Recent advances in LC-MS-based metabolomics for clinical biomarker discovery

Zhongfeng decoction attenuates cerebral ischemia-reperfusion injury by inhibiting autophagy via regulating the AGE-RAGE signaling pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Tackling phlegm and improving blood circulation is vital in the treatment of ischemic stroke (IS), culminating in the development of Zhongfeng Decoction (ZFD), a method grounded in this approach and serving as an effective therapy for IS. Nonetheless, the defensive mechanism of the ZFD in preventing cerebral ischemia-reperfusion damage remains ambiguous.

AIM OF THE STUDY

Determine the active ingredients in ZFD that have neuroprotective effects, and identify its mechanism of action against IS.

MATERIALS AND METHODS

A cerebral ischemia model in rats was developed, utilizing TTC, Nissl staining, and an oxidative stress kit to evaluate the neuroprotective impact of ZFD on this rat model. Following this, an amalgamation of LC-MS and network pharmacology techniques was employed to pinpoint potential active components, primary targets, and crucial action mechanisms of ZFD in treating IS. Finally, key targets and signaling pathways were detected using qRT-PCR, ELISA, Western blotting, electron microscopy, and other methods.

RESULTS

Through LC-MS and network analysis, 15 active ingredients and 6 hub targets were identified from ZFD. Analysis of pathway enrichment revealed that ZFD predominantly engages in the AGE-RAGE signaling route. Kaempferol, quercetin, luteolin, baicalein, and nobiletin in ZFD are the main active ingredients for treating IS. In vivo validation showed that ZFD can improve nerve damage in cerebral ischemic rats, reduce the mRNA expression of IL6, SERPINE1, CCL2, and TGFB1 related to inflammation. Furthermore, we also confirmed that ZFD can inhibit the protein expression of AGEs, RAGE, p-IKBα/IKBα, p-NF-κB p65/NF-κB p65, reduce autophagy levels, and thus decrease neuronal apoptosis.

CONCLUSIONS

The mechanism of action of ZFD in treating IS primarily includes inflammation suppression, oxidative stress response alleviation, post-stroke cell autophagy and apoptosis regulation, and potential mediation of the AGE-RAGE signaling pathway. This study elucidates how ZFD functions in treating IS, establishing a theoretical basis for its clinical application.

The association between metabolite profiles and impaired bone microstructure in adult growth hormone deficient rats

BACKGROUND

Adult growth hormone deficiency (AGHD) is associated with an increased risk of fractures and impaired bone microstructure. Understanding the metabolic changes accompanying bone deterioration in AGHD might provide insights into mechanisms behind molecular changes and develop new biomarkers or nutritional strategies for bone destruction. Our study aimed to investigate the association between altered metabolite patterns and impaired bone microstructure in adult rats with growth hormone deficiency.

METHODS

Thirty seven-week-aged adult Lewis dwarf homozygous (dw/dw) rats (five females and five males), and adult Lewis dwarf heterozygous (dw/ +) rats (five females and five males) rats were compared. Micro-computed tomography (Micro-CT) was used to examine the bone's microstructure. Hematoxylin and eosin (H&E) staining were used to quantify the histological characteristics. Liquid chromatography-mass spectrometry untargeted serum metabolomic analysis was applied in the study. ELISA was used to measure serum bone turnover markers and IGF-1 levels.

RESULTS

Adult dw/dw rats exhibited great reductions in trabecular volume bone density (Tb.vBMD), bone volume/total volume (BV/TV), and cortical thickness (Ct. Th) compared with adult dw/ + rats (all p values < 0.05), indicating significant impairment in bone microstructure. The serum metabolite profiles revealed substantial differences between the dw/dw rats and dw/ + rats. A total of 134 differential metabolites in positive ion mode and 49 differential metabolites in negative mode were identified. Five metabolites, including Lysophosphatidylcholine(LPC) 20:3, LPC22:6, LPC22:4, cortisol and histamine levels were upregulated in dw/dw rats. The steroid hormone biosynthesis and bile secretion pathways were the main perturbed metabolic pathways. There were significant associations between differential metabolites and the impaired bone microstructure parameters, indicating that the selected metabolites might serve as potential biomarkers for deteriorated bone microstructure in AGHD.

CONCLUSION

Adult dw/dw rats exhibit impaired bone microstructure and distinct serum metabolic profiles, and the altered metabolites were significantly associated with bone microstructure destruction. This provides a new insight into understanding the mechanism of bone deterioration in AGHD patients from a metabolic perspective.

Artificial intelligence with mass spectrometry-based multimodal molecular profiling methods for advancing therapeutic discovery of infectious diseases

Infectious diseases, driven by a diverse array of pathogens, can swiftly undermine public health systems. Accurate diagnosis and treatment of infectious diseases-centered around the identification of biomarkers and the elucidation of disease mechanisms-are in dire need of more versatile and practical analytical approaches. Mass spectrometry (MS)-based molecular profiling methods can deliver a wealth of information on a range of functional molecules, including nucleic acids, proteins, and metabolites. While MS-driven omics analyses can yield vast datasets, the sheer complexity and multi-dimensionality of MS data can significantly hinder the identification and characterization of functional molecules within specific biological processes and events. Artificial intelligence (AI) emerges as a potent complementary tool that can substantially enhance the processing and interpretation of MS data. AI applications in this context lead to the reduction of spurious signals, the improvement of precision, the creation of standardized analytical frameworks, and the increase of data integration efficiency. This critical review emphasizes the pivotal roles of MS based omics strategies in the discovery of biomarkers and the clarification of infectious diseases. Additionally, the review underscores the transformative ability of AI techniques to enhance the utility of MS-based molecular profiling in the field of infectious diseases by refining the quality and practicality of data produced from omics analyses. In conclusion, we advocate for a forward-looking strategy that integrates AI with MS-based molecular profiling. This integration aims to transform the analytical landscape and the performance of biological molecule characterization, potentially down to the single-cell level. Such advancements are anticipated to propel the development of AI-driven predictive models, thus improving the monitoring of diagnostics and therapeutic discovery for the ongoing challenge related to infectious diseases.

Amino acid metabolomics and machine learning-driven assessment of future liver remnant growth after hepatectomy in livers of various backgrounds

Accurate assessment of future liver remnant growth after partial hepatectomy (PH) in patients with different liver backgrounds is a pressing clinical issue. Amino acid (AA) metabolism plays a crucial role in liver regeneration. In this study, we combined metabolomics and machine learning (ML) to develop a generalized future liver remnant assessment model for multiple liver backgrounds. The liver index was calculated at 0, 6, 24, 48, 72 and 168 h after 70 % PH in healthy mice and mice with nonalcoholic steatohepatitis or liver fibrosis. The serum levels of 39 amino acids (AAs) were measured using UPLC-MS/MS. The dataset was randomly divided into training and testing sets at a 2:1 ratio, and orthogonal partial least squares regression (OPLS) and minimally biased variable selection in R (MUVR) were used to select a metabolite signature of AAs. To assess liver remnant growth, nine ML models were built, and evaluated using the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The post-Pareto technique for order preference by similarity to the ideal solution (TOPSIS) was employed for ranking the ML algorithms, and a stacking technique was utilized to establish consensus among the superior algorithms. Compared with those of OPLS, the signature AAs set identified by MUVR (Thr, Arg, EtN, Phe, Asa, 3MHis, Abu, Asp, Tyr, Leu, Ser, and bAib) are more concise. Post-Pareto TOPSIS ranking demonstrated that the majority of ML algorithm in combinations with MUVR outperformed those with OPLS. The established SVM-KNN consensus model performed best, with an R2 of 0.79, an MAE of 0.0029, and an RMSE of 0.0035 for the testing set. This study identified a metabolite signature of 12 AAs and constructed an SVM-KNN consensus model to assess future liver remnant growth after PH in mice with different liver backgrounds. Our preclinical study is anticipated to establish an alternative and generalized assessment method for liver regeneration.

Benzylic rearrangement for urinary analysis of guanidino and ureido compounds in cardiac surgery-associated acute kidney injury using high-performance liquid chromatography-tandem mass spectrometry

Rationale

Because acute kidney injury (AKI) is closely related to poor prognosis in critically ill patients, developing biomarkers for its prediction and early diagnosis is particularly important. Endogenous guanidino compounds (GCs) and ureido compounds (UCs) can participate in various biochemical processes because of their important physiological activities. The aim of this study was to investigate the alteration profiles of urinary GCs/UCs as potential biomarkers in patients with cardiac surgery–associated acute kidney injury (CSA‐AKI) at different stages.

Methods

GCs/UCs were reacted with benzil via benzylic rearrangement, and their derivatives were used to investigate fragmentation mechanisms using tandem mass spectrometry (MS/MS) in positive ion mode. Furthermore, a high‐performance liquid chromatography (HPLC)–MS/MS method was developed to measure the concentrations of GCs/UCs in urine samples taken from patients with CSA‐AKI at different time points.

Results

MS/MS analysis in positive ion mode showed that benzylic GCs/UCs exhibited similar fragmentation processes, which could produce the characteristic ion (C13H12N+) at m/z 182.0. Furthermore, an obviously different fragmentation pattern of benzylic UCs in the positive ion mode might be due to the neutral loss of the H2CO2 group under low collision energy. Of the eight selected GCs/UCs, methylguanidine exhibited significantly increased concentrations in urine when CSA‐AKI occurred, whereas guanidinoethyl sulfonate (GDS), homoarginine (HArg) and homocitrulline (HCit) exhibited decreased concentrations. After recovery from AKI, the urinary concentrations of the aforementioned GCs/UCs returned to normal. Some of the aforementioned metabolites with significant changes (GDS, HArg and HCit) had large areas under the curve in the receiver operating characteristic curve for distinguishing AKI stages on the third day after surgery.

Conclusions

In patients with CSA‐AKI, urinary GCs/UCs were significantly disrupted due to injured kidney, and some GC/UC metabolites exhibited a good ability to become potential biomarkers for AKI stages. The present study provides essential resources and new therapeutic targets for further research on CSA‐AKI.

A Deep Mining Strategy for Peptide Rapid Identification in Lactobacillus reuteri Based on LC-MS/MS Integrated with FBMN and De Novo Sequencing

Lactobacillus reuteri (L. reuteri) is widely recognized as a probiotic that produces prebiotics. However, studies on bioactive peptides or amino acid (AA) derivatives produced by L. reuteri are still lacking, whereas many bioactive peptides and AA derivatives have been found in other Lactobacillus species. In addition, rapid identification of peptides is challenged by the large amount of data and is limited by the coverage of protein databases. In this study, we performed a rapid and thorough profile of peptides in L. reuteri incorporating Global Natural Products Social Molecular Networking (GNPS) platform database searching, de novo sequencing, and deep mining, based on feature-based molecular networking (FBMN). According to FBMN, it was found that peptides containing identical or similar AA compositions were grouped into the same clusters, especially cyclic dipeptides (CDPs). Therefore, the grouping characteristics of clusters, differences in precursor ions, and characteristic fragment ions were utilized for the mining of deeply unknown compounds. Through this strategy, a total of 192 compounds, including 184 peptides, were rapidly identified. Among them, 53 CDPs, including four novel ones, were found for the first time in L. reuteri. Then, one of the novel CDPs, cyclo(5-OMe-Glu-4-OH-Pro), was isolated and characterized, which was consistent with the identification results. Moreover, some of the identified peptides exhibited considerable interactions with seven anti-inflammatory-related target proteins through molecular docking. According to the binding energies of peptides with different AA consistencies, it was considered that the existence of unnatural AAs in CDPs might contribute to their anti-inflammatory activity. These results provide a valuable strategy for the rapid identification of peptides, including CDPs. This study also reveals the substance basis for the potential anti-inflammatory effects exerted by L. reuteri.

Comprehensive immune cell spectral library for large-scale human primary T, B, and NK cell proteomics

Although proteomics is extensively used in immune research, there is currently no publicly accessible spectral assay library for the comprehensive proteome of immune cells. This study generated spectral assay libraries for five human immune cell lines and four primary immune cells: CD4 T, CD8 T, natural killer (NK) cells, and B cells. This was achieved by utilizing data-dependent acquisition (DDA) and employing fractionated samples from over 100 µg of proteins, which was applied to acquire the highest-quality MS/MS spectral data. In addition, Data-indedendent acquisition (DIA) was used to obtain sufficient data points for analyzing proteins from 10,000 primary CD4 T, CD8 T, NK, and B cells. The immune cell spectral assay library generated included 10,544 protein groups and 127,106 peptides. The proteomic profiles of 10,000 primary human immune cells obtained from 15 healthy volunteers analyzed using DIA revealed the highest heterogeneity of B cells among other immune cell types and the similarity between CD4 T and CD8 T cells. All data and spectral library are deposited in ProteomeXchange (PXD047742).

Effect of traditional Chinese medicine on metabolism disturbance in ischemic heart diseases

ETHNOPHARMACOLOGICAL RELEVANCE

Ischemic heart diseases (IHD), characterized by metabolic dysregulation, contributes majorly to the global morbidity and mortality. Glucose, lipid and amino acid metabolism are critical energy production for cardiomyocytes, and disturbances of these metabolism lead to the cardiac injury. Traditional Chinese medicine (TCM), widely used for treating IHD, have been demonstrated to effectively and safely regulate the cardiac metabolism reprogramming.

AIM OF THE REVIEW

This study discussed and analyzed the disturbed cardiac metabolism induced by IHD and development of formulas, extracts, single herb, bioactive compounds of TCM ameliorating IHD injury via metabolism regulation, with the aim of providing a basis for the development of clinical application of therapeutic strategies for TCM in IHD.

MATERIALS AND METHODS

With "ischemic heart disease", "myocardial infarction", "myocardial ischemia", "metabolomics", "Chinese medicine", "herb", "extracts" "medicinal plants", "glucose", "lipid metabolism", "amino acid" as the main keywords, PubMed, Web of Science, and other online search engines were used for literature retrieval.

RESULTS

IHD exhibits a close association with metabolism disorders, including but not limited to glycolysis, the TCA cycle, oxidative phosphorylation, branched-chain amino acids, fatty acid β-oxidation, ketone body metabolism, sphingolipid and glycerol-phospholipid metabolism. The therapeutic potential of TCM lies in its ability to regulate these disturbed cardiac metabolisms. Additionally, the active ingredients of TCM have depicted wonderful effects in cardiac metabolism reprogramming in IHD.

CONCLUSION

Drawing from the principles of TCM, we have pinpointed specific herbal remedies for the treatment of IHD, and leveraged advanced metabolomics technologies to uncover the effect of these TCMs on metabolomics alteration. In the future, further clinical experimental studies should be included to explore whether more TCM medicines can play a therapeutic role in IHD by reversing cardiac metabolism disorders; multi-omics would be conducted to explore more pathways and genes targeting such metabolism reprogramming by TCMs, and to seek more TCM therapies for IHD.

Untargeted Metabolomic Analysis Reveals Plasma Differences between Mares with Endometritis and Healthy Ones

The aim of this study was to explore alterations in plasma metabolites among mares afflicted with endometritis. Mares were divided into two groups, namely, the equine endometritis group (n = 8) and the healthy control group (n = 8), which included four pregnant and four non-pregnant mares, using a combination of clinical assessment and laboratory confirmation. Plasma samples from both groups of mares were analyzed through untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolomics. A total of 28 differentially abundant metabolites were identified by screening and identifying differentially abundant metabolites and analyzing the pathway enrichment of differentially. Ten metabolites were identified as potential biomarkers for the diagnosis of endometritis in mares. Among them, seven exhibited a decrease in the endometritis groups, including hexadecanedioic acid, oleoyl ethanolamide (OEA), [fahydroxy(18:0)]12_13-dihydroxy-9z-octa (12,13-diHOME), deoxycholic acid 3-glucuronide (DCA-3G), 2-oxindole, and (+/-)9-HPODE, and 13(S)-HOTRE. On the other hand, three metabolites, adenosine 5'-monophosphate (AMP), 5-hydroxy-dl-tryptophan (5-HTP), and l-formylkynurenine, demonstrated an increase. These substances primarily participate in the metabolism of tryptophan and linolenic acid, as well as fat and energy. In conclusion, metabolomics revealed differentially abundant metabolite changes in patients with mare endometritis. These specific metabolites can be used as potential biomarkers for the non-invasive diagnosis of mare endometritis.

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.

Comparative study of the anti-inflammatory activity of etoricoxib and Matcha green tea against acute kidney injury induced by gamma radiation in rats

PURPOSE

The primary objective of this study was to conduct a comparative analysis of the anti-inflammatory activity between Etoricoxib (ETO) and Matcha green tea (MG) in the context of acute kidney injury (AKI) induced by ionizing gamma radiation (IR) in female rats. Furthermore, the potential impact of whole body IR exposure on the intestinal system and serum estradiol levels was investigated. Additionally, it was acknowledged that the ETO and MG treatments might have exerted favorable effects on the intestinal and hormonal responses.

MATERIALS AND METHODS

Six groups of rats were assigned to different treatments: control, ETO, MG, irradiation (IRR), ETO + IRR, and MG + IRR. The evaluation included measuring the total phenolic and flavonoid contents of ETO and MG, as well as assessing their antioxidant activity, radical scavenging capacity, reducing power, and total antioxidant capacity. Kidney function was assessed through serum creatinine and urea levels. Oxidative stress markers, including superoxide dismutase, glutathione, malondialdehyde, and catalase, were measured to evaluate the antioxidant effects of ETO and MG. The anti-inflammatory potential of the treatments was evaluated by measuring STAT-3 and interleukins (IL-6, IL-23, and IL-17) using an ELISA assay. Prostaglandin E2 receptor (PGE-2) mRNA expression, histopathological examination, and immunohistochemistry for NF-κB inhibitors were performed to investigate the underlying mechanisms in kidney tissue homogenates. Histopathological changes and DNA fragmentation in the intestinal tissues were determined, and the characterization of Matcha green tea was performed using liquid chromatography-mass spectrometry (LC-MS). This allowed for the identification and quantification of various compounds present in Matcha green tea. Furthermore, the study assessed the effect of IR and treatments on estrogen levels in female rats.

RESULTS

Data showed that both ETO and MG had the potential to mitigate the adverse effects of AKI induced by IR. Notably, MG exhibited greater efficacy in attenuating oxidative stress and inflammation associated with renal injury. These findings revealed and compared the effects of ETO and MG in alleviating AKI caused by IR. MG demonstrated greater anti-inflammatory and antioxidant properties, highlighting its potential as a natural therapeutic agent.

CONCLUSIONS

These results contribute to the growing evidence supporting the use of MG in managing IR-induced renal complications. Future studies should focus on elucidating the molecular mechanisms and optimizing the application of MG in clinical settings.

Integrating the analysis of human biopsies using post-translational modifications proteomics

Proteome diversities and their biological functions are significantly amplified by post-translational modifications (PTMs) of proteins. Shotgun proteomics, which does not typically survey PTMs, provides an incomplete picture of the complexity of human biopsies in health and disease. Recent advances in mass spectrometry-based proteomic techniques that enrich and study PTMs are helping to uncover molecular detail from the cellular level to system-wide functions, including how the microbiome impacts human diseases. Protein heterogeneity and disease complexity are challenging factors that make it difficult to characterize and treat disease. The search for clinical biomarkers to characterize disease mechanisms and complexity related to patient diagnoses and treatment has proven challenging. Knowledge of PTMs is fundamentally lacking. Characterization of complex human samples that clarify the role of PTMs and the microbiome in human diseases will result in new discoveries. This review highlights the key role of proteomic techniques used to characterize unknown biological functions of PTMs derived from complex human biopsies. Through the integration of diverse methods used to profile PTMs, this review explores the genetic regulation of proteoforms, cells of origin expressing specific proteins, and several bioactive PTMs and their subsequent analyses by liquid chromatography and tandem mass spectrometry.

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.

The hidden treasures in endophytic fungi: a comprehensive review on the diversity of fungal bioactive metabolites, usual analytical methodologies, and applications

This review provides a comprehensive overview of the key aspects of the natural metabolite production by endophytic fungi, which has attracted significant attention due to its diverse biological activities and wide range of applications. Synthesized by various fungal species, these metabolites encompass compounds with therapeutic, agricultural, and commercial significance. We delved into strategies and advancements aimed at optimizing fungal metabolite production. Fungal cultivation, especially by Aspergillus, Penicillium, and Fusarium, plays a pivotal role in metabolite biosynthesis, and researchers have explored both submerged and solid-state cultivation processes to harness the full potential of fungal species. Nutrient optimization, pH, and temperature control are critical factors in ensuring high yields of the targeted bioactive metabolites especially for scaling up processes. Analytical methods that includes High-Performance Liquid Chromatography (HPLC), Liquid Chromatography-Mass Spectrometry (LC-MS), Gas Chromatography-Mass Spectrometry (GC-MS), Nuclear Magnetic Resonance (NMR), and Mass Spectrometry (MS), are indispensable for the identification and quantification of the compounds. Moreover, genetic engineering and metabolic pathway manipulation have emerged as powerful tools to enhance metabolite production and develop novel fungal strains with increased yields. Regulation and control mechanisms at the genetic, epigenetic, and metabolic levels are explored to fine-tune the biosynthesis of fungal metabolites. Ongoing research aims to overcome the complexity of the steps involved to ensure the efficient production and utilization of fungal metabolites.

Integrated post-acquisition data processing strategy for rapid steroid sulfate characterization in Toad gall-bladder

LC-MS serves as a workhorse for chemical profile characterization of Chinese medicinal materials (CMMs) attributing to the ability of measuring fruitful MS/MS spectral information. However, it is laborious to extract the information belonging to the compounds-of-interest from the massive data matrixes even employing those well-defined post-acquisition data processing strategies. Here, efforts were devoted to propose an integrated strategy allowing rapid chemical homologs-focused data filtering through integrating the fit-for-purpose existing strategies, such as molecular weight imprinting (MWI), diagnostic fragment ion filtering (DFIF), neutral loss filtering (NLF), and isotope pattern filtering (IPF). Homologs-focused chemical characterization of a precious CMM namely Toad gall-bladder (Chinese name: Chandan) that is rich of diverse effective steroid sulfates, particularly bufogenin sulfates, bile acid sulfates and bilichol sulfates, was employed as a proof-of-concept. Recombinant human SULT2A1-catalyzed in vitro metabolism was undertaken to generate eight bufogenin sulfates to facilitate summarizing MS/MS spectral behaviors. After in-house data library construction and MS1 and MS2 spectral acquisition, data filtering was conducted as follows: 1) MWI and IPF was utilized in combination to capture deprotonated molecular ions and the 34S isotopic ions for the sulfates of those reported steroids; 2) m/z 79.9568 (SO3-·) and 96.9596 (HSO4-) were applied to DFIF; and 3) SO3 (79.9568 Da) served as the feature to achieve NLF. Those captured MS/MS information subsequently participated in tentatively structural annotation through applying those empirical mass fragmentation rules. As a result, 71 compounds including 7 bufogenin sulfates, 17 bile acid sulfates, 13 bilichol sulfates and a C-23 steroid sulfate were detected from Toad gall-bladder and thereof, 39 ones received plausible identities assignment. Above all, the steroid sulfates in Toad gall-bladder were profiled in depth, and more importantly, the proposed strategy should be a meaningful option for, but not limited to, submetabolome characterization in CMMs.

Comparisons of different extraction methods and solvents for saliva samples

INTRODUCTION

Changes in the categories and concentrations of salivary metabolites may be closely related to oral, intestinal or systemic diseases. To study salivary metabolites, the first analytical step is to extract them from saliva samples as much as possible, while reducing interferences to a minimum. Frequently used extraction methods are protein precipitation (PPT), liquid-liquid extraction (LLE) and solid-phase extraction (SPE), with various organic solvents. The types and quantities of metabolites extracted with different methods may vary greatly, but few studies have systematically evaluated them.

OBJECTIVES

This study aimed to select the most suitable methods and solvents for the extraction of saliva according to different analytical targets.

METHODS

An untargeted metabolomics approach based on liquid chromatography-mass spectrometry was applied to obtain the raw data. The numbers of metabolites, repeatability of the data and intensities of mass spectrometry signals were used as evaluation criteria.

RESULTS

PPT resulted in the highest coverage. Among the PPT solvents, acetonitrile displayed the best repeatability and the highest coverage, while acetone resulted in the best signal intensities for the extracted compounds. LLE with the mixture of chloroform and methanol was the most suitable for the extraction of small hydrophobic compounds.

CONCLUSION

PPT with acetonitrile or acetone was recommended for untargeted analysis, while LLE with the mixture of chloroform and methanol was recommended for small hydrophobic compounds.

Interlaboratory evaluation of LC-MS-based biomarker assays

Validation of biomarker assays is crucial for effective drug development and clinical applications. Interlaboratory reproducibility is vital for reliable comparison and combination of data from different centers. This review summarizes interlaboratory studies of quantitative LC-MS-based biomarker assays using reference standards for calibration curves. The following points are discussed: trends in reports, reference and internal standards, evaluation of analytical validation parameters, study sample analysis and normalization of biomarker assay data. Full evaluation of these parameters in interlaboratory studies is limited, necessitating further research. Some reports suggest methods to address variations in biomarker assay data among laboratories, facilitating organized studies and data combination. Method validation across laboratories is crucial for reducing interlaboratory differences and reflecting target biomarker responses.

FNICM: A New Methodology To Identify Core Metabolites Based on Significantly Perturbed Metabolic Subnetworks

Metabolomics has emerged as a powerful tool in biomedical research to understand the pathophysiological processes and metabolic biomarkers of diseases. Nevertheless, it is a significant challenge in metabolomics to identify the reliable core metabolites that are closely associated with the occurrence or progression of diseases. Here, we proposed a new research framework by integrating detection-based metabolomics with computational network biology for function-guided and network-based identification of core metabolites, namely, FNICM. The proposed FNICM methodology is successfully utilized to uncover ulcerative colitis (UC)-related core metabolites based on the significantly perturbed metabolic subnetwork. First, seed metabolites were screened out using prior biological knowledge and targeted metabolomics. Second, by leveraging network topology, the perturbations of the detected seed metabolites were propagated to other undetected ones. Ultimately, 35 core metabolites were identified by controllability analysis and were further hierarchized into six levels based on confidence level and their potential significance. The specificity and generalizability of the discovered core metabolites, used as UC's diagnostic markers, were further validated using published data sets of UC patients. More importantly, we demonstrated the broad applicability and practicality of the FNICM framework in different contexts by applying it to multiple clinical data sets, including inflammatory bowel disease, colorectal cancer, and acute coronary syndrome. In addition, FNICM was also demonstrated as a practicality methodology to identify core metabolites correlated with the therapeutic effects of Clematis saponins. Overall, the FNICM methodology is a new framework for identifying reliable core metabolites for disease diagnosis and drug treatment from a systemic and a holistic perspective.

Untargeted urine metabolomics and machine learning provide potential metabolic signatures in children with autism spectrum disorder

Background

Complementary to traditional biostatistics, the integration of untargeted urine metabolomic profiling with Machine Learning (ML) has the potential to unveil metabolic profiles crucial for understanding diseases. However, the application of this approach in autism remains underexplored. Our objective was to delve into the metabolic profiles of autism utilizing a comprehensive untargeted metabolomics platform coupled with ML.

Methods

Untargeted metabolomics quantification (UHPLC/Q-TOF-MS) was performed for urine analysis. Feature selection was conducted using Lasso regression, and logistic regression, support vector machine, random forest, and extreme gradient boosting were utilized for significance stratification. Pathway enrichment analysis was performed to identify metabolic pathways associated with autism.

Results

A total of 52 autistic children and 40 typically developing children were enrolled. Lasso regression identified ninety-two urinary metabolites that significantly differed between the two groups. Distinct metabolites, such as prostaglandin E2, phosphonic acid, lysine, threonine, and phenylalanine, were revealed to be associated with autism through the application of four different ML methods (p<0.05). The alterations observed in the phosphatidylinositol and inositol phosphate metabolism pathways were linked to the pathophysiology of autism (p<0.05).

Conclusion

Significant urinary metabolites, including prostaglandin E2, phosphonic acid, lysine, threonine, and phenylalanine, exhibit associations with autism. Additionally, the involvement of the phosphatidylinositol and inositol phosphate pathways suggests their potential role in the pathophysiology of autism.

Characterization of Hair Metabolome in 5xFAD Mice and Patients with Alzheimer's Disease Using Mass Spectrometry-Based Metabolomics

Hair emerged as a biospecimen for long-term investigation of endogenous metabolic perturbations, reflecting the chemical composition circulating in the blood over the past months. Despite its potential, the use of human hair for metabolomics in Alzheimer's disease (AD) research remains limited. Here, we performed both untargeted and targeted metabolomic approaches to profile the key metabolic pathways in the hair of 5xFAD mice, a widely used AD mouse model. Furthermore, we applied the discovered metabolites to human subjects. Hair samples were collected from 6-month-old 5xFAD mice, a stage marked by widespread accumulation of amyloid plaques in the brain, followed by sample preparation and high-resolution mass spectrometry analysis. Forty-five discriminatory metabolites were discovered in the hair of 6-month-old 5xFAD mice compared to wild-type control mice. Enrichment analysis revealed three key metabolic pathways: arachidonic acid metabolism, sphingolipid metabolism, and alanine, aspartate, and glutamate metabolism. Among these pathways, six metabolites demonstrated significant differences in the hair of 2-month-old 5xFAD mice, a stage prior to the onset of amyloid plaque deposition. These findings suggest their potential involvement in the early stages of AD pathogenesis. When evaluating 45 discriminatory metabolites for distinguishing patients with AD from nondemented controls, a combination of l-valine and arachidonic acid significantly differentiated these two groups, achieving a 0.88 area under the curve. Taken together, these findings highlight the potential of hair metabolomics in identifying disease-specific metabolic alterations and developing biomarkers for improving disease detection and monitoring.

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.

Association between gut microbiome-related metabolites and symptomatic hand osteoarthritis in two independent cohorts

BACKGROUND

Since gut microbiome dysbiosis can cause inflammatory disorders by affecting host metabolism, we postulate that the gut microbiome and related metabolites could play a role in hand osteoarthritis. We characterised gut microbiome-related metabolites in people with symptomatic hand osteoarthritis (SHOA) in two independent cohorts.

METHODS

Using data collected from a large-sample community-based observational study (discovery cohort), we assessed the relations of the microbial function and plasma key metabolites related to altered microbial function with SHOA. Finally, we verified the relations of plasma metabolites to SHOA in an independent observational study (validation cohort).

FINDINGS

In the discovery cohort (n = 1359), compared to those without SHOA, participants with SHOA had significantly altered microbial functions related to tryptophan metabolism (Q = 0.025). Therefore we measured the plasma tryptophan metabolites and found that participants with SHOA had higher levels of 5-hydroxyindoleacetic acid (odds ratio [OR] = 1.25, 95% confidence interval [CI]: 1.09-1.42) and 5-hydroxytryptophol (OR = 1.13, 95% CI: 1.04-1.23), but lower levels of indole-3-lactic acid (ILA) (OR = 0.85, 95% CI: 0.72-1.00), skatole (OR = 0.93, 95% CI: 0.88-0.99) and 3-hydroxyanthranilic acid (OR = 0.90, 95% CI: 0.85-0.96). Findings from the validation cohort (n = 142) verified that lower levels of ILA were related to SHOA (OR = 0.70, 95% CI: 0.53-0.92).

INTERPRETATION

Alterations of the microbial function of tryptophan biosynthesis and tryptophan metabolites, especially lower levels of ILA, are associated with SHOA. These findings suggest the role of the microbiome and tryptophan metabolites in developing of SHOA and may contribute to future translational opportunities.

FUNDING

National Key Research and Development Plan and National Natural Science Foundation of China.

Metabolome subtyping reveals multi-omics characteristics and biological heterogeneity in major psychiatric disorders

Growing evidence suggests that major psychiatric disorders (MPDs) share common etiologies and pathological processes. However, the diagnosis is currently based on descriptive symptoms, which ignores the underlying pathogenesis and hinders the development of clinical treatments. This highlights the urgency of characterizing molecular biomarkers and establishing objective diagnoses of MPDs. Here, we collected untargeted metabolomics, proteomics and DNA methylation data of 327 patients with MPDs, 131 individuals with genetic high risk and 146 healthy controls to explore the multi-omics characteristics of MPDs. First, differential metabolites (DMs) were identified and we classified MPD patients into 3 subtypes based on DMs. The subtypes showed distinct metabolomics, proteomics and DNA methylation signatures. Specifically, one subtype showed dysregulation of complement and coagulation proteins, while the DNA methylation showed abnormalities in chemical synapses and autophagy. Integrative analysis in metabolic pathways identified the important roles of the citrate cycle, sphingolipid metabolism and amino acid metabolism. Finally, we constructed prediction models based on the metabolites and proteomics that successfully captured the risks of MPD patients. Our study established molecular subtypes of MPDs and elucidated their biological heterogeneity through a multi-omics investigation. These results facilitate the understanding of pathological mechanisms and promote the diagnosis and prevention of MPDs.

Identification of a potential prognostic plasma biomarker of acute ischemic stroke via untargeted LC-MS metabolomics

PURPOSE

Stroke is the sudden death of brain cells in a localized area due to an inadequate blood flow or blood vessel rupture, and it seriously affects the quality of life. The metabolite biomarkers are needed for predicting the functional outcome of acute ischemic stroke (AIS).

EXPERIMENTAL DESIGN

To identify biomarkers for AIS, untargeted LC/MS metabolomics was performed on plasma samples from subjects with favorable prognosis (mRS ≤ 2) and unfavorable prognosis (mRS > 2). The identified markers were further absolutely quantified by a targeted MRM approach.

RESULTS

There were 10 upregulated and 26 downregulated markers. Among these candidates, one was successfully identified as glycocholic acid and then absolutely quantified in plasma samples. Glycocholic acid could discriminate between subjects with favorable and unfavorable prognosis with an area under the curve (AUC) of 0.68 and odds ratio of 5.88.

CONCLUSIONS AND CLINICAL RELEVANCE

Glycocholic acid was identified as a potential plasma metabolite marker of non-progressive outcomes after ischemic stroke and could serve as predictive prognostic markers for clinical acute stroke outcomes.

Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis

Mass-Suite (MSS) is a Python-based, open-source software package designed to analyze high-resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) data, particularly for water quality assessment and other environmental applications. MSS provides flexible, user-defined workflows for HRMS data processing and analysis, including both basic functions (e.g., feature extraction, data reduction, feature annotation, data visualization, and statistical analyses) and advanced exploratory data mining and predictive modeling capabilities that are not provided by currently available open-source software (e.g., unsupervised clustering analyses, a machine learning-based source tracking and apportionment tool). As a key advance, most core MSS functions are supported by machine learning algorithms (e.g., clustering algorithms and predictive modeling algorithms) to facilitate function accuracy and/or efficiency. MSS reliability was validated with mixed chemical standards of known composition, with 99.5% feature extraction accuracy and ~ 52% overlap of extracted features relative to other open-source software tools. Example user cases of laboratory data evaluation are provided to illustrate MSS functionalities and demonstrate reliability. MSS expands available HRMS data analysis workflows for water quality evaluation and environmental forensics, and is readily integrated with existing capabilities. As an open-source package, we anticipate further development of improved data analysis capabilities in collaboration with interested users.

Advancements in Analyzing Tumor Metabolites through Chemical Derivatization-Based Chromatography

Understanding the metabolic abnormalities of tumors is crucial for early diagnosis, prognosis, and treatment. Accurate identification and quantification of metabolites in biological samples are essential to investigate the relationship between metabolite variations and tumor development. Common techniques like LC-MS and GC-MS face challenges in measuring aberrant metabolites in tumors due to their strong polarity, isomerism, or low ionization efficiency during MS detection. Chemical derivatization of metabolites offers an effective solution to overcome these challenges. This review focuses on the difficulties encountered in analyzing aberrant metabolites in tumors, the principles behind chemical derivatization methods, and the advancements in analyzing tumor metabolites using derivatization-based chromatography. It serves as a comprehensive reference for understanding the analysis and detection of tumor metabolites, particularly those that are highly polar and exhibit low ionization efficiency.

Serially coupled column liquid chromatography: An alternative separation tool

Despite the rapid development of liquid chromatography (LC) in recent decades, it remains a challenge to achieve the desired chromatographic separation of complex matrices using a single column. Multi-column LC techniques, particularly serially coupled column LC (SCC-LC), have emerged as a promising solution to overcome this challenge. While more attention has been focused on heart-cutting or comprehensive two-dimensional LC, reviews specifically focusing on SCC-LC, which offers advantages in terms of precision and facile instrumentation, are scarce. Here, our concerns are devoted to the progress summary regarding the instrumentation and applications of SCC-LC. Emphasis is placed on column selection aiming to enlarge peak capacity, selectivity, or both through the optimization of combination types (e.g. RPLC-RPLC, -RPLC-HILIC, and achiral-chiral LC), connection devices (e.g. zero dead volume connector, tubing, and T-type connector), elution program (i.e. isocratic or gradient) and detectors (e.g. mass spectrometer, ultraviolet detector, and fluorescence detector). The application of SCC-LC in pharmaceutical, biological, environmental, and food fields is also reviewed, and future perspectives and potential directions for SCC-LC are discussed. We envision that the review can give meaningful information to analytical scientists when facing heavy chromatographic separation tasks for complicated matrices.

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.

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.

LC/MS-based untargeted lipidomics reveals lipid signatures of nonpuerperal mastitis

BACKGROUND

Nonpuerperal mastitis (NPM) is a disease that presents with redness, swelling, heat, and pain during nonlactation and can often be confused with breast cancer. The etiology of NPM remains elusive; however, emerging clinical evidence suggests a potential involvement of lipid metabolism.

METHOD

Liquid chromatography‒mass spectrometry (LC/MS)-based untargeted lipidomics analysis combined with multivariate statistics was performed to investigate the NPM lipid change in breast tissue. Twenty patients with NPM and 10 controls were enrolled in this study.

RESULTS

The results revealed significant differences in lipidomics profiles, and a total of 16 subclasses with 14,012 different lipids were identified in positive and negative ion modes. Among these lipids, triglycerides (TGs), phosphatidylethanolamines (PEs) and cardiolipins (CLs) were the top three lipid components between the NPM and control groups. Subsequently, a total of 35 lipids were subjected to screening as potential biomarkers, and the chosen lipid biomarkers exhibited enhanced discriminatory capability between the two groups. Furthermore, pathway analysis elucidated that the aforementioned alterations in lipids were primarily associated with the arachidonic acid metabolic pathway. The correlation between distinct lipid populations and clinical phenotypes was assessed through weighted gene coexpression network analysis (WGCNA).

CONCLUSIONS

This study demonstrates that untargeted lipidomics assays conducted on breast tissue samples from patients with NPM exhibit noteworthy alterations in lipidomes. The findings of this study highlight the substantial involvement of arachidonic acid metabolism in lipid metabolism within the context of NPM. Consequently, this study offers valuable insights that can contribute to a more comprehensive comprehension of NPM in subsequent investigations.

TRIAL REGISTRATION

Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine (Number: 2019-702-57; Date: July 2019).

Metabolomics in drug research and development: The recent advances in technologies and applications

Emerging evidence has demonstrated the vital role of metabolism in various diseases or disorders. Metabolomics provides a comprehensive understanding of metabolism in biological systems. With advanced analytical techniques, metabolomics exhibits unprecedented significant value in basic drug research, including understanding disease mechanisms, identifying drug targets, and elucidating the mode of action of drugs. More importantly, metabolomics greatly accelerates the drug development process by predicting pharmacokinetics, pharmacodynamics, and drug response. In addition, metabolomics facilitates the exploration of drug repurposing and drug-drug interactions, as well as the development of personalized treatment strategies. Here, we briefly review the recent advances in technologies in metabolomics and update our knowledge of the applications of metabolomics in drug research and development.

High-throughput multitarget quantitative assay to profile the whole grain-specific phytochemicals alkylresorcinols, benzoxazinoids and avenanthramides in whole grain and grain-based foods

Currently, there is a growing interest in using whole grain (WG)-specific phytochemicals to perform WG research, including research on dietary assessment, health mechanisms, and quality control. However, the current approaches used for WG-specific phytochemical analysis cannot simultaneously achieve coverage, specificity, and sensitivity. In the present study, a series of WG-specific phytochemicals (alkylresorcinols (ARs), benzoxazinoids (BXs) and avenanthramides (AVAs)) were identified, and their mass spectrometry (MS) fragmentation mechanism was studied by TOF MS. Based on diagnostic fragmentation ions and retention time prediction models, a LC-MS/MS method was developed. Through this method, 56 ARs, 13 BXs, and 19 AVAs in WGs and grain-based foods were quantified for the first time. This method was validated and yielded excellent specificity, high sensitivity and negligible matrix effects. Finally, we established WG-specific phytochemical fingerprints in a variety of WG and grain-based foods. This method can be used for WG quality control and WG precision nutrition research.

Adverse events after the transjugular intrahepatic portal shunt are linked to serum metabolomic changes following the procedure

Background and Objective: Transjugular intrahepatic portal shunt (TIPS) insertion could promote weight gain and muscle and fat mass increase in patients with cirrhosis. However, few studies have focused on metabolic changes after TIPS. This study aims to explore metabolic changes after TIPS and potential biomarkers of adverse events. Methods: Peripheral and portal serum samples were collected before and after TIPS insertion. Untargeted metabolomics was performed using ultra-high-performance liquid chromatography-mass spectrometry. Spearman's correlation analysis was used to determine the relationship between metabolites and clinical parameters. Metabolite set enrichment analysis was performed to explore enriched pathways. The predictive value of the metabolites was calculated by receiver operating characteristic curve (ROC) analysis. Results: Metabolites in the peripheral and portal serum significantly changed early after TIPS. Some lipid metabolites were significantly correlated with liver function parameters. Both elevated and depleted metabolites were mainly enriched in amino acid metabolism. Nine and 12 portal metabolites have moderate predictive value in post-TIPS liver function decline and hepatic encephalopathy (HE), separately (area under curve >0.7). Conclusion: Metabolites in the peripheral and portal veins significantly changed after TIPS. Some metabolic changes might be ascribed to liver function decline early after TIPS. Nine and 12 portal metabolites might be potential biomarkers in prediction of liver function decline and HE, separately.

InSpectra - A platform for identifying emerging chemical threats

Non-target analysis (NTA) employing high-resolution mass spectrometry (HRMS) coupled with liquid chromatography is increasingly being used to identify chemicals of biological relevance. HRMS datasets are large and complex making the identification of potentially relevant chemicals extremely challenging. As they are recorded in vendor-specific formats, interpreting them is often reliant on vendor-specific software that may not accommodate advancements in data processing. Here we present InSpectra, a vendor independent automated platform for the systematic detection of newly identified emerging chemical threats. InSpectra is web-based, open-source/access and modular providing highly flexible and extensible NTA and suspect screening workflows. As a cloud-based platform, InSpectra exploits parallel computing and big data archiving capabilities with a focus for sharing and community curation of HRMS data. InSpectra offers a reproducible and transparent approach for the identification, tracking and prioritisation of emerging chemical threats.

Metabolomics profile of plasma in acute diquat-poisoned patients using gas chromatography-mass spectrometry

Diquat (DQ) has been confirmed to be toxic to humans and responsible for severe health impairment. While to date, very little is known about the toxicological mechanisms of DQ. Thus, investigations to discover the toxic targets and potential biomarkers of DQ poisoning are urgently needed. In this study, a metabolic profiling analysis was conducted to reveal the changes of metabolites of plasma and find out the potential biomarkers of DQ intoxication by GC-MS. First, multivariate statistical analysis demonstrated that acute DQ poisoning can lead to metabolomic changes in human plasma. Then, metabolomics studies showed that 31 of the identified metabolites were significantly altered by DQ. Pathway analysis indicated that three primarily metabolic pathways including phenylalanine, tyrosine and tryptophan biosynthesis, taurine and hypotaurine metabolism, and phenylalanine metabolism were affected by DQ, resulting in the perturbations of phenylalanine, tyrosine, taurine, and cysteine. Finally, the results of receiver operating characteristic analysis showed the above four metabolites could be used as reliable tools for the diagnosis and severity assessments of DQ intoxication. These data provided the theoretical basis for basic research to understand the potential mechanisms of DQ poisoning, and also identified the desirable biomarkers with great potential for clinical applications.

Precision Medicine Approaches with Metabolomics and Artificial Intelligence

Recent technological innovations in the field of mass spectrometry have supported the use of metabolomics analysis for precision medicine. This growth has been allowed also by the application of algorithms to data analysis, including multivariate and machine learning methods, which are fundamental to managing large number of variables and samples. In the present review, we reported and discussed the application of artificial intelligence (AI) strategies for metabolomics data analysis. Particularly, we focused on widely used non-linear machine learning classifiers, such as ANN, random forest, and support vector machine (SVM) algorithms. A discussion of recent studies and research focused on disease classification, biomarker identification and early diagnosis is presented. Challenges in the implementation of metabolomics-AI systems, limitations thereof and recent tools were also discussed.

Insight into Lotusine and Puerarin in Repairing Alcohol-Induced Metabolic Disorder Based on UPLC-MS/MS

Alcohol is an essential element in human culture. However, alcoholism has contributed to numerous health issues, including alcoholic fatty liver and sudden death. We found that the alkaloid lotusine possessed hepato- and neuroprotection against alcohol injuries. Lotusine showed comparable protective effects to puerarin, a widely recognized antagonist against alcohol damage. To better understand the metabolic response to alcohol injury and antagonist molecules, we applied sensitive zebrafish and LC-ESI-MS to collect metabolites related to alcohol, puerarin and lotusine exposure. LC-MS identified 119 metabolites with important physiological roles. Differential metabolomic analysis showed that alcohol caused abnormal expression of 82 metabolites (60 up-regulated and 22 down-regulated). These differential metabolites involved 18 metabolic pathways and modules, including apoptosis, necroptosis, nucleotide and fatty acid metabolism. Puerarin reversed seven metabolite variations induced by alcohol, which were related to necroptosis and sphingolipid metabolism. Lotusine was found to repair five metabolites disorders invoked by alcohol, mainly through nucleotide metabolism and glutathione metabolism. In phenotypic bioassay, lotusine showed similar activities to puerarin in alleviating behavioral abnormalities, neuroapoptosis and hepatic lipid accumulation induced by alcohol exposure. Our findings provided a new antagonist, lotusine, for alcohol-induced damage and explored the roles in repairing abnormal metabolism.

Application of metabolomics in osteoporosis research

Osteoporosis (OP) is a systemic disease characterized by bone metabolism imbalance and bone microstructure destruction, which causes serious social and economic burden. At present, the diagnosis and treatment of OP mainly rely on imaging combined with drugs. However, the existing pathogenic mechanisms, diagnosis and treatment strategies for OP are not clear and effective enough, and the disease progression that cannot reflect OP further restricts its effective treatment. The application of metabolomics has facilitated the study of OP, further exploring the mechanism and behavior of bone cells, prevention, and treatment of the disease from various metabolic perspectives, finally realizing the possibility of a holistic approach. In this review, we focus on the application of metabolomics in OP research, especially the newer systematic application of metabolomics and treatment with herbal medicine and their extracts. In addition, the prospects of clinical transformation in related fields are also discussed. The aim of this study is to highlight the use of metabolomics in OP research, especially in exploring the pathogenesis of OP and the therapeutic mechanisms of natural herbal medicine, for the benefit of interdisciplinary researchers including clinicians, biologists, and materials engineers.