Integrative analysis of SF-1 transcription factor dosage impact on genome-wide binding and gene expression regulation

Steroidogenic Factor-1 (SF-1) is a nuclear receptor that has a pivotal role in the development of adrenal glands and gonads and in the control of steroid hormone production, being also implicated in the pathogenesis of adrenocortical tumors. We have analyzed the mechanisms how SF-1 controls gene expression in adrenocortical cells and showed that it regulates different categories of genes according to its dosage. Significant correlations exist between the localization of SF-1-binding sites in chromatin under different dosage conditions and dosage-dependent regulation of gene expression. Our study revealed unexpected functional interactions between SF-1 and Neuron-Restrictive Silencer Factor/RE1-Silencing Transcription Factor (NRSF/REST), which was first characterized as a repressor of neuronal gene expression in non-neuronal tissues, in the regulation of gene expression in steroidogenic cells. When overexpressed, SF-1 reshapes the repertoire of NRSF/REST—regulated genes, relieving repression of key steroidogenic genes. These data show that NRSF/REST has a novel function in regulating gene expression in steroidogenic cells and suggest that it may have a broad role in regulating tissue-specific gene expression programs

Karpinski Score under Digital Investigation: A Fully Automated Segmentation Algorithm to Identify Vascular and Stromal Injury of Donors’ Kidneys

In kidney transplantations, the evaluation of the vascular structures and stromal areas is crucial for determining kidney acceptance, which is currently based on the pathologist’s visual evaluation. In this context, an accurate assessment of the vascular and stromal injury is fundamental to assessing the nephron status. In the present paper, the authors present a fully automated algorithm, called RENFAST (Rapid EvaluatioN of Fibrosis And vesselS Thickness), for the segmentation of kidney blood vessels and fibrosis in histopathological images. The proposed method employs a novel strategy based on deep learning to accurately segment blood vessels, while interstitial fibrosis is assessed using an adaptive stain separation method. The RENFAST algorithm is developed and tested on 350 periodic acid–Schiff (PAS) images for blood vessel segmentation and on 300 Massone’s trichrome (TRIC) stained images for the detection of renal fibrosis. In the TEST set, the algorithm exhibits excellent segmentation performance in both blood vessels (accuracy: 0.8936) and fibrosis (accuracy: 0.9227) and outperforms all the compared methods. To the best of our knowledge, the RENFAST algorithm is the first fully automated method capable of detecting both blood vessels and fibrosis in digital histological images. Being very fast (average computational time 2.91 s), this algorithm paves the way for automated, quantitative, and real-time kidney graft assessments

Karpinski Score under Digital Investigation: A Fully Automated Segmentation Algorithm to Identify Vascular and Stromal Injury of Donors’ Kidneys

In kidney transplantations, the evaluation of the vascular structures and stromal areas is crucial for determining kidney acceptance, which is currently based on the pathologist's visual evaluation. In this context, an accurate assessment of the vascular and stromal injury is fundamental to assessing the nephron status. In the present paper, the authors present a fully automated algorithm, called RENFAST (Rapid EvaluatioN of Fibrosis And vesselS Thickness), for the segmentation of kidney blood vessels and fibrosis in histopathological images. The proposed method employs a novel strategy based on deep learning to accurately segment blood vessels, while interstitial fibrosis is assessed using an adaptive stain separation method. The RENFAST algorithm is developed and tested on 350 periodic acid-Schiff (PAS) images for blood vessel segmentation and on 300 Massone's trichrome (TRIC) stained images for the detection of renal fibrosis. In the TEST set, the algorithm exhibits excellent segmentation performance in both blood vessels (accuracy: 0.8936) and fibrosis (accuracy: 0.9227) and outperforms all the compared methods. To the best of our knowledge, the RENFAST algorithm is the first fully automated method capable of detecting both blood vessels and fibrosis in digital histological images. Being very fast (average computational time 2.91 s), this algorithm paves the way for automated, quantitative, and real-time kidney graft assessments