A Protocol for Untargeted Metabolomic Analysis: From Sample Preparation to Data Processing

Heavy metal exposure and metabolomics analysis: an emerging frontier in environmental health

Exposure to heavy metals in various populations can lead to extensive damage to different organs, as these metals infiltrate and bioaccumulate in the human body, causing metabolic disruptions in various organs. To comprehensively understand the metal homeostasis, inter-organ "traffic," and extensive metabolic alterations resulting from heavy metal exposure, employing complementary analytical methods is crucial. Metabolomics is pivotal in unraveling the intricacies of disease vulnerability by furnishing thorough understandings of metabolic changes linked to different metabolic diseases. This field offers exciting prospects for enhancing the disease prevention, early detection, and tailoring treatment approaches to individual needs. This article consolidates the existing knowledge on disease-linked metabolic pathways affected by the exposure of diverse heavy metals providing concise overview of the underlying impact mechanisms. The main aim is to investigate the connection between the altered metabolic pathways and long-term complex health conditions induced by heavy metals such as diabetes mellitus, cardiovascular diseases, renal disorders, inflammation, neurodegenerative diseases, reproductive risks, and organ damage. Further exploration of common pathways may unveil the shared targets for treating associated pathological conditions. In this article, the role of metabolomics in disease susceptibility is emphasized that metabolomics is expected to be routinely utilized for the diagnosis and monitoring of diseases and practical value of biomarkers derived from metabolomics, as well as determining their appropriate integration into extensive clinical settings.

Assessment of estrogenic potential from exudates of microcystin-producing and non-microcystin-producing Microcystis by metabolomics, machine learning and E-screen assay

Cyanobacterial blooms, often dominated by Microcystis aeruginosa, are capable of producing estrogenic effects. It is important to identify specific estrogenic compounds produced by cyanobacteria, though this can prove challenging owing to the complexity of exudate mixtures. In this study, we used untargeted metabolomics to compare components of exudates from microcystin-producing and non-microcystin-producing M. aeruginosa strains that differed with respect to their ability to produce microcystins, and across two growth phases. We identified 416 chemicals and found that the two strains produced similar components, mainly organoheterocyclic compounds (20.2%), organic acids and derivatives (17.3%), phenylpropanoids and polyketides (12.7%), benzenoids (12.0%), lipids and lipid-like molecules (11.5%), and organic oxygen compounds (10.1%). We then predicted estrogenic compounds from this group using random forest machine learning. Six compounds (daidzin, biochanin A, phenylethylamine, rhein, o-Cresol, and arbutin) belonging to phenylpropanoids and polyketides (3), benzenoids (2), and organic oxygen compound (1) were tested and exhibited estrogenic potency based upon the E-screen assay. This study confirmed that both Microcystis strains produce exudates that contain compounds with estrogenic properties, a growing concern in cyanobacteria management.

Bioactives and metabolites of Tetrastigma hemsleyanum root extract alleviate DSS-induced ulcerative colitis by targeting the SYK protein in the B cell receptor signaling pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Tetrastigma hemsleyanum is an endemic Chinese herb with a wide range of pharmacological activities, including anti-inflammatory, antiviral, antioxidant, antitumor, and immunomodulatory activities. However, the effect and mechanisms of the anti-inflammatory activity of T. hemsleyanum root extract against dextran sodium sulfate (DSS)-induced ulcerative colitis (UC) have not yet been fully investigated.

AIM OF THE STUDY

This study aimed to explore the therapeutic effect and molecular mechanisms of T. hemsleyanum root extract in DSS-induced UC mice and knockdown cells.

MATERIALS AND METHODS

T. hemsleyanum root extract was obtained and analyzed by high-performance liquid chromatography (HPLC). The therapeutic effects of T. hemsleyanum root extract on DSS-induced UC mice were evaluated by the disease activity index (DAI) score, colon length, serum inflammatory cytokines and oxidant/antioxidant levels, and histopathological features of the ileum and colon. Genome-wide gene expression profiles of ileal and colonic tissues were collected by transcriptomics, and signaling pathways were analyzed by the KEGG database. UC-related pathways were uploaded to the STRING database, then the protein-protein interactions (PPIs) were determined by Cytoscape, and the enriched genes were evaluated by real-time quantitative PCR (qPCR). The protein-ligand complexes were docked by AutoDock, and the genes were knocked down in Caco-2 cells by shRNA. The non-targeted metabolomic profiling of ileal contents was analyzed by ultra-high-performance liquid chromatography (UHPLC), and gut microflora were sequenced by an Illumina MiSeq System.

RESULTS

Ten components that alleviated UC symptoms in mice by decreasing the DAI and serum inflammatory cytokines and oxidant levels, promoting intestinal development, and increasing serum antioxidant levels were identified in T. hemsleyanum root extract. T. hemsleyanum root extract activated the B cell receptor signaling pathway in the colon tissue of UC mice, in which two components, rutin and astragaline, bound to the spleen tyrosine kinase (SYK) protein but also restored gut microflora diversity and increased the proportion of probiotics. Furthermore, metabolites of T. hemsleyanum root extract were involved in vitamin metabolism, fatty acid metabolism, and ferroptosis.

CONCLUSIONS

The rutin and astragaline components of T. hemsleyanum root extract, by binding to SYK protein, activated the B cell receptor signaling pathway and restored gut microflora diversity to alleviate UC symptoms in mice.

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.

Dopamine modification of glycolytic enzymes impairs glycolysis: possible implications for Parkinson's disease

BACKGROUND

Parkinson's disease (PD), a chronic and severe neurodegenerative disease, is pathologically characterized by the selective loss of nigrostriatal dopaminergic neurons. Dopamine (DA), the neurotransmitter produced by dopaminergic neurons, and its metabolites can covalently modify proteins, and dysregulation of this process has been implicated in neuronal loss in PD. However, much remains unknown about the protein targets.

METHODS

In the present work, we designed and synthesized a dopamine probe (DA-P) to screen and identify the potential protein targets of DA using activity-based protein profiling (ABPP) technology in combination with liquid chromatography-tandem mass spectrometry (LC-MS/MS). In situ pull-down assays, cellular thermal shift assays (CETSAs) and immunofluorescence were performed to confirm the DA modifications on these hits. To investigate the effects of DA modifications, we measured the enzymatic activities of these target proteins, evaluated glycolytic stress and mitochondrial respiration by Seahorse tests, and systematically analyzed the changes in metabolites with unbiased LC-MS/MS-based non-targeted metabolomics profiling.

RESULTS

We successfully identified three glycolytic proteins, aldolase A, α-enolase and pyruvate kinase M2 (PKM2), as the binding partners of DA. DA bound to Glu166 of α-enolase, Cys49 and Cys424 of PKM2, and Lys230 of aldolase A, inhibiting the enzymatic activities of α-enolase and PKM2 and thereby impairing ATP synthesis, resulting in mitochondrial dysfunction.

CONCLUSIONS

Recent research has revealed that enhancing glycolysis can offer protection against PD. The present study identified that the glycolytic pathway is vulnerable to disruption by DA, suggesting a promising avenue for potential therapeutic interventions. Safeguarding glycolysis against DA-related disruption could be a potential therapeutic intervention for PD.

Single-polyp metabolomics reveals biochemical structuring of the coral holobiont at multiple scales

All biology happens in space, and spatial structuring plays an important role in mediating biological processes at all scales from cells to ecosystems. However, the metabolomic structuring of the coral holobiont has yet to be fully explored. Here, we present a method to detect high-quality metabolomic data from individual coral polyps and apply this method to study the patterning of biochemicals across multiple spatial (~1 mm - ~100 m) and organizational scales (polyp to population). The data show a strong signature for individual coral colonies, a weaker signature of branches within colonies, and variation at the polyp level related to the polyps' location along a branch. Mapping metabolites to either the coral or algal components of the holobiont reveals that polyp-level variation along the length of a branch was largely driven by molecules associated with the cnidarian host as opposed to the algal symbiont, predominantly putative sulfur-containing metabolites. This work yields insights on the spatial structuring of biochemicals in the coral holobiont, which is critical for design, analysis, and interpretation of studies on coral reef biochemistry.

Integrated metabolomics and network pharmacology revealing the mechanism of arsenic-induced hepatotoxicity in mice

Endemic arsenic (As) poisoning is a severe biogeochemical disease that endangers human health. Epidemiological investigations and animal experiments have confirmed the damaging effects of As on the liver, but there is an urgent need to investigate the underlying mechanisms. This study adopted a metabolomic approach using UHPLC-QE/MS to identify the different metabolites and metabolic mechanisms associated with As-induced hepatotoxicity in mice. A network pharmacology approach was applied to predict the potential target of As-induced hepatotoxicity. The predicted targets of differential metabolites were subjected to a deep matching for elucidating the integration mechanisms. The results demonstrate that the levels of ALT and AST in plasma significantly increased in mice after As exposure. In addition, the liver tissue showed disorganized liver lobules, lax cytoplasm and inflammatory cell infiltration. Metabolomic analysis revealed that As exposure caused disturbance to 40 and 75 potential differential metabolites in plasma and liver, respectively. Further investigation led to discovering five vital metabolic pathways, including phenylalanine, tyrosine, and tryptophan biosynthesis and nicotinate and nicotinamide metabolism pathways. These pathways may responded to As-induced hepatotoxicity primarily through lipid metabolism, apoptosis, and deoxyribonucleic acid damage. The network pharmacology suggested that As could induce hepatotoxicity in mice by acting on targets including Hsp90aa1, Akt2, Egfr, and Tnf, which regulate PI3K Akt, HIF-1, MAPK, and TNF signaling pathways. Finally, the integrated metabolomics and network pharmacology revealed eight key targets associated with As-induced hepatoxicity, namely DNMT1, MAOB, PARP1, MAOA, EPHX2, ANPEP, XDH, and ADA. The results also suggest that nicotinic acid and nicotinamide metabolisms may be involved in As-induced hepatotoxicity. This research identified the metabolites, targets, and mechanisms of As-induced hepatotoxicity, offering meaningful insights and establishing the groundwork for developing antidotes for widespread As poisoning.