Biology subjects

Quan, C.

Publications and source records attributed to Quan, C..

11 recordsLinked to original sources

bioRxiv2025

Dual-mode intramolecular agonist-dependent mechanoactivation of the adhesion GPCR ADGRG1/GPR56

Mechanical stimuli instruct cardinal cellular decisions pertaining to cell fate, proliferation, morphology, and movement1,2. How adhesion G protein-coupled receptors (aGPCRs), a large family of mechanosensors with more than 30 members in humans3-5, transduce mechanical cues into metabotropic commands has been a matter of debate due to the lack of suitable approaches to analyze receptor activation during mechanotransduction in live cells6. A central question is whether aGPCR activation strictly requires mechanical dissociation of the autoproteolyzed N- and C-terminal fragment (NTF and CTF) complex to expose the tethered intramolecular agonist (TIA; 'Stachel'). Here, we investigated human ADGRG1/GPR56 (G1), an aGPCR with roles in brain development7, skeletal muscle8, and platelet function9, to study the events during aGPCR mechanotransduction by monitoring receptor dissociation and G protein recruitment in real time during cell migration and substrate deformation. Magnetic-tweezer measurements at a physiologically relevant loading rate showed that isolated G1 GAIN domains can partially unfold and refold at forces below those required for NTF-CTF dissociation. In cells, G1 dissociation occurred at retracting fibers during cell migration10 over an adhesive substrate through its native extracellular matrix ligand, tissue transglutaminase 2 (TG2), and was enhanced by cell stretching. Simultaneous live recording of G protein recruitment and pharmacological assays during mechanical stimulation of G1 reveal a dual-mode activation mechanism: mechanical force elicits robust metabotropic signaling without requiring NTF dissociation and generates an intracellular response commensurate with the fully dissociated receptor. Both signaling modes depend on an intact Stachel element11,12 of the receptor under our experimental conditions. Our findings determine mechanotransduction principles governing aGPCR activation at the cell-ECM interface. We reveal that G1 does not function as a simple binary switch, but rather as a highly sophisticated mechanosensor that dynamically encodes localized extracellular tension into distinct, highly scalable metabotropic outputs.

cell biology↗

bioRxiv2025

Molecular surface mimicry enables CRBN to target G3BP2 for degradation

Molecular glue degraders (MGDs) are small molecule compounds that repurpose the ubiquitin-proteasome system to induce degradation of challenging therapeutic targets. Clinically effective MGDs bind cereblon (CRBN), a substrate receptor of the Cullin-4/RING E3 ubiquitin ligase (CRL4CRBN), and impose a gain-of-function activity to recruit, ubiquitinate and degrade so-called neosubstrate proteins. Known neosubstrates bind the CRBN/MGD neosurface on the CRBN CULT domain through a structural G-loop recognition motif that mimics contacts of a natural CRBN degron. Here we report the binding mode of G3BP2, a CRBN neosubstrate that bypasses the G-loop requirement by engaging an unconventional binding site on the CRBN LON domain. The MGD-induced ternary complex interface does not resemble known protein-protein interactions (PPI) with CRBN. Instead, CRBN mimics an endogenous binding partner of G3BP2 and repurposes a preexisting PPI hotspot on the target protein. Our findings provide a novel generalizable concept for the rationalization of unconventional neosubstrate binding modes on CRBN, demonstrate unprecedented potential for the reprogrammability of this substrate receptor by MGDs, and offer opportunities for rational expansion of the target repertoire accessible to this modality.

molecular biology↗

bioRxiv2025

Glycosylated IgG antibodies accelerated the recovery of haemorrhagic fever with renal syndrome patients

AbstractHaemorrhagic fever with renal syndrome (HFRS) is a fatal disease caused by Hantaan virus (HTNV) infection. Humoral immunity is essential for effective viral clearance, however, the glycosylation characteristics of immunoglobulin G (IgG) in HFRS patients is not well known. Peripheral blood mononuclear cells from HFRS patients were obtained for B subset analysis using scRNA-seq and flow cytometry. HTNV specific IgG antibody titers were detected by enzyme-linked immunosorbent assay, and IgG glycosylation was analyzed by ultra-performance liquid chromatography. The proportions of the antibody-secreting memory (ASM) B cells and plasmablasts (PB) were significantly expanded among acute HFRS patients. We discovered significantly increased fucosylated IgG and decreased bisecting N-acetylglucosamine during the convalescent phase of HTNV infection. Meanwhile, positive correlations were observed between ASM subsets and galactosylation/sialylation in the IgG Fc region, and between PB subsets and sialylation. Notably, the glycosylation-related genes, such as RPN1 and RPN2, were primarily expressed differentially in the ASM and PB subclusters which were enriched in the N-glycosylation modifications of proteins through asparagine. Our findings indicated that IgG N-glycosylation may play a crucial role in combating HTNV infection and contributing to clinical recovery, which provided new insights for optimizing glycoengineered therapeutic antibodies.

immunology↗

bioRxiv2024

Mining the CRBN Target Space Redefines Rules for Molecular Glue-induced Neosubstrate Recognition

The CRL4CRBN ubiquitin ligase is leveraged by molecular glue degraders, small molecules that reprogram ligase specificity to induce degradation of clinically relevant neosubstrate proteins. Known CRBN neosubstrates share a generalizable {beta}-hairpin G-loop recognition motif, yet systematic exploration of the CRBN target landscape is still pending. Through computational mining of the human proteome using structure-based approaches, we predict over 1,400 CRBN-compatible {beta}-hairpin G-loop proteins across diverse target classes, identify novel mechanisms of neosubstrate recognition through structurally differentiated helical motifs and molecular surface mimicry, and validate 22 representative neosubstrates with clinical implications. This work broadens the CRBN target space, redefines rules for neosubstrate recognition and establishes a platform for the elimination of challenging drug targets by repurposing CRL4CRBN through next-generation molecular glue degraders.

biochemistry↗

bioRxiv2022

A pathogen effector FOLD diversified in symbiotic fungi

Pathogenic fungi use secreted effector proteins to suppress immunity and support their infection, but effectors have also been reported from fungi that engage in nutritional symbioses with plants. Sequence based effector comparisons between pathogens and symbiotic arbuscular mycorrhizal (AM) fungi are hampered by the huge diversity of effector sequences even within closely related microbes. Here we used a systematic protein structure modelling approach to classify the secretome of the AM fungus Rhizophagus irregularis. We identified secreted proteins with high structural similarity to Fusarium oxysporum f. sp. lycopersici dual domain (FOLD) effectors, which occur in low numbers in fungal pathogen genomes. Contrastingly, genes encoding FOLD proteins from AM fungi (MycFOLDs) are found in enlarged and diversified gene families. Our structure-model comparison suggests that MycFOLDs are similar to carbohydrate binding motifs. Different MycFOLD genes are expressed during colonisation of different hosts and MycFOLD-17 transcripts accumulate in plant intracellular arbuscules. The exclusive presence of MycFOLDs across unrelated plant-colonising fungi, their inducible expression, lineage specific sequence diversification, and transcripts in arbuscules support the hypothesis that FOLD proteins act as effectors during plant colonisation by symbiotic and pathogenic fungi.

microbiology↗

bioRxiv2021

RegVar: Tissue-specific Prioritization of Noncoding Regulatory Variants

Noncoding genomic variants constitute the majority of trait-associated genome variations; however, identification of functional noncoding variants is still a challenge in human genetics, and a method systematically assessing the impact of regulatory variants on gene expression and linking them to potential target genes is still lacking. Here we introduce a deep neural network (DNN)-based computational framework, RegVar, that can accurately predict the tissue-specific impact of noncoding regulatory variants on target genes. We show that, by robustly learning the genomic characteristics of massive variant-gene expression associations in a variety of human tissues, RegVar vastly surpasses all current noncoding variants prioritization methods in predicting regulatory variants under different circumstances. The unique features of RegVar make it an excellent framework for assessing the regulatory impact of any variant on its putative target genes in a variety of tissues. RegVar is available as a webserver at http://regvar.cbportal.org/.

bioinformatics↗

bioRxiv2021

Transcriptional activity and epigenetic regulation of transposable elements in the symbiotic fungus Rhizophagus irregularis

Arbuscular mycorrhizal (AM) fungi form mutualistic relationships with most land plant species and have long been considered as ancient asexuals. Long-term clonal evolution would be remarkable for a eukaryotic lineage and suggests the importance of alternative mechanisms to promote genetic variability facilitating adaptation. Here, we assessed the potential of transposable elements (TEs) for generating genomic diversity. The dynamic expression of TEs during Rhizophagus irregularis spore development suggests ongoing TE activity. We find Mutator-like elements located near genes belonging to highly expanded gene families. Characterising the epigenomic status of R. irregularis provides evidence of DNA methylation and small RNA production occurring at TE loci. Our results support a potential role for TEs in shaping the genome, and roles for DNA methylation and small RNA-mediated silencing in regulating TEs. A well-controlled balance between TE activity and repression may therefore contribute to genome evolution in AM fungi.

genomics↗

bioRxiv2021

Artificial intelligence enables the identification and quantification of arbuscular mycorrhizal fungi in plant roots

Soil fungi establish mutualistic interactions with the roots of most vascular land plants. Arbuscular mycorrhizal (AM) fungi are among the most extensively characterised mycobionts to date. Current approaches to quantifying the extent of root colonisation and the abundance of hyphal structures in mutant roots rely on staining and human scoring involving simple, yet repetitive tasks prone to variations between experimenters. We developed AMFinder which allows for automatic computer vision-based identification and quantification of AM fungal colonisation and intraradical hyphal structures on ink-stained root images using convolutional neural networks. AMFinder delivered high-confidence predictions on image datasets of roots of multiple plant hosts (Nicotiana benthamiana, Medicago truncatula, Lotus japonicus, Oryza sativa) and captured the altered colonisation in ram1-1, str and smax1 mutants. A streamlined protocol for sample preparation and imaging allowed us to quantify mycobionts from the genera Rhizophagus, Claroideoglomus, Rhizoglomus and Funneliformis via flatbed scanning or digital microscopy including dynamic increases in colonisation in whole root systems over time. AMFinder adapts to a wide array of experimental conditions. It enables accurate, reproducible analyses of plant root systems and will support better documentation of AM fungal colonisation analyses. AMFinder can be accessed here: https://github.com/SchornacklabSLCU/amfinder.git

plant biology↗

bioRxiv2020

Characterization of Structural Variation in Tibetans Reveals New Evidence of High-altitude Adaptation and Introgression

Structural variation (SV) acts as an essential mutational force shaping the evolution and function of the human genome. To investigate the role of SVs in high-altitude adaptation (HAA), we here generated a comprehensive catalog of SVs in a Chinese Tibetan (n = 15) and Han (n = 10) population using the nanopore sequencing technology. Among a total of 38,216 unique SVs in the catalog, 27% were sequence-resolved for the first time. We systemically assessed the distribution of these SVs across repeat sequences and functional genomic regions. Through genotyping in additional 189 genomes, we identified 90 Tibetan-Han stratified SVs and 124 candidate adaptive genes. Besides, we discovered 15 adaptive introgressed SV candidates and provided evidence for a deletion of 335 base pairs at 1p36.32. Overall, our results highlight the important role of SVs in the evolutionary processes of Tibetans adaptation to the Qinghai-Tibet Plateau and provide a valuable resource for future HAA studies.

genetics↗

bioRxiv2019

DeepHiC: A Generative Adversarial Network for Enhancing Hi-C Data Resolution

Hi-C is commonly used to study three-dimensional genome organization. However, due to the high sequencing cost and technical constraints, the resolution of most Hi-C datasets is coarse, resulting in a loss of information and biological interpretability. Here we develop DeepHiC, a generative adversarial network, to predict high-resolution Hi-C contact maps from low-coverage sequencing data. We demonstrated that DeepHiC is capable of reproducing high-resolution Hi-C data from as few as 1% downsampled reads. Empowered by adversarial training, our method can restore fine-grained details similar to those in high-resolution Hi-C matrices, boosting accuracy in chromatin loops identification and TADs detection, and outperforms the state-of-the-art methods in accuracy of prediction. Finally, application of DeepHiC to Hi-C data on mouse embryonic development can facilitate chromatin loop detection with higher accuracy. We develop a web-based tool (DeepHiC, http://sysomics.com/deephic) that allows researchers to enhance their own Hi-C data with just a few clicks.\n\nAuthor summaryWe developed a novel method, DeepHiC, for enhancing Hi-C data resolution from low-coverage sequencing data using generative adversarial network. DeepHiC is capable of reproducing highresolution (10-kb) Hi-C data with high quality even using 1/100 downsampled reads. Our method outperforms the previous methods in Hi-C data resolution enhancement, boosting accuracy in chromatin loops identification and TADs detection. Application of DeepHiC on mouse embryonic development data shows that enhancements afforded by DeepHiC facilitates the chromatin loops identification of these data achieving higher accuracy. We also developed a user-friendly web server (http://sysomics.com/deephic) that allows researchers to enhance their own low-resolution Hi-C data (40kb-1 Mb) with just few clicks.

bioinformatics↗

bioRxiv2017

Time-resolved dual root-microbe transcriptomics reveals early induced Nicotiana benthamiana genes and conserved infection-promoting Phytophthora palmivora effectors

BackgroundPlant-pathogenic oomycetes are responsible for economically important losses on crops worldwide. Phytophthora palmivora, a broad-host-range tropical relative of the potato late blight pathogen, causes rotting diseases in many important tropical crops including papaya, cocoa, oil palm, black pepper, rubber, coconut, durian, mango, cassava and citrus.\n\nTranscriptomics have helped to identify repertoires of host-translocated microbial effector proteins which counteract defenses and reprogram the host in support of infection. As such, these studies have helped understanding of how pathogens cause diseases. Despite the importance of P. palmivora diseases, genetic resources to allow for disease resistance breeding and identification of microbial effectors are scarce.\n\nResultsWe employed the model plant N. benthamiana to study the P. palmivora root infections at the cellular and molecular level. Time-resolved dual transcriptomics revealed different pathogen and host transcriptome dynamics. De novo assembly of P. palmivora transcriptome and semi-automated prediction and annotation of the secretome enabled robust identification of conserved infection-promoting effectors. We show that one of them, REX3, suppresses plant secretion processes. In a survey for early transcriptionally activated plant genes we identified a N. benthamiana gene specifically induced at infected root tips that encodes a peptide with danger-associated molecular features.\n\nConclusionsThese results constitute a major advance in our understanding of P. palmivora diseases and establish extensive resources for P. palmivora pathogenomics, effector-aided resistance breeding and the generation of induced resistance to Phytophthora root infections. Furthermore, our approach to find infection relevant secreted genes is transferable to other pathogen-host interactions and not restricted to plants.

plant biology↗