LipidUNet-Machine Learning-Based Method of Characterization and Quantification of Lipid Deposits Using iPSC-Derived Retinal Pigment Epithelium.

The retinal pigment epithelium (RPE) is a monolayer of hexagonal cells located at the back of the eye. It provides nourishment and support to photoreceptors and choroidal capillaries, performs phagocytosis of photoreceptor outer segments (POS), and secretes cytokines in a polarized manner for maintaining the homeostasis of the outer retina. Dysfunctional RPE, caused by mutations, aging, and environmental factors, results in the degeneration of other retinal layers and causes vision loss. A hallmark phenotypic feature of degenerating RPE is intra and sub-cellular lipid-rich deposits. These deposits are a common phenotype across different retinal degenerative diseases. To reproduce the lipid deposit phenotype of monogenic retinal degenerations in vitro, induced pluripotent stem cell-derived RPE (iRPE) was generated from patients' fibroblasts. Cell lines generated from patients with Stargardt and Late-onset retinal degeneration (L-ORD) disease were fed with POS for 7 days to replicate RPE physiological function, which caused POS phagocytosis-induced pathology in these diseases. To generate a model for age-related macular degeneration (AMD), a polygenic disease associated with alternate complement activation, iRPE was challenged with alternate complement anaphylatoxins. The intra and sub-cellular lipid deposits were characterized using Nile Red, boron-dipyrromethene (BODIPY), and apolipoprotein E (APOE). To quantify the density of lipid deposits, a machine learning-based software, LipidUNet, was developed. The software was trained on maximum-intensity projection images of iRPE on culture surfaces. In the future, it will be trained to analyze three-dimensional (3D) images and quantify the volume of lipid droplets. The LipidUNet software will be a valuable resource for discovering drugs that decrease lipid accumulation in disease models.

Streptococcus pneumoniae surface protein PfbA is a versatile multidomain and multiligand-binding adhesin employing different binding mechanisms.

Streptococcus pneumoniae, one of the major human respiratory pathogens, uses its repertoire of surface proteins to adhere to the epithelium of the nasopharynx and lungs leading to colonization. PfbA is a conserved surface protein of S. pneumoniae and helps the bacterium to colonize the host by recognizing the extracellular matrix (ECM) molecule fibronectin, as well as blood proteins like plasminogen and human serum albumin. The crystal structure of rPfbA150-607 revealed it to possess a beta-helical region similar to those of carbohydrate-active enzymes as well as a C-terminal segment that resembles the fibronectin-binding regions of fibronectin-binding proteins. To get more insight into the putative carbohydrate-binding property of PfbA and its binding to various host molecules, we generated three different constructs of PfbA and characterized them by ELISA, isothermal titration calorimetry and bio-layer interferometry experiments. Importantly, the isothermal titration calorimetry experiments revealed that PfbA binds to different saccharides. Further, ELISA and bio-layer interferometry experiments identified that (a) apart from fibronectin and plasminogen, the beta helix of PfbA also binds to other ECM molecules (b) lysines are not responsible for PfbA's binding to plasminogen, (c) in comparison with native fibrinogen, deglycosylated-fibrinogen exhibits reduced binding affinity towards PfbA implying the importance of sugar molecule-PfbA interaction and (d) the C-terminal region of PfbA binds exclusively to the N-terminal F1 modules of fibronectin. Thus, the results of this study show PfbA to be a versatile multidomain and multiligand-binding protein employing different binding mechanisms. These results could be useful for structure-based designing of inhibitors against PfbA.