Sonic hedgehog signaling induces vascular smooth muscle cell proliferation via induction of the G1 cyclin-retinoblastoma axis.

OBJECTIVE: Proliferation of vascular smooth muscle cells (VSMCs) is a crucial event in the pathogenesis of intimal hyperplasia, the main cause of restenosis following vascular reconstruction. Here, the impact of sonic hedgehog (Shh)/Gli family zinc finger 2 (Gli2) signaling on VSMC proliferation was assessed. METHODS AND RESULTS: Increased Shh signaling was detected in VSMCs in the neointima of vein grafts obtained from mice undergoing restenosis. Comparable results were found in primary cultured human VSMCs (hVSMCs) obtained from patients undergoing coronary bypass surgery, which were used to further assess the impacts of Shh signaling on VSMC proliferation. Inhibition of Shh signaling in hVSMCs through treatment with cyclopamine or knockdown of Gli2 results in G(1) arrest and reduced cyclin D1, cyclin E, and phosphorylated retinoblastoma (pRB) levels. In contrast, activation of Shh/Gli2 signaling in hVSMCs results in increased levels of G(1) cyclins and promotes G(1)-S transition. Stimulation of hVSMC proliferation by Shh is abolished by cyclin D1 knockdown. CONCLUSIONS: Combined, these results demonstrate that Shh/Gli2 signaling stimulates VSMC proliferation via regulation of the G(1) cyclin-retinoblastoma axis and suggest that antagonists that target the Shh pathway may be therapeutically beneficial in the prevention of intimal hyperplasia.

Structural Dynamics of the N-Extension of Cardiac Troponin I Complexed with Troponin C by Site-Directed Spin Labeling Electron Paramagnetic Resonance.

The secondary structure of the N-extension of cardiac troponin I (cTnI) was determined by measuring the distance distribution between spin labels attached to the i and i + 4 residues: 15/19, 23/27, 27/31, 35/39, and 43/47. All of the EPR spectra of these regions in the monomeric state were broadened and had a amplitude that was reduced by two-thirds of that of the single spin-labeled spectra and was fit by two residual distance distributions, with a major distribution one spreading over the range from 1 to 2.5 nm and the other minor peak at 0.9 nm. Only slight or no obvious changes were observed when the extension was bound to cTnC in the cTnI-cTnC complex at 0.2 M KCl. However, at 0.1 M KCl, residues 43/47, located at the PKC phosphorylation sites Ser42/44 on the boundary of the extension, exclusively exhibited a 0.9 nm peak, as expected from α-helix in the crystal structure, in the complex. Furthermore, 23/27, which is located on the PKA phosphorylation sites Ser23/24, showed that the major distribution was markedly narrowed, centered at 1.4 nm and 0.5 nm wide, accompanying the spin label immobilization of residue 27. Residues 35 and 69 at site 1 and 2 of cTnC exhibited partial immobilization of the attached spin labels upon complex formation. The results show that the extension exhibited a primarily partially folded or unfolded structure equilibrated with a transiently formed α-helix-like short structure over the length. We hypothesize that the structure binds at least near sites 1 and 2 of cTnC and that the specific secondary structure of the extension on cTnC becomes uncovered when decreasing the ionic strength demonstrating that only the phosphorylation regions of cTnI interact stereospecifically with cTnC.

Quantum transport senses community structure in networks.

Quantum time evolution exhibits rich physics, attributable to the interplay between the density and phase of a wave function. However, unlike classical heat diffusion, the wave nature of quantum mechanics has not yet been extensively explored in modern data analysis. We propose that the Laplace transform of quantum transport (QT) can be used to construct an ensemble of maps from a given complex network to a circle S^{1}, such that closely related nodes on the network are grouped into sharply concentrated clusters on S^{1}. The resulting QT clustering (QTC) algorithm is as powerful as the state-of-the-art spectral clustering in discerning complex geometric patterns and more robust when clusters show strong density variations or heterogeneity in size. The observed phenomenon of QTC can be interpreted as a collective behavior of the microscopic nodes that evolve as macroscopic cluster "orbitals" in an effective tight-binding model recapitulating the network. python source code implementing the algorithm and examples are available at https://github.com/jssong-lab/QTC.

Emergent community agglomeration from data set geometry.

In the statistical learning language, samples are snapshots of random vectors drawn from some unknown distribution. Such vectors usually reside in a high-dimensional Euclidean space, and thus the "curse of dimensionality" often undermines the power of learning methods, including community detection and clustering algorithms, that rely on Euclidean geometry. This paper presents the idea of effective dissimilarity transformation (EDT) on empirical dissimilarity hyperspheres and studies its effects using synthetic and gene expression data sets. Iterating the EDT turns a static data distribution into a dynamical process purely driven by the empirical data set geometry and adaptively ameliorates the curse of dimensionality, partly through changing the topology of a Euclidean feature space R^{n} into a compact hypersphere S^{n}. The EDT often improves the performance of hierarchical clustering via the automatic grouping information emerging from global interactions of data points. The EDT is not restricted to hierarchical clustering, and other learning methods based on pairwise dissimilarity should also benefit from the many desirable properties of EDT.