Hypomorphic conditional deletion of E11/Podoplanin reveals a role in osteocyte dendrite elongation

The transmembrane glycoprotein E11/Podoplanin (Pdpn) has been implicated in the initial stages of osteocyte differentiation. However, its precise function and regulatory mechanisms are still unknown. Due to the known embryonic lethality induced by global Pdpn deletion, we have herein explored the effect of bone‐specific Pdpn knockdown on osteocyte form and function in the post‐natal mouse. Extensive skeletal phenotyping of male and female 6‐week‐old Oc‐cre;Pdpn^flox/flox (cKO) mice and their Pdpn^flox/flox controls (fl/fl) has revealed that Pdpn deletion significantly compromises tibial cortical bone microarchitecture in both sexes, albeit to different extents (p < 0.05). Consistent with this, we observed an increase in stiffness in female cKO mice in comparison to fl/fl mice (p < 0.01). Moreover, analysis of the osteocyte phenotype by phalloidin staining revealed a significant decrease in the dendrite volume (p < 0.001) and length (p < 0.001) in cKO mice in which deletion of Pdpn also modifies the bone anabolic loading response (p < 0.05) in comparison to age‐matched fl/fl mice. Together, these data confirm a regulatory role for Pdpn in osteocyte dendrite formation and as such, in the control of osteocyte function. As the osteocyte dendritic network is known to play vital roles in regulating bone modeling/remodeling, this highlights an essential role for Pdpn in bone homeostasis.     

FGF‐2 promotes osteocyte differentiation through increased E11/podoplanin expression

E11/podoplanin is critical in the early stages of osteoblast‐to‐osteocyte transitions (osteocytogenesis), however, the upstream events which regulate E11 expression are unknown. The aim of this study was to examine the effects of FGF‐2 on E11‐mediated osteocytogenesis and to reveal the nature of the underlying signaling pathways regulating this process. Exposure of MC3T3 osteoblast‐like cells and murine primary osteoblasts to FGF‐2 (10 ng/ml) increased E11 mRNA and protein expression (p < 0.05) after 4, 6, and 24 hr. FGF‐2 induced changes in E11 expression were also accompanied by significant (p < 0.01) increases in Phex and Dmp1 (osteocyte markers) expression and decreases in Col1a1, Postn, Bglap, and Alpl (osteoblast markers) expression. Immunofluorescent microscopy revealed that FGF‐2 stimulated E11 expression, facilitated the translocation of E11 toward the cell membrane, and subsequently promoted the formation of osteocyte‐like dendrites in MC3T3 and primary osteoblasts. siRNA knock down of E11 expression achieved >70% reduction of basal E11 mRNA expression (p < 0.05) and effectively abrogated FGF‐2‐related changes in E11 expression and dendrite formation. FGF‐2 strongly activated the ERK signaling pathway in osteoblast‐like cells but inhibition of this pathway did not block the ability of FGF‐2 to enhance E11 expression or to promote acquisition of the osteocyte phenotype. The results of this study highlight a novel mechanism by which FGF‐2 can regulate osteoblast differentiation and osteocyte formation. Specifically, the data suggests that FGF‐2 promotes osteocytogenesis through increased E11 expression and further studies will identify if this regulatory pathway is essential for bone development and maintenance in health and disease.     

A Control Chart Based on Cluster-Regression Adjustment for Retrospective Monitoring of Individual Characteristics

The tendency for experimental and industrial variables to include a certain proportion of outliers has become a rule rather than an exception. These clusters of outliers, if left undetected, have the capability to distort the mean and the covariance matrix of the Hotelling’s T ² multivariate control charts constructed to monitor individual quality characteristics. The effect of this distortion is that the control chart constructed from it becomes unreliable as it exhibits masking and swamping, a phenomenon in which an out-of-control process is erroneously declared as an in-control process or an in-control process is erroneously declared as out-of-control process. To handle these problems, this article proposes a control chart that is based on cluster-regression adjustment for retrospective monitoring of individual quality characteristics in a multivariate setting. The performance of the proposed method is investigated through Monte Carlo simulation experiments and historical datasets. Results obtained indicate that the proposed method is an improvement over the state-of-art methods in terms of outlier detection as well as keeping masking and swamping rate under control.