Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc. 2011;6:1060-1083. [PMID: 21720319 DOI: 10.1038/nprot.2011.335]

Evaluation of normalization strategies for GC-based metabolomics

INTRODUCTION

For many samples studied by GC-based metabolomics applications, extensive sample preparation involving extraction followed by a two-step derivatization procedure of methoximation and trimethylsilylation (TMS) is typically required to expand the metabolome coverage. Performing normalization is critical to correct for variations present in samples and any biases added during the sample preparation steps and analytical runs. Addressing the totality of variations with an adequate normalization method increases the reliability of the downstream data analysis and interpretation of the results.

OBJECTIVES

Normalizing to sample mass is one of the most commonly employed strategies, while the total peak area (TPA) as a normalization factor is also frequently used as a post-acquisition technique. Here, we present a new normalization approach, total derivatized peak area (TDPA), where data are normalized to the intensity of all derivatized compounds. TDPA relies on the benefits of silylation as a universal derivatization method for GC-based metabolomics studies.

METHODS

Two sample classes consisting of systematically incremented sample mass were simulated, with the only difference between the groups being the added amino acid concentrations. The samples were TMS derivatized and analyzed using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). The performance of five normalization strategies (no normalization, normalized to sample mass, TPA, total useful peak area (TUPA), and TDPA) were evaluated on the acquired data.

RESULTS

Of the five normalization techniques compared, TUPA and TDPA were the most effective. On PCA score space, they offered a clear separation between the two classes.

CONCLUSION

TUPA and TDPA carry different strengths: TUPA requires peak alignment across all samples, which depends upon the completion of the study, while TDPA is free from the requirement of alignment. The findings of the study would enhance the convenient and effective use of data normalization strategies and contribute to overcoming the data normalization challenges that currently exist in the metabolomics community.

Identification of organic pollutants and heavy metals in natural rubber wastewater and evaluation its phytotoxicity and cytogenotoxicity

The natural rubber industry consumes large volumes of water and annually releases wastewater with rich organic and inorganic loads. This wastewater is allowed for soil irrigation in developing countries. However, the pollutant composition in wastewater and its environmental effects remain unclear. Therefore, we aimed to assess the wastewater's physicochemical parameters, toxic organic pollutants, heavy metals, and phytotoxic and cytogenotoxic. The result revealed that values of comprehensive wastewater parameters were recorded as chemical oxygen demand (187432.1 mg/L), pH (4.23), total nitrogen (1157.1 mg/L), ammonia nitrogen (1113.0 mg/L), total phosphorus (1181.2 mg/L), Zn (593.3 mg/L), Cr (0.6127 mg/L), and Ni (0.2986 mg/L). The organic compounds detected by LC-MS were salbostatin, sirolimus, Gibberellin A34-catabolite, 1-(sn-glycero-3-phospho)-1D-myo-inositol, and methyldiphenylsilane. The toxicity of the identified toxic chemicals and heavy metals was confirmed by onion and mung bean phytotoxicity characterization tests. The wastewater affected the germination of mung bean seeds, reduced or inhibited the growth of onions, and induced various chromosomal aberrations in root apical meristems. Our study shows that the treatment of natural rubber wastewater needs to be improved, and the feasibility of irrigating soil with wastewater needs to be reconsidered.

HIF-1α/m6A/NF-κB/CCL3 axis-mediated immunosurveillance participates in low level benzene-related erythrohematopoietic development toxicity

Defective erythropoiesis is one of the causes of anemia and leukemia. However, the mechanisms underlying defective erythropoiesis under a low-dose environment of benzene are poorly understood. In the present study, multiple omics (transcriptomics and metabolomics) and methods from epidemiology to experimental biology (e.g., benzene-induced (WT and HIF-1α + ) mouse, hiPSC-derived HSPCs) were used. Here, we showed that erythropoiesis is more easily impacted than other blood cells, and the process is reversible, which involves HIF-1 and NF-kB signaling pathways in low-level benzene exposure workers. Decreased HIF-1α expression in benzene-induced mouse bone marrow resulted in DNA damage, senescence, and apoptosis in BMCs and HSCs, causing disturbances in iron homeostasis and erythropoiesis. We further revealed that HIF-1α mediates CCL3/macrophage-related immunosurveillance against benzene-induced senescent and damaged cells and contributes to iron homeostasis. Mechanistically, we showed that m6A modification is essential in this process. Benzene-induced depletion of m6A promotes the mRNA stability of gene NFKBIA and regulates the NF-κB/CCL3 pathway, which is regulated by HIF-1α/METTL3/YTHDF2. Overall, our results identified an unidentified role for HIF-1α, m6A, and the NF-kB signaling machinery in erythroid progenitor cells, suggesting that HIF-1α/METTL3/YTHDF2-m6A/NF-κB/CCL3 axis may be a potential prevention and therapeutic target for chronic exposure of humans to benzene-associated anemia and leukemia.

Cerebrospinal fluid neutral lipids predict progression from mild cognitive impairment to Alzheimer's disease

Genetic, metabolic, and clinical evidence links lipid dysregulation to an increased risk of Alzheimer's disease (AD). However, the role of lipids in the pathophysiological processes of AD and its clinical progression is unclear. We investigated the association between cerebrospinal fluid (CSF) lipidome and the pathological hallmarks of AD, progression from mild cognitive impairment (MCI) to AD, and the rate of cognitive decline in MCI patients. The CSF lipidome was analyzed by liquid chromatography coupled to mass spectrometry in an LC-ESI-QTOF-MS/MS platform for 209 participants: 91 AD, 92 MCI, and 26 control participants. The MCI patients were followed up for a median of 58 (± 12.5) months to evaluate their clinical progression to AD. Forty-eight (52.2%) MCI patients progressed to AD during follow-up. We found that higher CSF levels of hexacosanoic acid and ceramide Cer(d38:4) were associated with an increased risk of amyloid beta 42 (Aβ42) positivity in CSF, while levels of phosphatidylethanolamine PE(40:0) were associated with a reduced risk. Higher CSF levels of sphingomyelin SM(30:1) were positively associated with pathological levels of phosphorylated tau in CSF. Cholesteryl ester CE(11D3:1) and an unknown lipid were recognized as the most associated lipid species with MCI to AD progression. Furthermore, TG(O-52:2) was identified as the lipid most strongly associated with the rate of progression. Our results indicate the involvement of membrane and intracellular neutral lipids in the pathophysiological processes of AD and the progression from MCI to AD dementia. Therefore, CSF neutral lipids can be used as potential prognostic markers for AD.

Gut microbiome-based thiamine metabolism contributes to the protective effect of one acidic polysaccharide from Selaginella uncinata (Desv.) Spring against inflammatory bowel disease

Inflammatory bowel disease (IBD) is a serious disorder, and exploration of active compounds to treat it is necessary. An acidic polysaccharide named SUSP-4 was purified from Selaginella uncinata (Desv.) Spring, which contained galacturonic acid, galactose, xylose, arabinose, and rhamnose with the main chain structure of →4)-α-d-GalAp-(1→ and →6)-β-d-Galp-(1→ and the branched structure of →5)-α-l-Araf-(1→ . Animal experiments showed that compared with Model group, SUSP-4 significantly improved body weight status, disease activity index (DAI), colonic shortening, and histopathological damage, and elevated occludin and zonula occludens protein 1 (ZO-1) expression in mice induced by dextran sulfate sodium salt (DSS). 16S ribosomal RNA (rRNA) sequencing indicated that SUSP-4 markedly downregulated the level of Akkermansia and Alistipes. Metabolomics results confirmed that SUSP-4 obviously elevated thiamine levels compared with Model mice by adjusting thiamine metabolism, which was further confirmed by a targeted metabolism study. Fecal transplantation experiments showed that SUSP-4 exerted an anti-IBD effect by altering the intestinal flora in mice. A mechanistic study showed that SUSP-4 markedly inhibited macrophage activation by decreasing the levels of phospho-nuclear factor kappa-B (p-NF-κB) and cyclooxygenase-2 (COX-2) and elevating NF-E2-related factor 2 (Nrf2) levels compared with Model group. In conclusion, SUSP-4 affected thiamine metabolism by regulating Akkermania and inhibited macrophage activation to adjust NF-κB/Nrf2/COX-2-mediated inflammation and oxidative stress against IBD. This is the first time that plant polysaccharides have been shown to affect thiamine metabolism against IBD, showing great potential for in-depth research and development applications.

Sheathless CESI-MS versus LC-MS: Results of qualitative and quantitative analyses of the primary and secondary metabolites of Pleioblastus amarus bamboo shoots

The bamboo shoot of Pleioblastus amarus (Keng) Keng f. is a medicinal and edible resource in China. In this study, three separation techniques were applied to identify the primary and secondary metabolites component of P. amarus bamboo shoots, including sheathless capillary electrophoresis electrospray ionization-mass spectrometry (CESI-MS), reverse-phase liquid chromatography-MS (RPLC-MS), and hydrophilic interaction liquid chromatography-MS (HILIC-MS). A total of 201 metabolites were identified by the three methods. Among those metabolites, 146 were identified by RPLC-MS, 85 were identified by HILIC-MS, and 46 were identified by sheathless CESI-MS. These methods were complementary and had a linear coefficient. CESI-MS presented advantages in the identification of isomers, high sensitivity, very low sample usage, and good detection of polar and nonpolar metabolites, showing its unique applications in food analysis and prospects in metabolic research.

LC-MS/MS platform-based serum untargeted screening reveals the diagnostic biomarker panel and molecular mechanism of breast cancer

BACKGROUND

Breast cancer (BC), the most common form of malignant cancer affecting women worldwide, was characterized by heterogeneous metabolic disorder and lack of effective biomarkers for diagnosis. The purpose of this study is to search for reliable metabolite biomarkers of BC as well as triple-negative breast cancer (TNBC) using serum metabolomics approach.

METHODS

In this study, an untargeted metabolomics technique based on ultra-high performance liquid chromatography combined with mass spectrometry (UHPLC-MS) was utilized to investigate the differences in serum metabolic profile between the BC group (n = 53) and non-BC group (n = 57), as well as between TNBC patients (n = 23) and non-TNBC subjects (n = 30). The multivariate data analysis, determination of the fold change and the Mann-Whitney U test were used to screen out the differential metabolites. Additionally, machine learning methods including receiver operating curve analysis and logistic regression analysis were conducted to establish diagnostic biomarker panels.

RESULTS

There were 36 metabolites found to be significantly different between BC and non-BC groups, and 12 metabolites discovered to be significantly different between TNBC and non-TNBC patients. Results also showed that four metabolites, including N-acetyl-D-tryptophan, 2-arachidonoylglycerol, pipecolic acid and oxoglutaric acid, were considered as vital biomarkers for the diagnosis of BC and non-BC with an area under the curve (AUC) of 0.995. Another two-metabolite panel of N-acetyl-D-tryptophan and 2-arachidonoylglycerol was discovered to discriminate TNBC from non-TNBC and produced an AUC of 0.965.

CONCLUSION

This study demonstrated that serum metabolomics can be used to identify BC specifically and identified promising serum metabolic markers for TNBC diagnosis.

Beyond Volatile Phenols: An Untargeted Metabolomic Approach to Revealing Additional Markers of Smoke Taint in Grapevines (Vitis vinifera L.) cv. Merlot

When bushfires occur near wine regions, vineyards are frequently exposed to environmental smoke, which can negatively affect grapes and wine. For evaluating the severity of smoke exposure, volatile phenols and their glycosides are commonly used as biomarkers of smoke exposure. While critical to refining smoke taint diagnostics, few studies have comprehensively assessed the compositional impact of smoke exposure of grapes. In this study, Merlot grapevines were exposed to smoke post-véraison, with grapes being sampled both pre-smoke exposure and repeatedly post-smoke exposure, for analysis by liquid chromatography-high-resolution mass spectrometry. Volatile phenol glycosides were detected in control and smoke-affected grapes at ≤22 μg/kg and up to 160 μg/kg, respectively. The metabolite profiles of control and smoke-affected grapes were then compared using an untargeted metabolomics approach and compounds differentiating the sample types tentatively identified. The results demonstrate the presence of novel phenolic glycoconjugates as putative metabolites from environmental smoke together with stress-related grapevine metabolites and highlight the need to further characterize the consequences of grapevine smoke exposure with respect to the regulation of abiotic stress and plant defense mechanisms.

Multi-omics analysis reveals NNMT as a master metabolic regulator of metastasis in esophageal squamous cell carcinoma

Metabolic reprogramming has been observed in cancer metastasis, whereas metabolic changes required for malignant cells during lymph node metastasis of esophageal squamous cell carcinoma (ESCC) are still poorly understood. Here, we performed single-cell RNA sequencing (scRNA-seq) of paired ESCC tumor tissues and lymph nodes to uncover the reprogramming of tumor microenvironment (TME) and metabolic pathways. By integrating analyses of scRNA-seq data with metabolomics of ESCC tumor tissues and plasma samples, we found nicotinate and nicotinamide metabolism pathway was dysregulated in ESCC patients with lymph node metastasis (LN+), exhibiting as significantly increased 1-methylnicotinamide (MNA) in both tumors and plasma. Further data indicated high expression of N-methyltransferase (NNMT), which converts active methyl groups from the universal methyl donor, S-adenosylmethionine (SAM), to stable MNA, contributed to the increased MNA in LN+ ESCC. NNMT promotes epithelial-mesenchymal transition (EMT) and metastasis of ESCC in vitro and in vivo by inhibiting E-cadherin expression. Mechanically, high NNMT expression consumed too much active methyl group and decreased H3K4me3 modification at E-cadherin promoter and inhibited m6A modification of E-cadherin mRNA, therefore inhibiting E-cadherin expression at both transcriptional and post-transcriptional level. Finally, a detection method of lymph node metastasis was build based on the dysregulated metabolites, which showed good performance among ESCC patients. For lymph node metastasis of ESCC, this work supports NNMT is a master regulator of the cross-talk between cellular metabolism and epigenetic modifications, which may be a therapeutic target.

Multi-omics revealed the mechanisms of Codonopsis pilosula aqueous extract in improving UC through blocking abnormal activation of PI3K/Akt signaling pathway

ETHNOPHARMACOLOGICALRELEVANCE

Codonopsis pilosula (DS), a traditional Chinese medicine, had been used to regulate the immune, digestive and circulatory systems of the human, as well as protect the gastrointestinal tract, improve lung function.

AIM OF THE STUDY

The aim of study was to explore the effects and mechanism of Codonopsis pilosula aqueous extract (DS) intervention in improving ulcerative colitis (UC).

MATERIALS AND METHODS

UC model rats were established using combination of TNBS and ethanol. Tissue samples were collected for transcriptome and metabolomics analysis. Network pharmacology was performed on DS to identify bioactive compounds. Western blot was used to detect the key proteins involved in UC pathogenesis and PI3K/AKT pathways.

RESULTS

DS exerted the preventive and therapeutic effects in improving UC via inhibiting abnormal inflammatory responses and promoting antioxidant capacity. Levels of intestinal barrier, oxidative stress and inflammatory mediators were improved to nearly normal level in vivo by DS. Metabolome profiles showed that DS could restore the metabolic disorders associated with the UC pathogenesis. Further transcriptome results showed that DS mainly alleviate UC through inhibiting PI3K/Akt signaling pathway, and various related genes that dramatically expressed in UC Model rats were downregulated by DS. Typically, network pharmacology analysis identified that Glycitein was the hub compounds that involved in the mechanism of DS in improving UC.

CONCLUSIONS

The results show that Codonopsis pilosula (DS) was an potential excellent material in treating of UC depending on its suitable concentration. Possible therapeutic mechanisms of the DS involved in mitigating colonal inflammation, restoring metabolic disorders, promoting antioxidant capacity, and especially blocking the activation of PI3K/Akt pathway.

Quartet metabolite reference materials for inter-laboratory proficiency test and data integration of metabolomics profiling

BACKGROUND

Various laboratory-developed metabolomic methods lead to big challenges in inter-laboratory comparability and effective integration of diverse datasets.

RESULTS

As part of the Quartet Project, we establish a publicly available suite of four metabolite reference materials derived from B lymphoblastoid cell lines from a family of parents and monozygotic twin daughters. We generate comprehensive LC-MS-based metabolomic data from the Quartet reference materials using targeted and untargeted strategies in different laboratories. The Quartet multi-sample-based signal-to-noise ratio enables objective assessment of the reliability of intra-batch and cross-batch metabolomics profiling in detecting intrinsic biological differences among the four groups of samples. Significant variations in the reliability of the metabolomics profiling are identified across laboratories. Importantly, ratio-based metabolomics profiling, by scaling the absolute values of a study sample relative to those of a common reference sample, enables cross-laboratory quantitative data integration. Thus, we construct the ratio-based high-confidence reference datasets between two reference samples, providing "ground truth" for inter-laboratory accuracy assessment, which enables objective evaluation of quantitative metabolomics profiling using various instruments and protocols.

CONCLUSIONS

Our study provides the community with rich resources and best practices for inter-laboratory proficiency tests and data integration, ensuring reliability of large-scale and longitudinal metabolomic studies.

Using Data-Dependent and -Independent Hybrid Acquisitions for Fast Liquid Chromatography-Based Untargeted Lipidomics

Untargeted lipidomics using liquid chromatography (LC) coupled with tandem mass spectrometry (MS) is essential for large cohort studies. Using a fast LC gradient of less than 10 min for the rapid screening of lipids decreases the annotation rate, because of the lower coverage of the MS/MS spectra caused by the narrow peak width. A systematic procedure is proposed in this study to achieve a high annotation rate in fast LC-based untargeted lipidomics by integrating data-dependent acquisition (DDA) and sequential window acquisition of all-theoretical mass spectrometry data-independent acquisition (SWATH-DIA) techniques using the updated MS-DIAL program. This strategy uses variable SWATH-DIA methods for quality control (QC) samples, which are a mixture of biological samples that were analyzed multiple times to correct the MS signal drift. In contrast, biological samples are analyzed using DDA to facilitate the structural elucidation of lipids using the pure spectrum to the maximum extent. The workflow is demonstrated using an 8.6 min LC gradient, where the QC samples are analyzed using five different SWATH-DIA methods. The use of both DDA and SWATH-DIA achieves a 1.7-fold annotation coverage from publicly available benchmark data obtained using a fast LC-DDA-MS technique and offers 95.3% lipid coverage, as compared to the benchmark data set from a 25 min LC gradient. This study demonstrates that harmonized improvements in analytical conditions and informatics tools provide a comprehensive lipidome in fast LC-based untargeted lipidomics, not only for large-scale studies but also for small-scale experiments, contributing to both clinical applications and basic biology.

Comparison of the transcriptome and metabolome of wheat (Triticum aestivum L.) proteins content during grain formation provides insight

Introduction

Wheat is a food crop with a large global cultivation area, and the content and quality of wheat glutenin accumulation are important indicators of the quality of wheat flour.

Methods

To elucidate the gene expression regulation and metabolic characteristics related to the gluten content during wheat grain formation, transcriptomic and metabolomic analyses were performed for the high gluten content of the Xinchun 26 cultivar and the low proteins content of the Xinchun 34 cultivar at three periods (7 d, 14 d and 21 d) after flowering.

Results

Transcriptomic analysis revealed that 5573 unique differentially expressed genes (DEGs) were divided into two categories according to their expression patterns during the three periods. The metabolites detected were mainly divided into 12 classes. Lipid and lipid-like molecule levels and phenylpropanoid and polyketide levels were the highest, and the difference analysis revealed a total of 10 differentially regulated metabolites (DRMs) over the three periods. Joint analysis revealed that the DEGs and DRMs were significantly enriched in starch and sucrose metabolism; the citrate cycle; carbon fixation in photosynthetic organisms; and alanine, aspartate and glutamate metabolism pathways. The genes and contents of the sucrose and gluten synthesis pathways were analysed, and the correlation between gluten content and its related genes was calculated. Based on weighted correlation network analysis (WGCNA), by constructing a coexpression network, a total of 5 specific modules and 8 candidate genes that were strongly correlated with the three developmental stages of wheat grain were identified.

Discussion

This study provides new insights into the role of glutenin content in wheat grain formation and reveals potential regulatory pathways and candidate genes involved in this developmental process.

Silver Nanoparticles Induced Metabolic Perturbations in Pseudomonas aeruginosa: Evaluation Using the UPLC-QTof-MSE Platform

Silver nanoparticles (AgNPs) have been widely utilized in various biomedical and antimicrobial technologies, displaying broad-spectrum activities against Gram-negative and Gram-positive bacteria including multidrug-resistant strains. However, the emergence of resistance to AgNPs upon repeated exposure and the survival of bacteria after initial exposure to antimicrobial agents pose a threat, as they may lead to the development of new resistant populations. To combat the early stages of antibacterial resistance, systematic analysis is essential to understand the immediate response of bacteria to antimicrobial agents. In this study, green-synthesized AgNPs with a diameter of approximately 14 nm were exposed toPseudomonas aeruginosaat three different inhibitory concentrations and at two different time intervals (1 and 4 h) to investigate the perturbations in the metabolome using liquid chromatography-high-resolution mass spectrometry. MetaboAnalyst 5.0 was employed for univariate and multivariate analysis, and the affected metabolic pathways were constructed using a variable important in projection scores above 1 from PLS-DA. The study revealed significant alterations in metabolites associated with cell wall synthesis, energy metabolism, nucleotide metabolism, the TCA cycle, and anaplerotic intermediates of the TCA cycle. Our investigation aimed to comprehensively understand the effects of green-synthesized AgNPs onP. aeruginosa metabolism, providing a more precise snapshot of the bacterium's physiological state through metabolomics approach.

Development of a single mobile phase for LC-IM-MS-based discovery lipidomics and metabolic phenotyping: Application to methapyrilene hepatotoxicity in the rat

The untargeted global profiling of endogenous metabolites and lipids has the potential to increase knowledge and understanding in many areas of biology. LC-MS/MS is a key technology for such analyses however, several different LC methodologies, using different mobile phase compositions, are required to cover the diversity in polarity and analyte structure encountered in biological samples. Most notably many lipid screening methods make use of isopropanol (IPA) as a major component of mobile phases employed for comprehensive lipidomic profiling. In order to increase laboratory efficiency, and minimize opportunities for errors, a suite of methods, based on a single acetonitrile (ACN)-aqueous buffer mobile phase combination, has been developed. This mobile phase can be used for hydrophobic interaction liquid chromatography on an amide stationary phase (for polar analytes), reversed-phase (RP) LC analysis on a C8 stationary phase (for moderately polar-non-polar compounds) and RPLC using a CSH phenyl-hexyl bonded column (for lipids). All of these sub 10 minute separations had good throughput and reproducibility with CV's of analyte response <25 % whilst eliminating the need for complex mobile phase preparation and the use of IPA as an organic modifier for lipidomics. Advantages of removing IPA and replacing it with the ACN-based method were a 58 % increase in peak capacity for lipids, with improved resolution for the di- and triglycerides and cholesterol esters compared to current methods. Compared to the IPA-containing solvent system the ACN-based mobile phase also resulted in a 61 % increase in lipid feature detection. The utility of this "universal" mobile phase approach was demonstrated by its application to a rat toxicology study investigating the consequences of methapyrilene administration through on the endogenous metabolite profiles of plasma and urine. Methapyrilene and its metabolites were also profiled in these samples.

Oral magnesium prevents acetaminophen-induced acute liver injury by modulating microbial metabolism

Acetaminophen overuse is a common cause of acute liver failure (ALF). During ALF, toxins are metabolized by enzymes such as CYP2E1 and transformed into reactive species, leading to oxidative damage and liver failure. Here, we found that oral magnesium (Mg) alleviated acetaminophen-induced ALF through metabolic changes in gut microbiota that inhibit CYP2E1. The gut microbiota from Mg-supplemented humans prevented acetaminophen-induced ALF in mice. Mg exposure modulated Bifidobacterium metabolism and enriched indole-3-carboxylic acid (I3C) levels. Formate C-acetyltransferase (pflB) was identified as a key Bifidobacterium enzyme involved in I3C generation. Accordingly, a Bifidobacterium pflB knockout showed diminished I3C generation and reduced the beneficial effects of Mg. Conversely, treatment with I3C or an engineered bacteria overexpressing Bifidobacterium pflB protected against ALF. Mechanistically, I3C bound and inactivated CYP2E1, thus suppressing formation of harmful reactive intermediates and diminishing hepatocyte oxidative damage. These findings highlight how interactions between Mg and gut microbiota may help combat ALF.

Metabolic insights into how multifunctional microbial consortium enhances atrazine removal and phosphorus uptake at low temperature

Agricultural soils in the black soil region of northeast China often face negative stress due to low temperatures, pesticide contamination, and inadequate nutrient supply. In this study, a new cold-tolerant strain of Peribacillus simplex C1 (C1) was selectively isolated from atrazine contaminated soil. The artificially constructed microbial consortium (CPD) [C1, phosphorus-solubilizing bacterium Enterobacter sp. P1, and atrazine-degrading bacterium Acinetobacter lwoffii DNS32] demonstrated the most effective performance in enhancing atrazine degradation and phosphorus-solubilizing capacity when the initial inoculation ratio of 5:1:2 at 15 °C. CPD enhanced energy-related metabolic pathways and increased choline production to regulate bacterial adaptation to temperature decrease. Additionally, the strains could selectively utilize carbon sources (low molecular weight organic acids) or nitrogen sources (some metabolites of atrazine) provided by each other to enhance growth. Furthermore, strain C1 enhanced membrane fluidity through increased expression of the unsaturated fatty acids. Pot experiments demonstrated that CPD assisted soybean seedlings in resisting dual stresses of low temperature and atrazine contamination by inducing the expression of genes related to photosynthesis, membrane permeability, phosphorus response, and cold tolerance.

Continuous cropping of potato changed the metabolic pathway of root exudates to drive rhizosphere microflora

For potato production, continuous cropping (CC) could lead to autotoxicity buildup and microflora imbalance in the field soil, which may result in failure of crops and reduction in yield. In this study, non-targeted metabolomics (via liquid chromatography with tandem mass spectrometry (LC-MS/MS)) combined with metagenomic profiling (via high-throughput amplicon sequencing) were used to evaluate correlations between metabolomics of potato root exudates and communities of bacteria and fungi around potato plants to illustrate the impacts of CC. Potato plants were grown in soil collected from fields with various CC years (0, 1, 4, and 7 years). Metabolomic analysis showed that the contents and types of potential autotoxins in potato root exudates increased significantly in CC4 and CC7 plants (i.e., grown in soils with 4 and 7 years of CC). The differentially expressed metabolites were mainly produced via alpha-linolenic acid metabolism in plant groups CC0 and CC1 (i.e., no CC or 1 year CC). The metabolomics of the groups CC4 and CC7 became dominated by styrene degradation, biosynthesis of siderophore group non-ribosomal peptides, phenylpropanoid biosynthesis, and biosynthesis of various plant secondary metabolites. Continuous cropping beyond 4 years significantly changed the bacterial and fungal communities in the soil around the potato crops, with significant reduction of beneficial bacteria and accumulation of harmful fungi. Correlations between DEMs and microflora biomarkers were established with strong significances. These results suggested that continuous cropping of potato crops changed their metabolism as reflected in the plant root exudates and drove rhizosphere microflora to directions less favorable to plant growth, and it needs to be well managed to assure potato yield.

Serum Metabolic Fingerprints Characterize Systemic Lupus Erythematosus

Metabolic fingerprints in serum characterize diverse diseases for diagnostics and biomarker discovery. The identification of systemic lupus erythematosus (SLE) by serum metabolic fingerprints (SMFs) will facilitate precision medicine in SLE in an early and designed manner. Here, a discovery cohort of 731 individuals including 357 SLE patients and 374 healthy controls (HCs), and a validation cohort of 184 individuals (SLE/HC, 91/93) are constructed. Each SMF is directly recorded by nano-assisted laser desorption/ionization mass spectrometry (LDI MS) within 1 minute using 1 µL of native serum, which contains 908 mass to charge features. Sparse learning of SMFs achieves the SLE identification with sensitivity/specificity and area-under-the-curve (AUC) up to 86.0%/92.0% and 0.950 for the discovery cohort. For the independent validation cohort, it exhibits no performance loss by affording the sensitivity/specificity and AUC of 89.0%/100.0% and 0.992. Notably, a metabolic biomarker panel is screened out from the SMFs, demonstrating the unique metabolic pattern of SLE patients different from both HCs and rheumatoid arthritis patients. In conclusion, SMFs characterize SLE by revealing its unique metabolic pattern. Different regulation of small molecule metabolites contributes to the precise diagnosis of autoimmune disease and further exploration of the pathogenic mechanisms.

Low beauvericin concentrations promote PC-12 cell survival under oxidative stress by regulating lipid metabolism and PI3K/AKT/mTOR signaling

Beauvericin (BEA), a naturally occurring cyclic peptide with good pharmacological activity, has been widely explored in anticancer research. Although BEA is toxic, studies have demonstrated its antioxidant activity. However, to date, the antioxidant mechanisms of BEA remain unclear. Herein, we conducted a comprehensive and detailed study of the antioxidant mechanism of BEA using an untargeted metabolomics approach, subsequently validating the results. BEA concentrations of 0.5 and 1 μM significantly inhibited H2O2-induced oxidative stress (OS), decreased reactive oxygen species levels in PC-12 cells, and restored the mitochondrial membrane potential. Untargeted metabolomics indicated that BEA was primarily involved in lipid-related metabolism, suggesting its role in resisting OS in PC-12 cells by participating in lipid metabolism. BEA combated OS damage by increasing phosphatidylcholine, phosphatidylethanolamine, and sphingolipid levels. In the current study, BEA upregulated proteins related to the PI3K/AKT/mTOR pathway, thereby promoting cell survival. These findings support the antioxidant activity of BEA at low concentrations, warranting further research into its pharmacological effects.

Microbiome and metabolome dysbiosis analysis in impaired glucose tolerance for the prediction of progression to diabetes mellitus

Gut microbiota and circulating metabolite dysbiosis predate important pathological changes in glucose metabolic disorders; however, comprehensive studies on impaired glucose tolerance (IGT), a diabetes mellitus (DM) precursor, are lacking. Here, we perform metagenomic sequencing and metabolomics on 47 pairs of individuals with IGT and newly diagnosed DM and 46 controls with normal glucose tolerance (NGT); patients with IGT are followed up after 4 years for progression to DM. Analysis of baseline data reveals significant differences in gut microbiota and serum metabolites among the IGT, DM, and NGT groups. In addition, 13 types of gut microbiota and 17 types of circulating metabolites showed significant differences at baseline before IGT progressed to DM, including higher levels of Eggerthella unclassified, Coprobacillus unclassified, Clostridium ramosum, L-valine, L-norleucine, and L-isoleucine, and lower levels of Eubacterium eligens, Bacteroides faecis, Lachnospiraceae bacterium 3_1_46FAA, Alistipes senegalensis, Megaspaera elsdenii, Clostridium perfringens, α-linolenic acid, 10E,12Z-octadecadienoic acid, and dodecanoic acid. A random forest model based on differential intestinal microbiota and circulating metabolites can predict the progression from IGT to DM (AUC = 0.87). These results suggest that microbiome and metabolome dysbiosis occur in individuals with IGT and have important predictive values and potential for intervention in preventing IGT from progressing to DM.

Diagnosis of Esophageal Squamous Cell Carcinoma by High-Performance Serum Metabolic Fingerprints: A Retrospective Study

Esophageal squamous cell carcinoma (ESCC) is a highly prevalent and aggressive malignancy, and timely diagnosis of ESCC contributes to an increased cancer survival rate. However, current detection methods for ESCC mainly rely on endoscopic examination, limited by a relatively low participation rate. Herein, ferric-particle-enhanced laser desorption/ionization mass spectrometry (FPELDI MS) is utilized to record the serum metabolic fingerprints (SMFs) from a retrospective cohort (523 non-ESCC participants and 462 ESCC patients) to build diagnostic models toward ESCC. The PFELDI MS achieved high speed (≈30 s per sample), desirable reproducibility (coefficients of variation < 15%), and high throughput (985 samples with ≈124 200 data points for each spectrum). Desirable diagnostic performance with area-under-the-curves (AUCs) of 0.925-0.966 is obtained through machine learning of SMFs. Further, a metabolic biomarker panel is constructed, exhibiting superior diagnostic sensitivity (72.2-79.4%, p < 0.05) as compared with clinical protein biomarker tests (4.3-22.9%). Notably, the biomarker panel afforded an AUC of 0.844 (95% confidence interval [CI]: 0.806-0.880) toward early ESCC diagnosis. This work highlighted the potential of metabolic analysis for accurate screening and early detection of ESCC and offered insights into the metabolic characterization of diseases including but not limited to ESCC.

Effects of apolipoprotein H downregulation on lipid metabolism, fatty liver disease, and gut microbiota dysbiosis

Apolipoprotein H (APOH) downregulation can cause hepatic steatosis and gut microbiota dysbiosis. However, the mechanism by which APOH-regulated lipid metabolism contributes to metabolic dysfunction-associated steatotic liver disease (MASLD) remains undetermined. Herein, we aim to explore the regulatory effect of APOH, mediated through various pathways, on metabolic homeostasis and MASLD pathogenesis. We analyzed serum marker levels, liver histopathology, and cholesterol metabolism-related gene expression in global ApoH-/- C57BL/6 male mice. We used RNA sequencing and metabolomic techniques to investigate the association between liver metabolism and bacterial composition. Fifty-two differentially expressed genes were identified between ApoH-/- and WT mice. The mRNA levels of de novo lipogenesis genes were highly upregulated in ApoH-/- mice than in WT mice. Fatty acid, glycerophospholipid, sterol lipid, and triglyceride levels were elevated, while hyodeoxycholic acid levels were significantly reduced in the liver tissues of ApoH-/- mice than in those of WT mice. Microbial beta diversity was lower in ApoH-/- mice than in WT mice, and gut microbiota metabolic functions were activated in ApoH-/- mice. Moreover, ApoH transcripts were downregulated in patients with MASLD, and APOH-related differential genes were enriched in lipid metabolism. Open-source transcript-level data from human metabolic dysfunction-associated steatohepatitis livers reinforced a significant association between metabolic dysfunction-associated steatohepatitis and APOH downregulation. In conclusion, our studies demonstrated that APOH downregulation aggravates fatty liver and induces gut microbiota dysbiosis by dysregulating bile acids. Our findings offer a novel perspective on APOH-mediated lipid metabolic dysbiosis and provide a valuable framework for deciphering the role of APOH in fatty liver disease.

Label-Free Quantitation of Endogenous Peptides

Liquid chromatography-mass spectrometry (LC-MS)-based peptidomics methods allow for the detection and identification of many peptides in a complex biological mixture in an untargeted manner. Quantitative peptidomics approaches allow for comparisons of peptide abundance between different samples, allowing one to draw conclusions about peptide differences as a function of experimental treatment or physiology. While stable isotope labeling is a powerful approach for quantitative proteomics and peptidomics, advances in mass spectrometry instrumentation and analysis tools have allowed label-free methods to gain popularity in recent years. In a general label-free quantitative peptidomics experiment, peak intensity information for each peptide is compared across multiple LC-MS runs. Here, we outline a general approach for label-free quantitative peptidomics experiments, including steps for sample preparation, LC-MS data acquisition, data processing, and statistical analysis. Special attention is paid to address run-to-run variability, which can lead to several major problems in label-free experiments. Overall, our method provides researchers with a framework for the development of their own quantitative peptidomics workflows applicable to quantitation of peptides from a wide variety of different biological sources.

Characterization of the physicochemical Properties, bacterial community and non-volatile profiles of fermented Yu jiangsuan by Weissella cibaria and Lactobacillus plantarum

Yu jiangsuan (YJS) is a unique traditional fermented condiment in China. Physicochemical, bacterial communities, and non-volatile properties were examined in inoculation Autochthonous Weissella cibaria and Lactobacillus plantarum. The results indicated that inoculation samples did well in shortening fermentation time; amino acid nitrogen (AN) and TCA-soluble peptide contents of fermented YJS were 10.8% and 17.4% higher than those of naturally fermented YJS, respectively. However, its total volatile base nitrogen (TVB-N), thiobarbituric acid (TBARS), and nitrite were only 74.3%, 87.2% and 83.6% of those of naturally fermented YJS. In addition, the dominant bacterial genera were Lactobacillus, Weissella and Pectobacterium, whose contributions were 41.2%, 20.3% and 5.5%, respectively. Moreover, 26 significantly differential metabolites were identified, and involved in 10 metabolic pathways. The decomposition of substrates and the formation of differential metabolites in YJS were primarily centered on the TCA cycle and the metabolism of carbohydrates. Therefore, this study is conducive to discovering the bacterial community structure and metabolite composition of probiotic inoculated YJS fermentation, as well as the potential value of core functional bacteria genera in controlling YJS production in industry.

Current Practices in LC-MS Untargeted Metabolomics: A Scoping Review on the Use of Pooled Quality Control Samples

Untargeted metabolomics is an analytical approach with numerous applications serving as an effective metabolic phenotyping platform to characterize small molecules within a biological system. Data quality can be challenging to evaluate and demonstrate in metabolomics experiments. This has driven the use of pooled quality control (QC) samples for monitoring and, if necessary, correcting for analytical variance introduced during sample preparation and data acquisition stages. Described herein is a scoping literature review detailing the use of pooled QC samples in published untargeted liquid chromatography-mass spectrometry (LC-MS) based metabolomics studies. A literature query was performed, the list of papers was filtered, and suitable articles were randomly sampled. In total, 109 papers were each reviewed by at least five reviewers, answering predefined questions surrounding the use of pooled quality control samples. The results of the review indicate that use of pooled QC samples has been relatively widely adopted by the metabolomics community and that it is used at a similar frequency across biological taxa and sample types in both small- and large-scale studies. However, while many studies generated and analyzed pooled QC samples, relatively few reported the use of pooled QC samples to improve data quality. This demonstrates a clear opportunity for the field to more frequently utilize pooled QC samples for quality reporting, feature filtering, analytical drift correction, and metabolite annotation. Additionally, our survey approach enabled us to assess the ambiguity in the reporting of the methods used to describe the generation and use of pooled QC samples. This analysis indicates that many details of the QC framework are missing or unclear, limiting the reader's ability to determine which QC steps have been taken. Collectively, these results capture the current state of pooled QC sample usage and highlight existing strengths and deficiencies as they are applied in untargeted LC-MS metabolomics.

Effect of forest planting patterns on the formation of soil organic carbon during litter lignocellulose degradation from a microbial perspective

Litter decomposition is an important source of soil organic carbon, and it plays a key role in maintaining the stability of forest ecosystems. The microbial mechanism of soil organic carbon (SOC) formation in different urban forest planting patterns during litter lignocellulose degradation is still unclear. The key genes, microbes, and metabolites in the process of lignocellulose degradation and SOC formation were determined by metagenomics and metabolomics in different litter decomposition layers and soil layers in different urban forest planting patterns, including three types of broadleaf forests (BP forests), three types of coniferous forests (CP forests), and two types of mixed coniferous and broadleaf forests (MCBP forests). The results indicated that the cellulose, hemicellulose, and lignin concentrations from the undecomposed layer to the totally decomposed layer decreased by 70.07, 86.83, and 73.04% for CP litter; 74.30, 93.80, and 77.55% for BP litter; and 62.51, 48.58, and 90.61% for MCBP litter, respectively. The soil organic carbon of the BP forests and MCBP forests was higher than that of the CP forests by 38.06 and 94.43% for the 0-10 cm soil layer and by 38.55 and 20.87% for the 10-20 cm soil layer, respectively. Additionally, the gene abundances of glycoside hydrolases (GHs) and polysaccharide lyases (PLs) in the BP forests were higher than those in the MCBP forests and CP forests. Amino acid metabolism, sugar metabolism, TCA metabolism, and cAMP signaling metabolism were mainly between the CP forests and BP forests, while the TCA cycle, pyruvate metabolism, phenylalanine metabolism, and tyrosine metabolism were mainly between the BP forests and MCBP forests during litter decomposition. Additionally, ammonia nitrogen and hemicellulose were key factors driving SOC formation in the CP forests, while ammonia nitrogen, hemicellulose, and lignocellulose-degrading genes were key factors driving SOC formation in the BP forests. For the MCBP forests, cellulose, pH, ammonia nitrogen, and lignin were key factors driving SOC formation. Our findings revealed that the BP forests and MCBP forests had stronger lignocellulose degradation performance in the formation of SOC. This study provided a theoretical basis for the flow and transformation of nutrients in different urban forest management patterns.

Moderate altitude exposure impacts host fasting blood glucose and serum metabolome by regulation of the intestinal flora

Moderate altitude exposure has shown beneficial effects on diabetes incidence but the underlying mechanisms are not understood. Our study aimed to investigate how the human gut microbiome impacted the serum metabolome and associated with glucose homeostasis in healthy Chinese individuals upon moderate-altitude exposure. Faecal microbiome composition was assessed using shotgun metagenomic sequencing. Serum metabolome was acquired by untargeted metabolomics technology, and amino acids (AAs) and propionic acid in serum were quantified by targeted metabolomics technology. The results indicated that the moderate-altitude exposed individuals presented lowered fasting blood glucose (FBG) and propionic acid, increased circulating L-Glutamine but decreased L-Glutamate and L-Valine, which correlated with enriched Bacteroidetes and decreased Proteobacteria. Additionally, the silico causality associations among gut microbiota, serum metabolome and host FBG were analyzed by mediation analysis. It showed that increased Bacteroides ovatus (B. ovatus) and decreased Escherichia coli (E. coli) were identified as the main antagonistic species driving the association between L-Glutamate and FBG in silico causality. Furthermore, the high-fat diet (HFD) fed mice subjected to faecal microbiota transplantation (FMT) were applied to validate the cause-in-fact effects of gut microbiota on the beneficial glucose response. We found that microbiome in the moderate-altitude exposed donor could predict the extent of the FBG response in recipient mice, which showed lowered FBG, L-Glutamate and Firmicutes/Bacteroidetes ratio. Our findings suggest that moderate-altitude exposure targeting gut microbiota and circulating metabolome, may pave novel avenues to counter dysglycemia.

Untargeted Metabolomics Profiling Reveals Exercise Intensity-Dependent Alterations in Thoroughbred Racehorses' Plasma after Routine Conditioning Sessions

Thoroughbred (TB) racehorses undergo rigorous conditioning programs to optimize their physical and mental capabilities through varied exercise sessions. While conventional investigations focus on limited hematological and biochemical parameters, this field study employed untargeted metabolomics to comprehensively assess metabolic responses triggered by exercise sessions routinely used in TB conditioning. Blood samples were collected pre- and post-exercise from ten racehorses, divided into two groups based on exercise intensity: high intensity (n = 6, gallop at ± 13.38 m/s, 1400 m) and moderate intensity (n = 4, soft canter at ± 7.63 m/s, 2500 m). Intensity was evaluated through monitoring of the speed, heart rate, and lactatemia. Resting and 30 min post-exercise plasma samples were analyzed using ultraperformance liquid chromatography coupled with high-resolution mass spectrometry. Unsupervised principal component analysis revealed exercise-induced metabolome changes, with high-intensity exercise inducing greater alterations. Following high-intensity exercise, 54 metabolites related to amino acid, fatty acid, nucleic acid, and vitamin metabolism were altered versus 23 metabolites, primarily linked to fatty acid and amino acid metabolism, following moderate-intensity exercise. Metabolomics confirmed energy metabolism changes reported by traditional biochemistry studies and highlighted the involvement of lipid and amino acid metabolism during routine exercise and recovery, aspects that had previously been overlooked in TB racehorses.

Metabolomics: An Emerging "Omics" Platform for Systems Biology and Its Implications for Huntington Disease Research

Huntington's disease (HD) is a progressive, fatal neurodegenerative disease characterized by motor, cognitive, and psychiatric symptoms. The precise mechanisms of HD progression are poorly understood; however, it is known that there is an expansion of the trinucleotide cytosine-adenine-guanine (CAG) repeat in the Huntingtin gene. Important new strategies are of paramount importance to identify early biomarkers with predictive value for intervening in disease progression at a stage when cellular dysfunction has not progressed irreversibly. Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under certain conditions and is becoming an essential tool for the systemic characterization of metabolites to provide a snapshot of the functional and pathophysiological states of an organism and support disease diagnosis and biomarker discovery. This review briefly highlights the historical progress of metabolomic methodologies, followed by a more detailed review of the use of metabolomics in HD research to enable a greater understanding of the pathogenesis, its early prediction, and finally the main technical platforms in the field of metabolomics.

Omics analysis of the effect of cold normal saline stress through gastric gavage on LPS induced mice

Cold stress is a significant environmental stimulus that negatively affects the health, production, and welfare of animals and birds. However, the specific effects of cold stimulation combined with lipopolysaccharide (LPS) on the mouse intestine remain poorly understood. Therefore, we designed this research to explore the effect of cold stimulation + LPS on mice intestine via microbiome and microbiota sequencing. Forty-eight mice were randomly divided into four experimental groups (n = 12): Control (CC), LPS-induced (CL), cold normal saline-induced (MC) and LPS + cold normal saline-induced (ML). Our results showed body weight was similar among different groups of mice. However, the body weight of mice in groups CC and CL were slightly higher compared to those in groups MC and ML. The results of gene expressions reflected that CL and ML exposure caused gut injury and barrier dysfunction, as evident by decreased ZO-1, OCCLUDIN (P < 0.01), and CASPASE-1 (P < 0.01) expression in the intestine of mice. Moreover, we found that cold stress induced oxidative stress in LPS-challenged mice by increasing malondialdehyde (MDA) accumulation and decreasing the antioxidant capacity [glutathione peroxidase (GSH-Px), superoxide dismutase (SOD), total and antioxidant capacity (T-AOC)]. The cold stress promoted inflammatory response by increased IL-1β in mice treated with cold normal saline + LPS. Whereas, microbiome sequencing revealed differential abundance in four phyla and 24 genera among the mouse groups. Metabolism analysis demonstrated the presence of 4,320 metabolites in mice, with 43 up-regulated and 19 down-regulated in CC vs. MC animals, as well as 1,046 up-regulated and 428 down-regulated in ML vs. CL animals. It is Concluded that cold stress enhances intestinal damage by disrupting the balance of gut microbiota and metabolites, while our findings contribute in improving management practices of livestock in during cold seasons.

An interactive web application for exploring human plasma and fibroblast metabolomics data from patients with inborn errors of metabolism

Metabolomic profiling is instrumental in understanding the systemic and cellular impact of inborn errors of metabolism (IEMs), monogenic disorders caused by pathogenic genomic variants in genes involved in metabolism. This study encompasses untargeted metabolomics analysis of plasma from 474 individuals and fibroblasts from 67 subjects, incorporating healthy controls, patients with 65 different monogenic diseases, and numerous undiagnosed cases. We introduce a web application designed for the in-depth exploration of this extensive metabolomics database. The application offers a user-friendly interface for data review, download, and detailed analysis of metabolic deviations linked to IEMs at the level of individual patients or groups of patients with the same diagnosis. It also provides interactive tools for investigating metabolic relationships and offers comparative analyses of plasma and fibroblast profiles. This tool emphasizes the metabolic interplay within and across biological matrices, enriching our understanding of metabolic regulation in health and disease. As a resource, the application provides broad utility in research, offering novel insights into metabolic pathways and their alterations in various disorders.

Automated Identification of Modified Nucleosides during HRAM-LC-MS/MS using a Metabolomics ID Workflow with Neutral Loss Detection

The role of post-transcriptional modification in biological processes has been an ongoing field of study for several decades. Improvements in liquid chromatography platforms and mass spectrometry instrumentation have resulted in the enhanced identification, characterization, and quantification of modified nucleosides in biological systems. One consequence of the rapid technological improvements in the analytical acquisition of modified nucleosides has been a dearth of robust data processing workflows for analyzing more than a handful of samples at a time. To improve the utility of LC-MS/MS for batch analyses of modified nucleosides, a workflow for automated nucleoside identification has been developed. We adapted the Thermo Fisher Scientific metabolomics identification software package, Compound Discoverer, to accurately identify modified nucleosides from batch LC-MS/MS acquisitions. Three points of identification are used: accurate mass from a monoisotopic mass list, spectral matching from a spectral library, and neutral loss identification. This workflow was applied to a batch (n = 24) of urinary nucleosides, resulting in the accurate identification and relative quantification of 16 known nucleosides in less than 1 h.

Metabolic changes preceding bladder cancer occurrence among Korean men: a nested case-control study from the KCPS-II cohort

BACKGROUND

Bladder cancer (BLCA) research in Koreans is still lacking, especially in focusing on the prediction of BLCA. The current study aimed to discover metabolic signatures related to BLCA onset and confirm its potential as a biomarker.

METHODS

We designed two nested case-control studies using Korean Cancer Prevention Study (KCPS)-II. Only males aged 35-69 were randomly selected and divided into two sets by recruitment organizations [set 1, BLCA (n = 35) vs. control (n = 35); set 2, BLCA (n = 31) vs. control (n = 31)]. Baseline serum samples were analyzed by non-targeted metabolomics profiling, and OPLS-DA and network analysis were performed. Calculated genetic risk score (GRS) for BLCA from all KCPS participants was utilized for interpreting metabolomics data.

RESULTS

Critical metabolic signatures shown in the BLCA group were dysregulation of lysine metabolism and tryptophan-indole metabolism. Furthermore, the prediction model consisting of metabolites (lysine, tryptophan, indole, indoleacrylic acid, and indoleacetaldehyde) reflecting these metabolic signatures showed mighty BLCA predictive power (AUC: 0.959 [0.929-0.989]). The results of metabolic differences between GRS-high and GRS-low groups in BLCA indicated that the pathogenesis of BLCA is associated with a genetic predisposition. Besides, the predictive ability for BLCA on the model using GRS and five significant metabolites was powerful (AUC: 0.990 [0.980-1.000]).

CONCLUSION

Metabolic signatures shown in the present research may be closely associated with BLCA pathogenesis. Metabolites involved in these could be predictive biomarkers for BLCA. It could be utilized for early diagnosis, prognostic diagnosis, and therapeutic targets for BLCA.

Defence mechanisms of Pinctada fucata martensii to Vibrio parahaemolyticus infection: Insights from proteomics and metabolomics

Survival of pearl oysters is not only challenged by coastal pollution, but also pathogen infection that may eventually incur substantial economic losses in the pearl farming industry. Yet, whether pearl oysters can defend themselves against pathogen infection through molecular mechanisms remains largely unexplored. By using iTRAQ proteomic and metabolomic analyses, we analysed the proteins and metabolites in the serum of pearl oysters (Pinctada fucata martensii) when stimulated by pathogenic bacteria (Vibrio parahaemolyticus). Proteomic results found that a total of 2,242 proteins were identified in the experimental (i.e., Vibrio-stimulated) and control groups, where 166 of them were differentially expressed (120 upregulated and 46 downregulated in the experimental group). Regarding the immune response enrichment results, the pathway of signal transduction was significantly enriched, such as cytoskeleton and calcium signalling pathways. Proteins, including cathepsin L, heat shock protein 20, myosin and astacin-like protein, also contributed to the immune response of oysters. Pathogen stimulation also altered the metabolite profile of oysters, where 49 metabolites associated with metabolism of energy, fatty acids and amino acids were found. Integrated analysis suggests that the oysters could respond to pathogen infection by coordinating multiple cellular processes. Thus, the proteins and metabolites identified herein not only represent valuable genetic resources for developing molecular biomarkers and genetic breeding research, but also open new avenues for studies on the molecular defence mechanisms of pearl oysters to pathogen infection.

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.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Integrated multi-omics analysis of brain aging in female nonhuman primates reveals altered signaling pathways relevant to age-related disorders

The prefrontal cortex (PFC) has been implicated as a key brain region responsible for age-related cognitive decline. Little is known about aging-related molecular changes in PFC that may mediate these effects. To date, no studies have used untargeted discovery methods with integrated analyses to determine PFC molecular changes in healthy female primates. We quantified PFC changes associated with healthy aging in female baboons by integrating multiple omics data types (transcriptomics, proteomics, metabolomics) from samples across the adult age span. Our integrated omics approach using unbiased weighted gene co-expression network analysis to integrate data and treat age as a continuous variable, revealed highly interconnected known and novel pathways associated with PFC aging. We found Gamma-aminobutyric acid (GABA) tissue content associated with these signaling pathways, providing 1 potential biomarker to assess PFC changes with age. These highly coordinated pathway changes during aging may represent early steps for aging-related decline in PFC functions, such as learning and memory, and provide potential biomarkers to assess cognitive status in humans.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

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.

Activation of the JNK/COX-2/HIF-1α axis promotes M1 macrophage via glycolytic shift in HIV-1 infection

Chronic inflammation is recognized as a major risk factor for the severity of HIV infection. Whether metabolism reprogramming of macrophages caused by HIV-1 is related to chronic inflammatory activation, especially M1 polarization of macrophages, is inconclusive. Here, we show that HIV-1 infection induces M1 polarization and enhanced glycolysis in macrophages. Blockade of glycolysis inhibits M1 polarization of macrophages, indicating that HIV-1-induced M1 polarization is supported by enhanced glycolysis. Moreover, we find that this immunometabolic adaptation is dependent on hypoxia-inducible factor 1α (HIF-1α), a strong inducer of glycolysis. HIF-1α-target genes, including HK2, PDK1, and LDHA, are also involved in this process. Further research discovers that COX-2 regulates HIF-1α-dependent glycolysis. However, the elevated expression of COX-2, enhanced glycolysis, and M1 polarization of macrophages could be reversed by inactivation of JNK in the context of HIV-1 infection. Our study mechanistically elucidates that the JNK/COX-2/HIF-1α axis is activated to strengthen glycolysis, thereby promoting M1 polarization in macrophages in HIV-1 infection, providing a new idea for resolving chronic inflammation in clinical AIDS patients.

The low ratio of ghrelin in plasma and cerebrospinal fluid might be beneficial to sleep

OBJECTIVE

Ghrelin is physiologically important for maintaining sleep rhythm. Cigarette smoking has been demonstrated to significantly increase the risk of insufficient sleep by regulating ghrelin at the central and peripheral levels. No research has been published to study the relationship between active smoking and sleep via ghrelin level in cerebrospinal fluid (CSF).

METHODS

A total of 139 Chinese males were recruited and divided into active smokers (n = 77) and non-smokers (n = 62). The levels of CSF and plasma ghrelin were measured. The Pittsburgh Sleep Quality Index (PSQI) was used to evaluate sleep.

RESULTS

Non-smokers had lower PSQI scores (1.71 ± 1.93) than active smokers (3.70 ± 1.78). Non-smokers have significantly lower plasma ghrelin levels and lower plasma/CSF ghrelin ratio but higher CSF ghrelin than active smokers. Among non-smokers, plasma ghrelin levels were not correlated with PSQI scores (all p > 0.05), CSF ghrelin levels were positively correlated with PSQI scores (r = 0.309, p = 0.019), and the plasma/CSF ghrelin ratio was negatively correlated with PSQI scores (r = -0.346, p = 0.008).

CONCLUSIONS

This study is the first to reveal the relationship between cigarette smoking, high CSF ghrelin levels and insufficient sleep, suggesting that maintaining a normal plasma/CSF ghrelin ratio may be the physiological mechanism of healthy sleep, and the insufficient sleep population must quit smoking.

A comprehensive multiomics approach reveals that high levels of sphingolipids in cardiac cachexia adipose tissue are associated with inflammatory and fibrotic changes

Cardiac cachexia is a deadly consequence of advanced heart failure that is characterised by the dysregulation of adipose tissue homeostasis. Once cachexia occurs with heart failure, it prevents the normal treatment of heart failure and increases the risk of death. Targeting adipose tissue is an important approach to treating cardiac cachexia, but the pathogenic mechanisms are still unknown, and there are no effective therapies available. Transcriptomics, metabolomics, and lipidomics were used to examine the underlying mechanisms of cardiac cachexia. Transcriptomics investigation of cardiac cachexia adipose tissue revealed that genes involved in fibrosis and monocyte/macrophage migration were increased and strongly interacted. The ECM-receptor interaction pathway was primarily enriched, as shown by KEGG enrichment analysis. In addition, gene set enrichment analysis revealed that monocyte chemotaxis/macrophage migration and fibrosis gene sets were upregulated in cardiac cachexia. Metabolomics enrichment analysis demonstrated that the sphingolipid signalling pathway is important for adipose tissue remodelling in cardiac cachexia. Lipidomics analysis showed that the adipose tissue of rats with cardiac cachexia had higher levels of sphingolipids, including Cer and S1P. Moreover, combined multiomics analysis suggested that the sphingolipid metabolic pathway was associated with inflammatory-fibrotic changes in adipose tissue. Finally, the key indicators were validated by experiments. In conclusion, this study described a mechanism by which the sphingolipid signalling pathway was involved in adipose tissue remodelling by inducing inflammation and fat fibrosis in cardiac cachexia.

Targeted quantitative metabolic profiling of brain-derived cell cultures by semi-automated MEPS and LC-MS/MS

The accurate characterisation of metabolic profiles is an important prerequisite to determine the rate and the efficiency of the metabolic pathways taking place in the cells. Changes in the balance of metabolites involved in vital processes such as glycolysis, tricarboxylic acid (TCA) cycle, oxidative phosphorylation (OXPHOS), as well as in the biochemical pathways related to amino acids, lipids, nucleotides, and their precursors reflect the physiological condition of the cells and may contribute to the development of various human diseases. The feasible and reliable measurement of a wide array of metabolites and biomarkers possesses great potential to elucidate physiological and pathological mechanisms, aid preclinical drug development and highlight potential therapeutic targets. An effective, straightforward, sensitive, and selective liquid chromatography-tandem mass spectrometry (LC-MS/MS) approach was developed for the simultaneous quali-quantitative analysis of 41 compounds in both cell pellet and cell growth medium obtained from brain-derived cell cultures. Sample pretreatment miniaturisation was achieved thanks to the development and optimisation of an original extraction/purification approach based on digitally programmed microextraction by packed sorbent (eVol®-MEPS). MEPS allows satisfactory and reproducible clean-up and preconcentration of both low-volume homogenate cell pellet lysate and cell growth medium with advantages including, but not limited to, minimal sample handling and method sustainability in terms of sample, solvents, and energy consumption. The MEPS-LC-MS/MS method showed good sensitivity, selectivity, linearity, and precision. As a proof of concept, the developed method was successfully applied to the analysis of both cell pellet and cell growth medium obtained from a line of mouse immortalised oligodendrocyte precursor cells (OPCs; Oli-neu cell line), leading to the unambiguous determination of all the considered target analytes. This method is thus expected to be suitable for targeted, quantitative metabolic profiling in most brain cell models, thus allowing accurate investigations on the biochemical pathways that can be altered in central nervous system (CNS) neuropathologies, including e.g., mitochondrial respiration and glycolysis, or use of specific nutrients for growth and proliferation, or lipid, amino acid and nucleotide metabolism.

Metabolipidomic Analysis in Patients with Obstructive Sleep Apnea Discloses a Circulating Metabotype of Non-Dipping Blood Pressure

A non-dipping blood pressure (BP) pattern, which is frequently present in patients with obstructive sleep apnea (OSA), confers high cardiovascular risk. The mechanisms connecting these two conditions remain unclear. In the present study we performed a comprehensive analysis of the blood metabolipidome that aims to provide new insights into the molecular link between OSA and the dysregulation of circadian BP rhythmicity. This was an observational prospective longitudinal study involving adults with suspected OSA who were subjected to full polysomnography (PSG). Patients with an apnea-hypopnea index ≥ 5 events/h were included. Fasting plasma samples were obtained the morning after PSG. Based on the dipping ratio (DR; ratio of night/day BP values) measured via 24 h ambulatory BP monitoring, two groups were established: dippers (DR ≤ 0.9) and non-dippers (DR > 0.9). Treatment recommendations for OSA followed the clinical guidelines. Untargeted metabolomic and lipidomic analyses were performed in plasma samples via liquid chromatography-tandem mass spectrometry. Non-dipper patients represented 53.7% of the cohort (88/164 patients). A set of 31 metabolic species and 13 lipidic species were differentially detected between OSA patients who present a physiologic nocturnal BP decrease and those with abnormal BP dipping. Among the 44 differentially abundant plasma compounds, 25 were putatively identified, notably glycerophospholipids, glycolipids, sterols, and fatty acid derivates. Multivariate analysis defined a specific metabotype of non-dipping BP, which showed a significant dose-response relationship with PSG parameters of OSA severity, and with BP dipping changes after 6 months of OSA treatment with continuous positive airway pressure (CPAP). Bioinformatic analyses revealed that the identified metabolipidomic profile was found to be implicated in multiple systemic biological pathways, with potential physiopathologic implications for the circadian control of BP among individuals with OSA.

Multinomial machine learning identifies independent biomarkers by integrated metabolic analysis of acute coronary syndrome

A multi-class classification model for acute coronary syndrome (ACS) remains to be constructed based on multi-fluid metabolomics. Major confounders may exert spurious effects on the relationship between metabolism and ACS. The study aims to identify an independent biomarker panel for the multiclassification of HC, UA, and AMI by integrating serum and urinary metabolomics. We performed a liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics study on 300 serum and urine samples from 44 patients with unstable angina (UA), 77 with acute myocardial infarction (AMI), and 29 healthy controls (HC). Multinomial machine learning approaches, including multinomial adaptive least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), and assessment of the confounders were applied to integrate a multi-class classification biomarker panel for HC, UA and AMI. Different metabolic landscapes were portrayed during the transition from HC to UA and then to AMI. Glycerophospholipid metabolism and arginine biosynthesis were predominant during the progression from HC to UA and then to AMI. The multiclass metabolic diagnostic model (MDM) dependent on ACS, including 2-ketobutyric acid, LysoPC(18:2(9Z,12Z)), argininosuccinic acid, and cyclic GMP, demarcated HC, UA, and AMI, providing a C-index of 0.84 (HC vs. UA), 0.98 (HC vs. AMI), and 0.89 (UA vs. AMI). The diagnostic value of MDM largely derives from the contribution of 2-ketobutyric acid, and LysoPC(18:2(9Z,12Z)) in serum. Higher 2-ketobutyric acid and cyclic GMP levels were positively correlated with ACS risk and atherosclerosis plaque burden, while LysoPC(18:2(9Z,12Z)) and argininosuccinic acid showed the reverse relationship. An independent multiclass biomarker panel for HC, UA, and AMI was constructed using the multinomial machine learning methods based on serum and urinary metabolite signatures.

Identification of Serum Biomarkers of Ischemic Stroke in a Hypertensive Population Based on Metabolomics and Lipidomics

Hypertensive individuals are at a high risk of stroke, and thus, prevention of stroke in hypertensive patients is essential. Metabolomics and lipidomics can be used to identify diagnostic biomarkers and conduct early assessments of stroke risk in hypertensive populations. In this study, serum samples were collected from 30 hypertensive ischemic stroke (IS), 30 matched hypertensive and 30 matched healthy participants. Metabolomics and lipidomics analyses were conducted via liquid chromatography-tandem mass spectrometry, and the data were analyzed using multivariate and univariate statistical methods. A random forest algorithm and binary logistic regression were used to screen the biomarkers and establish diagnostic model. We detected 21 differential metabolites and 38 differential lipids between the hypertensive IS and healthy group. Moreover, we found 18 differential metabolites and 31 differential lipids between the hypertensive IS and hypertension group. In particular, the following seven metabolites or lipids distinguished the hypertensive IS from the healthy group: 4-hydroxyphenylpyruvic acid, cafestol, phosphatidylethanolamine (PE) (18:0p/18:2), PE (16:0e/20:4), (O-acyI)-1-hydroxy fatty acid (36:3), PE (16:0p/20:3) and PE (18:1p/18:2) (rep). The following seven biomarkers distinguished the hypertensive IS from the hypertension group: diglyceride (DG) (20:1/18:2), PE (18:0p/18:2), PE (16:0e/22:5), phosphatidylcholine (40:7), dimethylphosphatidylethanolamine (50:3), DG (18:1/18:2), and 4-hydroxyphenylpyruvic acid. The aforementioned panels had good diagnostic and predictive ability for hypertensive IS. Our study determines the metabolomic and lipidomic profiles of hypertensive IS patients and thereby identifies potential biomarkers of the presence of IS in hypertensive populations.

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.

Recovery and utilization of waste filtrate from industrial biological fermentation: Development and metabolite profile of the Bacillus cereus liquid bio-fertilizer

Most fermentation waste filtrates can be used as raw materials for producing bio-fertilizers to reduce wastewater emissions and environmental pollution, but their bio-fertilizer utilization depends on the nutrients contained and their metabolized by functional microorganism. To achieve bio-fertilizer utilization of Acremonium terricola fermented waste filtrate, this study systematically explored the functional microbial species for making good use of waste liquid, optimized material process parameters for bio-fertilizer production based on D-optimal mixture design method, and analyzed the composition of the waste filtrate and its metabolism by functional microorganisms using a non-targeted LC-MS metagenomics technique. The results showed that Bacillus cereus was the functional microbial candidate for producing bio-fertilizer because of its more efficiently utilize the waste filtrate than other Bacillus sp. The optimal material process parameters of the liquid bio-fertilizer were the inoculum dose of 5% (v:v, %), 80% of waste filtrate, 0.25% of N, 3.5% of P2O5, 3.25% of K2O of mass percentage. Under these conditions, the colony forming unit (CFU) of Bacillus cereus could reach (1.59 ± 0.01) × 108 CFU/mL, which met the bio-fertilizer standard requirements of the People's Republic of China (NY/T798). Furthermore, the potential functions of bio-fertilizer were studied based on comparison of raw materials and production components: on the one hand, waste filtrate contained abundant of nitrogen and carbon sources, and bioactive substances secreted by Acremonium terricola, such as β-alanyl-L-lysine, anserine, UMP, L-lactic acid and etc., which could meet the nutrient requirements of the growth of Bacillus cereus; On the other hand, some compounds of waste filtrate with the potential to benefit the plant growth and defense, such as betaine aldehyde, (2E,6E)-farnesol, homogentisic acid and etc., were significantly up regulated by Bacillus cereus utilization of the filtrate. To sum up, this work highlighted that the waste filtrate could be efficiently developed into liquid bio-fertilizer by Bacillus cereus.

Persistent Biofluid Small-Molecule Alterations Induced by Trypanosoma cruzi Infection Are Not Restored by Parasite Elimination

Chagas disease (CD), caused by Trypanosoma cruzi (T. cruzi) protozoa, is a complicated parasitic illness with inadequate medical measures for diagnosing infection and monitoring treatment success. To address this gap, we analyzed changes in the metabolome of T. cruzi-infected mice via liquid chromatography tandem mass spectrometry of clinically accessible biofluids: saliva, urine, and plasma. Urine was the most indicative of infection status across mouse and parasite genotypes. Metabolites perturbed by infection in urine include kynurenate, acylcarnitines, and threonylcarbamoyladenosine. Based on these results, we sought to implement urine as a tool for the assessment of CD treatment success. Strikingly, it was found that mice with parasite clearance following benznidazole antiparasitic treatment had an overall urine metabolome comparable to that of mice that failed to clear parasites. These results provide a complementary hypothesis to explain clinical trial data in which benznidazole treatment did not improve patient outcomes in late-stage disease, even in patients with successful parasite clearance. Overall, this study provides insights into new small-molecule-based CD diagnostic methods and a new approach to assess functional responses to treatment.