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Peer-reviewedCC BY 4.0PLOS One

A bioinformatic single-cell and structure-informed framework identifies a baicalin–CA2–keratinocyte state axis in atopic dermatitis

  • Boyan Yang1
  • Guilin Zhou2
  • Jun Dai
  1. 1Department of Dermatology, The First People’s Hospital of Yibin. No. 16 Puhe East Road, Yibin, Sichuan, PR China
  2. 2Department of Dermatology, The Fourth People’s Hospital of Sichuan Province, No. 12, Chengshou East Street, Jinjiang District, Chengdu, Sichuan, PR China
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Abstract

Atopic dermatitis (AD) is characterized by a self-reinforcing loop between epidermal barrier dysfunction and type 2-skewed inflammation; yet the most perturbed keratinocyte states and actionable epidermal targets remain incompletely defined. We integrated pharmacogenomic target mining, complementary machine-learning feature selection (LASSO and SVM-RFE), single-cell state–resolved perturbation analyses (Augur and scDist), and structure-based molecular modeling (molecular docking, MD simulation, and MM-PBSA free energy calculation) to prioritize candidate targets of baicalin in AD. CA2 emerged as a convergent epidermal candidate; scRNA-seq analyses localized CA2-associated transcriptional differences to keratinocytes, with the keratinocyte compartment exhibiting the disease-associated strongest separability and transcriptomic distance, accompanied by enrichment of metabolic reprogramming, epithelial junction and barrier remodeling, and proliferative quiescence gene programs. Structure-based evaluation supported a computationally plausible baicalin–CA2 interaction, with an estimated MM-PBSA binding free energy of −22.082 kcal/mol. Collectively, these findings nominate a computationally supported “baicalin–CA2–Kcs9” axis as a hypothesis-generating framework for epidermal stratification and experimental prioritization in AD.

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Cite

BibTeXRIS
BibTeX
@article{yang2026bioinformatic,
  title = {{A bioinformatic single-cell and structure-informed framework identifies a baicalin–CA2–keratinocyte state axis in atopic dermatitis}},
  author = {Boyan Yang and Guilin Zhou and Jun Dai},
  journal = {PLOS One},
  year = {2026},
  month = sep,
  doi = {10.1371/journal.pone.0356174},
  url = {https://doi.org/10.1371/journal.pone.0356174},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - A bioinformatic single-cell and structure-informed framework identifies a baicalin–CA2–keratinocyte state axis in atopic dermatitis
AU  - Boyan Yang
AU  - Guilin Zhou
AU  - Jun Dai
PY  - 2026
DA  - 2026/09/11
JO  - PLOS One
DO  - 10.1371/journal.pone.0356174
UR  - https://doi.org/10.1371/journal.pone.0356174
AB  - Atopic dermatitis (AD) is characterized by a self-reinforcing loop between epidermal barrier dysfunction and type 2-skewed inflammation; yet the most perturbed keratinocyte states and actionable epidermal targets remain incompletely defined. We integrated pharmacogenomic target mining, complementary machine-learning feature selection (LASSO and SVM-RFE), single-cell state–resolved perturbation analyses (Augur and scDist), and structure-based molecular modeling (molecular docking, MD simulation, and MM-PBSA free energy calculation) to prioritize candidate targets of baicalin in AD. CA2 emerged as a convergent epidermal candidate; scRNA-seq analyses localized CA2-associated transcriptional differences to keratinocytes, with the keratinocyte compartment exhibiting the disease-associated strongest separability and transcriptomic distance, accompanied by enrichment of metabolic reprogramming, epithelial junction and barrier remodeling, and proliferative quiescence gene programs. Structure-based evaluation supported a computationally plausible baicalin–CA2 interaction, with an estimated MM-PBSA binding free energy of −22.082 kcal/mol. Collectively, these findings nominate a computationally supported “baicalin–CA2–Kcs9” axis as a hypothesis-generating framework for epidermal stratification and experimental prioritization in AD.
SN  - 1932-6203
ER  -