RHBDL4-triggered downregulation of COPII adaptor protein TMED7 suppresses TLR4-mediated inflammatory signaling.

The toll-like receptor 4 (TLR4) is a central regulator of innate immunity that primarily recognizes bacterial lipopolysaccharide cell wall constituents to trigger cytokine secretion. We identify the intramembrane protease RHBDL4 as a negative regulator of TLR4 signaling. We show that RHBDL4 triggers degradation of TLR4's trafficking factor TMED7. This counteracts TLR4 transport to the cell surface. Notably, TLR4 activation mediates transcriptional upregulation of RHBDL4 thereby inducing a negative feedback loop to reduce TLR4 trafficking to the plasma membrane. This secretory cargo tuning mechanism prevents the over-activation of TLR4-dependent signaling in an in vitro Mycobacterium tuberculosis macrophage infection model and consequently alleviates septic shock in a mouse model. A hypomorphic RHBDL4 mutation linked to Kawasaki syndrome, an ill-defined inflammatory disorder in children, further supports the pathophysiological relevance of our findings. In this work, we identify an RHBDL4-mediated axis that acts as a rheostat to prevent over-activation of the TLR4 pathway.

Accurate and versatile 3D segmentation of plant tissues at cellular resolution.

Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.