Plasma membrane-specific interactome analysis reveals calpain 1 as a druggable modulator of rescued Phe508del-CFTR cell surface stability

Cystic fibrosis (CF) is a genetic disease caused by mutations in the gene encoding CF transmembrane conductance regulator (CFTR), a chloride channel normally expressed at the surface of epithelial cells. The most frequent mutation, resulting in Phe-508 deletion, causes CFTR misfolding and its premature degradation. Low temperature or pharmacological correctors can partly rescue the Phe508del-CFTR processing defect and enhance trafficking of this channel variant to the plasma membrane (PM). Nevertheless, the rescued channels have an increased endocytosis rate, being quickly removed from the PM by the peripheral protein quality-control pathway. We previously reported that rescued Phe508del-CFTR (rPhe508del) can be retained at the cell surface by stimulating signaling pathways that coax the adaptor molecule ezrin (EZR) to tether rPhe508del–Na+/H+-exchange regulatory factor-1 (NHERF1) complexes to the actin cytoskeleton, thereby averting the rapid internalization of this channel variant. However, the molecular basis for why rPhe508del fails to recruit active EZR to the PM remains elusive. Here, using a proteomics approach, we characterized and compared the core components of wt-CFTR– or rPhe508del–containing macromolecular complexes at the surface of human bronchial epithelial cells. We identified calpain 1 (CAPN1) as an exclusive rPhe508del interactor that prevents active EZR recruitment, impairs rPhe508del anchoring to actin, and reduces its stability in the PM. We show that either CAPN1 downregulation or its chemical inhibition dramatically improves the functional rescue of Phe508del-CFTR in airway cells. These observations suggest that CAPN1 constitutes an attractive target for pharmacological intervention, as part of CF combination therapies restoring Phe508del-CFTR function.This work was supported by a center grant UID/MULTI/04046/2019 to BioISI and project PTDC/BIA-CEL/28408/2017 and IF2012 to PM, both from FCT, Portugal. AMM was recipient of fellowship SFRH/BD/52490/2014 from BioSYS PhD programme PD65-2012, and PB of fellowship SFRH/BPD/94322/2013.N/

The endosomal transcriptional regulator RNF11 integrates degradation and transport of EGFR

Stimulation of cells with epidermal growth factor (EGF) induces internalization and partial degradation of the EGF receptor (EGFR) by the endo-lysosomal pathway. For continuous cell functioning, EGFR plasma membrane levels are maintained by transporting newly synthesized EGFRs to the cell surface. The regulation of this process is largely unknown. In this study, we find that EGF stimulation specifically increases the transport efficiency of newly synthesized EGFRs from the endoplasmic reticulum to the plasma membrane. This coincides with an up-regulation of the inner coat protein complex II (COPII) components SEC23B, SEC24B, and SEC24D, which we show to be specifically required for EGFR transport. Up-regulation of these COPII components requires the transcriptional regulator RNF11, which localizes to early endosomes and appears additionally in the cell nucleus upon continuous EGF stimulation. Collectively, our work identifies a new regulatory mechanism that integrates the degradation and transport of EGFR in order to maintain its physiological levels at the plasma membrane

Development of a Multiphoton Fluorescence Lifetime Imaging Microscopy (FLIM) system using a Streak Camera

We report the development and detailed calibration of a multiphoton fluorescence lifetime imaging system (FLIM) using a streak camera. The present system is versatile with high spatial (0.2 micron) and temporal (50 psec) resolution and allows rapid data acquisition and reliable and reproducible lifetime determinations. The system was calibrated with standard fluorescent dyes and the lifetime values obtained were in very good agreement with values reported in literature for these dyes. We also demonstrate the applicability of the system to FLIM studies in cellular specimens including stained pollen grains and fibroblast cells expressing green fluorescent protein. The lifetime values obtained matched well with those reported earlier by other groups for these same specimens. Potential applications of the present system include the measurement of intracellular physiology and Fluorescence Resonance Energy Transfer (FRET) imaging which are discussed in the context of live cell imaging

Reconstructing cell cycle and disease progression using deep learning

We show that deep convolutional neural networks combined with nonlinear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by reconstructing the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. In further analysis of Jurkat cells, we detect and separate a subpopulation of dead cells in an unsupervised manner and, in classifying discrete cell cycle stages, we reach a sixfold reduction in error rate compared to a recent approach based on boosting on image features. In contrast to previous methods, deep learning based predictions are fast enough for on-the-fly analysis in an imaging flow cytometer

Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images

<p>Abstract</p> <p>Background</p> <p>Several algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed.</p> <p>Results</p> <p>To better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies.</p> <p>Conclusions</p> <p>These results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our inclusion of methods that have not been used before in this context broadens the set of available detection methods and compares them against the current state-of-the-art methods for subcellular particle detection.</p