Gain of function of a metalloproteinase associated with multiple myeloma, bicuspid aortic valve, and Von Hippel-Lindau syndrome.

A patient diagnosed with multiple myeloma, bicuspid aortic valve, and Von Hippel-Lindau syndrome underwent whole-exome sequencing seeking a unified genetic cause for these three pathologies. The patient possessed a single-point mutation of arginine to cysteine (R24C) in the N-terminal region(pro-domain) of matrix metalloproteinase 9 (MMP-9). The pro-domain interacts with the catalytic site of this enzyme rendering it inactive.

Naturally occurring combinations of receptors from single cell transcriptomics in endothelial cells.

VEGF inhibitor drugs are part of standard care in oncology and ophthalmology, but not all patients respond to them. Combinations of drugs are likely to be needed for more effective therapies of angiogenesis-related diseases. In this paper we describe naturally occurring combinations of receptors in endothelial cells that might help to understand how cells communicate and to identify targets for drug combinations.

LIF, a mitogen for choroidal endothelial cells, protects the choriocapillaris: implications for prevention of geographic atrophy.

In the course of our studies aiming to discover vascular bed-specific endothelial cell (EC) mitogens, we identified leukemia inhibitory factor (LIF) as a mitogen for bovine choroidal EC (BCE), although LIF has been mainly characterized as an EC growth inhibitor and an anti-angiogenic molecule. LIF stimulated growth of BCE while it inhibited, as previously reported, bovine aortic EC (BAE) growth. The JAK-STAT3 pathway mediated LIF actions in both BCE and BAE cells, but a caspase-independent proapoptotic signal mediated by cathepsins was triggered in BAE but not in BCE.

Digital Cell Sorter (DCS): a cell type identification, anomaly detection, and Hopfield landscapes toolkit for single-cell transcriptomics.

Motivation: Analysis of singe cell RNA sequencing (scRNA-seq) typically consists of different steps including quality control, batch correction, clustering, cell identification and characterization, and visualization. The amount of scRNA-seq data is growing extremely fast, and novel algorithmic approaches improving these steps are key to extract more biological information. Here, we introduce: (i) two methods for automatic cell type identification (i.