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A Cocktail of Polyherbal Bioactive Compounds and Regular Mobility Training as Senolytic Approaches in Age-dependent Alzheimer’s: the In Silico Analysis, Lifestyle Intervention in Old Age

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Abstract

Alzheimer’s is a principal concern globally. Machine learning is a valuable tool to determine protective and diagnostic approaches for the elderly. We analyzed microarray datasets of Alzheimer’s cases based on artificial intelligence by R statistical software. This study provided a screened pool of ncRNAs and coding RNAs related to Alzheimer’s development. We designed hub genes as cut points in networks and predicted potential microRNAs and LncRNA to regulate protein networks in aging and Alzheimer’s through in silico algorithms. Notably, we collected effective traditional herbal medicines. A list of bioactive compounds prepared including capsaicin, piperine, crocetin, safranal, saffron oil, coumarin, thujone, rosmarinic acid, sabinene, thymoquinone, ascorbic acid, vitamin E, cyanidin, rhaponticin, isovitexin, coumarin, nobiletin, evodiamine, gingerol, curcumin, quercetin, fisetin, and allicin as an effective fusion that potentially modulates hub proteins and molecular signaling pathways based on pharmacophore model screening and chemoinformatics survey. We identified profiles of 21 mRNAs, 272 microRNAs, and eight LncRNA in Alzheimer’s based on prediction algorithms. We suggested a fusion of senolytic herbal ligands as an alternative therapy and preventive formulation in dementia. Also, we provided ncRNAs expression status as novel monitoring strategies in Alzheimer’s and new cut-point proteins as novel therapeutic approaches. Synchronizing fusion drugs and lifestyle could reverse Alzheimer’s hallmarks to amelioration via an offset of the signaling pathways, leading to increased life quality in the elderly.

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Data availability

The data and materials that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

AD:

Alzheimer’s disease

Aβ:

Amyloid-beta

AGEs:

Advanced glycation end products

CAM:

Complementary and alternative medicine

GEO:

Gene Expression Omnibus

lncRNA:

Long non-coding RNA

miRNA:

MicroRNA

ncRNAs:

Non-coding RNA

NHS:

National Health Service

RM:

Regular mobility

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Acknowledgements

We thank our colleagues for their association and helpful discussions in this study.

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Author notes

  1. Fatemeh Hajibabaie

    Present address: Department of Biology, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran

Authors and Affiliations

  1. Department of Physiology, Medicinal Plants Research Center, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

    Fatemeh Hajibabaie & Navid Abedpoor

  2. Department of Sports Physiology, Faculty of Sports Sciences, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

    Navid Abedpoor & Farzaneh Taghian

  3. Department of Plant Biotechnology, Medicinal Plants Research Centre, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

    Kamran Safavi

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  1. Fatemeh Hajibabaie
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  2. Navid Abedpoor
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Contributions

Planning of the study was performed with F.HB, F.T, and N.A. Conducting of the study was performed with F.H.B and N.A. The data collection was carried out by F.HB, N.A, K.S, and F.T. Interpretation of data was carried out by F.HB and N.A. Drafting the manuscript was performed with F.H.B, N.A, K.S, and F.T. All authors have approved the final draft and agreed to submit the manuscript to this journal.

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Correspondence to Navid Abedpoor or Farzaneh Taghian.

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Hajibabaie, F., Abedpoor, N., Taghian, F. et al. A Cocktail of Polyherbal Bioactive Compounds and Regular Mobility Training as Senolytic Approaches in Age-dependent Alzheimer’s: the In Silico Analysis, Lifestyle Intervention in Old Age. J Mol Neurosci 73, 171–184 (2023). https://doi.org/10.1007/s12031-022-02086-8

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