Activin receptor-like kinase 1 (ALK1)-mediated endothelial cell signalling in response to bone morphogenetic protein 9 (BMP9) and BMP10 is of significant importance in cardiovascular disease and cancer. However, detailed molecular mechanisms of ALK1-mediated signalling remain unclear. Here, we report crystal structures of the BMP10:ALK1 complex at 2.3 Å and the prodomain-bound BMP9:ALK1 complex at 3.3 Å. Structural analyses reveal a tripartite recognition mechanism that defines BMP9 and BMP10 specificity for ALK1, and predict that crossveinless 2 is not an inhibitor of BMP9, which is confirmed by experimental evidence. Introduction of BMP10-specific residues into BMP9 yields BMP10-like ligands with diminished signalling activity in C2C12 cells, validating the tripartite mechanism. The loss of osteogenic signalling in C2C12 does not translate into non-osteogenic activity in vivo and BMP10 also induces bone-formation. Collectively, these data provide insight into ALK1-mediated BMP9 and BMP10 signalling, facilitating therapeutic targeting of this important pathway.

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Bone morphogenetic protein 9 (BMP9), a member of the transforming growth factor β (TGFβ) superfamily, is a circulating vascular quiescence and endothelial protective factor, accounting for the majority of BMP activities in plasma. BMP9 and BMP10 bind preferentially to the high-affinity type I receptor activin receptor-like kinase 1 on vascular endothelial cells. Recently, many reports have highlighted the important roles of BMP9 in cardiovascular disease, particularly pulmonary arterial hypertension. In vivo, BMP9 activity and specificity are determined by tightly regulated protein–protein recognition with cognate receptors and a co-receptor, and may also be influenced by other proteins present on the endothelial cell surface (such as low-affinity receptors) and in circulation (such as TGFβ family ligands competing for the same receptors). In this review, we summarise recent findings on the role and therapeutic potential of BMP9 in cardiovascular disease and review the current understanding of how the extracellular protein–protein interaction milieu could play a role in regulating endothelial BMP9 signalling specificity and activity.

Objective: To predict post-operative depth of focus (DoF) using machine learning techniques after cataract surgery with Tecnis Symfony implantation and determine associated impact factors. Methods: This was a retrospective cohort study among patients receiving Tecnis Symfony implantation, an extended-range-of-vision intraocular lens, during October 2016–January 2020 at Daqing Oilfield General Hospital, China. Four different predictive models were used to predict good post-operative DoF (⩾2.5 D): Extreme Gradient Boost (XGBoost), random forest (RF), LASSO penalized regression, and multivariable logistic regression (MLR). Apriori algorithm was employed to further explore the association between patient attributes and DoF. Results: A total of 182 unique cases (143 patients) were included. The XGBoost model produced the best predictive accuracy compared to RF, LASSO, and MLR models. Overall performance of the best fitting XGBoost model was as follows: accuracy = 70.3%, AUC = 80.2%, sensitivity = 65.5%, and specificity = 87.5%. The Apriori algorithm identified six preoparative attributes with substantial effects on good post-operative DoF: low anterior chamber depth (ACD) (1.9 to <2.5 mm), smaller pupil size (1.7 to <2.5 mm), low-to-mid axial length (21 to <23 mm), minimum astigmatism degree (−0.2 to 0 diopter), low IOP (9 to <12 mmHg), and medium lens target refractive error (−0.5 to <−0.25 diopter). Conclusions: Machine Learning models were able to predict good post-operative DoF among cataract patients receiving a Tecnis Symfony ocular lens implantation. The accuracy of the model was above 70%. The Apriori algorithm identified six preoperative attributes with a strong association with post-operative DoF.