Integrating High-Resolution Mass Spectral Data, Bioassays and Computational Models to Annotate Bioactives in Botanical Extracts: Case Study – Analysis of <em>Centella asiatica</em> Extract Associates Di-Caffeoylquinic Acids with Protection against Amyloid-β Toxicity in an MC65 cell model
Rapid screening of botanical extracts for the discovery of bioactive natural products was performed using a fractionation approach in conjunction with flow-injection high-resolution mass spectrometry for obtaining chemical fingerprints of each fraction, enabling the correlation of the relative abundance of molecular features (representing individual phytochemicals) with the read-outs of bioassays. We applied this strategy for discovering and identifying constituents of Centella asiatica (C. asiatica) that protect against Aβ cytotoxicity in vitro. C. asiatica has been associated with improving mental health and cognitive function, with potential use in Alzheimer’s disease. Human neuroblastoma MC65 cells were exposed to subfractions of an aqueous extract of C. asiatica to evaluate the protective benefit derived from these subfractions against amyloid β-cytotoxicity. The % viability score of the cells exposed to each subfraction was used in conjunction with the intensity of the molecular features in two computational models, namely Elastic Net and selectivity ratio, to determine the relationship of the peak intensity of molecular features and % viability. Finally, the correlation of mass spectral features with MC65 protection and their abundance in different sub-fractions were visualized using GNPS molecular networking. Both computational methods unequivocally identified dicaffeoylquinic acids as providing strong protection against Aβ-toxicity in MC65 cells, in agreement with the protective effects observed for these compounds in previous preclinical model studies..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Preprints.org - (2024) vom: 19. Feb. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Alcazar-Magana, Armando [VerfasserIn] |
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| Links: |
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| doi: |
10.20944/preprints202401.0202.v1 |
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| PPN (Katalog-ID): |
preprintsorg042058945 |
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| 245 | 1 | 0 | |a Integrating High-Resolution Mass Spectral Data, Bioassays and Computational Models to Annotate Bioactives in Botanical Extracts: Case Study – Analysis of <em>Centella asiatica</em> Extract Associates Di-Caffeoylquinic Acids with Protection against Amyloid-β Toxicity in an MC65 cell model |
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| 520 | |a Rapid screening of botanical extracts for the discovery of bioactive natural products was performed using a fractionation approach in conjunction with flow-injection high-resolution mass spectrometry for obtaining chemical fingerprints of each fraction, enabling the correlation of the relative abundance of molecular features (representing individual phytochemicals) with the read-outs of bioassays. We applied this strategy for discovering and identifying constituents of Centella asiatica (C. asiatica) that protect against Aβ cytotoxicity in vitro. C. asiatica has been associated with improving mental health and cognitive function, with potential use in Alzheimer’s disease. Human neuroblastoma MC65 cells were exposed to subfractions of an aqueous extract of C. asiatica to evaluate the protective benefit derived from these subfractions against amyloid β-cytotoxicity. The % viability score of the cells exposed to each subfraction was used in conjunction with the intensity of the molecular features in two computational models, namely Elastic Net and selectivity ratio, to determine the relationship of the peak intensity of molecular features and % viability. Finally, the correlation of mass spectral features with MC65 protection and their abundance in different sub-fractions were visualized using GNPS molecular networking. Both computational methods unequivocally identified dicaffeoylquinic acids as providing strong protection against Aβ-toxicity in MC65 cells, in agreement with the protective effects observed for these compounds in previous preclinical model studies. | ||
| 700 | 1 | |a Vaswani, Ashish |4 aut | |
| 700 | 1 | |a Brown, Kevin S. |0 (orcid)0000-0003-2959-2401 |4 aut | |
| 700 | 1 | |a Jiang, Yuan |0 (orcid)0000-0001-6409-9159 |4 aut | |
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| 700 | 1 | |a Caruso, Maya |4 aut | |
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| 700 | 1 | |a Soumyanath, Amala |0 (orcid)0000-0003-1569-6453 |4 aut | |
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| 700 | 1 | |a Maier, Claudia S. |4 aut | |
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