HMDB 4.0: the human metabolome database for 2018

Gut Microbiome-Driven metabolites influence skin pigmentation in TYRP1 mutant Oujiang Color Common Carp

The gut microbiome plays a key role in regulating the gut-skin axis, and host genetics partially influence this regulation. The study investigated the role of gut microbiota and host genetics in the gut-skin axis, focusing on the unusual "coffee-like" color phenotype observed in TYRP1 mutant Oujiang Color Common Carp. We employed comparative high-throughput omics data from wild-type and mutant fish to quantify the influence of both genetics and gut microbes on skin transcriptomic expression and blood metabolites. We found 525 differential metabolites (DMs) and 45 distinct gut microbial genera in TYRP1 mutant fish compared to wild type. Interaction and causal mediation analyses revealed a complex interplay. The TYRP1 mutation likely triggers an inflammatory pathway involving Acinetobacter bacteria, Leukotrience-C4 and Spermine. This inflammatory response appears to be counterbalanced by an anti-inflammatory cardiovascular genetic network. The net effect is the upregulation of COMT, PLG, C2, C3, F10, TDO2, MHC1, and SERPINF2, leading to unusual coffee-like coloration. This study highlights the intricate interplay between gut microbiota, host genetics, and metabolic pathways in shaping complex phenotypes.

Discovery of AMPs from random peptides via deep learning-based model and biological activity validation

The ample peptide field is the best source for discovering clinically available novel antimicrobial peptides (AMPs) to address emerging drug resistance. However, discovering novel AMPs is complex and expensive, representing a major challenge. Recent advances in artificial intelligence (AI) have significantly improved the efficiency of identifying antimicrobial peptides from large libraries, whereas using random peptides as negative data increases the difficulty of discovering antimicrobial peptides from random peptides using discriminative models. In this study, we constructed three multi-discriminator models using deep learning and successfully screened twelve AMPs from a library of 30,000 random peptides. three candidate peptides (P2, P11, and P12) were screened by antimicrobial experiments, and further experiments showed that they not only possessed excellent antimicrobial activity but also had extremely low hemolytic activity. Mechanistic studies showed that these peptides exerted their bactericidal effects through membrane disruption, thus reducing the possibility of bacterial resistance. Notably, peptide 12 (P12) showed significant efficacy in a mouse model of Staphylococcus aureus wound infection with low toxicity to major organs at the highest tested dose (400 mg/kg). These results suggest deep learning-based multi-discriminator models can identify AMPs from random peptides with potential clinical applications.

Olive tree leaves as a great source of phenolic compounds: Comprehensive profiling of NaDES extracts

Waste from the olive industry is a noticeable source of antioxidant compounds that can be extracted and reused to produce raw materials related to the chemical, cosmetic, food and pharmaceutical sectors. This work studies the phenolic composition of olive leaf samples using liquid chromatography with ultraviolet detection coupled to mass spectrometry (LC-UV-MS). Olive leaf waste samples have been crushed, homogenized, and subjected to a solid-liquid extraction treatment with mechanical shaking at 80 °C for 2 h using Natural Deep Eutectic Solvents (NaDES). The phenolic compound identification in the resulting extracts has been carried out by high-resolution mass spectrometry (HRMS) using data-dependent acquisition mode using an Orbitrap HRMS instrument. >60 different phenolic compounds have been annotated tentatively, of which about 20 have been confirmed from the corresponding standards. Some of the most noticeable compounds are oleuropein and its aglycone and glucoside form, luteolin-7-O-glucoside, 3-hydroxytyrosol, and verbascoside.

Machine learning methods for compound annotation in non-targeted mass spectrometry-A brief overview of fingerprinting, in silico fragmentation and de novo methods

Non-targeted screenings (NTS) are essential tools in different fields, such as forensics, health and environmental sciences. NTSs often employ mass spectrometry (MS) methods due to their high throughput and sensitivity in comparison to, for example, nuclear magnetic resonance-based methods. As the identification of mass spectral signals, called annotation, is labour intensive, it has been used for developing supporting tools based on machine learning (ML). However, both the diversity of mass spectral signals and the sheer quantity of different ML tools developed for compound annotation present a challenge for researchers in maintaining a comprehensive overview of the field. In this work, we illustrate which ML-based methods are available for compound annotation in non-targeted MS experiments and provide a nuanced comparison of the ML models used in MS data analysis, unravelling their unique features and performance metrics. Through this overview we support researchers to judiciously apply these tools in their daily research. This review also offers a detailed exploration of methods and datasets to show gaps in current methods, and promising target areas, offering a starting point for developers intending to improve existing methodologies.

Proteomics and metabolomics analysis of American shad (Alosa sapidissima) liver responses to heat stress

The dramatic changes in the global climate pose a major threat to the survival of many organisms, including fish. To date, the regulatory mechanisms behind the physiological responses of fish to temperature changes have been studied, and a comprehensive analysis of the regulatory mechanisms of temperature tolerance will help to propose effective strategies for fish to cope with global warming. In this study, we investigated the expression profiles of proteins and metabolites in liver tissues of American shad (Alosa sapidissima) corresponding to different water temperatures (24 °C, 27 °C and 30 °C) at various times (1-month intervals) under natural culture conditions. Proteomic analysis showed that the expression levels of the heat shock protein family (e.g. HSPE1, HSP70, HSPA5 and HSPA.1) increase significantly with temperature and that many differentially expressed proteins were highly enriched especially in pathways related to the endoplasmic reticulum, oxidative phosphorylation and glycolysis/gluconeogenesis processes. In addition, the results of conjoint metabolomics and proteomics analysis suggested that the contents of several important amino acids and chemical compounds, including L-serine, L-isoleucine, L-cystine, choline and betaine, changed significantly under high-temperature environmental stress, affecting the metabolic levels of starch, amino acid and glucose, which is thought to represent a possible energy conservation method for A. sapidissima to cope with rapid changes in external temperature. In summary, our findings demonstrate that living under high temperatures for a long period of time leads to different physiological defense responses in A. sapidissima, which provides some new ideas for analyzing the molecular regulatory patterns of adaptation to high temperature and also provides a theoretical basis for the subsequent improvement of fish culture in response to global warming.

Knowledge translation and knowledge mobilization from the FoodBAll project

This report describes the knowledge mobilization and translation outcomes of the Canadian-funded portion of a large, international project called the Food Biomarker Alliance (FoodBAll), which ran from 2015 to 2019. This remarkably successful project led to a large number of important findings, outputs, and impacts. In particular, FoodBAll unequivocally demonstrated that metabolomics could be used to not only discover biomarkers of food intake (BFIs), but also to measure diet in a more objective manner. FoodBAll also created standards for assessing and validating BFIs, papers and databases describing BFIs, and kits for measuring BFIs and it laid the groundwork for many global studies exploring food composition and precision nutrition.

Indole-3-Acetic Acid Protects Against Lipopolysaccharide-induced Endothelial Cell Dysfunction and Lung Injury through the Activation of USP40

Lung microvascular endothelial cell (EC) dysfunction is the pathological hallmark of acute respiratory distress syndrome. Heat shock protein 90 (HSP90) is a key regulator in control of endothelial barrier disruption and inflammation. Our recent study has demonstrated that ubiquitin-specific peptidase 40 (USP40) preserves endothelial integrity by targeting HSP90β for its deubiquitination and inactivation. Indole-3-acetic acid (IAA), a plant hormone of the auxin class, can also be catabolized from dietary tryptophan by the intestinal microbiota. Accumulating evidence suggests that IAA reduces oxidative stress and inflammation and promotes intestinal barrier function. However, little is known about the role of IAA in endothelial cells and acute lung injury. In this study, we investigated the role of IAA in lung endothelial cell function in the context of acute lung injury. IAA exhibited EC barrier protection against LPS-induced reduction in transendothelial electrical resistance and inflammatory responses. The underlying mechanism of IAA on EC protective effects was investigated by examining the influence of IAA on degrees of HSP90 ubiquitination and USP40 activity. We identified that IAA, acting as a potential activator of USP40, reduces HSP90 ubiquitination, thereby protecting against LPS-induced inflammation in human lung microvascular endothelial cells as well as alleviating experimental lung injury. Furthermore, the EC protective effects of IAA against LPS-induced EC dysfunction and lung injury were abolished in USP40-deficient human lung microvascular endothelial cell and lungs of USP40 EC-specific knockout (USP40cdh5-ECKO) mice. Taken together, this study reveals that IAA protects against LPS-induced EC dysfunction and lung injury through the activation of USP40.

Efficacy and mechanism of the Ermiao San series of formulas for rheumatoid arthritis based on Chinmedomics strategy

BACKGROUND

The Ermiao San Series of Formulas (ESSF) refers to Ermiao San (TS), Sanmiao Wan (TW), and Simiao Wan (FW), which are widely used traditional Chinese medicine (TCM) formulas for treating rheumatoid arthritis (RA). However, the therapeutic advantages and underlying mechanisms of ESSF treatment are unclear, especially regarding the improper selection of these three formulas when treating RA.

PURPOSE

To explore the efficacy and mechanisms of ESSF treatment for RA.

METHODS

Complete Freund's adjuvant was used to induce RA in rats. Chinmedomics strategy, which included metabolomics, serum pharmacochemistry of TCM, molecular docking, western blotting and qPCR, was applied to reveal the therapeutic advantages, pathways, and targets of ESSF.

RESULTS

In the early stages of treatment, TS quickly reduced joint swelling and the arthritis score index and regulated pathways such as arachidonic acid metabolism and purine metabolism. TW increases the regulation of tryptophan metabolism and pyrimidine metabolism pathways, promoting the recovery of the thymus and spleen. FW increases the regulation of linoleic acid metabolism and has the greatest effect on immune organ and bone recovery. In addition, 54, 67, and 86 bioactive compounds were detected in the serum from TS, TW, and FW, respectively. Berberine, phellodendrine, atractylolide III, limonin, 25R-inokosterone, coixol, and stigmasterol were found to act on the key enzymes COX-2, mPGES-1, ALOX5, and XDH in arachidonic acid metabolism and purine metabolism pathways. Western blot and qPCR results showed that ESSF can reduce the activity of these targets, thereby inhibiting the expression of the inflammatory factors IL-1β, IL-6, IL-17, and TNF-α; the tissue injury factors MMP-3 and CRP; and the rheumatoid factors CCP Ab and RF, thereby achieving anti-RA efficacy.

CONCLUSION

ESSF has a good therapeutic effect on RA. TS focus on rapid swelling reduction in the early stages of RA, TW focus on the recovery of immune organ function, and FW can be used for bone recovery in the later stage of RA treatment. The key mechanism of treating RA is that ESSF reduces the activity of COX-2, mPGES-1, ALOX5, and XDH. These findings provide valuable guidance for targeted therapy for RA and for the clinical application of ESSF.

Dietary biomarkers-an update on their validity and applicability in epidemiological studies

The aim of this literature review was to identify and provide a summary update on the validity and applicability of the most promising dietary biomarkers reflecting the intake of important foods in the Western diet for application in epidemiological studies. Many dietary biomarker candidates, reflecting intake of common foods and their specific constituents, have been discovered from intervention and observational studies in humans, but few have been validated. The literature search was targeted for biomarker candidates previously reported to reflect intakes of specific food groups or components that are of major importance in health and disease. Their validity was evaluated according to 8 predefined validation criteria and adapted to epidemiological studies; we summarized the findings and listed the most promising food intake biomarkers based on the evaluation. Biomarker candidates for alcohol, cereals, coffee, dairy, fats and oils, fruits, legumes, meat, seafood, sugar, tea, and vegetables were identified. Top candidates for all categories are specific to certain foods, have defined parent compounds, and their concentrations are unaffected by nonfood determinants. The correlations of candidate dietary biomarkers with habitual food intake were moderate to strong and their reproducibility over time ranged from low to high. For many biomarker candidates, critical information regarding dose response, correlation with habitual food intake, and reproducibility over time is yet unknown. The nutritional epidemiology field will benefit from the development of novel methods to combine single biomarkers to generate biomarker panels in combination with self-reported data. The most promising dietary biomarker candidates that reflect commonly consumed foods and food components for application in epidemiological studies were identified, and research required for their full validation was summarized.

Causal Relationship Between Gut Microbiota, Metabolites, and Sarcopenia: A Mendelian Randomization Study

BACKGROUND

Gut microbiota imbalance and sarcopenia are frequently observed in older adults. Gut microbiota and their metabolites are considered risk factors contributing to the heightened risk of sarcopenia, but whether these associations are causal remains unclear.

METHODS

We conducted linkage disequilibrium score regression and 2-sample Mendelian randomization (MR) methods with single-nucleotide polymorphisms sourced from large-scale genome-wide association studies as instrumental variables to examine the causal associations linking gut microbiota with their metabolites to the sarcopenia. Following the MR analysis, subsequent sensitivity analyses were conducted to reinforce the robustness and credibility of the obtained results.

RESULTS

MR analysis yielded compelling evidence demonstrating the correlation between genetically predicted gut microbiota and metabolites and the risk of sarcopenia. The abundance of Porphyromonadaceae, Rikenellaceae, Terrisporobacter, and Victivallis was found to be associated with walking pace. Our study also found suggestive associations of 12 intestinal bacteria with appendicular lean mass, and of Streptococcaceae, Intestinibacter, Paraprevotella, Ruminococcaceae UCG009, and Sutterella with grip strength. Specifically, we identified 21 gut microbiota-derived metabolites that may be associated with the risk of sarcopenia.

CONCLUSIONS

Utilizing a 2-sample MR approach, our study elucidates the causal interplay among gut microbiota, gut microbiota-derived metabolites, and the occurrence of sarcopenia. These findings suggest that gut microbiota and metabolites may represent a potential underlying risk factor for sarcopenia, and offer the promise of novel therapeutic focal points.

A plasma lipid signature in acute human traumatic brain injury: Link with neuronal injury and inflammation markers

Traumatic brain injury (TBI) leads to major membrane lipid breakdown. We investigated plasma lipids over 3 days post-TBI, to identify a signature of acute human TBI and assess its correlation with neuronal injury and inflammation. Plasma from patients with TBI (Abbreviated Injury Scale (AIS)3 - serious injury, n = 5; AIS4 - severe injury, n = 8), and controls (n = 13) was analysed for lipidomic profile, neurofilament light (NFL) and cytokines, and the omega-3 index was measured in red blood cells. A lipid signature separated TBI from controls, at 24 and 72 h. Major species driving the separation were: lysophosphatidylcholine (LPC), phosphatidylcholine (PC) and hexosylceramide (HexCer). Docosahexaenoic acid (DHA, 22:6) and LPC (0:0/22:6) decreased post-injury. NFL levels were increased at 24 and 72 h post-injury in AIS4 TBI vs. controls. Interleukin (IL-)6, IL-2 and IL-13 were elevated at 24 h in AIS4 patients vs. controls. NFL and IL-6 were negatively correlated with several lipids. The omega-3 index at admission was low in all patients (controls: 4.3 ± 1.1% and TBI: 4.0 ± 1.1%) and did not change significantly over 3 days post-injury. We have identified specific lipid changes, correlated with markers of injury and inflammation in acute TBI. These observations could inform future lipid-based therapeutic approaches.

BCAA mediated microbiota-liver-heart crosstalk regulates diabetic cardiomyopathy via FGF21

BACKGROUND

Diabetic cardiomyopathy (DCM) is one of leading causes of diabetes-associated mortality. The gut microbiota-derived branched-chain amino acids (BCAA) have been reported to play a central role in the onset and progression of DCM, but the potential mechanisms remain elusive.

RESULTS

We found the type 1 diabetes (T1D) mice had higher circulating BCAA levels due to a reduced BCAA degradation ability of the gut microbiota. Excess BCAA decreased hepatic FGF21 production by inhibiting PPARα signaling pathway and thereby resulted in a higher expression level of cardiac LAT1 via transcription factor Zbtb7c. High cardiac LAT1 increased the levels of BCAA in the heart and then caused mitochondrial damage and myocardial apoptosis through mTOR signaling pathway, leading to cardiac fibrosis and dysfunction in T1D mice. Additionally, transplant of faecal microbiota from healthy mice alleviated cardiac dysfunction in T1D mice, but this effect was abolished by FGF21 knockdown.

CONCLUSIONS

Our study sheds light on BCAA-mediated crosstalk among the gut microbiota, liver and heart to promote DCM and FGF21 serves as a key mediator. Video Abstract.

Metabolomic analysis revealed the inflammatory and oxidative stress regulation in response to Vibrio infection in Plectropomus leopardus

Frequent outbreaks of infectious diseases in aquaculture have led to significant economic losses. The leopard coral grouper (Plectropomus leopardus) often suffers from vibriosis. Improving host immunity presents a superior strategy for disease control, with minimal side effects compared to the use of antibiotics, highlighting the necessity of exploring the mechanisms underlying the fish's response to pathogen infections. Here, we conducted a comparative metabolomic analysis on the livers of the P. leopardus infected with Vibrio harveyi. A total of 1124 differential metabolites (DMs) were identified, with 190, 218, 359, and 353 DMs being identified at 6, 12, 24, and 48 h post-infection (hpi), respectively. Then, based on the time series analysis, we found that the lipid metabolism pathways were modulated in response to the Vibrio infection, with an increase in the quantity of eicosanoids and gycerophospholipids (GPLs), as well as a decrease in the quantity of bile acids (BAs), vitamin D, and sex hormones. Furthermore, 13 enriched pathways involving 31 DMs were identified through KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analyses. We identified histamine, 15(S)-HpETE, and anandamide in the transient receptor potential (TRP) channels pathway, as well as (7S,8S)-DiHODE, 5S,8R-DiHODE, and 13(S)-HpODE in the linoleic acid (LA) metabolism pathway. The DM levels increased, which may be attributed to inflammation. The DMs in the thyroid hormone synthesis pathway were identified, and the contents of nicotinamide adenine dinucleotide phosphate (NADPH) and glutathione (GSH) decreased, which may be crucial in antioxidants. Our findings highlighted the dynamic adjustments in lipid metabolism and the response to inflammation and oxidative stress during the infection of V. harveyi in P. leopardus. This study not only deepens our understanding of the metabolic underpinnings of fish immune responses but also lays the groundwork for research into functional metabolomics and mechanisms of disease resistance.

Monoacylglycerol lipase blockades the senescence-associated secretory phenotype by interfering with NF-κB activation and promotes docetaxel efficacy in prostate cancer

Metabolic reprogramming and cellular senescence greatly contribute to cancer relapse and recurrence. In aging and treated prostate, persistent accumulating senescence-associated secretory phenotype (SASP) of cancer cells often limits the overall survival of patients. Novel strategic therapy with monoacylglycerol lipase (MGLL) upregulation that counters the cellular and docetaxel induced SASP might overcome this clinical challenge in prostate cancer (PCa). With primary comparative expression and survival analysis screening of fatty acid (FA) metabolism signature genes in the TCGA PCa dataset and our single center cohort, MGLL was detected to be downregulated in malignancy prostate tissues and its low expression predicted worse progression-free and overall survival. Functionally, overexpression of MGLL mainly suppresses NF-κB-driven SASP (N-SASP) which mostly restricts the cancer cell paracrine and autocrine tumorigenic manners and the corresponding cellular senescence. Further investigating metabolites, we determined that MGLL constitutive expression prevents lipid accumulation, decreases metabolites preferably, and consequently downregulates ATP levels. Overexpressed MGLL inhibited IκBα phosphorylation, NF-κB p65 phosphorylation, and NF-κB nuclear translocation to deactivate NF-κB transcriptional activities, and be responsible for the repressed N-SASP, partially through reducing ATP levels. Preclinically, combinational treatment with MGLL overexpression and docetaxel chemotherapy dramatically delays tumor progression in mouse models. Taken together, our findings identify MGLL as a switch for lipase-related N-SASP suppression and provide a potential drug candidate for promoting docetaxel efficacy in PCa.

Metabolite comparative variation related lipid metabolisms among fruit, leaf, and stem of Jatropha curcas

The issue of non-renewable energy scarcity has persisted over an extended period, primarily due to the depletion of fossil fuel reserves and the adverse effects of their utilization. This scarcity stems from the finite nature of fossil energy resources. The development of oil energy or biofuels aims to utilize oil-producing plants such as Jatropha curcas to develop alternative energy resources. However, metabolomic studies in Jatropha curcas are limited and need more investigations. Therefore, this research was essential to find biomarkers of metabolites among the fruit, leaf, and stem of Jatropha curcas using the GC-MS technique. We tested the metabolite profile with the R program, especially the metaboanalystR package, to determine fold change metabolite and pathway analysis. We found that 54 metabolites were detected in both fruit, leaf, and stem tissues of Jatropha curcas L, of which 19 metabolites were upregulated in the fruit, 20 metabolites in the leaf, and 15 up-regulated metabolites in the stem. The metabolites found formed three clusters based on correlation and networking metabolites analysis. The three clusters showed a relationship with the lipid biosynthesis pathway. In this study, provisional information was obtained that there was a different pattern of expression of metabolites between fruit, leaf, and stem tissues in Jatropha curcas, which was thought to be related to the critical metabolites of oleic acid and methylcyclohexane carboxylate in the biosynthetic pathway of fatty acids and unsaturated fatty acids. This information is essential as an initial reference for genetic engineering Jatropha curcas so that it can be used to transform plants, especially lipid-producing plants, as a source of oil.

Growth of complete ammonia oxidizers on guanidine

Guanidine is a chemically stable nitrogen compound that is excreted in human urine and is widely used in manufacturing of plastics, as a flame retardant and as a component of propellants, and is well known as a protein denaturant in biochemistry1-3. Guanidine occurs widely in nature and is used by several microorganisms as a nitrogen source, but microorganisms growing on guanidine as the only substrate have not yet been identified. Here we show that the complete ammonia oxidizer (comammox) Nitrospira inopinata and probably most other comammox microorganisms can grow on guanidine as the sole source of energy, reductant and nitrogen. Proteomics, enzyme kinetics and the crystal structure of a N. inopinata guanidinase homologue demonstrated that it is a bona fide guanidinase. Incubation experiments with comammox-containing agricultural soil and wastewater treatment plant microbiomes suggested that guanidine serves as substrate for nitrification in the environment. The identification of guanidine as a growth substrate for comammox shows an unexpected niche of these globally important nitrifiers and offers opportunities for their isolation.

Epigenome-wide association study on the plasma metabolome suggests self-regulation of the glycine and serine pathway through DNA methylation

BACKGROUND

The plasma metabolome reflects the physiological state of various biological processes and can serve as a proxy for disease risk. Plasma metabolite variation, influenced by genetic and epigenetic mechanisms, can also affect the cellular microenvironment and blood cell epigenetics. The interplay between the plasma metabolome and the blood cell epigenome remains elusive. In this study, we performed an epigenome-wide association study (EWAS) of 1183 plasma metabolites in 693 participants from the LifeLines-DEEP cohort and investigated the causal relationships in DNA methylation-metabolite associations using bidirectional Mendelian randomization and mediation analysis.

RESULTS

After rigorously adjusting for potential confounders, including genetics, we identified five robust associations between two plasma metabolites (L-serine and glycine) and three CpG sites located in two independent genomic regions (cg14476101 and cg16246545 in PHGDH and cg02711608 in SLC1A5) at a false discovery rate of less than 0.05. Further analysis revealed a complex bidirectional relationship between plasma glycine/serine levels and DNA methylation. Moreover, we observed a strong mediating role of DNA methylation in the effect of glycine/serine on the expression of their metabolism/transport genes, with the proportion of the mediated effect ranging from 11.8 to 54.3%. This result was also replicated in an independent population-based cohort, the Rotterdam Study. To validate our findings, we conducted in vitro cell studies which confirmed the mediating role of DNA methylation in the regulation of PHGDH gene expression.

CONCLUSIONS

Our findings reveal a potential feedback mechanism in which glycine and serine regulate gene expression through DNA methylation.

Impact of Partial Body Shielding from Very High Dose Rates on Untargeted Metabolomics in Biodosimetry

A realistic exposure to ionizing radiation (IR) from an improvised nuclear device will likely include individuals who are partially shielded from the initial blast delivered at a very high dose rate (VHDR). As different tissues have varying levels of radiosensitivity, e.g., hematopoietic vs gastrointestinal tissues, the effects of shielding on radiation biomarkers need to be addressed. Here, we explore how biofluid (urine and serum) metabolite signatures from male and female C57BL/6 mice exposed to VHDR (5-10 Gy/s) total body irradiation (TBI, 0, 4, and 8 Gy) compare to individuals exposed to partial body irradiation (PBI) (lower body irradiated [LBI] or upper body irradiated [UBI] at an 8 Gy dose) using a data-independent acquisition untargeted metabolomics approach. Although sex differences were observed in the spatial groupings of urine signatures from TBI and PBI mice, a metabolite signature (N6,N6,N6-trimethyllysine, carnitine, propionylcarnitine, hexosamine-valine-isoleucine, taurine, and creatine) previously developed from variable dose rate experiments was able to identify individuals with high sensitivity and specificity, irrespective of radiation shielding. A panel of serum metabolites composed from previous untargeted studies on nonhuman primates had excellent performance for separating irradiated cohorts; however, a multiomic approach to complement the metabolome could increase dose estimation confidence intervals. Overall, these results support the inclusion of small-molecule markers in biodosimetry assays without substantial interference from the upper or lower body shielding.

Integrative plasma and fecal metabolomics identify functional metabolites in adenoma-colorectal cancer progression and as early diagnostic biomarkers

Changes in plasma and fecal metabolomes in colorectal cancer (CRC) progression (normal-adenoma-CRC) remain unclear. Here, plasma and fecal samples were collected from four independent cohorts of 1,251 individuals (422 CRC, 399 colorectal adenoma [CRA], and 430 normal controls [NC]). By metabolomic profiling, signature plasma and fecal metabolites with consistent shift across NC, CRA, and CRC are identified, including CRC-enriched oleic acid and CRC-depleted allocholic acid. Oleic acid exhibits pro-tumorigenic effects in CRC cells, patient-derived organoids, and two murine CRC models, whereas allocholic acid has opposing effects. By integrative analysis, we found that oleic acid or allocholic acid directly binds to α-enolase or farnesoid X receptor-1 in CRC cells, respectively, to modulate cancer-associated pathways. Clinically, we establish a panel of 17 plasma metabolites that accurately diagnoses CRC in a discovery and three validation cohorts (AUC = 0.848-0.987). Overall, we characterize metabolite signatures, mechanistic significance, and diagnostic potential of plasma and fecal metabolomes in CRC.

NMR-based metabolomics for investigating urinary profiles of metal carpentry workers exposed to welding fumes and volatile organic compounds

Introduction

Metal carpentry includes a wide range of work activities such as welding and cutting metallic components, use of solvents and paints. Therefore, the employees in these types of activities are mainly exposed to welding fumes and volatile organic solvents. Here, we present an NMR-based metabolomic approach for assessing urinary profiles of workers in the same company that are exposed to two different risk factors.

Methods

The study enrolled 40 male subjects exposed to welding fumes, 13 male subjects exposed to volatile organic compounds of a metal carpentry company, and 24 healthy volunteers. All samples were collected, in the middle of the working week at fast. Thirty-five urinary metabolites belonging to different chemical classes such as amino acids, organic acids and amines were identified and quantified. Results were processed by multivariate statistical analysis for identifying significant metabolites for each working group examined, compared to controls.

Results

Workers exposed to welding fumes displayed urinary increase in glutamine, tyrosine, taurine, creatine, methylguanidine and pseudouridine associated to oxidative impairment, while workers exposed to volatile organic compounds showed higher urinary levels of branched chain aminoacids.

Conclusion

Our work identified specific urinary profile related to each occupational exposure, even if it is below the threshold limit values.

Ex-Vivo 13C NMR Spectroscopy of Rodent Brain: TNF Restricts Neuronal Utilization of Astrocyte-Derived Metabolites

Tumor necrosis factor (TNF) has well-established roles in neuroinflammatory disorders, but the effect of TNF on the biochemistry of brain cells remains poorly understood. Here, we microinjected TNF into the brain to study its impact on glial and neuronal metabolism (glycolysis, pentose phosphate pathway, citric acid cycle, pyruvate dehydrogenase, and pyruvate carboxylase pathways) using 13C NMR spectroscopy on brain extracts following intravenous [1,2-13C]-glucose (to probe glia and neuron metabolism), [2-13C]-acetate (probing astrocyte-specific metabolites), or [3-13C]-lactate. An increase in [4,5-13C]-glutamine and [2,3-13C]-lactate coupled with a decrease in [4,5-13C]-glutamate was observed in the [1,2-13C]-glucose-infused animals treated with TNF. As glutamine is produced from glutamate by astrocyte-specific glutamine synthetase the increase in [4,5-13C]-glutamine reflects increased production of glutamine by astrocytes. This was confirmed by infusion with astrocyte substrate [2-13C]-acetate. As lactate is metabolized in the brain to produce glutamate, the simultaneous increase in [2,3-13C]-lactate and decrease in [4,5-13C]-glutamate suggests decreased lactate utilization, which was confirmed using [3-13C]-lactate as a metabolic precursor. These results suggest that TNF rearranges the metabolic network, disrupting the energy supply chain perturbing the glutamine-glutamate shuttle between astrocytes and the neurons. These insights pave the way for developing astrocyte-targeted therapeutic strategies aimed at modulating effects of TNF to restore metabolic homeostasis in neuroinflammatory disorders.

Optimization of pre-enrichment strategies for mouse hematopoietic stem cell isolation and metabolomic analysis

Blood cell production arises from the activity of hematopoietic stem cells (HSCs), defined by their self-renewal capacity and ability to give rise to all mature blood cell types. The mouse remains one of the most studied species in hematological research, and markers to define and isolate mouse HSCs are well-established. Given the very low frequency of HSCs in the bone marrow, stem cell pre-enrichment by red blood cell lysis and magnetic cell separation is often performed as part of the isolation process to reduce sorting times. Several pre-enrichment strategies are available, differing in their speed, degree of enrichment, final cell yield, and cost. In the current study, we performed a side-by-side comparison and provide a decision tree to help researchers select a pre-enrichment strategy for mouse HSC isolation depending on their downstream application. We then compared different pre-enrichment techniques in combination with metabolomics analysis of HSCs, where speed, yield and temperature during pre-enrichment are crucial factors, and found that the choice of pre-enrichment strategy significantly impacts the number of metabolites detected and levels of individual metabolites in HSCs.

Exploring the co-exposure effects of environmentally relevant microplastics and an estrogenic mixture on the metabolome of the Sydney rock oyster

In aquatic environments the concurrent exposure of molluscs to microplastics (MPs) and estrogens is common, as these pollutants are frequently released by wastewater treatment plants into estuaries. Therefore, this study aimed to evaluate the independent and co-exposure impacts of polyethylene microplastics (PE-MPs) and estrogenic endocrine-disrupting chemicals (EEDCs) at environmentally relevant concentrations on polar metabolites and morphological parameters of the Sydney rock oyster. A seven-day acute exposure revealed no discernible differences in morphology; however, significant variations in polar metabolites were observed across oyster tissues. The altered metabolites were mostly amino acids, carbohydrates and intermediates of the Kreb's cycle. The perturbation of metabolites were tissue and sex-specific. All treatments generally showed an increase of metabolites relative to controls - a possible stimulatory and/or a potential hormetic response. The presence of MPs impeded the exposure of adsorbed and free EEDCs potentially due to the selective feeding behaviour of oysters to microplastics, favouring algae over similar-sized PE-MPs, and the formation of an eco/bio-corona involving faeces, pseudo-faeces, natural organic matter, and algae.

Study on the correlation and difference of qualitative information among three types of UPLC-HRMS and potential generalization in metabolites annotation

The variation of qualitative information among different types of mainstream hyphenated instruments of ultra-performance liquid chromatography coupled to high-resolution mass spectrometry (UPLC-HRMS) makes data sharing and standardization, and further comparison of results consistency in metabolite annotation not easy to attain. In this work, a quantitative study of correlation and difference was first achieved to systematically investigate the variation of retention time (tR), precursor ion (MS1), and product fragment ions (MS2) generated by three typical UPLC-HRMS instruments commonly used in metabolomics area. In terms of the findings of systematic and correlated variation of tR, MS1, and MS2 between different instruments, a computational strategy for integrated metabolite annotation was proposed to reduce the influence of differential ions, which made full use of the characteristic (common) and non-common fragments for scoring assessment. The regular variations of MS2 among three instruments under four collision energy voltages of high, medium, low, and hybrid levels were respectively inspected with three technical replicates at each level. These discoveries could improve general metabolite annotation with a known database and similarity comparison. It should provide the potential for metabolite annotation to generalize qualitative information obtained under different experimental conditions or using instruments from various manufacturers, which is still a big headache in untargeted metabolomics. The mixture of standard compounds and serum samples with the addition of standards were applied to demonstrate the principle and performance of the proposed method. The results showed that it could be an optional strategy for general use in HRMS-based metabolomics to offset the difference in metabolite annotation. It has some potential in untargeted metabolomics.

EFG-CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models

Nuclear magnetic resonance (NMR) crystallography is one of the main methods in structural biology for analyzing protein stereochemistry and structure. The chemical shift of the resonance frequency reflects the effect of the protons in a molecule producing distinct NMR signals in different chemical environments. Apprehending chemical shifts from NMR signals can be challenging since having an NMR structure does not necessarily provide all the required chemical shift information, making predictive models essential for accurately deducing chemical shifts, either from protein structures or, more ideally, directly from amino acid sequences. Here, we present EFG-CS, a web server that specializes in chemical shift prediction. EFG-CS employs a machine learning-based transfer prediction model for backbone atom chemical shift prediction, using ESMFold-predicted protein structures. Additionally, ESG-CS incorporates a graph neural network-based model to provide comprehensive side-chain atom chemical shift predictions. Our method demonstrated reliable performance in backbone atom prediction, achieving comparable accuracy levels with root mean square errors (RMSE) of 0.30 ppm for H, 0.22 ppm for Hα, 0.89 ppm for C, 0.89 ppm for Cα, 0.84 ppm for Cβ, and 1.69 ppm for N. Moreover, our approach also showed predictive capabilities in side-chain atom chemical shift prediction achieving RMSE values of 0.71 ppm for Hβ, 0.74-1.15 ppm for Hδ, and 0.58-0.94 ppm for Hγ, solely utilizing amino acid sequences without homology or feature curation. This work shows for the first time that generative AI protein models can predict NMR shifts nearly comparable to experimental models. This web server is freely available at https://biosig.lab.uq.edu.au/efg_cs, and the chemical shift prediction results can be downloaded in tabular format and visualized in 3D format.

Dysregulations of amino acid metabolism and lipid metabolism in urine of children and adolescents with major depressive disorder: a case-control study

RATIONALE

The mechanisms underlying major depressive disorder (MDD) in children and adolescents are unclear. Metabolomics has been utilized to capture metabolic signatures of various psychiatric disorders; however, urinary metabolic profile of MDD in children and adolescents has not been studied.

OBJECTIVES

We analyzed urinary metabolites in children and adolescents with MDD to identify potential biomarkers and metabolic signatures.

METHODS

Here, liquid chromatography-mass spectrometry was used to profile metabolites in urine samples from 192 subjects, comprising 80 individuals with antidepressant-naïve MDD (AN-MDD), 37 with antidepressant-treated MDD (AT-MDD) and 75 healthy controls (HC). We performed orthogonal partial least squares discriminant analysis to identify differential metabolites and employed logistic regression and receiver operating characteristic analysis to establish a diagnostic panel.

RESULTS

In total, 143 and 71 differential metabolites were identified in AN-MDD and AT-MDD, respectively. These were primarily linked to lipid metabolism, molecular transport, and small molecule biochemistry. AN-MDD additionally exhibited dysregulated amino acid metabolism. Compared to HC, a diagnostic panel of seven metabolites displayed area under the receiver operating characteristic curves of 0.792 for AN-MDD, 0.828 for AT-MDD, and 0.799 for all MDD. Furthermore, the urinary metabolic profiles of children and adolescents with MDD significantly differed from those of adult MDD.

CONCLUSIONS

Our research suggests dysregulated amino acid metabolism and lipid metabolism in the urine of children and adolescents with MDD, similar to results in plasma metabolomics studies. This contributes to the comprehension of mechanisms underlying children and adolescents with MDD.

Transformers and large language models in healthcare: A review

With Artificial Intelligence (AI) increasingly permeating various aspects of society, including healthcare, the adoption of the Transformers neural network architecture is rapidly changing many applications. Transformer is a type of deep learning architecture initially developed to solve general-purpose Natural Language Processing (NLP) tasks and has subsequently been adapted in many fields, including healthcare. In this survey paper, we provide an overview of how this architecture has been adopted to analyze various forms of healthcare data, including clinical NLP, medical imaging, structured Electronic Health Records (EHR), social media, bio-physiological signals, biomolecular sequences. Furthermore, which have also include the articles that used the transformer architecture for generating surgical instructions and predicting adverse outcomes after surgeries under the umbrella of critical care. Under diverse settings, these models have been used for clinical diagnosis, report generation, data reconstruction, and drug/protein synthesis. Finally, we also discuss the benefits and limitations of using transformers in healthcare and examine issues such as computational cost, model interpretability, fairness, alignment with human values, ethical implications, and environmental impact.

Enterobacter ludwigii b3 in the rhizosphere of wild rice assists cultivated rice in mitigating drought stress by direct and indirect methods

Drought is the primary factor limiting rice production in ecosystems. Wild rice rhizosphere bacteria possess the potential to assist in the stress resistance of cultivated rice. This study examines the impact of wild rice rhizosphere bacteria on cultivated rice under drought conditions. From the rhizosphere soil of wild rice, 20 potential drought-resistant strains were isolated. Subsequent to the screening, the most effective strain b3, was identified as Enterobacter ludwigii. Pot experiments were conducted on the cultivated Changbai 9 rice. It was found that inoculation with the E. ludwigii b3 strain improved the drought resistance of the rice, promotion of rice growth (shoot height increased by 13.47 %), increased chlorophyll content (chlorophyll a, chlorophyll b and carotenoid increased by 168.74 %, 130.68 % and 87.89 %), improved antioxidant system (content of glutathione was increased by 60.35 %), and accumulation of osmotic regulation substances (soluble sugar and soluble protein increased by 70.36 % and 142.03 %). Furthermore, E. ludwigii b3 had a transformative effect on the rhizosphere bacterial community of cultivated rice, increasing its abundance and diversity while simultaneously recruiting beneficial rhizosphere bacteria, resulting in a more complex community. Additionally, E. ludwigii b3 acted directly and indirectly on cultivated rice through its metabolites (organic acids, amino acids, flavonoids and other substances), which helped alleviate drought stress. In conclusion, the E. ludwigii b3 shows promise as a drought-resistant strain and has the potential to improve the growth and productivity of cultivated rice in arid agricultural ecosystems. This study represents the first investigation of E. ludwigii in the rhizosphere of wild rice under drought conditions on cultivated rice.

Metabolic Deficits in the Retina of a Familial Dysautonomia Mouse Model

Neurodegenerative retinal diseases such as glaucoma, diabetic retinopathy, Leber's hereditary optic neuropathy (LHON), and dominant optic atrophy (DOA) are marked by progressive death of retinal ganglion cells (RGC). This decline is promoted by structural and functional mitochondrial deficits, including electron transport chain (ETC) impairments, increased oxidative stress, and reduced energy (ATP) production. These cellular mechanisms associated with progressive optic nerve atrophy have been similarly observed in familial dysautonomia (FD) patients, who experience gradual loss of visual acuity due to the degeneration of RGCs, which is thought to be caused by a breakdown of mitochondrial structures, and a disruption in ETC function. Retinal metabolism plays a crucial role in meeting the elevated energetic demands of this tissue, and recent characterizations of FD patients' serum and stool metabolomes have indicated alterations in central metabolic processes and potential systemic deficits of taurine, a small molecule essential for retina and overall eye health. The present study sought to elucidate metabolic alterations that contribute to the progressive degeneration of RGCs observed in FD. Additionally, a critical subpopulation of retinal interneurons, the dopaminergic amacrine cells, mediate the integration and modulation of visual information in a time-dependent manner to RGCs. As these cells have been associated with RGC loss in the neurodegenerative disease Parkinson's, which shares hallmarks with FD, a targeted analysis of the dopaminergic amacrine cells and their product, dopamine, was also undertaken. One dimensional (1D) proton (1H) nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry, and retinal histology methods were employed to characterize retinae from the retina-specific Elp1 conditional knockout (CKO) FD mouse model (Pax6-Cre; Elp1LoxP/LoxP). Metabolite alterations correlated temporally with progressive RGC degeneration and were associated with reduced mitochondrial function, alterations in ATP production through the Cahill and mini-Krebs cycles, and phospholipid metabolism. Dopaminergic amacrine cell populations were reduced at timepoints P30-P90, and dopamine levels were 25-35% lower in CKO retinae compared to control retinae at P60. Overall, this study has expanded upon our current understanding of retina pathology in FD. This knowledge may apply to other retinal diseases that share hallmark features with FD and may help guide new avenues for novel non-invasive therapeutics to mitigate the progressive optic neuropathy in FD.

MetaPredictor: in silico prediction of drug metabolites based on deep language models with prompt engineering

Metabolic processes can transform a drug into metabolites with different properties that may affect its efficacy and safety. Therefore, investigation of the metabolic fate of a drug candidate is of great significance for drug discovery. Computational methods have been developed to predict drug metabolites, but most of them suffer from two main obstacles: the lack of model generalization due to restrictions on metabolic transformation rules or specific enzyme families, and high rate of false-positive predictions. Here, we presented MetaPredictor, a rule-free, end-to-end and prompt-based method to predict possible human metabolites of small molecules including drugs as a sequence translation problem. We innovatively introduced prompt engineering into deep language models to enrich domain knowledge and guide decision-making. The results showed that using prompts that specify the sites of metabolism (SoMs) can steer the model to propose more accurate metabolite predictions, achieving a 30.4% increase in recall and a 16.8% reduction in false positives over the baseline model. The transfer learning strategy was also utilized to tackle the limited availability of metabolic data. For the adaptation to automatic or non-expert prediction, MetaPredictor was designed as a two-stage schema consisting of automatic identification of SoMs followed by metabolite prediction. Compared to four available drug metabolite prediction tools, our method showed comparable performance on the major enzyme families and better generalization that could additionally identify metabolites catalyzed by less common enzymes. The results indicated that MetaPredictor could provide a more comprehensive and accurate prediction of drug metabolism through the effective combination of transfer learning and prompt-based learning strategies.

Role of blood metabolites in mediating the effect of gut microbiome on the mutated-RAS/BRAF metastatic colorectal cancer-specific survival

BACKGROUND

Recent studies have linked alterations in the gut microbiome and metabolic disruptions to the invasive behavior and metastasis of colorectal cancer (CRC), thus affecting patient prognosis. However, the specific relationship among gut microbiome, metabolite profiles, and mutated-RAS/BRAF metastatic colorectal cancer (M-mCRC) remains unclear. Furthermore, the potential mechanisms and prognostic implications of metabolic changes induced by gut microbiome alterations in patients with M-mCRC still need to be better understood.

METHODS

We conducted Mendelian randomization (MR) to evaluate the causal relationship of genetically predicted 196 gut microbiome features and 1400 plasma metabolites/metabolite ratios on M-mCRC-specific survival. Additionally, we identified significant gut microbiome-metabolites/metabolite ratio associations based on M-mCRC. Metabolite information was annotated, and functional annotation and pathway enrichment analyses were performed on shared proteins corresponding to significant metabolite ratios, aiming to reveal potential mechanisms by which gut microbiome influences M-mCRC prognosis via modulation of human metabolism.

RESULTS

We identified 11 gut microbiome features and 49 known metabolites/metabolite ratios correlated with M-mCRC-specific survival. Furthermore, we identified 17 gut microbiome-metabolite/metabolite ratio associations specific to M-mCRC, involving eight lipid metabolites and three bilirubin degradation products. The shared proteins corresponding to significant metabolite ratios were predominantly localized within the integral component of the membrane and exhibited enzymatic activities such as glucuronosyltransferase and UDP-glucuronosyltransferase, crucial in processes such as glucuronidation, bile secretion, and lipid metabolism. Moreover, these proteins were significantly enriched in pathways related to ascorbate and aldarate metabolism, pentose and glucuronate interconversions, steroid hormone biosynthesis, and bile secretion.

CONCLUSION

Our study offers novel insights into the potential mechanisms underlying the impact of the gut microbiome on the prognosis of M-mCRC. These findings serve as a meaningful reference for exploring potential therapeutic targets and strategies in the future.

Disrupted H2 synthesis combined with methyl viologen treatment inhibits photosynthetic electron flow to synergistically enhance glycogen accumulation in the cyanobacterium Synechocystis sp. PCC 6803

Under nitrogen deprivation (-N), cyanobacterium Synechocystis sp. PCC 6803 exhibits growth arrest, reduced protein content, and remarkably increased glycogen accumulation. However, producing glycogen under this condition requires a two-step process with cell transfer from normal to -N medium. Metabolic engineering and chemical treatment for rapid glycogen accumulation can bypass the need for two-step cultivation. For example, recent studies indicate that individually disrupting hydrogen (H2) or poly(3-hydroxybutyrate) (PHB) synthesis, or treatment with methyl viologen (MV), effectively increases glycogen accumulation in Synechocystis. Here we explore the effects of disrupted H2 or poly(3-hydroxybutyrate) synthesis, together with MV treatment to on enhanced glycogen accumulation in Synechocystis grown in normal medium. Wild-type cells without MV treatment exhibited low glycogen content of less than 6% w/w dry weight (DW). Compared with wild type, disrupting PHB synthesis combined with MV treatment did not increase glycogen content. Disrupted H₂ production without MV treatment yielded up to 11% w/w DW glycogen content. Interestingly, when combined, disrupted H2 production with MV treatment synergistically enhanced glycogen accumulation to 51% and 59% w/w DW within 3 and 7 days, respectively. Metabolomic analysis suggests that MV treatment mediated the conversion of proteins into glycogen. Metabolomic and transcriptional-expression analysis suggests that disrupted H2 synthesis under MV treatment positively influenced glycogen synthesis. Disrupted H₂ synthesis under MV treatment significantly increased NADPH levels. This increased NADPH content potentially contributed to the observed enhancements in antioxidant activity against MV-induced oxidants, O2 evolution, and metabolite substrates levels for glycogen synthesis in normal medium, ultimately leading to enhanced glycogen accumulation in Synechocystis. KEY MESSAGE: Combining disrupted hydrogen-gas synthesis and the treatment by photosynthesis electron-transport inhibitor significantly enhance glycogen production in cyanobacteria.

Polar metabolomics using trichloroacetic acid extraction and porous graphitic carbon stationary phase

INTRODUCTION

Accurately identifying and quantifying polar metabolites using untargeted metabolomics has proven challenging in comparison to mid to non-polar metabolites. Hydrophilic interaction chromatography and gas chromatography-mass spectrometry are predominantly used to target polar metabolites.

OBJECTIVES

This study aims to demonstrate a simple one-step extraction combined with liquid chromatography-mass spectrometry (LC-MS) that reliably retains polar metabolites.

METHODS

The method involves a MilliQ + 10% trichloroacetic acid extraction from 6 healthy individuals serum, combined with porous graphitic carbon liquid chromatography-mass spectrometry (LC-MS). The coefficient of variation (CV) assessed retention reliability of polar metabolites with logP as low as - 9. QreSS (Quantification, Retention, and System Suitability) internal standards determined the method's consistency and recovery efficiency.

RESULTS

The method demonstrated reliable retention (CV < 0.30) of polar metabolites within a logP range of - 9.1 to 5.6. QreSS internal standards confirmed consistent performance (CV < 0.16) and effective recovery (70-130%) of polar to mid-polar metabolites. Quality control dilution series demonstrated that ~ 80% of annotated metabolites could be accurately quantified (Pearson's correlation coefficient > 0.80) within their concentration range. Repeatability was demonstrated through clustering of repeated extractions from a single sample.

CONCLUSION

This LC-MS method is better suited to covering the polar segment of the metabolome than current methods, offering a reliable and efficient approach for accurate quantification of polar metabolites in untargeted metabolomics.

PI3K/AKT signaling alters glucose metabolism in uterine microenvironment of women with idiopathic recurrent spontaneous miscarriage

BACKGROUND

This study aims to identify metabolomic signatures in uterine fluid of women with idiopathic recurrent spontaneous miscarriage (IRSM) during window of implantation (WOI). Also, glucose transporters GLUT3 and GLUT4 and proteins of PI3K-Akt signaling pathway in endometrial tissue are assessed.

METHODS

Paired uterine fluid and endometrial biopsies were collected during WOI from women with IRSM (n = 24) and healthy women with azoospermic male partners as controls (n = 15). NMR metabolomics was used to identify the dysregulated metabolites in uterine fluid of IRSM women. Additionally, proteins and glucose transporters were investigated in the endometrial tissue using immunohistochemistry (IHC) and western blotting.

RESULTS

Uterine fluid metabolomics indicated eleven metabolites to be significantly downregulated in IRSM. While expression levels of PI3K (p85), PI3K (p110), p-Akt (Thr308), p-Akt (Ser473), GLUT3 and GLUT4 were significantly downregulated in endometrial tissue of these women, p-IKK α/β (Ser176/180) and p-NFkBp65 (Ser536) were significantly increased.

CONCLUSION

Our findings suggest that dysregulation of PI3K/Akt pathway in the uterine microenvironment could be a likely cause of endometrial dysfunction, thereby affecting implantation. Further studies on the downstream effects of the Akt signaling pathway in-vitro for improved understanding of the Akt-mediated cellular responses in IRSM is, therefore, warranted.

Metabolomics-based profiling of five Salvia L. (Lamiaceae) species using untargeted data analysis workflow

INTRODUCTION

The genus Salvia L., a member of the family Lamiaceae, is a keystone genus with a wide range of medicinal properties. It possesses a rich metabolite source that has long been used to treat different disorders.

OBJECTIVES

Due to a deficiency of untargeted metabolomic profiling in the genus Salvia, this work attempts to investigate a comprehensive mass spectral library matching, computational data annotations, exclusive biomarkers, specific chemotypes, intraspecific metabolite profile variation, and metabolite enrichment by a case study of five medicinal species of Salvia.

MATERIAL AND METHODS

Aerial parts of each species were subjected to QTRAP liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis workflow based on untargeted metabolites. A comprehensive and multivariate analysis was acquired on the metabolite dataset utilizing MetaboAnalyst 6.0 and the Global Natural Products Social Molecular Networking (GNPS) Web Platform.

RESULTS

The untargeted approach empowered the identification of 117 metabolites by library matching and 92 nodes annotated by automated matching. A machine learning algorithm as substructural topic modeling, MS2LDA, was further implemented to explore the metabolite substructures, resulting in four Mass2Motifs. The automated library newly discovered a total of 23 metabolites. In addition, 87 verified biomarkers of library matching, 58 biomarkers of GNPS annotations, and 11 specific chemotypes were screened.

CONCLUSION

Integrative spectral library matching and automated annotation by the GNPS platform provide comprehensive metabolite profiling through a workflow. In addition, QTRAP LC-MS/MS with multivariate analysis unveiled reliable information about inter and intraspecific levels of differentiation. The rigorous investigation of metabolite profiling presents a large-scale overview and new insights for chemotaxonomy and pharmaceutical studies.

Transplant of fecal microbiota from healthy young mice relieves cognitive defects in late-stage diabetic mice by reducing metabolic disorders and neuroinflammation

Fecal microbiota transplant (FMT) is becoming as a promising area of interest for treating refractory diseases. In this study, we investigated the effects of FMT on diabetes-associated cognitive defects in mice as well as the underlying mechanisms. Fecal microbiota was prepared from 8-week-aged healthy mice. Late-stage type 1 diabetics (T1D) mice with a 30-week history of streptozotocin-induced diabetics were treated with antibiotics for 7 days, and then were transplanted with bacterial suspension (100 μL, i.g.) once a day for 14 days. We found that FMT from healthy young mice significantly alleviated cognitive defects of late-stage T1D mice assessed in Morris water maze test. We revealed that FMT significantly reduced the relative abundance of Gram-negative bacteria in the gut microbiota and enhanced intestinal barrier integrity, mitigating LPS translocation into the bloodstream and NLRP3 inflammasome activation in the hippocampus, thereby reducing T1D-induced neuronal loss and astrocytic proliferation. FMT also reshaped the metabolic phenotypes in the hippocampus of T1D mice especially for alanine, aspartate and glutamate metabolism. Moreover, we showed that application of aspartate (0.1 mM) significantly inhibited NLRP3 inflammasome activation and IL-1β production in BV2 cells under a HG/LPS condition. We conclude that FMT can effectively relieve T1D-associated cognitive decline via reducing the gut-brain metabolic disorders and neuroinflammation, providing a potential therapeutic approach for diabetes-related brain disorders in clinic.

Metabolomics highlights biochemical perturbations occurring in the kidney and liver of mice administered a human dose of colistin

Introduction: Colistin (CMS) is used for the curation of infections caused by multidrug-resistant bacteria. CMS is constrained by toxicity, particularly in kidney and neuronal cells. The recommended human doses are 2.5-5 mg/kg/day, and the toxicity is linked to higher doses. So far, the in vivo toxicity studies have used doses even 10-fold higher than human doses. It is essential to investigate the impact of metabolic response of doses, that are comparable to human doses, to identify biomarkers of latent toxicity. The innovation of the current study is the in vivo stimulation of CMS's impact using a range of CMS doses that have never been investigated before, i.e., 1 and 1.5 mg/kg. The 1 and 1.5 mg/kg, administered in mice, correspond to the therapeutic and toxic human doses, based on previous expertise of our team, regarding the human exposure. The study mainly focused on the biochemical impact of CMS on the metabolome, and on the alterations provoked by 50%-fold of dose increase. The main objectives were i) the comprehension of the biochemical changes resulting after CMS administration and ii) from its dose increase; and iii) the determination of dose-related metabolites that could be considered as toxicity monitoring biomarkers. Methods: The in vivo experiment employed two doses of CMS versus a control group treated with normal saline, and samples of plasma, kidney, and liver were analysed with a UPLC-MS-based metabolomics protocol. Both univariate and multivariate statistical approaches (PCA, OPLS-DA, PLS regression, ROC) and pathway analysis were combined for the data interpretation. Results: The results pointed out six dose-responding metabolites (PAA, DA4S, 2,8-DHA, etc.), dysregulation of renal dopamine, and extended perturbations in renal purine metabolism. Also, the study determined altered levels of liver suberylglycine, a metabolite linked to hepatic steatosis. One of the most intriguing findings was the detection of elevated levels of renal xanthine and uric acid, that act as AChE activators, leading to the rapid degradation of acetylcholine. This evidence provides a naïve hypothesis, for the potential association between the CMS induced nephrotoxicity and CMS induced 39 neurotoxicity, that should be further investigated.

Causal impact of human blood metabolites and metabolic pathways on serum uric acid and gout: a mendelian randomization study

Objective

Hyperuricaemia and gout are common metabolic disorders. However, the causal relationships between blood metabolites and serum urate levels, as well as gout, remain unclear. A systematic evaluation of the causal connections between blood metabolites, hyperuricemia, and gout could enhance early screening and prevention of hyperuricemia and gout in clinical settings, providing novel insights and approaches for clinical treatment.

Methods

In this study, we employed a bidirectional two-sample Mendelian randomization analysis utilizing data from a genome-wide association study involving 7,286 participants, encompassing 486 blood metabolites. Serum urate and gout data were sourced from the Chronic Kidney Disease Genetics consortium, including 288,649 participants for serum urate and 9,819 African American and 753,994 European individuals for gout. Initially, LDSC methodology was applied to identify blood metabolites with a genetic relationship to serum urate and gout. Subsequently, inverse-variance weighting was employed as the primary analysis method, with a series of sensitivity and pleiotropy analyses conducted to assess the robustness of the results.

Results

Following LDSC, 133 blood metabolites exhibited a potential genetic relationship with serum urate and gout. In the primary Mendelian randomization analysis using inverse-variance weighting, 19 blood metabolites were recognized as potentially influencing serum urate levels and gout. Subsequently, the IVW p-values of potential metabolites were corrected using the false discovery rate method. We find leucine (IVW P FDR = 0.00004), N-acetylornithine (IVW P FDR = 0.0295), N1-methyl-3-pyridone-4-carboxamide (IVW P FDR = 0.0295), and succinyl carnitine (IVW P FDR = 0.00004) were identified as significant risk factors for elevated serum urate levels. Additionally, 1-oleoylglycerol (IVW P FDR = 0.0007) may lead to a substantial increase in the risk of gout. Succinyl carnitine exhibited acceptable weak heterogeneity, and the results for other blood metabolites remained robust after sensitivity, heterogeneity, and pleiotropy testing. We conducted an enrichment analysis on potential blood metabolites, followed by a metabolic pathway analysis revealing four pathways associated with serum urate levels.

Conclusion

The identified causal relationships between these metabolites and serum urate and gout offer a novel perspective, providing new mechanistic insights into serum urate levels and gout.

Impact of a legumes diet on the human gut microbiome articulated with fecal and plasma metabolomes: A pilot study

BACKGROUND & AIMS

Legumes intake is known to be associated with several health benefits the origins of which is still a matter of debate. This paper addresses a pilot small cohort to probe for metabolic aspects of the interplay between legumes intake, human metabolism and gut microbiota.

METHODS

Untargeted nuclear magnetic resonance (NMR) metabolomics of blood plasma and fecal extracts was carried out, in tandem with qPCR analysis of feces, to assess the impact of an 8-week pilot legumes diet intervention on the fecal and plasma metabolomes and gut microbiota of 19 subjects.

RESULTS

While the high inter-individual variability hindered the detection of statistically significant changes in the gut microbiome, increased fecal glucose and decreased threonine levels were noted. Correlation analysis between the microbiome and fecal metabolome lead to putative hypotheses regarding the metabolic activities of prevalent bacteria groups (Clostridium leptum subgroup, Roseburia spp., and Faecalibacterium prausnitzii). These included elevated fecal glucose as a preferential energy source, the involvement of valerate/isovalerate and reduced protein degradation in gut microbiota. Plasma metabolomics advanced mannose and betaine as potential markers of legume intake and unveiled a decrease in formate and ketone bodies, the latter suggesting improved energy utilization through legume carbohydrates. Amino acid metabolism was also apparently affected, as suggested by lowered urea, histidine and threonine levels.

CONCLUSIONS

Despite the high inter-individual gut microbiome variability characterizing the small cohort addressed, combination of microbiological measurements and untargeted metabolomics unveiled several metabolic effects putatively related to legumes intake. If confirmed in larger cohorts, our findings will support the inclusion of legumes in diets and contribute valuable new insight into the origins of associated health benefits.

Metabolomic profiling and accurate diagnosis of basal cell carcinoma by MALDI imaging and machine learning

Basal cell carcinoma (BCC), the most common keratinocyte cancer, presents a substantial public health challenge due to its high prevalence. Traditional diagnostic methods, which rely on visual examination and histopathological analysis, do not include metabolomic data. This exploratory study aims to molecularly characterize BCC and diagnose tumour tissue by applying matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) and machine learning (ML). BCC tumour development was induced in a mouse model and tissue sections containing BCC (n = 12) were analysed. The study design involved three phases: (i) Model training, (ii) Model validation and (iii) Metabolomic analysis. The ML algorithm was trained on MS data extracted and labelled in accordance with histopathology. An overall classification accuracy of 99.0% was reached for the labelled data. Classification of unlabelled tissue areas aligned with the evaluation of a certified Mohs surgeon for 99.9% of the total tissue area, underscoring the model's high sensitivity and specificity in identifying BCC. Tentative metabolite identifications were assigned to 189 signals of importance for the recognition of BCC, each indicating a potential tumour marker of diagnostic value. These findings demonstrate the potential for MALDI-MSI coupled with ML to characterize the metabolomic profile of BCC and to diagnose tumour tissue with high sensitivity and specificity. Further studies are needed to explore the potential of implementing integrated MS and automated analyses in the clinical setting.

Dual mass spectrometry imaging and spatial metabolomics to investigate the metabolism and nephrotoxicity of nitidine chloride

Evaluating toxicity and decoding the underlying mechanisms of active compounds are crucial for drug development. In this study, we present an innovative, integrated approach that combines air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI), time-of-flight secondary ion mass spectrometry (ToF-SIMS), and spatial metabolomics to comprehensively investigate the nephrotoxicity and underlying mechanisms of nitidine chloride (NC), a promising anti-tumor drug candidate. Our quantitive AFADESI-MSI analysis unveiled the region specific of accumulation of NC in the kidney, particularly within the inner cortex (IC) region, following single and repeated dose of NC. High spatial resolution ToF-SIMS analysis further allowed us to precisely map the localization of NC within the renal tubule. Employing spatial metabolomics based on AFADESI-MSI, we identified over 70 discriminating endogenous metabolites associated with chronic NC exposure. These findings suggest the renal tubule as the primary target of NC toxicity and implicate renal transporters (organic cation transporters, multidrug and toxin extrusion, and organic cation transporter 2 (OCT2)), metabolic enzymes (protein arginine N-methyltransferase (PRMT) and nitric oxide synthase), mitochondria, oxidative stress, and inflammation in NC-induced nephrotoxicity. This study offers novel insights into NC-induced renal damage, representing a crucial step towards devising strategies to mitigate renal damage caused by this compound.

Exploring Salivary Metabolic Alterations in Type 2 Diabetes: Implications for Dental Caries and Potential Influences of HbA1c and Vitamin D Levels

Diabetes mellitus is considered to be the most common health issue affecting almost 1 in 11 adults globally. Oral health complications including xerostomia, periodontal disease, dental caries, and soft tissue lesions are prevalent among individuals with diabetes, and therefore an understanding of the potential association between salivary metabolites and dental caries progression would enable the early detection and prevention of this non-communicable disease. Therefore, the aim of this study was to compare salivary biomarkers between individuals with type 2 diabetes (T2DM) with those without this disorder (ND) using 1H NMR-based metabolomics strategies. The objectives were to identify T2DM-associated biomarker signatures and their potential impact on dental caries. In addition, HbA1c and vitamin D levels were also analysed for this purpose.

METHODS

Stimulated whole-mouth saliva (SWS) samples were collected from T2DM and ND (n = 30 in each case) participants randomly selected from a group of 128 participants recruited for this case-control study. All participants were advised to refrain from eating, drinking, and smoking for at least 1-2 h prior to sample collection. Following preparation, SWS supernatants underwent 1H NMR analysis at an operating frequency of 800 MHz, and the dataset acquired was analysed using a range of multivariate metabolomics techniques.

RESULTS

Metabolomics analysis of data acquired demonstrated that, together with up- and downregulated blood HbA1c and vitamin D levels, key salivary discriminators between these two classifications included lactate, taurine, creatinine, α-glucose, and formate to a lesser extent. The bacterial catabolites lactate and formate were both significantly upregulated in the T2DM group, and these have previously been implicated in the pathogenesis of dental caries. Significance analysis of metabolites (SAM)-facilitated AUROC analysis yielded an 83% accuracy for this distinction.

CONCLUSION

In conclusion, this study highlights the significant differences in salivary metabolites between individuals with T2DM and healthy controls. Such differences appear to be related to the development and progression of dental caries in T2DM patients.

Disturbances in Muscle Energy Metabolism in Patients with Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neuromuscular disease type of motor neuron disorder characterized by degeneration of the upper and lower motor neurons resulting in dysfunction of the somatic muscles of the body. The ALS condition is manifested in progressive skeletal muscle atrophy and spasticity. It leads to death, mostly due to respiratory failure. Within the pathophysiology of the disease, muscle energy metabolism seems to be an important part. In our study, we used blood plasma from 25 patients with ALS diagnosed by definitive El Escorial criteria according to ALSFR-R (Revised Amyotrophic Lateral Sclerosis Functional Rating Scale) criteria and 25 age and sex-matched subjects. Aside from standard clinical biochemical parameters, we used the NMR (nuclear magnetic resonance) metabolomics approach to determine relative plasma levels of metabolites. We observed a decrease in total protein level in blood; however, despite accelerated skeletal muscle catabolism characteristic for ALS patients, we did not detect changes in plasma levels of essential amino acids. When focused on alterations in energy metabolism within muscle, compromised creatine uptake was accompanied by decreased plasma creatinine. We did not observe changes in plasma levels of BCAAs (branched chain amino acids; leucine, isoleucine, valine); however, the observed decrease in plasma levels of all three BCKAs (branched chain alpha-keto acids derived from BCAAs) suggests enhanced utilization of BCKAs as energy substrate. Glutamine, found to be increased in blood plasma in ALS patients, besides serving for ammonia detoxification, could also be considered a potential TCA (tricarboxylic acid) cycle contributor in times of decreased pyruvate utilization. When analyzing the data by using a cross-validated Random Forest algorithm, it finished with an AUC of 0.92, oob error of 8%, and an MCC (Matthew's correlation coefficient) of 0.84 when relative plasma levels of metabolites were used as input variables. Although the discriminatory power of the system used was promising, additional features are needed to create a robust discriminatory model.

The impact of high-IgE levels on metabolome and microbiome in experimental allergic enteritis

BACKGROUND

The pathological mechanism of the gastrointestinal forms of food allergies is less understood in comparison to other clinical phenotypes, such as asthma and anaphylaxis Importantly, high-IgE levels are a poor prognostic factor in gastrointestinal allergies.

METHODS

This study investigated how high-IgE levels influence the development of intestinal inflammation and the metabolome in allergic enteritis (AE), using IgE knock-in (IgEki) mice expressing high levels of IgE. In addition, correlation of the altered metabolome with gut microbiome was analysed.

RESULTS

Ovalbumin-sensitized and egg-white diet-fed (OVA/EW) BALB/c WT mice developed moderate AE, whereas OVA/EW IgEki mice induced more aggravated intestinal inflammation with enhanced eosinophil accumulation. Untargeted metabolomics detected the increased levels of N-tau-methylhistamine and 2,3-butanediol, and reduced levels of butyric acid in faeces and/or sera of OVA/EW IgEki mice, which was accompanied with reduced Clostridium and increased Lactobacillus at the genus level. Non-sensitized and egg-white diet-fed (NC/EW) WT mice did not exhibit any signs of AE, whereas NC/EW IgEki mice developed marginal degrees of AE. Compared to NC/EW WT mice, enhanced levels of lysophospholipids, sphinganine and sphingosine were detected in serum and faecal samples of NC/EW IgEki mice. In addition, several associations of altered metabolome with gut microbiome-for example Akkermansia with lysophosphatidylserine-were detected.

CONCLUSIONS

Our results suggest that high-IgE levels alter intestinal and systemic levels of endogenous and microbiota-associated metabolites in experimental AE. This study contributes to deepening the knowledge of molecular mechanisms for the development of AE and provides clues to advance diagnostic and therapeutic strategies of allergic diseases.

Pan-Cancer, Genome-Scale Metabolic Network Analysis of over 10,000 Patients Elucidates Relationship between Metabolism and Survival

Despite the high variability in cancer biology, cancers nevertheless exhibit cohesive hallmarks across multiple cancer types, notably dysregulated metabolism. Metabolism plays a central role in cancer biology, and shifts in metabolic pathways have been linked to tumor aggressiveness and likelihood of response to therapy. We therefore sought to interrogate metabolism across cancer types and understand how intrinsic modes of metabolism vary within and across indications and how they relate to patient prognosis. We used context specific genome-scale metabolic modeling to simulate metabolism across 10,915 patients from 34 cancer types from The Cancer Genome Atlas and the MMRF-COMMPASS study. We found that cancer metabolism clustered into modes characterized by differential glycolysis, oxidative phosphorylation, and growth rate. We also found that the simulated activities of metabolic pathways are intrinsically prognostic across cancer types, especially tumor growth rate, fatty acid biosynthesis, folate metabolism, oxidative phosphorylation, steroid metabolism, and glutathione metabolism. This work shows the prognostic power of individual patient metabolic modeling across multiple cancer types. Additionally, it shows that analyzing large-scale models of cancer metabolism with survival information provides unique insights into underlying relationships across cancer types and suggests how therapies designed for one cancer type may be repurposed for use in others.

Plasma metabolites as mediators in immune cell-pancreatic cancer risk: insights from Mendelian randomization

Background

Immune cells play a crucial role in the development and progression of pancreatic cancer, yet the causal relationship remains uncertain due to complex immune microenvironments and conflicting research findings. Mendelian randomization (MR), this study aims to delineate the causal relationships between immune cells and pancreatic cancer while identifying intermediary factors.

Methods

The genome-wide association study (GWAS) data on immune cells, pancreatic cancer, and plasma metabolites are derived from public databases. In this investigation, inverse variance weighting (IVW) as the primary analytical approach to investigate the causal relationship between exposure and outcome. Furthermore, this study incorporates MR-Egger, simple mode, weighted median, and weighted mode as supplementary analytical approaches. To ensure the reliability of our findings, we further assessed horizontal pleiotropy and heterogeneity and evaluated the stability of MR results using the Leave-one-out method. In conclusion, this study employed mediation analysis to elucidate the potential mediating effects of plasma metabolites.

Results

Our investigation revealed a causal relationship between immune cells and pancreatic cancer, highlighting the pivotal roles of CD11c+ monocytes (odds ratio, ORIVW=1.105; 95% confidence interval, 95%CI: 1.002-1.218; P=0.045), HLA DR+ CD4+ antigen-presenting cells (ORIVW=0.920; 95%CI: 0.873-0.968; P=0.001), and HLA DR+ CD8br T cells (ORIVW=1.058; 95%CI: 1.002-1.117; P=0.041) in pancreatic cancer progression. Further mediation analysis indicated that oxalate (proportion of mediation effect in total effect: -11.6%, 95% CI: -89.7%, 66.6%) and the mannose to trans-4-hydroxyproline ratio (-19.4, 95% CI: -136%, 96.8%) partially mediate the relationship between HLA DR+ CD8br T cells and pancreatic cancer in nature. In addition, our analysis indicates that adrenate (-8.39%, 95% CI: -18.3%, 1.54%) plays a partial mediating role in the association between CD11c+ monocyte and pancreatic cancer, while cortisone (-26.6%, 95% CI: 138%, -84.8%) acts as a partial mediator between HLA DR+ CD4+ AC and pancreatic cancer.

Conclusion

This MR investigation provides evidence supporting the causal relationship between immune cell and pancreatic cancer, with plasma metabolites serving as mediators. Identifying immune cell phenotypes with potential causal effects on pancreatic cancer sheds light on its underlying mechanisms and suggests novel therapeutic targets.

Gut microbial genomes with paired isolates from China illustrate probiotic and cardiometabolic effects

The gut microbiome displays genetic differences among populations, and characterization of the genomic landscape of the gut microbiome in China remains limited. Here, we present the Chinese Gut Microbial Reference (CGMR) set, comprising 101,060 high-quality metagenomic assembled genomes (MAGs) of 3,707 nonredundant species from 3,234 fecal samples across primarily rural Chinese locations, 1,376 live isolates mainly from lactic acid bacteria, and 987 novel species relative to worldwide databases. We observed region-specific coexisting MAGs and MAGs with probiotic and cardiometabolic functionalities. Preliminary mouse experiments suggest a probiotic effect of two Faecalibacillus intestinalis isolates in alleviating constipation, cardiometabolic influences of three Bacteroides fragilis_A isolates in obesity, and isolates from the genera Parabacteroides and Lactobacillus in host lipid metabolism. Our study expands the current microbial genomes with paired isolates and demonstrates potential host effects, contributing to the mechanistic understanding of host-microbe interactions.

Mitochondrial defects and metabolic vulnerabilities in Lynch syndrome-associated MSH2-deficient endometrial cancer

Lynch syndrome (LS) is defined by inherited mutations in DNA mismatch repair genes, including MSH2, and carries 60% lifetime risk of developing endometrial cancer (EC). Beyond hypermutability, specific mechanisms for LS-associated endometrial carcinogenesis are not well understood. Here, we assessed the effects of MSH2 loss on EC pathogenesis using a novel mouse model (PR-Cre Msh2 flox/flox , abbreviated Msh2KO), primary cell lines established from this model, human tissues, and human EC cell lines with isogenic MSH2 knockdown. Beginning at eight months of age, 30% of Msh2KO mice exhibited endometrial atypical hyperplasia (AH), a precancerous lesion. At 12 to 16 months of age, 47% of Msh2KO mice exhibited either AH or ECs with histologic features similar to human LS-related ECs. Transcriptomic profiling of EC from Msh2KO mice revealed a transcriptomic signature for mitochondrial dysfunction. Studies in vitro and in vivo revealed mitochondrial dysfunction based upon two mechanisms: marked mitochondrial content reduction, along with pronounced disruptions to the integrity of retained mitochondria. Human LS-related ECs also exhibited mitochondrial content reduction compared with non-LS-related ECs. Functional studies revealed metabolic reprogramming of MSH2-deficient EC cells in vitro , including reduced oxidative phosphorylation and increased susceptibility to glycolysis suppression. We are the first to identify mitochondrial dysfunction and metabolic disruption as a consequence of MSH2 deficiency-related EC. Mitochondrial and metabolic aberrations should be evaluated as novel biomarkers for endometrial carcinogenesis or risk stratification and could serve as targets for cancer interception in women with LS.

Significance

This is the first study to report mitochondrial dysfunction contributing to MSH2-deficient endometrial cancer development, identifying a noncanonical pathway for MSH2 deficient carcinogenesis, which also imparts vulnerability to metabolic targeting.

Impact of the delay in cryopreservation timing during biobanking procedures on human liver tissue metabolomics

The liver is a highly specialized organ involved in regulating systemic metabolism. Understanding metabolic reprogramming of liver disease is key in discovering clinical biomarkers, which relies on robust tissue biobanks. However, sample collection and storage procedures pose a threat to obtaining reliable results, as metabolic alterations may occur during sample handling. This study aimed to elucidate the impact of pre-analytical delay during liver resection surgery on liver tissue metabolomics. Patients were enrolled for liver resection during which normal tissue was collected and snap-frozen at three timepoints: before transection, after transection, and after analysis in Pathology. Metabolomics analyses were performed using 1H Nuclear Magnetic Resonance (NMR) and Liquid Chromatography-Mass Spectrometry (LC-MS). Time at cryopreservation was the principal variable contributing to differences between liver specimen metabolomes, which superseded even interindividual variability. NMR revealed global changes in the abundance of an array of metabolites, namely a decrease in most metabolites and an increase in β-glucose and lactate. LC-MS revealed that succinate, alanine, glutamine, arginine, leucine, glycerol-3-phosphate, lactate, AMP, glutathione, and NADP were enhanced during cryopreservation delay (all p<0.05), whereas aspartate, iso(citrate), ADP, and ATP, decreased (all p<0.05). Cryopreservation delays occurring during liver tissue biobanking significantly alter an array of metabolites. Indeed, such alterations compromise the integrity of metabolomic data from liver specimens, underlining the importance of standardized protocols for tissue biobanking in hepatology.