Integrated multi-omics and deep learning analysis reveals neurotransmitter metabolism regulatory mechanisms of Tianwang Buxin Dan.

Overview

1. Special Operations Department, The 940 Hospital of the Joint Logistics Support Force of Chinese PLA, Lanzhou, Gansu, China
2. Medical Service Office, The 943 Hospital of the Joint Logistics Support Force of Chinese PLA, Wuwei, Gansu, China
3. Health Management Center, Second People’s Hospital of Gansu Province, Lanzhou, Gansu, China
4. Endocrinology Department, Second People’s Hospital of Gansu Province, Lanzhou, Gansu, China

Abstract

Background: Tianwang Buxin Dan is a classical Traditional Chinese Medicine formula with documented clinical use in treating neuropsychiatric disorders, yet its molecular mechanisms remain incompletely understood.

Methods: We developed an integrated analytical framework combining transcriptomic and metabolomic profiling with deep learning to investigate the neurotransmitter metabolism regulatory mechanisms of Tianwang Buxin Dan. Data were collected from a para-chlorophenylalanine (PCPA)- induced insomnia rat model following formula intervention. A multi-omics feature fusion strategy incorporating autoencoder-based dimensionality reduction and cross-modal attention mechanisms was implemented to address data heterogeneity.

Results: A total of 1,847 differentially expressed genes and 286 differential metabolites were identified. The constructed deep neural network achieved 91.2% classification accuracy with an AUC of 0.956 in five-fold cross-validation, and permutation testing confirmed that performance was significantly above chance (p < 0.001). Ablation experiments demonstrated that integrated multiomics outperformed single-omics models. Tryptophan hydroxylase 2 (TPH2) upregulation and monoamine oxidase A (MAO-A) suppression were identified as key features and partially validated by qPCR and Western blot.

Discussion: Tianwang Buxin Dan may modulate neurotransmitter metabolism through coordinated regulation of biosynthetic and catabolic pathways. A component-target-pathway regulatory network identified 47 key molecular targets interconnected through 156 functional associations. This work provides a computational framework applicable to mechanism studies of other compound TCM formulations.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• github.com/uhuxwpkbnh/twbxd, at github.com; found in “Data availability statement”

Data availability statement

The original contributions presented in the study are publicly available. This data can be found here: https://github.com/UHUxwPKBNh/TWBXD.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 2, 28 September 2026

• Funding: added Lanzhou Science and Technology Bureau

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 6 keywords, 44 references.

Cite

This paper

Wan, M., Cai, R., Zhang, X., & Li, X. (2026). Integrated multi-omics and deep learning analysis reveals neurotransmitter metabolism regulatory mechanisms of Tianwang Buxin Dan. Frontiers in neuroscience, 20, 1837233. https://doi.org/10.3389/fnins.2026.1837233

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