Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices

Spatiotemporal metabolic mapping of ex-situ preserved hearts subjected to dialysis by integration of bio-SPME sampling with non-targeted metabolipidomic profiling

BACKGROUND

Normothermic ex situ heart perfusion (ESHP) has emerged as a valid modality for advanced cardiac allograft preservation and conditioning prior to transplantation though myocardial function declines gradually during ESHP thus limiting its potential for expanding the donor pool. Recently, the utilization of dialysis has been shown to preserve myocardial and coronary vasomotor function. Herein, we sought to determine the changes in myocardial metabolism that could support this improvement.

RESULTS

Male Yorkshire porcine hearts were subjected to ESHP for 8 h with or without dialysis. Alterations in metabolism were studied with an innovative in vivo solid-phase microextraction (SPME) technology coupled with global metabolite profiling at 15 min, 1.5, 4, and 8 h of perfusion. Bio-SPME sampling was performed by inserting SPME fibres coated with a PAN-based extraction phase containing mixed-mode (C8+benzenesulfonic acid) functionalities into the myocardium to a depth of their entire 8 mm coating or immersing them in the perfusate, followed by a 20-min extraction period for the analytes of interest. Dialyzed hearts demonstrated improved bioenergetics as evidenced by accelerated purine metabolism and less pronounced accumulation of intermediates of fatty acid β/ω-oxidation. Metabolic waste accumulation such as pro-inflammatory lipid mediators (e.g., leukotrienes) was mitigated thereby supporting the process of resolution of inflammation through excretion of specialized pro-resolving mediators (resolvins D1/D2, E2, protecin D1).

SIGNIFICANCE

Through implementing the unique analytical pipeline we demonstrated that the addition of dialysis may preserve cardiac metabolism allowing for prolonged ESHP. This strategy has the potential to facilitate high-risk donor organs' reconditioning prior to transplantation.

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 the 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.

Ion suppression correction and normalization for non-targeted metabolomics

Ion suppression is a major problem in mass spectrometry (MS)-based metabolomics; it can dramatically decrease measurement accuracy, precision, and sensitivity. Here we report a method, the IROA TruQuant Workflow, that uses a stable isotope-labeled internal standard (IROA-IS) library plus companion algorithms to: 1) measure and correct for ion suppression, and 2) perform Dual MSTUS normalization of MS metabolomic data. We evaluate the method across ion chromatography (IC), hydrophilic interaction liquid chromatography (HILIC), and reversed-phase liquid chromatography (RPLC)-MS systems in both positive and negative ionization modes, with clean and unclean ion sources, and across different biological matrices. Across the broad range of conditions tested, all detected metabolites exhibit ion suppression ranging from 1% to >90% and coefficients of variation ranging from 1% to 20%, but the Workflow and companion algorithms are highly effective at nulling out that suppression and error. To demonstrate a routine application of the Workflow, we employ the Workflow to study ovarian cancer cell response to the enzyme-drug L-asparaginase (ASNase). The IROA-normalized data reveal significant alterations in peptide metabolism, which have not been reported previously. Overall, the Workflow corrects ion suppression across diverse analytical conditions and produces robust normalization of non-targeted metabolomic data.

Applications of Metabolomics and Lipidomics in the Neonatal Intensive Care Unit

The metabolome and lipidome comprise the thousands of molecular compounds in an organism. Molecular compounds consist of the upstream metabolic components of intracellular reactions or the byproducts of cellular pathways. Molecular and biochemical perturbations are associated with disorders in newborns and infants. The diagnosis of inborn errors of metabolism has relied on targeted metabolomics for several decades. Newer approaches offer the potential to identify novel biomarkers for common diseases of the newborn and infant. They may also elucidate novel predictive or diagnostic measures for a variety of health trajectories. Here, we review the relevance of the metabolome and lipidome for common disorders and highlight challenges and opportunities for future investigations.

Bile metabolic fingerprints distinguish biliary tract cancer from benign biliary diseases

BACKGROUND AND AIMS

Biliary tract cancers are aggressive gastrointestinal malignancies characterized by a dismal 5-year overall survival rate <20%. Current diagnostic modalities suffer from limitations regarding sensitivity and specificity. This study aimed to develop a bile metabolite-based platform for precise discrimination between malignant and benign biliary diseases.

APPROACH AND RESULTS

Samples were collected from 336 patients with biliary tract cancer or benign biliary diseases across 3 independent cohorts. Untargeted metabolic fingerprinting was performed on 300 bile samples using novel nanoparticle-enhanced laser desorption/ionization mass spectrometry. Subsequently, a diagnostic assay was developed based on the exploratory cohort using a selected bile metabolic biomarker panel, with performance evaluated in the validation cohort. Further external validation of disease-specific metabolites from bile samples was conducted in a prospective cohort (n = 36) using quantitative analysis. As a result, we established a novel bile-based assay, BileMet, for the rapid and precise detection of malignancies in the biliary tract system with an AUC of 0.891. We identified 6-metabolite biomarker candidates and discovered the critical role of the chenodeoxycholic acid glycine conjugate as a protective metabolite associated with biliary tract cancer.

CONCLUSIONS

Our findings confirmed the improved diagnostic capabilities of BileMet assay in a clinical setting. If applied, the BileMet assay enables intraoperative testing and fast medical decision-making for cases with suspected malignancy where brush cytology detection fails to support malignancy, ultimately reducing the economic burden by over 90%.

Leveraging zebrafish models for advancing radiobiology: Mechanisms, applications, and future prospects in radiation exposure research

Ionizing radiation (IR) represents a significant risk to human health and societal stability. To effectively analyze the mechanisms of IR and enhance protective strategies, the development of more sophisticated animal models is imperative. The zebrafish, with its high degree of genomic homology to humans and the capacity for whole-body optical visualization and high-throughput screening, represents an invaluable model for the study of IR. This review examines the benefits of utilizing zebrafish as a model organism for research on IR, emphasizing recent advancements and applications. It presents a comprehensive overview of the methodologies for establishing IR models in zebrafish, addresses current challenges, and discusses future development trends. This paper provide theoretical support for elucidating the mechanisms of IR injury and developing effective treatment strategies.

Progress in toxicogenomics to protect human health

Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose-response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment.

QuanFormer: A Transformer-Based Precise Peak Detection and Quantification Tool in LC-MS-Based Metabolomics

In metabolomic analysis based on liquid chromatography coupled with mass spectrometry, detecting and quantifying intricate objects is a massive job. Current peak picking methods still cause high rates of incorrectly picked peaks to influence the reliability and reproducibility of results. To address these challenges, we developed QuanFormer, a deep learning method based on object detection designed to accurately quantify peak signals. Our algorithm combines the feature extraction capabilities of convolutional neural networks (CNNs) with the global computation capability of Transformer architecture. Data training in QuanFormer by using nearly 20,000 annotated regions-of-interest (ROIs) ensures unique prediction via bipartite matching, achieving 96.5% of the average precision value on the test set. Even without retraining, QuanFormer achieves over 90% accuracy in distinguishing true from false peaks. Performance was further analyzed using visualization techniques applied to the encoder and decoder layers. We also demonstrated that QuanFormer could correct retention time shifts for peak alignment and generally surpass the existing methods, including MZmine 3 and PeakDetective, to obtain a larger number of picked peaks and higher accurate quantification. Finally, we also carried out metabolomic analysis in a clinical cohort of breast cancer patients and utilized QuanFormer to detect and quantify the potential biomarkers. QuanFormer is open-source and available at https://github.com/LinShuhaiLAB/QuanFormer.

Multiple omics analysis reveals the regulation of SIRT4 on lipid deposition and metabolism during the differentiation of bovine preadipocytes

The differentiation and lipid metabolism of preadipocytes are crucial processes in IMF deposition. Studies have demonstrated that SIRT4 plays essential roles in energy metabolism and redox homeostasis, with its expression being coordinately regulated by multiple transcription factors associated with energy and lipid metabolism. In this study, the findings of multiple omics analysis reveal that SIRT4 significantly up-regulates the expression of genes involved in adipogenesis and enhances the differentiation and lipid deposition of bovine preadipocytes. Furthermore, SIRT4 profoundly influences the expression pattern of metabolites by increasing the abundance of substances involved in lipid synthesis while decreasing those that promote lipid oxidative decomposition. Additionally, SIRT4 broadly up-regulates the expression levels of various lipid classes, including glycerolipids, glycerophospholipids, sphingolipids, and sterol lipids. These findings not only provide a theoretical basis for molecular breeding and genetic improvement in beef cattle, but also offer potential therapeutic approaches for energy homeostasis disorders and obesity.

Setting standards for brain collection procedures in metabolomic studies

Current metabolomics technologies can measure hundreds of chemical entities in tissue extracts with good reliability. However, long-recognized requirements to halt enzyme activities during the initial moments of sample preparation are usually overlooked, allowing marked postmortem shifts in levels of labile metabolites representing diverse pathways. In brain many such changes occur in a matter of seconds. These comments overview the concern, contrast representative studies, and specify approaches to consider as standards in the field going forward. Comparison with established metabolite signatures of in vivo brain is an essential validation step when implementing any collection method.

Aqueous humor metabolomic profiling identifies a distinct signature in pseudoexfoliation syndrome

Purpose

PEXS was first described in 1917, yet its etiology still needs clarification. An imbalance between oxidants and antioxidants plays a significant role. PEXS leads to various ocular complications, including increased risk during cataract surgery due to weak zonules, lens dislocation, and reduced visual outcomes. Our study investigates whether metabolomics can provide insights into this ocular pathology.

Methods

The study included 183 patients undergoing cataract surgery at Pauls Stradins Clinical University Hospital. 104 patients did not have PEXS, while 79 were diagnosed with the condition. Intraocular fluid samples from these patients were analyzed using targeted metabolite analysis, performed through HILIC liquid chromatography coupled with mass spectrometry detection.

Results

The aqueous humor of PEXS patients contains statistically significant higher levels of cystine (p < 0.001), citrulline (p < 0.001), phenylalanine (p = 0.041), tyrosine (p = 0.025), serine (p = 0.030), arginine (p = 0.017), lactic acid (p = 0.055), tryptophan (p = 0.055), and creatinine (p = 0.022). These results suggest a potential link to ferroptosis.

Conclusion

Ferroptosis is a form of programmed cell death characterized by iron-dependent LPO. The inhibition of the antiporter system Xc - leads to increased oxidative stress, suggesting that the changes seen in PEXS could be linked to ferroptosis. Our findings indicate that cysteine synthesis occurs via the transsulfation pathway, attributable to inhibiting the antiporter system Xc -. Treatment of pseudoexfoliation should lower the oxidative stress inside the anterior chamber by reducing the uptake of PUFAs, lower iron levels, and cysteine supplementation.

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.

Collection of optimizations for untargeted metabolomics analysis of Leishmania promastigotes using gas chromatography-mass spectrometry

There is no consensus in the literature regarding the ideal protocol for obtaining and preparing cell samples for untargeted metabolomics. Nevertheless, the procedures must be carefully evaluated for proper and reliable results for each organism under study. This work proposes a novel protocol for determining intracellular metabolites in Leishmania promastigotes and is fully optimized for application in conjunction with gas chromatography-mass spectrometry platforms. Sample harvesting consisted of stopping metabolic activity by placing the parasite cells in a dry ice bath and removing extracellular interferants with two wash steps using cold PBS. The extraction is carried out with 1.0x108 promastigotes per sample using a mixture of cold 1:1 methanol:water and ultrasound mixing (1 min at 30 % power). Dried extracts were derivatized by oximation (at room temperature for 90 min), followed by silylation (at 40 °C for 30 min). The method developed here can cover a wide range of the Leishmania parasite metabolome, including amino acids and derivatives, organic and fatty acids, carbohydrates and derivatives, and steroids.

Integrating the milk microbiome signatures in mastitis: milk-omics and functional implications

Mammalian milk contains a variety of complex bioactive and nutritional components and microorganisms. These microorganisms have diverse compositions and functional roles that impact host health and disease pathophysiology, especially mastitis. The advent and use of high throughput omics technologies, including metagenomics, metatranscriptomics, metaproteomics, metametabolomics, as well as culturomics in milk microbiome studies suggest strong relationships between host phenotype and milk microbiome signatures in mastitis. While single omics studies have undoubtedly contributed to our current understanding of milk microbiome and mastitis, they often provide limited information, targeting only a single biological viewpoint which is insufficient to provide system-wide information necessary for elucidating the biological footprints and molecular mechanisms driving mastitis and milk microbiome dysbiosis. Therefore, integrating a multi-omics approach in milk microbiome research could generate new knowledge, improve the current understanding of the functional and structural signatures of the milk ecosystem, and provide insights for sustainable mastitis control and microbiome management.

Optimized MALDI2-Mass Spectrometry Imaging for Stable Isotope Tracing of Tissue-Specific Metabolic Pathways in Mice

Spatial stable isotope tracing metabolic imaging is a cutting-edge technique designed to investigate tissue-specific metabolic functions and heterogeneity. Traditional matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI) techniques often struggle with low coverage of low-molecular-weight (LMW) metabolites, which are often crucial for spatial metabolic studies. To address this, we developed a high-coverage spatial isotope tracing metabolic method that incorporates optimized matrix selection, sample preparation protocols, and enhanced post-ionization (MALDI2) techniques. We employed this approach to mouse kidney, brain, and breast tumors to visualize the spatial dynamics of metabolic flow. Our results revealed diverse regional distributions of nine labeled intermediates derived from 13C6-glucose across glycolysis, glycogen metabolism, and the tricarboxylic acid (TCA) cycle in kidney tissues. In brain sections, we successfully mapped six intermediates from the TCA cycle and glutamate-glutamine (Glu-Gln) cycle simultaneously in distinct neurological regions. Furthermore, in breast cancer tumor tissues, our approach facilitated the mapping of nine metabolic intermediates in multiple pathways, including glycolysis, the pentose phosphate pathway (PPP), and the TCA cycle, illustrating metabolic heterogeneity within the tumor microenvironment. This methodology enhances metabolite coverage, enabling more comprehensive imaging of isotope-labeled metabolites and opening new avenues for exploring the metabolic landscape in various biological contexts.

Single-Cell Mass Spectrometry Studies of Secondary Drug Resistance of Tumor Cells

Patients with epidermal growth factor receptor mutant nonsmall cell lung cancer (NSCLC) often fail to treat gefitinib because of secondary drug resistance. The development of tumor drug resistance is closely related to variations in cancer cell metabolism. Single-cell metabolomics analysis can provide unique information about tumor drug resistance. Herein, we constructed a platform to study the secondary resistance of tumor cells based on single-cell metabolomics (sSRTC-scM). A gefitinib-resistant NSCLC cell line (PC9GR) was constructed by increasing the dose step by step. The metabolic profiles of parental PC9 cells and PC9GR cells with different drug resistance levels were detected by intact living-cell electrolaunching ionization mass spectrometry at the single-cell level. The data were analyzed by statistical methods such as t-SNE, variance, volcano plot, heat map, and metabolic pathway analysis. Using this platform, we found that the metabolic fingerprints of PC9GR cells can evaluate drug resistance degrees. The metabolic fingerprints continue to be altered with the increase of drug resistance. We revealed 19 metabolic markers of secondary resistance by variance analysis and clarified that the glycerophospholipid metabolic pathway of PC9GR cells changed significantly. In addition, we found that with the increase in drug resistance levels, the heterogeneity of single-cell metabolism became greater and the number of cells with weak drug resistance gradually decreased. This phenomenon can be utilized to illustrate the drug resistance degrees of PC9GR cells. This study provides diagnostic markers for evaluating the drug resistance of tumors and gives new insight into overcoming the secondary resistance of tumors.

Bioinert UHPLC system improves sensitivity and peak shapes for ionic metabolites

The analysis of ionic compounds by liquid chromatography is challenging due to the interaction of analytes with the metal surface of the instrument and the column, leading to poor peak shape and decreased sensitivity. The use of bioinert materials in the chromatographic system minimizes these unrequired interactions. In this work, the ultrahigh-performance liquid chromatography (UHPLC) with bioinert components was connected to a high-resolution mass spectrometer to develop a method for untargeted metabolomic analysis. 81 standards of metabolites were used for the development and optimization of the method. In comparison to the conventional chromatographic system, the application of bioinert technology resulted in significantly improved peak shapes and increased sensitivity, especially for metabolites containing phosphate groups. The calibration curves were constructed for the evaluation of the method performance, showing a wide dynamic range, low limit of detection, and linear regression coefficients higher than 0.99 for all standards. The optimized method was applied to the analysis of NIST SRM 1950 human plasma, which allowed the detection of 156 metabolites and polar lipids based on the combination of mass accuracy in the full-scan mass spectra in both polarity modes, characteristic fragment ions in MS/MS, and logical chromatographic behavior leading to the high confidence level of annotation/identification. We have demonstrated an improvement in the peak shapes and sensitivity of ionic metabolites using bioinert technology, which indicates the potential for the analysis of other ionic compounds, e.g., molecules containing phosphate groups.

CRISPR/Cas9 editing of two adenine phosphoribosyl transferase coding genes reveals the functional specialization of adenine salvage proteins in common bean

Adenine metabolism is important for common bean (Phaseolus vulgaris L.) productivity since this legume uses ureides derived from the oxidation of purine nucleotides as its primary nitrogen storage molecules. Purine nucleotides are produced from de novo synthesis or through salvage pathways. Adenine phosphoribosyl transferase (APRT) is the enzyme dedicated to adenine nucleobase salvage for nucleotide synthesis, but it can also convert active cytokinin bases into their inactive nucleotide forms. In common bean, APRT is encoded by four genes. Gene expression analysis, biochemical properties, and subcellular location indicated functional differences among the common bean APRT isoforms. CRISPR/Cas9 targeted down-regulation of two of the four PvAPRTs followed by metabolomic and physiological analyses of targeted hairy roots revealed that, although the two proteins have redundant functions, PvAPRT1 mostly participated in the salvage of adenine, whereas PvAPRT5 was the predominant form in the regulation of cytokinin homeostasis and stress responses with a high impact in root and nodule growth.

Genome-wide association study reveals shared and distinct genetic architecture of fatty acids and oxylipins in the Hispanic Community Health Study/Study of Latinos

Bioactive fatty acid-derived oxylipin molecules play key roles mediating inflammation and oxidative stress. Circulating levels of fatty acids and oxylipins are influenced by environmental and genetic factors; characterizing the genetic architecture of bioactive lipids could yield new insights into underlying biology. We performed a genome-wide association study (GWAS) of 81 fatty acids and oxylipins in 11,584 Hispanic Community Health Study/Study of Latinos (HCHS/SOL) participants with genetic and lipidomic data measured at study baseline (58.6% female, mean age = 46.1 years (standard deviation 13.8)). Additionally, given the effects of central obesity on inflammation, we examined interactions with waist circumference using two-degree-of-freedom joint tests. Thirty-three of the 81 oxylipins and fatty acids were significantly heritable (heritability range: 0-32.7%). Forty (49.4%) oxylipins and fatty acids had at least one genome-wide significant (p < 6.94E-11) variant resulting in 19 independent genetic loci. Six loci (lead variant minor allele frequency [MAF] range: 0.08-0.50), including desaturase-encoding FADS and OATP1B1 transporter protein-encoding SLCO1B1, exhibited associations with two or more fatty acids and oxylipins. At several of these loci, there was evidence of colocalization of the top variant across fatty acids and oxylipins. The remaining loci were only associated with one oxylipin or fatty acid and included several CYP loci. We also identified an additional rare variant (MAF = 0.002) near CARS2 in two-degree-of-freedom tests. Our analyses revealed shared and distinct genetic architecture underlying fatty acids and oxylipins, providing insights into genetic factors and motivating work to characterize these compounds and elucidate their roles in disease.

Tailoring Super-Performed Chemo-Sensor via Simulation-Modeling and MEMS-Screening

Chemo-sensor designing involves a time-consuming trial-and-error screening process, which commonly cannot lead to optimal S4R features (Sensitivity, Selectivity, Speed, Stability, and Reversibility). Due to strong path dependence on reported groups/mechanisms, conventional chemo-sensors often fail to meet critical application demands, especially in achieving high reversibility without compromising other features. Here, a three-step screen and design strategy is developed for gaining customized chemo-sensors, through Structure modeling; MEMS (Micro Electro Mechanical Systems) analysis, and Performance verification. With such a strategy, the coordination hanging anion mechanism is screened out for reversible nerve agent detection and shows reversible emission enhancement by 25.8 times with DCP, ultrasensitive vapor phase detection (5.7 ppb), and rapid response(10 s) and recovery speed (20 s). Such tailored designing strategy for new organic chemo-sensors will probably play an important role in developing high-performance sensing system in the future.

Omics based technology application in poultry meat research

Omics techniques, including genomics, transcriptomics, proteomics, metabolomics, and lipidomics, analyze entire sets of biological molecules to seek comprehensive knowledge on a particular phenotype. These approaches have been extensively utilized to identify both biomarkers and biological mechanisms for various physiological conditions in livestock and poultry. The purpose of this symposium was not only to focus on how recent omics technologies can be used to gather, integrate, and interpret data produced by various methodologies in poultry research, but also to highlight how omics and bioinformatics have increased our understanding of poultry meat quality problems and other complex traits. This Poultry Science Association symposium paper includes 5 sections that cover: 1) functional annotation of cis-regulatory elements in the genome informs genetic control of complex traits in poultry, 2) mass spectrometry for proteomics, metabolomics, and lipidomics, 3) proteomic approaches to investigate meat quality, 4) spatial transcriptomics and metabolomics studies of wooden breast disease, and 5) multiomics analyses on chicken meat quality and spaghetti meat. These topics provide insights into the molecular components that contribute to the structure, function, and dynamics of the underlying mechanisms influencing meat quality traits, including chicken breast myopathies. This information will ultimately contribute to improving the quality and composition of poultry products.

Differential impact of impaired steryl ester biosynthesis on the metabolome of tomato fruits and seeds

Steryl esters (SE) are a storage pool of sterols that accumulates in cytoplasmic lipid droplets and helps to maintain plasma membrane sterol homeostasis throughout plant growth and development. Ester formation in plant SE is catalyzed by phospholipid:sterol acyltransferase (PSAT) and acyl-CoA:sterol acyltransferase (ASAT), which transfer long-chain fatty acid groups to free sterols from phospholipids and acyl-CoA, respectively. Comparative mass spectrometry-based metabolomic analysis between ripe fruits and seeds of a tomato (Solanum lycopersicum cv Micro-Tom) mutant lacking functional PSAT and ASAT enzymes (slasat1xslpsat1) shows that disruption of SE biosynthesis has a differential impact on the metabolome of these organs, including changes in the composition of free and glycosylated sterols. Significant perturbations were observed in the fruit lipidome in contrast to the mild effect detected in the lipidome of seeds. A contrasting response was also observed in phenylpropanoid metabolism, which is down-regulated in fruits and appears to be stimulated in seeds. Comparison of global metabolic changes using volcano plot analysis suggests that disruption of SE biosynthesis favours a general state of metabolic activation that is more evident in seeds than fruits. Interestingly, there is an induction of autophagy in both tissues, which may contribute along with other metabolic changes to the phenotypes of early seed germination and enhanced fruit tolerance to Botrytis cinerea displayed by the slasat1xslpsat1 mutant. The results of this study reveal unreported connections between SE metabolism and the metabolic status of plant cells and lay the basis for further studies aimed at elucidating the mechanisms underlying the observed effects.

An orally available P1'-5-fluorinated Mpro inhibitor blocks SARS-CoV-2 replication without booster and exhibits high genetic barrier

We identified a 5-fluoro-benzothiazole-containing small molecule, TKB272, through fluorine-scanning of the benzothiazole moiety, which more potently inhibits the enzymatic activity of SARS-CoV-2's main protease (Mpro) and more effectively blocks the infectivity and replication of all SARS-CoV-2 strains examined including Omicron variants such as SARS-CoV-2XBB1.5 and SARS-CoV-2EG.5.1 than two Mpro inhibitors: nirmatrelvir and ensitrelvir. Notably, the administration of ritonavir-boosted nirmatrelvir and ensitrelvir causes drug-drug interactions warranting cautions due to their CYP3A4 inhibition, thereby limiting their clinical utility. When orally administered, TKB272 blocked SARS-CoV-2XBB1.5 replication without ritonavir in B6.Cg-Tg(K18-hACE2)2-Prlmn/J-transgenic mice, comparably as did ritonavir-boosted nirmatrelvir. When the ancestral SARS-CoV-2 was propagated with nirmatrelvir in vitro, a highly nirmatrelvir-resistant E166V-carrying variant (SARS-CoV-2E166V-P14) readily emerged by passage 14; however, when propagated with TKB272, no variants emerged by passage 25. SARS-CoV-2E166V showed some cross-resistance to TKB272 but was substantially sensitive to the compound. X-ray structural analyses and mass-spectrometric data showed that the E166V substitution disrupts the critical dimerization-initiating Ser1'-E166 interactions, thereby limiting nirmatrelvir's Mpro inhibition but that TKB272 nevertheless forms a tight binding with Mpro's catalytic active sight even in the presence of the E166V substitution. TKB272 shows no apparent genotoxicity as tested in the micro-Ames test. Highly potent TKB272 may serve as a COVID-19 therapeutic, overcome resistance to existing Mpro inhibitors.

Systematic application of UPLC-Q-ToF-MS/MS coupled with chemometrics for the identification of natural food pigments from Davidson plum and native currant

This study investigates the potential of Australian Traditional foods as novel sources of natural colourants for food applications, employing untargeted metabolomics and chemometrics. Two native species were analysed: Davidson plum and native currant. The species were quantitatively assessed for colour properties using the CIELAB colour system in conjunction with Ultra Performance Liquid Chromatography-Quadrupole Time of Flight Tandem Mass Spectrometry (UPLC-Q-ToF-MS/MS). The results highlight diverse phenolic, flavonoid, and significant anthocyanin levels in Davidson plum and native currant, contributing to their robust red hues, comparable to commercial blueberry standards. Davidson plum and native currant exhibited high phenolic, flavonoid, and anthocyanin levels, contributing to vibrant red hues and significant bioactivity. Compared to blueberry, these species showed greater redness (a*) and chroma. Native currant demonstrated the highest phenolic content (146.73 mg g-1), anthocyanin content (14.48 mg g-1), and antioxidant activity (95.48 μmol Trolox equivalents/g). The chemometric analysis identified 46 key pigment metabolites, including anthocyanins and flavonoids, directly correlating to observed colour properties. UPLC-Q-ToF-MS/MS combined with CIELAB colourimetry facilitated pigment identification and colour analysis. These findings position Davidson plum and native currant as promising natural food colourants and functional ingredients. Additionally, the study underscores the efficacy of integrating chemometric analysis with CIELAB and UPLC-Q-ToF-MS/MS methodologies for pinpointing specific metabolites that influence the colour properties of these Traditional foods. This approach facilitates a deeper understanding of how indigenous Australian bushfoods can be innovatively incorporated into the food industry, aligning with consumer demand for natural and sustainable food options.

Metabolic profiling of pea (Pisum sativum) cultivars in changing environments: Implications for nutritional quality in animal feed

Field pea seeds have long been recognized as valuable feed ingredients for animal diets, due to their high-quality protein and starch digestibility. However, the chemical composition of pea cultivars can vary across different growing locations, consequently impacting their nutrient profiles. This study employs untargeted metabolomics in conjunction with the quantification of fatty acids and amino acids to explore the influence of three different growing locations in Spain (namely Andalusia, Aragon and Asturias), on the nutritional characteristics of seeds of various pea cultivars. Significant interactions between cultivar and environment were observed, with 121 metabolites distinguishing pea profiles. Lipids, lipid-like molecules, phenylpropanoids, polyketides, carbohydrates, and amino acids were the most affected metabolites. Fatty acid profiles varied across locations, with higher C16:0, C18:0, and 18:1 n-9 concentration in Aragón, while C18:2 n-6 predominated in Asturias and C18:3 n-3 in Andalusia. Amino acid content was also location-dependent, with higher levels in Asturias. These findings underscore the impact of environmental factors on pea metabolite profiles and emphasize the importance of selecting pea cultivars based on specific locations and animal requirements. Enhanced collaboration between research and industry is crucial for optimizing pea cultivation for animal feed production.

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.

A Novel Liquid Chromatography-Tandem Mass Spectrometry Method to Quantify Tryptophan and Its Major Metabolites in Serum to Support Biomarker Studies in Patients with Cancer Undergoing Immunotherapy

Tryptophan (TRP) is an essential amino acid crucial for the production of many bioactive compounds. Disturbances in TRP metabolism have been revealed in various diseases, many of which are closely related to the immune system. In recent years, we have focused on finding blood-based biomarkers of successful immunotherapy in cancer. Thus, we aimed to develop a robust liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for TRP and its metabolites that could be used in biomarker studies. Although analyzing TRP derivatives in biological matrices is not a new topic, we encountered multiple challenges during method development. One of them was the phenomenon of cross-interferences between the analyzed molecules, which has not been explored in most published papers. We noticed that injecting a pure single-compound solution often generated a signal in the other compounds' MS/MS channels. Specifically, TRP generated unexpected peaks in the channel for kynurenine, kynurenic acid, and xanthurenic acid, while kynurenine generated peaks in the channel for kynurenic acid. We also recorded a mutual cross-talk between kynurenine and isotope-labeled TRP. Different origins of the observed cross-signal contribution were proposed. This paper draws attention to investigating cross-interferences in LC-MS/MS, especially when structurally related compounds will be analyzed. Despite all the challenges, the method was successfully validated according to international guidelines (EMA/ICH), and its applicability was confirmed in a pilot study including 20 patients with lung cancer undergoing chemoimmunotherapy.

Congenital Zika Syndrome: Insights from Integrated Proteomic and Metabolomic Analysis

Background: In this study, we investigated the role of extracellular vesicles (EVs) in the pathogenesis of Congenital Zika Syndrome (CZS). Previous studies have highlighted the role of EVs in intercellular communication and the modulation of biological processes during viral infections, motivating our in-depth analysis. Our objective was to identify specific molecular signatures in the EVs of patients with CZS, focusing on their potential as biomarkers and on cellular pathways affected by the infection. Methods: We conducted advanced proteomic and metabolomic analyses using mass spectrometry for protein and metabolite identification. EVs were isolated from CZS patient samples and control groups using Izon qEV size-exclusion chromatography columns. Results: The analyzed EVs presented distinct molecular profiles in patients with CZS. Proteomic analysis revealed significant alterations in specific proteins, suggesting involvement in the PI3K-AKT-mTOR pathway, while metabolomics highlighted metabolites related to critical processes in Zika virus pathogenesis. These findings suggest a key role for the PI3K-AKT-mTOR pathway in regulating cellular processes during infection and indicate the involvement of EVs in intercellular communication. Additionally, the results identified potential biomarkers capable of aiding early diagnosis and assessing disease progression. Conclusions: This study demonstrates that EVs play a crucial role in intercellular communication during Zika virus infection. The identification of specific alterations in the PI3K-AKT-mTOR pathway highlights a possible therapeutic target, providing new opportunities for the development of more effective treatment strategies for CZS. Our findings significantly advance the understanding of CZS and underscore the need for further investigations using advanced techniques to validate and explore these potential molecular targets.

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.

Targeted and untargeted cross-sectional study for sex-specific identification of plasma biomarkers of COVID-19 severity

Coronavirus disease 2019 is a highly contagious respiratory illness caused by the coronavirus SARS-CoV-2. Symptoms can range from mild to severe and typically appear 2-14 days after virus exposure. While vaccination has significantly reduced the incidence of severe complications, strategies for the identification of new biomarkers to assess disease severity remains a critical area of research. Severity biomarkers are essential for personalizing treatment strategies and improving patient outcomes. This study aimed to identify sex-specific biomarkers for COVID-19 severity in a Chilean population (n = 123 female, n = 115 male), categorized as control, mild, moderate, or severe. Data were collected using clinical biochemistry parameters and mass spectrometry-based metabolomics and lipidomics to detect alterations in plasma cytokines, metabolites, and lipid profiles related to disease severity. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed to select significant characteristic features for each group. The results revealed distinct biomarkers for males and females. In males, COVID-19 severity of was associated with inflammation parameters, triglycerides content, and phospholipids profiles. For females, liver damage parameters, triglycerides content, cholesterol derivatives, and phosphatidylcholine were identified as severity biomarkers. For both sexes, most of the biomarker combinations evaluated got areas under the ROC curve greater than 0.8 and low prediction errors. These findings suggest that sex-specific biomarkers can help differentiate the levels of COVID-19 severity, potentially aiding in the development of tailored treatment approaches.

Metabolomic Profiling Reveals Biomarkers in Coronary Heart Disease Comorbidity

Background and Aims: Coronary heart disease (CHD), hypertension (HTN), depression (Dep), and Type 2 diabetes mellitus (T2DM) are often comorbid, resulting in an exacerbated patient condition and worsened prognosis. A lack of systematic metabolomic studies on comorbidities of CHD remains. Therefore, comprehensive metabolomic-based evaluation of comorbidities of CHD is necessary. Methods and Results: In the current study, 169 healthy subjects, 149 CHD subjects, 107 CHD + HTN subjects, 126 CHD + Dep subjects, and 58 CHD + T2DM subjects were recruited. Gas chromatography-mass spectrometry was used for metabolite determination, and multivariate statistical analysis was conducted to identify metabolites that are differentially expressed with the comorbidities of CHD. There were 9, 16, 14, and 10 metabolites identified in the healthy and CHD group, the CHD and CHD + HTN group, the CHD and CHD + Dep group, and the CHD and CHD + T2DM group, respectively. Six metabolic pathways were affected, involving starch and sucrose metabolism; fructose and mannose metabolism; citrate cycle; alanine, aspartate, and glutamate metabolism; fatty acid biosynthesis; and glycolysis. Conclusion: Our study has systematically elucidated the metabolic changes underlying the comorbidities of CHD, thereby providing insight into the mechanisms associated with these alterations.

Application of machine learning for mass spectrometry-based multi-omics in thyroid diseases

Thyroid diseases, including functional and neoplastic diseases, bring a huge burden to people's health. Therefore, a timely and accurate diagnosis is necessary. Mass spectrometry (MS) based multi-omics has become an effective strategy to reveal the complex biological mechanisms of thyroid diseases. The exponential growth of biomedical data has promoted the applications of machine learning (ML) techniques to address new challenges in biology and clinical research. In this review, we presented the detailed review of applications of ML for MS-based multi-omics in thyroid disease. It is primarily divided into two sections. In the first section, MS-based multi-omics, primarily proteomics and metabolomics, and their applications in clinical diseases are briefly discussed. In the second section, several commonly used unsupervised learning and supervised algorithms, such as principal component analysis, hierarchical clustering, random forest, and support vector machines are addressed, and the integration of ML techniques with MS-based multi-omics data and its application in thyroid disease diagnosis is explored.

Metabolomics for enhanced clinical understanding of inflammatory bowel disease

Metabolomics is an emerging field involving the systematic identification and quantification of numerous metabolites in biological samples. Precision medicine applies multiomics systems biology to individual patients for reliable diagnostic classification, disease monitoring, and treatment. Multiomics systems biology encompasses genomics, transcriptomics, proteomics, epigenomics, and metabolomics. Therefore, metabolomic techniques could be highly valuable for future clinical decision-making. This review provides a technical overview of two commonly used techniques for metabolomics measurements: mass spectrometry (MS) and proton nuclear magnetic resonance (1H NMR) spectroscopy. We also discuss recent clinical advances in these techniques. Individuals with inflammatory bowel disease (IBD) exhibit significant variability in prognosis and response to treatment. Since both genetic predisposition and environmental factors contribute to this condition, targeting the metabolome may provide key insights for distinguishing and profiling patients with different clinical needs. Additionally, the considerable overlap in the clinical presentation of various disease subtypes emphasizes the need for enhanced diagnostic methods to improve patient care.

Altered Cellular Metabolism Is a Consequence of Loss of the Ataxia-Linked Protein Sacsin

Mitochondrial dysfunction is implicated in the pathogenesis of the neurological condition autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS), yet precisely how the mitochondrial metabolism is affected is unknown. Thus, to better understand changes in the mitochondrial metabolism caused by loss of the sacsin protein (encoded by the SACS gene, which is mutated in ARSACS), we performed mass spectrometry-based tracer analysis, with both glucose- and glutamine-traced carbon. Comparing the metabolite profiles between wild-type and sacsin-knockout cell lines revealed increased reliance on aerobic glycolysis in sacsin-deficient cells, as evidenced by the increase in lactate and reduction of glucose. Moreover, sacsin knockout cells differentiated towards a neuronal phenotype had increased levels of tricarboxylic acid cycle metabolites relative to the controls. We also observed disruption in the glutaminolysis pathway in differentiated and undifferentiated cells in the absence of sacsin. In conclusion, this work demonstrates consequences for cellular metabolism associated with a loss of sacsin, which may be relevant to ARSACS.

Lactate dehydrogenase B noncanonically promotes ferroptosis defense in KRAS-driven lung cancer

Ferroptosis is an oxidative, non-apoptotic cell death frequently inactivated in cancer, but the underlying mechanisms in oncogene-specific tumors remain poorly understood. Here, we discover that lactate dehydrogenase (LDH) B, but not the closely related LDHA, subunits of active LDH with a known function in glycolysis, noncanonically promotes ferroptosis defense in KRAS-driven lung cancer. Using murine models and human-derived tumor cell lines, we show that LDHB silencing impairs glutathione (GSH) levels and sensitizes cancer cells to blockade of either GSH biosynthesis or utilization by unleashing KRAS-specific, ferroptosis-catalyzed metabolic synthetic lethality, culminating in increased glutamine metabolism, oxidative phosphorylation (OXPHOS) and mitochondrial reactive oxygen species (mitoROS). We further show that LDHB suppression upregulates STAT1, a negative regulator of SLC7A11, thereby reducing SLC7A11-dependent GSH metabolism. Our study uncovers a previously undefined mechanism of ferroptosis resistance involving LDH isoenzymes and provides a novel rationale for exploiting oncogene-specific ferroptosis susceptibility to treat KRAS-driven lung cancer.

Effective data visualization strategies in untargeted metabolomics

Covering: 2014 to 2023 for metabolomics, 2002 to 2023 for information visualizationLC-MS/MS-based untargeted metabolomics is a rapidly developing research field spawning increasing numbers of computational metabolomics tools assisting researchers with their complex data processing, analysis, and interpretation tasks. In this article, we review the entire untargeted metabolomics workflow from the perspective of information visualization, visual analytics and visual data integration. Data visualization is a crucial step at every stage of the metabolomics workflow, where it provides core components of data inspection, evaluation, and sharing capabilities. However, due to the large number of available data analysis tools and corresponding visualization components, it is hard for both users and developers to get an overview of what is already available and which tools are suitable for their analysis. In addition, there is little cross-pollination between the fields of data visualization and metabolomics, leaving visual tools to be designed in a secondary and mostly ad hoc fashion. With this review, we aim to bridge the gap between the fields of untargeted metabolomics and data visualization. First, we introduce data visualization to the untargeted metabolomics field as a topic worthy of its own dedicated research, and provide a primer on cutting-edge visualization research into data visualization for both researchers as well as developers active in metabolomics. We extend this primer with a discussion of best practices for data visualization as they have emerged from data visualization studies. Second, we provide a practical roadmap to the visual tool landscape and its use within the untargeted metabolomics field. Here, for several computational analysis stages within the untargeted metabolomics workflow, we provide an overview of commonly used visual strategies with practical examples. In this context, we will also outline promising areas for further research and development. We end the review with a set of recommendations for developers and users on how to make the best use of visualizations for more effective and transparent communication of results.

The oncomicrobiome: New insights into microorganisms in cancer

The discoveries of the oncomicrobiome (intratumoral microbiome) and oncomicrobiota (intratumoral microbiota) represent significant advances in tumor research and have rapidly become of key interest to the field. Within tumors, microorganisms such as bacteria, fungi, viruses, and archaea form the oncomicrobiota and are primarily found within tumor cells, immunocytes, and the intercellular matrix. The oncomicrobiome exhibits marked heterogeneity and is associated with tumor initiation, progression, metastasis, and treatment response. Interactions between the oncomicrobiome and the immune system can modulate host antitumor immunity, influencing the efficacy of immunotherapies. Oncomicrobiome research also faces numerous challenges, including overcoming methodological issues such as low target abundance, susceptibility to contamination, and biases in sample handling and analysis methods across different studies. Furthermore, studies of the oncomicrobiome may be confounded by baseline differences in microbiomes among populations driven by both environmental and genetic factors. Most studies to date have revealed associations between the oncomicrobiome and tumors, but very few have established mechanistic links between the two. This review introduces the relevant concepts, detection methods, sources, and characteristics of the oncomicrobiome. We then describe the composition of the oncomicrobiome in common tumors and its role in shaping the tumor microenvironment. We also discuss the current problems and challenges to be overcome in this rapidly progressing field.

Untargeted mutagenesis of brassinosteroid receptor SbBRI1 confers drought tolerance by altering phenylpropanoid metabolism in Sorghum bicolor

Drought is a critical issue in modern agriculture; therefore, there is a need to create crops with drought resilience. The complexity of plant responses to abiotic stresses, particularly in the field of brassinosteroid (BR) signalling, has been the subject of extensive research. In this study, we unveil compelling insights indicating that the BRASSINOSTEROID-INSENSITIVE 1 (BRI1) receptor in Arabidopsis and Sorghum plays a critical role as a negative regulator of drought responses. Introducing untargeted mutation in the sorghum BRI1 receptor (SbBRI1) effectively enhances the plant's ability to withstand osmotic and drought stress. Through DNA Affinity Purification sequencing (DAP-seq), we show that the sorghum BRI1-EMS-SUPPRESSOR 1 (SbBES1) transcription factor, a downstream player of the BR signalling, binds to a conserved G-box binding motif, and it is responsible for regulating BR homeostasis, as its Arabidopsis ortholog AtBES1. We further characterized the drought tolerance of sorghum bri1 mutants and decipher SbBES1-mediated regulation of phenylpropanoid pathway. Our findings suggest that SbBRI1 signalling serves a dual purpose: under normal conditions, it regulates lignin biosynthesis by SbBES1, but during drought conditions, BES1 becomes less active, allowing the activation of the flavonoid pathway. This adaptive shift improves the photosynthetic rate and photoprotection, reinforcing crop adaptation to drought.

Invited review: Research on ruminal biohydrogenation-Achievements, gaps in knowledge, and future approaches from the perspective of dairy science

Scientific knowledge about ruminal biohydrogenation (BH) has improved greatly since this metabolic process was empirically confirmed in 1951. For years, BH had mostly been perceived as a process to be avoided to increase the postruminal flow of UFA from the diet. Two milestones changed this perception and stimulated great interest in BH intermediates themselves: In 1987, the in vitro anticarcinogenic properties of CLA were described, and in 2000, the inhibition of milk fat synthesis by trans-10,cis-12 CLA was confirmed. Since then, numerous BH metabolites have been described in small and large ruminants, and the major deviation from the common BH pathway (i.e., the trans-10 shift) has been reasonably well established. However, there are some less well-characterized alterations, and the comprehensive description of new BH intermediates (e.g., using isotopic tracers) has not been coupled with research on their biological effects. In this regard, the low quality of some published fatty acid profiles may also be limiting the advance of knowledge in BH. Furthermore, although BH seems to no longer be considered a metabolic niche inhabited by a few bacterial species with a highly specific metabolic capability, researchers have failed to elucidate which specific microbial groups are involved in the process and the basis for alterations in BH pathways (i.e., changes in microbial populations or their activity). Unraveling both issues may be beneficial for the description of new microbial enzymes involved in ruminal lipid metabolism that have industrial interest. From the perspective of dairy science, other knowledge gaps that require additional research in the coming years are evaluation of the relationship between BH and feed efficiency and enteric methane emissions, as well as improving our understanding of how alterations in BH are involved in milk fat depression. Addressing these issues will have relevant practical implications in dairy science.

Untargeted metabolomics unravels the effects of ginkgolide B-producing Lactiplantibacillus plantarum and co-induced fermentation of ginkgo kernel juice and their underlying vascular endothelial cell protection activity

The objective of this study was to investigate the fermentation mechanism of ginkgo kernel juice (GKJ) under unfermented (Group A), Ginkgolide B (GB)-producing Lactiplantibacillus plantarum fermented (Group B), and co-induced fermented (Group C) conditions. The conditions were optimized and further evaluated for their vascular endothelial cell protective effects in vitro. The co-induced fermented GKJ group extensively promoted GB and total phenol contents, reaching 109.94 and 599.57 μg/mL, respectively. While pH declined from 5.90 to 3.42 during fermentation, the highest total viable count (8.85 log CFU/mL) was detected at 16 h in the L. plantarum group. The co-induced group recorded the highest total phenol contents (594.05 μg/mL) and markedly induced the survival rate, reactive oxygen species formation, and lactate dehydrogenase assay cytotoxicity of H2O2-induced human umbilical vein endothelial cells. An untargeted metabolomics analysis identified 2633 metabolites in the groups. The principal component and orthogonal partial least squares discriminant score plots showed a clear metabolite distinction among the fermentation groups. From the Kyoto Encyclopedia of Genes and Genomes analysis, 309 differential accumulated metabolites (DAMs) were up-regulated and 604 were down-regulated in the A vs. B group, while 702 downregulated and 304 upregulated DAMs were exhibited in the B vs. C group. These DAMs were primarily lipids and lipid-like molecules, organic acids and their derivatives, organoheterocyclic compounds, organic oxygen compounds, benzenoids, phenylpropanoids and polyketides, and unclassified compounds at the superclass level. Overall, the results indicated that L. plantarum and co-induced fermentation improved the cell protection efficacy of GKJ, showing excellent potential for drug delivery applications.

A strategy to comprehensively and quickly identify the chemical constituents in Wuteng tablets by ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry

Population growth and improved industrialization have led to a sharp rise in the demand for plant medicine. In recent years, there has been a general concern about developing new medicinal resources, cutting down on pharmaceutical waste, and discovering new, effective components of traditional Chinese medicine. A novel medication called Wuteng tablets is made from Schisandra chinensis stems and shows promise as a treatment for Alzheimer's disease. This work is the first development of an overall identification technique based on ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UHPLC-Q/TOF-MS). Using the MS-DIAL integrated informatics platform and UNIFI software, the chemical components of Wuteng tablets were identified, and the amount of lignin in the tablets was ascertained. This study will identify the chemical components of such medications, aid in the development and utilization of medicinal plant resources, and serve as a foundation for the analysis of the components of their biopharmaceutical origin.

Comprehensive lipidomic insights of differentially accumulating lipids in large pigmented rice sprout collection and the changes in the starch composition upon germination

Germination represents a vital bioprocess characterized by numerous biochemical transformations that significantly influence the nutritional characteristics of rice. The mobilization of starch and lipids during germination plays a pivotal role in altering the dietary profile of rice, thus potentially addressing the nutritional requirements of populations heavily reliant on rice as a staple food. To explore this potential, a comprehensive analysis encompassing lipidomics and starch composition was conducted on a diverse collection of pigmented rice sprouts. High-resolution mass spectrometry unveiled substantial shifts in the lipidome of pigmented rice sprouts, showcasing a notable enrichment in carotenoids and unsaturated triglycerides, with potential human health benefits. Notably, purple rice sprouts exhibited heightened levels of alpha- and beta-carotene. Analysis of starch composition revealed slight changes in amylose and amylopectin content; however, a consistent increase in digestible carbohydrates was observed across all rice varieties. Germination also led to a reduction in resistant starch content, with purple rice sprouts demonstrating a pronounced two-fold decrease (p < 0.05). These changes were corroborated by a 1.33% decrease in gelatinization enthalpy and a 0.40% reduction in the melting of the amylose-lipid complex. Furthermore, pasting property analysis indicated a substantial 42% decrease in the complexation index post-germination. We posit that the insights garnered from this study hold significant promise for the development of novel products enriched with health-promoting lipids and characterized by unique flour properties.

Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation

Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.

Elaborating the multifarious role of PGPB for sustainable food security under changing climate conditions

Changing climate creates a challenge to agricultural sustainability and food security by changing patterns of parameters like increased UV radiation, rising temperature, altered precipitation patterns, and higher occurrence of extreme weather incidents. Plants are vulnerable to different abiotic stresses such as waterlogging, salinity, heat, cold, and drought in their natural environments. The prevailing agricultural management practices play a major role in the alteration of the Earth's climate by causing biodiversity loss, soil degradation through chemical and physical degradation, and pollution of water bodies. The extreme usage of pesticides and fertilizers leads to climate change by releasing greenhouse gases (GHGs) and depositing toxic substances in the soil. At present, there is an urgent need to address these abiotic stresses to achieve sustainable growth in agricultural production and fulfill the rising global food demand. Several types of bacteria that are linked with plants can increase plant resistance to stress and lessen the negative effects of environmental challenges. This review aims to explore the environmentally friendly capabilities and prospects of multi-trait plant growth-promoting bacteria (PGPB) in the alleviation of detrimental impacts of harsh environmental conditions on plants.

Impact of Metabolites from Foodborne Pathogens on Cancer

Foodborne pathogens are microorganisms that cause illness through contamination, presenting significant risks to public health and food safety. This review explores the metabolites produced by these pathogens, including toxins and secondary metabolites, and their implications for human health, particularly concerning cancer risk. We examine various pathogens such as Salmonella sp., Campylobacter sp., Escherichia coli, and Listeria monocytogenes, detailing the specific metabolites of concern and their carcinogenic mechanisms. This study discusses analytical techniques for detecting these metabolites, such as chromatography, spectrometry, and immunoassays, along with the challenges associated with their detection. This study covers effective control strategies, including food processing techniques, sanitation practices, regulatory measures, and emerging technologies in pathogen control. This manuscript considers the broader public health implications of pathogen metabolites, highlighting the importance of robust health policies, public awareness, and education. This review identifies research gaps and innovative approaches, recommending advancements in detection methods, preventive strategies, and policy improvements to better manage the risks associated with foodborne pathogens and their metabolites.

Spectral Fingerprinting of Tencha Processing: Optimising the Detection of Total Free Amino Acid Content in Processing Lines by Hyperspectral Analysis

The quality and flavor of tea leaves are significantly influenced by chemical composition, with the content of free amino acids serving as a key indicator for assessing the quality of Tencha. Accurately and quickly measuring free amino acids during tea processing is crucial for monitoring and optimizing production processes. However, traditional chemical analysis methods are often time-consuming and costly, limiting their application in real-time quality control. Hyperspectral imaging (HSI) has shown significant effectiveness as a component detection tool in various agricultural applications. This study employs VNIR-HSI combined with machine learning algorithms to develop a model for visualizing the total free amino acid content in Tencha samples that have undergone different processing steps on the production line. Four pretreating methods were employed to preprocess the spectra, and partial least squares regression (PLSR) and least squares support vector machine regression (LS-SVR) models were established from the perspectives of individual processes and the entire process. Combining competitive adaptive reweighted sampling (CARS) and variable iterative space shrinkage approach (VISSA) methods for characteristic band selection, specific bands were chosen to predict the amino acid content. By comparing modeling evaluation indicators for each model, the optimal model was identified: the overall model CT+CARS+PLSR, with predictive indicators Rc2 = 0.9885, Rp2 = 0.9566, RMSEC = 0.0956, RMSEP = 0.1749, RPD = 4.8021, enabling the visualization of total free amino acid content in processed Tencha leaves. Here, we establish a benchmark for machine learning-based HSI, integrating this technology into the tea processing workflow to provide a real-time decision support tool for quality control, offering a novel method for the rapid and accurate prediction of free amino acids during tea processing. This achievement not only provides a scientific basis for the tea processing sector but also opens new avenues for the application of hyperspectral imaging technology in food science.

Language model-guided anticipation and discovery of unknown metabolites

Despite decades of study, large parts of the mammalian metabolome remain unexplored. Mass spectrometry-based metabolomics routinely detects thousands of small molecule-associated peaks within human tissues and biofluids, but typically only a small fraction of these can be identified, and structure elucidation of novel metabolites remains a low-throughput endeavor. Biochemical large language models have transformed the interpretation of DNA, RNA, and protein sequences, but have not yet had a comparable impact on understanding small molecule metabolism. Here, we present an approach that leverages chemical language models to discover previously uncharacterized metabolites. We introduce DeepMet, a chemical language model that learns the latent biosynthetic logic embedded within the structures of known metabolites and exploits this understanding to anticipate the existence of as-of-yet undiscovered metabolites. Prospective chemical synthesis of metabolites predicted to exist by DeepMet directs their targeted discovery. Integrating DeepMet with tandem mass spectrometry (MS/MS) data enables automated metabolite discovery within complex tissues. We harness DeepMet to discover several dozen structurally diverse mammalian metabolites. Our work demonstrates the potential for language models to accelerate the mapping of the metabolome.

Elucidating the gastroprotective mechanisms of Imperata cylindrica Beauv.var. major (Nees) C.E.Hubb through UHPLC-MS/MS and systems network pharmacology

Imperata cylindrica Beauv.var. major (Nees) C.E.Hubb., commonly known as BaiMaoGen (BMG), a medicinal and edible traditional Chinese medicinal (TCM) herb commonly used in health supplements, has been observed to offer protective effects against gastrointestinal disorders. However, the specific bioactive compounds and their molecular mechanisms have not been fully elucidated. This study employed ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) and systematic network pharmacology to analyze and identify the key active components and their interactions with biological targets. Thirty-six main active compounds, including 3,4-dihydroxybenzoic acid and p-hydroxybenzoic acid, were identified and analyzed for their interaction with key protein targets using molecular docking and dynamic simulations. This combined approach highlighted the therapeutic pathways involved, particularly the PI3K/AKT signaling pathways, providing new insights into the molecular basis of BMG's gastroprotective effects. Our findings suggested that BMG's complex multi-target action can potentially be harnessed to develop effective treatments for gastrointestinal toxicity.

Metabolomic signatures associated with fetal growth restriction and small for gestational age: a systematic review

The pathways involved in the pathophysiology of fetal growth restriction (FGR) and small for gestational age (SGA) are incompletely understood. We conduct a systematic review to identify metabolomic signatures in maternal and newborn tissues and body fluids samples associated with FGR/SGA. Here, we report that 825 non-duplicated metabolites were significantly altered across the 48 included studies using 10 different human biological samples, of which only 56 (17 amino acids, 12 acylcarnitines, 11 glycerophosphocholines, six fatty acids, two hydroxy acids, and eight other metabolites) were significantly and consistently up- or down-regulated in more than one study. Three amino acid metabolism-related pathways and one related with lipid metabolism are significantly associated with FGR and/or SGA: biosynthesis of unsaturated fatty acids in umbilical cord blood, and phenylalanine, tyrosine and tryptophan biosynthesis, valine, leucine and isoleucine biosynthesis, and phenylalanine metabolism in newborn dried blood spot. Significantly enriched metabolic pathways were not identified in the remaining biological samples. Whether these metabolites are in the causal pathways or are biomarkers of fetal nutritional deficiency needs to be explored in large, well-phenotyped cohorts.

Regulation of ROS metabolism in macrophage via xanthine oxidase is associated with disease progression in pulmonary tuberculosis

BACKGROUND

Pulmonary tuberculosis (PTB) exacerbation can lead to respiratory failure, multi-organ failure, and symptoms related to central nervous system diseases. The purpose of this study is to screen biomarkers and metabolic pathways that can predict the progression of PTB, and to verify the role of the metabolic enzyme xanthine oxidase (XO) in the progression of PTB.

METHODS

To explore the biomarkers and mechanisms underlying the progression of PTB, plasma metabolomics sequencing was conducted on patients with severe PTB, non-severe PTB, and healthy individuals. Screening differential metabolites and metabolic pathways that can predict the progression of PTB, and verifying the function and mechanism of action of XO through experiments.

RESULTS

The purine metabolism, sphingolipid metabolism, and amino acid metabolism between the three groups differ. In patients with severe PTB, the levels of xanthosine and hypoxanthine are increased, while the levels of D-tryptophan, dihydroceramide and uric acid are decreased. Inhibition of XO activity has been observed to reduce the levels of tumor necrosis factor (TNF-α), interleukin-1 beta (IL-1β), and interleukin-6 (IL-6), as well as to suppress the production of reactive oxygen species (ROS) and the activation of the NF-κB pathway, while also promoting the growth of MTB within cells.

CONCLUSION

D-tryptophan, xanthosine, and dihydroceramide can be utilized as biomarkers for progression of PTB, assisting in the evaluation of disease progression, and XO stands out as a potential therapeutic target for impeding the progression of PTB.