Ultra-high-performance liquid chromatography high-resolution mass spectrometry variants for metabolomics research

A cooperation mechanism between Bacillus thuringiensis and Citrobacter freundii that enhances cadmium biomineralization

The viability and tolerance of individual ureolytic bacteria are a bottleneck in the remediation of cadmium (Cd) by microbially induced carbonate precipitation (MICP) technology. To solve this issue, strains of Bacillus thuringiensis (B. thuringiensis, BT) and Citrobacter freundii (C. freundii, CF) were isolated from soil and studied for their growth characteristics and metabolism. A cooperation system (BT+CF, 1:1, v/v) was constructed and exposed to 20 mg/kg Cd2 + for 7 days, compared with individual bacteria. The synergistic mechanism of strains that immobilize Cd2+ was explored using characterization techniques. Results showed that the main metabolic pathways leading to urea up-regulation were pyrimidine metabolism, urea cycle, and lysine degradation by metabolomic analysis. The cooperation system can effectively remove Cd2+ with an efficiency of 97.68 %, which is higher than BT (66.66 %) and CF (88.61 %). The SEM-EDS, TEM, and XPS results revealed that the calcium carbonate polycrystals (vaterite and calcite) were formed during the MICP process, and the XRD and FTIR confirmed that the BT+CF produces more stable carbonate crystals. The BT+CF cooperation system was efficient at immobilizing Cd2+ by synergizing the molecular mechanisms of ureolytic bacteria. These results provide a novel perspective for the application of MICP.

Root Exudation: An In-Depth Experimental Guide

Plants exude a wide variety of compounds into the rhizosphere, modulating soil functioning and diversity. The number of studies investigating exudation has exponentially increased over the past decades. Yet, the high inter-study variability of the results is slowing down our understanding of root-soil interactions. This variability is partly due to the absence of harmonized methodologies to collect and characterize exudation. Here, we discuss how various experimental aspects influence exudation profiles by performing a literature review, and we suggest best practices for different experimental setups. We discuss state-of-the-art of spatially resolved exudate collection, collection in controlled versus field conditions and plant growth setups ranging from hydroponics to soil. We highlight the importance of preparing experimental blanks, in situ versus ex situ exudate collection, various collection media and timing of collection, exudate storage and processing and analytical considerations. We summarize best practices for experimental setup and reporting of parameters in an easily accessible table format to facilitate discussion of best practices in the field. An increased standardization in the field together with the systematic studies suggested will improve our knowledge of how plant exudation shapes interactions with organisms in soil.

Evaluation of ion source parameters and liquid chromatography methods for plasma untargeted metabolomics using orbitrap mass spectrometer

Although untargeted metabolomics holds promise for study of metabolites in human health and disease, robust method development and optimization are needed to reduce potential analytical biases and to ensure comprehensive, high-throughput results. In this study, the effect of mass spectrometer (MS) ion source parameters on the signal reproducibility and number of metabolite annotations during untargeted metabolomics is shown. Furthermore, different mobile phase gradients and columns (five reversed phase (RP)-C18 and two hydrophilic interaction liquid chromatography (HILIC) columns) were evaluated for untargeted metabolomics of blood plasma extracts. Positioning the electrospray needle at the farthest on the Z-direction and the closest tested position on the Y-direction with respect to the mass spectrometry inlet produced the best signal reproducibility and the greatest number of metabolite annotations. Moreover, optimal ion source conditions included a positive spray voltage between 2.5 and 3.5 kV, a negative spray voltage between 2.5 and 3.0 kV, vaporization and ion transfer tube (ITT) temperature between 250 and 350 °C, 30 to 50 arbitrary units of sheath gas, and at least 10 auxiliary gas units. Despite the differences in chromatographic characteristics, the different RP columns assessed showed comparable performance in terms of number of metabolites annotated. For HILIC columns, a zwitterionic column demonstrated better performance than an amide column. Finally, as compared with use of a RP column alone, use of both the optimal RP and HILIC approaches expanded metabolome coverage: the number of metabolites annotated increased by 60 %. This study highlights the significance of fine-tuning the MS ion source parameters and optimizing chromatographic conditions on metabolome coverage during untargeted metabolomics of plasma samples.

Dissecting inflammation in the immunemetabolomic era

The role of immune metabolism, specific metabolites and cell-intrinsic and -extrinsic metabolic states across the time course of an inflammatory response are emerging knowledge. Targeted and untargeted metabolomic analysis is essential to understand how immune cells adapt their metabolic program throughout an immune response. In addition, metabolomic analysis can aid to identify pathophysiological patterns in inflammatory disease. Here, we discuss new metabolomic findings within the transition from inflammation to resolution, focusing on three key programs of immunity: Efferocytosis, IL-10 signaling and trained immunity. Particularly the tryptophan-derived metabolite kynurenine was identified as essential for efferocytosis and inflammation resolution as well as a potential biomarker in diverse inflammatory conditions. In summary, metabolomic analysis and integration with transcriptomic and proteomic data, high resolution imaging and spatial information is key to unravel metabolic drivers and dependencies during inflammation and progression to tissue-repair.

Monodisperse Silica Microsphere with Extremely Large Specific Surface Area: Preparation and Characterization

Monodisperse SiO2 microspheres are widely used in catalysis, separation, adsorption, and drug delivery. Their particle size, uniformity, and specific surface area are crucial for these applications. This study reports the novel preparation of monodisperse SiO2 microspheres using cetyltrimethylammonium bromide as the template agent, employing hexadecylamine serving concurrently as a pore-expanding agent and catalyst. By controlling the reactant quantities and reaction conditions, we achieved monodisperse SiO2 microspheres with tunable particle sizes ranging from 800 nm to 2.5 μm with exceptionally large specific surface areas. It is worth mentioning that microspheres with a particle size of 2 μm and extremely uniform size distribution were produced at room temperature. Excitingly, it has a BET specific surface area of 1543 m2/g. Various effects on the preparation of the microspheres were investigated in detail, and the growth mechanism of these microspheres was elucidated.

Serum metabolic fingerprints encode functional biomarkers for ovarian cancer diagnosis: a large-scale cohort study

BACKGROUND

Ovarian cancer (OC) ranks as the most lethal gynaecological malignancy worldwide, with early diagnosis being crucial yet challenging. Current diagnostic methods like transvaginal ultrasound and blood biomarkers show limited sensitivity/specificity. This study aimed to identify and validate serum metabolic biomarkers for OC diagnosis using the largest cohort reported to date.

METHODS

We constructed a large-scale OC-associated cohort of 1432 subjects, including 662 OC, 563 benign ovarian disease, and 207 healthy control subjects, across retrospective (n = 1073) and set-aside validation (n = 359) cohorts. Serum metabolic fingerprints (SMFs) were recorded using nanoparticle-enhanced laser desorption/ionization mass spectrometry (NELDI-MS). A diagnostic panel was developed through machine learning of SMFs in the discovery cohort and validated in independent verification and set-aside validation cohorts. The identified metabolic biomarkers were further validated using liquid chromatography MS and their biological functions were assessed in OC cell lines.

FINDINGS

We identified a metabolic biomarker panel including glucose, histidine, pyrrole-2-carboxylic acid, and dihydrothymine. This panel achieved consistent areas under the curve (AUCs) of 0.87-0.89 for distinguishing between malignant and benign ovarian masses across all cohorts, and improved to AUCs of 0.95-0.99 when combined with risk of ovarian malignancy algorithm (ROMA). In vitro validation provided initial biological context for the metabolic alterations observed in our diagnostic panel.

INTERPRETATION

Our study established a reliable serum metabolic biomarker panel for OC diagnosis with potential clinical translations. The NELDI-MS based approach offers advantages of fast analytical speed (∼30 s/sample) and low cost (∼2-3 dollars/sample), making it suitable for large-scale clinical applications.

FUNDING

MOST (2021YFA0910100), NSFC (82421001, 823B2050, 824B2059, and 82173077), Medical-Engineering Joint Funds of Shanghai Jiao Tong University (YG2021GD02, YG2024ZD07, and YG2023ZD08), Shanghai Science and Technology Committee Project (23JC1403000), Shanghai Institutions of Higher Learning (2021-01-07-00-02-E00083), Shanghai Jiao Tong University Inner Mongolia Research Institute (2022XYJG0001-01-16), Sichuan Provincial Department of Science and Technology (2024YFHZ0176), Innovation Research Plan by the Shanghai Municipal Education Commission (ZXWF082101), Innovative Research Team of High-Level Local Universities in Shanghai (SHSMU-ZDCX20210700), Basic-Clinical Collaborative Innovation Project from Shanghai Immune Therapy Institute, Guangdong Basic and Applied Basic Research Foundation (2024A1515013255).

The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions

Researchers have invested much effort into developing kinetic models due to their ability to capture dynamic behaviors, transient states, and regulatory mechanisms of metabolism, providing a detailed and realistic representation of cellular processes. Historically, the requirements for detailed parametrization and significant computational resources created barriers to their development and adoption for high-throughput studies. However, recent advancements, including the integration of machine learning with mechanistic metabolic models, the development of novel kinetic parameter databases, and the use of tailor-made parametrization strategies, are reshaping the field of kinetic modeling. In this Review, we discuss these developments and offer future directions, highlighting the potential of these advances to drive progress in systems and synthetic biology, metabolic engineering, and medical research at an unprecedented scale and pace.

Library Enabling Annotation of Botanical Natural Products (LEAFBot): An Open-Access Library of Mass Spectrometry Fragmentation Spectra for Plant Metabolites

Many existing mass spectral libraries focus on human or microbially derived molecules. Few plant-specific MS2 databases exist, making annotation of botanical samples difficult. To fill this gap in mass spectrometry data availability, the Library Enabling Annotation of Botanical Natural Products (LEAFBot) was constructed. Using a flow injection mass spectrometry method that allowed for rapid throughput data collection, the MS2 spectra of >300 pure botanical secondary metabolites were experimentally measured and complied into a single library housed in the Global Natural Products Social Molecular Networking (GNPS) spectral database. Of these compounds, over 20% were not present in the existing GNPS database, and 11% were not present in any of three main mass spectral databases (GNPS, Metlin, and MassBank). Additionally, LEAFBot contains a wider range of adducts compared to other plant-based mass spectral libraries, enabling more effective annotation of unknown features. The LEAFBot database represents a new resource to the mass spectrometry and metabolomics community seeking to characterize plant-based samples. The possibility of searching against a taxonomically specific library decreases the likelihood of false positives in database searches, and the ease of adding new spectra, following procedures outlined herein, will enable community-lead expansion of the database.

Improving fat globule structure to narrow metabolite gap between human milk and infant formulae

We hypothesized that improving the fat globule structure of infant formulae based on the milk fat globule membrane (MFGM) would regulate metabolites and metabolic pathways, making it more similar to the metabolic properties of human milk. Therefore, we prepared infant formulae with different fat globule structures, including two model infant formulae (F1: fat globules surrounded by MFGM; F2: fat globules surrounded by protein) and one commercial infant formulae containing MFGM, and compared their metabolic differences with those of human milk. The number of differential metabolites between each sample and human milk reached 60 (F1), 132 (F2) and 126 (IF1). Glycerophosphocholines were screened as potential biomarkers to distinguish infant formulae from human milk. Compared with F2 and IF1, the enrichment of amino acid metabolism and lipid metabolism pathways was not significant between F1 and human milk. These results emphasized that F1 had the highest similarity to human milk in metabolic properties.

HeuSMA: A Multigradient LC-MS Strategy for Improving Peak Identification in Untargeted Metabolomics

Metabolomics, which involves the comprehensive analysis of small molecules within biological systems, plays a crucial role in elucidating the biochemical underpinnings of physiological processes and disease conditions. However, current coverage of the metabolome remains limited. In this study, we present a heuristic strategy for untargeted metabolomics analysis (HeuSMA) based on multiple chromatographic gradients to enhance the metabolome coverage in untargeted metabolomics. This strategy involves performing LC-MS analysis under multiple gradient conditions on a given sample (e.g., a pooled sample or a quality control sample) to obtain a comprehensive metabolomics data set, followed by constructing a heuristic peak list using a retention index system. Guided by this list, heuristic peak picking in quantitative metabolomics data is achieved. The benchmarking and validation results demonstrate that HeuSMA outperforms existing tools (such as MS-DIAL and MZmine) in terms of metabolite coverage and peak identification accuracy. Additionally, HeuSMA improves the accessibility of MS/MS data, thereby facilitating the metabolite annotation. The effectiveness of the HeuSMA strategy was further demonstrated through its application in serum metabolomics analysis of human hepatocellular carcinoma (HCC). To facilitate the adoption of the HeuSMA strategy, we also developed two user-friendly graphical interface software solutions (HPLG and HP), which automate the analysis process, enabling researchers to efficiently manage data and derive meaningful conclusions (https://github.com/Lacterd/HeuSMA).

Advancing Gut Microbiome Research: The Shift from Metagenomics to Multi-Omics and Future Perspectives

The gut microbiome, a dynamic and integral component of human health, has co-evolved with its host, playing essential roles in metabolism, immunity, and disease prevention. Traditional microbiome studies, primarily focused on microbial composition, have provided limited insights into the functional and mechanistic interactions between microbiota and their host. The advent of multi-omics technologies has transformed microbiome research by integrating genomics, transcriptomics, proteomics, and metabolomics, offering a comprehensive, systems-level understanding of microbial ecology and host-microbiome interactions. These advances have propelled innovations in personalized medicine, enabling more precise diagnostics and targeted therapeutic strategies. This review highlights recent breakthroughs in microbiome research, demonstrating how these approaches have elucidated microbial functions and their implications for health and disease. Additionally, it underscores the necessity of standardizing multi-omics methodologies, conducting large-scale cohort studies, and developing novel platforms for mechanistic studies, which are critical steps toward translating microbiome research into clinical applications and advancing precision medicine.

Metabolomic and lipidomic profiling of traditional Chinese medicine Testudinis Carapax et Plastrum and its substitutes

Introduction

Chinemys reevesii (Gray) species-sourced Testudinis Carapax et Plastrum (TCP) is an animal-based traditional Chinese medical material, and its decoction or extract possesses multiple pharmacological effects. However, other species-sourced substitutes are sometimes used in the market, potentially impairing the quality and effectiveness of TCP medications. To address this issue, it is very necessary to develop applicable approaches that can accurately differentiate genuine TCP from its counterfeit counterparts.

Methods

In this study, liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomic and lipidomic analyses were performed to comprehensively detect water-soluble metabolites and organic-soluble lipids in water decoctions of genuine TCP and its substitutes, such as Trachemys scripta elegans (Wied)- and Ocadia sinensis (Gray)-sourced tortoise shells. Differential analyses based on fold change (FC), principal component analysis (PCA), and Orthogonal partial least squares-discriminant analysis (OPLS-DA) were performed to assess the differences among TCP decoctions from different origins, as well as between decoctions of TCP samples and the two substitutes. Further, Kyoto Encyclopedia of Genes and Genomes (KEGG) database-based pathway enrichment analysis was performed for differential metabolites and lipids among them. Besides, LC-MS/MS-based absolute quantitative method was used to quantify the amino acid-relevant metabolites in decoctions of TCP and substituted tortoise shell samples.

Results

All told, 1117 water-soluble metabolites (including amino acids, organic acids, nucleotides and their metabolites or derivatives, etc.) and 574 organic-soluble lipids (including glycerolipids, sphingolipids, glycerophospholipids, fatty acids, and sterol lipids) were detected in decoctions of TCP and two substitutes. Comparative analyses revealed that there were significantly differential metabolites and lipids among TCP decoctions from different origins, as well as between decoctions of TCP samples and the two substitutes. Of particular interest, the content of N-methyl-4-aminobutyric acid was lower in the substituted samples than TCP samples. Furthermore, the content of 27 amino acids, 22 amino acid derivatives, and 18 small peptides in the decoctions of TCP and two substitutes were absolutely quantified, constituting up to tens of milligrams per 10 g of tortoise shell.

Discussion

In conclusion, our study provides comprehensive metabolomic and lipidomic information of TCP decoction. However, the current results represent preliminary data, and further extensive research is required to validate these findings.

Critical assessment of quenching and extraction/sample preparation methods for microorganisms in metabolomics

BACKGROUND

Advancements in the research of intracellular metabolome have the potential to affect our understanding of biological processes. The applications and findings of intracellular metabolome analysis are useful in understanding cellular pathways, microbial interactions, and the detection of secreted metabolites and their functions.

AIM OF REVIEW

This work focuses on the analysis of intracellular metabolomes in microorganisms. The techniques used for analyzing the intracellular metabolomes including metabolomics approaches such as mass spectrometry, nuclear magnetic resonance, liquid chromatography, and gas chromatography are discussed.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Challenges such as sample preparation, data analysis, metabolite extraction, sample storage and collection, and processing techniques were investigated, as they can highlight emerging technologies and advancements in metabolome analysis, future applications in drug discovery, personalized medicine, systems biology, and the limitations and challenges in studying the metabolome of microorganisms.

Determination of Urolithin A in Health Products by Ultra-High-Performance Liquid Chromatography

This study establishes and validates a novel ultra-high-performance liquid chromatography (UHPLC) method for the determination of urolithin A content in health products, a bioactive compound with potential anti-aging properties. Given the lack of standardized analytical methods for urolithin A in health products, this research addresses a critical gap in quality control. The method employs a methanol-water mobile phase, optimized gradient elution, and a specialized UPLC column (ACQUITY UPLC CSH Fluoro Phenyl) to achieve high resolution and specificity in the separation of urolithin A from its impurities. A variety of diluents, extraction solvents, and extraction times were tested to maximize analyte recovery and stability, with pure methanol yielding the highest recovery rate (over 95%) in 30 min. The method was validated in terms of linearity, sensitivity, repeatability, specificity, and precision. The calibration curve for urolithin A exhibited excellent linearity (r2 = 0.9998) over a concentration range of 0.100-10.000 µg/mL. Detection and quantification limits were found to be 0.051 µg/mL and 0.103 µg/mL, respectively. Precision testing revealed an inter-operator RSD of 1.3%, and recovery rates for spiked samples consistently fell within the 98-102% range. The developed method was successfully applied to analyze the urolithin A content in a commercially available health product, demonstrating its practicality for routine quality control. However, this method may currently be affected by the excipient matrix. This research contributes to the establishment of robust, reliable, and high-sensitivity analytical methods for the bioactive compounds found in health products, with significant implications for regulatory compliance and consumer safety.

A Primer on Proteomic Characterization of Intercellular Communication in a Virus Microenvironment

Intercellular communication is fundamental to multicellular life and a core determinant of outcomes during viral infection, where the common goals of virus and host for persistence and replication are generally at odds. Hosts rely on encoded innate and adaptive immune responses to detect and clear viral pathogens, while viruses can exploit or disrupt these pathways and other intercellular communication processes to enhance their spread and promote pathogenesis. While virus-induced signaling can result in systemic changes to the host, striking alterations are observed within the cellular microenvironment directly surrounding a site of infection, termed the virus microenvironment (VME). Mechanisms employed by viruses to condition their VMEs are emerging and are critical for understanding the biology and pathologies of viral infections. Recent advances in experimental approaches, including proteomic methods, have enabled study of the VME in unprecedented detail. In this review article, we provide a primer on proteomic approaches used to study how viral infections alter intercellular communication, highlighting the ways in which these approaches have been implemented and the exciting biology they have uncovered. First, we consider the different molecules secreted by an infected cell, including proteins, either soluble or contained within extracellular vesicles, and metabolites. We further discuss the modalities of interactions facilitated by alteration at the cell surface of infected cells, including immunopeptide presentation and interactions with the extracellular matrix. Finally, we review spatial profiling approaches that have allowed distinguishing how specific subpopulations of cells within a VME respond to infection and alter their protein composition, discussing valuable insights these methods have offered.

Alkaline Phosphatase-Regulated DNAzyme Cleavage Coupled with CRISPR/Cas12a for Quantitative Detection of Deoxynivalenol in Agricultural Crops

Sensitive and simplified detection of a mycotoxin such as deoxynivalenol (DON) is crucial for food safety. In recent years, the CRISPR/Cas technology has demonstrated significant potential in detecting non-nucleic acids. Herein, we present a triple enzyme-assisted fluorescence immunoassay (TEFIA) that integrates alkaline phosphatase (ALP)-regulated DNAzyme cleavage with the CRISPR/Cas12a assay for the accurate detection of mycotoxin. By employing this method for detecting DON, we exhibit a low detection limit of 0.05 ng/mL and a satisfactory linear response between 0.1 and 10 ng/mL. This performance exceeds the conventional sensitivity levels found in traditional methods. TEFIA also demonstrates a good correlation with ic-ELISA for testing DON in real samples. Thus, it offers a robust and efficient detection platform for DON in complex matrices. Furthermore, TEFIA can be employed to identify various targets of interest by merely altering the antibody-antigen pairs, indicating its great potential in a wide range of applications.

Potential Biomarkers of Fatal Hypothermia Revealed by UHPLC-MS Metabolomics in Mice

BACKGROUND

The postmortem diagnosis of fatal hypothermia presents a considerable challenge in forensic medicine. Metabolomics, a powerful tool reflecting comprehensive changes in endogenous metabolites, offers significant potential for exploring disease mechanisms and identifying diagnostic markers.

METHODS

In this study, we employed ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) to perform a non-targeted metabolomic analysis of liver, stomach, spleen, and musculus gastrocnemius tissues from mice subjected to fatal hypothermia.

RESULT

A substantial number of differential metabolites were identified in each tissue: 1601 in the liver, 420 in the stomach, 732 in the spleen, and 668 in the gastrocnemius muscle. The most significantly altered metabolites were as follows: magnoflorine (liver, upregulated, ranked first in fold-change), gibberellic acid (stomach, downregulated, ranked first in fold-change), nitrofurantoin (spleen, upregulated, ranked first in fold-change), and isoreserpin (gastrocnemius muscle, downregulated, ranked first in fold-change). Glycerophospholipid metabolism exhibited notable enrichment in all tissues (spleen: second, liver: tenth, stomach: eleventh, gastrocnemius muscle: twenty-first), as did tryptophan metabolism (spleen: thirteenth, liver: eighth, stomach: third, gastrocnemius muscle: seventeenth).

CONCLUSIONS

Our findings provide insights into the metabolic perturbations associated with fatal hypothermia in different tissues and lay a foundation for the identification of potential tissue biomarkers for forensic diagnosis.

Efficient Coupling of Structures for Lossless Ion Manipulations with Ion Trap Mass Analyzers Using Phase Modulation

Phased structures for lossless ion manipulation offer significant improvements over the scanning second gate method for coupling with ion trap mass analyzers. With an experimental run time of under 1 min for select conditions and an average run time of less than 4 min, this approach significantly reduces experimental time while enhancing the temporal duty cycle. The outlined SLIM system connects to an ion trap mass analyzer via a PCB stacked ring ion guide, which replaces the commercial ion optics and capillary inlet. By applying a discrete and repeating injection pulse and solving a series of algebraic equations, the system reconstructs an arrival time distribution with a minimal degree of error with enhanced ion throughput. To demonstrate the feasibility of this approach, the 3.4-m SLIM system resolves gas-phase conformers for various small peptides and proteins. This system and methodology also enable direct implementation between SLIM and ion trap mass analyzers traditionally interfaced with front separation systems such as liquid chromatography.

tDDA: A Targeted Data-Dependent Acquisition Mode for Rapid Screening of Targets in Complex Matrices

Miniaturized mass spectrometers offer significant potential for in situ analysis due to their high specificity and portability. In traditional data-dependent acquisition (DDA) mode, precursor ions for tandem analysis are selected based on the full-scan mass spectrum. However, in situ applications often require the direct analysis of complex samples without extensive sample pretreatment, making them susceptible to chemical noise that can result in false negatives. To address this challenge, we propose a targeted data-dependent acquisition (tDDA) mode that substantially improves the accurate detection of target compounds in complex matrices. Unlike conventional DDA, the tDDA method eliminates reliance on the full-scan mass spectrum, where signals of interest are often obscured by matrix effects. This approach leverages sine amplitude modulation of sinusoidal frequency modulated (SAM-SFM) waveforms technology, which enables the real-time generation of isolated waveforms, allowing tDDA to achieve parallel, high-speed screening. Additionally, targeted automatic gain control (AGC) technology enhances the detection of low-concentration analytes, further reducing the false-negative rate. The tDDA mode was successfully integrated and validated on a modified "Brick" miniaturized ion trap mass spectrometer. Experimental results demonstrated its capability to detect low concentrations of illicit drugs spiked in blood and saliva samples, highlighting its potential for effective in situ screening.

Color and fluorescence orthogonal dual-functional visual turn-on sensing for acidic and alkaline glyphosate and additive

In this work, benefitting from the sensitive pH-responsiveness of both meso-tetra-(4-sulfonatophenyl) porphyrin (TPPS4) and calixpyridinium, and their controllable strong noncovalent interactions, the first orthogonal dual-functional visual sensor for simultaneously and separately detecting acidic and alkaline substances without interference by using UV-Vis absorption and fluorescence emission spectra with both "turn on" signal changes was constructed by the supramolecular assembly of calixpyridinium with TPPS4. Color and fluorescence orthogonal dual-functional visual "turn-on" sensing for acidic and alkaline glyphosate and additive by calixpyridinium-TPPS4 sensor was further practically applied. The preparation of this sensor is quite simple in an environmentally friendly water medium. Only 2 μM calixpyridinium and 3 μM TPPS4 are needed to construct this assembly sensor. This sensor has a good biocompatibility, a high selectivity and sensitivity. Moreover, calixpyridinium-TPPS4 sensor can also be applied as a thermal switch and a light controlled anti-counterfeit material.

Metabolomics studies in cushing's syndrome: recent developments and perspectives

INTRODUCTION

Exogenous Cushing's syndrome is the result of long-term exposure to glucocorticoids, while endogenous Cushing's syndrome occurs due to excessive production of glucocorticoids within the body. Cushing's syndrome remains a diagnostic challenge for the treating physician.Mass spectrometry, with its better resolution, detectability, and specificity, paved the way to understanding the cellular and molecular mechanisms involved in several diseases that facilitated the evolution of biomarkers and personalized medicine, which can be applicable to manage Cushing's syndrome as well.

AREAS COVERED

There are only a few reports of mass spectrometry-based metabolomic approaches to endogenous Cushing's syndrome of certain etiologies. However, the application of this approach in the diagnosis of exogenous Cushing has not been explored much. This review attempts to discuss the application of the mass spectrometry-based metabolomic approach in the evaluation of Cushing's syndrome.

EXPERT OPINION

Global metabolomics has the potential to discover altered metabolites and associated signaling and metabolic pathways, which may serve as potential biomarkers that would help in developing tools to accelerate precision medicine. Multi-omics approaches will provide innovative solutions to develop molecular tests for multi-molecule panel assays.

Integration of LC-MS-based untargeted and targeted metabolomics to uncover novel whole-grain wheat dietary intake biomarkers in the plasma of the Chinese population

Biomarkers of food intake (BFIs) for whole grains (WGs) would enable more precise dietary assessments and help investigate WG's health effects. However, no reports have been published on the biomarkers that reflect the intake of WG wheat in the Chinese diet. In an acute, randomized, crossover intervention study performed on 22 Chinese subjects, WG wheat BFI candidates were screened using an LC-HRMS untargeted metabolomics technique. Screening results indicate that alkylresorcinol (AR) metabolites are WG wheat-specific metabolites. These metabolites were systematically characterized by in vitro metabolism reaction, and the matched high-throughput LC-MS/MS-targeted quantitative method was developed. Time-response plots generated via targeted analysis revealed AR oxidation products (AR-OOH) and their sulfate conjugates (AR-OOH-sul) increased rapidly following the WG wheat consumption, which were identified as novel and short-term WG wheat BFIs. Another attractive biomarker was glucuronidated ARs (AR-glu), which can distinguish between WG and refined grain (RG) groups 24 h after WG wheat ingestion and is considered a promising medium- and long-term biomarker. Subsequently an independent dose-response study was performed for 38 volunteers who consumed different WG wheat amounts (0, 25, 50 or 100 g) for further validation. Identified BFIs perform well in classifying participants into WG wheat consumers or non-consumers, and could capture dose-dependent changes with WG intake increased. Overall, this is the first study to discover and validate WG BFIs in the Chinese population via dietary intervention trials, indicating the potential usefulness for WG wheat intake assessments and dietary compliance monitoring. Future work will examine their use in large-scale free-living populations.

Non-Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning

Moyamoya disease (MMD) is a progressive cerebrovascular disorder that increases the risk of intracranial ischemia and hemorrhage. Timely diagnosis and intervention can significantly reduce the risk of new-onset stroke in patients with MMD. However, the current diagnostic methods are invasive and expensive, and non-invasive diagnosis using biomarkers of MMD is rarely reported. To address this issue, nanoparticle-enhanced laser desorption/ionization mass spectrometry (LDI MS) was employed to record serum metabolic fingerprints (SMFs) with the aim of establishing a non-invasive diagnosis method for MMD. Subsequently, a diagnostic model was developed based on deep learning algorithms, which exhibited high accuracy in differentiating the MMD group from the HC group (AUC = 0.958, 95% CI of 0.911 to 1.000). Additionally, hierarchical clustering analysis revealed a significant association between SMFs across different groups and vascular cognitive impairment in MMD. This approach holds promise as a novel and intuitive diagnostic method for MMD. Furthermore, the study may have broader implications for the diagnosis of other neurological disorders.

Quantitative Site-Specific Glycoproteomics Reveals Glyco-Signatures for Breast Cancer Diagnosis

Intact glycopeptide characterization by mass spectrometry has proven to be a versatile tool for site-specific glycoproteomics analysis and biomarker screening. Here, we present a method using a new model of a Q-TOF instrument equipped with a Zeno trap for intact glycopeptide identification and demonstrate its ability to analyze large-cohort glycoproteomes. From 124 clinical serum samples of breast cancer, noncancerous diseases, and nondisease controls, a total of 6901 unique site-specific glycans on 807 glycosites of proteins were detected. Much more differences of glycoproteome were observed in breast diseases than the proteome. By employing machine learning, 15 site-specific glycans were determined as potential glyco-signatures in detecting breast cancer. The results demonstrate that our method provides a powerful tool in glycoproteomic studies.

New acorane-sesequiterpenes and anti-retinoblastoma constituents from the marine algicolous fungus Trichoderma harzianum NTU2180 guided by molecular networking strategy

BACKGROUND

Trichoderma species, known as biocontrol agents against plant diseases, contain diverse compounds, especially terpenoids, with various bioactivities. To facilitate the exploration of bioactive secondary metabolites of Trichoderma harzianum NTU2180, the OSMAC approach MS/MS molecular networking was applied in the current study.

RESULTS

The feature-based molecular networking (FBMN) analysis showed that T. harzianum NTU2180 fermented on germinated brown rice (GBR) produced more terpenoids. Here, two new acorane-sesequiterpenes, trichospirols A (1) and B (2), and 12 known compounds (3 - 14) were isolated from the EtOAc layer of T. harzianum NTU2180 fermentation on GBR. Structures of these compounds were determined through NMR, UV, IR, and MS analyses. The absolute configuration of trichospirols A (1) was also elucidated by x-ray with Cu K-α radiation. Among them, six compounds (1, 2, 3, 4, 5, and 11) were annotated as terpenoids by the NPClassifier on FBMN. 5-Hydroxy-3-hydroxmethyl-2-methyl-7-methoxychromone (7) and ergosterol peroxide (11) showed significant anti-angiogenic activity in ex vivo experiments with respective 0.57 ± 0.12- and 0.20 ± 0.12-fold changes. In addition, compound 11 displayed cytotoxicity against Y79 retinoblastoma cells with IC50 value of 35.3 ± 6.9 µM.

CONCLUSIONS

The current study utilizes FBMN concept with OSMAC approach to accelerate the exploration of potential metabolites of the fungus Trichoderma harzianum NTU2180. Through a series of FBMN-guided isolation and purification, two new acorane-sesequiterpenes and 12 known compounds were obtained. The ex vivo and in vitro experiments were evaluated to assess anticancer isolates. It is worth noting that compound 11 was identified as a dual inhibitor targeting both angiogenesis and proliferation of retinoblastomas. Altogether, the results revealed the novel potential of T. harzianum for developing natural therapeutics against retinoblastomas.

Optimization of metabolomics pretreatment method of cholangiocarcinoma cells based on ultrahigh performance liquid chromatography coupled with mass spectrometry

Metabolomics intends to maximize the quantity of available metabolites for the global metabolome, which largely depends on sample pretreatment protocols. However, there are few studies that comprehensively examined the effects of extraction and reconstitution solvents on metabolome coverage of adherent mammalian cells. In this study, the human cholangiocarcinoma TFK-1 cells were chosen as a cell model, and eight extraction solvents and five reconstitution solvents were used for the pretreatment based on ultrahigh performance liquid chromatography coupled with mass spectrometry (UPLC/MS). The coverage, reproducibility, and stability of the data were norms to evaluate the effectiveness of different extraction solvents and reconstitution solvents. Based on the number of metabolites, the mean Euclidean distance (EDMEAN) in the principal component analysis (PCA) 3D score plots and the relative standard deviation (RSD) distribution of metabolites, it was demonstrated that MeOH-CHCl3-H2O (8:1:1, v/v/v) was the optimal extraction solvent and MeOH-H2O (1:1, v/v) or H2O was superior to other reconstitution solvents for RP column analysis, and the extraction solvent MeOH-ACN-H2O (2:2:1, v/v/v) and the reconstitution solvents ACN-H2O (4:1, v/v) or MeOH-H2O (1:1, v/v) provide the best performance for HILIC column analysis. The optimized pretreatment methods explored in this study expand the coverage of polar and non-polar metabolites and improve the reproducibility and stability of the metabolic data, which can be applied to UPLC/MS-based global metabolomics study on cholangiocarcinoma cells, potentially providing better extraction solvents and reconstitution solvents for other adherent mammalian cells with similar chemical and physical properties.

Understanding LC/MS-Based Metabolomics: A Detailed Reference for Natural Product Analysis

Liquid chromatography, when used in conjunction with mass spectrometry (LC/MS), is a powerful tool for conducting accurate and reproducible investigations of numerous metabolites in natural products (NPs). LC/MS has gained prominence in metabolomic research due to its high throughput, the availability of multiple ionization techniques and its ability to provide comprehensive metabolite coverage. This unique method can significantly influence various scientific domains. This review offers a comprehensive overview of the current state of LC/MS-based metabolomics in the investigation of NPs. This review provides a thorough overview of the state of the art in LC/MS-based metabolomics for the investigation of NPs. It covers the principles of LC/MS, various aspects of LC/MS-based metabolomics such as sample preparation, LC modes, method development, ionization techniques and data pre-processing. Moreover, it presents the applications of LC/MS-based metabolomics in numerous fields of NPs research such as including biomarker discovery, the agricultural research, food analysis, the study of marine NPs and microbiological research. Additionally, this review discusses the challenges and limitations of LC/MS-based metabolomics, as well as emerging trends and developments in this field.

Advances and future trends in the detection of beta-amyloid: A comprehensive review

The neurodegenerative condition known as Alzheimer's disease is typified by the build-up of beta-amyloid plaques within the brain. The timely and precise identification of beta-amyloid is essential for understanding disease progression and developing effective therapeutic interventions. This comprehensive review explores the diverse landscape of beta-amyloid detection methods, ranging from traditional immunoassays to cutting-edge technologies. The review critically examines the strengths and limitations of established techniques such as ELISA, PET, and MRI, providing insights into their roles in research and clinical settings. Emerging technologies, including electrochemical methods, nanotechnology, fluorescence techniques, point-of-care devices, and machine learning integration, are thoroughly discussed, emphasizing recent breakthroughs and their potential for revolutionizing beta-amyloid detection. Furthermore, the review delves into the challenges associated with current detection methods, such as sensitivity, specificity, and accessibility. By amalgamating knowledge from multidisciplinary approaches, this review aims to guide researchers, clinicians, and policymakers in navigating the complex landscape of beta-amyloid detection, ultimately contributing to advancements in Alzheimer's disease diagnostics and therapeutics.

Metabolic profiling and antibacterial activity of tree wood extracts obtained under variable extraction conditions

INTRODUCTION

Tree bacterial diseases are a threat in forestry due to their increasing incidence and severity. Understanding tree defence mechanisms requires evaluating metabolic changes arising during infection. Metabolite extraction affects the chemical diversity of the samples and, therefore, the biological relevance of the data. Metabolite extraction has been standardized for several biological models. However, little information is available regarding how it influences wood extract's chemical diversity.

OBJECTIVES

This study aimed to develop a methodological approach to obtain extracts from different tree species with the highest reproducibility and chemical diversity possible, to ensure proper coverage of the trees' metabolome.

METHODS

A full factorial design was used to evaluate the effect of solvent type, extraction temperature and number of extraction cycles on the metabolic profile, chemical diversity and antibacterial activity of four tree species.

RESULTS

Solvent, temperature and their interaction significantly affected the extracts' chemical diversity, while the number of extraction cycles positively correlated with yield and antibacterial activity. Although 60% of the features were recovered in all the tested conditions, differences in the presence and abundance of specific chemical classes per tree were observed, including organooxygen compounds, prenol lipids, carboxylic acids, and flavonoids.

CONCLUSIONS

Each tree species has a unique metabolic profile, which means that no single protocol is universally effective. Extraction at 50 °C for three cycles using 80% methanol or chloroform/methanol/water showed the best results and is suggested for studying wood metabolome. These observations highlight the need to tailor extraction protocols to each tree species to ensure comprehensive metabolome coverage for metabolic profiling.

Muscle Biopsy Sample Preparation and Proteomics Analysis Based on UHPLC-MS/MS

Proteomics analysis is crucial for understanding the molecular mechanisms underlying muscle adaptations to different types of exercise, such as concentric and eccentric training. Traditional methods like two-dimensional gel electrophoresis and standard mass spectrometry have been used to analyze muscle protein content and modifications. This protocol details the preparation of muscle samples for proteomics analysis using ultra-high-performance liquid chromatography (UHPLC). It includes steps for muscle biopsy collection, protein extraction, digestion, and UHPLC-based analysis. The UHPLC method offers high-resolution separation of complex protein mixtures, providing more detailed and accurate proteomic profiles compared to conventional techniques. This protocol significantly enhances sensitivity, reproducibility, and efficiency, making it ideal for comprehensive muscle proteomics studies. Key features • Developed for analyzing muscle adaptations in response to concentric and eccentric training, applicable to various physiology exercise studies. • Utilizes UHPLC-MS/MS for high-resolution separation and detailed proteomic profiling. • Requires access to advanced UHPLC-MS/MS equipment and muscle biopsy collection tools. • The protocol can be completed within one week, including sample preparation and analysis.

Serum Metabolomics Study to Screen Potential Biomarkers of Lung Cancer Risk in High Natural Background Radiation Areas of Thailand: A Pilot Study

Background: Indoor radon is a significant risk factor for the development of LC. This study aimed to identify potential biomarkers for LC risk in high background radiation areas using a metabolomics approach (UHPLC-HRMS). Methods: Based on the indoor radon activity concentration measurements in the Kong Khaek subdistrict, serum samples were collected from 45 nonsmoker or former smoker participants, comprising 15 LC patients and 30 matched healthy controls (low- and high-radon groups, respectively). Results: A total of 90 and 111 differential metabolites were identified in the LC group compared with the low- and high-radon groups, respectively, using criteria such as a variable importance in projection (VIP) of >1, a fold change (FC) of >1 or <0.5, and a p value of <0.05. Receiver operating characteristic (ROC) curves (an AUC of ≥ 0.9) indicated that 30 and 21 of these metabolites had the potential to serve as biomarkers of LC development in the low- and high-radon groups, respectively. The KEGG pathway enrichment analysis suggested that D-sphingosine may have been a candidate biomarker associated with LC in both groups. Conclusions: Overall, this study provides new insights into metabolic biomarkers for screening LC development in high-risk individuals with prolonged exposure to indoor radon. Further large-scale studies are needed to validate our results.

Emerging Biomarkers in Metabolomics: Advancements in Precision Health and Disease Diagnosis

Metabolomics has come to the fore as an efficient tool in the search for biomarkers that are critical for precision health approaches and improved diagnostics. This review will outline recent advances in biomarker discovery based on metabolomics, focusing on metabolomics biomarkers reported in cancer, neurodegenerative disorders, cardiovascular diseases, and metabolic health. In cancer, metabolomics provides evidence for unique oncometabolites that are important for early disease detection and monitoring of treatment responses. Metabolite profiling for conditions such as neurodegenerative and mental health disorders can offer early diagnosis and mechanisms into the disease especially in Alzheimer's and Parkinson's diseases. In addition to these, lipid biomarkers and other metabolites relating to cardiovascular and metabolic disorders are promising for patient stratification and personalized treatment. The gut microbiome and environmental exposure also feature among the influential factors in biomarker discovery because they sculpt individual metabolic profiles, impacting overall health. Further, we discuss technological advances in metabolomics, current clinical applications, and the challenges faced by metabolomics biomarker validation toward precision medicine. Finally, this review discusses future opportunities regarding the integration of metabolomics into routine healthcare to enable preventive and personalized approaches.

Promises and challenges of crop translational genomics

Crop translational genomics applies breeding techniques based on genomic datasets to improve crops. Technological breakthroughs in the past ten years have made it possible to sequence the genomes of increasing numbers of crop varieties and have assisted in the genetic dissection of crop performance. However, translating research findings to breeding applications remains challenging. Here we review recent progress and future prospects for crop translational genomics in bringing results from the laboratory to the field. Genetic mapping, genomic selection and sequence-assisted characterization and deployment of plant genetic resources utilize rapid genotyping of large populations. These approaches have all had an impact on breeding for qualitative traits, where single genes with large phenotypic effects exert their influence. Characterization of the complex genetic architectures that underlie quantitative traits such as yield and flowering time, especially in newly domesticated crops, will require further basic research, including research into regulation and interactions of genes and the integration of genomic approaches and high-throughput phenotyping, before targeted interventions can be designed. Future priorities for translation include supporting genomics-assisted breeding in low-income countries and adaptation of crops to changing environments.

Analysis of the quadruple lsu mutant reveals molecular determinants of the role of LSU proteins in sulfur assimilation in Arabidopsis

Because plants are immobile, they have developed intricate mechanisms to sense and absorb nutrients, adjusting their growth and development accordingly. Sulfur is an essential macroelement, but our understanding of its metabolism and homeostasis is limited. LSU (RESPONSE TO LOW SULFUR) proteins are plant-specific proteins with unknown molecular functions and were first identified during transcriptomic studies on sulfur deficiency in Arabidopsis. These proteins are crucial hubs that integrate environmental signals and are involved in the response to various stressors. Herein, we report the direct involvement of LSU proteins in primary sulfur metabolism. Our findings revealed that the quadruple lsu mutant, q-lsu-KO, which was grown under nonlimiting sulfate conditions, exhibited a molecular response resembling that of sulfur-deficient wild-type plants. This led us to explore the interactions of LSU proteins with sulfate reduction pathway enzymes. We found that all LSU proteins interact with ATPS1 and ATPS3 isoforms of ATP sulfurylase, all three isoforms of adenosine 5´ phosphosulfate reductase (APR), and sulfite reductase (SiR). Additionally, in vitro assays revealed that LSU1 enhances the enzymatic activity of SiR. These results highlight the supportive role of LSU proteins in the sulfate reduction pathway.

Characterization and optimization of magnetic molecularly imprinted nanofibers for determination of sunitinib in human serum and capsule samples

This article reports a spectrofluorometric method for the determination of sunitinib (STB) drug based on molecularly imprinted nanofibers fabricated by the electrospinning method and modified by magnetic nanoparticles as sorbent. The characterization of magnetic molecularly imprinted nanofibers (MMINs) was carried out using X-ray diffraction (XRD), scanning electron microscope (SEM), and transmission electron microscopy (TEM), which confirmed the successful synthesis of MMINs with well-distributed magnetite nanoparticles. Drug adsorption and desorption were optimized and important parameters such as sample pH, nanofiber mass, adsorption and desorption time, eluent solvent and sample volume were analyzed. The results demonstrated that the MMINs act as a selective sorbent for STB and can be readily collected through an external magnetic field. Methanol was used as the best eluent solvent for STB desorption from MNIN. A linear correlation was observed between the STB concentrations and fluorescence intensities in the range of 0.01-15.0 mg L-1. The detection limit for this method was 0.002 mg L-1. The relative standard deviation (RSD) of 2.6 % for 1.0 mg L-1 and 1.1 % for 10 mg L-1 of STB (n = 3) were obtained, which indicates that the developed method is precise in determining STB. Human serum and capsule analysis show the applicability of the proposed sensor for real samples.

Degradation of a novel herbicide fluchloraminopyr in soil: Dissipation kinetics, degradation pathways, transformation products identification and ecotoxicity assessment

With the continuous application of new agricultural chemicals in agricultural systems, it is imperative to study the environmental fate and potential transformation products (TPs) of these chemicals to better assess their ecological and health risks, as well as guide scientific application. The dissipation of fluchloraminopyr was firstly evaluated under aerobic/anaerobic condition in four representative soils, with Dissipation Time 50 (DT50) values ranging from 0.107 to 4.76 days. Eight TPs generated by soil degradation were identified via Ultra-High Performance Liquid Chromatography coupled with Quadrupole Time-of-Flight Mass Spectrometry (UHPLC-QTOF/MS) and Density Functional Theory (DFT) calculations. The predominant transformation reactions of fluchloraminopyr in soil include oxidation, dechlorination, hydroxylation, and acetylation. The predictions from toxicological software indicated that the acute and chronic toxicity of TPs to aquatic organisms was significantly lower than that of fluchloraminopyr. Moreover, both M267 and M221 exhibited higher acute oral toxicity to terrestrial organisms compared to the parent compound. Consequently, these findings offer essential ecological risk evaluation data for the judicious application of fluchloraminopyr.

Metabolomics-driven approaches for identifying therapeutic targets in drug discovery

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics-based methods, such as dose-response metabolomics, stable isotope-resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose-dependent metabolite-drug interactions. Emerging techniques, including single-cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field.

Simultaneous detection of multiple aging characteristic components in oil-paper insulation using sensitive Raman technology and microfluidics

The aging characteristic components of oil-paper insulation reflect the aging status of the power equipment. In this study, we designed a novel microfluidic chip capable of automatic and rapid extraction of aging components from insulating oil. Combined with Raman spectroscopy technology, it enables simultaneous detection of various aging components. By optimizing the microfluidic chip structural and adopting an optical window encapsulation, it eliminates interference from the Polydimethylsiloxane (PDMS). Measurements and analyses were carried out on multiple oil samples containing three aging products (furfural, acetone, and methanol). The results indicate that this novel microfluidic chip facilitates simultaneous detection of multiple components, significantly improving the detection sensitivity of complex oil. The detection limits for furfural, acetone, and methanol in insulating oil are 0.43 mg/L, 1.04 mg/L, and 2.31 mg/L, respectively. This provides a new approach for the online detection of oil-paper insulation equipment.

Multiomics and biotechnologies for understanding and influencing cadmium accumulation and stress response in plants

Cadmium (Cd) is one of the most toxic heavy metals faced by plants and, additionally, via the food chain, threatens human health. It is principally dispersed through agro-ecosystems via anthropogenic activities and geogenic sources. Given its high mobility and persistence, Cd, although not required, can be readily assimilated by plants thereby posing a threat to plant growth and productivity as well as animal and human health. Thus, breeding crop plants in which the edible parts contain low to zero Cd as safe food stuffs and harvesting shoots of high Cd-containing plants as a route for decontaminating soils are vital strategies to cope with this problem. Recently, multiomics approaches have been employed to considerably enhance our understanding of the mechanisms underlying (i) Cd toxicity, (ii) Cd accumulation, (iii) Cd detoxification and (iv) Cd acquisition tolerance in plants. This information can be deployed in the development of the biotechnological tools for developing plants with modulated Cd tolerance and detoxification to safeguard cellular and genetic integrity as well as to minimize food chain contamination. The aim of this review is to provide a current update about the mechanisms involved in Cd uptake by plants and the recent developments in the area of multiomics approach in terms of Cd stress responses, as well as in the development of Cd tolerant and low Cd accumulating crops.

Machine learning powered CN-coordinated cobalt nanoparticles embedded cellulosic nanofibers to assess meat quality via clenbuterol monitoring

The World Anti-Doping Agency (WADA) has prohibited the use of clenbuterol (CLN) because it induces anabolic muscle growth while potentially causing adverse effects such as palpitations, anxiety, and muscle tremors. Thus, it is vital to assess meat quality because, athletes might have positive test for CLN even after consuming very low quantity of CLN contaminated meat. Numerous materials applied for CLN monitoring faced potential challenges like sluggish ion transport, non-uniform ion/molecule movement, and inadequate electrode surface binding. To overcome these shortcomings, herein we engineered bimetallic zeolitic imidazole framework (BM-ZIF) derived N-doped porous carbon embedded Co nanoparticles (CN-CoNPs), dispersed on conductive cellulose acetate-polyaniline (CP) electrospun nanofibers for sensitive electrochemical monitoring of CLN. Interestingly, the smartly designed CN-CoNPs wrapped CP (CN-CoNPs-CP) electrospun nanofibers offers rapid diffusion of CLN molecules to the sensing interface through amine and imine groups of CP, thus minimizing the inhomogeneous ion transportation and inadequate electrode surface binding. Additionally, to synchronize experiments, machine learning (ML) algorithms were applied to optimize, predict, and validate voltametric current responses. The ML-trained sensor demonstrated high selectivity, even amidst interfering substances, with notable sensitivity (4.7527 μA/μM/cm2), a broad linear range (0.002-8 μM), and a low limit of detection (1.14 nM). Furthermore, the electrode exhibited robust stability, retaining 98.07% of its initial current over a 12-h period. This ML-powered sensing approach was successfully employed to evaluate meat quality in terms of CLN level. To the best of our knowledge, this is the first study of using ML powered system for electrochemical sensing of CLN.

The marriage between stable isotope ecology and plant metabolomics - new perspectives for metabolic flux analysis and the interpretation of ecological archives

Even though they share many thematical overlaps, plant metabolomics and stable isotope ecology have been rather separate fields mainly due to different mass spectrometry demands. New high-resolution bioanalytical mass spectrometers are now not only offering high-throughput metabolite identification but are also suitable for compound- and intramolecular position-specific isotope analysis in the natural isotope abundance range. In plant metabolomics, label-free metabolic pathway and metabolic flux analysis might become possible when applying this new technology. This is because changes in the commitment of substrates to particular metabolic pathways and the activation or deactivation of others alter enzyme-specific isotope effects. This leads to differences in intramolecular and compound-specific isotope compositions. In plant isotope ecology, position-specific isotope analysis in plant archives informed by metabolic pathway analysis could be used to reconstruct and separate environmental impacts on complex metabolic processes. A technology-driven linkage between the two disciplines could allow us to extract information on environment-metabolism interaction from plant archives such as tree rings but also within ecosystems. This would contribute to a holistic understanding of how plants react to environmental drivers, thus also providing helpful information on the trajectories of the vegetation under the conditions to come.

Toward the apoplast metabolome: Establishing a leaf apoplast collection approach suitable for metabolomics analysis

The leaf apoplast contains several compounds that play important roles in the regulation of different physiological processes in plants. However, this compartment has been neglected in several experimental and modelling studies, which is mostly associated to the difficulty to collect apoplast washing fluid (AWF) in sufficient amount for metabolomics analysis and as free as possible from symplastic contamination. Here, we established an approach based in an infiltration-centrifugation technique that use little leaf material but allows sufficient AWF collection for gas chromatography mass spectrometry (GC-MS)-based metabolomics analysis in both tobacco and Arabidopsis. Up to 54 metabolites were annotated in leaf and apoplast samples from both species using either 20% (v/v) methanol (20% MeOH) or distilled deionized water (ddH2O) as infiltration fluids. The use of 20% MeOH increased the yield of the AWF collected but also the level of symplastic contamination, especially in Arabidopsis. We propose a correction factor and recommend the use of multiple markers such as MDH activity, protein content and conductivity measurements to verify the level of symplastic contamination in MeOH-based protocols. Neither the concentration of sugars nor the level of primary metabolites differed between apoplast samples extracted with ddH2O or 20% MeOH. This indicates that ddH2O can be preferentially used, given that it is a non-toxic and highly accessible infiltration fluid. The infiltration-centrifugation-based approach established here uses little leaf material and ddH2O as infiltration fluid, being suitable for GC-MS-based metabolomics analysis in tobacco and Arabidopsis, with great possibility to be extended for other plant species and tissues.

Multi-omic analysis identifies metabolic biomarkers for the early detection of breast cancer and therapeutic response prediction

Reliable blood-based tests for identifying early-stage breast cancer remain elusive. Employing single-cell transcriptomic sequencing analysis, we illustrate a close correlation between nucleotide metabolism in the breast cancer and activation of regulatory T cells (Tregs) in the tumor microenvironment, which shows distinctions between subtypes of patients with triple-negative breast cancer (TNBC) and non-TNBC, and is likely to impact cancer prognosis through the A2AR-Treg pathway. Combining machine learning with absolute quantitative metabolomics, we have established an effective approach to the early detection of breast cancer, utilizing a four-metabolite panel including inosine and uridine. This metabolomics study, involving 1111 participants, demonstrates high accuracy across the training, test, and independent validation cohorts. Inosine and uridine prove predictive of the response to neoadjuvant chemotherapy (NAC) in patients with TNBC. This study deepens our understanding of nucleotide metabolism in breast cancer development and introduces a promising non-invasive method for early breast cancer detection and predicting NAC response in patients with TNBC.

LC-MS analysis of chiral amino acids in human urine reveals D-amino acids as potential biomarkers for colorectal cancer

Colorectal cancer (CRC) is a common malignant tumor in the gastrointestinal tract. Changes in amino acid metabolites have been implicated in tumorigenesis and disease progression. Biomarkers on the basis of chiral amino acids, especially D-amino acids, have not been established for early diagnosis of CRC. Quantification of chiral amino acids, especially very low concentrations of endogenous D-amino acids, is technically challenging. We report here the quantification of L- and D-amino acids in urine samples collected from 115 CRC patients and 155 healthy volunteers, using an improved method. The method of chiral labeling, liquid chromatography, and tandem mass spectrometry enabled separation and detection of 28 amino acids (14 L-amino acids, 13 D-amino acids and Gly). Orthogonal partial least squares discriminant analysis identified 14 targeted variables among these chiral amino acids that distinguished the CRC from the healthy controls. Binary logistic regression analysis revealed that D-α-aminobutyric acid (D-AABA), L-alanine (L-Ala), D-alanine (D-Ala), D-glutamine (D-Gln) and D-serine (D-Ser) could be potential biomarkers for CRC. A receiver operating characteristic curve analysis of combined multi-variables contributed to an area under the curve (AUC) of 0.995 with 98.3 % sensitivity and 96.8 % specificity. A model constructed with D-AABA, D-Ala, D-Gln, and D-Ser achieved an AUC of 0.988, indicating important contributions of D-amino acids to the association with CRC. Further analysis also demonstrated that the metabolic aberration was associated with age and the development of CRC, D-methionine (D-Met) was decreased in CRC patients with age over 50, and D/L-Gln in patients at stage IV was higher than patients at stage I. This study provides the signature of D-amino acids in urine samples and offers a promising strategy for developing non-invasive diagnosis of CRC.

Analyzing the differences and correlations between key metabolites and dominant microorganisms in different regions of Daqu based on off-target metabolomics and high-throughput sequencing

Daqu is usually produced in an open environment, which makes its quality unstable. The microbial community of Daqu largely determines its quality. Therefore, in order to improve the fermentation characteristics of Daqu, samples were collected from Jinsha County (MT1), Xishui County (MT2), and Maotai Town (MT3) in Guizhou Province to explore the microbial diversity of Daqu and its impact on Daqu's metabolites.Off-target metabolomics was used to detect metabolites, and high-throughput sequencing was used to detect microorganisms. Metabolomics results revealed that MT1 and MT2 had the highest relative fatty acid content, whereas MT3 had the highest organooxygen compound content. Principal component analysis and partial least squares discriminant analysis revealed significant differences in the metabolites among the three groups, followed by the identification of 33 differential metabolites (key metabolites) filtered using the criteria of variable importance in projection >1 and p < 0.001. According to the microbiological results, Proteobacteria was the dominant bacteria phylum in three samples. Gammaproteobacteria was the dominant class in MT1(26.84 %) and MT2(36.54 %), MT3 is Alphaproteobacteria(25.66 %). And Caulobacteraceae was the dominant family per the abundance analysis, MTI was 24.32 %, MT2 and MT3 were 33.71 % and 24.40 % respectively. Three samples dominant fungi phylum were Ascomycota, and dominant fungi family were Thermoascaceae. Pseudomonas showed a significant positive connection with various fatty acyls, according to correlation analyses between dominant microorganisms (genus level) and key metabolites. Fatty acyls and Thermomyces showed a positive correlation, but Thermoascus had the reverse relation. These findings offer a theoretical framework for future studies on the impact of metabolites on Baijiu quality during fermentation.

Validation of two LCHRMS methods for large-scale untargeted metabolomics of serum samples: Strategy to establish method fitness-for-purpose

Untargeted metabolomics by LCHRMS is a powerful tool to enhance our knowledge of pathophysiological processes. Whereas validation of a bioanalytical method is customary in most analytical chemistry fields, it is rarely performed for untargeted metabolomics. This study aimed to establish and validate an analytical platform for a long-term, clinical metabolomics study. Sample preparation was performed with an automated liquid handler and four analytical methods were developed and evaluated. The validation study spanned three batches with twelve runs using individual serum samples and various quality control samples. Data was acquired with untargeted acquisition and only metabolites identified at level 1 were evaluated. Validation parameters were set to evaluate key performance metrics relevant for the intended application: reproducibility, repeatability, stability, and identification selectivity, emphasizing dataset intrinsic variance. Concordance of semi-quantitative results between methods was evaluated to identify potential bias. Spearman rank correlation coefficients (rs) were calculated from individual serum samples. Of the four methods tested, two were selected for validation. A total of 47 and 55 metabolites (RPLC-ESI+- and HILIC-ESI--HRMS, respectively) met specified validation criteria. Quality assurance involved system suitability testing, sample release, run release, and batch release. The median repeatability and within-run reproducibility as coefficient of variation% for metabolites that passed validation on RPLC-ESI+- and HILIC-ESI--HRMS were 4.5 and 4.6, and 1.5 and 3.8, respectively. Metabolites that passed validation on RPLC-ESI+-HRMS had a median D-ratio of 1.91, and 89 % showed good signal intensity after ten-fold dilution. The corresponding numbers for metabolites with the HILIC-ESI--HRMS method was 1.45 and 45 %, respectively. The rs median ({range}) for metabolites that passed validation on RPLC-ESI+- was 0.93 (N = 9 {0.69-0.98}) and on HILIC-ESI--HRMS was 0.93 (N = 22 {0.55-1.00}). The validated methods proved fit-for-purpose and the laboratory thus demonstrated its capability to produce reliable results for a large-scale, untargeted metabolomics study. This validation not only bolsters the reliability of the assays but also significantly enhances the impact and credibility of the hypotheses generated from the studies. Therefore, this validation study serves as a benchmark in the documentation of untargeted metabolomics, potentially guiding future endeavors in the field.

Techniques, Databases and Software Used for Studying Polar Metabolites and Lipids of Gastrointestinal Parasites

Gastrointestinal parasites (GIPs) are organisms known to have coevolved for millennia with their mammalian hosts. These parasites produce small molecules, peptides, and proteins to evade or fight their hosts' immune systems and also to protect their host for their own survival/coexistence. The small molecules include polar compounds, amino acids, lipids, and carbohydrates. Metabolomics and lipidomics are emerging fields of research that have recently been applied to study helminth infections, host-parasite interactions and biochemicals of GIPs. This review comprehensively discusses metabolomics and lipidomics studies of the small molecules of GIPs, providing insights into the available tools and techniques, databases, and analytical software. Most metabolomics and lipidomics investigations employed LC-MS, MS or MS/MS, NMR, or a combination thereof. Recent advancements in artificial intelligence (AI)-assisted software tools and databases have propelled parasitomics forward, offering new avenues to explore host-parasite interactions, immunomodulation, and the intricacies of parasitism. As our understanding of AI technologies and their utilisation continue to expand, it promises to unveil novel perspectives and enrich the knowledge of these complex host-parasite relationships.

Spatio-temporal plant hormonomics: from tissue to subcellular resolution

Due to technological advances in mass spectrometry, significant progress has been achieved recently in plant hormone research. Nowadays, plant hormonomics is well established as a fully integrated scientific field focused on the analysis of phytohormones, mainly on their isolation, identification, and spatiotemporal quantification in plants. This review represents a comprehensive meta-study of the advances in the phytohormone analysis by mass spectrometry over the past decade. To address current trends and future perspectives, Web of Science data were systematically collected and key features such as mass spectrometry-based analyses were evaluated using multivariate data analysis methods. Our findings showed that plant hormonomics is currently divided into targeted and untargeted approaches. Both aim to miniaturize the sample, allowing high-resolution quantification to be covered in plant organs as well as subcellular compartments. Therefore, we can study plant hormone biosynthesis, metabolism, and signalling at a spatio-temporal resolution. Moreover, this trend has recently been accelerated by technological advances such as fluorescence-activated cell sorting or mass spectrometry imaging.

Advances in mass spectrometry imaging for plant metabolomics-Expanding the analytical toolbox

Mass spectrometry imaging (MSI) has become increasingly popular in plant science due to its ability to characterize complex chemical, spatial, and temporal aspects of plant metabolism. Over the past decade, as the emerging and unique features of various MSI techniques have continued to support new discoveries in studies of plant metabolism closely associated with various aspects of plant function and physiology, spatial metabolomics based on MSI techniques has positioned it at the forefront of plant metabolic studies, providing the opportunity for far higher resolution than was previously available. Despite these efforts, profound challenges at the levels of spatial resolution, sensitivity, quantitative ability, chemical confidence, isomer discrimination, and spatial multi-omics integration, undoubtedly remain. In this Perspective, we provide a contemporary overview of the emergent MSI techniques widely used in the plant sciences, with particular emphasis on recent advances in methodological breakthroughs. Having established the detailed context of MSI, we outline both the golden opportunities and key challenges currently facing plant metabolomics, presenting our vision as to how the enormous potential of MSI technologies will contribute to progress in plant science in the coming years.