Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, USA.
Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA.
Sci Rep. 2023 Aug 10;13(1):12968. doi: 10.1038/s41598-023-40328-w.
Diabetic retinopathy is a common complication of long-term diabetes and that could lead to vision loss. Unfortunately, early diabetic retinopathy remains poorly understood. There is no effective way to prevent or treat early diabetic retinopathy until patients develop later stages of diabetic retinopathy. Elevated acellular capillary density is considered a reliable quantitative trait present in the early development of retinopathy. Hence, in this study, we interrogated whole retinal vascular transcriptomic changes via a Nile rat model to better understand the early pathogenesis of diabetic retinopathy. We uncovered the complexity of associations between acellular capillary density and the joint factors of blood glucose, diet, and sex, which was modeled through a Bayesian network. Using segmented regressions, we have identified different gene expression patterns and enriched Gene Ontology (GO) terms associated with acellular capillary density increasing. We developed a random forest regression model based on expression patterns of 14 genes to predict the acellular capillary density. Since acellular capillary density is a reliable quantitative trait in early diabetic retinopathy, and thus our model can be used as a transcriptomic clock to measure the severity of the progression of early retinopathy. We also identified NVP-TAE684, geldanamycin, and NVP-AUY922 as the top three potential drugs which can potentially attenuate the early DR. Although we need more in vivo studies in the future to support our re-purposed drugs, we have provided a data-driven approach to drug discovery.
糖尿病性视网膜病变是长期糖尿病的常见并发症，可导致视力丧失。不幸的是，早期糖尿病性视网膜病变仍未被充分了解。直到患者发展到糖尿病性视网膜病变的晚期，才有一种有效的方法可以预防或治疗早期糖尿病性视网膜病变。无细胞毛细血管密度升高被认为是视网膜病变早期发生的一种可靠的定量特征。因此，在这项研究中，我们通过尼罗鼠模型研究了整个视网膜血管转录组的变化，以更好地了解糖尿病性视网膜病变的早期发病机制。我们揭示了无细胞毛细血管密度与血糖、饮食和性别等联合因素之间的复杂关联，并通过贝叶斯网络对其进行建模。使用分段回归，我们已经确定了与无细胞毛细血管密度增加相关的不同基因表达模式和丰富的基因本体论（GO）术语。我们基于 14 个基因的表达模式开发了一个随机森林回归模型，用于预测无细胞毛细血管密度。由于无细胞毛细血管密度是早期糖尿病性视网膜病变中可靠的定量特征，因此我们的模型可以用作转录组钟来衡量早期视网膜病变进展的严重程度。我们还确定了 NVP-TAE684、geldanamycin 和 NVP-AUY922 作为前三种潜在药物，它们可能减轻早期 DR。尽管我们未来需要更多的体内研究来支持我们重新定位的药物，但我们已经提供了一种基于数据的药物发现方法。
