Metabolites

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26 pages, 9061 KiB

Open AccessArticle

Bayesian ¹³C-Metabolic Flux Analysis of Parallel Tracer Experiments in Granulocytes: A Directional Shift within the Non-Oxidative Pentose Phosphate Pathway Supports Phagocytosis

by Melanie Hogg, Eva-Maria Wolfschmitt, Ulrich Wachter, Fabian Zink, Peter Radermacher and Josef Albert Vogt

Metabolites 2024, 14(1), 24; https://doi.org/10.3390/metabo14010024 - 29 Dec 2023

Cited by 3 | Viewed by 2293

Abstract

The pentose phosphate pathway (PPP) plays a key role in the cellular regulation of immune function; however, little is known about the interplay of metabolic adjustments in granulocytes, especially regarding the non-oxidative PPP. For the determination of metabolic mechanisms within glucose metabolism, we [...] Read more.

The pentose phosphate pathway (PPP) plays a key role in the cellular regulation of immune function; however, little is known about the interplay of metabolic adjustments in granulocytes, especially regarding the non-oxidative PPP. For the determination of metabolic mechanisms within glucose metabolism, we propose a novel set of measures for ¹³C-metabolic flux analysis based on ex vivo parallel tracer experiments ([1,2-¹³C]glucose, [U-¹³C]glucose, [4,5,6-¹³C]glucose) and gas chromatography–mass spectrometry labeling measurements of intracellular metabolites, such as sugar phosphates and their fragments. A detailed constraint analysis showed that the permission range for net and irreversible fluxes was limited to a three-dimensional space. The overall workflow, including its Bayesian flux estimation, resulted in precise flux distributions and pairwise confidence intervals, some of which could be represented as a line due to the strength of their correlation. The principal component analysis that was enabled by these behaviors comprised three components that explained 99.6% of the data variance. It showed that phagocytic stimulation reversed the direction of non-oxidative PPP net fluxes from ribose-5-phosphate biosynthesis toward glycolytic pathways. This process was closely associated with the up-regulation of the oxidative PPP to promote the oxidative burst. Full article

(This article belongs to the Special Issue Application of Mass Spectrometry Analysis in Metabolomics)

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17 pages, 2830 KiB

Open AccessArticle

Quantitative Analytical and Computational Workflow for Large-Scale Targeted Plasma Metabolomics

by Antonia Fecke, Nay Min Min Thaw Saw, Dipali Kale, Siva Swapna Kasarla, Albert Sickmann and Prasad Phapale

Metabolites 2023, 13(7), 844; https://doi.org/10.3390/metabo13070844 - 13 Jul 2023

Cited by 3 | Viewed by 2626

Abstract

Quantifying metabolites from various biological samples is necessary for the clinical and biomedical translation of metabolomics research. One of the ongoing challenges in biomedical metabolomics studies is the large-scale quantification of targeted metabolites, mainly due to the complexity of biological sample matrices. Furthermore, [...] Read more.

Quantifying metabolites from various biological samples is necessary for the clinical and biomedical translation of metabolomics research. One of the ongoing challenges in biomedical metabolomics studies is the large-scale quantification of targeted metabolites, mainly due to the complexity of biological sample matrices. Furthermore, in LC-MS analysis, the response of compounds is influenced by their physicochemical properties, chromatographic conditions, eluent composition, sample preparation, type of MS ionization source, and analyzer used. To facilitate large-scale metabolite quantification, we evaluated the relative response factor (RRF) approach combined with an integrated analytical and computational workflow. This approach considers a compound’s individual response in LC-MS analysis relative to that of a non-endogenous reference compound to correct matrix effects. We created a quantitative LC-MS library using the Skyline/Panorama web platform for data processing and public sharing of data. In this study, we developed and validated a metabolomics method for over 280 standard metabolites and quantified over 90 metabolites. The RRF quantification was validated and compared with conventional external calibration approaches as well as literature reports. The Skyline software environment was adapted for processing such metabolomics data, and the results are shared as a “quantitative chromatogram library” with the Panorama web application. This new workflow was found to be suitable for large-scale quantification of metabolites in human plasma samples. In conclusion, we report a novel quantitative chromatogram library with a targeted data analysis workflow for biomedical metabolomic applications. Full article

(This article belongs to the Special Issue Application of Mass Spectrometry Analysis in Metabolomics)

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14 pages, 2227 KiB

Open AccessArticle

The Virulence Factor Macrophage Infectivity Potentiator (Mip) Influences Branched-Chain Amino Acid Metabolism and Pathogenicity of Legionella pneumophila

by Fabian Nikolka, Mustafa Safa Karagöz, Mohamed Zakaria Nassef, Karsten Hiller, Michael Steinert and Thekla Cordes

Metabolites 2023, 13(7), 834; https://doi.org/10.3390/metabo13070834 - 11 Jul 2023

Viewed by 2171

Abstract

Legionella pneumophila (Lp) is a common etiological agent of bacterial pneumonia that causes Legionnaires’ disease (LD). The bacterial membrane-associated virulence factor macrophage infectivity potentiator (Mip) exhibits peptidyl-prolyl-cis/trans-isomerase (PPIase) activity and contributes to the intra- and extracellular pathogenicity of Lp [...] Read more.

Legionella pneumophila (Lp) is a common etiological agent of bacterial pneumonia that causes Legionnaires’ disease (LD). The bacterial membrane-associated virulence factor macrophage infectivity potentiator (Mip) exhibits peptidyl-prolyl-cis/trans-isomerase (PPIase) activity and contributes to the intra- and extracellular pathogenicity of Lp. Though Mip influences disease outcome, little is known about the metabolic consequences of altered Mip activity during infections. Here, we established a metabolic workflow and applied mass spectrometry approaches to decipher how Mip activity influences metabolism and pathogenicity. Impaired Mip activity in genetically engineered Lp strains decreases intracellular replication in cellular infection assays, confirming the contribution of Mip for Lp pathogenicity. We observed that genetic and chemical alteration of Mip using the PPIase inhibitors rapamycin and FK506 induces metabolic reprogramming in Lp, specifically branched-chain amino acid (BCAA) metabolism. Rapamycin also inhibits PPIase activity of mammalian FK506 binding proteins, and we observed that rapamycin induces a distinct metabolic signature in human macrophages compared to bacteria, suggesting potential involvement of Mip in normal bacteria and in infection. Our metabolic studies link Mip to alterations in BCAA metabolism and may help to decipher novel disease mechanisms associated with LD. Full article

(This article belongs to the Special Issue Application of Mass Spectrometry Analysis in Metabolomics)

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14 pages, 2405 KiB

Open AccessEditor’s ChoiceArticle

Effects of Different Storage Conditions on Lipid Stability in Mice Tissue Homogenates

by Erika Dorochow, Robert Gurke, Samuel Rischke, Gerd Geisslinger and Lisa Hahnefeld

Metabolites 2023, 13(4), 504; https://doi.org/10.3390/metabo13040504 - 31 Mar 2023

Cited by 6 | Viewed by 2485

Abstract

Lipids are biomolecules involved in numerous (patho-)physiological processes and their elucidation in tissue samples is of particular interest. However, tissue analysis goes hand in hand with many challenges and the influence of pre-analytical factors can intensively change lipid concentrations ex vivo, compromising the [...] Read more.

Lipids are biomolecules involved in numerous (patho-)physiological processes and their elucidation in tissue samples is of particular interest. However, tissue analysis goes hand in hand with many challenges and the influence of pre-analytical factors can intensively change lipid concentrations ex vivo, compromising the results of the whole research project. Here, we study the influence of pre-analytical factors on lipid profiles during the processing of homogenized tissues. Homogenates from four different mice tissues (liver, kidney, heart, spleen) were stored at room temperature as well as in ice water for up to 120 min and analyzed via ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS). Lipid class ratios were calculated since their suitability as indicators for sample stability has been previously illustrated. Only approx. 40% of lipid class ratios were unchanged after 35 min, which was further reduced to 25% after 120 min during storage at room temperature. In contrast, lipids in tissue homogenates were generally stable when samples were kept in ice water, as more than 90% of investigated lipid class ratios remained unchanged after 35 min. Ultimately, swift processing of tissue homogenates under cooled conditions represents a viable option for lipid analysis and pre-analytical factors require more attention to achieve reliable results. Full article

(This article belongs to the Special Issue Application of Mass Spectrometry Analysis in Metabolomics)

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19 pages, 8714 KiB

Open AccessArticle

Metabolomic Analysis Demonstrates the Impacts of Polyketide Synthases PKS14 and PKS15 on the Production of Beauvericins, Bassianolide, Enniatin A, and Ferricrocin in Entomopathogen Beauveria bassiana

by Wachiraporn Toopaang, Kullyanee Panyawicha, Chettida Srisuksam, Wei-Chen Hsu, Ching-Chih Lin, Morakot Tanticharoen, Yu-Liang Yang and Alongkorn Amnuaykanjanasin

Metabolites 2023, 13(3), 425; https://doi.org/10.3390/metabo13030425 - 14 Mar 2023

Cited by 6 | Viewed by 2818

Abstract

Beauveria bassiana is a globally distributed entomopathogenic fungus that produces various secondary metabolites to support its pathogenesis in insects. Two polyketide synthase genes, pks14 and pks15, are highly conserved in entomopathogenic fungi and are important for insect virulence. However, understanding of their [...] Read more.

Beauveria bassiana is a globally distributed entomopathogenic fungus that produces various secondary metabolites to support its pathogenesis in insects. Two polyketide synthase genes, pks14 and pks15, are highly conserved in entomopathogenic fungi and are important for insect virulence. However, understanding of their mechanisms in insect pathogenicity is still limited. Here, we overexpressed these two genes in B. bassiana and compared the metabolite profiles of pks14 and pks15 overexpression strains to those of their respective knockout strains in culture and in vivo using tandem liquid chromatography-mass spectrometry (LC-MS/MS) with Global Natural Products Social Molecular Networking (GNPS). The pks14 and pks15 clusters exhibited crosstalk with biosynthetic clusters encoding insect-virulent metabolites, including beauvericins, bassianolide, enniatin A, and the intracellular siderophore ferricrocin under certain conditions. These secondary metabolites were upregulated in the pks14-overexpressing strain in culture and the pks15-overexpressing strain in vivo. These data suggest that pks14 and pks15, their proteins or their cluster components might be directly or indirectly associated with key pathways in insect pathogenesis of B. bassiana, particularly those related to secondary metabolism. Information about interactions between the polyketide clusters and other biosynthetic clusters improves scientific understanding about crosstalk among biosynthetic pathways and mechanisms of pathogenesis. Full article

(This article belongs to the Special Issue Application of Mass Spectrometry Analysis in Metabolomics)

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10 pages, 2338 KiB

Open AccessEditor’s ChoiceCommunication

Surface-Coated Acupuncture Needles as Solid-Phase Microextraction Probes for In Vivo Analysis of Bioactive Molecules in Living Plants by Mass Spectrometry

by Huiyun Cheng, Xu Zhao, Lin Zhang, Mingying Ma and Xiaoxiao Ma

Metabolites 2023, 13(2), 220; https://doi.org/10.3390/metabo13020220 - 2 Feb 2023

Cited by 4 | Viewed by 2025

Abstract

In this work, we report the coupling of solid-phase microextraction (SPME) enabled by surface-coated acupuncture needles with nano-electrospray mass spectrometry (nanoESI-MS) for the analysis of bioactive molecules in living plants. The needle tip was oxidized by a mixture of nitric acid and hydrogen [...] Read more.

In this work, we report the coupling of solid-phase microextraction (SPME) enabled by surface-coated acupuncture needles with nano-electrospray mass spectrometry (nanoESI-MS) for the analysis of bioactive molecules in living plants. The needle tip was oxidized by a mixture of nitric acid and hydrogen peroxide solution and then subject to surface coating via carbonization of paraffin. A combination of oxidation and surface coating resulted in a thin coating of carbon film, whereby the significantly increased surface area promoted both analyte enrichment and ionization for MS analysis. The analytical performances were evaluated through the characterization of small molecules, peptides and proteins. Compared with conventional nanoESI, our new strategy of employing surface-coated needles had a high salt tolerance. The streamlined experimental workflow could be completed within one minute. The linear dynamic ranges for L-histidine and L-lysine, as two representatives, were over two orders of magnitude with a limit of detection (LOD) of 3.0~5.0 ng/mL. A mark is made on the needle at 2 mm from the tip, the needle is then kept in the sample for 30 s. In vivo sampling and identification of α-tomatine and organic acids from the stem of a living tomato plant were demonstrated as a practical application, while the physiological activities of the plant were not disrupted due to the minimally invasive sampling. We anticipate that the developed strategy may be of potential use for real-time clinical and other on-site analyses. Full article

(This article belongs to the Special Issue Application of Mass Spectrometry Analysis in Metabolomics)

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14 pages, 26764 KiB

Open AccessArticle

Rapid Profiling of Metabolites Combined with Network Pharmacology to Explore the Potential Mechanism of Sanguisorba officinalis L. against Thrombocytopenia

by Yubei Dai, Kailian Zhang, Long Wang, Ling Xiong, Feihong Huang, Qianqian Huang, Jianming Wu and Jing Zeng

Metabolites 2022, 12(11), 1074; https://doi.org/10.3390/metabo12111074 - 5 Nov 2022

Cited by 2 | Viewed by 2028

Abstract

Sanguisorba officinalis L. (SO), a well-known herbal medicine, has been proven to show effect against thrombocytopenia. However, metabolites of SO in vivo are still unclear, and the underlying mechanism of SO against thrombocytopenia from the aspect of metabolites have not been [...] Read more.

Sanguisorba officinalis L. (SO), a well-known herbal medicine, has been proven to show effect against thrombocytopenia. However, metabolites of SO in vivo are still unclear, and the underlying mechanism of SO against thrombocytopenia from the aspect of metabolites have not been well elucidated. In this study, an improved analytical method combined with UHPLC-QTOF MS and a molecular network was developed for the rapid characterization of metabolites in vivo based on fragmentation patterns. Then, network pharmacology (NP) was used to elucidate the potential mechanism of SO against thrombocytopenia. As a result, a total of 1678 exogenous metabolites were detected in urine, feces, plasma, and bone marrow, in which 104 metabolites were tentatively characterized. These characterized metabolites that originated from plasma, urine, and feces were then imported to the NP analysis. The results showed that the metabolites from plasma, urine, and feces could be responsible for the pharmacological activity against thrombocytopenia by regulating the PI3K-Akt, MAPK, JAK-STAT, VEGF, chemokine, actin cytoskeleton, HIF-1, and pluripotency of stem cells. This study provides a rapid method for metabolite characterization and a new perspective of underlying mechanism study from the aspect of active metabolites in vivo. Full article

(This article belongs to the Special Issue Application of Mass Spectrometry Analysis in Metabolomics)

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Review

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16 pages, 987 KiB

Open AccessEditor’s ChoiceReview

Instrumental Drift in Untargeted Metabolomics: Optimizing Data Quality with Intrastudy QC Samples

by Andre Märtens, Johannes Holle, Brit Mollenhauer, Andre Wegner, Jennifer Kirwan and Karsten Hiller

Metabolites 2023, 13(5), 665; https://doi.org/10.3390/metabo13050665 - 16 May 2023

Cited by 15 | Viewed by 4255

Abstract

Untargeted metabolomics is an important tool in studying health and disease and is employed in fields such as biomarker discovery and drug development, as well as precision medicine. Although significant technical advances were made in the field of mass-spectrometry driven metabolomics, instrumental drifts, [...] Read more.

Untargeted metabolomics is an important tool in studying health and disease and is employed in fields such as biomarker discovery and drug development, as well as precision medicine. Although significant technical advances were made in the field of mass-spectrometry driven metabolomics, instrumental drifts, such as fluctuations in retention time and signal intensity, remain a challenge, particularly in large untargeted metabolomics studies. Therefore, it is crucial to consider these variations during data processing to ensure high-quality data. Here, we will provide recommendations for an optimal data processing workflow using intrastudy quality control (QC) samples that identifies errors resulting from instrumental drifts, such as shifts in retention time and metabolite intensities. Furthermore, we provide an in-depth comparison of the performance of three popular batch-effect correction methods of different complexity. By using different evaluation metrics based on QC samples and a machine learning approach based on biological samples, the performance of the batch-effect correction methods were evaluated. Here, the method TIGER demonstrated the overall best performance by reducing the relative standard deviation of the QCs and dispersion-ratio the most, as well as demonstrating the highest area under the receiver operating characteristic with three different probabilistic classifiers (Logistic regression, Random Forest, and Support Vector Machine). In summary, our recommendations will help to generate high-quality data that are suitable for further downstream processing, leading to more accurate and meaningful insights into the underlying biological processes. Full article

(This article belongs to the Special Issue Application of Mass Spectrometry Analysis in Metabolomics)

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