Liquid Chromatography Combined With Mass Spectrometry Research Articles

Toxoplasma gondii Infection of BALB/c Mice Perturbs Host Neurochemistry.

Toxoplasma gondii infection has been associated with psychoneurological disease in humans and behavioural changes in rodents. However, the mechanisms accounting for this have not been fully described and in some cases could be argued to reflect the severe neuropathology that some mice suffer during infection. Herein we employ a multi-omics approach to extensively examine BALB/c mice that are resistant to toxoplasmic encephalitis. Using a combination of LCMS (liquid chromatography-mass spectrometry) and RNAseq we demonstrate that infection alters the neurochemistry and the transcriptome of the brains of BALB/c mice. Notable changes to tryptophan, purine, arginine, nicotinamide and carnitine metabolism were observed in infected mice and this was accompanied with changes to the levels of a number of transcripts associated with enzymes these metabolic pathways. In addition, changes were seen in transcripts of many immunologically important genes known to contribute to immunity to T. gondii. Changes in the levels of additional transcripts during infection have previously been associated with psychoneurological diseases. The results demonstrate that the BALB/c mouse, with its relatively mild neurological disease, is a useful model for characterising the effects of T. gondii infection on murine neurochemistry. The results also implicate specific biochemical pathways in mediating these changes and should inform further mechanistic studies and suggest therapeutic targets.

Metabolomic Profiling of Pulmonary Neuroendocrine Neoplasms.

Pulmonary neuroendocrine neoplasms (NENs) account for 20% of malignant lung tumors. Their management is challenging due to their diverse clinical features and aggressive nature. Currently, metabolomics offers a range of potential cancer biomarkers for diagnosis, monitoring tumor progression, and assessing therapeutic response. However, a specific metabolomic profile for early diagnosis of lung NENs has yet to be identified. This study aims to identify specific metabolomic profiles that can serve as biomarkers for early diagnosis of lung NENs. We measured 153 metabolites using liquid chromatography combined with mass spectrometry (LC-MS) in the plasma of 120 NEN patients and compared them with those of 71 healthy individuals. Additionally, we compared these profiles with those of 466 patients with non-small-cell lung cancers (NSCLCs) to ensure clinical relevance. We identified 21 metabolites with consistently altered plasma concentrations in NENs. Compared to healthy controls, 18 metabolites were specific to carcinoid tumors, 5 to small-cell lung carcinomas (SCLCs), and 10 to large-cell neuroendocrine carcinomas (LCNECs). These findings revealed alterations in various metabolic pathways, such as fatty acid biosynthesis and beta-oxidation, the Warburg effect, and the citric acid cycle. Our study identified biomarker metabolites in the plasma of patients with each subtype of lung NENs and demonstrated significant alterations in several metabolic pathways. These metabolomic profiles could potentially serve as biomarkers for early diagnosis and better management of lung NENs.

First comprehensive untargeted metabolomics study of suramin-treated Trypanosoma brucei: an integrated data analysis workflow from multifactor data modelling to functional analysis.

Human African trypanosomiasis, commonly known as sleeping sickness, is a vector-borne parasitic disease prevalent in sub-Saharan Africa and transmitted by the tsetse fly. Suramin, a medication with a long history of clinical use, has demonstrated varied modes of action against Trypanosoma brucei. This study employs a comprehensive workflow to investigate the metabolic effects of suramin on T. brucei, utilizing a multimodal metabolomics approach. The primary aim of this study is to comprehensively analyze the metabolic impact of suramin on T. brucei using a combined liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance spectroscopy (NMR) approach. Statistical analyses, encompassing multivariate analysis and pathway enrichment analysis, are applied to elucidate significant variations and metabolic changes resulting from suramin treatment. A detailed methodology involving the integration of high-resolution data from LC-MS and NMR techniques is presented. The study conducts a thorough analysis of metabolite profiles in both suramin-treated and control T. brucei brucei samples. Statistical techniques, including ANOVA-simultaneous component analysis (ASCA), principal component analysis (PCA), ANOVA 2 analysis, and bootstrap tests, are employed to discern the effects of suramin treatment on the metabolomics outcomes. Our investigation reveals substantial differences in metabolic profiles between the control and suramin-treated groups. ASCA and PCA analysis confirm distinct separation between these groups in both MS-negative and NMR analyses. Furthermore, ANOVA 2 analysis and bootstrap tests confirmed the significance of treatment, time, and interaction effects on the metabolomics outcomes. Functional analysis of the data from LC-MS highlighted the impact of treatment on amino-acid, and amino-sugar and nucleotide-sugar metabolism, while time effects were observed on carbon intermediary metabolism (notably glycolysis and di- and tricarboxylic acids of the succinate production pathway and tricarboxylic acid (TCA) cycle). Through the integration of LC-MS and NMR techniques coupled with advanced statistical analyses, this study identifies distinctive metabolic signatures and pathways associated with suramin treatment in T. brucei. These findings contribute to a deeper understanding of the pharmacological impact of suramin and have the potential to inform the development of more efficacious therapeutic strategies against African trypanosomiasis.

Parthian dyed fabric discovered from Kuh-e Khwaja archaeological site, Sistan, Iran: A multi-analytical study for dye identification

The purpose of this investigation is to examine a dyed fabric recovered from Kuh-e Khwaja, an archaeological site in the Sistan area of southeastern Iran. This fabric, dating back to the Parthian period (3rd century BCE-3rd century CE), is a rarity due to its red and blue dyed fibers. A combination of liquid chromatography-mass spectrometry (LC-MS), micro-spectrophotometry, and scanning electron microscopy equipped with an energy-dispersive X-ray spectrometer (SEM-EDS) were employed to explore the fibers morphology, dyes and mordants. The results reveal that wool fibers were used in the production of the dyed yarns. Using micro-spectrophotometry, single fibers could be studied and preliminary dye identification was possible through transmittance spectra recording in microscopic dimensions. The spectra recorded indicate that indigo and madder were used to dye blue and red fibers respectively. LC-MS analysis corroborated these findings, demonstrating the presence of alizarin, purpurin, and munjistin in red dyes and indigotin in blue dyes. Evaluation of the fibers with SEM-EDS supports the possibility of using non-mordanted fiber in blue yarn and chalk as a pre-treatment in dyeing red fibers. These results showcase the use of madder and indigo dyeing during the Parthian era in this important region of southeastern Iran, which has been subject to limited exploration concerning its textile history. These findings contribute to a more comprehensive understanding of the history of dyeing in Iran.

Exploring the effect of Yinzhihuang granules on alcoholic liver disease based on pharmacodynamics, network pharmacology and molecular docking

BackgroundYinzhihuang granules (YZHG) is a commonly used Chinese patent medicine for the treatment of liver disease. However, the mechanism of YZHG in alcoholic liver disease (ALD) is still unclear.MethodsThis study combined liquid chromatography-mass spectrometry technology, pharmacodynamics, network pharmacology and molecular docking methods to evaluate the potential mechanism of YZHG in the treatment of ALD.ResultsA total of 25 compounds including 4-hydroxyacetophenone, scoparone, geniposide, quercetin, baicalin, baicalein, chlorogenic acid and caffeic acid in YZHG were identified by ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). The pharmacodynamic investigations indicated that YZHG could improve liver function and the degree of liver tissue lesions, and reduce liver inflammation and oxidative stress in ALD mice. Network pharmacology analysis showed that YZHG treated ALD mainly by regulating inflammation-related signaling pathways such as the PI3K-Akt signaling pathway. The results of the PPI network and molecular docking showed that the targets of SRC, HSP90AA1, STAT3, EGFR and AKT1 could be the key targets of YZHG in the treatment of ALD.ConclusionThis study explored the potential compounds, potential targets and signaling pathways of YZHG in the treatment of ALD, which is helpful to clarify the efficacy and mechanism of YZHG and provide new insights for the clinical application of YZHG.

Metabolomics Reveals a "Trimeric" γ-Actinorhodin from Streptomyces coelicolor M145.

Streptomyces coelicolor is a prolific producer of natural products and serves as a model organism for their study. It produces several pigmented antibiotics, the best-studied of which are the actinorhodins. We used a combination of liquid chromatography-mass spectrometry (LC-MS) and computational tools used for annotating the detected species (e. g., spectral matching, in-silico predictors, molecular networking) to identify putative new actinorhodin analogs. These studies led to the discovery of the first trimeric benzoisochromanequinone, θ-actinorhodin (1). Further metabolomics analysis revealed that the relative amounts of shunt products produced were similar between the two growth conditions explored. This suggests that, while substantially different products were being produced, the biosynthetic gene clusters were similarly active. Overall, this work describes the discovery of the first trimeric benzoisochromanequinone and explores the biosynthetic processes that might lead to its production by metabolomics analysis of relevant intermediates.

Survival analysis of patient groups defined by unsupervised machine learning clustering methods based on patient metabolomic data.

PurposeMeta-analyses failed to accurately identify patients with non-metastatic breast cancer who are likely to benefit from chemotherapy, and metabolomics could provide new answers. In our previous published work, patients were clustered using five different unsupervised machine learning (ML) methods resulting in the identification of three clusters with distinct clinical and simulated survival data. The objective of this study was to evaluate the survival outcomes, with extended follow-up, using the same 5 different methods of unsupervised machine learning. Experimental designForty-nine patients, diagnosed between 2013 and 2016, with non-metastatic BC were included retrospectively. Median follow-up was extended to 85.8 months. 449 metabolites were extracted from tumor resection samples by combined Liquid chromatography-mass spectrometry (LC–MS). Survival analyses were reported grouping together Cluster 1 and 2 versus cluster 3. Bootstrap optimization was applied. ResultsPCA k-means, K-sparse and Spectral clustering were the most effective methods to predict 2-year progression-free survival with bootstrap optimization (PFSb); as bootstrap example, with PCA k-means method, PFSb were 94% for cluster 1&2 versus 82% for cluster 3 (p = 0.01). PCA k-means method performed best, with higher reproducibility (mean HR=2 (95%CI [1.4–2.7]); probability of p ≤ 0.05 85%). Cancer-specific survival (CSS) and overall survival (OS) analyses highlighted a discrepancy between the 5 ML unsupervised methods. ConclusionOur study is a proof-of-principle that it is possible to use unsupervised ML methods on metabolomic data to predict PFS survival outcomes, with the best performance for PCA k-means. A larger population study is needed to draw conclusions from CSS and OS analyses.

Pregnancy-Related Diseases and Delivery Mode can Affect the Content of Human Milk Oligosaccharides: A Preliminary Study.

Human milk oligosaccharides (HMOs) are the second most abundant carbohydrates in colostrum. In this study, we performed a quantitative analysis of 13 oligosaccharides in 99 colostrum samples obtained from mothers living in Northwest China. The analysis combined liquid chromatography-mass spectrometry (LC-MS) with 2-amino-N-(2-aminoethyl)benzamide (AEAB) labeling and nonsecretors accounted for 17%. Compared with healthy secretor mothers, those with gestational diabetes mellitus presented lower levels of sialylated oligosaccharides, especially 3'-sialyllactose. Colostrum from mothers with pregnancy-induced hypertension had higher levels of fucosylated oligosaccharides, but the difference was not significant, and hypothyroidism appeared to have no effect on HMOs. Most HMOs (especially 6'-sialyllactose) were more abundant in colostrum from mothers who underwent vaginal delivery than a C-section. These findings show that the concentration of total or individual HMOs is affected by multiple factors. These findings provide a reference for evaluating variations in HMO expression among different populations and potential guidance for providing personalized clinical nutrition.

Discovery of the Potential Novel Pharmacodynamic Substances From Zhi-Zi-Hou-Po Decoction Based on the Concept of Co-Decoction Reaction and Analysis Strategy.

Background: Zhi-Zi-Hou-Po Decoction (ZZHPD), a classic traditional Chinese medicine (TCM) formula, is clinically used to treat insomnia and depression. The analysis strategy based on the concept of co-decoction of TCM is helpful to analyse the effective substances of TCM formula in depth. Aim of the study: This manuscript intends to take ZZHPD as a model sample to explore the phenomenon of co-decoction of complex formula in the combination of liquid chromatography-mass spectrometry (LC-MS) technology, data analysis, and molecular docking. Materials and methods: In the current research, an innovative LC-MS method has been established to study the active ingredients in ZZHPD, and to identify the ingredients absorbed into the blood and brain tissues of mice. And molecular docking was used to study the binding pattern and affinities of known compounds of the brain tissue toward insomnia related proteins. Results: Based on new processing methods and analysis strategies, 106 chemical components were identified in ZZHPD, including 28 blood components and 18 brain components. Then, by comparing the different compounds in the co-decoction and single decoction, it was surprisingly found that 125 new ingredients were produced during the co-decoction, 2 of which were absorbed into the blood and 1 of which was absorbed into brain tissue. Ultimately, molecular docking studies showed that 18 brain components of ZZHPD had favourable binding conformation and affinity with GABA, serotonin and melatonin receptors. The docking results of GABRA1 with naringenin and hesperidin, HCRTR1 with naringenin-7-O-glucoside, poncirenin and genipin 1-gentiobioside, and luteolin with SLC6A4, GLO1, MAOB and MTNR1A may clarify the mechanism of action of ZZHPD in treating insomnia and depression. Conclusion: Our study may provide new ideas for further exploring the effective substances in ZZHPD.

Identification of adducts between oxidized rosmarinic acid and glutathione compounds by electrochemistry, liquid chromatography and mass spectrometry.

Natural polyphenols are omnipresent and are an integral part of the human diet as well as quinones. Glutathione (GSH) is present in a significant amount inside cells and consequently, GSH conjugates of polyphenols will be encountered in the body. In the current work, voltammetry and liquid chromatography-mass spectrometry were carried out to characterize the reaction mechanism of the electrochemical oxidation of polyphenolic rosmarinic acid (RA) with GSH nucleophiles in aqueous solution. Electrochemical investigation of RA revealed that two consecutive transfer steps (which depend on pH) of two electrons and protons occur during the reversible oxidation of RA. Moreover, it was found that the first oxidation step is associated with the 3,4-dihydroxyphenyl lactic acid moiety, whereas the second oxidation step corresponds to the oxidation of the caffeic acid one. By using ultrahigh-performance liquid chromatography-diode array detection-mass spectrometry (UPLC-DAD-MS) in the negative ion mode, the oxidation pathways of RA in the presence of GSH were analyzed, and a total of four RA-GSH conjugates were identified. The oxidative degradation pathway of RA can be better apprehended and forecasted by the acquired results in this study.

Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea.

BackgroundConventional diagnostic approaches for adrenal tumors require multi-step processes, including imaging studies and dynamic hormone tests. Therefore, this study aimed to discriminate adrenal tumors from a single blood sample based on the combination of liquid chromatography-mass spectrometry (LC-MS) and machine learning algorithms in serum profiling of adrenal steroids.MethodsThe LC-MS-based steroid profiling was applied to serum samples obtained from patients with nonfunctioning adenoma (NFA, n=73), Cushing’s syndrome (CS, n=30), and primary aldosteronism (PA, n=40) in a prospective multicenter study of adrenal disease. The decision tree (DT), random forest (RF), and extreme gradient boost (XGBoost) were performed to categorize the subtypes of adrenal tumors.ResultsThe CS group showed higher serum levels of 11-deoxycortisol than the NFA group, and increased levels of tetrahydrocortisone (THE), 20α-dihydrocortisol, and 6β-hydroxycortisol were found in the PA group. However, the CS group showed lower levels of dehydroepiandrosterone (DHEA) and its sulfate derivative (DHEA-S) than both the NFA and PA groups. Patients with PA expressed higher serum 18-hydroxycortisol and DHEA but lower THE than NFA patients. The balanced accuracies of DT, RF, and XGBoost for classifying each type were 78%, 96%, and 97%, respectively. In receiver operating characteristics (ROC) analysis for CS, XGBoost, and RF showed a significantly greater diagnostic power than the DT. However, in ROC analysis for PA, only RF exhibited better diagnostic performance than DT.ConclusionThe combination of LC-MS-based steroid profiling with machine learning algorithms could be a promising one-step diagnostic approach for the classification of adrenal tumor subtypes.

The key role of mass spectrometry in comprehensive research on new psychoactive substances

This month's Special Feature is authored by four accomplished investigators from the Department of Physical and Analytical Chemistry, University Jaume I, Castellón de la Plana, Spain. In their article, Dr. Ibáñez and colleagues direct their attention towards issues relating to new psychoactive substances (NPS), or emerging designer drugs, a heterogeneous group of compounds that aim to mimic the actions of ‘classical’ illicit drugs and circumvent scheduling guidelines. Each year these drugs contribute to a growing number of drug-related adverse events and deaths. The authors describe their work by way of a three-step strategy for the analysis of these agents by mass spectrometry: first, detection and analytical characterization of the NPS; second, metabolic and pharmacokinetics studies to select the most suitable biomarkers of their consumption; and third, the detection, identification and quantification of these biomarkers in biological tissues and fluids. They discuss the application of a range of mass spectrometric techniques, including conventional approaches such as combined liquid chromatography-mass spectrometry, but also the more recent adoption of direct ionization sources for the rapid identification of seized lots of drugs. To illustrate their points this Special Feature focuses on two model psychoactive substances: the synthetic cannabinoid XLR-11 and the synthetic cathinone 5-PPDi. Their Special Feature illustrates some of the challenges involved in the analysis of new psychoactive substances and highlights the essential role of mass spectrometry in this important and rapidly evolving area.

DDRE-01. METABOLIC PLASTICITY AND HETEROGENEITY IN IDH1MUT CELL LINES PRODUCES RESISTANCE TO GLUTAMINASE INHIBITION BY CB839

BACKGROUNDMutant IDH1 (IDH1mut) gliomas have characteristic genetic and metabolic profiles and exhibit phenotype that is distinct from their wild-type counterparts. The glutamine/glutamate pathway has been hypothesized as a selective therapeutic target in IDH1mut gliomas. However, little information exists on the contribution of this pathway to the formation of D-2-hydroxyglutarate (D-2HG), a hallmark of IDHmut cells, and the metabolic consequences of inhibiting this pathway.METHODSWe employed an untargeted metabolic profiling approach in order to detect metabolic changes arising from glutaminase (GLS) inhibition treatment. Subsequently, 13C metabolic tracing analysis through a combined Nuclear Magnetic Resonance and Liquid Chromatography-Mass Spectrometry approach, we explored the fate of glutamine and glucose under treatment with CB839 a glutaminase-GLS-inhibitor and their respective contributions to D-2HG formation.RESULTS AND CONCLUSIONSThe effects of CB839 on cellular proliferation differed among the cell lines tested, leading to designations of GLS-inhibition super-sensitive, -sensitive or -resistant. Our data indicates a decrease in the production of downstream metabolites of glutamate, including those involved in the TCA cycle, when treating the sensitive cells with CB839 (glutaminase -GLS- inhibitor). Notably, CB839-sensitive IDH1mutcells respond to GLS inhibition by upregulating glycolysis and lactate production. In contrast, CB839-resistantIDH1mut cell lines do not rely only on glutamine for the sustenance of TCA cycle. In these cells, glucose contribution to TCA is enough to compensate the downregulation of glutamine-derived TCA metabolites. This investigation reveals that the glutamine/glutamate pathway contributes differentially to D-2HG in a cell-line dependent fashion on a panel of IDHmut cell lines. Further, these results demonstrate that there is a heterogeneous landscape of IDH1mut metabolic phenotypes. This underscores the importance of detailed metabolic profiling of IDH1mut patients prior to the decision to target glutamine/glutamate pathway clinically.

Anoectochilus roxburghii polysaccharide prevents carbon tetrachloride-induced liver injury in mice by metabolomic analysis

Anoectochilus roxburghii (Wall.) Lindl, a traditional Chinese medicine, is used for the effective treatment of liver disease in China. Anoectochilus roxburghii polysaccharide (ARPT) is an important constituent of Anoectochilus roxburghii. ARPT exerts a hepatoprotective effect and contributes directly to the therapeutic benefit of Anoectochilus roxburghii. However, the hepatoprotective mechanism of ARPT requires further elucidation. The present study was designed to assess the effects and underlying mechanism of ARPT when used to pretreat carbon tetrachloride (CCl4)-induced liver injury in mice. Mice were randomly divided into three groups: control group (no ARPT treatment or liver injury), model group (liver injury induced with CCl4), and the ARPT group (ARPT pretreatment followed by liver injury). A metabolomic method, based on liquid chromatography combined with mass spectrometry (LC-MS) and pattern recognition analysis, was applied. The data were analyzed with principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA), to determine differentiating metabolites in the serum and liver tissue between the experimental groups. The PCA and OPLS-DA scores plots of the serum and liver tissue samples based on the LC-MS data showed a clear separation between the control and liver injury model group, while the ARPT-treated group showed a trend of close with the control. There were eleven metabolites [PS(O-18:0/0:0), phosphocholine, phenylalanine, hippuric acid, α-ketoisovaleric acid, metyrosine, leucinic acid, ketoleucine, Cer(d18:1/19:0), α-kamlolenic acid, and 4-formyl indole] were identified as candidate biomarkers in the serum samples, eight such metabolites (valine, phosphohydroxypyruvic acid, phosphocholine, ornithine, indole, xanthine, uridine, and glucose 6-phosphate) were found in the liver tissue samples, and one metabolite (phosphocholine) was observed in both the serum and liver tissue samples. These endogenous metabolites are considered to be in response to the hepatoprotective effects of ARPT and are involved in amino acid metabolism, lipid metabolism, gut bacteria metabolism, energy metabolism, and the methylation pathway. These findings suggest that ARPT mitigates the metabolic effect of CCl4-induced hepatotoxicity in mice by affecting at least five different pathways. LC-MS-based metabolomics provides a powerful approach for identifying potential biomarkers and for elucidating the protective mechanisms of traditional Chinese medicines against disease.

Distinguishing NASH Histological Severity Using a Multiplatform Metabolomics Approach.

Nonalcoholic fatty liver disease (NAFLD) is categorized based on histological severity into nonalcoholic fatty liver (NAFL) or nonalcoholic steatohepatitis (NASH). We used a multiplatform metabolomics approach to identify metabolite markers and metabolic pathways that distinguish NAFL from early NASH and advanced NASH. We analyzed fasting serum samples from 57 prospectively-recruited patients with histologically-proven NAFLD, including 12 with NAFL, 31 with early NASH and 14 with advanced NASH. Metabolite profiling was performed using a combination of liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy analyzed with multivariate statistical and pathway analysis tools. We targeted 237 metabolites of which 158 were quantified. Multivariate analysis uncovered metabolite profile clusters for patients with NAFL, early NASH, and advanced NASH. Also, multiple individual metabolites were associated with histological severity, most notably spermidine which was more than 2-fold lower in advanced fibrosis vs. early fibrosis, in advanced NASH vs. NAFL and in advanced NASH vs. early NASH, suggesting that spermidine exercises a protective effect against development of fibrosing NASH. Furthermore, the results also showed metabolic pathway perturbations between early-NASH and advanced-NASH. In conclusion, using a combination of two reliable analytical platforms (LC-MS and NMR spectroscopy) we identified individual metabolites, metabolite clusters and metabolic pathways that were significantly different between NAFL, early-NASH, and advanced-NASH. These differences provide mechanistic insights as well as potentially important metabolic biomarker candidates that may noninvasively distinguish patients with NAFL, early-NASH, and advanced-NASH. The associations of spermidine levels with less advanced histology merit further assessment of the potential protective effects of spermidine in NAFLD.

Genome-based analysis for the bioactive potential of Streptomyces yeochonensis CN732, an acidophilic filamentous soil actinobacterium

BackgroundAcidophilic members of the genus Streptomyces can be a good source for novel secondary metabolites and degradative enzymes of biopolymers. In this study, a genome-based approach on Streptomyces yeochonensis CN732, a representative neutrotolerant acidophilic streptomycete, was employed to examine the biosynthetic as well as enzymatic potential, and also presence of any genetic tools for adaptation in acidic environment.ResultsA high quality draft genome (7.8 Mb) of S. yeochonensis CN732 was obtained with a G + C content of 73.53% and 6549 protein coding genes. The in silico analysis predicted presence of multiple biosynthetic gene clusters (BGCs), which showed similarity with those for antimicrobial, anticancer or antiparasitic compounds. However, the low levels of similarity with known BGCs for most cases suggested novelty of the metabolites from those predicted gene clusters. The production of various novel metabolites was also confirmed from the combined high performance liquid chromatography-mass spectrometry analysis. Through comparative genome analysis with related Streptomyces species, genes specific to strain CN732 and also those specific to neutrotolerant acidophilic species could be identified, which showed that genes for metabolism in diverse environment were enriched among acidophilic species. In addition, the presence of strain specific genes for carbohydrate active enzymes (CAZyme) along with many other singletons indicated uniqueness of the genetic makeup of strain CN732. The presence of cysteine transpeptidases (sortases) among the BGCs was also observed from this study, which implies their putative roles in the biosynthesis of secondary metabolites.ConclusionsThis study highlights the bioactive potential of strain CN732, an acidophilic streptomycete with regard to secondary metabolite production and biodegradation potential using genomics based approach. The comparative genome analysis revealed genes specific to CN732 and also those among acidophilic species, which could give some insights into the adaptation of microbial life in acidic environment.

FUNCTIONAL METABOLOMICS DECIPHER BIOCHEMICAL FUNCTIONS AND ASSOCIATED MECHANISMS UNDERLIE SMALL-MOLECULE METABOLISM.

Metabolism is the collection of biochemical reactions enabled by chemically diverse metabolites, which facilitate different physiological processes to exchange substances and synthesize energy in diverse living organisms. Metabolomics has emerged as a cutting-edge method to qualify and quantify the metabolites in different biological matrixes, and it has the extraordinary capacity to interrogate the biological significance that underlies metabolic modification and modulation. Liquid chromatography combined with mass spectrometry (LC/MS), as a robust platform for metabolomics analysis, has increased in popularity over the past 10 years due to its excellent sensitivity, throughput, and versatility. However, metabolomics investigation currently provides us with only phenotype data without revealing the biochemical functions and associated mechanisms. This limitation indeed weakens the core value of metabolomics data in a broad spectrum of the life sciences. In recent years, the scientific community has actively explored the functional features of metabolomics and translated this cutting-edge approach to be used to solve key multifaceted questions, such as disease pathogenesis, the therapeutic discovery of drugs, nutritional issues, agricultural problems, environmental toxicology, and microbial evolution. Here, we are the first to briefly review the history and applicable progression of LC/MS-based metabolomics, with an emphasis on the applications of metabolic phenotyping. Furthermore, we specifically highlight the next era of LC/MS-based metabolomics to target functional metabolomes, through which we can answer phenotype-related questions to elucidate biochemical functions and associated mechanisms implicated in dysregulated metabolism. Finally, we propose many strategies to enhance the research capacity of functional metabolomics by enabling the combination of contemporary omics technologies and cutting-edge biochemical techniques. The main purpose of this review is to improve the understanding of LC/MS-based metabolomics, extending beyond the conventional metabolic phenotype toward biochemical functions and associated mechanisms, to enhance research capability and to enlarge the applicable scope of functional metabolomics in small-molecule metabolism in different living organisms.

Characterization of the Impact of Drug Metabolism on the Gas-Phase Structures of Drugs Using Ion Mobility-Mass Spectrometry.

Conventional strategies for drug metabolite identification employ a combination of liquid chromatography-mass spectrometry (LC-MS), which offers higher throughput but provides limited structural information, and nuclear magnetic resonance spectroscopy, which can achieves the most definitive identification but lacks throughput. Ion mobility-mass spectrometry (IM-MS) is a rapid, two-dimensional analysis that separates ions on the basis of their gas-phase size and shape (reflected by collision cross section, CCS) and their mass-to-charge (m/z) ratios. The rapid nature of IM separation combined with the structural information provided by CCS make IM-MS a promising technique for obtaining more structural information on drug metabolites without sacrificing analytical throughput. Here, we present an in vitro biosynthesis coupled with IM-MS strategy for rapid generation and analysis of drug metabolites. Drug metabolites were generated in vitro using pooled subcellular fractions derived from human liver and analyzed using a rapid flow injection-IM-MS method. We measured CCS values for 19 parent drugs and their 37 metabolites generated in vitro (78 values in total), representing a wide variety of metabolic modifications. Post-IM fragmentation and computational modeling were used to support metabolite identifications and explore the structural characteristics driving behaviors observed in IM separation. Overall, we found the effects of metabolic modifications on the gas-phase structures of the metabolites to be highly dependent upon the structural characteristics of the parent compounds and the specific position of the modification. This in vitro biosynthesis coupled with rapid IM-MS analysis workflow represents a promising platform for rapid and high-confidence identification of drug metabolites, applicable at a large scale.

Xanthine Oxidase Inhibitors Screening, Antioxidation, and DNA Protection Properties of Geranium wilfordii Maxim

Geranium wilfordii Maxim is a commonly used traditional medicine and as decoction pieces in drinks for treating acute and chronic rheumatalgia. Earlier research showed it could inhibit xanthine oxidase and had a strong antioxidative activity. In this study, four compounds namely, corilagin, geraniin, ellagic acid, and myricetrin were screened and identified as xanthine oxidase inhibitors and antioxidants using ultrafiltration combined liquid chromatography—mass spectrometry (LC—MS) and 1,1-diphenyl-2-picrylhydrazyl (DPPH) spiking combined LC—MS methods. Ellagic acid showed the greatest inhibition with IC50 of 0.018 ± 0.001 mM better than that of allopurinol. The kinetic parameters exhibited the mode of xanthine oxidase inhibitions by corilagin, geraniin, and myricetrin were the competitive types, whereas the ellagic acid was found as an uncompetitive inhibitor. In vitro DNA nicking assay showed these four compounds protected DNA from reactive oxygen species. Correlation analysis and partial least squares analysis showed there was a moderate correlation between ellagic acid and DPPH˙ scavenging activity. Our findings revealed four compounds in G. wilfordii Maxim contributed satisfied radicals scavenging, xanthine oxidase inhibition, and DNA damage protective activities. More comprehensive research on pharmaceutical applications and phytochemical profile of G. wilfordii Maxim could be further studied.

A Transcript and Metabolite Atlas of Blackcurrant Fruit Development Highlights Hormonal Regulation and Reveals the Role of Key Transcription Factors.

Blackcurrant fruit collected at six stages of development were assessed for changes in gene expression using custom whole transcriptome microarrays and for variation in metabolite content using a combination of liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry. Principal components analysis demonstrated that fruit development could be clearly defined according to their transcript or metabolite profiles. During early developmental stages, metabolite profiles were dominated by amino acids and tannins, whilst transcript profiles were enriched in functions associated with cell division, anatomical structure morphogenesis and cell wall metabolism. During mid fruit development, fatty acids accumulated and transcript profiles were consistent with seed and embryo development. At the later stages, sugars and anthocyanins accumulated consistent with transcript profiles that were associated with secondary metabolism. Transcript data also indicated active signaling during later stages of fruit development. A targeted analysis of signaling networks revealed a dynamic activation and repression of almost 60 different transcripts encoding transcription factors across the course of fruit development, many of which have been demonstrated as pivotal to controlling such processes in other species. Transcripts associated with cytokinin and gibberellin were highly abundant at early fruit development, whilst those associated with ABA and ethylene tended to be more abundant at later stages. The data presented here provides an insight into fruit development in blackcurrant and provides a foundation for further work in the elucidation of the genetic basis of fruit quality.