A Metabolomics Workflow for Analyzing Complex Biological Samples Using a Combined Method of Untargeted and Target-List Based Approaches

An Untargeted Metabolomic Analysis of Lacticaseibacillus (L.) rhamnosus, Lactobacillus (L.) acidophilus, Lactiplantibacillus (L.) plantarum and Limosilactobacillus (L.) reuteri Reveals an Upregulated Production of Inosine from L. rhamnosus

Lactic acid bacteria are considered an inexhaustible source of bioactive compounds; indeed, products from their metabolism are known to have immunomodulatory and anti-inflammatory activity. Recently, we demonstrated that Cell-Free Supernatants (CFS) obtained from Lactobacillus (L.) acidophilus, Lactiplantibacillus (L.) plantarum, Lacticaseibacillus (L.) rhamnosus, and Limosilactobacillus (L.) reuteri can impair Candida pathogenic potential in an in vitro model of epithelial vaginal infection. This effect could be ascribed to a direct effect of living lactic acid bacteria on Candida virulence and to the production of metabolites that are able to impair fungal virulence. In the present work, stemming from these data, we deepened our knowledge of CFS from these four lactic acid bacteria by performing a metabolomic analysis to better characterize their composition. By using an untargeted metabolomic approach, we detected consistent differences in the metabolites produced by these four different lactic acid bacteria. Interestingly, L. rhamnosus and L. acidophilus showed the most peculiar metabolic profiles. Specifically, after a hierarchical clustering analysis, L. rhamnosus and L. acidophilus showed specific areas of significantly overexpressed metabolites that strongly differed from the same areas in other lactic acid bacteria. From the overexpressed compounds in these areas, inosine from L. rhamnosus returned with the best identification profile. This molecule has been described as having antioxidant, anti-inflammatory, anti-infective, and neuroprotective properties. The biological significance of its overproduction by L. rhamnosus might be important in its probiotic and/or postbiotic activity.

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.

Comprehensive quality control of Qingjin Yiqi granule based on UHPLC-Q-Orbitrap-MS and UPLC-QQQ-MS

INTRODUCTION

Qingjin Yiqi granule (QYG) is a prescription medicine of traditional Chinese medicine which is widely used clinically for the recovery of coronavirus patients. However, there is currently limited research on the quality control of QYG.

OBJECTIVE

To evaluate the quality of QYG qualitatively and quantitatively by making full use of advanced chromatography-mass spectrometry techniques.

METHODS

Firstly, a multicomponent characterisation of QYG was performed by ultrahigh-performance liquid chromatography coupled with a Q Exactive™ hybrid quadrupole-Orbitrap mass spectrometry (UHPLC-Q-Orbitrap-MS) system using a rapid negative/positive switching mode. Secondly, the co-condition fingerprint analysis of constituted herbal medicines of QYG was performed to unveil active ingredients as the quality markers of QYG. Thirdly, the marker compounds in 10 batches of QYG were quantified by ultrahigh-performance liquid chromatography coupled with a Waters Xevo TQ-S triple quadrupole mass spectrometry (UPLC-QQQ-MS) system.

RESULTS

A comprehensive method that combined the inclusion list and data-dependent acquisition (DDA) to achieve a systematic characterisation of QYG was established by UHPLC-Q-Orbitrap-MS. After analysis based on Compound Discoverer software and Global Natural Products Social (GNPS) platform, a total of 332 compounds were detected. Eleven Q-markers were determined for the quality evaluation of QYG by comparison with the fingerprint of nine constituted herbal medicines. An adjusted multiple reaction monitoring (MRM) quantification method was further established to simultaneously determine the 11 Q-markers for holistic quality evaluation of QYG.

CONCLUSION

This is the first study to report comprehensive multicomponent characterisation, identification, and quality assessment of QYG, which could be used for effective guarantee of the quality of QYG.

Fluid shear stress induces a shift from glycolytic to amino acid pathway in human trophoblasts

BACKGROUND

The human placenta, a tissue with a lifespan limited to the period of pregnancy, is exposed to varying shear rates by maternal blood perfusion depending on the stage of development. In this study, we aimed to investigate the effects of fluidic shear stress on the human trophoblast transcriptome and metabolism.

RESULTS

Based on a trophoblast cell line cultured in a fluidic flow system, changes caused by shear stress were analyzed and compared to static conditions. RNA sequencing and bioinformatics analysis revealed an altered transcriptome and enriched gene ontology terms associated with amino acid and mitochondrial metabolism. A decreased GLUT1 expression and reduced glucose uptake, together with downregulated expression of key glycolytic rate-limiting enzymes, hexokinase 2 and phosphofructokinase 1 was observed. Altered mitochondrial ATP levels and mass spectrometry data, suggested a shift in energy production from glycolysis towards mitochondrial oxidative phosphorylation. This shift in energy production could be supported by increased expression of glutamic-oxaloacetic transaminase variants in response to shear stress as well as under low glucose availability or after silencing of GLUT1. The shift towards amino acid metabolic pathways could be supported by significantly altered amino acid levels, like glutamic acid, cysteine and serine. Downregulation of GLUT1 and glycolytic rate-limiting enzymes, with concomitant upregulation of glutamic-oxaloacetic transaminase 2 was confirmed in first trimester placental explants cultured under fluidic flow. In contrast, high fluid shear stress decreased glutamic-oxaloacetic transaminase 2 expression in term placental explants when compared to low flow rates. Placental tissue from pregnancies with intrauterine growth restriction are exposed to high shear rates and showed also decreased glutamic-oxaloacetic transaminase 2, while GLUT1 was unchanged and glycolytic rate-limiting enzymes showed a trend to be upregulated. The results were generated by using qPCR, immunoblots, quantification of immunofluorescent pictures, padlock probe hybridization, mass spectrometry and FRET-based measurement.

CONCLUSION

Our study suggests that onset of uteroplacental blood flow is accompanied by a shift from a predominant glycolytic- to an alternative amino acid converting metabolism in the villous trophoblast. Rheological changes with excessive fluidic shear stress at the placental surface, may disrupt this alternative amino acid pathway in the syncytiotrophoblast and could contribute to intrauterine growth restriction.

Ultraviolet exposure regulates skin metabolome based on the microbiome

Skin metabolites (< 1500 Da) play a critical role in barrier function, hydration, immune response, microbial invasion, and allergen penetration. We aimed to understand the global metabolic profile changes of the skin in relation to the microbiome and UV exposure and exposed germ-free (devoid of microbiome), disinfected mice (partially devoid of skin microbiome) and control mice with intact microbiome to immunosuppressive doses of UVB radiation. Targeted and untargeted lipidome and metabolome profiling was performed with skin tissue by high-resolution mass spectrometry. UV differentially regulated various metabolites such as alanine, choline, glycine, glutamine, and histidine in germ-free mice compared to control mice. Membrane lipid species such as phosphatidylcholine, phosphatidylethanolamine, and sphingomyelin were also affected by UV in a microbiome-dependent manner. These results shed light on the dynamics and interactions between the skin metabolome, microbiome, and UV exposure and open new avenues for the development of metabolite- or lipid-based applications to maintain skin health.

Untargeted Metabolomics to Characterize the Urinary Chemical Landscape of E-Cigarette Users

The health and safety of using e-cigarette products (vaping) have been challenging to assess and further regulate due to their complexity. Inhaled e-cigarette aerosols contain chemicals with under-recognized toxicological profiles, which could influence endogenous processes once inhaled. We urgently need more understanding on the metabolic effects of e-cigarette exposure and how they compare to combustible cigarettes. To date, the metabolic landscape of inhaled e-cigarette aerosols, including chemicals originated from vaping and perturbed endogenous metabolites in vapers, is poorly characterized. To better understand the metabolic landscape and potential health consequences of vaping, we applied liquid chromatography-mass spectrometry (LC-MS) based nontargeted metabolomics to analyze compounds in the urine of vapers, cigarette smokers, and nonusers. Urine from vapers (n = 34), smokers (n = 38), and nonusers (n = 45) was collected for verified LC-HRMS nontargeted chemical analysis. The altered features (839, 396, and 426 when compared smoker and control, vaper and control, and smoker and vaper, respectively) among exposure groups were deciphered for their structural identities, chemical similarities, and biochemical relationships. Chemicals originating from e-cigarettes and altered endogenous metabolites were characterized. There were similar levels of nicotine biomarkers of exposure among vapers and smokers. Vapers had higher urinary levels of diethyl phthalate and flavoring agents (e.g., delta-decalactone). The metabolic profiles featured clusters of acylcarnitines and fatty acid derivatives. More consistent trends of elevated acylcarnitines and acylglycines in vapers were observed, which may suggest higher lipid peroxidation. Our approach in monitoring shifts of the urinary chemical landscape captured distinctive alterations resulting from vaping. Our results suggest similar nicotine metabolites in vapers and cigarette smokers. Acylcarnitines are biomarkers of inflammatory status and fatty acid oxidation, which were dysregulated in vapers. With higher lipid peroxidation, radical-forming flavoring, and higher level of specific nitrosamine, we observed a trend of elevated cancer-related biomarkers in vapers as well. Together, these data present a comprehensive profiling of urinary biochemicals that were dysregulated due to vaping.

Progress of metabolomics in atopic dermatitis: a systematic review

Atopic dermatitis (AD), a chronic inflammatory skin disorder characterized by recurrent eczema and intense pruritus, is a major skin-related burden worldwide. The diagnosis and treatment of AD is often challenging due to the high heterogeneity of AD, and its exact etiology is unknown. Metabolomics offers the opportunity to follow continuous physiological and pathological changes in individuals, which allows accurate diagnosis and management as well as providing deep insights into the etiopathogenesis of AD. Several metabolomic studies of AD have been published over the past few years. The aim of this review is to summarize these findings and help researchers to understand the rapid development of metabolomics for AD. A comprehensive and systematic search was performed using the PubMed, Embase, Cochrane Library and Web of Science databases. Twenty-six papers were finally included in the review after quality assessment. Significant differences in metabolite profiles were found between patients with AD and healthy individuals. This study provides a comprehensive overview of metabolomic research in AD. A better understanding of the metabolomics of AD may offer novel diagnostic, prognostic, and therapeutic approaches.

Discovery of metabolic markers for the discrimination of Helwingia species based on bioactivity evaluation, plant metabolomics, and network pharmacology

RATIONALE

Helwingia japonica (HJ), a traditional medicinal plant, is commonly used for the treatment of dysentery, blood in the stool, and scald burns. Three major HJ species, Helwingia japonica (Thunb.) Dietr. (QJY), Helwingia himalaica Hook. f. et Thoms. ex C. B. Clarke, and Helwingia chinensis Batal., share great similarities in both morphology and chemical constituents. The discrimination of medicinal plants directly affects their pharmacological and clinical effects. Here, we solved the taxonomy uncertainty of these three HJ species and explored the discrimination and study of other traditional medicines (TMs).

METHODS

First, the anti-inflammatory effects of the three HJ species were compared using lipopolysaccharide (LPS)-induced inflammatory responses in mouse leukemia cells of monocyte macrophage (RAW) 264.7 cells. Then, plant metabolomics were performed in 48 batches of samples to discover chemical markers for discriminating different HJ species. Finally, network pharmacology was applied to explore the linkages among constituents, targets, and signaling pathways.

RESULTS

In vitro experiments showed that the QJY exhibited the most potential anti-inflammatory activities. Meanwhile, 172 compounds were tentatively identified and eight metabolites with higher relative content in QJY were designated as chemical markers to distinguish QJY and the other two species. According to the property of absorbed in vivo, threonic acid, arginine, and tyrosine were selected to construct a component-target-pathway network. The network pharmacology analysis confirmed that the chemotaxonomy differentiation was consistent with the bioactive assessment.

CONCLUSIONS

The present study demonstrates that bioactivity evaluation integrated with plant metabolomics and network pharmacology could be used as an effective approach to discriminate different TMs and discover the active compounds.

Untargeted metabolomics of pulmonary tuberculosis patient serum reveals potential prognostic markers of both latent infection and outcome

Currently, there are no particularly effective biomarkers to distinguish between latent tuberculosis infection (LTBI) and active pulmonary tuberculosis (PTB) and evaluate the outcome of TB treatment. In this study, we have characterized the changes in the serum metabolic profiles caused by Mycobacterium tuberculosis (Mtb) infection and standard anti-TB treatment with isoniazid-rifampin-pyrazinamide-ethambutol (HRZE) using GC-MS and LC-MS/MS. Seven metabolites, including 3-oxopalmitic acid, akeboside ste, sulfolithocholic acid, 2-decylfuran (4,8,8-trimethyldecahydro-1,4-methanoazulen-9-yl)methanol, d-(+)-camphor, and 2-methylaminoadenosine, were identified to have significantly higher levels in LTBI and untreated PTB patients (T0) than those in uninfected healthy controls (Un). Among them, akeboside Ste and sulfolithocholic acid were significantly decreased in PTB patients with 2-month HRZE (T2) and cured PTB patients with 2-month HRZE followed by 4-month isoniazid-rifampin (HR) (T6). Receiver operator characteristic curve analysis revealed that the combined diagnostic model showed excellent performance for distinguishing LT from T0 and Un. By analyzing the biochemical and disease-related pathways, we observed that the differential metabolites in the serum of LTBI or TB patients, compared to healthy controls, were mainly involved in glutathione metabolism, ascorbate and aldarate metabolism, and porphyrin and chlorophyll metabolism. The metabolites with significant differences between the T0 group and the T6 group were mainly enriched in niacin and nicotinamide metabolism. Our study provided more detailed experimental data for developing laboratory standards for evaluating LTBI and cured PTB.

Development of LC-HRMS untargeted analysis methods for nasal epithelial lining fluid exposomics

BACKGROUND

The nasal mucosa, as a primary site of entry for inhaled substances, contains both inhaled xenobiotic and endogenous biomarkers. Nasal mucosa can be non-invasively sampled (nasal epithelial lining fluid "NELF") and analyzed for biological mediators. However, methods for untargeted analysis of compounds inhaled and/or retained in the nasal mucosa are needed.

OBJECTIVES

This study aimed to develop a high resolution LC-MS untargeted method to analyze collected NELF. Profiling of compounds in NELF samples will also provide baseline data for future comparative studies to reference.

METHODS

Extracted NELF analytes were injected to LC-ESI-MS. After spectrum processing, an in-house library provided annotations with high confidence, while more tentative annotation proposals were obtained via ChemSpider database matching.

RESULTS

The established method successfully detected unique molecular signatures within NELF. Baseline profiling of 27 samples detected 2002 unknown molecules, with 77 and 463 proposed structures by our in-house library and Chemspider matching. High confidence annotations revealed common metabolites and tentative annotations implied various environmental exposure biomarkers are also present in NELF.

SIGNIFICANCE

The experimental pipeline for analyzing NELF samples serves as simple and robust method applicable for future studies to characterize identities/effects of inhaled substances and metabolites retained in the nasal mucosa.

IMPACT STATEMENT

The nasal mucosa contains exogenous and endogenous compounds. The development of an untargeted analysis is necessary to characterize the nasal exposome by deciphering the identity and influence of inhaled compounds on nasal mucosal biology. This study established a high resolution LC-MS based untargeted analysis of non-invasively collected nasal epithelial lining fluid. Baseline profiling of the nasal mucosa (n = 27) suggests the presence of environmental pollutants, along with detection of endogenous metabolites. Our results show high potential for the analytical pipeline to facilitate future respiratory health studies involving inhaled pollutants or pharmaceutical compounds and their effects on respiratory biology.

Biosynthetic regulatory network of flavonoid metabolites in stems and leaves of Salvia miltiorrhiza

Flavonoid secondary metabolites can treat and prevent many diseases, but systematic studies on regulation of the biosynthesis of such metabolites in aboveground parts of Salvia miltiorrhiza are lacking. In this study, metabonomic and transcriptomic analyses of different S. miltiorrhiza phenotypes were conducted to explore pathways of synthesis, catalysis, accumulation, and transport of the main flavonoid secondary metabolites regulating pigment accumulation. Tissue localization and quantitative analysis of flavonoid secondary metabolites were conducted by laser scanning confocal microscopy (LSCM). A total 3090 differentially expressed genes were obtained from 114,431 full-length unigenes in purple and green phenotypes, and 108 functional genes were involved in flavonoid biosynthesis. Five key phenylpropane structural genes (PAL, 4CL, ANS, 3AT, HCT) were highly differentially expressed, and four transcription factor genes (MYB, WRKY, bHLH, bZiP) were identified. In addition, six GST genes, nine ABC transporters, 22 MATE genes, and three SNARE genes were detected with key roles in flavonoid transport. According to LSCM, flavonoids were mainly distributed in epidermis, cortex, and collenchyma. Thus, comprehensive and systematic analyses were used to determine biosynthesis, accumulation, and transport of flavonoids in stems and leaves of different S. miltiorrhiza phenotypes. The findings will provide a reference for flavonoid production and cultivar selection.

Changes in the Cerebrospinal Fluid and Plasma Lipidome in Patients with Rett Syndrome

Rett syndrome (RTT) is defined as a rare disease caused by mutations of the methyl-CpG binding protein 2 (MECP2). It is one of the most common causes of genetic mental retardation in girls, characterized by normal early psychomotor development, followed by severe neurologic regression. Hitherto, RTT lacks a specific biomarker, but altered lipid homeostasis has been found in RTT model mice as well as in RTT patients. We performed LC-MS/MS lipidomics analysis to investigate the cerebrospinal fluid (CSF) and plasma composition of patients with RTT for biochemical variations compared to healthy controls. In all seven RTT patients, we found decreased CSF cholesterol levels compared to age-matched controls (n = 13), whereas plasma cholesterol levels were within the normal range in all 13 RTT patients compared to 18 controls. Levels of phospholipid (PL) and sphingomyelin (SM) species were decreased in CSF of RTT patients, whereas the lipidomics profile of plasma samples was unaltered in RTT patients compared to healthy controls. This study shows that the CSF lipidomics profile is altered in RTT, which is the basis for future (functional) studies to validate selected lipid species as CSF biomarkers for RTT.

Applications of spatially resolved omics in the field of endocrine tumors

Endocrine tumors derive from endocrine cells with high heterogeneity in function, structure and embryology, and are characteristic of a marked diversity and tissue heterogeneity. There are still challenges in analyzing the molecular alternations within the heterogeneous microenvironment for endocrine tumors. Recently, several proteomic, lipidomic and metabolomic platforms have been applied to the analysis of endocrine tumors to explore the cellular and molecular mechanisms of tumor genesis, progression and metastasis. In this review, we provide a comprehensive overview of spatially resolved proteomics, lipidomics and metabolomics guided by mass spectrometry imaging and spatially resolved microproteomics directed by microextraction and tandem mass spectrometry. In this regard, we will discuss different mass spectrometry imaging techniques, including secondary ion mass spectrometry, matrix-assisted laser desorption/ionization and desorption electrospray ionization. Additionally, we will highlight microextraction approaches such as laser capture microdissection and liquid microjunction extraction. With these methods, proteins can be extracted precisely from specific regions of the endocrine tumor. Finally, we compare applications of proteomic, lipidomic and metabolomic platforms in the field of endocrine tumors and outline their potentials in elucidating cellular and molecular processes involved in endocrine tumors.

Metabolic Rewiring and the Characterization of Oncometabolites

Simple Summary

Oncometabolites are produced by cancer cells and assist the cancer to proliferate and progress. Oncometabolites occur as a result of mutated enzymes in the tumor tissue or due to hypoxia. These processes result in either the abnormal buildup of a normal metabolite or the accumulation of an unusual metabolite. Definition of the metabolic changes that occur due to these processes has been accomplished using metabolomics, which mainly uses mass spectrometry platforms to define the content of small metabolites that occur in cells, tissues, organs and organisms. The four classical oncometabolites are fumarate, succinate, (2R)-hydroxyglutarate and (2S)-hydroxyglutarate, which operate by inhibiting 2-oxoglutarate-dependent enzyme reactions that principally regulate gene expression and response to hypoxia. Metabolomics has also revealed several putative oncometabolites that include lactate, kynurenine, methylglyoxal, sarcosine, glycine, hypotaurine and (2R,3S)-dihydroxybutanoate. Metabolomics will continue to be critical for understanding the metabolic rewiring involving oncometabolite production that underpins many cancer phenotypes.

Abstract

The study of low-molecular-weight metabolites that exist in cells and organisms is known as metabolomics and is often conducted using mass spectrometry laboratory platforms. Definition of oncometabolites in the context of the metabolic phenotype of cancer cells has been accomplished through metabolomics. Oncometabolites result from mutations in cancer cell genes or from hypoxia-driven enzyme promiscuity. As a result, normal metabolites accumulate in cancer cells to unusually high concentrations or, alternatively, unusual metabolites are produced. The typical oncometabolites fumarate, succinate, (2R)-hydroxyglutarate and (2S)-hydroxyglutarate inhibit 2-oxoglutarate-dependent dioxygenases, such as histone demethylases and HIF prolyl-4-hydroxylases, together with DNA cytosine demethylases. As a result of the cancer cell acquiring this new metabolic phenotype, major changes in gene transcription occur and the modification of the epigenetic landscape of the cell promotes proliferation and progression of cancers. Stabilization of HIF1α through inhibition of HIF prolyl-4-hydroxylases by oncometabolites such as fumarate and succinate leads to a pseudohypoxic state that promotes inflammation, angiogenesis and metastasis. Metabolomics has additionally been employed to define the metabolic phenotype of cancer cells and patient biofluids in the search for cancer biomarkers. These efforts have led to the uncovering of the putative oncometabolites sarcosine, glycine, lactate, kynurenine, methylglyoxal, hypotaurine and (2R,3S)-dihydroxybutanoate, for which further research is required.