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Biology subjects

Marschall, P.

Publications and source records attributed to Marschall, P..

2 recordsLinked to original sources

Megalin deficiency perturbs retinal homeostasis and impairs cathepsin D processing and phagosome-lysosome maturation in the retinal pigment epithelium

The multiligand endocytic receptor, megalin (LRP2), is expressed in the retinal pigment epithelium (RPE) and patients lacking the receptor develop high myopia. Despite its established role in retinal development, the contribution of megalin to retinal homeostasis in the normally developed/mature eye remains poorly understood. Here, we investigated megalin function using an inducible knockout mouse (KO) model and human iPSC-derived RPE with megalin knockdown (KD) to distinguish post-developmental homeostatic functions from developmental effects. In vivo, megalin ablation caused progressive retinal degeneration and visual impairment, with morphological abnormalities in the RPE but no changes in myopia-associated ocular phenotypes including axial length and intraocular pressure. Proteomic profiling of megalin-KO RPE revealed reduction of autophagy-related proteins. In line with this, megalin deficiency was associated with accumulation of pro-cathepsin D, and perturbed rhodopsin turnover. This was supported in vitro, where trafficking of photoreceptor outer segment (POS) containing phagosomes to lysosomes was reduced, suggesting disturbed phagosome maturation. Megalin KD did not measurably impair initial uptake of POS discs, but delayed rhodopsin degradation, indicating defective post-ingestion processing. Together, these findings establish megalin as a key regulator of retinal homeostasis in the mature eye by controlling phagosome-lysosome fusion in the RPE and suggest that megalin dysfunction contributes to slowly progressive retinal degeneration. This positions megalin as a potential therapeutic target in lysosomal degenerative diseases in the retina.

Cell Biology↗

EVA: a Foundation Model Advancing Translational Drug Development in Immuno-Inflammation

Drug development is a lengthy and high-risk process, with most investigational drug candidates failing in phase II randomized clinical trials (RCT) due to insufficient efficacy. It makes early prediction of trial outcomes crucial for reducing attrition and guiding strategic decisions, especially in immunology and inflammation (I&I) diseases. Herein, we present EVA, the first pre-trained foundation model in complex inflammatory diseases tailored to support drug development. EVA learns generalizable patterns from large-scale data of cell biology and immunology, enabling superior predictive performance and generalization compared to traditional approaches. EVA is pre-trained on tens of millions of single-cell RNA-seq samples and tens of thousands of bulk RNA-seq samples from I&I diseases patients, enabling it to learn disease-relevant transcriptomic patterns in this therapeutic area. By fine-tuning EVA in few-shot settings on both preclinical (mouse) and clinical (human) data and harnessing its wide pre-training knowledge, EVA predicts drug responses in I&I with high precision at both cohort and patient levels, as illustrated by accurate forecasting of anti-TNF therapeutic activity in ulcerative colitis. By deciphering its decision process, we further highlight that EVAs ability to stratify patients based on predicted drug response can also be leveraged to discover drug response biomarkers as early as preclinical stages. EVAs applications in precision immunology encompass therapeutic target validation prior to clinical entry, identification of patient subpopulations most likely to benefit from treatment, and comparative efficacy analysis against competitor compounds. EVAs versatility makes it an invaluable tool for strategic decision-making throughout the drug development pipeline: by leveraging it to prioritize the most promising drug candidates and optimize RCT designs, it can contribute to reduce late-stage failures and accelerate the delivery of effective therapies. Overall, this work represents a significant advancement in utilizing a pre-trained foundation model for precision drug development in complex inflammatory diseases. Graphical abstractEVA is a pre-trained foundation model specific to immune-mediated inflammatory diseases. It enables the prediction of therapeutic efficacy in patients leveraging data from preclinical disease models. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=73 SRC="FIGDIR/small/651839v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@1c783f1org.highwire.dtl.DTLVardef@1a760d9org.highwire.dtl.DTLVardef@1c7748aorg.highwire.dtl.DTLVardef@1b44f82_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗