Raj, Y. | bioRxiv Science

bioRxiv2026

K63-linked ubiquitin chains mark inactive Smoothened for packaging into ciliary extracellular vesicles

Signaling receptors can exit cilia either by retrieval back into the cell or by secretion in extracellular vesicles (EVs), a process known as ectocytosis. The mechanisms that govern ectocytosis remain poorly understood. Here, we leverage quantitative proteomic profiling to identify the Hedgehog signaling receptor Smoothened (SMO) as a major cargo of cilia-derived EVs. Surprisingly, ligands that promote ciliary accumulation of SMO strongly suppressed its packaging into EVs, indicating that ectocytosis selectively packages specific conformational states of SMO. We further find that SMO packaged into EVs is extensively modified with K63-linked ubiquitin chains. Preventing SMO ubiquitination or selectively removing K63-linked ubiquitin chains from SMO markedly reduced its secretion into EVs and caused SMO accumulation within cilia. Together, these results identify K63-linked ubiquitin chains as a sorting signal for ciliary ectocytosis and indicate that ubiquitin-dependent packaging of inactive SMO contributes to the dynamic redistribution of SMO during Hedgehog signaling.

cell biology↗

bioRxiv2022

Deep multi-omic network fusion for marker discovery of Alzheimer's Disease

Multi-omic data spanning from genotype, gene expression to protein expression have been increasingly explored to interpret findings from genome wide association studies of Alzheimers disease (AD) and to gain more insight of the disease mechanism. However, each -omics data type is usually examined individually and the functional interactions between genetic variations, genes and proteins are only used after discovery to interpret the findings, but not beforehand. In this case, multi-omic findings are likely not functionally related and therefore give rise to challenges in interpretation. To address this problem, we propose a new interpretable deep neural network model MoFNet to jointly model the prior knowledge of functional interactions and multi-omic data set. It aims to identify a subnetwork of functional interactions predictive of AD evidenced by multi-omic measures. Particularly, prior functional interaction network was embedded into the architecture of MoFNet in a way that it resembles the information flow from DNA to gene and protein. The proposed model MoFNet significantly outperformed all other state-of-art classifiers when evaluated using multi-omic data from the ROS/MAP cohort. Instead of individual markers, MoFNet yielded multi-omic sub-networks related to innate immune system, clearance of misfolded proteins, and neurotransmitter release respectively. Around 50% of these findings were replicated in another independent cohort. Our identified gene/proteins are highly related to synaptic vesicle function. Altered regulation or expression of these genes/proteins could cause disruption in neuron-neuron or neuron-glia cross talk and further lead to neuronal and synapse loss in AD. Further investigation of these identified genes/proteins could possibly help decipher the mechanisms underlying synaptic dysfunction in AD, and ultimately inform therapeutic strategies to modify AD progression in the early stage.

systems biology↗