ArticleBioFactors (Oxford, England)
Betanin Promotes Wound Closure and Drives a Context-Specific Transcriptional Repair Program in HaCaT Keratinocytes: A Multi-Evidence AI-Guided Prioritization Study.
Article in BioFactors (Oxford, England). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Keratinocytes coordinate re-epithelialization, barrier restoration, inflammatory control, and matrix remodeling during cutaneous repair. Although betanin has reported cytoprotective and antioxidant properties, its wound-relevant transcriptional program in keratinocytes remains incompletely defined. This study therefore asked whether betanin engages barrier, inflammatory, and remodeling-related keratinocyte programs under inflammatory conditions, and whether existing wound-healing databases can account for the resulting transcriptional pattern. Scratch closure was quantified by ImageJ measurement and by an independent rule-based automated image-analysis pipeline with first-order kinetic modeling; transcriptional responses in IFN-γ/TNF-α-stimulated HaCaT cells were interpreted through database-weighted evidence scoring and machine learning-based stress testing. Betanin (20 μg/mL) significantly reduced residual wound area at 24 h (p = 0.020) and 48 h (p = 0.035) by ImageJ quantification, and automated image analysis with kinetic modeling yielded an approximately 1.5-fold higher first-order closure rate constant relative to the vehicle-treated wounded control. Betanin partially restored FLG, reduced KRT14, and selectively attenuated inflammatory mediators including IL1B, ICAM1, CCL22, and CXCL8. Among remodeling-associated genes, TGFB1, COL1A1, and VEGFA were suppressed while COL3A1 was partially restored. NFE2L2 and HMOX1 were only modestly affected, indicating a dominant anti-inflammatory and barrier-modulating response rather than canonical antioxidant axis activation. Database-weighted evidence scoring stably prioritized FLG, COL3A1, COL1A1, and ICAM1, corroborated by betanin-specific experimental responses. Three architecturally distinct machine learning models showed no evidence that database-derived wound-healing features predict betanin-induced transcriptional responses, indicating that the selected prior-knowledge annotations were insufficient to account for the observed pattern in this 15-gene, single-context dataset.
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