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

Energy band engineered nanomatrix assisted mass spectrometry for metabolite detection

Metabolites participate in the regulation of various physiological and pathophysiological processes in organisms. Metabolite detection can identify disease biomarkers, facilitating early disease diagnosis. Laser desorption/ionization mass spectrometry (LDI MS) holds promise in metabolite detection, but the performance of mass spectrometry depends on the precise design and preparation of matrix materials. The lack of clear understanding of LDI mechanisms hinders the rational design of matrices. This paper proposes a matrix design strategy by developing an energy band engineered Indium-doped CuCrO2 (ICCO), thereby achieving hole regulation, for precisely controlling charge-driven desorption to enhance LDI performance. Furthermore, by integrating density functional theory (DFT), this strategy is capable of enhancing the charge transfer ability at the matrix-glucose interface, to the consequent improvement in LDI performance. Compared to CCO, other concentrations of ICCO and traditional organic matrices, 2.5 % ICCO could achieve ≈2-500-fold signal enhancement. The optimized ICCO-assisted laser desorption ionization mass spectrometry platform can quantify glucose concentration in diabetic patient serum samples with only 1 μL of serum. This method has been validated to exhibit high accuracy (Pearson's r = 0.997 compared with the clinical gold standard) and good reproducibility (CV < 7 %). This work has facilitated the development of advanced substrates and underscored the potential of metabolite quantification in practical clinical applications going forward.

The role of SLC7A11 in arsenite-induced oncogenic phenotypes of human bronchial epithelial cells: A metabolic perspective

Chronic arsenic exposure enhances the probability of lung cancer with the underlying mechanisms remain unknown. Glutamine-driven synthetic metabolism, including nucleotide synthesis, amino acid production, TCA cycle replenishment, glutathione synthesis, and lipid biosynthesis, is crucial for both cancer initiation and progression. This study demonstrated that chronic exposure to 0.1 μM arsenite for as long as 36 weeks induced malignant transformation in human bronchial epithelial cells (BEAS-2B). Metabolomics were used to systematically disclose metabolic characteristics in arsenic-transformed malignant (As-TM) cells. Significantly changed metabolites were enriched in alanine, aspartate and glutamate metabolism, arginine biosynthesis, glutamine and glutamate metabolism, glutathione metabolism, butanoate metabolism, TCA cycle, and arginine and proline metabolism. It is worth noting that glutamate located at the intersection of the enriched metabolism pathways. Glutamine deprivation attenuated the oncogenic phenotypes, including capacity of wound healing and proliferation, in As-TM cells. And the expression levels of mRNA and proteins associated with glutamine metabolism-related transporters and enzymes, including SLC7A11, GCLM, and GCLC, were significantly increased, with SLC7A11 exhibiting the most substantial increase. Moreover, arsenite transformation progressively elevated SLC7A11 mRNA and protein levels over time. The SLC7A11 inhibitor sulfasalazine remarkably attenuated arsenite-induced oncogenic phenotypes. Collectively, our data suggest that chronic arsenite exposure enhances glutamine metabolism through upregulation of SLC7A11, thereby promoting cell proliferation and malignant transformation. These results provide new insights for preventive and therapeutic strategies for lung cancer linked to arsenic exposure.

Integrating NMR and multi-LC-MS-based untargeted metabolomics for comprehensive analysis of blood serum samples

BACKGROUND

Mass spectrometry (MS) and nuclear magnetic resonance (NMR) have emerged as pivotal tools in biofluid metabolomics, facilitating investigation of disease mechanisms and biomarker discovery. Despite complementary capabilities, these techniques are rarely combined, although their integration is often beneficial. Typically, different sample preparation approaches are used, and compatibility challenges potentially arise due to the requirement for deuterated buffered solvents in NMR but not MS techniques. Additionally, MS-based approaches necessitate protein removal from samples whilst in NMR proteins can be potentially useful biomarkers. In this study, we developed a blood serum preparation protocol enabling sequential NMR and multi-LC-MS untargeted metabolomics analysis using a single serum aliquot in a research discovery setting.

RESULTS

We analysed human serum samples using various untargeted NMR and multi-LC-MS platforms to assess the impact of deuterated solvents and buffers on detected compound-features. Employing multiple LC-MS profiling approaches, we observed no evidence of deuterium incorporation into metabolites following sample preparation with deuterated solvents. Furthermore, we demonstrated that buffers used in NMR were well tolerated by LC-MS. Protein removal, involving both solvent precipitation and molecular weight cut-off (MWCO) filtration, was identified as a primary factor influencing metabolite abundance. Our findings led to the development and validation of a serum sample preparation protocol enabling a combined NMR and multi-LC-MS analysis.

SIGNIFICANCE

Using a single clinical serum aliquot for simultaneous untargeted profiling via NMR and multi-LC-MS represents a highly efficient alternative to current methods. This approach reduces sample volume requirements and substantially expands the potential for broader metabolome coverage. Our study offers comprehensive insights into the impact of sample preparation on complex metabolic biofluid profiles, highlighting the compatibility and complementarity of LC-MS and NMR in metabolomics research.

Bile acids produced by gut microbiota activate TGR5 to promote colorectal liver metastasis progression by inducing MDSCs infiltration in liver

BACKGROUND

CRLM (Colorectal liver metastasis), a prevalent form of distant metastasis in colorectal cancer, is a leading cause of mortality in affected patients. Despite advancements in immunotherapy for colorectal cancer, clinical benefits in CRLM patients remain limited. The immunosuppressive liver microenvironment plays a pivotal role in facilitating metastatic colonization and disease progression.

METHODS

We performed fecal metabolomics in ABX (antibiotic-treated) mice and single-cell RNA sequencing on hepatic tissues from four cohorts: CRC (colorectal cancer) , CRLM, LCA-fed CRC, and LCA-fed CRLM mice, to delineate intergroup immune heterogeneity. Cellular and molecular profiling across groups was conducted via Luminex multiplex assays, flow cytometry, and immunofluorescence. Integrated multi-omics analyses elucidated LCA-driven pathways modulating metastatic progression RESULTS: We demonstrated that LCA (lithocholic acid), a gut microbiota-derived metabolite, activates TGR5 in hepatic CAFs (cancer-associated fibroblasts) to upregulate CCL3 secretion. Elevated CCL3 levels subsequently recruit MDSCs (myeloid-derived suppressor cells) into metastatic niches. While MDSCs primarily suppress T-cell activation, we identified a paradoxical role of MDSC-derived CCL2 in attenuating immunosuppression via CCR2 signaling, suggesting a compensatory pro-inflammatory axis within the tumor microenvironment CONCLUSIONS: These findings suggest new immunotherapeutic strategies for the treatment of CRLM.

Metal-organic framework nanomaterials alter cellular metabolism in bladder cancer

While nanomaterial-mediated metabolic reprogramming emerges as a promising anticancer strategy, the precise mechanisms remain elusive due to limited metabolomics investigations. The objective of this study is to design an aluminum (Al) based metal organic frameworks (Al-MOF) and investigate its cytotoxic effects on bladder cancer cells (T24), and elucidate the specific molecular mechanisms. Comprehensive characterization (scanning electron microscopy, particle size and potential analysis, infrared spectroscopy, powder X-ray diffraction, and N2 desorption/desorption experiment) confirmed the successful preparation of Al-MOF. Subsequently, in vitro assays demonstrated the selective cytotoxicity of Al-MOF, showing an inhibitory effect on the proliferation of T24 compared to human immortalized urothelial cells. At the same time, when the concentration of Al-MOF exceeded 100 μg/mL, it exhibited significant migration inhibition on T24. Then, the effect of Al-MOF on T24 metabolites was investigated using ultra-high performance liquid chromatography quadrupole Orbitrap high-resolution mass spectrometry. After 24 h of incubation, we identified 38 key differential metabolites from expression patterns and metabolic pathways, predominantly in fatty acid synthesis. Research has found that Al-MOF reduced fatty acid biosynthesis by inhibiting FASN expression, thereby inhibiting the progression of T24. This work provides evidence of MOF-mediated intervention in cancer cell metabolism, offering valuable insights for the design of novel multifunctional nanotherapies.

A comprehensive evaluation of stability and safety for HEK293F-derived extracellular vesicles as promising drug delivery vehicles

HEK293F-derived extracellular vesicles (HEK293F-EVs) have great potential as next-generation drug delivery vehicles. A comprehensive understanding of their batch stability and in vivo safety is prerequisite for clinical translation. HEK293F-EVs were purified using ultracentrifugation combined with size exclusion chromatography, and their physicochemical properties, such as morphology, size distribution, and biomarkers, were thoroughly characterized. Raman spectroscopy and multi-omics analyses were employed to elaborate their molecular composition. Blood kinetics and biodistribution were assessed via IVIS spectrum imaging. Additionally, long-term in vivo safety was evaluated following multiple-dose administration through hematology, serum biochemistry, cytokine/chemokine profiling, and histopathology. HEK293F-EVs exhibited stable yields, purity, physicochemical properties (morphology, size, zeta potential, and marker proteins), and chemical composition across different cell passages (P10, P20, P30), with no significant variations. Content profiling, including protein, miRNA, metabolite, and lipid, confirmed consistent molecular stability across five production batches. GO, Reactome, and KEGG analyses revealed minimal enrichment in pathways related to acute immune response or cytotoxicity. Blood kinetics studies indicated rapid clearance of HEK293F-EVs from circulation, though slightly slower than PEG-Liposomes. Organ biodistribution was comparable between HEK293F-EVs and PEG-Liposomes, with HEK293F-EVs potentially having longer retention times. Importantly, HEK293F-EVs exhibited a favorable preclinical long-term safety profile, showing low immunogenicity and fewer tissue lesions compared to PEG-Liposomes. Our study demonstrates that HEK293F-EVs maintain stable physicochemical characteristics and compositions across batches and possess a superior safety profile, suggesting their significant potential as a safe and reliable drug delivery platform for clinical applications.

Neurotoxic effects of per- and polyfluoroalkyl substances (PFAS) mixture exposure in mice: Accumulations in brain and associated changes of behaviors, metabolome, and transcriptome

Humans are exposed to complex per- and polyfluoroalkyl substances (PFAS) mixtures, yet their neurotoxicity and mechanisms remains unclear. This study exposed male mice to 17 PFAS mixtures at low levels (0.2-20 µg/L) for 49 days via drinking water. Perfluoropentanoic acid (PFPeA), perfluoroheptanoic acid (PFHpA), 6:2 fluorotelomer sulfonic acid (6:2 FTS), and perfluorooctane Sulfonate (PFOS) accumulated in brain tissues, with brain/plasma ratios of 2.03-5.87, 2.94-12.88, 1.90-3.19, and 0.62-0.93, respectively. Electroencephalogram (EEG) results showed significant alterations, including a reduction in beta spectral edge (21.47-13.85 Hz) and an increase in gamma spectral edge (57.64-79.07 Hz). Histopathological analysis revealed necrosis in the hippocampus, contributing to the observed anxiety-like behaviors and memory impairments in exposed mice. Plasma metabolomics highlighted disrupted osmoprotectants, impaired glutamatergic synapse function, and tryptophan metabolism. Brain metabolomics demonstrated suppression of purine metabolism and activation of arachidonic acid metabolism, suggesting involvement in neurotoxic effects. Transcriptomic profiling further identified dysregulation in neuroactive ligand-receptor interactions, cholinergic and GABAergic synapses, and calcium signaling pathways, with oxytocin signaling highlighted as a critical mechanism. This study, for the first time, links PFAS mixture to neurotoxicity via neurotransmitter-related pathways, underscoring the need for public health policies and preventive strategies to mitigate PFAS exposure risks.

Inhibitory effects of phenolic compounds from blueberry leaf on α-amylase and α-glucosidase: kinetics, mode of action, and molecular interactions

BACKGROUND

The interactions between blueberry leaf polyphenols (BLPs) and digestive enzymes were analyzed using multiple techniques to gain insights into their inhibitory effects on enzyme kinetics and modes of action.

RESULTS

3-O-Caffeoylquinic acid (3-CQA) was the most abundant compound identified. Quercetin (QR) exhibited the strongest inhibitory activity against α-amylase (α-AMY) and α-glucosidase (α-GLU). The BLP extracts acted as typical mixed-type inhibitors for both digestive enzymes, showing stronger inhibition of α-GLU (IC50 = 7.36 ± 0.03 μg mL-1) than α-AMY (IC50 = 12.52 ± 0.65 μg mL-1). Stern-Volmer plots showed static quenching of enzyme fluorescence intensity. The quenching and binding constants of α-GLU were higher than those of α-AMY, showing greater affinity of the former for BLP. The conformational changes of 3-CQA and QR in the BLP were studied at the molecular level. The stability of the complexes formed followed this order: α-GLU-3-CQA > α-AMY-QR > α-GLU-QR > α-AMY-3-CQA. This trend supported the observation that QR had a greater impact on α-AMY conformation, whereas 3-CQA more effectively altered α-GLU.

CONCLUSION

These findings elucidated the inhibitory mechanisms of BLP on glucose-regulating enzymes, providing novel insights relevant for the treatment of diabetes. © 2025 Society of Chemical Industry.

The Role of IDO Activity in Cataract Progression: Correlation to Age and Cataract Severity

PURPOSE

Cataract is a prevalent, progressive lens disorder characterized by gradual opacity, often related to aging. Oxidative stress, ultraviolet radiation, and inflammation contribute to lens damage and protein modifications. Indoleamine 2,3-dioxygenase plays a critical role in tryptophan metabolism, with kynurenines implicated in various diseases. This study aimed to assess IDO activity in cataract patients by analyzing IDO activity in aqueous humor.

METHODS

The study included 170 cataract surgery patients (60 males, 110 females; mean age 73.9 ± 9.43 years). Cataract severity was categorized using the SPONCS system: SPONCS 1 (1.8%), SPONCS 2 (30.6%), SPONCS 3 (35.3%), SPONCS 4 (11.8%), and SPONCS 5 (20.6%). Aqueous humor samples (50-120 µL) were collected via paracentesis and analyzed using HILIC liquid chromatography with mass spectrometry. Target metabolite quantification was performed using internal standards and calibration curves.

RESULTS

Significant differences in IDO activity were observed across SPONCS groups (X2(4) = 12.0, p = .018, Ɛ2= 0.0707), particularly between SPONCS 2 and SPONCS 5 (p = .013). Age differences across SPONCS groups were also significant (p < .001). Males had lower tryptophan levels than females (p = .027). Correlations were found between SPONCS and IDO activity (rs = -0.255, p < .001), SPONCS and kynurenine (rs = 0.196, p = .011), and kynurenic acid with kynurenine (rs = 0.355, p < .001).

CONCLUSION

Patients with SPONCS 2 cataract exhibit increased susceptibility to elevated IDO activity and heightened kynurenine production. IDO serves as a more reliable prognostic marker for cataract progression than chronological age. Furthermore, IDO activity may be associated with reduced glutathione levels in human lens epithelial cells, suggesting a potential link between the enzyme and oxidative stress within the lens.

Harnessing technologies to unravel gastric cancer heterogeneity

Gastric cancer arises from complex carcinogenic factor interactions, with limited treatment options due to the lack of targetable driver gene mutations and significant tumor heterogeneity. Recent studies have provided promising novel approaches to improve our understanding of gastric cancer heterogeneity through integrated characterization, combining genomics with emerging technologies. Delineating the molecular changes and targeting specific molecular subtypes will enhance the efficacy of gastric cancer treatment and improve clinical outcomes. This review provides a comprehensive overview of current technologies used in gastric cancer research, highlighting key discoveries and treatment strategies driven by these innovations. Finally, we discuss the emerging technology-guided directions and potential breakthroughs that could enhance the understanding of gastric cancer tumor heterogeneity, ultimately improving clinical outcomes.

Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS

Characterizing biological and environmental samples at a molecular level primarily uses tandem mass spectroscopy (MS/MS), yet the interpretation of tandem mass spectra from untargeted metabolomics experiments remains a challenge. Existing computational methods for predictions from mass spectra rely on limited spectral libraries and on hard-coded human expertise. Here we introduce a transformer-based neural network pre-trained in a self-supervised way on millions of unannotated tandem mass spectra from our GNPS Experimental Mass Spectra (GeMS) dataset mined from the MassIVE GNPS repository. We show that pre-training our model to predict masked spectral peaks and chromatographic retention orders leads to the emergence of rich representations of molecular structures, which we named Deep Representations Empowering the Annotation of Mass Spectra (DreaMS). Further fine-tuning the neural network yields state-of-the-art performance across a variety of tasks. We make our new dataset and model available to the community and release the DreaMS Atlas-a molecular network of 201 million MS/MS spectra constructed using DreaMS annotations.

MetCohort: Precise Feature Detection and Correspondence for Untargeted Metabolomics in Large-Scale Cohort Studies

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics is becoming increasingly popular in large-scale cohort studies. However, its data processing is complex and challenging. We present MetCohort, a computational tool for performing metabolomics raw data alignment for large-scale sample analysis, and accurate feature detection and quantification. By combining chromatogram profile alignment and local anchor matching with an outlier removal algorithm, the retention times of the raw data were aligned. With aligned retention times across all the samples, regions of interest (ROIs) are detected and stacked among samples to form a two-dimensional (2D) ROI-matrix. This 2D ROI-matrix, resembling an image with rows representing samples and columns corresponding to the time, allows the application of image processing techniques. Since the peaks are already aligned in the alignment step, features can be accurately detected and quantified with automatic correspondence of all the samples. Based on the 2D image processing technique, holistic scale feature detection is performed, which not only significantly decreases the number of false-positives and improves the detection of low-intensity compounds, but also avoids tricky peak matching and quantification uncertainty. Overall, MetCohort has potential to enhance the accuracy and efficiency of data processing in large-scale LC-HRMS.

Systems and synthetic biology for plant natural product pathway elucidation

Plants are one of the major reservoirs of medicinal compounds, serving as a cornerstone of both traditional and modern medicine. However, despite their importance, the complex biosynthetic pathways of many plant-derived compounds remain only partially understood, hindering their full potential in therapeutic applications. This review paper summarizes the advances in systems and synthetic biology utilized in the characterization and engineering of plant metabolic pathways. We discuss various strategies such as (1) co-expression analysis, (2) gene cluster identification, (3) metabolite profiling, (4) deep learning approaches, (5) genome-wide association studies, and (6) protein complex identification. Through case studies on several biosynthesis pathways, we highlight how these methods are applied to unravel complex pathways and enhance the production of important natural products. Finally, we discuss future directions in the context of metabolic engineering, including metabolon engineering, AI integration, and sustainable production strategies, underscoring the potential for cheaper and greener production of plant natural products.

HSV-1 alters lipid metabolism and induces lipid droplet accumulation in functionally impaired mouse dendritic cells

Herpes simplex virus type 1 (HSV-1) significantly impairs dendritic cell (DC) function, ultimately eliciting the death of these cells. Here, we sought to assess whether HSV-1 modulates lipid metabolism in mouse DCs as a mechanism of immune evasion. For this, we performed RT-qPCR gene arrays with ingenuity pathway analysis (IPA), RNA sequencing (RNA-seq) and gene set enrichment analysis (GSEA), confocal microscopy, transmission electron microscopy, ultra-high-performance liquid chromatography-quadrupole time-of-flight (UHPLC-QTOF) analysis, pharmacological inhibition of eight lipid-metabolism-related enzymes in HSV-1-infected DCs, co-cultures between virus-specific transgenic CD4+ and CD8+ T cells and HSV-1-infected DCs, and in vivo assays with mice. We found that HSV-1 significantly alters lipid metabolism in DCs and induces lipid droplet (LD) accumulation in these cells. Pharmacological inhibition of two particular lipid metabolism enzymes was found to partially restore DC function. Overall, these results suggest that lipid metabolism plays an important role in the impairment of DC function by HSV-1.

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.

Metabolomics: a new frontier in neurodegenerative disease biomarker discovery

BACKGROUND

Neurodegenerative disorders are a group of debilitating diseases affecting the central nervous system, and are characterized by the progressive loss of neurons, leading to declines in cognitive function, movement, and overall quality of life. While the exact causes remain elusive, it's believed that a combination of genetic, environmental, and lifestyle factors contribute to their development. Metabolites, the end products of cellular processes, reflect the physiological state of an organism. By analysing these molecules, researchers can gain a deeper understanding of the underlying metabolic changes associated with neurodegenerative disorders.

AIM OF REVIEW

This review aims to explore the possibilities between metabolites and their association with neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's disease (PD), Multiple sclerosis (MS) and Huntington's disease (HD).

KEY SCIENTIFIC CONCEPTS OF REVIEW

Metabolomic studies could potentially illuminate altered biochemical pathways, facilitating earlier detection and treatment of these conditions. Metabolomic investigations have revealed the role of oxidative stress, alterations in glucose and fat metabolism, mitochondrial dysfunction, apoptosis, glutamate excitotoxicity and alterations in myelin composition in neurodegenerative disorders. The common metabolic biomarkers identified includes glutamate, taurine, uric acid, branched chain amino acids, acylcarnitine, creatinine, choline, with some more amino acids and lipids. Metabolomics offers valuable insights into disease mechanisms and potential therapeutic targets by identifying biochemical and metabolic alterations, but still there are several aspects to be explored for accurate mapping of metabolites with specific pathway involved in the disease.

Metabolomic Analysis Revealed the Differences in Metabolites Between Three Different Sugarcane Stems and Leaves

Background: Sugarcane is an important sugar crop. Sugarcane stems are mainly used for sugar extraction, while leaves can only be burned as waste. However, sugarcane leaves can also produce a large number of secondary metabolites, and these metabolites have significant nutritional and pharmacological value. At present, there are few studies on sugarcane compounds. Methods: Therefore, the stems and leaves of three sugarcane varieties (Yacheng 89-159, Dianzhe 01-58, ROC22) were selected as experimental materials, and the compounds of stems and leaves of different sugarcane were studied using high-performance liquid chromatography. Results: Metabolomics analysis detected 1197 metabolites that could be broadly divided into 11 categories. Orthogonal partial least squares discriminant analysis identified metabolites that were differentially abundant across groups (stems and leaves within and across the three varieties). Flavonoids, phenolic acids, and lipids were the main differential metabolites. Notably, tricin-4'-O-(guaiacylglycerol)ether-7-O-glucoside, quercetin-3,4-O-di-glucoside, cyanidin-3-O-(6''-O-malony)glucoside were significantly higher in the stems than in the leaves across all three varieties. The content of methylenesuccinic acid was higher in the leaves of Dianzhe 01-58 and ROC22. In the comparative analysis of the top 20 differential metabolites among different varieties, it was found that the metabolite content of stems and leaves of Yacheng 89-9 and ROC22 was significantly higher than that of Dianzhe 01-58. Next, KEGG analysis showed that these differential metabolites were mainly enriched in pathways related to flavonoid, phenylpropanoid, and isoflavonoid biosynthesis, as well as starch and sucrose metabolism. Leaves also had significantly fewer metabolites involved in starch and sucrose metabolism than stems did. Conclusion: In conclusion, this study provides a scientific basis for utilization of sugarcane compounds, laying a theoretical foundation for further processing of sugarcane by-products into higher-value materials.

Mass spectrometry-based metabolomic as a powerful tool to unravel the component and mechanism in TCM

Mass spectrometry (MS)-based metabolomics has emerged as a transformative tool to unraveling components and their mechanisms in traditional Chinese medicine (TCM). The integration of advanced analytical platforms, such as LC-MS and GC-MS, coupled with metabolomics, has propelled the qualitative and quantitative characterization of TCM's complex components. This review comprehensively examines the applications of MS-based metabolomics in elucidating TCM efficacy, spanning chemical composition analysis, molecular target identification, mechanism-of-action studies, and syndrome differentiation. Recent innovations in functional metabolomics, spatial metabolomics, single-cell metabolomics, and metabolic flux analysis have further expanded TCM research horizons. Artificial intelligence (AI) and bioinformatics integration offer promising avenues for overcoming analytical bottlenecks, enhancing database standardization, and driving interdisciplinary breakthroughs. However, challenges remain, including the need for improved data processing standardization, database expansion, and understanding of metabolite-gene-protein interactions. By addressing these gaps, metabolomics can bridge traditional practices and modern biomedical research, fostering global acceptance of TCM. This review highlights the synergy of advanced MS techniques, computational tools, and TCM's holistic philosophy, presenting a forward-looking perspective on its clinical translation and internationalization.

LC-MS/MS based metabolomics reveals the mechanism of skeletal muscle regeneration

BACKGROUND

Skeletal muscle possesses a robust regenerative capacity and can effectively repair itself following injury. However, research on the metabolic changes during skeletal muscle regeneration in large animals remains relatively limited. Therefore, in this study, we used pigs as a model and applied non-targeted LC-MS/MS metabolomic technology to reveal the metabolic changes during skeletal muscle regeneration, and conducted an in-depth exploration of important signaling pathways, which can provide a reference for further research on the mechanisms promoting skeletal muscle regeneration.

METHODS

In this study, we used 18 piglets aged 35 days and weighing 7.10 ± 0.90 kg to construct a skeletal muscle regeneration model. These piglets were randomly divided into three treatment groups (n = 6) and injected with cardiotoxins (CTX) in the right longissimus dorsi muscle. They were euthanized on the 1st, 4th, and 16th days post-injection to collect right longissimus dorsi muscle samples as the treatment group. Additionally, the left longissimus dorsi muscle of piglets on the 4th day post-injection was selected as the control group. Phenotypic changes in skeletal muscle regeneration were determined through H&E staining, immunofluorescence, and Western Blot analysis, and LC-MS/MS untargeted metabolomics technology was utilized to explore the differential expressed metabolites (DEMs) involved in skeletal muscle regeneration.

RESULTS

Phenotyping results showed that the regeneration model showed 3 stages of inflammation, regeneration and remodeling, which indicated successful model construction. Non-targeted LC-MS/MS metabolomics analysis showed significant differences in the structure of metabolites in these 3 stages. (1) In the inflammatory stage, a total of 198 DEMs were identified, which were mainly enriched in the pathways regulating the inflammatory response. (2) in the repair stage, 264 DEMs were identified, which were mainly enriched in pathways that inhibit inflammatory response and promote protein synthesis. (3) During the remodeling stage, 102 DEMs were identified, which were mainly enriched in the pathways that inhibit protein depletion and promote protein deposition. Temporal expression analysis revealed metabolites consistent with changes in the skeletal muscle regeneration process and found that these metabolite functions were mainly enriched in inhibiting inflammatory responses, alleviating myofibrillar lysis, and promoting muscle growth. Among them, (R)-Lipoic acid, 8-Hydroxyguanosine, and Uridine 5'-monophosphate maybe key metabolites associated with skeletal muscle regeneration.

CONCLUSION

The skeletal muscle regeneration mechanism was systematically explored, and the metabolite time series analysis during skeletal muscle regeneration revealed some key metabolites that reflect the degree of skeletal muscle damage.

Metabolic pathway analysis of tumors using stable isotopes

Metabolic reprogramming is pivotal in malignant transformation and cancer progression. Tumor metabolism is shaped by a complex interplay of both intrinsic and extrinsic factors that are not yet fully elucidated. It is of great value to unravel the complex metabolic activity of tumors in patients. Metabolic flux analysis (MFA) is a versatile technique for investigating tumor metabolism in vivo, it has increasingly been applied to the assessment of metabolic activity in cancer in the past decade. Stable-isotope tracing have shown that human tumors use diverse nutrients to fuel central metabolic pathways, such as the tricarboxylic acid cycle and macromolecule synthesis. Precisely how tumors use different fuels, and the contribution of alternative metabolic pathways in tumor progression, remain areas of intensive investigation. In this review, we systematically summarize the evidence from in vivo stable- isotope tracing in tumors and describe the catabolic and anabolic processes involved in altered tumor metabolism. We also discuss current challenges and future perspectives for MFA of human cancers, which may provide new approaches in diagnosis and treatment of cancer.

Anti-diabetes activity of (R)-gentiandiol in KKAy type 2 mice

Swertiamarin is a major component of many traditional Chinese Swertia herbs that show significant antidiabetic activity. (R)-Gentiandiol and (S)-gentiandiol are metabolites of swertiamarin found in vivo. The antidiabetic activity of swertiamarin and its nitrogen-containing metabolites (R)-gentiandiol and (S)-gentiandiol was evaluated in this research, and their mechanism of action was investigated after evaluating the serum metabolic profile of KK/Upj-Ay type 2 mice. The pharmaceutical effects of swertiamarin, (R)-gentiandiol, and (S)-gentiandiol were tested by biochemical indices and histopathological observations. Moreover, the mechanism underlying the action of three compounds against type 2 diabetes was elucidated using a metabolomic method. It was shown that (R)-gentiandiol significantly improved pathological changes in the kidney and pancreas. The levels of total cholesterol, triglyceride, and high-density and low-density lipoprotein cholesterol improved considerably after treatment with (R)-gentiandiol, compared to their levels in model mice. However, the levels of these compounds showed no improvement after treatment with (S)-gentiandiol. In total, 15 biomarkers were identified in KK/Upj-Ay type 2 mice, and the levels of 10 biomarkers were measured after treatment with (R)-gentiandiol. (R)-Gentiandiol reduced the abnormalities in metabolic pathways, including lipid metabolism, amino acid metabolism, carbohydrate metabolism, and nucleotide metabolism. Additionally, glycine, serine, and threonine metabolism related to the regulation of glycine was affected the most. The study indicated that the antidiabetic effects of Swertia herbs may due to (R)-gentiandiol which is a metabolite of swertiamarin in vivo. This study helps clarify the active metabolites of swertiamarin, provide greater insights into the clinical antidiabetic effects of Swertia herbs and bring novel ideas for developing new drugs from antidiabetic herbs.

Evaluation of Violacein Metabolic Stability and Metabolite Identification in Human, Mouse, and Rat Liver Microsomes

Background: Malaria significantly impacts the health of populations living in poverty and vulnerable conditions. Resistance to current antimalarial drugs remains a major challenge and highlights the urgent need for novel, effective, and safer therapies. Violacein, a purple pigment, has demonstrated potent antiplasmodial activity, making it a promising antimalarial candidate. However, to date, no in vitro metabolism studies of violacein have been published. In this study, the metabolic stability of violacein was evaluated using human (HLMs), mouse (MLMs), and rat (RLMs) liver microsomes and the metabolites generated by HLMs and RLMs were assessed. Methods: Liquid chromatography quadrupole mass spectrometry (LC-MS/MS) was used to investigate the metabolic stability of violacein, while liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) was used to identify the metabolites. In silico analyses were used to support in vitro metabolite identification by providing insights into potential metabolic pathways and predicting metabolite structures, thereby enhancing the accuracy and efficiency of the identification process. Results: The half-life (t1/2) for violacein in RLMs, MLMs, and HLMs was 36, 81, and 216 min, respectively. The in vitro intrinsic clearance (CLint, in vitro) values were 38.4, 17.0, and 6.4 µL/min/mg for RLMs, MLMs, and HLMs, respectively, while the in vivo intrinsic clearance (CLint, in vivo) was 93.7, 67.0, and 6.6 mL/min/kg, respectively. A slow elimination profile was observed in HLMs followed by MLMs, with rapid elimination in RLMs, indicating greater stability of violacein in HLMs and MLMs when compared with RLMs. Four violacein metabolites were identified in HLMs and RLMs, two of which were formed by phase I metabolism, one by phase II metabolism, and one by phase I + II metabolism. Conclusions: This study provides the first published analysis of the metabolic stability of violacein.

Metabolism in hematology: Technological advances open new perspectives on disease biology and treatment

The term metabolism refers to the multi-faceted biochemical reactions within a cell or an organism that occur to maintain energy homeostasis, cell growth, and oxidative balance. Cells possess a high metabolic plasticity, allowing them to adapt to the dynamic requirements of their functional state and environment. Deregulated cellular metabolism is a hallmark of many diseases, including benign and malignant hematological conditions. In the last decade, multiple technological innovations in the metabolism field have made in-depth metabolic analysis broadly applicable. Such studies are shedding new light on normal and malignant hematopoiesis and open avenues to a better understanding of the biology of hematological diseases. In this review, we will first give a brief overview of central metabolic processes. Furthermore, we discuss the most commonly used methods to study metabolism. We begin by elaborating on the use of next-generation sequencing to detect metabolism-related genomic mutations and study transcriptional signatures. Furthermore, we discuss methods for measuring protein expression, such as mass spectrometry (MS), flow cytometry, and cytometry time-of-flight. Next, we describe the use of nuclear magnetic resonance spectroscopy, MS, and flow cytometry for metabolite quantification. Finally, we highlight functional assays to probe metabolic pathways in real-time. We illustrate how these technologies and their combination have advanced our understanding of the role of metabolism. Our goal is to provide hematologists with a comprehensive guide to modern techniques in metabolism research, their benefits and disadvantages, and how they guide our understanding of disease and potentially future personalized therapy decisions.

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.

Direct mapping of tyrosine sulfation states in native peptides by nanopore

Sulfation is considered the most prevalent post-translational modification (PTM) on tyrosine; however, its importance is frequently undervalued due to difficulties in direct and unambiguous determination from phosphorylation. Here we present a sequence-independent strategy to directly map and quantify the tyrosine sulfation states in universal native peptides using an engineered protein nanopore. Molecular dynamics simulations and nanopore mutations reveal specific interactions between tyrosine sulfation and the engineered nanopore, dominating identification across diverse peptide sequences. We show a nanopore framework to discover tyrosine sulfation in unknown peptide fragments digested from a native protein and determine the sequence of the sulfated fragment based on current blockade enhancement induced by sulfation. Moreover, our method allows direct observation of peptide sulfation in ultra-low abundance, down to 1%, and distinguishes it from isobaric phosphorylation. This sequence-independent strategy suggests the potential of nanopore to explore specific PTMs in real-life samples and at the omics level.

Characterization of the cyclic dipeptide cyclo(His-Pro) in Arabidopsis

Diketopiperazines (DKPs) are chemically and functionally diverse cyclic dipeptides associated primarily with microbes. Few DKPs have been reported from plants and animals; the best characterized is cyclo(His-Pro), found in the mammalian central nervous system, where it arises from the proteolytic cleavage of a thyrotropin-releasing tripeptide hormone. Herein, we report the identification of cyclo(His-Pro) in Arabidopsis (Arabidopsis thaliana), where its levels increase upon abiotic stress conditions, including high salt, heat, and cold. To screen for potential protein targets, we used isothermal shift assays, which examine changes in protein-melting stability upon ligand binding. Among the identified proteins, we focused on the glycolytic enzyme, cytosolic glyceraldehyde-3-phosphate dehydrogenase (GAPC1). Binding between the GAPC1 protein and cyclo(His-Pro) was validated using nano-differential scanning fluorimetry and microscale thermophoresis, and we could further demonstrate that cyclo(His-Pro) inhibits GAPC1 activity with an IC50 of ∼200 μm. This inhibition was conserved in human GAPDH. Inhibition of glyceraldehyde-3-phosphate dehydrogenase activity has previously been reported to reroute carbon from glycolysis toward the pentose phosphate pathway. Accordingly, cyclo(His-Pro) supplementation augmented NADPH levels, increasing the NADPH/NADP+ ratio. Phenotypic screening revealed that plants supplemented with cyclo(His-Pro) were more tolerant to high-salt stress, as manifested by higher biomass, which we show is dependent on GAPC1/2. Our work reports the identification and functional characterization of cyclo(His-Pro) as a modulator of glyceraldehyde-3-phosphate dehydrogenase in plants.

Research Progress of Genomics Applications in Secondary Metabolites of Medicinal Plants: A Case Study in Safflower

Medicinal plants, recognized as significant natural resources, have gained prominence in response to the increasing global demand for herbal medicines, necessitating the large-scale production of these plants and their derivatives. Medicinal plants are exposed to a variety of internal and external factors that interact to influence the biosynthesis and accumulation of secondary metabolites. With the rapid development of omics technologies such as genomics, transcriptomics, proteomics, and metabolomics, multi-omics technologies have become important tools for revealing the complexity and functionality of organisms. They are conducive to further uncovering the biological activities of secondary metabolites in medicinal plants and clarifying the molecular mechanisms underlying the production of secondary metabolites. Also, artificial intelligence (AI) technology accelerates the comprehensive utilization of high-dimensional datasets and offers transformative potential for multi-omics analysis. However, there is currently no systematic review summarizing the genomic mechanisms of secondary metabolite biosynthesis in medicinal plants. Safflower (Carthamus tinctorius L.) has rich and diverse bioactive flavonoids, among of which Hydroxysafflor yellow A (HSYA) is specific to safflower and emerging as a potential medication for treating a wide range of diseases. Hence, significant progress has been made in the study of safflower as an excellent example for the regulation of secondary metabolites in medicinal plants in recent years. Here, we review the progress on the understanding of the regulation of main secondary metabolites at the multi-omics level, and summarize the influence of various factors on their types and contents, with a particular focus on safflower flavonoids. This review aims to provide a comprehensive insight into the regulatory mechanisms of secondary metabolite biosynthesis from the perspective of genomics.

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).

A targetable antioxidant defense mechanism to EZH2 inhibitors enhances tumor cell vulnerability to ferroptosis

Epigenetic changes are present in all human cancers and are responsible for switching on or off genes, thus controlling tumor cell transcriptome. These changes occur through DNA methylation, histone modifiers and readers, chromatin remodelers, and microRNAs. The histone H3 methyl-transferase EZH2 gene is overexpressed in several cancer types, including adrenocortical carcinoma (ACC), a rare cancer still lacking a targeted therapy. EZH2 inhibitors (EZH2i) have been tested in several clinical trials, but their effectiveness was limited by the toxic effects of the therapeutic doses. We tested several EZH2i on ACC cells, and observed a significant reduction in cell growth only with doses much higher than those required to prevent H3 methylation. We found that all tested EZH2i doses affected lipid metabolism genes, ROS, and glutathione production. Transcript changes correlated with metabolic data, which suggested the effects of EZH2i on ferroptosis. We found that EZH2i dose-dependently increased SLC7A11/glutathione axis and glutathione peroxidase-4 (GPX4), required to counteract lipid peroxidation and ferroptosis. A GPX4 inhibitor synergized with EZH2i, making low doses - which otherwise do not affect cell viability - able to significantly reduce ACC cell growth in vitro and in vivo. Importantly, we found that the anti-ferroptosis defense mechanism induced by EZH2i is a common response for several aggressive tumor phenotypes, uncovering a general co-targetable mechanism that could limit EZH2i effectiveness. Correcting this antioxidant response by ferroptosis inducers may be a new combination therapy that will easily find clinical applications.

Unveiling Key Genes and Crucial Pathways in Goose Muscle Satellite Cell Biology Through Integrated Transcriptomic and Metabolomic Analyses

Skeletal muscle satellite cells (SMSCs) are quiescent stem cells located in skeletal muscle tissue and function as the primary reservoir of myogenic progenitors for muscle growth and regeneration. However, the molecular and metabolic mechanisms governing their differentiation in geese remain largely unexplored. This study comprehensively examined the morphological, transcriptional, and metabolic dynamics of goose SMSCs across three critical differentiation stages: the quiescent stage (DD0), the differentiation stage (DD4), and the late differentiation stage (DD6). By integrating transcriptomic and metabolomic analyses, stage-specific molecular signatures and regulatory networks involved in SMSC differentiation were identified. Principal component analysis revealed distinct clustering patterns in gene expression and metabolite profiles across these stages, highlighting dynamic shifts in lipid metabolism and myogenesis. The PPAR signaling pathway emerged as a key regulator, with crucial genes such as PPARG, IGF1, ACSL5, FABP5, and PLIN1 exhibiting differentiation-dependent expression patterns. Notably, PPARG and IGF1 displayed negative correlations with adenosine and L-carnitine levels, suggesting their role in metabolic reprogramming during myotube formation. Additionally, MYOM2 and MYBPC1 exhibited stage-specific regulation and positively correlated with 2,3-dimethoxyphenylamine. This study provides a foundational framework for understanding muscle development and regeneration, offering valuable insights for both agricultural and biomedical research.

Isotope tracing-based metabolite identification for mass spectrometry metabolomics

Modern mass spectrometry-based metabolomics is a key technology for biomedicine, enabling discovery and quantification of a wide array of biomolecules critical for human physiology. Yet, only a fraction of human metabolites have been structurally determined, and the majority of features in typical metabolomics data remain unknown. To date, metabolite identification relies largely on comparing MS 2 fragmentation patterns against known standards, related compounds or predicted spectra. Here, we propose an orthogonal approach to identification of endogenous metabolites, based on mass isotopomer distributions (MIDs) measured in an isotope-labeled reference material. We introduce a computational measure of pairwise distance between metabolite MIDs that allows identifying novel metabolites by their similarity to previously known peaks. Using cell material labeled with 20 individual 13 C tracers, this method identified 62% of all unknown peaks, including previously never seen metabolites. Importantly, MID-based identification is highly complementary to MS 2 -based methods in that MIDs reflect the biochemical origin of metabolites, and therefore also yields insight into their synthesis pathways, while MS 2 spectra mainly reflect structural features. Accordingly, our method performed best for small molecules, while MS 2 -based identification was stronger on lipids and complex natural products. Among the metabolites discovered was trimethylglycyl-lysine, a novel amino acid derivative that is altered in human muscle tissue after intensive lifestyle treatment. MID-based annotation using isotope-labeled reference materials enables identification of novel endogenous metabolites, extending the reach of mass spectrometry-based metabolomics.

Spatiotemporal molecular tracing of ultralow-volume biofluids via a soft skin-adaptive optical monolithic patch sensor

Molecular tracing of extremely low amounts of biofluids is vital for precise diagnostic analysis. Although optical nanosensors for real-time spatiotemporal molecular tracing exist, integrating them into simple devices that capture low-volume fluids on rough, dynamic surfaces remains challenging. We present a bioinspired 3D microstructured patch monolithically integrated with optical nanosensors (3D MIN) for real-time, multivariate molecular tracing of ultralow-volume fluids. Inspired by tree frog toe pads, the 3D MIN features soft, hexagonally aligned pillars and microchannels for conformal adhesion and targeted fluid management. Embedding near-infrared fluorescent single-walled carbon nanotube nanosensors in a hydrogel enables simultaneous fluid capture and detection. Softening the elastomer microarchitecture and optimizing water management promote stable adhesion on wet biosurfaces, allowing rapid collection of ultralow-volume fluids (~0.1 µL/min·cm²). We demonstrate real-time, remote sweat analysis with ≥75 nL volumes collected in 45 s, without exercise or iontophoresis, showcasing high biocompatibility and efficient spatiotemporal molecular tracing.

Enhanced fatty acid oxidation via SCD1 downregulation fuels cardiac reprogramming

Direct cardiac reprogramming has emerged as a promising therapeutic strategy to remuscularize injured myocardium. This approach converts non-contractile fibroblasts to induced cardiomyocytes (iCMs) that spontaneously contract, yet the intrinsic metabolic requirements driving cardiac reprogramming are not fully understood. Using single-cell metabolic flux estimation and flux balance analysis, we characterized the metabolic heterogeneity of iCMs and identified fatty acid oxidation (FAO) as a critical factor in iCM conversion. Both pharmacological and genetic inhibition of FAO impairs iCM generation. We further identified stearoyl-coenzyme A desaturase 1 (SCD1) as a metabolic switch that suppresses iCM reprogramming. Mechanistically, Scd1 knockdown activates PGC1α and PPARβ signaling, enhancing FAO-related gene expression and mitochondrial biogenesis, thereby improving reprogramming efficacy. Pharmacological manipulations targeting SCD1, PGC1α, and the PPARβ signaling axis further improved iCM generation and mitochondrial function. Our findings collectively highlight FAO as a key determinant of iCM fate and offer new therapeutic avenues for advancing reprogramming strategies.

Regulation of starch metabolism in banana fruit: Mechanisms shaping the nutritional quality

Bananas are nutrient-rich fruits that provide starch, essential vitamins, and minerals and play significant importance in the global economy through extensive production, trade, and consumption. Nutrient metabolic processes, such as starch-to-sugar conversion, are fundamental in shaping the quality of banana fruits. Starch accounts for 15%-35% of fresh fruit weight, and its degradation mediated by ethylene signaling components can increase sweetness, soften texture, and increase the palatability of banana fruit. This review summarizes recent advances in the regulatory mechanism underlying starch metabolism in banana fruits, highlights key research questions for future investigation, and proposes promising strategies to manipulate starch levels to develop new banana varieties with enhanced nutritional quality.

Untargeted metabolomics reveals anion and organ-specific metabolic responses of salinity tolerance in willow

Willows can alleviate soil salinisation while generating sustainable feedstock for biorefinery, yet the metabolomic adaptations underlying their tolerance remain poorly understood. Salix miyabeana was treated with two environmentally abundant salts, NaCl and Na2SO4, in a 12-week pot trial. Willows tolerated salts across all treatments (up to 9.1 dS m-1 soil ECe), maintaining biomass while selectively partitioning ions, confining Na+ to roots and accumulating Cl- andSO 4 2 - $$ {\mathrm{SO}}_4^{2-} $$ in the canopy and adapting to osmotic stress via reduced stomatal conductance. Untargeted metabolomics captured >5000 putative compounds, including 278 core willow metabolome compounds constitutively produced across organs. Across all treatments, salinity drove widespread metabolic reprogramming, altering 28% of the overall metabolome, with organ-tailored strategies. Comparing salt forms at equimolar sodium, shared differentially abundant metabolites were limited to 3% of the metabolome, representing the generalised salinity response, predominantly in roots. Anion-specific metabolomic responses were extensive. NaCl reduced carbohydrates and tricarboxylic acid cycle intermediates, suggesting potential carbon and energy resource pressure, and accumulated root structuring compounds, antioxidant flavonoids, and fatty acids. Na2SO4 salinity triggered accumulation of sulphur-containing larger peptides, suggesting excess sulphate incorporation leverages ion toxicity to produce specialised salt-tolerance-associated metabolites. This high-depth picture of the willow metabolome underscores the importance of capturing plant adaptations to salt stress at organ scale and considering ion-specific contributions to soil salinity.

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Unravelling disease complexity: integrative analysis of multi-omic data in clinical research

INTRODUCTION

A holistic view on biological systems is today a reality with the application of multi-omic technologies. These technologies allow the profiling of genome, epigenome, transcriptome, proteome, metabolome as well as newly emerging 'omes.' While the multiple layers of data accumulate, their integration and reconciliation in a single system map is a cumbersome exercise that faces many challenges. Application to human health and disease requires large sample sizes, robust methodologies and high-quality standards.

AREAS COVERED

We review the different methods used to integrate multi-omics, as recent ones including artificial intelligence. With proteomics as an anchor technology, we then present selected applications of its data combination with other omics layers in clinical research, mainly covering literature from the last five years in the Scopus and/or PubMed databases.

EXPERT OPINION

Multi-omics is powerful to comprehensively type molecular layers and link them to phenotype. Yet, technologies and data are very diverse and still strategies and methodologies to properly integrate these modalities are needed.

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.

LDHB silencing enhances the effects of radiotherapy by impairing nucleotide metabolism and promoting persistent DNA damage

Lung cancer is the leading cause of cancer-related deaths globally, with radiotherapy as a key treatment modality for inoperable cases. Lactate, once considered a by-product of anaerobic cellular metabolism, is now considered critical for cancer progression. Lactate dehydrogenase B (LDHB) converts lactate to pyruvate and supports mitochondrial metabolism. In this study, a re-analysis of our previous transcriptomic data revealed that LDHB silencing in the NSCLC cell lines A549 and H358 dysregulated 1789 genes, including gene sets associated with cell cycle and DNA repair pathways. LDHB silencing increased H2AX phosphorylation, a surrogate marker of DNA damage, and induced cell cycle arrest at the G1/S or G2/M checkpoint depending on the p53 status. Long-term LDHB silencing sensitized A549 cells to radiotherapy, resulting in increased DNA damage and genomic instability as evidenced by increased H2AX phosphorylation levels and micronuclei accumulation, respectively. The combination of LDHB silencing and radiotherapy increased protein levels of the senescence marker p21, accompanied by increased phosphorylation of Chk2, suggesting persistent DNA damage. Metabolomics analysis revealed that LDHB silencing decreased nucleotide metabolism, particularly purine and pyrimidine biosynthesis, in tumor xenografts. Nucleotide supplementation partially attenuated DNA damage caused by combined LDHB silencing and radiotherapy. These findings suggest that LDHB supports metabolic homeostasis and DNA damage repair in NSCLC, while its silencing enhances the effects of radiotherapy by impairing nucleotide metabolism and promoting persistent DNA damage.

Exploring the relationship between serum 25-hydroxyvitamin D levels and intestinal fungal communities and their metabolites in postmenopausal Chinese women

BACKGROUND

Research gaps persist in understanding the interactions between serum 25 (OH)D levels, intestinal fungi, and their metabolites in postmenopausal women.

METHODS

This study, approved by the Ethics Committee of Zhongshan Hospital, Xiamen University, recruited postmenopausal women from Xiamen. Clinical assessments included Body Mass Index (BMI) calculations and blood tests for various bone-related markers using Roche's electrochemiluminescence system. Bone density was measured via dual-energy X-ray absorptiometry. Fecal DNA was extracted for Internal Transcribed Spacer (ITS) sequencing with a two-stage PCR process and analyzed using high-throughput Illumina sequencing. Metabolites were extracted from fecal samples and analyzed by ultra-high-performance liquid chromatography combined with mass spectrometry. Statistical analyses and data visualization were performed using R, focusing on fungal community structure and correlations with metabolites.

RESULTS

The study analyzed 81 postmenopausal women, categorized into vitamin D deficient (VDD), insufficient (VDI), and sufficient (VDS) groups based on serum 25 (OH)D levels. Other health markers, including age and BMI, were consistent across groups. Notably, Linear discriminant analysis identified distinct fungal communities across VDD, VDI, and VDS groups. In the VDD group, notable fungi included Hanseniaspora occidentalis and Pichia. The VDI group showed enrichment of Candida, while the VDS group had higher abundances Such as Phanerochaete, and Nectriaceae. Alpha diversity metrics, such as the Chao1 index, differed significantly among the groups (p < 0.05). Correlation analysis (Spearman) revealed that fungi like Trichosporon and Penicillium positively associated with 25 (OH)D3, whereas fungi such as Cystofilobasidium were negatively correlated with bone mineral density (BMD). Metabolites like Glutaric acid positively correlated with 25 (OH)D3, while L-Citrulline and Deoxycholic acid were negatively correlated. Additionally, Argininosuccinic acid correlated positively with BMD, whereas Acamprosate and p-Hydroxyphenylacetic acid were negatively associated.

CONCLUSION

In postmenopausal women, fungal community composition varies significantly with vitamin D status, potentially correlating with serum 25 (OH)D levels and BMD, indicating that specific fungal species may be relevant for therapeutic strategies with osteoporosis and offering insights into the broader bone health effects of vitamin D.

Hollow Mesoporous Carbon Nanospheres/Ni Hybrids Aid in Metabolic Encoding for COVID-19 Recovery Assessment in Mothers and Fetuses

Metabolite analysis of body fluids is an advanced method for disease diagnosis and status assessment. Laser desorption/ionization-mass spectrometry (LDI-MS) has been widely employed for metabolic analysis due to the fast detection speed and simple sample pretreatment. Here, we designed and synthesized hollow mesoporous carbon nanospheres anchored with Ni (HMCSs/Ni) to simultaneously enhance the ionization and thermal desorption processes of the LDI process owing to their hollow and mesoporous structure, large surface area, and abundant Ni-N bonds. Based on HMCSs/Ni, we built an LDI-MS platform that can be used for metabolic information extraction and achieved the rapid detection (about seconds per sample) of metabolic fingerprints in trace serum samples (∼0.1 μL) without complicated preprocessing procedures. Then, we conducted serum metabolic screening in a cohort of COVID-19-recovered pregnant women. The optimized machine learning model could distinguish recovered pregnant women from uninfected pregnant women based on metabolic features with an AUC value of 0.901. In addition, the model indicates that maternal COVID-19 infection does not significantly affect the metabolic fingerprints of the fetuses. Overall, our work shows the prospect of HMCSs/Ni-assisted LDI-MS in disease recovery assessment and metabolite analysis.

Fatty Acid Metabolic Impairment in Soil Microbes Induced by PFAS: Dependence on the Fluorocarbon Chain Length, Headgroups, and Ether Attached

Per- and polyfluoroalkyl substances (PFASs) with varying chain lengths, headgroups, and alternative structures are widespread and persistent in soil, yet knowledge of their biological effects and toxic mechanisms on soil microorganisms is limited. This study identified the structure-dependent impacts of PFAS on microbial fatty acid (FA) profiles by integrated field-laboratory investigations. The field investigation uncovered distinct PFAS-FA interaction patterns, varying by PFAS fluorocarbon (FC) chain length and functional group, and FA hydrocarbon (HC) chain length and unsaturation degree. Specifically, long-chain perfluoroalkyl carboxylic acids (PFCAs, FC7-17) increased medium/long-chain FAs (HC ≤ 17) and monounsaturated FAs (MUFAs), while long-chain perfluoroalkyl sulfonic acids (PFSAs, FC6-8) enhanced longer-chain FAs (HC > 17) and MUFAs. Additionally, perfluoroether carboxylic acids (PFECAs) as well as short-chain PFCAs (FC < 7) and PFSAs (FC < 6), which commonly used as alternatives to long-chain PFCAs/PFSAs, were associated with polyunsaturated FAs (PUFAs). The laboratory investigation with Pseudomonas aeruginosa PAO1 validated these FA metabolic disruptions and revealed broader perturbations in phospholipids, acetyl-CoA, and secondary metabolite metabolisms, providing insights into dysregulated membrane synthesis, C/N metabolisms, and bacteria quorum sensing (QS) processes. These findings reveal novel structure-dependent effects of PFASs on microorganisms, highlighting microbial FA profiles as potential metabolic biomarkers for assessing PFAS toxicity and soil health.

Reprogramming yeast metabolism for customized starch-rich micro-grain through low-carbon microbial manufacturing

Starch is a primary food ingredient and industrial feedstock. Low-carbon microbial manufacturing offers a carbon-neutral/negative arable land-independent strategy for starch production. Here, we reconfigure the oleaginous yeast as a starch-rich micro-grain producer by rewiring the starch biosynthesis and gluconeogenesis pathways and regulating cell morphology. With the CO2 electro-synthesized acetate as the substrate, the strain accumulates starch 47.18% of dry cell weight. The optimized system renders spatial-temporal starch productivity (243.7 g/m2/d) approximately 50-fold higher than crop cultivation and volumetric productivity (160.83 mg/L/h) over other microbial systems by an order of magnitude. We demonstrate tunable starch composition and starch-protein ratios via strain and process engineering. The engineered artificial strains adopt a cellular resources reallocation strategy to ensure high-level starch production in micro-grain and could facilitate a highly efficient straw/cellulose-to-starch conversion. This work elucidates starch biosynthesis machinery and establishes a superior-to-nature platform for customizable starch synthesis, advancing low-carbon nutritional manufacturing.

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.

Impact of Oil Temperature and Splashing Frequency on Chili Oil Flavor: Volatilomics and Lipidomics

In this study, headspace gas chromatography-ion mobility spectrometry, headspace gas chromatography-mass spectrometry, and lipidomics were used to explore the effects of three oil temperatures (210 °C, 180 °C, 150 °C) with single- and traditional triple-oil-splashing processes (210 °C → 180 °C → 150 °C) on the formation of key chili oil aromas. A total of 31 key aroma compounds were identified, with 2,4-nonadienal, α-pinene, α-phellandrene, and β-ocimene being found in all treatment groups. Lipidomics suggested that oleic acid, linoleic acid, and α-linolenic acid were highly positively correlated with key chili oil key aroma compounds, such as (E)-2-heptenal, 2-methylbutyraldehyde, limonene, (E, E)-2,4-heptadienal, 2,4-nonadienal, and 2,4-decadienal. The temperature and frequency of oil splashing significantly affected the chili oil aroma profile (p < 0.05). The citrus, woody, and grassy notes were richer in chili oil prepared at 150 °C, malty and fatty aromas were more prominent at 180 °C, and the nutty aroma was stronger in 210 °C prepared and triple-splashed chili oil. The present study reveals how sequential oil splashing processes synergistically activate distinct lipid degradation pathways compared to single-temperature treatments, providing new insights into lipid-rich condiment preparation, enabling chefs and food manufacturers to target specific aroma profiles.

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.

Effect of Antifreeze Glycopeptides on the Quality and Microstructure of Frozen Lamb Meatballs

This study explored the protective effects of antifreeze glycopeptide and alginate on the quality of -18 °C frozen lamb meatballs across various storage periods.

METHODS

Measurements of volatile salt nitrogen (TVB-N), thiobarbituric acid (TBARS), water retention, water distribution, microstructure, and metabolite changes were taken in the lamb meatballs.

RESULTS

The results showed that the addition of antifreeze glycopeptides (AFGs) significantly preserved the quality characteristics of lamb meatballs. In particular, the 0.30% antifreeze glycopeptide demonstrated the strongest protective effect on water retention and metabolites during freezing. The ice crystal area within the microstructure of lamb meatballs with added antifreeze glycopeptides was markedly reduced compared to the others after 14 days of freezing (p < 0.05). Additionally, AFGs lessened the lipid oxidation reaction and prolonged the oxidation time of lamb after 28 days of freezing.

CONCLUSION

In summary, AFGs beneficially affected the quality of frozen lamb meatballs and are a potential, safe, and efficient cryoprotectant.

Contribution of metabolomics to the taxonomy and systematics of octocorals from the Tropical Eastern Pacific

Octocorals are sessile invertebrates that play a key role in marine habitats, with significant diversity in the Tropical Eastern Pacific, especially in Ecuador's shallow waters. This study focuses on the most representative octocorals within the Marine Protected Area El Pelado, Santa Elena, Ecuador, as a part of a marine biodiscovery project employing an integrative approach. While molecular techniques have advanced, challenges persist in distinguishing closely related species. Octocorals produce a wide range of compounds, characterized by unique chemical structures and diverse biological properties. Therefore, the main objective of this study was to assess the potential of metabolomics and advanced analytical techniques to analyze the metabolome of these organisms, aiming to refine species classification and improve understanding of octocoral systematics in this region. Untargeted metabolomics effectively discriminates 12 octocoral species across five genera: Muricea, Leptogorgia, Pacifigorgia, Psammogorgia, and Heterogorgia, with notable differentiation between species within the genus Muricea, reinforcing its utility as an additional data set for species characterization. Secondary metabolites such as sterols, steroids, and terpenes (furanocembranolides and sesquiterpenes), were identified in Leptogorgia and Muricea. Overall, this method enabled the identification of 11 known species and a potentially new one, Leptogorgia cf. alba, confirming the extreme diversity of this group in the Tropical Eastern Pacific and within the Ecuadorian marine ecosystem. The study highlights the value of metabolomics in octocoral systematics and encourages for its broader application in marine biodiversity research.

Ultrasensitive Determination of Amino Acids in Single Cells by Chemical Isotope Labeling with Liquid Chromatography Mass Spectrometry Analysis

Amino acids play multiple critical roles in the regulation of various metabolic pathways and physiological processes in living organisms. Mass spectrometry (MS) has become the most pioneering platform for amino acid analysis. However, the simultaneous and sensitive determination of amino acids is still challenging because of their structural similarity and broad ranges of concentrations. To this end, a pair of isotope labeling reagents, d0/d3-2-((diazomethyl)phenyl)(9-methyl-1,3,4,9-tetrahydro-2H-pyrido[3,4-b]indol-2-yl) methanone (DMPI/d3-DMPI), were applied to label amino acid metabolites. The diazo groups on the pair of isotopomers (DMPI/d3-DMPI) can specifically react with the carboxyl groups on the amino acids. The results showed that the retention on reversed-phase column were enhanced and the detection sensitivities of 19 amino acids were increased benefiting from DMPI labeling strategy that transfers the hydrophobic indole heterocycle group of DMPI to the hydrophilic compounds of amino acids. The obtained limits of detection (LODs) of amino acids were in the range of 0.002-0.082 fmol. With this established method, we achieved the sensitive detection of amino acids in a single HUVE cell. Meanwhile, we found that the contents of amino acids in the serum of premature neonates were higher compared to normal neonates. Overall, this developed method provides great support of detection tool for the clinical metabolomic study of amino acids and the investigation of dynamic changes of amino acid metabolism in single cells.