Identification Of Metabolites Research Articles

Environmental metabolomics characterization of modern stromatolites and annotation of ibhayipeptolides.

Lithified layers of complex microbial mats known as microbialites are ubiquitous in the fossil record, and modern forms are increasingly identified globally. A key challenge to developing an understanding of microbialite formation and environmental role is how to investigate complex and diverse communities in situ. We selected living, layered microbialites (stromatolites) in a peritidal environment near Schoenmakerskop, Eastern Cape, South Africa to conduct a spatial survey mapping the composition and small molecule production of the microbial communities from environmental samples. Substrate core samples were collected from nine sampling stations ranging from the upper point of the freshwater inflow to the lower marine interface where tidal overtopping takes place. Substrate cores provided material for parallel analyses of microbial community diversity by 16S rRNA gene amplicon sequencing and metabolomics using LC-MS2. Species and metabolite diversities were correlated, and prominent specialized metabolites were targeted for preliminary characterization. A new series of cyclic hexadepsipeptides, named ibhayipeptolides, was most abundant in substrate cores of submerged microbialites. These results demonstrate the detection and identification of metabolites from mass-limited environmental samples and contribute knowledge about microbialite chemistry and biology, which facilitates future targeted studies of specialized metabolite function and biosynthesis.

In-depth organic mass cytometry reveals differential contents of 3-hydroxybutanoic acid at the single-cell level

Comprehensive single-cell metabolic profiling is critical for revealing phenotypic heterogeneity and elucidating the molecular mechanisms underlying biological processes. However, single-cell metabolomics remains challenging because of the limited metabolite coverage and inability to discriminate isomers. Herein, we establish a single-cell metabolomics platform for in-depth organic mass cytometry. Extended single-cell analysis time guarantees sufficient MS/MS acquisition for metabolite identification and the isomers discrimination while online sampling ensures the high-throughput of the method. The largest number of identified metabolites (approximately 600) are achieved in single cells and fine subtyping of MCF-7 cells is first demonstrated by an investigation on the differential levels of 3-hydroxybutanoic acid among clusters. Single-cell transcriptome analysis reveals differences in the expression of 3-hydroxybutanoic acid downstream antioxidative stress genes, such as metallothionein 2 (MT2A), while a fluorescence-activated cell sorting assay confirms the positive relationship between 3-hydroxybutanoic acid and target proteins; these results suggest that the heterogeneity of 3-hydroxybutanoic acid provides cancer cells with different ability to resist surrounding oxidative stress. Our method paves the way for deep single-cell metabolome profiling and investigations on the physiological and pathological processes that occur during cancer.

Exploring anti-inflammatory non-essential oil metabolites in Mentha canadensis: Insights into neutrophil extracellular trap inhibition for functional health promotion

Mentha canadensis, commonly known as Canadian mint, has been utilized in herbal medicine for its menthol content, addressing digestive and respiratory issues. It also serves as a popular culinary herb, adding a refreshing minty flavor to dishes and beverages. However, research on its non-essential oils and their anti-inflammatory potential is lacking. This study utilized multi-informative molecular networking to comprehensively profile the non-essential oils of M. canadensis. Cinnamic acid derivatives were identified as potent anti-inflammatory agents, with high activity ratings. By analyzing the Molecular Networking (MN) database, fourteen metabolites were identified. Compounds within the bioactive MN clusters demonstrate significant inhibition of reactive oxygen species generation, playing a crucial role in the management of oxidative stress. These compounds also effectively suppressed the formation of neutrophil extracellular traps. In conclusion, salvianolic acid B (IC50: 0.08 μM) was promoted as the chemical marker of M. canadensis for its potential anti-inflammatory effect. This study pioneers the identification of anti-inflammatory metabolites in M. canadensis' non-essential oils, offering insights into its therapeutic properties and potential for health-centric culinary applications.

Changes in urinary metabolomic profile show the effectiveness of a nutritional intervention in children 6–12 years old: The ALINFA study

AbstractDiet plays an essential role in health and disease. Therefore, its determination is an important component of many investigations. The aim of the study was to evaluate the effect of a nutritional intervention on the urinary metabolome in children aged 6–12 years. Also, it was intended to identify biomarkers of diet quality and dietary intake. A 2‐month, randomized, controlled, parallel trial was conducted in Spanish children. The analyses focused on the ALINFA group, which followed a full‐fixed meal plan including healthy products, ready‐to‐eat meals, and healthy recipes. Diet quality was assessed by the KIDMED index and dietary intake by a food frequency questionnaire. Untargeted metabolomic analysis on urine samples was carried out, and multivariate analyses were performed for pattern recognition and characteristic metabolite identification. PLS‐DA and Volcano plot analyses were performed to identify the discriminating metabolites of this group. 12 putative metabolites were found to be the most relevant to this intervention. Most of them were products derived from protein and amino acid metabolism (N‐Ribosylhistidine, indolacrylic acid, and peptides) and lipid metabolism (3‐oxo‐2‐pentylcyclopentane‐1‐hexanoic acid methyl, Suberoyl‐L‐carnitine, and 7‐Dehydrodichapetalin E). All these metabolites decreased after the intervention, which was mainly associated with a decrease in the consumption of fatty meat and total fat, especially saturated fat. In turn, N‐Ribosylhistidine and Suberoyl‐L‐carnitine were negatively associated with diet quality, as well as able to predict the change in KIDMED index. In conclusion, the changes observed in urinary metabolome demonstrate the effectiveness of the ALINFA nutritional intervention.

Causality of Genetically Determined Metabolites on Chronic Kidney Disease: A Two-Sample Mendelian Randomization Study In Silico.

Introduction: Chronic kidney disease (CKD) is associated with metabolic disorders. However, the evidence for the causality of circulating metabolites to promote or prevent CKD is still lacking. Methods: The two-sample Mendelian randomization (MR) analysis was conducted to evaluate the latent causal relationship between the genetically proxied 486 blood metabolites and CKD. Genome-wide association study (GWAS) data for exposures were derived from 7824 European GWAS on metabolite levels, which have been extensively utilized in the medical field to elucidate the mechanisms underlying disease onset and progression. The random inverse variance weighted (IVW) is the primary analysis for causality analysis while MR-Egger and weighted median as complementary analyses. For the further identification of metabolites, reverse MR and linkage disequilibrium score regression were performed for further evaluation. The drug target for N-acetylornithine was subsequently supplemented into the analysis, with MR and colocalization analysis being utilized. Key metabolic pathways were identified via MetaboAnalyst 4.0 (https://www.metaboanalyst.ca/) online website. Results: N-acetylornithine was identified as a reliable metabolite that increases the susceptibility to estimated glomerular filtration rate (eGFR) decrease (β = 0.047; 95% confidence interval: -0.068 to -0.026; PIVW = 1.5E-5). The "glyoxylate and dicarboxylate metabolism" pathway showed significant relevance to CKD development (P = 6E-4), whereas the "glycine, serine, and threonine metabolism" pathway was also recognized as associated with CKD by general practitioners (P = 7E-4). Colocalization analysis revealed a robust genetic link between N-acetylornithine and both CKD and eGFR, with 85.1% and 99.4% colocalization rates, respectively. IVW-MR analysis substantiated these findings with a significant positive association for CKD (odds ratio = 1.43, P = 4.7E-5) and a negative correlation with eGFR (b = -0.04, P = 1.13E-31). Conclusions: MR was utilized to explore the potential causal links between 61 genetic serum metabolites and CKD. N-acetylornithine and NAT8 were further explored as a potential therapeutic target for CKD treatment.

Identification of Secondary Metabolites from Actinomycetes Isolated from the Hilly Region of Nepal

Actinomycetes are a unique class of Gram-positive bacteria with a fungus-like filamentous mycelium. They are often called ‘antibiotics factories’ because they can produce many potent secondary metabolites. At present, multidrug-resistant (MDR) pathogens are the main problem for humans and animals and to overcome this problem, more potent antibiotics are urgently required. Fortunately, Nepal has great geographical and climatic diversity serving as a home for various actinomycetes. This study identified several actinomycetes using morphological and biochemical assays from seven soil samples collected from different elevations in Nepal. The primary objective of this study was to screen the potent metabolites-producing actinomycetes. For this purpose, antimicrobial assays of seven isolates were carried out against Escherichia coli ATCC 25922, Salmonella typhi ATCC 14028, Shigella sonnei ATCC 25931, and Staphylococcus aureus ATCC 43300. The sample BT36 demonstrated the highest zone of inhibition against the tested microorganisms among others. Similarly, minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of all isolates were carried out. Among them, the MIC of samples BT36, PT7, and AB1 were found to be 1.952 mg/mL, 0.012 mg/mL, and 0.029 mg/mL, respectively. Ethyl acetate extracts of resulting potent isolates PT7 and AB1 were subjected to mass spectrometric analysis. A total of 22 bioactive compounds were annotated from liquid chromatography-high resolution mass spectroscopy (LC-HRMS), including epopromycin A, myxopyronin B, gilvocarcin HE, and okilactomycin A. It is concluded that this study will be useful in setting up a strategy to isolate useful secondary metabolites from soil microbes from the hilly region of Nepal.

Ion Moblity Mass Spectrometry in Multi-omics Studies

Mass spectrometry (MS) as an analytical technique enables the identification and quantitative determination of proteins, metabolites, or lipids in a studied sample. However, this method has limitations regarding the number of molecules that can be identified at a given time. To increase the number of identifications, the application of ion mobility spectrometry (IMS) can be employed. This technique allows the separation of ions based on their mobility while traversing the analyser in a gradient of an electromagnetic field and opposing gas pressure. The separation is performed in conjunction with MS analysis, adding another dimension to the analysis, resulting in a significant improvement in the number of identified compounds and a reduction in noise. Alternatively, while maintaining the same number of identifications, analysis can be performed in a shorter time period. It is crucial to pay special attention to the type of IMS analyser used, as its specific implementation dictates further stages of analysis and ion detection capabilities.

Metabolomics analysis using matrix-assisted diffusion-ordered spectroscopy (DOSY) and its application in acrylamide-induced cardiovascular toxicity

Untargeted NMR-based metabolomics strategies are increasingly applied for metabolite screening in a wide variety of medical conditions. However, the identification of metabolites remains a great challenge in NMR metabolomics research. Here, we used matrix-assisted diffusion-ordered spectroscopy (DOSY) to identify new biomarkers in body fluids. DOSY-based metabolomics can exploit the unique strengths of NMR and, thanks to improvements in its resolution, can be successfully used to address a significantly wider range of biological issues. In this study, a metabolite model consisting of ten common metabolites was employed to optimize the matrix-assisted DOSY experimental parameters. Polyvinylpyrrolidone (PVP), a high-molecular-weight polymer, was found to be the most appropriate matrix with which to accomplish the separation of the mixture components. 1H NMR and PVP-assisted DOSY analyses were performed to characterize the metabolic profile of serum samples from acrylamide (ACR)-treated rats and study the mechanism of ACR-induced cardiovascular toxicity. Biomarker identification by 1H NMR and PVP-assisted DOSY yielded similar results. However, multivariate statistical analysis showed that, compared with 1H NMR, PVP-assisted DOSY achieved a better discrimination of the serum samples obtained from rats treated with different doses of ACR. A total of 11 differential metabolites were identified. An enrichment analysis of the metabolic pathways showed that, in rats treated with a high dose of ACR, the glutamate and glutathione metabolic pathways and the energy metabolism pathway of the tricarboxylic acid cycle were mainly affected.

Nuclear Magnetic Resonance (NMR) Metabolomics: Current Applications in Equine Health Assessment.

Metabolomics can allow for the comprehensive identification of metabolites within biological systems, at given time points, in physiological and pathological conditions. In the last few years, metabolomic analysis has gained popularity both in human and in veterinary medicine, showing great potential for novel applications in clinical activity. The aim of applying metabolomics in clinical practice is understanding the mechanisms underlying pathological conditions and the influence of certain stimuli (i.e., drugs, nutrition, exercise) on body systems, in the attempt of identifying biomarkers that can help in the diagnosis of diseases. Proton Nuclear Magnetic Resonance spectroscopy (1H-NMR) is well tailored to be used as an analytical platform for metabolites' detection at the base of metabolomics studies, due to minimal sample preparation and high reproducibility. In this mini-review article, the scientific production of NMR metabolomic applications to equine medicine is examined. The research works are very different in methodology and difficult to compare. Studies are mainly focused on exercise, reproduction, and nutrition, other than respiratory and musculoskeletal diseases. The available information on this topic is still scant, but a greater collection of data could allow researchers to define new reliable markers to be used in clinical practice for diagnostic and therapeutical purposes.

Identification of New Hepatic Metabolites of Miconazole by Biological and Electrochemical Methods Using Ultra-High-Performance Liquid Chromatography Combined with High-Resolution Mass Spectrometry.

The main objective of this study was to investigate the metabolism of miconazole, an azole antifungal drug. Miconazole was subjected to incubation with human liver microsomes (HLM) to mimic phase I metabolism reactions for the first time. Employing a combination of an HLM assay and UHPLC-HRMS analysis enabled the identification of seven metabolites of miconazole, undescribed so far. Throughout the incubation with HLM, miconazole underwent biotransformation reactions including hydroxylation of the benzene ring and oxidation of the imidazole moiety, along with its subsequent degradation. Additionally, based on the obtained results, screen-printed electrodes (SPEs) were optimized to simulate the same biotransformation reactions, by the use of a simple, fast, and cheap electrochemical method. The potential toxicity of the identified metabolites was assessed using various in silico models.

Untargeted Metabolomics Based on Ultra-High-Performance Liquid Chromatography Coupled with Quadrupole Orbitrap High-Resolution Mass Spectrometry for Differential Metabolite Analysis of Pinelliae Rhizoma and Its Adulterants.

The present study investigates the chemical composition variances among Pinelliae Rhizoma, a widely used Chinese herbal medicine, and its common adulterants including Typhonium flagelliforme, Arisaema erubescens, and Pinellia pedatisecta. Utilizing the non-targeted metabolomics technique of employing UHPLC-Q-Orbitrap HRMS, this research aims to comprehensively delineate the metabolic profiles of Pinelliae Rhizoma and its adulterants. Multivariate statistical methods including PCA and OPLS-DA are employed for the identification of differential metabolites. Volcano plot analysis is utilized to discern upregulated and downregulated compounds. KEGG pathway analysis is conducted to elucidate the differences in metabolic pathways associated with these compounds, and significant pathway enrichment analysis is performed. A total of 769 compounds are identified through metabolomics analysis, with alkaloids being predominant, followed by lipids and lipid molecules. Significant differential metabolites were screened out based on VIP > 1 and p-value < 0.05 criteria, followed by KEGG enrichment analysis of these differential metabolites. Differential metabolites between Pinelliae Rhizoma and Typhonium flagelliforme, as well as between Pinelliae Rhizoma and Pinellia pedatisecta, are significantly enriched in the biosynthesis of amino acids and protein digestion and absorption pathways. Differential metabolites between Pinelliae Rhizoma and Arisaema erubescens are mainly enriched in tyrosine metabolism and phenylalanine metabolism pathways. These findings aim to provide valuable data support and theoretical references for further research on the pharmacological substances, resource development and utilization, and quality control of Pinelliae Rhizoma.

Development of an analytical platform for the affinity screening of natural extracts by SEC-MS towards PPARα and PPARγ receptors

BackgroundPeroxisome proliferator-activated receptors (PPARs) belong to the superfamily of nuclear receptors and represent the targets for the therapeutical treatment of type 2 diabetes, dyslipidemia and hyperglycemia associated with metabolic syndrome. Some medicinal plants have been traditionally used to treat this kind of metabolic diseases. Today only few drugs targeting PPARs have been approved and for this reason, the rapid identification of novel ligands and/or chemical scaffolds starting from natural extracts would benefit of a selective affinity ligand fishing assay. ResultsIn this paper we describe the development of a new ligand fishing assay based on size exclusion chromatography (SEC) coupled to LC-MS for the analysis of complex samples such as botanical extracts. The known PPARα and PPARγ ligands, WY-14643 and rosiglitazone respectively, were used for system development and evaluation. The system has found application on an Allium lusitanicum methanolic extract, containing saponins, a class of chemical compounds which have attracted interest as PPARs ligands because of their hypolipidemic and insulin-like properties. SignificanceA new SEC-AS-MS method has been developed for the affinity screening of PPARα and PPARγ ligands. The system proved to be highly specific and will be used to improve the throughput for the identification of new selective metabolites from natural souces targeting PPARα and PPARγ.

In vitro antileishmanial activities of hydro-methanolic crude extracts and solvent fractions of Clematis simensis fresen leaf, and Euphorbia abyssinica latex.

As a result of increasing drug resistance, crossover resistance development, prolonged therapy, and the absence of different agents with innovative methods for implementation, the efficacy of recent antileishmanial medications is severely declining. So, it is vital to look for other medications from botanical remedies that have antileishmanial activity. The latex of Euphorbia abyssinica (E abyssinica) and the leaves of Clematis simensis fresen (C simensis) were macerated in methanol (80%). In vitro antileishmanial activity of the preparation was tried on promastigotes of Leishmania aethiopica (L aethiopica) and Leishmania donovani (L donovani) using resazurin assay, and fluorescence intensity was measured. One percent of dimethyl sulfoxide (DMSO) and media as negative control and amphotericin B as positive control were used. Additionally, hemolytic & phytochemical tests of the preparation were done. The mean and standard errors of each extract were evaluated and interpreted for statistical significance using one-way analysis of variance. From sigmoidal dose-response curves of % inhibition, half maximal inhibitory concentration (IC50) values were determined by GraphPad Prism and Microsoft Excel; outcomes were presented as mean ± standard error of mean of triplicate trials. P < .05 was statistical significance. The phytochemical screening of C simensis and E abyssinica confirmed the existence of steroids, phenols, tannins, saponins, alkaloids, terpenoids, flavonoids and glycosides. C simensis possesses antileishmanial activity with IC50 outcomes of 46.12 ± 0.03 and 8.18 ± 0.10 µg/mL on the promastigotes of L aethiopica and L donovani, respectively. However, E abyssinica showed stronger activity with IC50 outcomes of 16.07 ± 0.05 µg/mL and 4.82 ± 0.07 µg/mL on L aethiopica and L donovani, respectively. C simensis and E abyssinica have a less hemolytic effect on human red blood cells at low concentrations. The outcomes from this investigation demonstrated that the preparation of C simensis and E abyssinica indicated significant antileishmanial activity. Therefore, further in vivo assessment of antileishmanial, cytotoxicity activity and quantitative identification of secondary metabolites are highly recommended.

Software-aided efficient identification of the components of compound formulae and their metabolites in rats by UHPLC/IM-QTOF-MS and an in-house high-definition MS2 library: Sishen Formula as a case

Identifying the compound formulae-related xenobiotics in bio-samples is full of challenges. Conventional strategies always exhibit the insufficiencies in overall coverage, analytical efficiency, and degree of automation, and the results highly rely on the personal knowledge and experience. The goal of this work was to establish a software-aided approach, by integrating ultra-high performance liquid chromatography/ion-mobility quadrupole time-of-flight mass spectrometry and in-house high-definition MS2 library, to enhance the identification of prototypes and metabolites of the compound formulae in vivo, taking Sishen formula (SSF) as a template. Seven different MS2 acquisition methods were compared, which demonstrated the potency of a hybrid scan approach (namely HDDIDDA) in the identification precision, MS1 coverage, and MS2 spectra quality. The HDDIDDA data for 55 reference compounds, four component drugs, and SSF, together with the rat bio-samples (e.g., plasma, urine, feces, liver, and kidney), were acquired. Based on the UNIFI™ platform (Waters), the efficient data processing workflows were established by combining mass defect filtering (MDF)-induced classification, diagnostic product ions (DPIs), and neutral loss filtering (NLF)-dominated structural confirmation. The high-definition MS2 spectral libraries, dubbed in vitro-SSF and in vivo-SSF, were elaborated, enabling the efficient and automatic identification of SSF-associated xenobiotics in diverse rat bio-samples. Consequently, 118 prototypes and 206 metabolites of SSF were identified, with the identification rate reaching 80.51% and 79.61%, respectively. The metabolic pathways mainly involved the oxidation, reduction, hydrolysis, sulfation, methylation, demethylation, acetylation, glucuronidation, and the combined reactions. Conclusively, the proposed strategy can drive the identification of compound formulae-related xenobiotics in vivo in an intelligent manner.

Identification of renal protective gut microbiome derived-metabolites in diabetic chronic kidney disease: An integrated approach using network pharmacology and molecular docking

Metabolites from the gut microbiota define molecules in the gut-kidney cross talks. However, the mechanistic pathway by which the kidneys actively sense gut metabolites and their impact on diabetic chronic kidney disease (DCKD) remains unclear. This study is an attempt to investigate the gut microbiome metabolites, their host targeting genes, and their mechanistic action against DCKD.Gut microbiome, metabolites, and host targets were extracted from the gutMgene database and metabolites from the PubChem database. DCKD targets were identified from DisGeNET, GeneCard, NCBI, and OMIM databases. Computational examination such as protein–protein interaction networks, enrichment pathway, identification of metabolites for potential targets using molecular docking, hubgene-microbes-metabolite-samplesource-substrate (HMMSS) network architecture were executed using Network analyst, ShinyGo, GeneMania, Cytoscape, Autodock tools. There were 574 microbial metabolites, 2861 DCKD targets, and 222 microbes targeting host genes. After screening, we obtained 27 final targets, which are used for computational examination. From enrichment analysis, we found NF-ΚB1, AKT1, EGFR, JUN, and RELA as the main regulators in the DCKD development through mitogen activated protein kinase (MAPK) pathway signalling. The (HMMSS) network analysis found F.prausnitzi, B.adolescentis,and B.distasonisprobiotic bacteriafound in the intestinal epithelium, colonic region, metabolize the substrates like tryptophan, other unknown substrates might have direct interaction with the NF-kB1 and epidermal growth factor receptor (EGFR) targets. Docking of these target proteins with 3- Indole propionic acid (IPA) showed high binding energy affinity.From this study we identified, the 3 IPA produced byF. prausnitzi A2-165was found to have renal sensing properties inhibiting MAPK/NF-KB1 inflammatory pathway and would be useful in treating CKD in diabetics.

Metabolic profiling of lumateperone in vitro and in vivo by UPLC-Q Exactive Orbitrap HRMS, and its pharmacokinetic study in rat plasma by LC-MS/MS

Lumateperone is a novel agent approved by FDA for treatment of schizophrenia in adults. To elucidate the species differences in the of biotransformation of lumateperone and its PK characteristics in rats, the metabolite identification of lumateperone was carried out in rat, dog and human liver microsomes, and rat plasma after oral administration using UPLC-Q Exactive Orbitrap high-resolution mass spectrometry HRMS. Furtherly, the PK characteristics of lumateperone and its N-demethylated metabolite (M3) in rat plasma were investigated using a validated LC-MS/MS method following intravenous and oral administration. Fourteen phase I metabolites were found in liver microsomes and ten of them were observed in rat plasma. N-demethylation, carbonylation, dehydrogenation, and piperazine ring cleavage were main metabolic pathway of lumateperone. No unique metabolites were formed in human liver microsomes. After rapid absorption in rats, lumateperone was quickly metabolized and eliminated with bioavailability of less than 5%. The exposure level of M3 was about 1.5-fold higher than that of lumateperone in rat plasma. Lumatperone underwent extensive metabolism and was absorbed rapidly in rats. Metabolite M3 had equivalent or slightly higher exposure levels than lumateperone. This study provides essential PK information to facilitate further pharmacodynamic researches of lumateperone.

Identification of active main metabolites of anti-infective inhibitors of the macrophage infectivity potentiator protein by liquid chromatography using mass detection.

Due to increasing antibiotic resistance, the development of anti-infectives with new mechanisms of action is crucial. Virulence factors such as the "macrophage infectivity potentiator" (Mip) protein, which catalyzes the folding of proline-containing proteins by means of their cis-trans isomerase (PPIase) activity, have come into focus as a potential new target. Since the inhibition of Mip by small molecules has been shown to lead to reduced virulence and survival in vitro, especially of Gram-negative bacteria such as Burkholderia pseudomallei (Bp), Neisseria meningitidis (Nm), and Neisseria gonorrhoeae (Ng), or Coxiella burnetii (Cb), among many others, a library of Mip inhibitors was developed. As drug metabolism has a significant impact on the overall therapeutic outcome, this report describes the biotransformation of the most potent Mip inhibitors. Therefore, the anti-infectives were treated using human liver microsomes in vitro. Liquid chromatography with tandem mass spectrometry (LC/MS-MS) methods were applied to identify the metabolites and quantify the metabolic degradation of the hit compounds. Active metabolites, N-oxides, were found, leading to new opportunities for further drug development.

Computational methods for processing and interpreting mass spectrometry-based metabolomics.

Metabolomics has emerged as an indispensable tool for exploring complex biological questions, providing the ability to investigate a substantial portion of the metabolome. However, the vast complexity and structural diversity intrinsic to metabolites imposes a great challenge for data analysis and interpretation. Liquid chromatography mass spectrometry (LC-MS) stands out as a versatile technique offering extensive metabolite coverage. In this mini-review, we address some of the hurdles posed by the complex nature of LC-MS data, providing a brief overview of computational tools designed to help tackling these challenges. Our focus centers on two major steps that are essential to most metabolomics investigations: the translation of raw data into quantifiable features, and the extraction of structural insights from mass spectra to facilitate metabolite identification. By exploring current computational solutions, we aim at providing a critical overview of the capabilities and constraints of mass spectrometry-based metabolomics, while introduce some of the most recent trends in data processing and analysis within the field.

Echinacea: Bioactive Compounds and Agronomy.

For centuries, medicinal plants have been used as sources of remedies and treatments for various disorders and diseases. Recently, there has been renewed interest in these plants due to their potential pharmaceutical properties, offering natural alternatives to synthetic drugs. Echinacea, among the world's most important medicinal plants, possesses immunological, antibacterial, antifungal, and antiviral properties. Nevertheless, there is a notable lack of thorough information regarding the echinacea species, underscoring the vital need for a comprehensive review paper to consolidate existing knowledge. The current review provides a thorough analysis of the existing knowledge on recent advances in understanding the physiology, secondary metabolites, agronomy, and ecology of echinacea plants, focusing on E. purpurea, E. angustifolia, and E. pallida. Pharmacologically advantageous effects of echinacea species on human health, particularly distinguished for its ability to safeguard the nervous system and combat cancer, are discussed. We also highlight challenges in echinacea research and provide insights into diverse approaches to boost the biosynthesis of secondary metabolites of interest in echinacea plants and optimize their large-scale farming. Various academic databases were employed to carry out an extensive literature review of publications from 2001 to 2024. The medicinal properties of echinacea plants are attributed to diverse classes of compounds, including caffeic acid derivatives (CADs), chicoric acid, echinacoside, chlorogenic acid, cynarine, phenolic and flavonoid compounds, polysaccharides, and alkylamides. Numerous critical issues have emerged, including the identification of active metabolites with limited bioavailability, the elucidation of specific molecular signaling pathways or targets linked to echinacoside effects, and the scarcity of robust clinical trials. This raises the overarching question of whether scientific inquiry can effectively contribute to harnessing the potential of natural compounds. A systematic review and analysis are essential to furnish insights and lay the groundwork for future research endeavors focused on the echinacea natural products.

Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation.

Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.