Interplay between transglutaminases and heparan sulphate in progressive renal scarring

Transglutaminase-2 (TG2) is a new anti-fibrotic target for chronic kidney disease, for its role in altering the extracellular homeostatic balance leading to excessive build-up of matrix in kidney. However, there is no confirmation that TG2 is the only transglutaminase involved, neither there are strategies to control its action specifically over that of the conserved family-members. In this study, we have profiled transglutaminase isozymes in the rat subtotal nephrectomy (SNx) model of progressive renal scarring. All transglutaminases increased post-SNx peaking at loss of renal function but TG2 was the predominant enzyme. Upon SNx, extracellular TG2 deposited in the tubulointerstitium and peri-glomerulus via binding to heparan sulphate (HS) chains of proteoglycans and co-associated with syndecan-4. Extracellular TG2 was sufficient to activate transforming growth factor-β1 in tubular epithelial cells, and this process occurred in a HS-dependent way, in keeping with TG2-affinity for HS. Analysis of heparin binding of the main transglutaminases revealed that although the interaction between TG1 and HS is strong, the conformational heparin binding site of TG2 is not conserved, suggesting that TG2 has a unique interaction with HS within the family. Our data provides a rationale for a novel anti-fibrotic strategy specifically targeting the conformation-dependent TG2-epitope interacting with HS

Female Institutional Directors on Boards and Firm Value

The aim of this research is to examine what impact female institutional directors on boards have on corporate performance. Previous research shows that institutional female directors cannot be considered as a homogeneous group since they represent investors who may or may not maintain business relations with the companies on whose corporate boards they sit. Thus, it is not only the effect of female institutional directors as a whole on firm value that has been analysed, but also the impact of pressure-resistant female directors, who represent institutional investors (investment, pension and mutual funds) that only invest in the company, and do not maintain a business relation with the firm. We hypothesize that there is a non-linear association, specifically quadratic, between institutional and pressure-resistant female directors on boards and corporate performance. Our results report that female institutional directors on boards enhance corporate performance, but when they reach a certain threshold on boards (11.72 %), firm value decreases. In line with female institutional directors, pressure-resistant female directors on boards also increase firm value, but only up to a certain figure (12.71 % on boards), above which they have a negative impact on firm performance. These findings are consistent with an inverted U-shaped relationship between female institutional directors and pressure-resistant female directors and firm performance

Stochastic Algorithms For Exploratory Data Analysis: Data Clustering And Data Visualization

. Iterative, EM-type algorithms for data clustering and data visualization are derived on the basis of the maximum entropy principle. These algorithms allow the data analyst to detect structure in vectorial or relational data. Conceptually, the clustering and visualization procedures are formulated as combinatorial or continuous optimization problems which are solved by stochastic optimization. 1. INTRODUCTION Exploratory Data Analysis addresses the question of how to discover and model structure hidden in a data set. Data clustering (JD88) and data visualization are important algorithmic tools in this quest for explanation of data relations. The structural relationships between data points, e.g., pronounced similarity of groups of data vectors, have to be detected in an unsupervised fashion. This search for prototypes poses a delicate tradeoff: a sufficiently rich modelling approach should be able to capture the essential structure in a data set but we should restrain ourselves from ..