Unveiling the molecular complexity of proliferative diabetic retinopathy through scRNA-seq, AlphaFold 2, and machine learning

Wang, Jun1,2; Sun, Hongyan2,3; Mou, Lisha4,5; Lu, Ying4,5; Wu, Zijing4,5; Pu, Zuhui4,5; Yang, Ming-ming2,3

2024-05-10

10.3389/fendo.2024.1382896

FRONTIERS IN ENDOCRINOLOGY   影响因子和分区

1664-2392

Background Proliferative diabetic retinopathy (PDR), a major cause of blindness, is characterized by complex pathogenesis. This study integrates single-cell RNA sequencing (scRNA-seq), Non-negative Matrix Factorization (NMF), machine learning, and AlphaFold 2 methods to explore the molecular level of PDR.Methods We analyzed scRNA-seq data from PDR patients and healthy controls to identify distinct cellular subtypes and gene expression patterns. NMF was used to define specific transcriptional programs in PDR. The oxidative stress-related genes (ORGs) identified within Meta-Program 1 were utilized to construct a predictive model using twelve machine learning algorithms. Furthermore, we employed AlphaFold 2 for the prediction of protein structures, complementing this with molecular docking to validate the structural foundation of potential therapeutic targets. We also analyzed protein-protein interaction (PPI) networks and the interplay among key ORGs.Results Our scRNA-seq analysis revealed five major cell types and 14 subcell types in PDR patients, with significant differences in gene expression compared to those in controls. We identified three key meta-programs underscoring the role of microglia in the pathogenesis of PDR. Three critical ORGs (ALKBH1, PSIP1, and ATP13A2) were identified, with the best-performing predictive model demonstrating high accuracy (AUC of 0.989 in the training cohort and 0.833 in the validation cohort). Moreover, AlphaFold 2 predictions combined with molecular docking revealed that resveratrol has a strong affinity for ALKBH1, indicating its potential as a targeted therapeutic agent. PPI network analysis, revealed a complex network of interactions among the hub ORGs and other genes, suggesting a collective role in PDR pathogenesis.Conclusion This study provides insights into the cellular and molecular aspects of PDR, identifying potential biomarkers and therapeutic targets using advanced technological approaches.

diabetic retinopathy single-cell analysis oxidative stress AlphaFold 2 NMF PPI machine learning ALKBH1

SCI

英语

通讯

Shenzhen Science and Technology Program["JCYJ20220818102603007","GCZX2015043017281705"] ; General Project of the Shenzhen Natural Science Foundation["JCYJ20210324113808023","JCYJ20220530152813030"]

Endocrinology & Metabolism

Endocrinology & Metabolism

WOS:001229865200001

FRONTIERS MEDIA SA

Web of Science

1.Jinan Univ, Shenzhen Peoples Hosp, Clin Med Coll 2, Dept Endocrinol, Shenzhen, Peoples R China
2.Southern Univ Sci & Technol, Affiliated Hosp 1, Shenzhen, Peoples R China
3.Jinan Univ, Shenzhen Peoples Hosp, Clin Med Coll 2, Dept Ophthalmol, Shenzhen, Peoples R China
4.Shenzhen Univ, Affiliated Hosp 1, Shenzhen Peoples Hosp 2, Shenzhen Inst Translat Med,Imaging Dept, Shenzhen, Peoples R China
5.Shenzhen Inst Translat Med, MetaLife Ctr, Shenzhen, Guangdong, Peoples R China

Wang, Jun,Sun, Hongyan,Mou, Lisha,et al. Unveiling the molecular complexity of proliferative diabetic retinopathy through scRNA-seq, AlphaFold 2, and machine learning[J]. FRONTIERS IN ENDOCRINOLOGY,2024,15.

Wang, Jun.,Sun, Hongyan.,Mou, Lisha.,Lu, Ying.,Wu, Zijing.,...&Yang, Ming-ming.(2024).Unveiling the molecular complexity of proliferative diabetic retinopathy through scRNA-seq, AlphaFold 2, and machine learning.FRONTIERS IN ENDOCRINOLOGY,15.

Wang, Jun,et al."Unveiling the molecular complexity of proliferative diabetic retinopathy through scRNA-seq, AlphaFold 2, and machine learning".FRONTIERS IN ENDOCRINOLOGY 15(2024).