Biology subjects

LI, S.

Publications and source records attributed to LI, S..

6 recordsLinked to original sources

bioRxiv2024

Chemically Induced Nuclear Pore Complex Protein Degradation via E3 Ligase TRIM21

Despite the exciting progress of the bifunctional degrader molecules, also known as proteolysis-targeting chimeras (PROTACs), the rapidly expanding field is still significantly hampered by the lacking of available E3 ligase ligands. Our research bridges this gap by uncovering a series of small-molecule ligands to the E3 ligase TRIM21 through DNA-Encoded Library (DEL) technology. We confirmed their interaction with TRIM21 using crystallography and demonstrated their anti-proliferative effects across various cancer cell types. Furthermore, proteomic studies identified that the mRNA Export Factor GLE1, and the Nuclear Pore Complex Protein NUP155, were significantly down-regulated upon TRIM21 ligand treatment. This degradation required TRIM21 and was ubiquitin-proteasome-dependent. More specifically, NUP155 was the primary target for the TRIM21 ligands, while GLE1 was considered a passenger target upon the initial degradation of NUP155. Using immunofluorescence techniques, we further demonstrated that the degradation of GLE1 and NUP155 proteins impaired the integrity of the nuclear envelope, leading to cell death. Highlighted by this research, a novel mode of action has been discovered for the TRIM21 E3 ligase ligand, acting as a monovalent degrader that triggers de novo interaction with functional complex proteins and induces their degradation.

cell biology↗

bioRxiv2022

Resolving single-cell copy number profiling for large datasets

The advances of single-cell DNA sequencing (scDNA-seq) enable us to characterize the genetic heterogeneity of cancer cells. However, the high noise and low coverage of scDNA-seq impede the estimation of copy number variations (CNVs). In addition, existing tools suffer from intensive execution time and often fail on large datasets. Here, we propose SeCNV, a novel method that leverages structural entropy, to profile the copy numbers. SeCNV adopts a local Gaussian kernel to construct a matrix, depth congruent map, capturing the similarities between any two bins along the genome. Then SeCNV partitions the genome into segments by minimizing the structural entropy from the depth congruent map. With the partition, SeCNV estimates the copy numbers within each segment for cells. We simulate nine datasets with various breakpoint distributions and amplitudes of noise to benchmark SeCNV. SeCNV achieves a robust performance, i.e., the F1-scores are higher than 0.95 for breakpoint detections, significantly outperforming state-of-the-art methods. SeCNV successfully processes large datasets (>50,000 cells) within four minutes while other tools failed to finish within the time limit, i.e., 120 hours. We apply SeCNV to single-nucleus sequencing (SNS) datasets from two breast cancer patients and acoustic cell tagmentation (ACT) sequencing datasets from eight breast cancer patients. SeCNV successfully reproduces the distinct subclones and infers tumor heterogeneity. SeCNV is available at https://github.com/deepomicslab/SeCNV.

bioinformatics↗

bioRxiv2021

Recombinant NAGLU-IGF2 prevents physical and neurological disease and improves survival in Sanfilippo B syndrome

Recombinant human alpha-N-acetylglucosaminidase-insulin-like growth factor-2 (rhNAGLU-IGF2) is an investigational enzyme replacement therapy for Sanfilippo B, a lysosomal storage disease. Because recombinant human NAGLU (rhNAGLU) is poorly mannose 6-phosphorylated, we generated a fusion protein of NAGLU with IGF2 to permit its binding to the cation-independent mannose 6-phosphate receptor. We previously administered rhNAGLU-IGF2 intracerebroventricularly to Sanfilippo B mice, and demonstrated therapeutic restoration of NAGLU, normalization of lysosomal storage, and improvement in markers of neurodegeneration and inflammation. Here, we studied repeated intracerebroventricular rhNAGLU-IGF2 delivery in both murine and canine Sanfilippo B to determine potential effects on their behavioral phenotypes and survival. Treated mice showed improvement in disease markers such as heparan sulfate glycosaminoglycans, beta-hexosaminidase, microglial activation, and lysosomal-associated membrane protein-1. Sanfilippo B mice treated with rhNAGLU-IGF2 displayed partial normalization of their stretch attend postures, a defined fear pose in mice (p<0.001). We found an improved rotarod performance in Sanfilippo B mice treated with rhNAGLU-IGF2 compared to vehicle-treated Sanfilippo B mice (p=0.002). We also found a 61% increase in survival in Sanfilippo B mice treated with rhNAGLU-IGF2 (mean 53w, median 48w) compared to vehicle-treated Sanfilippo B mice (mean 33w, median 37w; p<0.001). In canine Sanfilippo B, we found that rhNAGLU-IGF2 administered into cerebrospinal fluid normalized HS and beta-hexosaminidase activity in gray and white matter brain regions. Proteomic analysis of cerebral cortex showed restoration of protein expression levels in pathways relevant to cognitive, synaptic, and lysosomal functions. These data suggest that treatment with rhNAGLU-IGF2 may improve the phenotype of Sanfilippo B disease.

neuroscience↗

bioRxiv2021

The globular C1q receptor is required for epidermal growth factor receptor signaling during Candida albicans infection

During oropharyngeal candidiasis, Candida albicans activates the epidermal growth factor receptor (EGFR), which induces oral epithelial cells to both endocytose the fungus and synthesize proinflammatory mediators that orchestrate the host immune response. To elucidate the signaling pathways that are stimulated when C. albicans interacts with EGFR, we analyzed the proteins that associate with EGFR when C. albicans infects human oral epithelial cells. We identified 1214 proteins that were associated with EGFR in C. albicans-infected cells. We investigated the function of seven of these proteins that either showed increased association with EGFR in response to C. albicans or that mediated the interaction of other microbial pathogens with epithelial cells. Among these proteins, EGFR was found to associate with WW domain-binding protein 2, toll-interacting protein, interferon-induced transmembrane protein 3, and the globular C1q receptor (gC1qR) in viable epithelial cells. Each of these proteins was required for maximal endocytosis of C. albicans and they all regulated fungal-induced production of IL-1{beta} and/or IL-8, either positively or negatively. gC1qR functioned as a key coreceptor with EGFR. Interacting with the C. albicans Als3 invasin, gC1qR was required for the fungus to stimulate both EGFR and the ephrin type-A receptor 2. The combination of gC1qR and EGFR was necessary for maximal endocytosis of C. albicans and secretion of IL-1{beta}, IL-8, and GM-CSF. Thus, this work provides an atlas of proteins that associate with EGFR and identifies several that play a central role in the response of human oral epithelial cells to C. albicans infection. IMPORTANCEOral epithelial cells play a key role in the pathogenesis of oropharyngeal candidiasis. In addition to being target host cells for C. albicans adherence and invasion, they secrete proinflammatory cytokines and chemokines that recruit T cells and activated phagocytes to foci of infection. It is known that C. albicans activates EGFR on oral epithelial cells, which induces these cells to endocytose the organism and stimulates them to secrete proinflammatory mediators. To elucidate the EGFR signaling pathways that govern these responses, we analyzed the epithelial cell proteins that associate with EGFR in C. albicans-infected epithelial cells. We identified four proteins that physically associate with EGFR and that regulate different aspects of the epithelial response to C. albicans. One of these is gC1qR, which is required for C. albicans to activate EGFR, induce endocytosis, and stimulate the secretion of proinflammatory mediators, indicating that gC1qR functions as a key co-receptor with EGFR.

microbiology↗

bioRxiv2020

Bystander CD4+ T cells infiltrate human tumors and are phenotypically distinct

Tumor-specific T cells likely underpin effective immune checkpoint-blockade therapies. Yet, most studies focus on Treg cells and CD8+ tumor-infiltrating lymphocytes (TILs). Here we study CD4+ TILs in human lung and colorectal cancers and observe that non-Treg CD4+ TILs average more than 70% of total CD4+ TILs in both cancer types. Leveraging high dimensional analyses including mass cytometry and single-cell sequencing, we reveal that CD4+ TILs are heterogeneous at both gene and protein levels, within each tumor and across patients. Consistently, we find different subsets of CD4+ TILs showing characteristics of effectors, tissue resident memory (Trm) or exhausted cells (expressing PD-1, CTLA-4 and CD39). In both cancer types, the frequencies of CD39- non-Treg CD4+ TILs strongly correlate with frequencies of CD39- CD8+ TILs, which we and others have previously shown to be enriched for cells specific for cancer-unrelated antigens (bystanders). Ex-vivo, we demonstrate that CD39- CD4+ TILs can be specific for cancer unrelated antigens, such as HCMV epitopes. Overall, our findings highlight that CD4+ TILs cells are not necessarily tumor-specific and suggest measuring CD39 expression as a straightforward way to quantify or isolate bystander CD4+ T cells. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=158 SRC="FIGDIR/small/204172v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@13de05org.highwire.dtl.DTLVardef@553e34org.highwire.dtl.DTLVardef@66e9f1org.highwire.dtl.DTLVardef@8903ca_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2018

Analysis of biological record data: improvements of the occupancy-detection models

Many indicators are used to monitor the progress of the target which aims to stop the biodiversity loss by 2020. However, the occupancy-detection model which is currently applied to calculate the indicator is biased. Hence, more robust models are required to track the trend of the species precisely. This research first reviews the previous works on improving this occupancy-detection model by changing the prior distributions of one of the quantities and of the models considered previously, a model based on a random walk is found to be the most appropriate although it has some potential deficiencies. Then this research provides some potential improvements of the random walk model by changing the way of modelling the prior distributions of each quantity and changing the model structure. Then the hoverflies datasets are used in this research to analyse the performance of the models. These models are compared by the running times of fitting the models and the plots of the trend of the species of all models. As a result, the categorical list length model is considered to be the most precise model among all models with a reasonable running time. Then, we fit this model with a large dataset, however, it takes a long running time to get the result. Finally, some potential improvements are suggested which may be useful for further research.

ecology↗