Windows Scanning Multiomics: Integrated Metabolomics and Proteomics

Shi, Jiachen; Zhao, Jialiang; Zhang, Yu; Wang, Yanan; Tan, Chin Ping; Xu, Yong-Jiang; Liu, Yuanfa

doi:10.1021/acs.analchem.3c03785

Cited by 4 publications

(4 citation statements)

References 48 publications

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“…For instance, look at metabolites D and HH1 of ferroquine detailed in work [33] (see, also Figure 9, above.) The latter fact raises a problem of broad implementation of omics-approaches to structural analysis; thus highlighting at about 5-15 % effectiveness of omics-tools for MS massive of data on metabolites in biological fluids and tissues [49,50].…”

Section: Discussionmentioning

confidence: 99%

“…Consider detail on work [11], dealing with mass spectrometric analysis of pharmaceutics in environmental samples and biota. This aspect of the mass spectrometric analysis needs to be stressed, because of not only the incompleted chemical derivatization of metabolites, side reactions, or presence of multiple analytes per m/z value are the only factors decreasing the performances of the metabolomics based annotation and identification of chemical compounds up to 5-15 % examining massive of analytes and standard spectra via database searching algorithms via omicsapproaches, as aforementioned [49,50].…”

Section: ( ) ( )mentioning

confidence: 99%

“…There is frequently incompleted chemical derivatization of metabolites; and multiple metabolites per MS peak as result from degradation or side reactions. In total, at about 5-15 % MS massive of spectral features are effectively identified and annotated via omicsapproaches [49,50].…”

Section: Introductionmentioning

confidence: 99%

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Stochastic dynamics mass spectrometric determination of ferrocenes

Ivanova

2024

Preprint

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The paper treats stochastic dynamics mass spectrometric formulas of exact data-processing of measurands; thus, answering a question: How can be inferred reliably analyte identification and annotation from mass spectrometric measurands when omics-methods produce comparable performances among structurally similar analytes? Omics-methods are treated in context of instrumental measurements and data-processing. Exact quantitative and multi-dimensional structural mass spectrometric-based methods for metabolomics contributes crucially to understand biochemical mechanisms, because of metabolites are downstream biochemical products in living systems. Reliable metabolomics provides in-depth knowledge of response within biological fluids and tissues depending on internal and external agents, which is crucial for real time monitoring of human diseases. The study deals with structural analysis of ferroquine and its metabolites in human hepatic models utilizing ultra-high accuracy mass spectrometry, static and molecular dynamics quantum chemical approaches, and chemometrics. It provides excellent-to-exact performances in examining standard organometallics ferrocene and [FeII(L)(CO)2(CH3CN)] (L = 2-(5-oxo-4,6-bis-trimethylsilanyl-3,5-dihydro-1H-cyclopenta[c]pyrrol-2-yl)-ethanesulfonic acid) (|r|=0.99992.) The analysis of ferroquine and its metabolites show |r|=0.99815–0.90814.

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Section: Discussionmentioning

confidence: 99%

Section: ( ) ( )mentioning

confidence: 99%

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Stochastic dynamics mass spectrometric determination of ferrocenes

Ivanova

2024

Preprint

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“…The dynamic changes information is a high-dimensional readout of the changes in the biochemical reaction system that provides a signature about the biological processes affected by the perturbation-induced phenotypic changes. As a quality information collection strategy, which is extraordinarily well matched with multidimensional omics investigation, the emerging of variable data-independent acquisition (vDIA) enables the acquisition of enormous mass information through fragmenting all parent ions within a predetermined mass range. − However, due to the DIA-based omics generated copious highly multiplexed data and the mixed spectra produced by various precursor ions, manual curation of the results is hardly an option. MS/MS molecular network construction, on the basis of spectral similarity and homologous mass spectrometry fragments, is considered as an effective approach in qualification and mining of unknown compounds in complex matrices .…”

Section: Introductionmentioning

confidence: 99%

3D-MPEA: A Graph Attention Model-Guided Computational Approach for Annotating Unknown Metabolites in Interactomics via Mass Spectrometry-Focused Multilayer Molecular Networking

Fan,

Jia

2024

Anal. Chem.

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The spectral matching strategy of MS2 fragment spectrograms serves as a ubiquitous method for compound characterization within the matrix. Nevertheless, challenges arise due to the deficiency of distinctions in spectra across instruments caused by coelution peak-derived fragments and incompleteness of the current spectral reference database, leading to dilemma of multidimensional omics annotation. The graph attention model embedded with long short-term memory was proposed as an optimized approach involving integrating similar MS2 spectra into molecular networks according to the isotopic ion peak cluster spacing features to collapse diverse ion species and expand the spectral reference library, which efficiently evaluated the substance capture capacity to 123.1% than classic substance perception tactics. The versatility and utility of the established annotation procedure were showcased in a study on the stimulation of pork mediated by 2,2-bis(4-hydroxyphenyl)propane and enabled the global metabolite annotation from knowns to unknowns at metabolite-lipid-protein level. On the spectra for which in silico extended spectral library search provided a group truth, 83.5−117.1% accuracy surpassed 1.2−14.3% precision after manual validation. β-Ala-His dipeptidase was first evidenced as the critical node related to the transformation of α-helical (36.57 to 35.74%) to random coil (41.53 to 42.36%) mediated by 2,2-bis(4-hydroxyphenyl)propane, ultimately triggering an augment of catalytic performance, inducing a series of oxidative stress, and further intervening in the availability of animal-derived substrates. The integration of ionic fragment feature networks and long short-term memory models allows the effective annotation of recurrent unknowns in organisms and the deciphering of unacquainted matter in multiomics.

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Monomethyl Branched‐Chain Fatty Acids Suppress M1 Macrophage Polarization via FABP4/PPAR‐γ Signaling Pathway

He,

Zhang,

Zhu

et al. 2024

Molecular Nutrition Food Res

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ScopeMonomethyl‐branched chain fatty acids (mmBCFAs) are found in a variety of food sources and are of great interest due to their potent antiinflammatory properties. However, most of the current researches have concentrated on the relationship between mmBCFAs and intestinal inflammation, and there is a large gap in the biological mechanisms involved behind their antiinflammatory effects.Methods and resultsThe present study examines the role of mmBCFAs in modulating macrophage polarization. The results demonstrate that iso‐C16:0 significantly inhibits macrophages M1 proinflammatory polarization through regulating FABP4/PPAR‐γ pathway. Proteomics and molecular biology experiments verify that metabolic reprogramming is involved in the inhibition of M1 macrophage, referring to the upregulation of fatty acid oxidation, TCA cycle, and oxidative phosphorylation, as well as downregulation of glycolytic flux.ConclusionIn summary, this study offers a novel perspective on the antiinflammatory effects mediated by mmBCFAs.

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Windows Scanning Multiomics: Integrated Metabolomics and Proteomics

Cited by 4 publications

References 48 publications

Stochastic dynamics mass spectrometric determination of ferrocenes

Stochastic dynamics mass spectrometric determination of ferrocenes

3D-MPEA: A Graph Attention Model-Guided Computational Approach for Annotating Unknown Metabolites in Interactomics via Mass Spectrometry-Focused Multilayer Molecular Networking

Monomethyl Branched‐Chain Fatty Acids Suppress M1 Macrophage Polarization via FABP4/PPAR‐γ Signaling Pathway

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