Isotope Labeling-Assisted Evaluation of Hydrophilic and Hydrophobic Liquid Chromatograph-Mass Spectrometry for Metabolomics Profiling

CYP3A5 unexpectedly regulates glucose metabolism through the AKT-TXNIP-GLUT1 axis in pancreatic cancer

CYP3A5 is a cytochrome P450 (CYP) enzyme that metabolizes drugs and contributes to drug resistance in cancer. However, it remains unclear whether CYP3A5 directly influences cancer progression. In this report, we demonstrate that CYP3A5 regulates glucose metabolism in pancreatic ductal adenocarcinoma. Multi-omics analysis showed that CYP3A5 knockdown results in a decrease in various glucose-related metabolites through its effect on glucose transport. A mechanistic study revealed that CYP3A5 enriches the glucose transporter GLUT1 at the plasma membrane by restricting the translation of TXNIP, a negative regulator of GLUT1. Notably, CYP3A5-generated reactive oxygen species were proved to be responsible for attenuating the AKT-4EBP1-TXNIP signaling pathway. CYP3A5 contributes to cell migration by maintaining high glucose uptake in pancreatic cancer. Taken together, our results, for the first time, reveal a role of CYP3A5 in glucose metabolism in pancreatic ductal adenocarcinoma and identify a novel mechanism that is a potential therapeutic target.

A green-footprint approach for parallel multiclass analysis of contaminants in roasted coffee via LC-HRMS

The development and validation of a simple, comprehensive, and environment-friendly procedure to determine pesticide residues, naturally occurring and processing contaminants in roasted coffee is presented. A solid-liquid extraction of pesticides and mycotoxins with ethyl acetate and the concurrent partition of acrylamide to an aqueous phase follows a parallel analytical strategy that requires a single analytical portion to determine contaminants that are typically analyzed by dedicated single residue methods. The partition rules the lipids out of the aqueous extract before an "in-tube" dispersive solid phase microextraction (dSPME) for acrylamide retention. This is followed by the elution with buffer prior to injection. This extract is independently introduced into the system front end followed by the injection of the compounds from the organic phase, yet all spotted in the same run. A novel liquid chromatography high-resolution mass spectrometry (LC-HRMS) method setup enables the quantification of 186 compounds at 10 µg/kg, 226 at 5 µg/kg, and the acrylamide at 200 µg/kg for a total of 414 molecules, with acceptable recoveries (70-120%) and precision (RSD < 20%) making this strategy significantly faster and cost-effective than the dedicated single residue methods. Even though the presence of chlorpyrifos, acrylamide, and ochratoxin A was confirmed on samples of different origins, the findings were below the limit of quantification. During the storage of raw coffee, no proof of masking of OTA was found; however, condensation with glucose was evidenced during thermal processing experiments with sucrose by using stable isotope labeling (SIL). No detected conjugates were found in roasted nor in commercial sugar-added torrefacto samples, an industrial processing usually carried out above the decomposition temperature of the disaccharide.

Multiomics Analysis-Based Biomarkers in Diagnosis of Polycystic Ovary Syndrome

Polycystic ovarian syndrome is an utmost communal endocrine, psychological, reproductive, and metabolic disorder that occurs in women of reproductive age with extensive range of clinical manifestations. This may even lead to long-term multiple morbidities including obesity, diabetes mellitus, insulin resistance, cardiovascular disease, infertility, cerebrovascular diseases, and ovarian and endometrial cancer. Women affliction from PCOS in midst assemblage of manifestations allied with menstrual dysfunction and androgen exorbitance, which considerably affects eminence of life. PCOS is recognized as a multifactorial disorder and systemic syndrome in first-degree family members; therefore, the etiology of PCOS syndrome has not been copiously interpreted. The disorder of PCOS comprehends numerous allied health conditions and has influenced various metabolic processes. Due to multifaceted pathophysiology engaging several pathways and proteins, single genetic diagnostic tests cannot be supportive to determine in straight way. Clarification of cellular and biochemical pathways and various genetic players underlying PCOS could upsurge our consideration of pathophysiology of this syndrome. It is requisite to know pathophysiological relationship between biomarker and their reflection towards PCOS disease. Biomarkers deliver vibrantly and potent ways to apprehend the spectrum of PCOS with applications in screening, diagnosis, characterization, and monitoring. This paper relies on the endeavor to point out many candidates as potential biomarkers based on omics technologies, thus highlighting correlation between PCOS disease with innovative technologies. Therefore, the objective of existing review is to encapsulate more findings towards cutting-edge advances in prospective use of biomarkers for PCOS disease. Discussed biomarkers may be fruitful in guiding therapies, addressing disease risk, and predicting clinical outcomes in future.

Sulfhydryl functionalized carbon quantum dots as a turn-off fluorescent probe for sensitive detection of Hg2

Mercury ion (Hg2+) is one of the most toxic heavy metal ions and lowering the detection limit of Hg2+ is always a challenge in analytical chemistry and environmental analysis. In this work, sulfhydryl functionalized carbon quantum dots (HS-CQDs) were synthesized through a one-pot hydrothermal method. The obtained HS-CQDs were able to detect mercury ions Hg2+ rapidly and sensitively through fluorescence quenching, which may be ascribed to the formation of nonfluorescent ground-state complexes and electron transfer reaction between HS-CQDs and Hg2+. A modification of the HS-CQD surface by -SH was confirmed using Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). The HS-CQDs sensing system obtained a good linear relationship over a Hg2+ concentration ranging from 0.45 μM to 2.1 μM with a detection limit of 12 nM. Delightfully, the sensor has been successfully used to detect Hg2+ in real samples with satisfactory results. This means that the sensor has the potential to be used for testing actual samples.

Anti-depressant effect of Zhi-zi-chi decoction on CUMS mice and elucidation of its signaling pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Zhi-zi-chi decoction (ZZCD) is used for treating depression as an effectively traditional Chinese medicine. Until now, studies on pharmacological research of ZZCD have mostly been centered in pharmacokinetic level. Little was known about its pharmacological mechanism of relieving depression.

AIM OF THE STUDY

This study was to evaluate the effect of ZZCD on relieving depression via behavioral tests, serum metabolomics and signaling target expression analysis on chronic unpredictable mild stress (CUMS) model mice.

MATERIALS AND METHODS

The CUMS exposure lasted 7 consecutive weeks. The mice were administrated with ZZCD for the last 3 weeks. Behavioral tests were applied and a serum metabolomics method based on UFLC/Q-TOF-MS with multivariate statistical and global metabolic network analysis was performed to identify relevant metabolites and pathways. Finally, the protein expressions in mouse hippocampi were determined by western blot to verify the metabolomics deduction.

RESULTS

Behavioral parameters were visibly changed after modeling, while high and medium dosage groups showed status improvement compared to the model group. Seventy six metabolites were identified as potential biomarkers from the metabolomics profiles in C₁₈ and HILIC systems. In addition, 9 significant pathways related to changed biomarkers were conducted. The pathways were closely connected by some key targets, which were significantly reduced in the model group compared with those in control group, while ZZCD treated groups showed corrections after 3-week administration. The results revealed that the anti-depression efficacy of ZZCD might be associated with PKA-CREB-BDNF-TrkB-PSD-95 pathway influenced by metabolic changes, verifying the pathway annotation speculation.

CONCLUSION

This study demonstrated that ZZCD had a positive treatment effect on CUMS depression model mice. Metabolomics results revealed the holistic and interconnected metabolic changes of ZZCD in CUMS mice. The metabolic pathway annotation suggested that the anti-depression mechanism of ZZCD might be related to signaling pathway in brain. PKA-CREB-BDNF-TrkB-PSD-95 signaling expression was a verification and complement to the metabolomics results.

Disturbances in Microbial and Metabolic Communication across the Gut-Liver Axis Induced by a Dioxin-like Pollutant: An Integrated Metagenomics and Metabolomics Analysis

To determine how the aryl hydrocarbon receptor (AhR) signaling acts along the gut-liver axis, we employed an integrated metagenomic and metabolomic approach to comprehensively profile the microbial and metabolic networks. Adult zebrafish were exposed to a model agonist of the AhR: polychlorinated biphenyl (PCB) 126. The metagenomic analysis showed that PCB126 suppressed microbial activities related to primary bile acid metabolism in male intestines. Accordingly, a suite of primary bile acids consistently showed higher concentrations, suggesting that bacterial conversion of primary bile acids was blocked. PCB126 also disturbed bacterial metabolism of bile acids in female intestines, as revealed by higher concentrations of primary bile acids (e.g., chenodeoxycholic acid) and activation of the nuclear farnesoid X receptor signaling. In addition, PCB126 exposure impaired the metabolism of various essential vitamins (e.g., retinol, vitamin B6, and folate). Degradation of vitamin B6 by bacterial enzymes was inhibited in male intestines, resulting in its intestinal accumulation. However, PCB126 suppressed the bacterial metabolism of vitamins in female intestines, causing systematic deficiency of essential vitamins. Overall, we found that PCB126 exposure dysregulated gut microbial activities, consequently interrupting bile acid and vitamin metabolism along the gut-liver axis. The findings provided an insight of the AhR action in microbe-host metabolic communication related to PCBs.

JUMPm: A Tool for Large-Scale Identification of Metabolites in Untargeted Metabolomics

Metabolomics is increasingly important for biomedical research, but large-scale metabolite identification in untargeted metabolomics is still challenging. Here, we present Jumbo Mass spectrometry-based Program of Metabolomics (JUMPm) software, a streamlined software tool for identifying potential metabolite formulas and structures in mass spectrometry. During database search, the false discovery rate is evaluated by a target-decoy strategy, where the decoys are produced by breaking the octet rule of chemistry. We illustrated the utility of JUMPm by detecting metabolite formulas and structures from liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) analyses of unlabeled and stable-isotope labeled yeast samples. We also benchmarked the performance of JUMPm by analyzing a mixed sample from a commercially available metabolite library in both hydrophilic and hydrophobic LC-MS/MS. These analyses confirm that metabolite identification can be significantly improved by estimating the element composition in formulas using stable isotope labeling, or by introducing LC retention time during a spectral library search, which are incorporated into JUMPm functions. Finally, we compared the performance of JUMPm and two commonly used programs, Compound Discoverer 3.1 and MZmine 2, with respect to putative metabolite identifications. Our results indicate that JUMPm is an effective tool for metabolite identification of both unlabeled and labeled data in untargeted metabolomics.

MS2 and LC libraries for untargeted metabolomics: Enhancing method development and identification confidence

As part of the "omics" technologies in the life sciences, metabolomics is becoming increasingly important. In untargeted metabolomics, unambiguous metabolite identification and the inevitable coverage bias that comes with the selection of analytical conditions present major challenges. Reliable compound annotation is essential for translating metabolomics data into meaningful biological information. Here, we developed a fast and transferable method for generating in-house MS² libraries to improve metabolite identification. Using the new method we established an in-house MS² library that includes over 4,000 fragmentation spectra of 506 standard compounds for 6 different normalized collision energies (NCEs). Additionally, we generated a comprehensive liquid chromatography (LC) library by testing 57 different LC-MS conditions for 294 compounds. We used the library information to develop an untargeted metabolomics screen with maximum coverage of the metabolome that was successfully tested in a study of 360 human serum samples. The current work demonstrates a workflow for LC-MS/MS-based metabolomics, with enhanced metabolite identification confidence and the possibility to select suitable analysis conditions according to the specific research interest.

A large-scale analysis of targeted metabolomics data from heterogeneous biological samples provides insights into metabolite dynamics

INTRODUCTION

We previously developed a tandem mass spectrometry-based label-free targeted metabolomics analysis framework coupled to two distinct chromatographic methods, reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC), with dynamic multiple reaction monitoring (dMRM) for simultaneous detection of over 200 metabolites to study core metabolic pathways.

OBJECTIVES

We aim to analyze a large-scale heterogeneous data compendium generated from our LC-MS/MS platform with both RPLC and HILIC methods to systematically assess measurement quality in biological replicate groups and to investigate metabolite abundance changes and patterns across different biological conditions.

METHODS

Our metabolomics framework was applied in a wide range of experimental systems including cancer cell lines, tumors, extracellular media, primary cells, immune cells, organoids, organs (e.g. pancreata), tissues, and sera from human and mice. We also developed computational and statistical analysis pipelines, which include hierarchical clustering, replicate-group CV analysis, correlation analysis, and case-control paired analysis.

RESULTS

We generated a compendium of 42 heterogeneous deidentified datasets with 635 samples using both RPLC and HILIC methods. There exist metabolite signatures that correspond to various phenotypes of the heterogeneous datasets, involved in several metabolic pathways. The RPLC method shows overall better reproducibility than the HILIC method for most metabolites including polar amino acids. Correlation analysis reveals high confidence metabolites irrespective of experimental systems such as methionine, phenylalanine, and taurine. We also identify homocystine, reduced glutathione, and phosphoenolpyruvic acid as highly dynamic metabolites across all case-control paired samples.

CONCLUSIONS

Our study is expected to serve as a resource and a reference point for a systematic analysis of label-free LC-MS/MS targeted metabolomics data in both RPLC and HILIC methods with dMRM.

Tools and resources for metabolomics research community: A 2017-2018 update

The scale at which MS- and NMR-based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences-ranging from biomedical to agricultural-is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017-2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.