Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling

Liver Lipidomics Analysis Revealed the Protective mechanism of Zuogui Jiangtang Qinggan Formula in type 2 diabetes mellitus with non-alcoholic fatty liver disease

ETHNOPHARMACOLOGICAL RELEVANCE

Hepatic steatosis, a hallmark of non-alcoholic fatty liver disease (NAFLD), represents a significant global health issue. Liver lipidomics has garnered increased focus recently, highlighting Traditional Chinese Medicine's (TCM) role in mitigating such conditions through lipid metabolism regulation. The Zuogui Jiangtang Qinggan Formula (ZGJTQGF), a longstanding TCM regimen for treating Type 2 Diabetes Mellitus (T2DM) with NAFLD, lacks a definitive mechanism for its lipid metabolism regulatory effects.

AIM OF THE STUDY

This research aims to elucidate ZGJTQGF's mechanism on lipid metabolism in T2DM with NAFLD.

MATERIALS AND METHODS

The study, utilized db/db mice to establish T2DM with NAFLD models. Evaluations included Hematoxylin-Eosin (HE) and Oil Red O stainedstaining of liver tissues, alongside biochemical lipid parameter analysis. Liver lipidomics and Western blotting further substantiated the findings, systematically uncovering the mechanism of action mechanism.

RESULTS

ZGJTQGF notably reduced body weight, and Fasting Blood Glucose (FBG), enhancing glucose tolerance in db/db mice. HE, and Oil Red O staining, complemented by biochemical and liver lipidomics analyses, confirmed ZGJTQGF's efficacy in ameliorating liver steatosis and lipid metabolism anomalies. Lipidomics identified 1571 significantly altered lipid species in the model group, primarily through the upregulation of triglycerides (TG) and diglycerides (DG), and the downregulation of phosphatidylcholine (PC) and phosphatidylethanolamine (PE). Post-ZGJTQGF treatment, 496 lipid species were modulated, with increased PC and PE levels and decreased TG and DG, showcasing significant lipid metabolism improvement in T2DM with NAFLD. Moreover, ZGJTQGF's influence on lipid synthesis-related proteins was observed, underscoring its anti-steatotic impact through liver lipidomic alterations and offering novel insights into hepatic steatosis pathogenesis.

CONCLUSIONS

Liver lipidomics analysis combined with protein verification further demonstrated that ZGJTQGF could ameliorate the lipid disturbance of TG, DG, PC, PE in T2DM with NAFLD, as well as improve fatty acid and cholesterol synthesis and metabolism through De novo lipogenesis pathway.

A multi-omics study to monitor senescence-associated secretory phenotypes of Alzheimer's disease

OBJECTIVE

Alzheimer's disease (AD) is characterized by the progressive degeneration and damage of neurons in the brain. However, developing an accurate diagnostic assay using blood samples remains a challenge in clinic practice. The aim of this study was to explore senescence-associated secretory phenotypes (SASPs) in peripheral blood using mass spectrometry based multi-omics approach and to establish diagnostic assays for AD.

METHODS

This retrospective study included 88 participants, consisting of 29 AD patients and 59 cognitively normal (CN) individuals. Plasma and serum samples were examined using high-resolution mass spectrometry to identify proteomic and metabolomic profiles. Receiver operating characteristic (ROC) analysis was employed to screen biomarkers with diagnostic potential. K-nearest neighbors (KNN) algorithm was utilized to construct a multi-dimensional model for distinguishing AD from CN.

RESULTS

Proteomics analysis revealed upregulation of five plasma proteins in AD, including RNA helicase aquarius (AQR), zinc finger protein 587B (ZNF587B), C-reactive protein (CRP), fibronectin (FN1), and serum amyloid A-1 protein (SAA1), indicating their potential for AD classification. Interestingly, KNN-based three-dimensional model, comprising AQR, ZNF587B, and CRP, demonstrated its high accuracy in AD recognition, with evaluation possibilities of 0.941, 1.000, and 1.000 for the training, testing, and validation datasets, respectively. Besides, metabolomics analysis suggested elevated levels of serum phenylacetylglutamine (PAGIn) in AD.

INTERPRETATION

The multi-omics outcomes highlighted the significance of the SASPs, specifically AQR, ZNF587B, CRP, and PAGIn, in terms of their potential for diagnosing AD and suggested neuronal aging-associated pathophysiology.

Targeting of Antifungal Metabolites from Grapevine Byproducts by UPLC-HRMS/MS Approaches Using Bioactivity-Based Molecular Networking

This study focuses on countering Fusarium graminearum, a harmful fungal pathogen impacting cereal crops and human health through mycotoxin production. These mycotoxins, categorized as type B trichothecenes, pose significant health risks. Research explores natural alternatives to synthetic fungicides, particularly investigating phenolics in grapevine byproducts. Thirteen eco-extracts from five French grape varieties (Merlot, Cabernet Sauvignon, Sauvignon blanc, Tannat, and Artaban) exhibited substantial antifungal properties, with ten extracts displaying remarkable effects. Extracts from grapevine stems and roots notably reduced fungal growth by over 91% after five days. Through UHPLC-HRMS/MS analysis and metabolomics, the study identified potent antifungal compounds such as ampelopsin A and cyphostemmin B, among other oligomeric stilbenes. Interestingly, this approach showed that flavan-3-ols have been identified as markers for extracts that induce fungal growth. Root extracts from rootstocks, rich in oligostilbenes, demonstrated the highest antifungal activity. This research underscores grapevine byproducts' potential both as a sustainable approach to control F. graminearum and mycotoxin contamination in cereal crops and the presence of different metabolites from the cultivars of grapevine, suggesting different activities.

The Metabolic and Lipidomic Fingerprint of Torin1 Exposure in Mouse Embryonic Fibroblasts Using Untargeted Metabolomics

Torin1, a selective kinase inhibitor targeting the mammalian target of rapamycin (mTOR), remains widely used in autophagy research due to its potent autophagy-inducing abilities, regardless of its unspecific properties. Recognizing the impact of mTOR inhibition on metabolism, our objective was to develop a reliable and thorough untargeted metabolomics workflow to study torin1-induced metabolic changes in mouse embryonic fibroblast (MEF) cells. Crucially, our quality assurance and quality control (QA/QC) protocols were designed to increase confidence in the reported findings by reducing the likelihood of false positives, including a validation experiment replicating all experimental steps from sample preparation to data analysis. This study investigated the metabolic fingerprint of torin1 exposure by using liquid chromatography-high resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics platforms. Our workflow identified 67 altered metabolites after torin1 exposure, combining univariate and multivariate statistics and the implementation of a validation experiment. In particular, intracellular ceramides, diglycerides, phosphatidylcholines, phosphatidylethanolamines, glutathione, and 5'-methylthioadenosine were downregulated. Lyso-phosphatidylcholines, lyso-phosphatidylethanolamines, glycerophosphocholine, triglycerides, inosine, and hypoxanthine were upregulated. Further biochemical pathway analyses provided deeper insights into the reported changes. Ultimately, our study provides a valuable workflow that can be implemented for future investigations into the effects of other compounds, including more specific autophagy modulators.

Untargeted metabolomics based on ultra-high performance liquid chromatography-mass spectrometry/MS reveals the lipid-lowering mechanism of taurine in hyperlipidemia mice

Introduction

Taurine has a prominent lipid-lowering effect on hyperlipidemia. However, a comprehensive analysis of the effects of taurine on endogenous metabolites in hyperlipidemia has not been documented. This study aimed to explore the impact of taurine on multiple metabolites associated with hyperlipidemia.

Methods

The hyperlipidemic mouse model was induced by high-fat diet (HFD). Taurine was administered via oral gavage at doses of 700  mg/kg/day for 14  weeks. Evaluation of body weight, serum lipid levels, and histopathology of the liver and adipose tissue was performed to confirm the lipid-lowering effect of taurine. Ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS)-based metabonomics analyses of serum, urine, feces, and liver, coupled with multivariate data analysis, were conducted to assess changes in the endogenous metabolites.

Results and discussion

Biochemical and histological examinations demonstrated that taurine administration prevented weight gain and dyslipidemia, and alleviated lipid deposition in the liver and adipose tissue in hyperlipidemic mice. A total of 76 differential metabolites were identified by UPLC-MS-based metabolomics approach, mainly involving BAs, GPs, SMs, DGs, TGs, PUFAs and amino acids. Taurine was found to partially prevent HFDinduced abnormalities in the aforementioned metabolites. Using KEGG database and MetaboAnalyst software, it was determined that taurine effectively alleviates metabolic abnormalities caused by HFD, including fatty acid metabolism, sphingolipid metabolism, glycerophospholipid metabolism, diacylglycerol metabolism, amino acid metabolism, bile acid and taurine metabolism, taurine and hypotaurine metabolism. Moreover, DGs, GPs and SMs, and taurine itself may serve as active metabolites in facilitating various anti-hyperlipidemia signal pathways associated with taurine. This study provides new evidence for taurine to prevent hyperlipidemia.

Integrated transcriptomic and metabolomic data reveal the cold stress responses molecular mechanisms of two coconut varieties

Among tropical fruit trees, coconut holds significant edible and economic importance. The natural growth of coconuts faces a challenge in the form of low temperatures, which is a crucial factor among adverse environmental stresses impacting their geographical distribution. Hence, it is essential to enhance our comprehension of the molecular mechanisms through which cold stress influences various coconut varieties. We employed analyses of leaf growth morphology and physiological traits to examine how coconuts respond to low temperatures over 2-hour, 8-hour, 2-day, and 7-day intervals. Additionally, we performed transcriptome and metabolome analyses to identify the molecular and physiological shifts in two coconut varieties displaying distinct sensitivities to the cold stress. As the length of cold stress extended, there was a prominent escalation within the soluble protein (SP), proline (Pro) concentrations, the activity of peroxidase (POD) and superoxide dismutase (SOD) in the leaves. Contrariwise, the activity of glutathione peroxidase (GSH) underwent a substantial reduction during this period. The widespread analysis of metabolome and transcriptome disclosed a nexus of genes and metabolites intricately cold stress were chiefly involved in pathways centered around amino acid, flavonoid, carbohydrate and lipid metabolism. We perceived several stress-responsive metabolites, such as flavonoids, carbohydrates, lipids, and amino acids, which unveiled considerably, lower in the genotype subtle to cold stress. Furthermore, we uncovered pivotal genes in the amino acid biosynthesis, antioxidant system and flavonoid biosynthesis pathway that presented down-regulation in coconut varieties sensitive to cold stress. This study broadly enriches our contemporary perception of the molecular machinery that contributes to altering levels of cold stress tolerance amid coconut genotypes. It also unlocks several unique prospects for exploration in the areas of breeding or engineering, aiming to identifying tolerant and/or sensitive coconut varieties encompassing multi-omics layers in response to cold stress conditions.

Biological characteristics of pregnancy in captive Yangtze finless porpoises revealed by urinary metabolomics†

The Yangtze finless porpoises (Neophocaena asiaeorientalis a.) are an endemic and critically endangered species in China. Intensive captive breeding is essential for understanding the biology of critically endangered species, especially their pregnancy characteristics, knowledge of which is crucial for effective breeding management. Urine metabolomics can reveal metabolic differences, arising from physiological changes across pregnancy stages. Therefore, we used the urinary metabolomic technology, to explore urinary metabolite changes in pregnant Yangtze finless porpoises. A total of 2281 metabolites were identified in all samples, which including organic acids and derivatives (24.45%), organoheterocyclic compounds (20.23%), benzenoids (18.05%), organic oxygen compounds (7.73%), and phenylpropanoids and polyketides (6.48%). There were 164, 387, and 522 metabolites demonstrating differential abundance during early pregnancy, mid pregnancy, and late pregnancy, respectively, from the levels observed in nonpregnancy. The levels of pregnenolone, 17α-hydroxyprogesterone, and tetrahydrocortisone were significantly higher during all pregnancy stages, indicating their important roles in fetal development. The differential metabolites between nonpregnancy and pregnancy were mainly associated with amino acid and carbohydrate metabolism. Moreover, metabolic activity varied across pregnancy stages; steroid hormone biosynthesis was predominant in early pregnancy, and amino acid biosynthesis and carbohydrate metabolism were predominant in mid pregnancy and late pregnancy, respectively. Our results provide new insights into metabolic characteristics in the Yangtze finless porpoises' urine during pregnancy, and indicate that the differential levels of urine metabolites can determine pregnancy in Yangtze finless porpoises, providing valuable information for the husbandry and management of pregnant Yangtze finless porpoises in captivity.

Intestine proteomic and metabolomic alterations in dogs infected with Toxocara canis

Toxocariasis is an important zoonotic parasitic disease. Toxocaris canis adults live and reproduce in the intestinal tract of dogs and other canine hosts, and the infectious eggs are continuously excreted in feces, which causes environmental contamination and has an important public health significance. In this study, TMT proteomic and untargeted metabolomic methods were used to explore the physiological and pathological effects on the intestinal tract of dogs which infected with T. canis, and a series of bioinformatics analyses were conducted to identify differentially expressed proteins (DEPs) and differentially expressed metabolites (DEMs). The proteomics results showed that 198 DEPs were mainly enriched in the immune system and signal transduction pathway, and involved in the regulation of the occurrence and development of cancer and infectious diseases. T. canis could disrupt intestinal permeability by increasing the expression of proteins such as zinc finger protein DZIP1L and myosin heavy chain 10. Additionally, T. canis infection could also inhibit the host immune response by decreasing the expression of MHC-II, NF-κB, DLA and other immune-related molecules. While, the metabolomics results revealed that the expression of oxoglutaric acid, glutamate, d-aspartate, arginine, taurochenodeoxycholic acid and taurocholic acid which participated in tricarboxylic acid (TCA) cycle, glycolysis/gluconeogenesis, bile secretion, biosynthesis of amino acids pathway were significantly decreased. The correlation results of proteomics and metabolomics showed that DEPs and DEMs were mainly co-enriched in bile secretion pathway to regulate intestinal peristalsis. Analyzing DEPs and DEMs will not only provide insights into the mechanisms of host parasite interaction, but also aid in identifying potential targets for therapy and diagnosis, thus setting the groundwork for effectively preventing and managing toxocariasis.

Convenient synthesis and delivery of a megabase-scale designer accessory chromosome empower biosynthetic capacity

Synthetic biology confers new functions to hosts by introducing exogenous genetic elements, yet rebuilding complex traits that are based on large-scale genetic information remains challenging. Here, we developed a CRISPR/Cas9-mediated haploidization method that bypasses the natural process of meiosis. Based on the programmed haploidization in yeast, we further developed an easy-to-use method designated HAnDy (Haploidization-based DNA Assembly and Delivery in yeast) that enables efficient assembly and delivery of large DNA, with no need for any fussy in vitro manipulations. Using HAnDy, a de novo designed 1.024 Mb synthetic accessory chromosome (synAC) encoding 542 exogenous genes was parallelly assembled and then directly transferred to six phylogenetically diverse yeasts. The synAC significantly promotes hosts' adaptations and increases the scope of the metabolic network, which allows the emergence of valuable compounds. Our approach should facilitate the assembly and delivery of large-scale DNA for expanding and deciphering complex biological functions.

Protocol for visualizing complex volatile metabolomics data in clinical setups using EDaViS software

Here, we present a protocol for using Early Data Visualization Script, a user-friendly software tool to visualize complex volatile metabolomics data in clinical setups. We describe steps for tabulating data and adjusting visual output to visualize complex time-resolved volatile omics data using simple charts and graphs. We then demonstrate possible modifications by detailing procedures for the adaptation of four basic functions. For complete details on the use and execution of this protocol, please refer to Sukul et al. (2022)1 and Remy et al. (2022).2.

Chronic cortisol stimulation enhances hypothalamus-specific enrichment of metabolites in the rainbow trout brain

The hypothalamus is a key integrating center that is involved in the initiation of the corticosteroid stress response, and in regulating nutrient homeostasis. Although cortisol, the principal glucocorticoid in humans and teleosts, plays a central role in feeding regulation, the mechanisms are far from clear. We tested the hypothesis that the metabolic changes to cortisol exposure signal an energy excess in the hypothalamus, leading to feeding suppression during stress in fish. Rainbow trout (Oncorhynchus mykiss) were administered a slow-release cortisol implant for 3 days, and the metabolite profiles in the plasma, hypothalamus, and the rest of the brain were assessed. Also, U-13C-glucose was injected into the hypothalamus by intracerebroventricular (ICV) route, and the metabolic fate of this energy substrate was followed in the brain regions by metabolomics. Chronic cortisol treatment reduced feed intake, and this corresponded with a downregulation of the orexigenic gene agrp, and an upregulation of the anorexigenic gene cart in the hypothalamus. The U-13C-glucose-mediated metabolite profiling indicated an enhancement of glycolytic flux and tricarboxylic acid intermediates in the rest of the brain compared with the hypothalamus. There was no effect of cortisol treatment on the phosphorylation status of AMPK or mechanistic target of rapamycin in the brain, whereas several endogenous metabolites, including leucine, citrate, and lactate were enriched in the hypothalamus, suggesting a tissue-specific metabolic shift in response to cortisol stimulation. Altogether, our results suggest that the hypothalamus-specific enrichment of leucine and the metabolic fate of this amino acid, including the generation of lipid intermediates, contribute to cortisol-mediated feeding suppression in fish.NEW & NOTEWORTHY Elevated cortisol levels during stress suppress feed intake in animals. We tested whether the feed suppression is associated with cortisol-mediated alteration in hypothalamus metabolism. The brain metabolome revealed a hypothalamus-specific metabolite profile suggesting nutrient excess. Specifically, we noted the enrichment of leucine and citrate in the hypothalamus, and the upregulation of pathways involved in leucine metabolism and fatty acid synthesis. This cortisol-mediated energy substrate repartitioning may modulate the feeding/satiety centers leading to the feeding suppression.

Untargeted Pixel-by-Pixel Imaging of Metabolite Ratio Pairs as a Novel Tool for Biomedical Discovery in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of structurally identified and yet-undefined metabolites across tissue cryosections. While numerous software packages enable pixel-by-pixel imaging of individual metabolites, the research community lacks a discovery tool that images all metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs informs discovery of unanticipated molecules contributing to shared metabolic pathways, uncovers hidden metabolic heterogeneity across cells and tissue subregions, and indicates single-timepoint flux through pathways of interest. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling and instrument drift, markedly enhances spatial image resolution, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent tool to enhance knowledge obtained from current spatial metabolite profiling technologies.

Self-Organizing Maps: An AI Tool for Identifying Unexpected Source Signatures in Non-Target Screening Analysis of Urban Wastewater by HPLC-HRMS

(1) Background: Monitoring effluent in water treatment plants has a key role in identifying potential pollutants that might be released into the environment. A non-target analysis approach can be used for identifying unknown substances and source-specific multipollutant signatures. (2) Methods: Urban and industrial wastewater effluent were analyzed by HPLC-HRMS for non-target analysis. The anomalous infiltration of industrial wastewater into urban wastewater was investigated by analyzing the mass spectra data of "unknown common" compounds using principal component analysis (PCA) and the Self-Organizing Map (SOM) AI tool. The outcomes of the models were compared. (3) Results: The outlier detection was more straightforward in the SOM model than in the PCA one. The differences among the samples could not be completely perceived in the PCA model. Moreover, since PCA involves the calculation of new variables based on the original experimental ones, it is not possible to reconstruct a chromatogram that displays the recurring patterns in the urban WTP samples. This can be achieved using the SOM outcomes. (4) Conclusions: When comparing a large number of samples, the SOM AI tool is highly efficient in terms of calculation, visualization, and identifying outliers. Interpreting PCA visualization and outlier detection becomes challenging when dealing with a large sample size.

Evaluating the Effect of Data Merging and Postacquisition Normalization on Statistical Analysis of Untargeted High-Resolution Mass Spectrometry Based Urinary Metabolomics Data

Urine is one of the most widely used biofluids in metabolomic studies because it can be collected noninvasively and is available in large quantities. However, it shows large heterogeneity in sample concentration and consequently requires normalization to reduce unwanted variation and extract meaningful biological information. Biological samples like urine are commonly measured with electrospray ionization (ESI) coupled to a mass spectrometer, producing data sets for positive and negative modes. Combining these gives a more complete picture of the total metabolites present in a sample. However, the effect of this data merging on subsequent data analysis, especially in combination with normalization, has not yet been analyzed. To address this issue, we conducted a neutral comparison study to evaluate the performance of eight postacquisition normalization methods under different data merging procedures using 1029 urine samples from the Food Chain plus (FoCus) cohort. Samples were measured with a Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR-MS). Normalization methods were evaluated by five criteria capturing the ability to remove sample concentration variation and preserve relevant biological information. Merging data after normalization was generally favorable for quality control (QC) sample similarity, sample classification, and feature selection for most of the tested normalization methods. Merging data after normalization and the usage of probabilistic quotient normalization (PQN) in a similar setting are generally recommended. Relying on a single analyte to capture sample concentration differences, like with postacquisition creatinine normalization, seems to be a less preferable approach, especially when data merging is applied.

Data processing solutions to render metabolomics more quantitative: case studies in food and clinical metabolomics using Metabox 2.0

In classic semiquantitative metabolomics, metabolite intensities are affected by biological factors and other unwanted variations. A systematic evaluation of the data processing methods is crucial to identify adequate processing procedures for a given experimental setup. Current comparative studies are mostly focused on peak area data but not on absolute concentrations. In this study, we evaluated data processing methods to produce outputs that were most similar to the corresponding absolute quantified data. We examined the data distribution characteristics, fold difference patterns between 2 metabolites, and sample variance. We used 2 metabolomic datasets from a retail milk study and a lupus nephritis cohort as test cases. When studying the impact of data normalization, transformation, scaling, and combinations of these methods, we found that the cross-contribution compensating multiple standard normalization (ccmn) method, followed by square root data transformation, was most appropriate for a well-controlled study such as the milk study dataset. Regarding the lupus nephritis cohort study, only ccmn normalization could slightly improve the data quality of the noisy cohort. Since the assessment accounted for the resemblance between processed data and the corresponding absolute quantified data, our results denote a helpful guideline for processing metabolomic datasets within a similar context (food and clinical metabolomics). Finally, we introduce Metabox 2.0, which enables thorough analysis of metabolomic data, including data processing, biomarker analysis, integrative analysis, and data interpretation. It was successfully used to process and analyze the data in this study. An online web version is available at http://metsysbio.com/metabox.

Spermine deficiency shifts the balance between jasmonic acid and salicylic acid-mediated defence responses in Arabidopsis

Polyamines are small aliphatic polycations present in all living organisms. In plants, the most abundant polyamines are putrescine (Put), spermidine (Spd) and spermine (Spm). Polyamine levels change in response to different pathogens, including Pseudomonas syringae pv. tomato DC3000 (Pst DC3000). However, the regulation of polyamine metabolism and their specific contributions to defence are not fully understood. Here we report that stimulation of Put biosynthesis by Pst DC3000 is dependent on coronatine (COR) perception and jasmonic acid (JA) signalling, independently of salicylic acid (SA). Conversely, lack of Spm in spermine synthase (spms) mutant stimulated galactolipids and JA biosynthesis, and JA signalling under basal conditions and during Pst DC3000 infection, whereas compromised SA-pathway activation and defence outputs through SA-JA antagonism. The dampening of SA responses correlated with COR and Pst DC3000-inducible deregulation of ANAC019 expression and its key SA-metabolism gene targets. Spm deficiency also led to enhanced disease resistance to the necrotrophic fungal pathogen Botrytis cinerea and stimulated endoplasmic reticulum (ER) stress signalling in response to Pst DC3000. Overall, our findings provide evidence for the integration of polyamine metabolism in JA- and SA-mediated defence responses, as well as the participation of Spm in buffering ER stress during defence.

Systematic assessment of transcriptomic and metabolic reprogramming by blue light exposure coupled with aging

The prevalent use of light-emitting diodes (LEDs) has caused revolutionary changes in modern life, but the potential hazards to health of blue light are poorly understood. N6-methyladenosine (m6A) is the most prevalent posttranscriptional modification in eukaryotes and can modulate diverse physiological processes by regulating mRNA fate. Here, to understand the effects and molecular mechanisms of daily low-intensity blue light exposure (BLE) and ascertain whether m6A methylation plays a role in BLE-induced phenotypes, we constructed a series of Drosophila models under different durations of daily low-intensity BLE and obtained multiomics profiles. Our results revealed that BLE could induce transcriptomic, m6A epitranscriptomic, and metabolomic reprogramming in Drosophila along with aging process. Importantly, the m6A methylation sites enriched in the 5' untranslated regions (UTRs) of Drosophila transcripts showed strong age specificity and could be altered by BLE. We experimentally validated that aging-related gene Tor and circadian rhythm-related gene per were regulated by 5' UTR-enriched m6A methylation. Overall, our study provides a systematic assessment of m6A RNA methylome reprogramming by BLE and aging in Drosophila model.

Machine learning algorithms for integrating clinical features to predict intracranial hemorrhage in patients with acute leukemia

BACKGROUND

Intracranial hemorrhage (ICH) in acute leukemia (AL) patients leads to high morbidity and mortality, treatment approaches for ICH are generally ineffective. Thus, early identification of which subjects are at high risk of ICH is of key importance. Currently, machine learning can achieve well predictive capability through constructing algorithms that simultaneously exploit the information coming from clinical features.

METHODS

After rigid data preprocessing, 42 different clinical features from 948 AL patients were used to train different machine learning algorithms. We used the feature selection algorithms to select the top 10 features from 42 clinical features. To test the performance of the machine learning algorithms, we calculated area under the curve (AUC) values from receiver operating characteristic (ROC) curves along with 95% confidence intervals (CIs) by cross-validation.

RESULTS

With the 42 features, RF exhibited the best predictive power. After feature selection, the top 10 features were international normalized ratio (INR), prothrombin time (PT), creatinine (Cr), indirect bilirubin (IBIL), albumin (ALB), monocyte (MONO), platelet (PLT), lactic dehydrogenase (LDH), fibrinogen (FIB) and prealbumin (PA). Among the top 10 features, INR, PT, Cr, IBIL and ALB had high predictive performance with an AUC higher than 0.8 respectively.

CONCLUSIONS

The RF algorithm exhibited a higher cross-validated performance compared with the classical algorithms, and the selected important risk features should help in individualizing aggressive treatment in AL patients to prevent ICH. Efforts that will be made to test and optimize in independent samples will warrant the application of such algorithm and predictors in the future.

Comprehensive analysis of the relationship between xanthine oxidoreductase activity and chronic kidney disease

Chronic kidney disease (CKD) is a common disease that seriously endangers human health. However, the potential relationship between xanthine oxidoreductase (XOR) activity and CKD remains unclear. In this study, we used clinical data, CKD datasets from the Gene Expression Omnibus database, and untargeted metabolomics to explain the relationship between XOR activity and CKD. First, XOR activity showed high correlation with the biomarkers of CKD, such as serum creatinine, blood urea nitrogen, uric acid, and estimated glomerular filtration rate. Then, we used least absolute shrinkage and selection operator logical regression algorithm and random forest algorithm to screen CKD molecular markers from differentially expressed genes, and the results of qRT-PCR of XDH, KOX-1, and ROMO1 were in accordance with the results of bioinformatics analyses. In addition, untargeted metabolomics analysis revealed that the purine metabolism pathway was significantly enriched in CKD patients in the simulated models of kidney fibrosis.

Global metabolic profile and multiple phytometabolites in the different varieties of Gastrodia elata Blume

Gastrodia elata Blume (Tianma in Chinese), a myco-heterotrophic orchid, is widely distributed in China. Tubers derived from this orchid are traditionally used as both medicinal and edible materials. At present, five primary varieties of G. elata are recorded in the "Flora of China." Among them, the three main varieties currently in artificial cultivation are G. elata f. elata (GR, red stem), G. elata f. glauca (GB, black stem), and G. elata f. viridis (GG, green stem). In our study, the metabolic profiles and chemical composition of these three varieties were determined via UPLC-MS/MS and HPLC-UV. In total, 11,132 metabolites were detected, from which multiple phytometabolites were identified as aromatic compounds, heteroatomic compounds, furans, carbohydrates, organic acids, and their derivatives. A number of differentially expressed metabolites (DEMs) were annotated as bioactive ingredients. Overall, parishins, vanilloloside, and gastrodin A/B in the GB group were markedly higher, whereas gastrodin, gastrol, and syringic acid were more enriched in the GG or GR groups. Moreover, HPLC fingerprint analysis also found six metabolites used as markers for the identification of Gastrodiae Rhizoma in the Chinese Pharmacopoeia, which were also typical DEMs in metabolomics. Of these, gastrodin, 4-hydroxybenzyl alcohol, citric acid, and adenosine were quantitatively detected, showing a similar result with the metabolomic data. In summary, our findings provide novel insights into the phytochemical ingredients of different G. elata varieties, highlighting diverse biological activities and healthcare value.

Insight into the effect of a heavy metal mixture on neurological damage in rats through combined serum metabolomic and brain proteomic analyses

The heavy metals lead (Pb), cadmium (Cd), and mercury (Hg) that cause neurocognitive impairment have been extensively studied. These elements typically do not exist alone in the environment; they are often found with other heavy metals and can enter the body through various routes, thereby impacting health. Our previous research showed that low Pb, Cd, and Hg levels cause neurobehavioral impairments in weaning and adult rats. However, little is known about the biomarkers and mechanisms underlying Pb, Cd, and Hg mixture-induced neurological impairments. A combined analysis of metabolomic and proteomic data may reveal heavy metal-induced alterations in metabolic and protein profiles, thereby improving our understanding of the molecular mechanisms underlying heavy metal-induced neurological impairments. Therefore, brain tissue and serum samples were collected from rats exposed to a Pb, Cd, and Hg mixture for proteomic and metabolomic analyses, respectively. The analysis revealed 363 differential proteins in the brain and 206 metabolites in serum uniquely altered in the Pb, Cd, and Hg mixture exposure group, compared to those of the control group. The main metabolic impacted pathways were unsaturated fatty acids biosynthesis, linoleic acid metabolism, phenylalanine metabolism, and tryptophan metabolism. We further identified that the levels of arachidonic acid (C20:4 n-3) and, adrenic acid (C22:4 n-3) were elevated and that kynurenic acid (KA) and quinolinic acid (QA) levels and the KA/QA ratio, were decreased in the group exposed to the Pb, Cd, and Hg mixture. A joint analysis of the proteome and metabolome showed that significantly altered proteins such as LPCAT3, SLC7A11, ASCL4, and KYAT1 may participate in the neurological impairments induced by the heavy metal mixture. Overall, we hypothesize that the dysregulation of ferroptosis and kynurenine pathways is associated with neurological damage due to chronic exposure to a heavy metal mixture.

The efficacy of prevention for colon cancer based on the microbiota therapy and the antitumor mechanisms with intervention of dietary Lactobacillus

Gut microbiota and their secreted metabolites have an influence on the initiation and progression of colon cancer. Probiotics are extensively perceived as a potential microbiota-modulation strategy to promote the health of the host, while the effectiveness of preventing colon cancer based on microbiota therapy has not been confirmed, and antitumor mechanisms influenced by microbiota and their metabolites with the intervention of probiotics remain to be further investigated. In vitro, Lactobacillus (JY300-8 and JMR-01) significantly inhibited the proliferation of CT26, HT29, and HCT116 cells. Moreover, we studied the prevention and therapy efficiency of Lactobacillus and its underlying antitumor mechanism through the alteration of gut microbiota and their metabolites regulated by Lactobacillus in colon cancer models in mice. We demonstrated that the pre-administration of Lactobacillus (JY300-8 and JMR-01) for 20 days before establishing tumor models resulted in an 86.21% reduction in tumor formation rate compared to tumor control group. Subsequently, continuous oral administration of living Lactobacillus significantly suppresses tumor growth, and tumor volumes decrease by 65.2%. Microbiome and metabolome analyses reveal that Lactobacillus suppresses colonic tumorigenesis and progression through the modulation of gut microbiota homeostasis and metabolites, including the down-regulation of secondary bile acids, sphingosine 1-phosphate (S1P), and pyrimidine metabolism, as well as the production of anticarcinogenic compounds in tumor-bearing mice. Additionally, metabolome analyses of Lactobacillus (JY300-8 and JMR-01) indicate that living Lactobacillus could reduce the relative abundance of alanine and L-serine to suppress tumor progression by regulating the tumor microenvironment, including down-regulation of pyrimidine metabolism and S1P signaling in cancer. These findings provide a potential prevention strategy and therapeutic target for colon cancer through the intervention of dietary Lactobacillus. IMPORTANCE The modulation of gut microbiota and metabolites has a significant influence on the progression of colon cancer. Our research indicated that the intervention of probiotics is a potentially feasible strategy for preventing colon cancer. We have also revealed the underlying antitumor mechanism through the alteration of gut microbiota and their metabolites, which could lead to broader biomedical impacts on the prevention and therapy of colon cancer with microbiota-based therapy regulated by probiotics.

Mass spectrometry-based metabolomic analysis as a tool for quality control of natural complex products

Metabolomics is an area of intriguing and growing interest. Since the late 1990s, when the first Omic applications appeared to study metabolite's pool ("metabolome"), to understand new aspects of the global regulation of cellular metabolism in biology, there have been many evolutions. Currently, there are many applications in different fields such as clinical, medical, agricultural, and food. In our opinion, it is clear that developments in metabolomics analysis have also been driven by advances in mass spectrometry (MS) technology. As natural complex products (NCPs) are increasingly used around the world as medicines, food supplements, and substance-based medical devices, their analysis using metabolomic approaches will help to bring more and more rigor to scientific studies and industrial production monitoring. This review is intended to emphasize the importance of metabolomics as a powerful tool for studying NCPs, by which significant advantages can be obtained in terms of elucidation of their composition, biological effects, and quality control. The different approaches of metabolomic analysis, the main and basic techniques of multivariate statistical analysis are also briefly illustrated, to allow an overview of the workflow associated with the metabolomic studies of NCPs. Therefore, various articles and reviews are illustrated and commented as examples of the application of MS-based metabolomics to NCPs.

The mechanism of action of Ophiocordyceps sinensis mycelia for prevention of acute lung injury based on non-targeted serum metabolomics

Ophiocordyceps sinensis is a fungus with medicinal value in treating lung diseases, but no study has reported how to prevent acute lung injury using this fungus. The mice were divided into normal, model, positive control, and O. sinensis groups to observe lung histopathological sections and transmission electron microscopy, along with liquid chromatography-mass spectrometry and hematoxylin and eosin (H&E) staining to closely identify structural differences resulting from destruction between the groups. The results of the H&E staining showed that, compared with the normal group, the model group showed alveolar collapse. Compared with the model group, the infiltration of inflammatory cells in the alveolar cavity of the O. sinensis group was significantly reduced. Mitochondrial plate-like cristae were observed in type II alveolar cells of the normal group, with normal coloration of the mitochondrial matrix. Type II alveolar cells in the model group showed obvious edema. The statuses of type II alveolar cells in the O. sinensis and positive groups were similar to that in the normal group. Twenty-nine biomarkers and 10 related metabolic pathways were identified by serum metabolomics screening. The results showed that O. sinensis mycelia had a significant effect on the prevention of lipopolysaccharide-induced inflammation.

Food Fingerprinting: LC-ESI-IM-QTOF-Based Identification of Blumeatin as a New Marker Metabolite for the Detection of Origanum majorana Admixtures to O. onites/vulgare

Oregano (Origanum vulgare and O. onites) is one of the most frequently counterfeited herbs in the world and is diluted with the leaves of a wide variety of plants. In addition to olive leaves, marjoram (O. majorana) is often used for this purpose in order to achieve a higher profit. However, apart from arbutin, no marker metabolites are known to reliably detect marjoram admixtures in oregano batches at low concentrations. In addition, arbutin is relatively widespread in the plant kingdom, which is why it is of great relevance to look for further marker metabolites in order to secure the analysis accordingly. Therefore, the aim of the present study was to use a metabolomics-based approach to identify additional marker metabolites with the aid of an ion mobility mass spectrometry instrument. The focus of the analysis was on the detection of non-polar metabolites, as this study was preceded by nuclear magnetic resonance spectroscopic investigations of the same samples based mainly on the detection of polar analytes. Using the MS-based approach, numerous marjoram specific features could be detected in admixtures of marjoram >10% in oregano. However, only one feature was detectable in admixtures of >5% marjoram. This feature was identified as blumeatin, which belongs to the class of flavonoid compounds. Initially, blumeatin was identified based on MS/MS spectra and collision cross section values using a database search. In addition, the identification of blumeatin was confirmed by a reference standard. Moreover, dried leaves of olive, myrtle, thyme, sage and peppermint, which are also known to be used to adulterate oregano, were measured. Blumeatin could not be detected in these plants, so this substance can be considered as an excellent marker compound for the detection of marjoram admixtures.

Integrative transcriptome and metabolome revealed the molecular mechanism of Bacillus megaterium BT22-mediated growth promotion in Arabidopsis thaliana

Plant growth-promoting rhizobacteria (PGPR) can promote plant growth and protect plants from pathogens, which contributes to sustainable agricultural development. Several studies have reported their beneficial characteristics in facilitating plant growth and development and enhancing plant stress resistance through different mechanisms. However, there is still a challenge to study the molecular mechanism of plant response to PGPR. We integrated the transcriptome and metabolome of Arabidopsis thaliana (Arabidopsis) to understand its responses to the inoculation with an isolated PGPR strain (BT22) of Bacillus megaterium. Fresh shoot weight, dry shoot weight and leaf number of Arabidopsis were increased by BT22 treatment, showing a positive growth-promoting effect. According multi-omics analysis, 878 differentially expressed genes (296 up-regulated, 582 down-regulated) and 139 differentially expressed metabolites (66 up-regulated, 73 down-regulated) response to BT22 inoculation. GO enrichment results indicate that the up-regulated genes mainly enriched in the regulation of growth and auxin response pathways. In contrast, the down-regulated genes mainly enriched in wounding response, jasmonic acid and ethylene pathways. BT22 inoculation regulated plant hormone signal transduction of Arabidopsis, including auxin and cytokinin response genes AUX/IAA, SAUR, and A-ARR related to cell enlargement and cell division. The contents of nine flavonoids and seven phenylpropanoid metabolites were increased, which help to induce systemic resistance in plants. These results suggest that BT22 promoted Arabidopsis growth by regulating plant hormone homeostasis and inducing metabolome reprogramming.

Banxia Xiexin decoction alleviates AS co-depression disease by regulating the gut microbiome-lipid metabolic axis

ETHNOPHARMACOLOGICAL RELEVANCE

Banxia Xiexin decoction (BXD) is a classic Chinese herbal formulation consisting of 7 herbs including Pinelliae Rhizoma, Scutellariae Radix, Zingiberis Rhizoma, Ginseng Radix, Glycyrrhizae Radix, Coptidis Rhizoma, and Jujubae Fructus, which can exert effects on lowering lipids and alleviating depressive mood disorders via affecting gastrointestinal tract.

AIM OF THE STUDY

The pathogenesis of atherosclerosis (AS) co-depression disease has not been well studied, and the current clinical treatment strategies are not satisfactory. As a result, it is critical to find novel methods of treatment. Based on the hypothesis that the gut microbiome may promote the development of AS co-depression disease by regulating host lipid metabolism, this study sought to evaluate the effectiveness and action mechanism of BXD in regulation of the gut microbiome via an intervention in AS co-depression mice.

MATERIALS AND METHODS

To determine the primary constituents of BXD, UPLC-Q/TOF-MS analysis was carried out. Sixteen C56BL/6 mice were fed normal chow as a control group; 64 ApoE^-/- mice were randomized into four groups (model group and three treatment groups) and fed high-fat chow combined with daily bind stimulation for sixteen weeks to develop the AS co-depression mouse model and were administered saline or low, medium or high concentrations of BXD during the experimental modeling period. The antidepressant efficacy of BXD was examined by weighing, a sucrose preference test, an open field test, and a tail suspension experiment. The effectiveness of BXD as an anti-AS treatment was evaluated by means of biochemical indices, the HE staining method, and the Oil red O staining method. The impacts of BXD on the gut microbiome structure and brain (hippocampus and prefrontal cortex tissue) lipids in mice with the AS co-depression model were examined by 16S rDNA sequencing combined with lipidomics analysis.

RESULTS

The main components of BXD include baicalin, berberine, ginsenoside Rb1, and 18 other substances. BXD could improve depression-like behavioral characteristics and AS-related indices in AS co-depression mice; BXD could regulate the abundance of some flora (phylum level: reduced abundance of Proteobacteria and Deferribacteres; genus level: reduced abundance of Clostridium_IV, Helicobacter, and Pseudoflavonifractor, Acetatifactor, Oscillibacter, which were significantly different). The lipidomics analysis showed that the differential lipids between the model and gavaged high-dose BXD (BXH) groups were enriched in glycerophospholipid metabolism, and lysophosphatidylcholine (LPC(20:3)(rep)(rep)) in the hippocampus and LPC(20:4)(rep) in the prefrontal cortex both showed downregulation in BXH. The correlation analysis illustrated that the screened differential lipids were mainly linked to Deferribacteres and Actinobacteria.

CONCLUSION

BXD may exert an anti-AS co-depression therapeutic effect by modulating the abundance of some flora and thus intervening in peripheral lipid and brain lipid metabolism (via downregulation of LPC levels).

A novel N-arachidonoyl-l-alanine-catabolizing strain of Serratia marcescens for the bioremediation of Cd and Cr co-contamination

Cadmium (Cd) and chromium (Cr) are widespread contaminants with a high risk to the environment and humans. Herein we isolated a novel strain of Serratia marcescens, namely strain S27, from soil co-contaminated with Cd and Cr. This strain showed strong resistance to Cd as well as Cr. S27 cells demonstrated Cd adsorption rate of 45.8% and Cr reduction capacity of 84.4% under optimal growth conditions (i.e., 30 °C, 200 rpm, and pH 7.5). Microscopic characterization of S27 cells revealed the importance of the functional groups C-O-C, C-H-O, C-C, C-H, and -OH, and also indicated that Cr reduction occurred on bacterial cell membrane. Cd(II) and Cr(VI) bioaccumulation on S27 cell surface was mainly in the form of Cd(OH)₂ and Cr₂O₃, respectively. Further, metabolomic analyses revealed that N-arachidonoyl-l-alanine was the key metabolite that promoted Cd and Cr complexation by S27; it primarily promotes γ-linolenic acid (GLA) metabolism, producing siderophores and coordinating with organic acids to enhance metal bioavailability. To summarize, our results suggest that S27 is promising for the bioremediation of environments contaminated with Cd and Cr in tropical regions.

Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects

Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders. The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results. The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.

Bile metabolites as diagnostic biomarkers for perihilar cholangiocarcinoma

It is difficult to directly obtain pathological diagnosis of perihilar cholangiocarcinoma (pCCA). Analysis of bile in the pCCA microenvironment, based on metabolomics and statistical methods, can help in clinical diagnosis. Clinical information, bile samples, blood liver function, blood CA199, CEA, and other indicators were collected from 33 patients with pCCA and 16 patients with gallstones. Bile samples were analyzed using untargeted metabolomics methods. A combination of multivariate and univariate analyses were used to screen for potential differential metabolites Through Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment and differential metabolite remodeling, we explored changes in the pCCA pathway and potential therapeutic targets. There were significant differences in patient blood TBIL, ALT, AST, TBA, CA19-9, and CEA indices (p < 0.05, |log2(fc)| ≥ 1) between two groups. A significant correlation was found between these different indicators by Spearman's analysis. The clinical parameters were correlated with mass-to-charge ratios of 305 (Positive Ion Mode, POS) and 246 (Negative Ion Mode, NEG) in the metabolic group (|r| ≥ 0.7, P ≤ 10^-7). The result of this study indicated that bile untargeted metabolomics combined with statistical analysis techniques may be used for diagnose and treatment of pCCA.

Metabolic landscape dysregulation in bronchoalveolar lavage fluid of checkpoint inhibitor pneumonitis

BACKGROUND

Checkpoint inhibitor pneumonitis (CIP) is a potentially fatal adverse event resulting from immunotherapy in patients with malignant tumors. However, the pathogenesis of CIP remains poorly understood.

METHODS

We collected bronchoalveolar lavage fluid (BALF) from cohorts of patients with CIP, new-onset lung cancer (LC), and idiopathic pulmonary fibrosis (IPF). Non-targeted metabolomics analysis was conducted to analyze metabolic signatures. Flow cytometry was used to evaluate immune cell subsets.

RESULTS

Lymphocytes were predominant in the BALF of patients with CIP. A total of 903 metabolites were identified, among which lipid compounds were the most abundant. In a comparison between patients with CIP and LC, enrichment analysis of the altered metabolites showed suppressed amino sugar metabolism, and spermidine and spermine biosynthesis in the CIP group. Metabolism of alpha linolenic acid, linoleic acid, and their fatty acid derivatives was enriched in the CIP group relative to the IPF group. The twelve metabolites found to be enriched in the CIP group were positively correlated with the proportion of CD8⁺ T cells. One cluster of BALF metabolites, 57.14% of which were lipid molecules, was inversely correlated with the proportion of natural killer cells.

CONCLUSIONS

In this study, the metabolomic landscape of BALF in patients with CIP was determined. We elucidated suppressed tumor metabolic signatures, enhanced pulmonary inflammatory signaling, and the characteristics of responsible immune cells, which helps to understand the pathogenesis of CIP.

An Untargeted Metabolomics Workflow that Scales to Thousands of Samples for Population-Based Studies

The success of precision medicine relies upon collecting data from many individuals at the population level. Although advancing technologies have made such large-scale studies increasingly feasible in some disciplines such as genomics, the standard workflows currently implemented in untargeted metabolomics were developed for small sample numbers and are limited by the processing of liquid chromatography/mass spectrometry data. Here we present an untargeted metabolomics workflow that is designed to support large-scale projects with thousands of biospecimens. Our strategy is to first evaluate a reference sample created by pooling aliquots of biospecimens from the cohort. The reference sample captures the chemical complexity of the biological matrix in a small number of analytical runs, which can subsequently be processed with conventional software such as XCMS. Although this generates thousands of so-called features, most do not correspond to unique compounds from the samples and can be filtered with established informatics tools. The features remaining represent a comprehensive set of biologically relevant reference chemicals that can then be extracted from the entire cohort's raw data on the basis of m/z values and retention times by using Skyline. To demonstrate applicability to large cohorts, we evaluated >2000 human plasma samples with our workflow. We focused our analysis on 360 identified compounds, but we also profiled >3000 unknowns from the plasma samples. As part of our workflow, we tested 14 different computational approaches for batch correction and found that a random forest-based approach outperformed the others. The corrected data revealed distinct profiles that were associated with the geographic location of participants.

Antioxidant processes involving epicatechin decreased symptoms of pine wilt disease

Since the pine wood nematode (PWN, Bursaphelenchus xylophilus) invasion of Northeast China, both symptomatic and asymptomatic PWN carriers have been found. Asymptomatic PWN carriers, which are more dangerous than symptomatic carriers, constitute a source of infection in the following spring. The simultaneous presence of symptomatic and asymptomatic PWN carriers indicates that Pinus koraiensis has different tolerance levels to PWN. In this study, validity of susceptibility testing discovered differential types of P. koraiensis including Latent Reservoirs, Low Susceptibles, High Susceptibles and Bell Ringers. Among those types, the Low Susceptibles and Latent Reservoirs were asymptomatic PWN carriers, and Latent Reservoirs were the most dangerous. Transcriptome and metabolomic data showed that 5 genes (3 ans and 2 anr gene) involved in the epicatechin (EC) synthesis pathway were significantly upregulated, which increased the content of EC antioxidants in Latent Reservoirs. Hydrogen peroxide (H₂O₂) staining and content determination showed that the hypersensitive response (HR) and H₂O₂, which functions as a signaling molecule in systemic acquired resistance, decreased in Latent Reservoirs. However, low contents of EC and high contents of H₂O₂ were found in the High Susceptibles of P. koraiensis. RT-PCR results showed that the expression of ans and anr was upregulated together only in Latent Reservoirs. These results show that the susceptibility of P. koraiensis to PWN differed among different individuals, although no resistant individuals were found. Latent Reservoirs, in which more PWNs resided without visible symptoms via prolonged incubation period, inhibited the symptoms caused by H₂O₂ because of increased contents of the EC antioxidants.

Metabolomic analysis of porcine intestinal epithelial cells during swine acute diarrhea syndrome coronavirus infection

Swine acute diarrhea syndrome coronavirus (SADS-CoV) is an enveloped, positive single-stranded RNA virus belonging to Coronaviridae family, Orthocoronavirinae subfamily, Alphacoronavirus genus. As one of the main causes of swine diarrhea, SADS-CoV has brought huge losses to the pig industry. Although we have a basic understanding of SADS-CoV, the research on the pathogenicity and interactions between host and virus are still limited, especially the metabolic changes induced by SADS-CoV infection. Here, we utilized a combination of untargeted metabolomics and lipomics to analyze the metabolic alteration in SADS-CoV infected cells. Significant changes were observed in 1257 of 2225 metabolites identified in untargeted metabolomics, while the number of lipomics was 435 out of 868. Metabolic pathway enrichment analysis showed that amino acid metabolism, tricarboxylic acid (TCA) cycle and ferroptosis were disrupted during viral infection, suggesting that these metabolic pathways may partake in pathological processes related to SADS-CoV pathogenesis. Collectively, our findings gain insights into the cellular metabolic disorder during SADS-CoV infection, offer a valuable resource for further exploration of the relationship between virus and host metabolic activities, and provide potential targets for the development of antiviral drugs.

Epichloë bromicola from wild barley improves salt-tolerance of cultivated barley by altering physiological responses to salt stress

Introduction

Epichloë bromicola is a cultivable fungal endophyte that lives in symbiosis with wild barley (Hordeum brevisubulatum) to which it confers salt tolerance. This study tested the hypothesis that E. bromicola derived from wild barley has the potential to increase salt tolerance in cultivated barley under salt stress.

Methods

To test this hypothesis, the growth response, physiological parameters, and metabolic profiles of barley plants inoculated with E. bromicola (E+) and those not inoculated with E. bromicola (E-) were compared under salt stress.

Results

Compared with E- barley plants, E+ barley plants had significantly increased plant height, shoot biomass, total biomass, chlorophyll content, osmotic synthesis, and accumulation of stress adaptation metabolites. E. bromicola increased the salt stress tolerance of cultivated barley, and the positive effects correlated with different salt stress conditions.

Discussion

These results suggest that E. bromicola has promising potential for enhancing the salt tolerance of barley. New insights into the mechanisms underlying this barley-fungal endophyte association are provided, and interesting questions regarding the role of E. bromicola in fungus-enhanced tolerance to salt stress in this symbiosis are raised.

Metabolic alterations in dairy cattle with lameness revealed by untargeted metabolomics of dried milk spots using direct infusion-tandem mass spectrometry and the triangulation of multiple machine learning models

Lameness is a major challenge in the dairy cattle industry in terms of animal welfare and economic implications. Better understanding of metabolic alteration associated with lameness could lead to early diagnosis and effective treatment, there-fore reducing its prevalence. To determine whether metabolic signatures associated with lameness could be discovered with untargeted metabolomics, we developed a novel workflow using direct infusion-tandem mass spectrometry to rapidly analyse (2 min per sample) dried milk spots (DMS) that were stored on commercially available Whatman® FTA® DMPK cards for a prolonged period (8 and 16 days). An orthogonal partial least squares-discriminant analysis (OPLS-DA) method validated by triangulation of multiple machine learning (ML) models and stability selection was employed to reliably identify important discriminative metabolites. With this approach, we were able to differentiate between lame and healthy cows based on a set of lipid molecules and several small metabolites. Among the discriminative molecules, we identified phosphatidylglycerol (PG 35:4) as the strongest and most sensitive lameness indicator based on stability selection. Overall, this untargeted metabolomics workflow is found to be a fast, robust, and discriminating method for determining lameness in DMS samples. The DMS cards can be potentially used as a convenient and cost-effective sample matrix for larger scale research and future routine screening for lameness.

Combined metabolomics and network pharmacology to elucidate the mechanisms of Dracorhodin Perchlorate in treating diabetic foot ulcer rats

Background: Diabetic foot ulcer (DFU) is a severe chronic complication of diabetes, that can result in disability or death. Dracorhodin Perchlorate (DP) is effective for treating DFU, but the potential mechanisms need to be investigated. We aimed to explore the mechanisms underlying the acceleration of wound healing in DFU by the topical application of DP through the combination of metabolomics and network pharmacology. Methods: A DFU rat model was established, and the rate of ulcer wound healing was assessed. Different metabolites were found in the skin tissues of each group, and MetaboAnalyst was performed to analyse metabolic pathways. The candidate targets of DP in the treatment of DFU were screened using network pharmacology. Cytoscape was applied to construct an integrated network of metabolomics and network pharmacology. Moreover, the obtained hub targets were validated using molecular docking. After the topical application of DP, blood glucose, the rate of wound healing and pro-inflammatory cytokine levels were assessed. Results: The levels of IL-1, hs-CRP and TNF-α of the Adm group were significantly downregulated. A total of 114 metabolites were identified. These could be important to the therapeutic effects of DP in the treatment of DFU. Based on the network pharmacology, seven hub genes were found, which were partially consistent with the metabolomics results. We focused on four hub targets by further integrated analysis, namely, PAH, GSTM1, DHFR and CAT, and the crucial metabolites and pathways. Molecular docking results demonstrated that DP was well combined with the hub targets. Conclusion: Our research based on metabolomics and network pharmacology demonstrated that DP improves wound healing in DFU through multiple targets and pathways, and it can potentially be used for DFU treatment.

Application of Metabolomics in Fungal Research

Metabolomics is an essential method to study the dynamic changes of metabolic networks and products using modern analytical techniques, as well as reveal the life phenomena and their inherent laws. Currently, more and more attention has been paid to the development of metabolic histochemistry in the fungus field. This paper reviews the application of metabolomics in fungal research from five aspects: identification, response to stress, metabolite discovery, metabolism engineering, and fungal interactions with plants.

Untargeted Metabolomic Liquid Chromatography High-Resolution Mass Spectrometry Fingerprinting of Apple Cultivars for the Identification of Biomarkers Related to Resistance to Rosy Apple Aphid

Liquid chromatography high-resolution mass spectrometry fingerprinting together with pattern recognition techniques was used to determine the metabolites involved in the susceptibility of apple cultivars to rosy apple aphid (RAA). Preprocessing of ultra-high-performance liquid chromatography coupled to electrospray ionization and quadrupole time-of-flight mass spectrometry raw data of resistant and susceptible apple cultivars was carried out with XCMS and CAMERA packages. Univariate statistical tools and multivariate data analysis highlighted significant different profiles of the apple metabolomes according to their tolerance to RAA. Optimized and cross-validated Partial least squares discriminant analysis and orthogonal projections to latent structures discriminant analysis models confirmed trans-4-caffeoylquinic acid and 4-p-coumaroylquinic acid as biomarkers for the identification of resistant and susceptible apple cultivars to RAA and disclosed that only hydroxycinnamic acids are involved in the disease susceptibility of cultivars. In this sense, the final steps of the biosynthesis of caffeoylquinic acid (CQA) and p-coumaroylquinic acid (p-CoQA) become decisive because the isomerization of 5-CQA to 4-CQA is favored in resistant cultivars, whereas the isomerization of 5-p-CoQA to 4-p-CoQA is favored in susceptible cultivars.

Probabilistic quotient’s work and pharmacokinetics’ contribution: countering size effect in metabolic time series measurements

Metabolomic time course analyses of biofluids are highly relevant for clinical diagnostics. However, many sampling methods suffer from unknown sample sizes, commonly known as size effects. This prevents absolute quantification of biomarkers. Recently, several mathematical post acquisition normalization methods have been developed to overcome these problems either by exploiting already known pharmacokinetic information or by statistical means. Here we present an improved normalization method, MIX, that combines the advantages of both approaches. It couples two normalization terms, one based on a pharmacokinetic model (PKM) and the other representing a popular statistical approach, probabilistic quotient normalization (PQN), in a single model. To test the performance of MIX, we generated synthetic data closely resembling real finger sweat metabolome measurements. We show that MIX normalization successfully tackles key weaknesses of the individual strategies: it (i) reduces the risk of overfitting with PKM, and (ii), contrary to PQN, it allows to compute sample volumes. Finally, we validate MIX by using real finger sweat as well as blood plasma metabolome data and demonstrate that MIX allows to better and more robustly correct for size effects. In conclusion, the MIX method improves the reliability and robustness of quantitative biomarker detection in finger sweat and other biofluids, paving the way for biomarker discovery and hypothesis generation from metabolomic time course data.

Association of the characteristics of the blood metabolome and gut microbiome with the outcome of methotrexate therapy in psoriasis

Metabolic status and gut microecology are implicated in psoriasis. Methotrexate (MTX) is usually the first-line treatment for this disease. However, the relationship between MTX and host metabolic status and the gut microbiota is unclear. This study aimed to characterize the features of blood metabolome and gut microbiome in patients with psoriasis after treatment with MTX. Serum and stool samples were collected from 15 patients with psoriasis. Untargeted liquid chromatography–mass spectrometry and metagenomics sequencing were applied to profile the blood metabolome and gut microbiome, respectively. We found that the response to MTX varied according to metabolomic and metagenomic features at baseline; for example, patients who had high levels of serum nutrient molecular and more enriched gut microbiota had a poor response. After 16 weeks of MTX, we observed a reduction in microbial activity pathways, and patients with a good response showed more microbial activity and less biosynthesis of serum fatty acid. We also found an association between the serum metabolome and the gut microbiome before intervention with MTX. Carbohydrate metabolism, transporter systems, and protein synthesis within microbes were associated with host metabolic clusters of lipids, benzenoids, and organic acids. These findings suggest that the metabolic status of the blood and the gut microbiome is involved in the effectiveness of MTX in psoriasis, and that inhibition of symbiotic intestinal microbiota may be one of the mechanisms of action of MTX. Prospective studies in larger sample sizes are needed to confirm these findings.

Comprehensive transcriptomic and metabolomic analysis revealed distinct flavonoid biosynthesis regulation during abnormal pistil development in Japanese apricot

Japanese apricot is an imperative stone fruit plant with numerous processing importance. The failure of reproductive system is the most common cause of fruit loss, through which pistil abortion is the fundamental one. To understand this mechanism, we used a combination of transcriptomic and metabolomic approaches to investigate the biochemical and molecular basis of flavonoid biosynthesis. Due to the regulated expression of flavonoid pathway-related genes in plants, flavonoid biosynthesis is largely regulated at the transcriptional level. A total of 2272 differently expressed genes and 215 differential metabolites were found. The expression of the genes and metabolites encoding flavonoid biosynthesis was lower in abnormal pistils that are in line with the flavonoid quantification from abnormal pistils. Besides, a couple of genes were also detected related to MYB, MADS, NAC and bHLH transcription factors. Remarkably, we found 'hydroxycinnamoyl transferase (LOC103323133)' and flavonoid related metabolite '2-hydroxycinnamic acid' was lower expressed in abnormal pistil, proposing the cause of pistil abortion. Collectively, the present study delivers inclusive transcriptional and metabolic datasets that proposed valuable prospects to unravel the genetic mechanism underlying pistil abortion.

Characterizing Powdered Activated Carbon Treatment of Surface Water Samples Using Polarity-Extended Non-Target Screening Analysis

Advanced wastewater treatment such as powdered activated carbon (PAC) reduces the load of organic micropollutants entering the aquatic environment. Since mobile and persistent compounds accumulate in water cycles, treatment strategies need to be evaluated for the removal of (very) polar compounds. Thereby, non-targeted analysis gives a global picture of the molecular fingerprint (including these very polar molecules) of water samples. Target and non-target screening were conducted using polarity-extended chromatography hyphenated with mass spectrometry. Samples treated with different types and concentrations of PAC were compared to untreated samples. Molecular features were extracted from the analytical data to determine fold changes, perform a principal component analysis and for significance testing. The results suggest that a part of the polar target analytes was adsorbed but also some byproducts might be formed or desorbed from the PAC.

Impact of PM2.5 exposure on plasma metabolome in healthy adults during air pollution waves: A randomized, crossover trial

Air pollution, especially PM_2.5 (particulate matter with an aerodynamic diameter ≤2.5 µm) in China, is severe and related to a variety of diseases while the potential mechanisms have not been clearly clarified yet. This study was conducted using a randomized crossover trial protocol among young and healthy college students. Plasma samples were collected before, during, and post two typical air pollution waves with a washout interval of at least 2 weeks under true and sham air purification treatments, respectively. A total of 144 blood samples from 24 participants were included in the final analysis. Metabolomics analysis for the plasma samples was completed by Ultrahigh Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS). Orthogonal Partial Least Squares Discrimination Analysis (OPLS-DA) and linear mixed-effect models were used to identify the differentially expressed metabolites and their associations with PM_2.5 exposure. MetaboAnalyst 5.0 was further used to conduct pathway enrichment analysis and correlation analysis of differentially expressed metabolites. A total of 40 metabolites were identified to be differentially expressed between the true and sham air purification treatments, and eleven metabolites showed consistent significant changes upon outdoor, indoor, and time-weighted personal PM_2.5 exposures. Short-term exposure to PM_2.5 may cause disturbances in metabolic pathways such as linoleic acid metabolism, arachidonic acid metabolism, and tryptophan metabolism.

Metabolic Profiling of Serum for Osteoarthritis Biomarkers

Osteoarthritis is a prevalent aging disease in the world, and in recent years it has shown a trend toward younger age, which is becoming a major health problem in the world and seriously endangers the health of the elderly. However, the etiology and pathogenesis of osteoarthritis are still unclear, causing great trouble for treatment. To screen out candidate biomarkers that could be used for the identification of osteoarthritis and explore the pathogenesis of osteoarthritis, we performed an untargeted metabolomics analysis of nine New Zealand rabbit serum samples by LC-MS/MS, including three normal serum samples (control group) and six osteoarthritis serum samples (case group). Finally, 44 differential metabolites were identified, and the ROC analysis results indicated that a total of 36 differential metabolites could be used as candidate biomarkers. Further metabolic pathway enrichment analysis was performed on these differential metabolites, and we found that a total of 17 metabolic pathways were affected, which may provide directions for the study of osteoarthritis mechanisms.

Addressing Missing Data in GC × GC Metabolomics: Identifying Missingness Type and Evaluating the Impact of Imputation Methods on Experimental Replication

Missing data is a significant issue in metabolomics that is often neglected when conducting data preprocessing, particularly when it comes to imputation. This can have serious implications for downstream statistical analyses and lead to misleading or uninterpretable inferences. In this study, we aim to identify the primary types of missingness that affect untargeted metabolomics data and compare strategies for imputation using two real-world comprehensive two-dimensional gas chromatography (GC × GC) data sets. We also present these goals in the context of experimental replication whereby imputation is conducted in a within-replicate-based fashion─the first description and evaluation of this strategy─and introduce an R package MetabImpute to carry out these analyses. Our results conclude that, in these two GC × GC data sets, missingness was most likely of the missing at-random (MAR) and missing not-at-random (MNAR) types as opposed to missing completely at-random (MCAR). Gibbs sampler imputation and Random Forest gave the best results when imputing MAR and MNAR compared against single-value imputation (zero, minimum, mean, median, and half-minimum) and other more sophisticated approaches (Bayesian principal component analysis and quantile regression imputation for left-censored data). When samples are replicated, within-replicate imputation approaches led to an increase in the reproducibility of peak quantification compared to imputation that ignores replication, suggesting that imputing with respect to replication may preserve potentially important features in downstream analyses for biomarker discovery.

Challenges and opportunities for prevention and removal of unwanted variation in lipidomic studies

Large 'omics studies are of particular interest to population and clinical research as they allow elucidation of biological pathways that are often out of reach of other methodologies. Typically, these information rich datasets are produced from multiple coordinated profiling studies that may include lipidomics, metabolomics, proteomics or other strategies to generate high dimensional data. In lipidomics, the generation of such data presents a series of unique technological and logistical challenges; to maximize the power (number of samples) and coverage (number of analytes) of the dataset while minimizing the sources of unwanted variation. Technological advances in analytical platforms, as well as computational approaches, have led to improvement of data quality - especially with regard to instrumental variation. In the small scale, it is possible to control systematic bias from beginning to end. However, as the size and complexity of datasets grow, it is inevitable that unwanted variation arises from multiple sources, some potentially unknown and out of the investigators control. Increases in cohort sizes and complexity has led to new challenges in sample collection, handling, storage, and preparation stages. If not considered and dealt with appropriately, this unwanted variation may undermine the quality of the data and reliability of any subsequent analysis. Here we review the various experimental phases where unwanted variation may be introduced and review general strategies and approaches to handle this variation, specifically addressing issues relevant to lipidomics studies.

Suitability of Dried Blood Spots for Accelerating Veterinary Biobank Collections and Identifying Metabolomics Biomarkers With Minimal Resources

Biomarker discovery using biobank samples collected from veterinary clinics would deliver insights into the diverse population of pets and accelerate diagnostic development. The acquisition, preparation, processing, and storage of biofluid samples in sufficient volumes and at a quality suitable for later analysis with most suitable discovery methods remain challenging. Metabolomics analysis is a valuable approach to detect health/disease phenotypes. Pre-processing changes during preparation of plasma/serum samples may induce variability that may be overcome using dried blood spots (DBSs). We report a proof of principle study by metabolite fingerprinting applying UHPLC-MS of plasma and DBSs acquired from healthy adult dogs and cats (age range 1–9 years), representing each of 4 dog breeds (Labrador retriever, Beagle, Petit Basset Griffon Vendeen, and Norfolk terrier) and the British domestic shorthair cat (n = 10 per group). Blood samples (20 and 40 μL) for DBSs were loaded onto filter paper, air-dried at room temperature (3 h), and sealed and stored (4°C for ~72 h) prior to storage at −80°C. Plasma from the same blood draw (250 μL) was prepared and stored at −80°C within 1 h of sampling. Metabolite fingerprinting of the DBSs and plasma produced similar numbers of metabolite features that had similar abilities to discriminate between biological classes and correctly assign blinded samples. These provide evidence that DBSs, sampled in a manner amenable to application in in-clinic/in-field processing, are a suitable sample for biomarker discovery using UHPLC-MS metabolomics. Further, given appropriate owner consent, the volumes tested (20–40 μL) make the acquisition of remnant blood from blood samples drawn for other reasons available for biobanking and other research activities. Together, this makes possible large-scale biobanking of veterinary samples, gaining sufficient material sooner and enabling quicker identification of biomarkers of interest.