Challenges and emergent solutions for LC-MS/MS based untargeted metabolomics in diseases

The combination of deep learning and pseudo-MS image improves the applicability of metabolomics to congenital heart defect prenatal screening

To investigate the metabolic alterations in maternal individuals with fetal congenital heart disease (FCHD), establish the FCHD diagnostic models, and assess the performance of these models, we recruited two batches of pregnant women. By metabolomics analysis using Ultra High-performance Liquid Chromatography-Mass/Mass (UPLC-MS/MS), a total of 36 significantly altered metabolites (VIP >1.0) were identified between FCHD and non-FCHD groups. Two logistic regression models and four support vector machine (SVM) models exhibited strong performance and clinical utility in the training set (area under the curve (AUC) = 1.00). The convolutional neural network (CNN) model also demonstrated commendable performance and clinical utility (AUC = 0.89 in the training set). Notably, in the validation set, the performance of the CNN model (AUC = 0.66, precision = 0.714) exhibited better robustness than the six models above (AUC≤0.50). In conclusion, the CNN model based on pseudo-MS images holds promise for real-world and clinical applications due to its better repeatability.

Tumor Metabolism: A New Field for the Treatment of Glioma

The clinical treatment of glioma remains relatively immature. Commonly used clinical treatments for gliomas are surgery combined with chemotherapy and radiotherapy, but there is a problem of drug resistance. In addition, immunotherapy and targeted therapies also suffer from the problem of immune evasion. The advent of metabolic therapy holds immense potential for advancing more efficacious and tolerable therapies against this aggressive disease. Metabolic therapy alters the metabolic processes of tumor cells at the molecular level to inhibit tumor growth and spread, and lead to better outcomes for patients with glioma that are insensitive to conventional treatments. Moreover, compared with conventional therapy, it has less impact on normal cells, less toxicity and side effects, and higher safety. The objective of this review is to examine the changes in metabolic characteristics throughout the development of glioma, enumerate the current methodologies employed for studying tumor metabolism, and highlight the metabolic reprogramming pathways of glioma along with their potential molecular mechanisms. Importantly, it seeks to elucidate potential metabolic targets for glioblastoma (GBM) therapy and summarize effective combination treatment strategies based on various studies.

Quantitative analysis of six sesquiterpene glycosides from Dendrobium nobile Lindl. under different growth conditions by high-performance liquid chromatography coupled with triple quadrupole tandem mass spectrometry in MRM mode

INTRODUCTION

The sesquiterpene glycosides (SGs) from Dendrobium nobile Lindl. have immunomodulatory effects. However, there are no studies on the growth conditions affecting its contents and quantitative analysis methods.

OBJECTIVE

In the present study, a quantitative analysis method for six SGs from D. nobile was established. We explored which growth conditions could affect the contents of SGs, providing a basis for the cultivation and clinical application of D. nobile.

METHODS

Firstly, based on the optimization of mass spectrometry parameters and extraction conditions for six SGs in D. nobile, a method for the determination of the contents of six SGs was established using high-performance liquid chromatography coupled with triple quadrupole tandem mass spectrometry (HPLC-QqQ-MS/MS) in multiple reaction monitoring (MRM) mode. Then, the methodology of the established method was validated. Secondly, the established method was applied to determine the contents of six SGs from 78 samples of D. nobile grown under different growth conditions. Finally, chemometrics analysis was employed to analyze the results and select optimal growth conditions for D. nobile.

RESULTS

The results indicated significant variations in the contents of SGs from D. nobile grown under different growth conditions. The primary factors influencing SG contents included age, geographical origin, altitude, and epiphytic pattern.

CONCLUSION

Therefore, the established method for determining SG contents from D. nobile is stable. In particular, the SG contents were relatively high in samples of 3-year-old D. nobile grown at an altitude of approximately 500 m on Danxia rocks in Chishui, Guizhou.

Optimization and validation of metabolomics methods for feline urine and serum towards application in veterinary medicine

BACKGROUND

Metabolomics is an emerging and powerful technology that offers a comprehensive view of an organism's physiological status. Although widely applied in human medicine, it is only recently making its introduction in veterinary medicine. As a result, validated metabolomics protocols in feline medicine are lacking at the moment. Since biological interpretation of metabolomics data can be misled by the extraction method used, species and matrix-specific optimized and validated metabolomic protocols are sorely needed.

RESULTS

Systematic optimization was performed using fractional factorial experiments for both serum (n = 57) and urine (n = 24), evaluating dilution for both matrices, and aliquot and solvent volume, protein precipitation time and temperature for serum. For the targeted (n = 76) and untargeted (n = 1949) validation of serum respectively, excellent instrumental, intra-assay and inter-day precision were observed (CV ≤ 15% or 30%, respectively). Linearity deemed sufficient both targeted and untargeted (R2 ≥ 0.99 or 0.90, respectively). An appropriate targeted recovery between 70 and 130% was achieved. For the targeted (n = 69) and untargeted (n = 2348) validation of the urinary protocol, excellent instrumental and intra-assay precision were obtained (CV ≤ 15% or 30%, respectively). Subsequently, the discriminative ability of our metabolomics methods was confirmed for feline chronic kidney disease (CKD) by univariate statistics (n = 41 significant metabolites for serum, and n = 55 for urine, p-value<0.05) and validated OPLS-DA models (R2(Y) > 0.95, Q2(Y) > 0.65, p-value<0.001 for both matrices).

SIGNIFICANCE

This study is the first to present an optimized and validated wholistic metabolomics methods for feline serum and urine using ultra-high performance liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry. This robust methodology opens avenues for biomarker panel selection and a deeper understanding of feline CKD pathophysiology and other feline applications.

Metabolomic discoveries for early diagnosis and traditional Chinese medicine efficacy in ischemic stroke

Ischemic stroke (IS), a devastating cerebrovascular accident, presents with high mortality and morbidity. Following IS onset, a cascade of pathological changes, including excitotoxicity, inflammatory damage, and blood-brain barrier disruption, significantly impacts prognosis. However, current clinical practices struggle with early diagnosis and identifying these alterations. Metabolomics, a powerful tool in systems biology, offers a promising avenue for uncovering early diagnostic biomarkers for IS. By analyzing dynamic metabolic profiles, metabolomics can not only aid in identifying early IS biomarkers but also evaluate Traditional Chinese Medicine (TCM) efficacy and explore its mechanisms of action in IS treatment. Animal studies demonstrate that TCM interventions modulate specific metabolite levels, potentially reflecting their therapeutic effects. Identifying relevant metabolites in cerebral ischemia patients holds immense potential for early diagnosis and improved outcomes. This review focuses on recent metabolomic discoveries of potential early diagnostic biomarkers for IS. We explore variations in metabolites observed across different ages, genders, disease severity, and stages. Additionally, the review examines how specific TCM extracts influence IS development through metabolic changes, potentially revealing their mechanisms of action. Finally, we emphasize the importance of integrating metabolomics with other omics approaches for a comprehensive understanding of IS pathophysiology and TCM efficacy, paving the way for precision medicine in IS management.

LC-MS metabolomics analysis of serum metabolites during neoadjuvant chemoradiotherapy in locally advanced rectal cancer

BACKGROUND

This study aimed to investigate the serum metabolite profiles during neoadjuvant chemoradiotherapy (NCRT) in locally advanced rectal cancer (LARC) using liquid chromatography-mass spectrometry (LC-MS) metabolomics analysis.

METHODS

60 serum samples were collected from 20 patients with LARC before, during, and after radiotherapy. LC-MS metabolomics analysis was performed to identify the metabolite variations. Functional annotation was applied to discover altered metabolic pathways. The key metabolites were screened and their ability to predict sensitivity to radiotherapy was calculated using random forests and ROC curves.

RESULTS

The results showed that NCRT led to significant changes in the serum metabolite profiles. The serum metabolic profiles showed an apparent separation between different time points and different sensitivity groups. Moreover, the functional annotation showed that the differential metabolites were associated with a series of important metabolic pathways. Pre-radiotherapy (3Z,6Z)-3,6-Nonadiena and pro-radiotherapy 1-Hydroxyibuprofen showed good predictive performance in discriminating the sensitive and non-sensitive group to NCRT, with an AUC of 0.812 and 0.75, respectively. Importantly, the combination of different metabolites significantly increased the predictive ability.

CONCLUSION

This study demonstrated the potential of LC-MS metabolomics for revealing the serum metabolite profiles during NCRT in LARC. The identified metabolites may serve as potential biomarkers and therapeutic targets for the management of this disease. Furthermore, the understanding of the affected metabolic pathways may help design more personalized therapeutic strategies for LARC patients.

Exploring machine learning for untargeted metabolomics using molecular fingerprints

BACKGROUND

Metabolomics, the study of substrates and products of cellular metabolism, offers valuable insights into an organism's state under specific conditions and has the potential to revolutionise preventive healthcare and pharmaceutical research. However, analysing large metabolomics datasets remains challenging, with available methods relying on limited and incompletely annotated metabolic pathways.

METHODS

This study, inspired by well-established methods in drug discovery, employs machine learning on metabolite fingerprints to explore the relationship of their structure with responses in experimental conditions beyond known pathways, shedding light on metabolic processes. It evaluates fingerprinting effectiveness in representing metabolites, addressing challenges like class imbalance, data sparsity, high dimensionality, duplicate structural encoding, and interpretable features. Feature importance analysis is then applied to reveal key chemical configurations affecting classification, identifying related metabolite groups.

RESULTS

The approach is tested on two datasets: one on Ataxia Telangiectasia and another on endothelial cells under low oxygen. Machine learning on molecular fingerprints predicts metabolite responses effectively, and feature importance analysis aligns with known metabolic pathways, unveiling new affected metabolite groups for further study.

CONCLUSION

In conclusion, the presented approach leverages the strengths of drug discovery to address critical issues in metabolomics research and aims to bridge the gap between these two disciplines. This work lays the foundation for future research in this direction, possibly exploring alternative structural encodings and machine learning models.

Integrative multiomics analysis identifies molecular subtypes and potential targets of hepatocellular carcinoma

BACKGROUND

The liver is anatomically divided into eight segments based on the distribution of Glisson's triad. However, the molecular mechanisms underlying each segment and its association with hepatocellular carcinoma (HCC) heterogeneity are not well understood. In this study, our objective is to conduct a comprehensive multiomics profiling of the segmentation atlas in order to investigate potential subtypes and therapeutic approaches for HCC.

METHODS

A high throughput liquid chromatography-tandem mass spectrometer strategy was employed to comprehensively analyse proteome, lipidome and metabolome data, with a focus on segment-resolved multiomics profiling. To classify HCC subtypes, the obtained data with normal reference profiling were integrated. Additionally, potential therapeutic targets for HCC were identified using immunohistochemistry assays. The effectiveness of these targets were further validated through patient-derived organoid (PDO) assays.

RESULTS

A multiomics profiling of 8536 high-confidence proteins, 1029 polar metabolites and 3381 nonredundant lipids was performed to analyse the segmentation atlas of HCC. The analysis of the data revealed that in normal adjacent tissues, the left lobe was primarily involved in energy metabolism, while the right lobe was associated with small molecule metabolism. Based on the normal reference atlas, HCC patients with segment-resolved classification were divided into three subtypes. The C1 subtype showed enrichment in ribosome biogenesis, the C2 subtype exhibited an intermediate phenotype, while the C3 subtype was closely associated with neutrophil degranulation. Furthermore, using the PDO assay, exportin 1 (XPO1) and 5-lipoxygenase (ALOX5) were identified as potential targets for the C1 and C3 subtypes, respectively.

CONCLUSION

Our extensive analysis of the segmentation atlas in multiomics profiling defines molecular subtypes of HCC and uncovers potential therapeutic strategies that have the potential to enhance the prognosis of HCC.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Unveiling the gut microbiota and metabolite profiles in guinea pigs with form deprivation myopia through 16S rRNA gene sequencing and untargeted metabolomics

Aim

The aim of this study was to confirm the presence of the form deprivation myopia (FDM) guinea pig eye-gut axis and investigate the relationship between serum vasoactive intestinal peptide (VIP), lipopolysaccharides (LPS), specific gut microbiota and their metabolites.

Method

20 specific-pathogen-free (SPF) guinea pigs were divided into the FDM and the control(Con) group. Following model induction, serum levels of VIP and LPS were quantified. A combination of 16S ribosomal ribosomal Ribonucleic Acid (rRNA) gene sequencing, non-targeted metabolomics and bioinformatics analysis were employed to identify disparities in gut microbiota and metabolites between the two groups of guinea pigs.

Result

Compared to the control group, FDM guinea pigs exhibited a significant trend towards myopia, along with significantly elevated concentrations of LPS and VIP (p < 0.0001). Furthermore, Ruminococcus_albus emerged as the predominant bacterial community enriched in FDM (p < 0.05), and demonstrated positive correlations with 10 metabolites, including l-Glutamic acid, Additionally, Ruminococcus_albus exhibited positive correlations with VIP and LPS levels (p < 0.05).

Conclusion

The findings suggest that the Ruminococcus_Albus and glutamate metabolic pathways play a significant role in myopia development, leading to concurrent alterations in serum VIP and LPS levels in FDM guinea pigs. This underscores the potential of specific gut microbiota and their metabolites as pivotal biomarkers involved in the pathogenesis of myopia.

Urban and agricultural influences on the coastal dissolved organic matter pool in the Algoa Bay estuaries

While anthropogenic pollution is a major threat to aquatic ecosystem health, our knowledge of the presence of xenobiotics in coastal Dissolved Organic Matter (DOM) is still relatively poor. This is especially true for water bodies in the Global South with limited information gained mostly from targeted studies that rely on comparison with authentic standards. In recent years, non-targeted tandem mass spectrometry has emerged as a powerful tool to collectively detect and identify pollutants and biogenic DOM components in the environment, but this approach has yet to be widely utilized for monitoring ecologically important aquatic systems. In this study we compared the DOM composition of Algoa Bay, Eastern Cape, South Africa, and its two estuaries. The Swartkops Estuary is highly urbanized and severely impacted by anthropogenic pollution, while the Sundays Estuary is impacted by commercial agriculture in its catchment. We employed solid-phase extraction followed by liquid chromatography tandem mass spectrometry to annotate more than 200 pharmaceuticals, pesticides, urban xenobiotics, and natural products based on spectral matching. The identification with authentic standards confirmed the presence of methamphetamine, carbamazepine, sulfamethoxazole, N-acetylsulfamethoxazole, imazapyr, caffeine and hexa(methoxymethyl)melamine, and allowed semi-quantitative estimations for annotated xenobiotics. The Swartkops Estuary DOM composition was strongly impacted by features annotated as urban pollutants including pharmaceuticals such as melamines and antiretrovirals. By contrast, the Sundays Estuary exhibited significant enrichment of molecules annotated as agrochemicals widely used in the citrus farming industry, with predicted concentrations for some of them exceeding predicted no-effect concentrations. This study provides new insight into anthropogenic impact on the Algoa Bay system and demonstrates the utility of non-targeted tandem mass spectrometry as a sensitive tool for assessing the health of ecologically important coastal ecosystems and will serve as a valuable foundation for strategizing long-term monitoring efforts.

Multiple-matrix metabolomics analysis for the distinct detection of colorectal cancer and adenoma

OBJECTIVES

Although colorectal cancer (CRC) is the leading cause of cancer-related morbidity and mortality, current diagnostic tests for early-stage CRC and colorectal adenoma (CRA) are suboptimal. Therefore, there is an urgent need to explore less invasive screening procedures for CRC and CRA diagnosis.

METHODS

Untargeted gas chromatography-mass spectrometry (GC-MS) metabolic profiling approach was applied to identify candidate metabolites. We performed metabolomics profiling on plasma samples from 412 subjects including 200 CRC patients, 160 CRA patients and 52 normal controls (NC). Among these patients, 45 CRC patients, 152 CRA patients and 50 normal controls had their fecal samples tested simultaneously.

RESULTS

Differential metabolites were screened in the adenoma-carcinoma sequence. Three diagnostic models were further developed to identify cancer group, cancer stage, and cancer microsatellite status using those significant metabolites. The three-metabolite-only classifiers used to distinguish the cancer group always keeps the area under the receiver operating characteristic curve (AUC) greater than 0.7. The AUC performance of the classifiers applied to discriminate CRC stage is generally greater than 0.8, and the classifiers used to distinguish microsatellite status of CRC is greater than 0.9.

CONCLUSION

This finding highlights potential early-driver metabolites in CRA and early-stage CRC. We also find potential metabolic markers for discriminating the microsatellite state of CRC. Our study and diagnostic model have potential applications for non-invasive CRC and CRA detection.

Comprehensive metabolomics analysis reveals novel biomarkers and pathways in falsely suspected glutaric aciduria Type-1 newborns

BACKGROUND

Glutaric aciduria type-1 (GA-1) is a rare metabolic disorder due to glutaryl coenzyme A dehydrogenase deficiency, causing elevated levels of glutaryl-CoA and its derivatives. GA-1 exhibits symptoms like macrocephaly, developmental delays, and movement disorders. Timely diagnosis through genetic testing and newborn screening is crucial. However, in some cases, transiently elevated level of glutarylcarnitine (C5DC) challenges accurate diagnosis, highlighting the need for alternative diagnostic methods, like mass spectrometry-based untargeted metabolomics, to identify additional biomarkers for distinguishing falsely suspected GA-1 from healthy newborns.

METHODOLOGY

DBS samples from falsely suspected GA-1 newborns (n = 47) and matched control were collected through the NBS program. Untargeted metabolomics using liquid chromatography-high-resolution mass spectrometry (LC-HRMS) was performed to enable biomarker and pathway investigations for significantly altered metabolites.

RESULTS

582 and 546 were up- and down-regulated metabolites in transient GA-1. 155 endogenous metabolites displayed significant variations compared to the control group. Furthermore, our data identified novel altered metabolic biomarkers, such as N-palmitoylcysteine, heptacarboxyporphyrin, 3-hydroxylinoleoylcarnitine, and monoacylglyceride (MG) (0:0/20:1/0:0), along with perturbed metabolic pathways like sphingolipid and thiamine metabolism associated with the transient elevated C5DC levels in DBS samples.

CONCLUSIONS

A distinct metabolic pattern linked to the transient C5DC elevation in newborns was reported to enhance the prediction of the falsely positive cases, which could help avoiding unnecessary medical treatments and minimizing the financial burdens in the health sector.

Challenges in the Metabolomics-Based Biomarker Validation Pipeline

As end-products of the intersection between the genome and environmental influences, metabolites represent a promising approach to the discovery of novel biomarkers for diseases. However, many potential biomarker candidates identified by metabolomics studies fail to progress beyond analytical validation for routine implementation in clinics. Awareness of the challenges present can facilitate the development and advancement of innovative strategies that allow improved and more efficient applications of metabolite-based markers in clinical settings. This minireview provides a comprehensive summary of the pre-analytical factors, required analytical validation studies, and kit development challenges that must be resolved before the successful translation of novel metabolite biomarkers originating from research. We discuss the necessity for strict protocols for sample collection, storage, and the regulatory requirements to be fulfilled for a bioanalytical method to be considered as analytically validated. We focus especially on the blood as a biological matrix and liquid chromatography coupled with tandem mass spectrometry as the analytical platform for biomarker validation. Furthermore, we examine the challenges of developing a commercially viable metabolomics kit for distribution. To bridge the gap between the research lab and clinical implementation and utility of relevant metabolites, the understanding of the translational challenges for a biomarker panel is crucial for more efficient development of metabolomics-based precision medicine.

LC-MS/DIA-based strategy for comprehensive flavonoid profiling: an Ocotea spp. applicability case

We introduce a liquid chromatography - mass spectrometry with data-independent acquisition (LC-MS/DIA)-based strategy, specifically tailored to achieve comprehensive and reliable glycosylated flavonoid profiling. This approach facilitates in-depth and simultaneous exploration of all detected precursors and fragments during data processing, employing the widely-used open-source MZmine 3 software. It was applied to a dataset of six Ocotea plant species. This framework suggested 49 flavonoids potentially newly described for these plant species, alongside 45 known features within the genus. Flavonols kaempferol and quercetin, both exhibiting O-glycosylation patterns, were particularly prevalent. Gas-phase fragmentation reactions further supported these findings. For the first time, the apigenin flavone backbone was also annotated in most of the examined Ocotea species. Apigenin derivatives were found mainly in the C-glycoside form, with O. porosa displaying the highest flavone : flavonol ratio. The approach also allowed an unprecedented detection of kaempferol and quercetin in O. porosa species, and it has underscored the untapped potential of LC-MS/DIA data for broad and reliable flavonoid profiling. Our study annotated more than 50 flavonoid backbones in each species, surpassing the current literature.

Synergistic mechanism of stir-baked curcumae radix with vinegar in dysmenorrhea rats based on UPLC-Q-TOF/MS metabolomics

Curcumae Radix (i.e. Huangsiyujin: HSYJ), a well-known traditional Chinese medicine (TCM), has been widely used in clinical practice for many years to treat depression and primary dysmenorrhea. Modern pharmacological researches have demonstrated its anti-inflammatory, antidepressant, and dysmenorrhea relief effects. According to the processing theory of TCM, it is believed that stir-baked HSYJ with vinegar may enhance the ability to disperse stagnant hepatoqi and alleviate pain. However, whether the vinegar concoction of HSYJ can enhance the therapeutic effect on the Qi stagnation due to liver depression (LDQS) type of dysmenorrhea and what its mechanism has not been well explained. Based on the processing drugs theory of "stir-baked with vinegar into liver", a metabolomic approach was used to investigate the therapeutic effect and mechanism of stir-baked HSYJ with vinegar to enhance the treatment of dysmenorrhea in rats. By establishing a rat model of dysmenorrhea of the "LDQS" type, observation of hemorheology, uterine pathological sections, COX-2 and OTR protein expression and other indicators; analysis of urinary metabolic changes in rats by UPLC-Q-TOF-MS technique, to compare the differential biomarkers and metabolic pathways in the treatment of dysmenorrhea due to "liver stagnation and qi stagnation" before and after stir-baked HSYJ with vinegar. Stir-baked HSYJ with vinegar significantly inhibited the writhing response of rats, improved hemorheology, repaired damaged diseased uterus and inhibited high expression of COX-2 and OTR proteins in uterus; 68 differential metabolites were screened from the urine of rats, compared with the raw HSYJ, the levels of 14 metabolites were significantly changed in stir-baked HSYJ with vinegar, involving the pathways of phenylalanine, tyrosine and tryptophan metabolism, cysteine and methionine metabolism, aspartate and glutamate metabolism. The potentiating effect of stir-baked HSYJ with vinegar may be related to the regulation of multiple amino acid metabolic pathways.

UPLC-Q-TOF/MS based serum and urine metabolomics strategy to analyze the mechanism of nervonic acid in treating Alzheimer's disease

Nervonic acid is a natural component of breast milk and is frequently used as a food additive due to its excellent neuroprotective effects. Although it has been reported that nervonic acid may play a role in the recovery of human cognitive impairment, its specific mechanism of action is still unclear. In this study, the results of serum biochemical indexes showed that nervonic acid improved inflammation and reduced amyloid β peptide (Aβ) deposition and tau protein phosphorylation in Alzheimer's disease (AD) rats. Subsequently, we further used a metabolomics approach to investigate the potential mechanism of action of nervonic acid in the treatment of AD. The results of serum and urine metabolomics study showed that the intervention of nervonic acid significantly reversed the metabolic profile disorder in AD rats. A total of 52 metabolites were identified. They mainly involved linoleic acid metabolism, alpha-linolenic acid metabolism, phenylalanine metabolism and arachidonic acid metabolism, and all these metabolic pathways were associated with the emergence of inflammation in vivo. It suggests that the therapeutic effect of nervonic acid on AD is likely to be produced by ameliorating inflammation. The results obtained in this study provide new insights into the mechanism of nervonic acid treatment of AD and lay a foundation for the clinical application of nervonic acid in the treatment of Alzheimer's disease.

LC-MS based untargeted metabolomics studies of the metabolic response of Ginkgo biloba extract on arsenism patients

Arsenic is an environmentally ubiquitous toxic metalloid. Chronic exposure to arsenic may lead to arsenicosis, while no specific therapeutic strategies are available for the arsenism patients. And Ginkgo biloba extract (GBE) exhibited protective effect in our previous study. However, the mechanisms by which GBE protects the arsenism patients remain poorly understood. A liquid chromatography-mass spectrometry (LC-MS) based untargeted metabolomics analysis was used to study metabolic response in arsenism patients upon GBE intervention. In total, 39 coal-burning type of arsenism patients and 50 healthy residents were enrolled from Guizhou province of China. The intervention group (n = 39) were arsenism patients orally administered with GBE (three times per day) for continuous 90 days. Plasma samples from 50 healthy controls (HC) and 39 arsenism patients before and after GBE intervention were collected and analyzed by established LC-MS method. Statistical analysis was performed by MetaboAnalyst 5.0 to identify differential metabolites. Multivariate analysis revealed a separation in arsenism patients between before (BG) and after GBE intervention (AG) group. It was observed that 35 differential metabolites were identified between BG and AG group, and 30 of them were completely or partially reversed by GBE intervention, with 14 differential metabolites significantly up-regulated and 16 differential metabolites considerably down-regulated. These metabolites were involved in promoting immune response and anti-inflammatory functions, and alleviating oxidative stress. Taken together, these findings indicate that the GBE intervention could probably exert its protective effects by reversing disordered metabolites modulating these functions in arsenism patients, and provide insights into further exploration of mechanistic studies.

High-throughput omics technologies in inflammatory bowel disease

Inflammatory bowel disease (IBD) is a chronic, relapsing intestinal disease. Elucidation of the pathogenic mechanisms of IBD requires high-throughput technologies (HTTs) to effectively obtain and analyze large amounts of data. Recently, HTTs have been widely used in IBD, including genomics, transcriptomics, proteomics, microbiomics, metabolomics and single-cell sequencing. When combined with endoscopy, the application of these technologies can provide an in-depth understanding on the alterations of intestinal microbe diversity and abundance, the abnormalities of signaling pathway-mediated immune responses and functionality, and the evaluation of therapeutic effects, improving the accuracy of early diagnosis and treatment of IBD. This review comprehensively summarizes the development and advancement of HTTs, and also highlights the challenges and future directions of these technologies in IBD research. Although HTTs have made striking breakthrough in IBD, more standardized methods and large-scale dataset processing are still needed to achieve the goal of personalized medicine.

Metabolite profiling and antioxidant capacity of natural Ophiocordyceps gracilis and its cultures using LC-MS/MS-based metabolomics: Comparison with Ophiocordyceps sinensis

INTRODUCTION

Ophiocordyceps gracilis is an entomopathogenic fungus and a precious traditional Chinese medicine with similar medicinal properties to Ophiocordyceps sinensis. However, information on the metabolite profiles of natural O. gracilis and its cultures is lacking, which limits their utilization.

OBJECTIVE

The metabolic variations and antioxidant activities of O. gracilis cultures and natural O. gracilis were analyzed to evaluate the nutritional and medicinal value of O. gracilis and its cultures.

METHOD

The metabolite profiles of O. gracilis cultures (fruiting bodies and aerial mycelia), natural O. gracilis, and natural O. sinensis were compared by LC-MS/MS coupled with multivariate data analysis. Furthermore, their antioxidant activities were evaluated based on their DPPH• , ABTS•+ , and • OH scavenging abilities.

RESULTS

A total of 612 metabolites were identified, and the metabolic compositions of the four Cordyceps samples were similar, with differences observed in the levels of some metabolites. There were 126 differential metabolites between natural O. gracilis and natural O. sinensis, among which fatty acids, carbohydrates, and secondary metabolites are predominant in natural O. gracilis. Furthermore, 116 differential metabolites between O. gracilis cultures and natural Cordyceps were identified, with generally higher levels in O. gracilis cultures than in natural Cordyceps. O. gracilis cultivated fruiting bodies exhibited the strongest antioxidant capacity among Cordyceps samples. Additionally, 46 primary and 24 secondary differential metabolites contribute to antioxidant activities.

CONCLUSION

This study provides a reference for the application of natural O. gracilis and its cultures in functional food and medicine from the perspective of metabolites and antioxidant capacity.

Gut microbiota and metabolic biomarkers in metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD), a replacement of the nomenclature employed for NAFLD, is the most prevalent chronic liver disease worldwide. Despite its high global prevalence, NAFLD is often under-recognized due to the absence of reliable noninvasive biomarkers for diagnosis and staging. Growing evidence suggests that the gut microbiome plays a significant role in the occurrence and progression of NAFLD by causing immune dysregulation and metabolic alterations due to gut dysbiosis. The rapid advancement of sequencing tools and metabolomics has enabled the identification of alterations in microbiome signatures and gut microbiota-derived metabolite profiles in numerous clinical studies related to NAFLD. Overall, these studies have shown a decrease in α-diversity and changes in gut microbiota abundance, characterized by increased levels of Escherichia and Prevotella, and decreased levels of Akkermansia muciniphila and Faecalibacterium in patients with NAFLD. Furthermore, bile acids, short-chain fatty acids, trimethylamine N-oxide, and tryptophan metabolites are believed to be closely associated with the onset and progression of NAFLD. In this review, we provide novel insights into the vital role of gut microbiome in the pathogenesis of NAFLD. Specifically, we summarize the major classes of gut microbiota and metabolic biomarkers in NAFLD, thereby highlighting the links between specific bacterial species and certain gut microbiota-derived metabolites in patients with NAFLD.

An inulin-type fructan CP-A from Codonopsis pilosula alleviates TNBS-induced ulcerative colitis based on serum-untargeted metabolomics

Ulcerative colitis (UC) is an inflammatory disease with abdominal pain, diarrhea, and bloody stool as the main symptoms. Several studies have confirmed that polysaccharides are effective against UC. It is commonly accepted that the traditional benefits of Radix Codonopsis can be attributed to its polysaccharide contents, and inulin-type fructan CP-A is the main active monomer in the polysaccharide components. Herein, we established a 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced UC rat model and lipopolysaccharide (LPS)-induced colonic epithelial cell model (NCM460) to investigate the effect of CP-A on UC. Untargeted metabolomics studies were conducted to identify differential metabolites using ultra-high performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UHPLC-Q-TOF/MS) and enrich metabolic pathways in rat serum. The in vivo assays demonstrated that CP-A reduces colonic macroscopic injury, disease activity index (DAI), histopathological score, interleukin (IL)-8, and tumor necrosis factor-α (TNF-α) levels, as well as the expression of intercellular adhesion molecules. On the other hand, CP-A increases IL-10 and transforming growth factor-β (TGF-β) levels. The in vitro experiments indicated that CP-A treatment could reduce nitric oxide (NO) and IL-1β after LPS stimulation. The metabolomics results suggested that CP-A therapy for UC may be related to the mammalian target of rapamycin (mTOR) signaling pathway. The in vitro and in vivo validation of the pathway showed similar results, indicating that CP-A alleviates UC by preventing the activation of mTOR/p70S6K signaling pathway. These findings offer a fresh approach to treating UC and a theoretical foundation for the future advancement of CP-A.NEW & NOTEWORTHY We report that an inulin-type fructan from Codonopsis pilosula CP-A exhibits a therapeutic effect on experimental colitis. Its mechanism may be to alleviate intestinal inflammation by preventing the activation of mammalian target of rapamycin (mTOR)/p70S6K signaling pathway. These findings offer a fresh approach to treating ulcerative colitis (UC) and a theoretical foundation for the future advancement of CP-A.

Environmental chemical TCPOBOP exposure alters milk liposomes and offspring growth trajectories in mice

Exposure to environmental endocrine disruptors (EEDs) has become a global health concern, and EEDs are known to be potent inducers of constitutive androstane receptor (CAR). 1,4-bis [2-(3,5-dichloropyridyloxy)] benzene (TCPOBOP, hereafter abbreviated as TC), a specific ligand for CAR, has been considered as a potential EED. Here, we analyzed the effect of TC exposure to female mice on the histological morphology of their alveoli in the basic unit of lactation. We quantified differences in the milk metabolome of the control and TC-exposed group while assessing the correlations between metabolites and neonatal growth. Mammary histological results showed that TC exposure inhibited alveolar development. Based on the milk metabolomic data, we identified a total of 1505 differential metabolites in both the positive and negative ion mode, which indicated that TC exposure affected milk composition. As expected, the differential metabolites were significantly enriched in the drug metabolism pathway. Further analyses revealed that differential metabolites were significantly enriched in multiple lipid metabolic pathways, such as fatty acid biosynthesis, suggesting that most differential metabolites were concentrated in lipids. Simultaneously, a quantitative analysis showed that TC exposure led to a decrease in the relative abundance of total milk lipids, affecting the proportion of some lipid subclasses. Notably, a portion of lipid metabolites were associated with neonatal growth. Taken together, these findings suggest that TC exposure may affect milk lipidomes, resulting in the inability of mothers to provide adequate nutrients, ultimately affecting the growth and health of their offspring.

Harnessing Schistosoma-associated metabolite changes in the human host to identify biomarkers of infection and morbidity: Where are we and what should we do next?

Schistosomiasis is the second most widespread parasitic disease affecting humans. A key component of today's infection control measures is the diagnosis and monitoring of infection, informing individual- and community-level treatment. However, newly acquired infections and/or low parasite burden are still difficult to diagnose reliably. Furthermore, even though the pathological consequence of schistosome egg sequestration in host tissues is well described, the evidence linking egg burden to morbidity is increasingly challenged, making it inadequate for pathology monitoring. In the last decades, omics-based instruments and methods have been developed, adjusted, and applied in parasitic research. In particular, the profiling of the most reliable determinants of phenotypes, metabolites by metabolomics, emerged as a powerful boost in the understanding of basic interactions within the human host during infection. As such, the fine detection of host metabolites produced upon exposure to parasites such as Schistosoma spp. and the ensuing progression of the disease are believed to enable the identification of Schistosoma spp. potential biomarkers of infection and associated pathology. However, attempts to provide such a comprehensive understanding of the alterations of the human metabolome during schistosomiasis are rare, limited in their design when performed, and mostly inconclusive. In this review, we aimed to briefly summarize the most robust advances in knowledge on the changes in host metabolic profile during Schistosoma infections and provide recommendations for approaches to optimize the identification of metabolomic signatures of human schistosomiasis.

User-friendly relative quantification procedure for gas chromatography/mass spectrometry-based plasma metabolome analysis

RATIONALE

Recently, metabolome analysis has been applied to a variety of research fields, but differences between batches or facilities can cause discrepancies in the results of such analyses. To resolve these issues using comprehensive metabolome analysis, in which it is difficult to perform quantitative analyses of all detected metabolites, internal standard compounds are used to obtain relative metabolite levels. This study investigated gas chromatography/mass spectrometry-based plasma metabolome analysis methods that are superior to relative quantification using internal standard compounds.

METHODS

In experiment I, four analyses were performed under different analytical conditions at one facility, and then the data from the four analyses were compared. In experiment II, the same samples were analyzed at three facilities, and then the data from the three facilities were compared.

RESULTS

Regarding the relative values obtained through comparisons with the internal standard compound, differences in the analytical results were observed among the four analytical conditions in experiment I and among the three facilities in experiment II, and the differences observed among the three facilities (experiment II) were larger. When correction was performed using plasma as a quality control, which is the procedure suggested in this study, these differences were markedly ameliorated.

CONCLUSION

The suggested procedure involves the analysis of a plasma standard as a quality control for each batch and the calculation of relative target plasma to quality-control plasma values for each metabolite. This is an easy and low-cost method and could be readily employed by researchers during comprehensive plasma metabolome analysis.

Untargeted Plasma Metabolomic Profiling in Patients with Depressive Disorders: A Preliminary Study

Depressive disorder is a multifactorial disease that is based on dysfunctions in mental and biological processes. The search for biomarkers can improve its diagnosis, personalize therapy, and lead to a deep understanding of the biochemical processes underlying depression. The purpose of this work was a metabolomic analysis of blood serum to classify patients with depressive disorders and healthy individuals using Compound Discoverer software. Using high-resolution mass spectrometry, blood plasma samples from 60 people were analyzed, of which 30 were included in a comparison group (healthy donors), and 30 were patients with a depressive episode (F32.11) and recurrent depressive disorder (F33.11). Differences between patient and control groups were identified using the built-in utilities in Compound Discoverer software. Compounds were identified by their accurate mass and fragment patterns using the mzCloud database and tentatively identified by their exact mass using the ChemSpider search engine and the KEGG, ChEBI, FDA UNII-NLM, Human Metabolome and LipidMAPS databases. We identified 18 metabolites that could divide patients with depressive disorders from healthy donors. Of these, only two compounds were tentatively identified using the mzCloud database (betaine and piperine) based on their fragmentation spectra. For three compounds ((4S,5S,8S,10R)-4,5,8-trihydroxy-10-methyl-3,4,5,8,9,10-hexahydro-2H-oxecin-2-one, (2E,4E)-N-(2-hydroxy-2-methylpropyl)-2,4-tetradecadienamide and 17α-methyl-androstan-3-hydroxyimine-17β-ol), matches were found in the mzCloud database but with low score, which could not serve as reliable evidence of their structure. Another 13 compounds were identified by their exact mass in the ChemSpider database, 9 (g-butyrobetaine, 6-diazonio-5-oxo-L-norleucine, 11-aminoundecanoic acid, methyl N-acetyl-2-diazonionorleucinate, glycyl-glycyl-argininal, dilaurylmethylamine, 12-ketodeoxycholic acid, dicetylamine, 1-linoleoyl-2-hydroxy-sn-glycero-3-PC) had only molecular formulas proposed, and 4 were unidentified. Thus, the use of Compound Discoverer software alone was not sufficient to identify all revealed metabolites. Nevertheless, the combination of the found metabolites made it possible to divide patients with depressive disorders from healthy donors.

Mapping the metabolic responses to oxaliplatin-based chemotherapy with in vivo spatiotemporal metabolomics

Adjuvant chemotherapy improves the survival outlook for patients undergoing operations for lung metastases caused by colorectal cancer (CRC). However, a multidisciplinary approach that evaluates several factors related to patient and tumor characteristics is necessary for managing chemotherapy treatment in metastatic CRC patients with lung disease, as such factors dictate the timing and drug regimen, which may affect treatment response and prognosis. In this study, we explore the potential of spatial metabolomics for evaluating metabolic phenotypes and therapy outcomes during the local delivery of the anticancer drug, oxaliplatin, to the lung. 12 male Yorkshire pigs underwent a 3 h left lung in vivo lung perfusion (IVLP) with various doses of oxaliplatin (7.5, 10, 20, 40, and 80 mg/L), which were administered to the perfusion circuit reservoir as a bolus. Biocompatible solid-phase microextraction (SPME) microprobes were combined with global metabolite profiling to obtain spatiotemporal information about the activity of the drug, determine toxic doses that exceed therapeutic efficacy, and conduct a mechanistic exploration of associated lung injury. Mild and subclinical lung injury was observed at 40 mg/L of oxaliplatin, and significant compromise of the hemodynamic lung function was found at 80 mg/L. This result was associated with massive alterations in metabolic patterns of lung tissue and perfusate, resulting in a total of 139 discriminant compounds. Uncontrolled inflammatory response, abnormalities in energy metabolism, and mitochondrial dysfunction next to accelerated kynurenine and aldosterone production were recognized as distinct features of dysregulated metabolipidome. Spatial pharmacometabolomics may be a promising tool for identifying pathological responses to chemotherapy.

A novel endogenous retention-index for minimizing retention-time variations in metabolomic analysis with reversed-phase ultrahigh-performance liquid-chromatography and mass spectrometry

Consistent retention time (tR) of metabolites is vital for identification in metabolomic analysis with ultrahigh-performance liquid-chromatography (UPLC). To minimize inter-experimental tR variations from the reversed-phase UPLC-MS, we developed an endogenous retention-index (endoRI) using in-sample straight-chain acylcarnitines with different chain-length (LC, C0-C26) without additives. The endoRI-corrections reduced the tR variations caused by the combined changes of mobile phases, gradients, flow-rates, elution time, columns and temperature from up to 5.1 min-0.2 min for most metabolites in a model metabolome consisting of 91 metabolites and multiple biological matrices including human serum, plasma, fecal, urine, A549 cells and rabbit liver extracts. The endoRI-corrections also reduced the inter-batch and inter-platform tR variations from 1.5 min to 0.15 min for 95 % of detected features in the above biological samples. We further established a quantitative model between tR and LC for predicting tR values of acylcarnitines when absent in samples. This makes it possible to compare metabolites' tR from different tR databases and the UPLC-based metabolomic data from different batches.

Metabolomic Changes in Rat Serum after Chronic Exposure to Glyphosate-Based Herbicide

Glyphosate-based herbicides (GBHs) have gained extensive popularity in recent decades. For many years, glyphosate has been regarded as harmless or minimally toxic to mammals due to the absence of its primary target, the shikimic acid pathway in humans. Nonetheless, mounting evidence suggests that glyphosate may cause adverse health effects in humans via other mechanisms. In this study, we described the metabolomic changes in the serum of experimental rats exposed to chronic GBH using the highly sensitive LC-MS/MS technique. We investigated the possible relationship between chronic exposure to GBH and neurological disorders. Our findings suggest that chronic exposure to GBH can alter spatial learning memory and the expression of some important metabolites that are linked to neurophysiological disorders in young rats, with the female rats showing higher susceptibility compared to the males. This indicates that female rats are more likely to show early symptoms of the disorder on exposure to chronic GBH compared to male rats. We observed that four important metabolites (paraxanthine, epinephrine, L-(+)-arginine, and D-arginine) showed significant changes and involvement in neurological changes as suggested by ingenuity pathway analysis. In conclusion, our results indicate that chronic exposure to GBH can increase the risk of developing neurological disorders.

Altered metabolic profiles and targets relevant to the protective effect of acteoside on diabetic nephropathy in db/db mice based on metabolomics and network pharmacology studies

ETHNOPHARMACOLOGICAL RELEVANCE

Diabetic nephropathy (DN) was a major cause of end-stage renal failure and a common microvascular complication in patients with diabetes mellitus (DM). Acteoside (ACT) was the main ingredient extracted from the leaves of Rehmannia glutinosa, which had the functions of entering the lung, moisturizing the skin and relieving itching, nourishing yin and tonifying the kidney, cooling blood, and stopping bleeding. ACT had attracted worldwide interest because of its therapeutic effects on DM and its complications.

AIM OF THE STUDY

To clarify the metabolic profiles and targets of ACT in db/db mice based on metabolomics and network pharmacology studies.

MATERIALS AND METHODS

Db/db mice were used to observe the biochemical indices and histopathological changes in the kidney to evaluate the pharmacological effects of ACT on DN. Untargeted metabolomics studies were performed to investigate by UHPLC-LTQ-Orbitrap MS on urine, serum, and kidney samples. The key targets and pathways were analyzed by network pharmacology. For the pathways enriched by untargeted metabolomics, targeted metabolomics by UHPLC-QQQ-MS/MS was performed in kidney samples for validation. Sensitive biomarkers in kidney samples were evaluated. The effect of ACT on the improvement of DN from the perspective of metabolism of small molecules in vivo was described.

RESULTS

ACT could delay the progression of DN and improve the degree of histopathological damage to the kidney. The pathways were focused on amino acid metabolism by untargeted metabolomics. Through network pharmacology analysis, the effect pathways were related to signal transduction, carbohydrate, lipid, amino acid metabolism and mainly affected the endocrine and immune systems. Amino acid metabolism was disturbed in the kidney of db/db mice, which could be callback by ACT, such as tryptophan, glutamine, cysteine, leucine, threonine, proline, phenylalanine, histidine, serine, arginine, asparagine by targeted metabolomics.

CONCLUSIONS

In conclusion, this study provided strong support for ACT on DN treatment in clinics. Meanwhile, the Rehmannia glutinosa was used fully to raise the income level of farmers economically, while achieving the social benefit of empowering rural revitalization.

Exploring biomarkers associated with severity of knee osteoarthritis in Southern China using widely targeted metabolomics

BACKGROUND

Metabolomics is a tool to study the pathogenesis of diseases and their associated metabolites, but there are still insufficient metabolomic studies on severe knee osteoarthritis.To investigate the differences in serum metabolites between healthy populations and knee osteoarthritis (KOA) patients in Southern China using widely targeted metabolomics, and to explore biomarkers and their metabolic pathways that could be associated with the severity of KOA.

METHODS

There were 10 healthy individuals in the control group and 32 patients with KOA. According to the Kellgren-Lawrence (KL) grading system, KOA was further divided into mild (n = 13, KL grade 1 and 2) and severe (n = 19, KL grade 3 and 4). Serum samples from all participants were collected and analyzed metabolomics based on ultra-performance liquid chromatography/electrospray ionization/tandem mass spectrometry. We screened for differential metabolites between patients and controls, and between mild and severe KOA. We explored the metabolic pathways involved in differential metabolism using the Kyoto Encyclopedia of Genes and Genomes database.

RESULTS

Sixty-one metabolites were differentially expressed in the sera of the patient group compared with the control group (45 upregulated and 16 downregulated). Analysis of the mild and severe KOA groups showed a total of 12 differential metabolites. Receiver operating characteristic curve analysis showed N-alpha-acetyl-L-asparagine was a good predictor of advanced osteoarthritis(OA).Differential metabolites are enriched in multiple pathways such as arachidonic acid metabolism.

CONCLUSION

Widely targeted metabolomics found that upregulation of the amino acid metabolite N-α-acetyl-L-asparagine was significantly associated with severe KOA and could be a biomarker for predicting severity of KOA. Arachidonic acid metabolism may play an important role in patients with severe KOA.

Corneal metabolic biomarkers for moderate and high myopia in human

This study aimed to identify the corneal metabolic biomarkers for moderate and high myopia in human. We enrolled 221 eyes from 221 subjects with myopia to perform the femtosecond laser small incision lenticule extraction (SMILE) surgery. Among these, 71 eyes of 71 subjects were enrolled in the low myopic group, 75 eyes of 75 subjects in the moderate myopic group and 75 eyes of 75 subjects in the high myopic group. The untargeted metabolomics analysis was performed to analyze the corneal tissues extracted during the SMILE surgery using an ultra-high-performance liquid chromatography (UHPLC) coupled to a quadrupole time-of-flight (Q-TOF) mass spectrometry (MS). The one-way analysis of variance (ANOVA) was used to identify the different metabolites among the three myopic groups, the orthogonal partial least-squares discriminant analysis (OPLS-DA) model was used to reveal the different metabolites between moderate myopia and low myopia, and between high myopia and low myopia. The Venn gram was used to find the overlapped metabolites of the three datasets of the different metabolites. The stepwise multiple linear regression analysis was used to determine the metabolic molecules associated with manifest refractive spherical equivalents (MRSE). The Receiver Operating Characteristics (ROC) analysis was performed to reveal the corneal biomarkers for moderate and high myopia. The hub biomarker was further selected by the networks among different metabolites created by the Cytoscape software. A total of 1594 metabolites were identified in myopic corneas. 321 metabolites were different among the three myopic groups, 106 metabolites were different between high myopic corneas and low myopic corneas, 104 metabolites were different between moderate myopic corneas and low myopic corneas, and 30 metabolic molecules overlapped among the three datasets. The multivariate linear regression analysis revealed the myopic degree was significantly influenced by the corneal levels of azelaic acid, arginine-proline (Arg-Pro), 1-stearoyl-2-myristoyl-sn-glycero-3-phosphocholine, and hypoxanthine. The ROC curve analysis showed that azelaic acid, Arg-Pro and hypoxanthine were effective in discriminating low myopia from moderate to high myopia with the area under the curve (AUC) values as 0.982, 0.991 and 0.982 for azelaic acid, Arg-Pro and hypoxanthine respectively. The network analysis suggested that Arg-Pro had the maximum connections among these three biomarkers. Thus, this study identified azelaic acid, Arg-Pro and hypoxanthine as corneal biomarkers to discriminate low myopia from moderate to high myopia, with Arg-Pro serving as the hub biomarker for moderate and high myopia.

Opportunities and challenges for sample preparation and enrichment in mass spectrometry for single-cell metabolomics

Single-cell heterogeneity in metabolism, drug resistance and disease type poses the need for analytical techniques for single-cell analysis. As the metabolome provides the closest view of the status quo in the cell, studying the metabolome at single-cell resolution may unravel said heterogeneity. A challenge in single-cell metabolome analysis is that metabolites cannot be amplified, so one needs to deal with picolitre volumes and a wide range of analyte concentrations. Due to high sensitivity and resolution, MS is preferred in single-cell metabolomics. Large numbers of cells need to be analysed for proper statistics; this requires high-throughput analysis, and hence automation of the analytical workflow. Significant advances in (micro)sampling methods, CE and ion mobility spectrometry have been made, some of which have been applied in high-throughput analyses. Microfluidics has enabled an automation of cell picking and metabolite extraction; image recognition has enabled automated cell identification. Many techniques have been used for data analysis, varying from conventional techniques to novel combinations of advanced chemometric approaches. Steps have been set in making data more findable, accessible, interoperable and reusable, but significant opportunities for improvement remain. Herein, advances in single-cell analysis workflows and data analysis are discussed, and recommendations are made based on the experimental goal.

The Kynurenine/Tryptophan Ratio as a Promising Metabolomic Biomarker for Diagnosing the Spectrum of Tuberculosis Infection and Disease

The metabolic system and immunology used to be seen as distinct fields of study. Recent developments in our understanding of how the immune system operates in health and disease have connected these fields to complex systems. An effective technique for identifying probable abnormalities of metabolic homeostasis brought on by disease is metabolomics, which is defined as the thorough study of small molecule metabolic intermediates within a biological system that collectively make up the metabolome. A prognostic metabolic biomarker with adequate prognostic accuracy for tuberculosis progression has recently been created. The rate-limiting host enzyme for the conversion of tryptophan to kynurenine, indoleamine 2,3-dioxygenase (IDO), is greatly elevated in the lungs of tuberculosis disease patients. Targeted study on tryptophan in tuberculosis disease indicates that such decreases may also resembled this upregulation. Although tuberculosis diagnosis has improved with the use of interferon release assay and tuberculosis nucleic acid amplification, tuberculosis control is made difficult by the lack of a biomarker to diagnose active tuberculosis disease. We hope that the reader of this work can develop an understanding of the advantages of metabolomics testing, particularly as a sort of testing that can be used for both diagnosing and monitoring a patient's response to treatment for tuberculosis.

Exploring Human Metabolome after Wine Intake-A Review

Wine has a rich history dating back to 2200 BC, originally recognized for its medicinal properties. Today, with the aid of advanced technologies like metabolomics and sophisticated analytical techniques, we have gained remarkable insights into the molecular-level changes induced by wine consumption in the human organism. This review embarks on a comprehensive exploration of the alterations in human metabolome associated with wine consumption. A great number of 51 studies from the last 25 years were reviewed; these studies systematically investigated shifts in metabolic profiles within blood, urine, and feces samples, encompassing both short-term and long-term studies of the consumption of wine and wine derivatives. Significant metabolic alterations were observed in a wide variety of metabolites belonging to different compound classes, such as phenolic compounds, lipids, organic acids, and amino acids, among others. Within these classes, both endogenous metabolites as well as diet-related metabolites that exhibited up-regulation or down-regulation following wine consumption were included. The up-regulation of short-chain fatty acids and the down-regulation of sphingomyelins after wine intake, as well as the up-regulation of gut microbial fermentation metabolites like vanillic and syringic acid are some of the most important findings reported in the reviewed literature. Our results confirm the intact passage of certain wine compounds, such as tartaric acid and other wine acids, to the human organism. In an era where the health effects of wine consumption are of growing interest, this review offers a holistic perspective on the metabolic underpinnings of this centuries-old tradition.

Identification of canine mammary tumor-associated metabolites using untargeted metabolomics

The canine mammary tumor is the most common tumor type in female dogs and seriously threatens their life. Currently, no effective treatments are available for this condition. Hence, it is essential to identify biomarkers that positively influence the early diagnosis and treatment and prognosis of this disease. To provide a basis for early diagnosis of canine breast tumors, in this study, 23 dogs with mammary tumors were identified via histopathological examination combined with ancillary diagnoses via blood examinations and diagnostic imaging. The canine mammary tumor and tumor-adjacent healthy tissues were collected, and their metabolites were identified utilizing a UHPLC-qTOF-MS-based untargeted metabolomics approach. The metabolic results revealed a total of 979 ion features in the positive polarity mode and 371 ion features in the negative polarity mode in the tissues of two groups; among them, 536 differential metabolites (385 in the positive and 151 in the negative polarity mode) were analyzed by PCA and PLS-DA. Subsequently, the enrichment pathways purine metabolism, alpha-linolenic acid metabolism, vitamin B6 metabolism, and fatty acid biosynthesis were analyzed using Metaboanalyst 4.0, which suggested that these pathways were valuable diagnostic and therapeutic targets. Moreover, the receiver operating characteristic curves further confirmed 13Z,16Z-docosadienoic acid, 23-nordeoxycholic acid, and (±)12(13)-DiHOME as expected candidate biomarkers of canine mammary tumors. In conclusion, the discovery of tumor biomarkers based on untargeted metabolomics is informative for pathological mechanism studies and facilitates the early diagnosis of canine mammary tumors.

Study on the Common Molecular Mechanism of Metabolic Acidosis and Myocardial Damage Complicated by Neonatal Pneumonia

Pneumonia is a common clinical disease in the neonatal period and poses a serious risk to infant health. Therefore, the understanding of molecular mechanisms is of great importance for the development of methods for the rapid and accurate identification, classification and staging, and even disease diagnosis and therapy of pneumonia. In this study, a nontargeted metabonomic method was developed and applied for the analysis of serum samples collected from 20 cases in the pneumonia control group (PN) and 20 and 10 cases of pneumonia patients with metabolic acidosis (MA) and myocardial damage (MD), respectively, with the help of ultrahigh-performance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS). The results showed that compared with the pneumonia group, 23 and 21 differential metabolites were identified in pneumonia with two complications. They showed high sensitivity and specificity, with the area under the curve (ROC) of the receiver operating characteristic curve (ROC) larger than 0.7 for each differential molecule. There were 14 metabolites and three metabolic pathways of sphingolipid metabolism, porphyrin and chlorophyll metabolism, and glycerophospholipid metabolism existing in both groups of PN and MA, and PN and MD, all involving significant changes in pathways closely related to amino acid metabolism disorders, abnormal cell apoptosis, and inflammatory responses. These findings of molecular mechanisms should help a lot to fully understand and even treat the complications of pneumonia in infants.

Facilitating structural elucidation of small environmental solutes in RPLC-HRMS by retention index prediction

Implementing effective environmental management strategies requires a comprehensive understanding of the chemical composition of environmental pollutants, particularly in complex mixtures. Utilizing innovative analytical techniques, such as high-resolution mass spectrometry and predictive retention index models, can provide valuable insights into the molecular structures of environmental contaminants. Liquid Chromatography-High-Resolution Mass Spectrometry is a powerful tool for the identification of isomeric structures in complex samples. However, there are some limitations that can prevent accurate isomeric structure identification, particularly in cases where the isomers have similar mass and fragmentation patterns. Liquid chromatographic retention, determined by the size, shape, and polarity of the analyte and its interactions with the stationary phase, contains valuable 3D structural information that is vastly underutilized. Therefore, a predictive retention index model is developed which is transferrable to LC-HRMS systems and can assist in the structural elucidation of unknowns. The approach is currently restricted to carbon, hydrogen, and oxygen-based molecules <500 g mol-1. The methodology facilitates the acceptance of accurate structural formulas and the exclusion of erroneous hypothetical structural representations by leveraging retention time estimations, thereby providing a permissible tolerance range for a given elemental composition and experimental retention time. This approach serves as a proof of concept for the development of a Quantitative Structure-Retention Relationship model using a generic gradient LC approach. The use of a widely used reversed-phase (U)HPLC column and a relatively large set of training (101) and test compounds (14) demonstrates the feasibility and potential applicability of this approach for predicting the retention behaviour of compounds in complex mixtures. By providing a standard operating procedure, this approach can be easily replicated and applied to various analytical challenges, further supporting its potential for broader implementation.

UHPLC-HRMS-based serum untargeted lipidomics: Phosphatidylcholines and sphingomyelins are the main disturbed lipid markers to distinguish colorectal advanced adenoma from cancer

Colorectal advanced adenoma (CAA) is a key precancerous lesion of colorectal cancer (CRC), and early diagnosis can lessen CRC morbidity and mortality. Although abnormal lipid metabolism is associated with the development of CRC, there are no studies on the biomarkers and mechanism of lipid metabolism linked to CAA carcinogenesis. Hence, we performed a lipidomics study of serum samples from 46 CAA, and 50 CRC patients by the ultra high-performance liquid chromatography tandem high resolution mass spectrometry (UHPLC-HRMS) in both electrospray ionization (ESI) modes. Differential lipids were selected by univariate and multivariate statistics analysis, and their diagnostic performance was evaluated using a receiver operating characteristic curve (ROC) analysis. Combining P < 0.05 and variable importance in projection (VIP) > 1, 59 differential lipids were obtained totally. Ten of them showed good discriminant ability for CAA and CRC (AUC > 0.900). Especially, the lipid panel consisting of PC 44:5, PC 35:6e, and SM d40:3 showed the highest selection frequency and outperformed (AUC = 0.952). Additionally, phosphatidylcholine (PC) and sphingomyelin (SM) were the main differential and high-performance lipids. In short, this is the first study to explore the biomarkers and mechanism for CAA-CRC sequence with large-scale serum lipidomics. The findings should provide valuable reference and new clues for the development of diagnostic and therapeutic strategies of CRC.

Novel Peak Shift Correction Method Based on the Retention Index for Peak Alignment in Untargeted Metabolomics

Peak alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based large-scale untargeted metabolomics workflows, as it enables the integration of metabolite peaks across multiple samples, which is essential for accurate data interpretation. Slight differences or fluctuations in chromatographic separation conditions, however, can cause the chromatographic retention time (RT) shift between consecutive analyses, ultimately affecting the accuracy of peak alignment between samples. Here, we introduce a novel RT shift correction method based on the retention index (RI) and apply it to peak alignment. We synthesized a series of N-acyl glycine (C2-C23) homologues via the amidation reaction between glycine with normal saturated fatty acids (C2-C23) as calibrants able to respond proficiently in both mass spectrometric positive- and negative-ion modes. Using these calibrants, we established an N-acyl glycine RI system. This RI system is capable of covering a broad chromatographic space and addressing chromatographic RT shift caused by variations in flow rate, gradient elution, instrument systems, and LC separation columns. Moreover, based on the RI system, we developed a peak shift correction model to enhance peak alignment accuracy. Applying the model resulted in a significant improvement in the accuracy of peak alignment from 15.5 to 80.9% across long-term data spanning a period of 157 days. To facilitate practical application, we developed a Python-based program, which is freely available at https://github.com/WHU-Fenglab/RI-based-CPSC.

Metabolomic Profiling of Female Mink Serum during Early to Mid-Pregnancy to Reveal Metabolite Changes

Mink embryos enter a period of diapause after the embryo develops into the blastocyst, and its reactivation is mainly caused by an increase in polyamine. The specific process of embryo diapause regulation and reactivation remains largely unexamined. This study aimed to identify changes in metabolites in the early pregnancy of mink by comparing and analyzing in serum metabolites up to twenty-nine days after mating. Blood samples were taken on the first day of mating, once a week until the fifth week. Metabolomic profiles of the serum samples taken during this period were analyzed by ultra-performance liquid chromatography/mass spectrometry. Multivariate statistical analyses identified differential metabolite expression at different time points in both positive and negative ion modes. The levels of dopamine, tyramine, L-phenylalanine, L-tyrosine, tyrosine, L-kynurenine, L-lysine, L-arginine, D-ornithine, and leucine changed significantly. These metabolites may be associated with the process of embryo diapause and subsequent reactivation.

Recent Review on Selected Xenobiotics and Their Impacts on Gut Microbiome and Metabolome

As it is well known, the gut is one of the primary sites in any host for xenobiotics, and the many microbial metabolites responsible for the interactions between the gut microbiome and the host. However, there is a growing concern about the negative impacts on human health induced by toxic xenobiotics. Metabolomics, broadly including lipidomics, is an emerging approach to studying thousands of metabolites in parallel. In this review, we summarized recent advancements in mass spectrometry (MS) technologies in metabolomics. In addition, we reviewed recent applications of MS-based metabolomics for the investigation of toxic effects of xenobiotics on microbial and host metabolism. It was demonstrated that metabolomics, gut microbiome profiling, and their combination have a high potential to identify metabolic and microbial markers of xenobiotic exposure and determine its mechanism. Further, there is increasing evidence supporting that reprogramming the gut microbiome could be a promising approach to the intervention of xenobiotic toxicity.

Collision cross section measurement and prediction methods in omics

Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant-independent and calibrant-dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.

Improving the Data Quality of Untargeted Metabolomics through a Targeted Data-Dependent Acquisition Based on an Inclusion List of Differential and Preidentified Ions

Metabolomics based on high-resolution mass spectrometry has become a powerful technique in biomedical research. The development of various analytical tools and online libraries has promoted the identification of biomarkers. However, how to make mass spectrometry collect more data information is an important but underestimated research topic. Herein, we combined full-scan and data-dependent acquisition (DDA) modes to develop a new targeted DDA based on the inclusion list of differential and preidentified ions (dpDDA). In this workflow, the MS1 datasets for statistical analysis and metabolite preidentification were first obtained using full-scan, and then, the MS/MS datasets for metabolite identification were obtained using targeted DDA of quality control samples based on the inclusion list. Compared with the current methods (DDA, data-independent acquisition, targeted DDA with time-staggered precursor ion list, and iterative exclusion DDA), dpDDA showed better stability, higher characteristic ion coverage, higher differential metabolites' MS/MS coverage, and higher quality MS/MS spectra. Moreover, the same trend was verified in the analysis of large-scale clinical samples. More surprisingly, dpDDA can distinguish patients with different severities of coronary heart disease (CHD) based on the Canadian Cardiovascular Society angina classification, which we cannot distinguish through conventional metabolomics data collection. Finally, dpDDA was employed to differentiate CHD from healthy control, and targeted metabolomics confirmed that dpDDA could identify a more complete metabolic pathway network. At the same time, four unreported potential CHD biomarkers were identified, and the area under the receiver operating characteristic curve was greater than 0.85. These results showed that dpDDA would expand the discovery of biomarkers based on metabolomics, more comprehensively explore the key metabolites and their association with diseases, and promote the development of precision medicine.

A Solid-Phase Microextraction-Liquid Chromatography-Mass Spectrometry Method for Analyzing Serum Lipids in Psoriatic Disease

Approximately 25% of psoriasis patients have an inflammatory arthritis termed psoriatic arthritis (PsA). There is strong interest in identifying and validating biomarkers that can accurately and reliably predict conversion from psoriasis to PsA using novel technologies such as metabolomics. Lipids, in particular, are of key interest in psoriatic disease. We sought to develop a liquid chromatography-mass spectrometry (LC-MS) method to be used in conjunction with solid-phase microextraction (SPME) for analyzing fatty acids and similar molecules. A total of 25 chromatographic methods based on published lipid studies were tested on two LC columns. As a proof of concept, serum samples from psoriatic disease patients (n = 27 psoriasis and n = 26 PsA) were processed using SPME and run on the selected LC-MS method. The method that was best for analyzing fatty acids and fatty acid-like molecules was optimized and applied to serum samples. A total of 18 tentatively annotated features classified as fatty acids and other lipid compounds were statistically significant between psoriasis and PsA groups using both multivariate and univariate approaches. The SPME-LC-MS method developed and optimized was capable of detecting fatty acids and similar lipids that may aid in differentiating psoriasis and PsA patients.

Research progress of metabolomics in psoriasis

ABSTRACT

Psoriasis is a chronic inflammatory skin disease with significant physical and psychological burdens. The interplay between the innate and adaptive immune systems is thought to contribute to the pathogenesis; however, the details of the pathogenesis remain unclear. In addition, reliable biomarkers for diagnosis, assessment of disease activity, and monitoring of therapeutic response are limited. Metabolomics is an emerging science that can be used to identify and analyze low molecular weight molecules in biological systems. During the past decade, metabolomics has been widely used in psoriasis research, and substantial progress has been made. This review summarizes and discusses studies that applied metabolomics to psoriatic disease. These studies have identified dysregulation of amino acids, carnitines, fatty acids, lipids, and carbohydrates in psoriasis. The results from these studies have advanced our understanding of: (1) the molecular mechanisms of psoriasis pathogenesis; (2) diagnosis of psoriasis and assessment of disease activity; (3) the mechanism of treatment and how to monitor treatment response; and (4) the link between psoriasis and comorbid diseases. We discuss common research strategies and progress in the application of metabolomics to psoriasis, as well as emerging trends and future directions.

Dysregulated lipid metabolism in TMZ-resistant glioblastoma: pathways, proteins, metabolites and therapeutic opportunities

Glioblastoma (GBM) is a highly aggressive and lethal brain tumor with limited treatment options, such as the chemotherapeutic agent, temozolomide (TMZ). However, many GBM tumors develop resistance to TMZ, which is a major obstacle to effective therapy. Recently, dysregulated lipid metabolism has emerged as an important factor contributing to TMZ resistance in GBM. The dysregulation of lipid metabolism is a hallmark of cancer and alterations in lipid metabolism have been linked to multiple aspects of tumor biology, including proliferation, migration, and resistance to therapy. In this review, we aimed to summarize current knowledge on lipid metabolism in TMZ-resistant GBM, including key metabolites and proteins involved in lipid synthesis, uptake, and utilization, and recent advances in the application of metabolomics to study lipid metabolism in GBM. We also discussed the potential of lipid metabolism as a target for novel therapeutic interventions. Finally, we highlighted the challenges and opportunities associated with developing these interventions for clinical use, and the need for further research to fully understand the role of lipid metabolism in TMZ resistance in GBM. Our review suggests that targeting dysregulated lipid metabolism may be a promising approach to overcome TMZ resistance and improve outcomes in patients with GBM.

Applications of multi-omics analysis in human diseases

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.

Cerebrospinal fluid metabolite alterations in patients with different etiologies, diagnoses, and prognoses of disorders of consciousness

INTRODUCTION

Medical management of disorders of consciousness (DoC) is a growing issue imposing a major burden on families and societies. Recovery rates vary widely among patients with DoC, and recovery predictions strongly influence decisions on medical care. However, the specific mechanisms underlying different etiologies, consciousness levels, and prognoses are still unclear.

METHODS

We analyzed the comprehensive cerebrospinal fluid (CSF) metabolome through liquid chromatography-mass spectrometry. Metabolomic analyses were used to identify the metabolic differences between patients with different etiologies, diagnoses, and prognoses.

RESULTS

We found that the CSF levels of multiple acylcarnitines were lower in patients with traumatic DoC, suggesting mitochondrial function preservation in the CNS, which might contribute to the better consciousness outcomes of these patients. Metabolites related to glutamate and GABA metabolism were altered and showed a good ability to distinguish the patients in the minimally conscious state and the vegetative state. Moreover, we identified 8 phospholipids as potential biomarkers to predict the recovery of consciousness.

CONCLUSIONS

Our findings shed light on the differences in physiological activities underlying DoC with different etiologies and identified some potential biomarkers used for DoC diagnosis and prognosis.

Simultaneous quantitation of multiple myeloma related dietary metabolites in serum using HILIC-LC-MS/MS

Background

Recent studies from targeted and untargeted metabolomics have consistently revealed that diet-related metabolites, including carnitine (C0), several species of acylcarnitines (AcyCNs), amino acids, ceramides, and lysophosphatidylcholines (LPCs) may serve as potential multiple myeloma (MM) biomarkers. However, most of these approaches had some intrinsic limitations, namely low reproducibility and compromising the accuracy of the results.

Objective

This study developed and validated a precise, efficient, and reliable liquid chromatography tandem mass spectrometric (LC-MS/MS) method for measuring these 28 metabolic risk factors in human serum.

Design

This method employed isopropanol to extract the metabolites from serum, gradient elution on a hydrophilic interaction liquid chromatographic column (HILIC) for chromatographic separation, and multiple reaction monitor (MRM) mode with positive electrospray ionization (ESI) for mass spectrometric detection.

Results

The correlation coefficients of linear response for this method were more than 0.9984. Analytical recoveries ranged from 91.3 to 106.3%, averaging 99.5%. The intra-run and total coefficients of variation were 1.1-5.9% and 2.0-9.6%, respectively. We have simultaneously determined the serological levels of C0, several subclasses of AcyCNs, amino acids, ceramides, and LPCs within 15 min for the first time.

Conclusion

The established LC-MS/MS method was accurate, sensitive, efficient, and could be valuable in providing insights into the association between diet patterns and MM disease and added value in further clinical research.