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Machine Learning Unmasked Nutritional Imbalances on the Medicinal Plant Bryophyllum sp. Cultured in vitro.

ABSTRACT: Plant nutrition is a crucial factor that is usually underestimated when designing plant in vitro culture protocols of unexploited plants. As a complex multifactorial process, the study of nutritional imbalances requires the use of time-consuming experimental designs and appropriate statistical and multiple regression analysis for the determination of critical parameters, whose results may be difficult to interpret when the number of variables is large. The use of machine learning (ML) supposes a cutting-edge approach to investigate multifactorial processes, with the aim of detecting non-linear relationships and critical factors affecting a determined response and their concealed interactions. Thus, in this work we applied artificial neural networks coupled to fuzzy logic, known as neurofuzzy logic, to determine the critical factors affecting the mineral nutrition of medicinal plants belonging to Bryophyllum subgenus cultured in vitro. The application of neurofuzzy logic algorithms facilitate the interpretation of the results, as the technology is able to generate useful and understandable "IF-THEN" rules, that provide information about the factor(s) involved in a certain response. In this sense, ammonium, sulfate, molybdenum, copper and sodium were the most important nutrients that explain the variation in the in vitro culture establishment of the medicinal plants in a species-dependent manner. Thus, our results indicate that Bryophyllum spp. display a fine-tuning regulation of mineral nutrition, that was reported for the first time under in vitro conditions. Overall, neurofuzzy model was able to predict and identify masked interactions among such factors, providing a source of knowledge (helpful information) from the experimental data (non-informative per se), in order to make the exploitation and valorization of medicinal plants with high phytochemical potential easier.

SUBMITTER: Garcia-Perez P

PROVIDER: S-EPMC7729169 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Machine Learning Unmasked Nutritional Imbalances on the Medicinal Plant Bryophyllum sp. Cultured in vitro.

García-Pérez Pascual P Lozano-Milo Eva E Landin Mariana M Gallego Pedro Pablo PP

Frontiers in plant science 20201201

Plant nutrition is a crucial factor that is usually underestimated when designing plant in vitro culture protocols of unexploited plants. As a complex multifactorial process, the study of nutritional imbalances requires the use of time-consuming experimental designs and appropriate statistical and multiple regression analysis for the determination of critical parameters, whose results may be difficult to interpret when the number of variables is large. The use of machine learning (ML) sup ...[more]

PMID: 33329638

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Project description:BACKGROUND:Human Immunodeficiency Virus (HIV) persists to be a significant public health issue worldwide. The current strategy for the treatment of HIV infection, Highly Active Antiretroviral Therapy (HAART), has reduced deaths from AIDS related disease, but it can be an expensive regime for the underdeveloped and developing countries where the supply of drugs is scarce and often not well tolerated, especially in persons undergoing long term treatment. The present therapy also has limitations of development of multidrug resistance, thus there is a need for the discovery of novel anti-HIV compounds from plants as a potential alternative in combating HIV disease. METHODS:Ten Indian medicinal plants were tested for entry and replication inhibition against laboratory adapted strains HIV-1IIIB, HIV-1Ada5 and primary isolates HIV-1UG070, HIV-1VB59 in TZM-bl cell lines and primary isolates HIV-1UG070, HIV-1VB59 in PM1 cell lines. The plant extracts were further evaluated for toxicity in HEC-1A epithelial cell lines by transwell epithelial model. RESULTS:The methanolic extracts of Achyranthes aspera, Rosa centifolia and aqueous extract of Ficus benghalensis inhibited laboratory adapted HIV-1 strains (IC80 3.6-118 μg/ml) and primary isolates (IC80 4.8-156 μg/ml) in TZM-bl cells. Methanolic extract of Strychnos potatorum, aqueous extract of Ficus infectoria and hydroalcoholic extract of Annona squamosa inhibited laboratory adapted HIV-1 strains (IC80 4.24-125 μg/ml) and primary isolates (IC80 18-156 μg/ml) in TZM-bl cells. Methanolic extracts of Achyranthes aspera and Rosa centifolia, (IC801-9 μg/ml) further significantly inhibited HIV-1 primary isolates in PM1cells. Methanolic extracts of Tridax procumbens, Mallotus philippinensis, Annona reticulate, aqueous extract of Ficus benghalensis and hydroalcoholic extract of Albizzia lebbeck did not exhibit anti-HIV activity in all the tested strains. Methanolic extract of Rosa centifolia also demonstrated to be non-toxic to HEC-1A epithelial cells and maintained epithelial integrity (at 500 μg/ml) when tested in transwell dual-chamber. CONCLUSION:These active methanolic extracts of Achyranthes aspera and Rosa centifolia, could be further subjected to chemical analysis to investigate the active moiety responsible for the anti-HIV activity. Methanolic extract of Rosa centifolia was found to be well tolerated maintaining the epithelial integrity of HEC-1A cells in vitro and thus has potential for investigating it further as candidate microbicide.

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Dataset Information

Machine Learning Unmasked Nutritional Imbalances on the Medicinal Plant Bryophyllum sp. Cultured in vitro.

Publications

Machine Learning Unmasked Nutritional Imbalances on the Medicinal Plant <i>Bryophyllum</i> sp. Cultured <i>in vitro</i>.

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