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Hultin, S.

Publications and source records attributed to Hultin, S..

2 recordsLinked to original sources

The VE-cadherin/AmotL2 mechanosensory pathway suppresses aortic inflammation and the formation of abdominal aortic aneurysms

Arterial endothelial cells (ECs) have the ability to respond to mechanical forces exerted by fluid shear stress. This response is of importance, as it is protective against vascular diseases such as atherosclerosis and aortic aneurysms. Mechanical forces are transmitted at the sites of adhesion to the basal membrane as well as cell-cell junctions where protein complexes connect to the cellular cytoskeleton to relay force into the cell. Here we present a novel protein complex that connects junctional VE-cadherin and radial actin filaments to the LINC complex in the nuclear membrane. We show that the scaffold protein AmotL2 is essential for the formation of radial actin filaments and the flow-induced alignment of aortic and arterial ECs. The deletion of endothelial AmotL2 alters nuclear shape as well as subcellular positioning. Molecular analysis shows that VE-cadherin is mechanically associated with the nuclear membrane via binding to AmotL2 and Actin. Furthermore, the deletion of AmotL2 in ECs provokes a pro-inflammatory response and abdominal aortic aneurysms (AAA) in the aorta of mice on a normal diet. Remarkably, transcriptome analysis of AAA samples from human patients revealed a negative correlation between AmotL2 expression and aneurysm diameters, as well as a positive correlation between AmotL2 and YAP expression. These findings provide a conceptual framework regarding how mechanotransduction in the junctions is coupled with vascular disease.

cell biology↗

Deep learning identifies pathological abnormalities predictive of graft loss in kidney transplant biopsies

BackgroundInterstitial fibrosis, tubular atrophy, and inflammation are major contributors to renal allograft failure. Here we seek an objective, quantitative pathological assessment of these lesions to improve predictive utility. MethodsWe constructed a deep-learning-based pipeline recognizing normal vs. abnormal kidney tissue compartments and mononuclear leukocyte (MNL) infiltrates from Periodic acid-Schiff (PAS) stained slides of transplant biopsies (training: n=60, testing: n=33) that quantified pathological lesions specific for interstitium, tubules and MNL infiltration. The pipeline was applied to 789 whole slide images (WSI) from baseline (n=478, pre-implantation) and 12-month post-transplant (n=311) protocol biopsies in two independent cohorts (GoCAR: 404 patients, AUSCAD: 212 patients) of transplant recipients to correlate composite lesion features with graft loss. ResultsOur model accurately recognized kidney tissue compartments and MNLs. The digital features significantly correlated with Banff scores, but were more sensitive to subtle pathological changes below the thresholds in Banff scores. The Interstitial and Tubular Abnormality Score (ITAS) in baseline samples was highly predictive of 1-year graft loss (p=2.8e-05), while a Composite Damage Score (CDS) in 12-month post-transplant protocol biopsies predicted later graft loss (p=7.3e-05). ITAS and CDS outperformed Banff scores or clinical predictors with superior graft loss prediction accuracy. High/intermediate risk groups stratified by ITAS or CDS also demonstrated significantly higher incidence of eGFR decline and subsequent graft damage. ConclusionsThis deep-learning approach accurately detected and quantified pathological lesions from baseline or post-transplant biopsies, and demonstrated superior ability for prediction of posttransplant graft loss with potential application as a prevention, risk stratification or monitoring tool.

pathology↗