Blocking CHOP-dependent TXNIP shuttling to mitochondria attenuates albuminuria and mitigates kidney injury in nephrotic syndrome.

Albuminuria is a hallmark of glomerular disease of various etiologies. It is not only a symptom of glomerular disease but also a cause leading to glomerulosclerosis, interstitial fibrosis, and eventually, a decline in kidney function. The molecular mechanism underlying albuminuria-induced kidney injury remains poorly defined. In our genetic model of nephrotic syndrome (NS), we have identified CHOP (C/EBP homologous protein)-TXNIP (thioredoxin-interacting protein) as critical molecular linkers between albuminuria-induced ER dysfunction and mitochondria dyshomeostasis. TXNIP is a ubiquitously expressed redox protein that binds to and inhibits antioxidant enzyme, cytosolic thioredoxin 1 (Trx1), and mitochondrial Trx2. However, very little is known about the regulation and function of TXNIP in NS. By utilizing Chop-/- and Txnip-/- mice as well as 68Ga-Galuminox, our molecular imaging probe for detection of mitochondrial reactive oxygen species (ROS) in vivo, we demonstrate that CHOP up-regulation induced by albuminuria drives TXNIP shuttling from nucleus to mitochondria, where it is required for the induction of mitochondrial ROS. The increased ROS accumulation in mitochondria oxidizes Trx2, thus liberating TXNIP to associate with mitochondrial nod-like receptor protein 3 (NLRP3) to activate inflammasome, as well as releasing mitochondrial apoptosis signal-regulating kinase 1 (ASK1) to induce mitochondria-dependent apoptosis. Importantly, inhibition of TXNIP translocation and mitochondrial ROS overproduction by CHOP deletion suppresses NLRP3 inflammasome activation and p-ASK1-dependent mitochondria apoptosis in NS. Thus, targeting TXNIP represents a promising therapeutic strategy for the treatment of NS.

Automated endocardial cushion segmentation and cellularization quantification in developing hearts using optical coherence tomography.

Of all congenital heart defects (CHDs), anomalies in heart valves and septa are among the most common and contribute about fifty percent to the total burden of CHDs. Progenitors to heart valves and septa are endocardial cushions formed in looping hearts through a multi-step process that includes localized expansion of cardiac jelly, endothelial-to-mesenchymal transition, cell migration and proliferation. To characterize the development of endocardial cushions, previous studies manually measured cushion size or cushion cell density from images obtained using histology, immunohistochemistry, or optical coherence tomography (OCT). Manual methods are time-consuming and labor-intensive, impeding their applications in cohort studies that require large sample sizes. This study presents an automated strategy to rapidly characterize the anatomy of endocardial cushions from OCT images. A two-step deep learning technique was used to detect the location of the heart and segment endocardial cushions. The acellular and cellular cushion regions were then segregated by K-means clustering. The proposed method can quantify cushion development by measuring the cushion volume and cellularized fraction, and also map 3D spatial organization of the acellular and cellular cushion regions. The application of this method to study the developing looping hearts allowed us to discover a spatial asymmetry of the acellular cardiac jelly in endocardial cushions during these critical stages, which has not been reported before.