfortunately, no scientific knowledge of its phytochemical composition exists, except for the isolation of two compounds from the leaves. Scientific study has been limited to the leaves only, despite the applications of stems and roots in traditional medicine practice and natural product drug discovery programs.

OBJECTIVE

Omics was applied to identify and classify different volatile and semivolatile bioactive compounds in the leaf, stem, and root parts of C. platypetalum. The thermal stability of the plant constituents at 60-65°C extraction temperature by Soxhlet and maceration at room temperature on the type, class, and concentration of compounds in the leaf was further investigated.

METHOD

A GC-MS untargeted metabolomics approach, automated deconvolution by the Automated Mass Spectral Deconvolution and Identification System (AMDIS) for GC-MS data, preprocessing by Metab R, and multivariate statistical data analysis were employed in this study.

RESULTS

A total of 97 phytoconstituents, including 17 bioactive compounds belonging to the terpenoids, flavonoids, long-chain fatty acids, and other unclassified structural arrangements distributed across C. platypetalum, were identified for the first time. A correlation (r = 0.782; P = 0.000) between Soxhlet and maceration extraction methods relative to resolved chromatographic peak areas of metabolites was established.

CONCLUSION

Findings corroborate the reported bio-investigation of its leaf extracts, its traditional uses, and previous findings from the Combretum genus. The results substantiate the possible applications of C. platypetalum in natural product drug discovery and provide a guide for future investigations.

Lipidomics Analysis in Ferroptosis

Ferroptosis is a form of regulated cell death that occurs due to abnormal lipid metabolism. Lipids, which have been identified in over 45,000 different molecular species, play essential roles in modulating basic life processes. The process of ferroptosis is highly reliant on various lipid species, with polyunsaturated fatty acids (PUFAs) playing a central role in driving this process. Recent advances in mass spectrometry-based lipidomics have led to a surge in studies on ferroptosis. To explore the mechanism of lipid homeostasis in ferroptosis, the development of lipidomics techniques is critical. Currently, liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatography-mass spectrometry (GC-MS) are the most widely used analytical techniques in lipidomics. These techniques offer deeper insights into the complex lipid mechanisms that underlie ferroptosis.

A broadly conserved fungal alcohol oxidase (AOX) facilitates fungal invasion of plants

Alcohol oxidases (AOXs) are ecologically important enzymes that facilitate a number of plant-fungal interactions. Within Ascomycota they are primarily associated with methylotrophy, as a peroxisomal AOX catalysing the conversion of methanol to formaldehyde in methylotrophic yeast. In this study we demonstrate that AOX orthologues are phylogenetically conserved proteins that are common in the genomes of nonmethylotrophic, plant-associating fungi. Additionally, AOX orthologues are highly expressed during infection in a range of diverse pathosystems. To study the role of AOX in plant colonization, AOX knockout mutants were generated in the broad host range pathogen Sclerotinia sclerotiorum. Disease assays in soybean showed that these mutants had a significant virulence defect as evidenced by markedly reduced stem lesions and mortality rates. Chemical genomics suggested that SsAOX may function as an aromatic AOX, and growth assays demonstrated that ΔSsAOX is incapable of properly utilizing plant extract as a nutrient source. Profiling of known aromatic alcohols pointed towards the monolignol coniferyl alcohol (CA) as a possible substrate for SsAOX. As CA and other monolignols are ubiquitous among land plants, the presence of highly conserved AOX orthologues throughout Ascomycota implies that this is a broadly conserved protein used by ascomycete fungi during plant colonization.

Unveiling Microbial Chemical Interactions Based on Metabolomics Approaches

Microorganisms are ubiquitous in diverse habitats and studying their chemical interactions with the environment and comprehend its complex relations with both hosts and environment, are crucial for the development of strategies to control microbial diseases. This chapter discusses the importance of studying microorganisms with agricultural benefits, using specialized metabolites as examples. Herein we highlight the challenges and opportunities in utilizing microorganisms as alternatives to synthetic pesticides and fertilizers in agriculture. Genome-guided investigations and improved analytical methodologies are necessary to characterize diverse and complex biomolecules produced by microorganisms. Predicting and isolating bioproducts based on genetic information have become a focus for researchers, aided by tools like antiSMASH, BiG-SCAPE, PRISM, and others. However, translating genomic data into practical applications can be complex. Therefore, integrating genomics, transcriptomics, and metabolomics enhances chemical characterization, aiding in discovering new metabolic pathways and specialized metabolites. Additionally, elicitation is one promising strategy to enhance beneficial metabolite production. Finally, identify and characterize microbial secondary metabolites remain challenging due to their low production, complex chemical structure characterization and different environmental factors necessary for metabolite in vitro production.

Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics

The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.

Targeted metabolomics reveals serum changes of amino acids in mild to moderate ischemic stroke and stroke mimics

Background

The pathophysiological processes linked to an acute ischemic stroke (IS) can be reflected in the circulating metabolome. Amino acids (AAs) have been demonstrated to be one of the most significant metabolites that can undergo significant alteration after a stroke.

Methods

We sought to identify the potential biomarkers for the early detection of IS using an extensive targeted technique for reliable quantification of 27 different AAs based on ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). A cohort with 216 participants was enrolled, including 70 mild to moderate ischemic stroke patients (National Institutes of Health Stroke Scale < 15, MB group), 76 stroke mimics (MM group) and 70 healthy controls (NC group).

Results

It was found that upon comparing MB and MM to control patients, AAs shifts were detected via partial least squares discrimination analysis (PLS-DA) and pathway analysis. Interestingly, MB and MM exhibited similar AAs pattern. Moreover, ornithine, asparagine, valine, citrulline, and cysteine were identified for inclusion in a biomarker panel for early-stage stroke detection based upon an AUC of 0.968 (95% CI 0.924-0.998). Levels of ornithine were positively associated with infract volume, 3 months mRS score, and National Institutes of Health Stroke Scale (NIHSS) score in MB. In addition, a metabolites biomarker panel, including ornithine, taurine, phenylalanine, citrulline, cysteine, yielded an AUC of 0.99 (95% CI 0.966-1) which can be employed to effectively discriminate MM patients from control.

Conclusion

Overall, alternations in serum AAs are characteristic metabolic features of MB and MM. AAs could serve as promising biomarkers for the early diagnosis of MB patients since mild to moderate IS patients were enrolled in the study. The metabolism of AAs can be considered as a key indicator for both the prevention and treatment of IS.

Metabolomics Applied to Cyanobacterial Toxins and Natural Products

The biological and chemical diversity of Cyanobacteria is remarkable. These ancient prokaryotes are widespread in nature and can be found in virtually every habitat on Earth where there is light and water. They are producers of an array of secondary metabolites with important ecological roles, toxic effects, and biotechnological applications. The investigation of cyanobacterial metabolites has benefited from advances in analytical tools and bioinformatics that are employed in metabolomic analyses. In this chapter, we review selected articles highlighting the use of targeted and untargeted metabolomics in the analyses of secondary metabolites produced by cyanobacteria. Here, cyanobacterial secondary metabolites have been didactically divided into toxins and natural products according to their relevance to toxicological studies and drug discovery, respectively. This review illustrates how metabolomics has improved the chemical analysis of cyanobacteria in terms of speed, sensitivity, selectivity, and/or coverage, allowing for broader and more complex scientific questions.

Metabolomics in Cell Biology

Metabolomics has long been used in a biomedical context. The most typical samples are body fluids in which small molecules can be detected and quantified using technologies such as Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS). Many studies, in particular in the wider field of cancer research, are based on cellular models. Different cancer cells can have vastly different ways of regulating metabolism and responses to drug treatments depend on specific metabolic mechanisms which are often cell type specific. This has led to a series of publications using metabolomics to study metabolic mechanisms. Cell-based metabolomics has specific requirements and allows for interesting approaches where metabolism is followed in real-time. Here applications of metabolomics in cell biology have been reviewed, providing insight into specific technologies used and showing exemplary case studies with an emphasis towards applications which help to understand drug mechanisms.

Prostate cancer cells demonstrate unique metabolism and substrate adaptability acutely after androgen deprivation therapy

BACKGROUND

Androgen deprivation therapy (ADT) has been the standard of care for advanced hormone-sensitive prostate cancer (PC), yet tumors invariably develop resistance resulting in castrate-resistant PC. The acute response of cancer cells to ADT includes apoptosis and cell death, but a large fraction remains arrested but viable. In this study, we focused on intensively characterizing the early metabolic changes that result after ADT to define potential metabolic targets for treatment.

METHODS

A combination of mass spectrometry, optical metabolic imaging which noninvasively measures drug responses in cells, oxygen consumption rate, and protein expression analysis was used to characterize and block metabolic pathways over several days in multiple PC cell lines with variable hormone response status including ADT sensitive lines LNCaP and VCaP, and resistant C4-2 and DU145.

RESULTS

Mass spectrometry analysis of LNCaP pre- and postexposure to ADT revealed an abundance of glycolytic intermediates after ADT. In LNCaP and VCaP, a reduction in the optical redox ratio [NAD(P)H/FAD], extracellular acidification rate, and a downregulation of key regulatory enzymes for fatty acid and glutamine utilization was acutely observed after ADT. Screening several metabolic inhibitors revealed that blocking fatty acid oxidation and synthesis reversed this stress response in the optical redox ratio seen with ADT alone in LNCaP and VCaP. In contrast, both cell lines demonstrated increased sensitivity to the glycolytic inhibitor 2-Deoxy- d-glucose(2-DG) and maintained sensitivity to electron transport chain inhibitor Malonate after ADT exposure. ADT followed by 2-DG results in synergistic cell death, a result not seen with simultaneous administration.

CONCLUSIONS

Hormone-sensitive PC cells displayed altered metabolic profiles early after ADT including an overall depression in energy metabolism, induction of a quiescent/senescent phenotype, and sensitivity to selected metabolic inhibitors. Glycolytic blocking agents (e.g., 2-DG) as a sequential treatment after ADT may be promising.

Root exudate composition reflects drought severity gradient in blue grama (Bouteloua gracilis)

Plant survival during environmental stress greatly affects ecosystem carbon (C) cycling, and plant–microbe interactions are central to plant stress survival. The release of C-rich root exudates is a key mechanism plants use to manage their microbiome, attracting beneficial microbes and/or suppressing harmful microbes to help plants withstand environmental stress. However, a critical knowledge gap is how plants alter root exudate concentration and composition under varying stress levels. In a greenhouse study, we imposed three drought treatments (control, mild, severe) on blue grama (Bouteloua gracilis Kunth Lag. Ex Griffiths), and measured plant physiology and root exudate concentration and composition using GC–MS, NMR, and FTICR. With increasing drought severity, root exudate total C and organic C increased concurrently with declining predawn leaf water potential and photosynthesis. Root exudate composition mirrored the physiological gradient of drought severity treatments. Specific compounds that are known to alter plant drought responses and the rhizosphere microbiome mirrored the drought severity-induced root exudate compositional gradient. Despite reducing C uptake, these plants actively invested C to root exudates with increasing drought severity. Patterns of plant physiology and root exudate concentration and composition co-varied along a gradient of drought severity.

Metabolic signatures associated with oncolytic myxoma viral infections

Oncolytic viral therapy is a recent advance in cancer treatment, demonstrating promise as a primary treatment option. To date, the secondary metabolic effects of viral infection in cancer cells has not been extensively studied. In this work, we have analyzed early-stage metabolic changes in cancer cells associated with oncolytic myxoma virus infection. Using GC–MS based metabolomics, we characterized the myxoma virus infection induced metabolic changes in three cancer cell lines—small cell (H446) and non-small cell (A549) lung cancers, and glioblastoma (SFxL). We show that even at an early stage (6 and 12 h) myxoma infection causes profound changes in cancer cell metabolism spanning several important pathways such as the citric acid cycle, fatty acid metabolism, and amino acid metabolism. In general, the metabolic effects of viral infection across cell lines are not conserved. However, we have identified several candidate metabolites that can potentially serve as biomarkers for monitoring oncolytic viral action in general.

Clostridium butyricum potentially improves inflammation and immunity through alteration of the microbiota and metabolism of gastric cancer patients after gastrectomy

BACKGROUND

Gastrectomy is the most effective treatment to improve the clinical survival rate of patients with gastric cancer. However, the pathophysiological changes caused by gastrectomy have seriously affected the postoperative recovery.

METHODS

In the present trial, Ataining (containing C. butyricum, CGMCC0313.1) was applied in patients after gastrectomy to investigate the effect of C. butyricum on the early postoperative recovery by monitoring the inflammatory immune response with blood indicators, detecting the gut microbiota with high-throughput sequencing, and analyzing the short-chain fatty acids (SCFAs) with targeted metabolomics. This study is registered with the number ChiCTR2000040915.

RESULTS

Our outcomes revealed that C. butyricum had significantly reduced the number of Leucocyte (P < 0.001), the percentage of Neutrophil (P < 0.001), the expression of IL-1β (P < 0.01), IL-6 (P < 0.05), and TNF-α (P < 0.01), while markedly enhanced the immunity indexes (immunoglobulin and lymphocyte) (P < 0.05) and nutrition indexes (albumin and total protein) (P < 0.05). In addition, the use of the C. butyricum greatly enriched the relative abundance of beneficial bacteria Bacteroides, Faecalibacterium and Gemmiger, while the abundance of pathogenic Streptococcus, Desulfovibrio and Actinomyces were markedly decreased at genus level. We also observed significant up-regulation of SCFAs, including acetic acid, propionic acid, butyric acid and isobutyric acid, after C. butyricum administration in patients receiving gastrectomy.

CONCLUSION

Therefore, evidence supported that oral administration of C. butyricum after gastrectomy can reduce early postoperative inflammation, enhance immune ability, restore intestinal microbiota eubiosis, increase intestinal SCFAs, reduce the occurrence of postoperative complications, and ultimately promote the early recovery of the patient.

CLINICAL TRIAL REGISTRATION

http://www.chictr.org.cn/, identifier (ChiCTR2000040915).

Disturbing effect of cepharanthine on valve interstitial cells calcification via regulating glycolytic metabolism pathways

Osteogenic differentiation of valve interstitial cells (VICs) directly leads to aortic valve calcification, which is a common cardiovascular disease caused by inflammation and metabolic disorder. There is still no ideal drug for its treatment and prevention. The purpose of this study was to explore the effect and molecular mechanism of cepharanthine (CEP), a natural product, on inhibiting the osteogenic differentiation of VICs. First, CCK8 assay was used to evaluate cell viability of CEP on VICs. CEP concentration of 10 μM was the effective dose with slight cytotoxicity, which was used for further study. The alizarin red staining analysis showed that CEP significantly inhibited calcium deposition caused by osteogenic medium related calcification induction. In order to explore the anti-calcification molecular mechanism of CEP, transcriptome and metabolome were synchronously used to discover the possible molecular mechanism and target of CEP. The results showed that CEP inhibited valve calcification by regulating the glycolytic pathway. The molecular docking of CEP and selected key factors in glycolysis showed significant binding energies for GLUT1 (-11.3 kcal/mol), ENO1 (-10.6 kcal/mol), PKM (-9.8 kcal/mol), HK2 (-9.2 kcal/mol), PFKM (-9.0 kcal/mol), and PFKP (-8.9 kcal/mol). The correlation analysis of RUNX2 expression and cellular lactate content showed R2 of 0.7 (p < 0.001). In conclusion, this study demonstrated that CEP inhibited osteoblastic differentiation of VICs by interfering with glycolytic metabolisms via downregulation of the production of lactate and glycolysis-associated metabolites.

Sex differences in brain tumor glutamine metabolism reveal sex-specific vulnerabilities to treatment

BACKGROUND

Brain cancer incidence and mortality rates are greater in males. Understanding the molecular mechanisms that underlie those sex differences could improve treatment strategies. Although sex differences in normal metabolism are well described, it is currently unknown whether they persist in cancerous tissue.

METHODS

Using positron emission tomography (PET) imaging and mass spectrometry, we assessed sex differences in glioma metabolism in samples from affected individuals. We assessed the role of glutamine metabolism in male and female murine transformed astrocytes using isotope labeling, metabolic rescue experiments, and pharmacological and genetic perturbations to modulate pathway activity.

FINDINGS

We found that male glioblastoma surgical specimens are enriched for amino acid metabolites, including glutamine. Fluoroglutamine PET imaging analyses showed that gliomas in affected male individuals exhibit significantly higher glutamine uptake. These sex differences were well modeled in murine transformed astrocytes, in which male cells imported and metabolized more glutamine and were more sensitive to glutaminase 1 (GLS1) inhibition. The sensitivity to GLS1 inhibition in males was driven by their dependence on glutamine-derived glutamate for α-ketoglutarate synthesis and tricarboxylic acid (TCA) cycle replenishment. Females were resistant to GLS1 inhibition through greater pyruvate carboxylase (PC)-mediated TCA cycle replenishment, and knockdown of PC sensitized females to GLS1 inhibition.

CONCLUSION

Our results show that clinically important sex differences exist in targetable elements of metabolism. Recognition of sex-biased metabolism may improve treatments through further laboratory and clinical research.

FUNDING

This work was supported by NIH grants, Joshua's Great Things, the Siteman Investment Program, and the Barnard Research Fund.

Exploring Dynamic Metabolome of the HepG2 Cell Line: Rise and Fall

Both biological and technical variations can discredit the reliability of obtained data in omics studies. In this technical note, we investigated the effect of prolonged cultivation of the HepG2 hepatoma cell line on its metabolomic profile. Using the GC × GC-MS approach, we determined the degree of metabolic variability across HepG2 cells cultured in uniform conditions for 0, 5, 10, 15, and 20 days. Post-processing of obtained data revealed substantial changes in relative abundances of 110 metabolites among HepG2 samples under investigation. Our findings have implications for interpreting metabolomic results obtained from immortal cells, especially in longitudinal studies. There are still plenty of unanswered questions regarding metabolomics variability and many potential areas for future targeted and panoramic research. However, we suggest that the metabolome of cell lines is unstable and may undergo significant transformation over time, even if the culture conditions remain the same. Considering metabolomics variability on a relatively long-term basis, careful experimentation with particular attention to control samples is required to ensure reproducibility and relevance of the research results when testing both fundamentally and practically significant hypotheses.

Fused Raman spectroscopic analysis of blood and saliva delivers high accuracy for head and neck cancer diagnostics

As a rapid, label-free, non-destructive analytical measurement requiring little to no sample preparation, Raman spectroscopy shows great promise for liquid biopsy cancer detection and diagnosis. We carried out Raman analysis and mass spectrometry of plasma and saliva from more than 50 subjects in a cohort of head and neck cancer patients and benign controls (e.g., patients with benign oral masses). Unsupervised data models were built to assess diagnostic performance. Raman spectra collected from either biofluid provided moderate performance to discriminate cancer samples. However, by fusing together the Raman spectra of plasma and saliva for each patient, subsequent analytical models delivered an impressive sensitivity, specificity, and accuracy of 96.3%, 85.7%, and 91.7%, respectively. We further confirmed that the metabolites driving the differences in Raman spectra for our models are among the same ones that drive mass spectrometry models, unifying the two techniques and validating the underlying ability of Raman to assess metabolite composition. This study bolsters the relevance of Raman to provide additive value by probing the unique chemical compositions across biofluid sources. Ultimately, we show that a simple data augmentation routine of fusing plasma and saliva spectra provided significantly higher clinical value than either biofluid alone, pushing forward the potential of clinical translation of Raman spectroscopy for liquid biopsy cancer diagnostics.

Plasma metabolomic profiling as a tool to identify predictive biomarkers of methotrexate efficacy in rheumatoid arthritis

OBJECTIVE

Methotrexate (MTX) remains the first-choice disease-modifying therapy in rheumatoid arthritis (RA). However, clinical response is variable, and no reliable predictive biomarkers of efficacy currently exist. In this study, plasma metabolomic profiling is evaluated as a tool to identify pretreatment biomarkers of MTX response in RA.

METHODS

Plasma collected from RA patients initiating MTX therapy (n = 20) were analyzed by metabolomic profiling totaling 648 identified metabolites. Pretreatment metabolomic profiles were compared based on clinical response after 16-weeks of MTX therapy. Clinical response to MTX was defined by a clinically meaningful reduction in disease activity score in 28 joints (DAS28-ESR) of greater than 1.2.

RESULTS

Pretreatment plasma levels of 19 metabolites were found to differ (p < 0.05) between RA patients based on response to MTX at 16-weeks. Spearman's correlation of pretreatment plasma metabolite levels with change in DAS28-ESR over the treatment period further supported three of the identified metabolites as associated with MTX response (p < 0.05). The identified metabolite levels were all found to be lower in RA patients responsive to MTX but were not found to be intercorrelated. Receiver operating characteristic analysis of each of the identified metabolites, alone or in combination, demonstrated an excellent discrimination between responders and non-responders based on pretreatment plasma levels of nornicotine (AUC = 0.84), N-methylisoleucine (AUC = 0.82), 2,3-dihydroxybutanoic acid (AUC = 0.82), and a combination biomarker panel score (AUC = 0.98).

CONCLUSION

Pretreatment plasma metabolomic profiling identified multiple metabolites associated with early response to MTX therapy in RA and represents a promising approach for the identification of clinical biomarkers of MTX response in RA.

Prolonged phloem feeding by the spotted lanternfly, an invasive planthopper, alters resource allocation and inhibits gas exchange in grapevines

Spotted lanternfly (Lycorma delicatula White; SLF) is a phloem-feeding planthopper invasive to the Eastern United States that can feed on a range of wild and cultivated plant species. Since its 2014 introduction in the United States, large infestations and subsequent economic damage have been reported in cultivated grapevines, but no studies have detailed grapevine physiological responses to SLF phloem feeding. This study investigated grapevine-SLF interactions, detailing how different infestation densities affect leaf gas exchange and end-season concentrations of nonstructural carbohydrates and nitrogen in vegetative and perennial tissues of two Vitis species. Effects on fruit ripeness parameters and dormant bud freeze tolerance were examined, in addition to other year-after effects. Phloem feeding by low densities (≤4 SLF shoot-1) had minimal effects, whereas greater densities (5-15 SLF shoot-1) increasingly affected carbohydrate and nitrogen dynamics in both Vitis species. Phloem feeding substantially affected starch and, to a lesser extent, total nitrogen concentrations of woody roots. Prolonged exposure strongly reduced leaf gas exchange. We conclude that intensive late-season phloem feeding by large adult SLF population densities (≥8 SLF shoot-1) can induce carbon limitation, with the potential for negative year-after effects in cases of severe belowground carbon depletion. This work presents novel insights into SLF-grapevine interactions, identifies avenues of future SLF-plant research, and assists the development of action thresholds for SLF management in vineyards.

d-lactate-triggered extracellular trap formation in cattle polymorphonuclear leucocytes is glucose metabolism dependent

D-lactic acidosis is a metabolic disease of cattle caused by the digestive overgrowth of bacteria that are highly producers of d-lactate, a metabolite that then reaches and accumulates in the bloodstream. d-lactate is a proinflammatory agent in cattle that induces the formation of extracellular traps (ETs) in polymorphonuclear leucocytes (PMN), although information on PMN metabolic requirements for this response mechanism is insufficient. In the present study, metabolic pathways involved in ET formation induced by d-lactate were studied. We show that d-lactate but not l-lactate induced ET formation in cattle PMN. We analyzed the metabolomic changes induced by d-lactate in bovine PMN using gas chromatography-mass spectrometry (GC-MS). Several metabolic pathways were altered, including glycolysis/gluconeogenesis, amino sugar and nucleotide sugar metabolism, galactose metabolism, starch and sucrose metabolism, fructose and mannose metabolism, and pentose phosphate pathway. d-lactate increased intracellular levels of glucose and glucose-6-phosphate, and increased uptake of the fluorescent glucose analog 2-NBDG, suggesting improved glycolytic activity. In addition, using an enzymatic assay and transmission electron microscopy (TEM), we observed that d-lactate was able to decrease intracellular glycogen levels and the presence of glycogen granules. Relatedly, d-lactate increased the expression of enzymes of glycolysis, gluconeogenesis and glycogen metabolism. In addition, 2DG (a hexokinase inhibitor), 3PO (a 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 inhibitor), MB05032 (inhibitor of fructose-1,6-bisphosphatase) and CP-91149 (inhibitor of glycogen phosphorylase) reduced d-lactate-triggered ETosis. Taken together, these results suggest that d-lactate induces a metabolic rewiring that increases glycolysis, gluconeogenesis and glycogenolysis, all of which are required for d-lactate-induced ET release in cattle PMN.

Optimization of a GC-MS Injection-Port Derivatization Methodology to Enhance Metabolomics Analysis Throughput in Biological Samples

Advances in metabolomics analysis and data treatment increase the knowledge of complex biological systems. One of the most used methodologies is gas chromatography-mass spectrometry (GC-MS) due to its robustness, high separation efficiency, and reliable peak identification through curated databases. However, methodologies are not standardized, and the derivatization steps in GC-MS can introduce experimental errors and take considerable time, exposing the samples to degradation. Here, we propose the injection-port derivatization (IPD) methodology to increase the throughput in plasma metabolomics analysis by GC-MS. The IPD method was evaluated and optimized for different families of metabolites (organic acids, amino acids, fatty acids, sugars, sugar phosphates, etc.) in terms of residence time, injection-port temperature, and sample/derivatization reagent ratio. Finally, the method's usefulness was validated in a study consisting of a cohort of obese patients with or without nonalcoholic steatohepatitis. Our results show a fast, reproducible, precise, and reliable method for the analysis of biological samples by GC-MS. Raw data are publicly available at MetaboLights with Study Identifier MTBLS5151.

Optimization of a GC-MS method for the profiling of microbiota-dependent metabolites in blood samples: An application to type 2 diabetes and prediabetes

Changes in serum or plasma metabolome may reflect gut microbiota dysbiosis, which is also known to occur in patients with prediabetes and type 2 diabetes (T2DM). Thus, developing a robust method for the analysis of microbiota-dependent metabolites (MDMs) is an important issue. Gas chromatography with mass spectrometry (GC–MS) is a powerful approach enabling detection of a wide range of MDMs in biofluid samples with good repeatability and reproducibility, but requires selection of a suitable solvents and conditions. For this reason, we conducted for the first time the study in which, we demonstrated an optimisation of samples preparation steps for the measurement of 75 MDMs in two matrices. Different solvents or mixtures of solvents for MDMs extraction, various concentrations and volumes of derivatizing reagents as well as temperature programs at methoxymation and silylation step, were tested. The stability, repeatability and reproducibility of the 75 MDMs measurement were assessed by determining the relative standard deviation (RSD). Finally, we used the developed method to analyse serum samples from 18 prediabetic (PreDiab group) and 24 T2DM patients (T2DM group) from our 1000PLUS cohort. The study groups were homogeneous and did not differ in age and body mass index. To select statistically significant metabolites, T2DM vs. PreDiab comparison was performed using multivariate statistics. Our experiment revealed changes in 18 MDMs belonging to different classes of compounds, and seven of them, based on the SVM classification model, were selected as a panel of potential biomarkers, able to distinguish between patients with T2DM and prediabetes.

Mass Spectrometry-Based Nontargeted and Targeted Analytical Approaches in Fingerprinting and Metabolomics of Food and Agricultural Research

Mass spectrometry (MS)-based techniques have been extensively applied in food and agricultural research. This review aims to address the advances and applications of MS-based analytical strategies in nontargeted and targeted analysis and summarizes the recent publications of MS-based techniques, including flow injection MS fingerprinting, chromatography-tandem MS metabolomics, direct analysis using ambient mass spectrometry, as well as development in MS data deconvolution software packages and databases for metabolomic studies. Various nontargeted and targeted approaches are employed in marker compounds identification, material adulteration detection, and the analysis of specific classes of secondary metabolites. In the newly emerged applications, the recent advances in computer tools for the fast deconvolution of MS data in targeted secondary metabolite analysis are highlighted.

Metabolic profiles alteration of Southern Thailand traditional sweet pickled mango during the production process

Sweet pickled mango named Ma-Muang Bao Chae-Im (MBC), a delicacy from the Southern part of Thailand, has a unique aroma and taste. The employed immersion processes (brining 1, brining 2, and immersion in a hypertonic sugar solution, sequentially) in the MBC production process bring changes to the unripe mango, which indicate the occurrence of metabolic profiles alteration during the production process. This occurrence was never been explored. Thus, this study investigated metabolic profile alteration during the MBC production process. The untargeted metabolomics profiling method was used to reveal the changes in volatile and non-volatile metabolites. Headspace solid-phase micro-extraction tandem with gas chromatography quadrupole time of flight (GC/QTOF) was employed for the volatile analysis, while metabolites derivatization for non-volatile analysis. In conclusion, a total of 82 volatile and 41 non-volatile metabolites were identified during the production process. Terpenes, terpenoids, several non-volatile organic acids, and sugars were the major mango metabolites that presented throughout the process. Gamma-aminobutyric acid (GABA) was only observed during the brining processes, which suggested the microorganism’s stress response mechanism to an acidic environment and high chloride ions in brine. Esters and alcohols were abundant during the last immersion process, which had an important role in MBC flavor characteristics. The knowledge of metabolites development during the MBC production process would be beneficial for product development and optimization.

Overexpression of bacteriophage T4 and T7 endolysins differentially regulate the metabolic fingerprint of host Escherichia coli

Bioactive proteins are often overexpressed in different host systems for biotechnological/biomedical applications. Endolysins are natural bactericidal proteins that cleave the bacterial peptidoglycan membrane, and have the potential to be the next-generation enzybiotics. Therefore, the present study aims to elucidate the impact of two endolysins (T4L, T7L) overexpression on metabolic fingerprint of E. coli using NMR spectroscopy. The ¹H NMR-based metabolomics analysis revealed global metabolite profiles of E. coli in response to endolysins. The study has identified nearly 75 metabolites, including organic acids, amino acids, sugars and nucleic acids. RNA Polymerase (RNAP) has been considered as reference protein for marking the specific alterations in metabolic pathways. The data suggested downregulation of central carbon metabolic pathway in both endolysins overexpression, but to a different extent. Also, the endolysin overexpression have highlighted the enhanced metabolic load and stress generation in the host cells, thus leading to the activation of osmoregulatory pathways. The overall changes in metabolic fingerprint of E. coli highlights the enhanced perturbations during the overexpression of T4L as compared to T7L. These untargeted metabolic studies shed light on the regulation of molecular pathways during the heterologous overexpression of these lytic enzymes that are lethal to the host.

Microbial Tryptophan Metabolism Tunes Host Immunity, Metabolism, and Extraintestinal Disorders

The trillions of commensal microorganisms comprising the gut microbiota have received growing attention owing to their impact on host physiology. Recent advances in our understandings of the host–microbiota crosstalk support a pivotal role of microbiota-derived metabolites in various physiological processes, as they serve as messengers in the complex dialogue between commensals and host immune and endocrine cells. In this review, we highlight the importance of tryptophan-derived metabolites in host physiology, and summarize the recent findings on the role of tryptophan catabolites in preserving intestinal homeostasis and fine-tuning immune and metabolic responses. Furthermore, we discuss the latest evidence on the effects of microbial tryptophan catabolites, describe their mechanisms of action, and discuss how perturbations of microbial tryptophan metabolism may affect the course of intestinal and extraintestinal disorders, including inflammatory bowel diseases, metabolic disorders, chronic kidney diseases, and cardiovascular diseases.