HMDB 4.0: the human metabolome database for 2018

Deep multiomic profiling reveals molecular signatures that underpin preschool wheeze and asthma

BACKGROUND

Wheezing in childhood is prevalent, with over one-half of all children experiencing at least 1 episode by age 6. The pathophysiology of wheeze, especially why some children develop asthma while others do not, remains unclear.

OBJECTIVES

This study addresses the knowledge gap by investigating the transition from preschool wheeze to asthma using multiomic profiling.

METHODS

Unsupervised, group-agnostic integrative multiomic factor analysis was performed using host/bacterial (meta)transcriptomic and bacterial shotgun metagenomic datasets from bronchial brush samples paired with metabolomic/lipidomic data from bronchoalveolar lavage samples acquired from children 1-17 years old.

RESULTS

Two multiomic factors were identified: one characterizing preschool-aged recurrent wheeze and another capturing an inferred trajectory from health to wheeze and school-aged asthma. Recurrent wheeze was driven by type 1-immune signatures, coupled with upregulation of immune-related and neutrophil-associated lipids and metabolites. Comparatively, progression toward asthma from ages 1 to 18 was dominated by changes related to airway epithelial cell gene expression, type 2-immune responses, and constituents of the airway microbiome, such as increased Haemophilus influenzae.

CONCLUSIONS

These factors highlighted distinctions between an inflammation-related phenotype in preschool wheeze, and the predominance of airway epithelial-related changes linked with the inferred trajectory toward asthma. These findings provide insights into the differential mechanisms driving the progression from wheeze to asthma and may inform targeted therapeutic strategies.

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.

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.

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.

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.

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.

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.

Consistent Multi-Omic Relationships Uncover Molecular Basis of Pediatric Asthma IgE Regulation

Serum total immunoglobulin E levels (total IgE) capture the state of the immune system in relation to allergic sensitization. High levels are associated with airway obstruction and poor clinical outcomes in pediatric asthma. Inconsistent patient response to anti-IgE therapies motivates discovery of molecular mechanisms underlying serum IgE level differences in children with asthma. To uncover these mechanisms using complementary metabolomic and transcriptomic data, abundance levels of 529 named metabolites and expression levels of 22,772 genes were measured among children with asthma in the Childhood Asthma Management Program (CAMP, N=564) and the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS, N=309) via the TOPMed initiative. Gene-metabolite associations dependent on IgE were identified within each cohort using multivariate linear models and were interpreted in a biochemical context using network topology, pathway and chemical enrichment, and representation within reactions. A total of 1,617 total IgE-dependent gene-metabolite associations from GACRS and 29,885 from CAMP met significance cutoffs. Of these, glycine and guanidinoacetic acid (GAA) were associated with the most genes in both cohorts, and the associations represented reactions central to glycine, serine, and threonine metabolism and arginine and proline metabolism. Pathway and chemical enrichment analysis further highlighted additional related pathways of interest. The results of this study suggest that GAA may modulate total IgE levels in two independent pediatric asthma cohorts with different characteristics, supporting the use of L-Arginine as a potential therapeutic for asthma exacerbation. Other potentially new targetable pathways are also uncovered.

Metabolic signatures of two scleractinian corals from the northern South China sea in response to extreme high temperature events

Coral bleaching events are becoming increasingly common worldwide, causing widespread coral mortality. However, not all colonies within the same coral taxa show sensitivity to bleaching events, and the current understanding of the metabolic mechanisms underlying thermal bleaching in corals remains limited. We used untargeted metabolomics to analyze the biochemical processes involved in the survival of two bleaching phenotypes of the common corals Pavona decussata and Acropora pruinosa, during a severe bleaching event in the northern South China Sea in 2020. During thermal bleaching, P. decussata and A. pruinosa significantly accumulated energy products such as succinate and EPA, antioxidants and inflammatory markers, and reduced energy storage substances like glutamate and thymidine. KEGG analysis revealed enrichment of energy production pathways such as ABC transporters, nucleotide metabolism and lipid metabolism, suggesting the occurrence of oxidative stress and energy metabolism disorders in bleached corals. Notably, heat stress exerted distinct effects on metabolic pathways in the two coral species, e.g., P. decussata activating carbohydrate metabolism pathways like glycolysis and the TCA cycle, along with amino acid metabolism pathways, whereas A. pruinosa significantly altered the content of multiple small peptides affected amino acid metabolism. Furthermore, the osmoregulatory potential of corals correlates with their ability to survive in heat-stress environments in the wild. This study provides valuable insights into the metabolic mechanisms linked to thermal tolerance in reef-building corals, contributes to the understanding of corals' adaptive potential to heat stress induced by global warming and lays the foundation for developing targeted conservation strategies in the future.

Discovery and determination of misuse and chemotypes of Pogostemon cablin by liquid chromatography-quadrupole time-of-flight mass spectrometry and liquid chromatography with diode-array detector

Traditional Chinese medicine (TCM) has garnered significant scientific interest in healthcare but faces increased regulatory scrutiny due to concerns about uncontrolled usage. This study focuses on characterizing Pogostemon cablin (PC) to mitigate potential misuse and identify chemotype differences. Leveraging untargeted metabolomics, we identified 222 distinctive features effectively differentiating PC from Agastache rugosa (AR), reducing misidentification risks. Pogostone and tilianin emerged as potential markers, leading to a high-performance liquid chromatography-diode array detection (HPLC-DAD) method development for PC and AR discrimination. Evaluation of PC chromatograms revealed notable profile and pogostone level differences among samples, suggesting chemotype associations. Untargeted metabolic profiling identified 78 features with significant differences, highlighting 7,3',4'-tri-O-methyleriodictyol as a potential discriminatory marker between PC chemotypes. The developed HPLC-DAD method quantified pogostone and 7,3',4'-tri-O-methyleriodictyol, effectively discriminating PC chemotypes. This platform differentiates PC and AR and distinguishes chemical types within PC, like pogostone-type and patchoulol-type. Applied to local TCM stores, it ensures PC authenticity. This approach addresses TCM control concerns, enhancing understanding and application of herbal medicine by providing insights into PC chemical composition and discrimination.

Associations of Early Gut Microbiome and Metabolome with Growth and Body Composition of Preterm Infants Within the First 6 Months

Objectives: This study aimed to explore the associations of growth and body composition with gut microbiome and metabolome in preterm infants. Materials and Methods: A prospective cohort study including 73 human milk-fed very preterm infants was conducted. During hospitalization, fecal samples were collected to detect microbes and metabolites using 16S rRNA gene sequencing and liquid chromatography-mass spectrometry. Growth and body composition indices were measured at term equivalent age (TEA) and 6 months of corrected age (CA). Associations of the fecal microbiome and metabolome profiles with growth and body composition indices, as well as their changes, were analyzed. Results: A higher abundance of Streptococcus was associated with a lower fat-free mass (FFM) z-score at 6 months of CA (p = 0.002) and a smaller increase in FFM z-score from TEA to 6 months of CA (p = 0.018). Higher levels of 3'-sialyllactose and 6'-sialyllactose (6'-SL) in feces were correlated with a lower z-score of percentage body fat (PBF) (p = 0.018 and 0.020, respectively) and a lower z-score of fat mass (p = 0.044 and 0.043, respectively) at 6 months of CA. A higher level of 6'-SL in feces was correlated with a greater increase in FFM z-score from TEA to 6 months of CA (p = 0.021). Conclusions: This study sheds light on the role of specific microbial-host interactions in metabolic changes in preterm infants, indicating the potential role of sialylated human milk oligosaccharides in optimizing body composition.

Biological activities, identification, method development, and validation for analysis of polyphenolic compounds in Nymphaea rubra flowers and leaves by UHPLC-Q-cIM-TOF-MS and UHPLC-TQ-MS

INTRODUCTION

Nymphaea rubra belongs to the Nymphaea family and is regarded as a vegetable used in traditional medicine to cure several ailments. These species are rich in phenolic acid, flavonoids, and hydrolysable tannin.

OBJECTIVE

This study aimed to assess the biological activities of Nymphaea rubra flowers (NRF) and leaves (NRL) by identifying and quantifying their polyphenolic compounds using ultra-performance liquid chromatography coupled to quadrupole cyclic ion mobility time-of-flight mass spectrometry (UHPLC-Q-cIM-TOF-MS) and triple quadrupole mass spectrometry (UHPLC-TQ-MS).

METHODOLOGY

NRF and NRL powder was extracted with methanol and fractionated using hexane, ethylacetate, and water. Antioxidant and α-glucosidase, and tyrosinase enzyme inhibitory activities were evaluated. The polyphenolic components of NRF and NRL were identified and quantified using UHPLC-Q-cIM-TOF-MS and UHPLC-TQ-MS. The method was validated using linearity, precision, accuracy, limit of detection (LOD), and lower limit of quantification (LLOQ).

RESULTS

Bioactive substances and antioxidants were highest in the ethylacetate fraction of flowers and leaves. Principal component analysis showed how solvent and plant components affect N. rubra's bioactivity and bioactive compound extraction. A total of 67 compounds were identified, and among them 21 significant polyphenols were quantified. Each calibration curve had R2 > 0.998. The LOD and LLOQ varied from 0.007 to 0.09 μg/mL and from 0.01 to 0.1 μg/mL, respectively. NRF contained a significant amount of gallic acid (10.1 mg/g), while NRL contained abundant pentagalloylglucose (2.8 mg/g).

CONCLUSION

The developed method is simple, rapid, and selective for the identification and quantification of bioactive molecules. These findings provide a scientific basis for N. rubra's well-documented biological effects.

Anti-hemostatic, antithrombotic, and chemical profiles of a curly-leaf variety of Petroselinum crispum (Apiaceae), a food and medicinal aromatic herb

Thrombosis is currently among the major causes of morbidity and mortality in the World. New prevention and therapy alternatives have been increasingly sought in medicinal plants. In this context, we have been investigating parsley, Petroselinum crispum (Mill.) Nym, an aromatic herb with two leaf varieties. We report here the in vitro, in vivo, and ex vivo anti-hemostatic and antithrombotic activities of a parsley curly-leaf variety. Aqueous extracts of aerial parts (PCC-AP), stems (PCC-S), and leaves (PCC-L) showed significant in vitro antiplatelet activity. PCC-AP extract exhibited the highest activity (IC50 2.92 mg/mL) when using ADP and collagen as agonists. All extracts also presented in vitro anticoagulant activity (APTT and PT) and anti-thrombogenic activity. PCC-S was the most active, with more significant interference in the factors of the intrinsic coagulation pathway. The oral administration of PCC-AP extract in rats caused a greater inhibitory activity in the deep vein thrombi (50%; 65 mg/kg) than in arterial thrombi formation (50%; 200 mg/kg), without cumulative effect after consecutive five-day administration. PCC-AP extract was safe in the induced bleeding time test. Its anti-aggregating profile was similar in ex vivo and in vitro conditions but was more effective in the extrinsic pathway when compared to in vitro results. Apiin and coumaric acid derivatives are the main compounds in PCC-AP according to the HPLC-DAD-ESI-MS/MS profile. We demonstrated for the first time that extracts from different parts of curly parsley have significant antiplatelet, anticoagulant, and antithrombotic activity without inducing hemorrhage, proving its potential as a source of antithrombotic compounds.

Screening of Environmental Chemicals to Characterize Exposures in Participants With Systemic Lupus Erythematosus

OBJECTIVE

There is a need to characterize exposures associated with the pathogenesis of systemic lupus erythematosus (SLE). In this pilot study, we explore a hypothesis-free approach that can measure thousands of exogenous chemicals in blood ("exposome") in patients with SLE and unaffected controls.

METHODS

This cross-sectional study analyzed a cohort of patients with prevalent SLE (n = 285) and controls (n = 106). Plasma was analyzed by liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF/MS). Mass spectrometry features present in at least 25% of all samples were selected for association analysis (n = 2,737). Features were matched to potential chemicals using available databases. Association analysis of abundances of features with SLE status was performed, adjusting for age and sex. We also explored features associated with SLE phenotypes, sociodemographic factors, and current medication use.

RESULTS

We found 30 features significantly associated with SLE status (Bonferroni P < 0.05). Of these, seven matched chemical names based on databases. These seven features included phthalate metabolites, a formetanate metabolite, and eugenol. The abundance of acid pesticides differed between patients with SLE and controls (Bonferroni P < 0.05). Two unmatched features were associated with a history of lupus nephritis, and one with anti-double-stranded DNA antibody production (Bonferroni P < 0.05). Seventeen features varied by self-reported race and ethnicity, including a polyfluoroalkyl substance (analysis of variance P < 1.69 × 10-5). Eleven features correlated with antimalarials, 6 with mycophenolate mofetil, and 29 with prednisone use.

CONCLUSION

This proof-of-concept study demonstrates that LC-QTOF/MS is a powerful tool that agnostically detects circulating exogenous compounds. These analyses can generate hypotheses of disease-related exposures for future prospective, longitudinal studies.

Utilising a Clinical Metabolomics LC-MS Study to Determine the Integrity of Biological Samples for Statistical Modelling after Long Term -80 °C Storage: A TOFI_Asia Sub-Study

Biological samples of lipids and metabolites degrade after extensive years in -80 °C storage. We aimed to determine if associated multivariate models are also impacted. Prior TOFI_Asia metabolomics studies from our laboratory established multivariate models of metabolic risks associated with ethnic diversity. Therefore, to compare multivariate modelling degradation after years of -80 °C storage, we selected a subset of aged (≥5-years) plasma samples from the TOFI_Asia study to re-analyze via untargeted LC-MS metabolomics. Samples from European Caucasian (n = 28) and Asian Chinese (n = 28) participants were evaluated for ethnic discrimination by partial least squares discriminative analysis (PLS-DA) of lipids and polar metabolites. Both showed a strong discernment between participants ethnicity by features, before (Initial) and after (Aged) 5-years of -80 °C storage. With receiver operator characteristic curves, sparse PLS-DA derived confusion matrix and prediction error rates, a considerable reduction in model integrity was apparent with the Aged polar metabolite model relative to Initial modelling. Ethnicity modelling with lipids maintained predictive integrity in Aged plasma samples, while equivalent polar metabolite models reduced in integrity. Our results indicate that researchers re-evaluating samples for multivariate modelling should consider time at -80 °C when producing predictive metrics from polar metabolites, more so than lipids.

Blood metabolites and chronic kidney disease: a Mendelian randomization study

BACKGROUND

Human blood metabolites have demonstrated close associations with chronic kidney disease (CKD) in observational studies. Nonetheless, the causal relationship between metabolites and CKD is still unclear. This study aimed to assess the associations between metabolites and CKD risk.

METHODS

We applied a two-sample Mendelian randomization (MR) analysis to evaluate relationships between 1400 blood metabolites and eight phenotypes (outcomes) (CKD, estimated glomerular filtration rate(eGFR), urine albumin to creatinine ratio, rapid progress to CKD, rapid decline of eGFR, membranous nephropathy, immunoglobulin A nephropathy, and diabetic nephropathy). The inverse variance weighted (IVW), MR-Egger, and weighted median were used to investigate the causal relationship. Sensitivity analyses were performed with Cochran's Q, MR-Egger intercept, MR-PRESSO Global test, and leave-one-out analysis. Bonferroni correction was used to test the strength of the causal relationship.

RESULTS

Through the MR analysis of 1400 metabolites and eight clinical phenotypes, a total of 48 metabolites were found to be associated with various outcomes. Among them, N-acetylleucine (OR = 0.923, 95%CI: 0.89-0.957, PIVW = 1.450 × 10-5) has a strong causal relationship with lower risk of CKD after the Bonferroni-corrected test, whereas Glycine to alanine ratio has a strong causal relationship with higher risk of CKD (OR = 1.106, 95%CI: 1.063-1.151, PIVW = 5.850 × 10-7). No horizontal pleiotropy and heterogeneity were detected.

CONCLUSION

Our study offers groundbreaking insights into the integration of metabolomics and genomics to reveal the pathogenesis of and therapeutic strategies for CKD. It underscores 48 metabolites as potential causal candidates, meriting further investigation.

Extending PROXIMAL to predict degradation pathways of phenolic compounds in the human gut microbiota

Despite significant advances in reconstructing genome-scale metabolic networks, the understanding of cellular metabolism remains incomplete for many organisms. A promising approach for elucidating cellular metabolism is analysing the full scope of enzyme promiscuity, which exploits the capacity of enzymes to bind to non-annotated substrates and generate novel reactions. To guide time-consuming costly experimentation, different computational methods have been proposed for exploring enzyme promiscuity. One relevant algorithm is PROXIMAL, which strongly relies on KEGG to define generic reaction rules and link specific molecular substructures with associated chemical transformations. Here, we present a completely new pipeline, PROXIMAL2, which overcomes the dependency on KEGG data. In addition, PROXIMAL2 introduces two relevant improvements with respect to the former version: i) correct treatment of multi-step reactions and ii) tracking of electric charges in the transformations. We compare PROXIMAL and PROXIMAL2 in recovering annotated products from substrates in KEGG reactions, finding a highly significant improvement in the level of accuracy. We then applied PROXIMAL2 to predict degradation reactions of phenolic compounds in the human gut microbiota. The results were compared to RetroPath RL, a different and relevant enzyme promiscuity method. We found a significant overlap between these two methods but also complementary results, which open new research directions into this relevant question in nutrition.

Exploratory Untargeted Metabolomics of Dried Blood Spot Samples from Newborns with Maple Syrup Urine Disease

Currently, tandem mass spectrometry-based newborn screening (NBS), which examines targeted biomarkers, is the first approach used for the early detection of maple syrup urine disease (MSUD) in newborns, followed by confirmatory genetic mutation tests. However, these diagnostic approaches have limitations, demanding the development of additional tools for the diagnosis/screening of MUSD. Recently, untargeted metabolomics has been used to explore metabolic profiling and discover the potential biomarkers/pathways of inherited metabolic diseases. Thus, we aimed to discover a distinctive metabolic profile and biomarkers/pathways for MSUD newborns using untargeted metabolomics. Herein, untargeted metabolomics was used to analyze dried blood spot (DBS) samples from 22 MSUD and 22 healthy control newborns. Our data identified 210 altered endogenous metabolites in MSUD newborns and new potential MSUD biomarkers, particularly L-alloisoleucine, methionine, and lysoPI. In addition, the most impacted pathways in MSUD newborns were the ascorbate and aldarate pathways and pentose and glucuronate interconversions, suggesting that oxidative and detoxification events may occur in early life. Our approach leads to the identification of new potential biomarkers/pathways that could be used for the early diagnosis/screening of MSUD newborns but require further validation studies. Our untargeted metabolomics findings have undoubtedly added new insights to our understanding of the pathogenicity of MSUD, which helps us select the appropriate early treatments for better health outcomes.

Host microbiome depletion attenuates biofluid metabolite responses following radiation exposure

Development of novel biodosimetry assays and medical countermeasures is needed to obtain a level of radiation preparedness in the event of malicious or accidental mass exposures to ionizing radiation (IR). For biodosimetry, metabolic profiling with mass spectrometry (MS) platforms has identified several small molecules in easily accessible biofluids that are promising for dose reconstruction. As our microbiome has profound effects on biofluid metabolite composition, it is of interest how variation in the host microbiome may affect metabolomics based biodosimetry. Here, we 'knocked out' the microbiome of male and female C57BL/6 mice (Abx mice) using antibiotics and then irradiated (0, 3, or 8 Gy) them to determine the role of the host microbiome on biofluid radiation signatures (1 and 3 d urine, 3 d serum). Biofluid metabolite levels were compared to a sham and irradiated group of mice with a normal microbiome (Abx-con mice). To compare post-irradiation effects in urine, we calculated the Spearman's correlation coefficients of metabolite levels with radiation dose. For selected metabolites of interest, we performed more detailed analyses using linear mixed effect models to determine the effects of radiation dose, time, and microbiome depletion. Serum metabolite levels were compared using an ANOVA. Several metabolites were affected after antibiotic administration in the tryptophan and amino acid pathways, sterol hormone, xenobiotic and bile acid pathways (urine) and lipid metabolism (serum), with a post-irradiation attenuative effect observed for Abx mice. In urine, dose×time interactions were supported for a defined radiation metabolite panel (carnitine, hexosamine-valine-isoleucine [Hex-V-I], creatine, citric acid, and Nε,Nε,Nε-trimethyllysine [TML]) and dose for N1-acetylspermidine, which also provided excellent (AUROC ≥ 0.90) to good (AUROC ≥ 0.80) sensitivity and specificity according to the area under the receiver operator characteristic curve (AUROC) analysis. In serum, a panel consisting of carnitine, citric acid, lysophosphatidylcholine (LysoPC) (14:0), LysoPC (20:3), and LysoPC (22:5) also gave excellent to good sensitivity and specificity for identifying post-irradiated individuals at 3 d. Although the microbiome affected the basal levels and/or post-irradiation levels of these metabolites, their utility in dose reconstruction irrespective of microbiome status is encouraging for the use of metabolomics as a novel biodosimetry assay.

A cell-free nutrient-supplemented perfusate allows four-day ex vivo metabolic preservation of human kidneys

The growing disparity between the demand for transplants and the available donor supply, coupled with an aging donor population and increasing prevalence of chronic diseases, highlights the urgent need for the development of platforms enabling reconditioning, repair, and regeneration of deceased donor organs. This necessitates the ability to preserve metabolically active kidneys ex vivo for days. However, current kidney normothermic machine perfusion (NMP) approaches allow metabolic preservation only for hours. Here we show that human kidneys discarded for transplantation can be preserved in a metabolically active state up to 4 days when perfused with a cell-free perfusate supplemented with TCA cycle intermediates at subnormothermia (25 °C). Using spatially resolved isotope tracing we demonstrate preserved metabolic fluxes in the kidney microenvironment up to Day 4 of perfusion. Beyond Day 4, significant changes were observed in renal cell populations through spatial lipidomics, and increases in injury markers such as LDH, NGAL and oxidized lipids. Finally, we demonstrate that perfused kidneys maintain functional parameters up to Day 4. Collectively, these findings provide evidence that this approach enables metabolic and functional preservation of human kidneys over multiple days, establishing a solid foundation for future clinical investigations.

Transcriptomic, epigenomic, and spatial metabolomic cell profiling redefines regional human kidney anatomy

A large-scale multimodal atlas that includes major kidney regions is lacking. Here, we employed simultaneous high-throughput single-cell ATAC/RNA sequencing (SHARE-seq) and spatially resolved metabolomics to profile 54 human samples from distinct kidney anatomical regions. We generated transcriptomes of 446,267 cells and chromatin accessibility profiles of 401,875 cells and developed a package to analyze 408,218 spatially resolved metabolomes. We find that the same cell type, including thin limb, thick ascending limb loop of Henle and principal cells, display distinct transcriptomic, chromatin accessibility, and metabolomic signatures, depending on anatomic location. Surveying metabolism-associated gene profiles revealed non-overlapping metabolic signatures between nephron segments and dysregulated lipid metabolism in diseased proximal tubule (PT) cells. Integrating multimodal omics with clinical data identified PLEKHA1 as a disease marker, and its in vitro knockdown increased gene expression in PT differentiation, suggesting possible pathogenic roles. This study highlights previously underrepresented cellular heterogeneity underlying the human kidney anatomy.

Rats Orally Administered with Ethyl Alcohol for a Prolonged Time Show Histopathology of the Epididymis and Seminal Vesicle Together with Changes in the Luminal Metabolite Composition

Prolonged ethanol (EtOH) consumption is associated with male infertility, with a decreased spermatogenesis rate as one cause. The defective maturation and development of sperm during their storage in the cauda epididymis and transit in the seminal vesicle can be another cause, possibly occurring before the drastic spermatogenesis disruption. Herein, we demonstrated that the cauda epididymis and seminal vesicle of rats, orally administered with EtOH under a regimen in which spermatogenesis was still ongoing, showed histological damage, including lesions, a decreased height of the epithelial cells and increased collagen fibers in the muscle layer, which implicated fibrosis. Lipid peroxidation (shown by malondialdehyde (MDA) levels) was observed, indicating that reactive oxygen species (ROS) were produced along with acetaldehyde during EtOH metabolism by CYP2E1. MDA, acetaldehyde and other lipid peroxidation products could further damage cellular components of the cauda epididymis and seminal vesicle, and this was supported by increased apoptosis (shown by a TUNEL assay and caspase 9/caspase 3 expression) in these two tissues of EtOH-treated rats. Consequently, the functionality of the cauda epididymis and seminal vesicle in EtOH-treated rats was impaired, as demonstrated by a decreases in 1H NMR-analyzed metabolites (e.g., carnitine, fructose), which were important for sperm development, metabolism and survival in their lumen.