Genetic variation in retinal vascular patterning predicts variation in pial collateral extent and stroke severity.
Genetic variation in retinal vascular patterning predicts variation in pial collateral extent and stroke severity.
复制标题
视网膜血管模式的遗传变异可预测软脑膜侧支范围和中风严重程度的变化。
DOI：
10.1007/s10456-014-9449-y
复制
发表时间：
2015
期刊：
影响因子：
9.8
通讯作者：
Faber,JamesE
中科院分区：
文献类型：
--
作者：
Prabhakar,Pranay;Zhang,Hua;Chen,De;Faber,JamesE
组织中天然侧支循环的存在减轻了闭塞性血管疾病的损伤。然而，遗传背景的差异导致小鼠侧枝数目和直径的广泛变化，从而导致保护的较大变化。侧支灌注的间接估计表明，在人类中也存在广泛的差异。不幸的是，用于获得这些估计值的方法是侵入性的，并且不广泛可用。我们试图确定小鼠遗传背景的差异是否导致视网膜动脉循环分支模式的变化，以及这些差异是否预测了软脑膜侧支范围和缺血性卒中严重程度的应变依赖性差异。视网膜图案的度量，侧支程度，和梗死体积获得了10株已知的侧支程度差异很大。进行多变量回归，并使用K折交叉验证评估模型性能。21个指标因应变而异（p< 0.01）。十个指标（例如，分叉角度、空隙度、最优性）预测了7个回归模型中的侧支数目和直径，最佳模型接近预测（p< 0.0001）数目（±1.2-3.4侧支，K倍R2 = 0.83-0.98）、直径（±1.2-1.9 μm，R2= 0.73-0.88）和梗死体积（±5.1 mm 3，R2= 0.85-0.87）。在上述菌株的一个子集中，从大脑中动脉（MCA）树获得的一组类似的最具预测性的指标也预测了（p< 0.0001）侧支数量（±3.3侧支，K倍R2 = 0.78）和直径（±1.6 μm，R2= 0.86）。因此，视网膜和大脑中动脉树中动脉分支模式的差异由遗传背景指定，并预测侧支范围和中风严重程度的变化。如果在人类中也是如此，并且由于脑侧支的遗传变异至少在小鼠中延伸到其他组织，则类似的“视网膜预测指数”可以用作脑和其他组织中侧支程度的非侵入性或微创生物标志物。这有助于预测发生闭塞事件或阻塞性疾病时的组织损伤严重程度，并有助于患者分层以进行治疗选择和临床研究。
The presence of a native collateral circulation in tissues lessens injury in occlusive vascular diseases. However, differences in genetic background cause wide variation in collateral number and diameter in mice, resulting in large variation in protection. Indirect estimates of collateral perfusion suggest that wide variation also exists in humans. Unfortunately, methods used to obtain these estimates are invasive and not widely available. We sought to determine whether differences in genetic background in mice result in variation in branch patterning of the retinal arterial circulation, and whether these differences predict strain-dependent differences in pial collateral extent and severity of ischemic stroke. Retinal patterning metrics, collateral extent, and infarct volume were obtained for 10 strains known to differ widely in collateral extent. Multivariate regression was conducted, and model performance was assessed using K-fold cross-validation. Twenty-one metrics varied with strain (p< 0.01). Ten metrics (e.g., bifurcation angle, lacunarity, optimality) predicted collateral number and diameter across seven regression models, with the best model closely predicting (p< 0.0001) number (±1.2–3.4 collaterals, K-foldR2= 0.83–0.98), diameter (±1.2–1.9 μm,R2= 0.73–0.88), and infarct volume (±5.1 mm3,R2= 0.85–0.87). An analogous set of the most predictive metrics, obtained for the middle cerebral artery (MCA) tree in a subset of the above strains, also predicted (p< 0.0001) collateral number (±3.3 collaterals, K-foldR2= 0.78) and diameter (±1.6 μm, R2= 0.86). Thus, differences in arterial branch patterning in the retina and the MCA trees are specified by genetic background and predict variation in collateral extent and stroke severity. If also true in human, and since genetic variation in cerebral collaterals extends to other tissues at least in mice, a similar “retinal predictor index” could serve as a non- or minimally invasive biomarker for collateral extent in brain and other tissues. This could aid prediction of severity of tissue injury in the event of an occlusive event or development of obstructive disease and in patient stratification for treatment options and clinical studies.