Biology subjects

DU, Y.

Publications and source records attributed to DU, Y..

4 recordsLinked to original sources

bioRxiv2025

Establishment of a Systemic Lupus Erythematosus Mouse Model in Humanized CD3/CD19 Mice and Evaluation of the Efficacy of a Human Bispecific Antibody

Systemic Lupus Erythematosus (SLE) is a complex autoimmune disease affecting multiple organ systems and is considered one of the most severe autoimmune diseases worldwide, significantly impacting womens health. B cells play a crucial role in disease pathogenesis by becoming abnormally activated and producing autoantibodies that attack the bodys normal tissues, leading to inflammation and organ damage. Therefore, targeting the human B cell receptor has emerged as a promising strategy for therapeutic intervention. In this study, we established an SLE animal model using double-humanized CD3/CD19 mice, in which human CD3 and CD19 genes replaced their murine counterparts. The phenotype was confirmed by ELISA and flow cytometry. hCD3/hCD19 mice induced by a combination of pristine x 2, imiquimod (IMQ) and nephrectomy exhibited several key features of SLE, including anti-dsDNA antibody production, proteinuria, increased immune cell populations, and kidney lesions. Furthermore, treatment of these humanized mice with anti-human CD3/CD19 bispecific antibodies significantly inhibited these pathological indicators. Thus, pristine x 2, IMQ and nephrectomy induced hCD3/hCD19 mice provide a validated preclinical model for investigating novel therapeutic agents targeting B cells in SLE. Graphical abstractThis study explores the effects of different modeling strategies on agent induction, with a particular emphasis on the link between the induced agents and their corresponding clinical manifestations. Through an extensive literature review and experimental validation, a novel approach for inducing systemic lupus erythematosus (SLE) has been established. By employing gene editing techniques, mouse CD3 and CD19 antibody recognition sites were substituted with their human equivalents. This humanized mouse model enables the direct assessment of humanized bispecific antibodies in the context of SLE induction. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/668572v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@652c83org.highwire.dtl.DTLVardef@4d6233org.highwire.dtl.DTLVardef@6a2f8forg.highwire.dtl.DTLVardef@1587652_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2021

2-kupl: mapping-free variant detection from DNA-seq data of matched samples

The detection of genome variants, including point mutations, indels and structural variants, is a fundamental and challenging computational problem. We address here the problem of variant detection between two deep-sequencing (DNA-seq) samples, such as two human samples from an individual patient, or two samples from distinct bacterial strains. The preferred strategy in such a case is to align each sample to a common reference genome, collect all variants and compare these variants between samples. Such mapping-based protocols have several limitations. DNA sequences with large indels, aggregated mutations and structural variants are hard to map to the reference. Furthermore, DNA sequences cannot be mapped reliably to genomic low complexity regions and repeats. Herein, we introduce 2-kupl, a k-mer based, mapping-free protocol to detect variants between two DNA-seq samples. On simulated and actual data, 2-kupl achieves a higher precision than other mapping-free protocols. Applying 2-kupl to prostate cancer whole exome data, we identify a number of candidate variants in hard-to-map regions and propose potential novel recurrent variants in this disease.

bioinformatics↗

bioRxiv2020

precisionFDA Truth Challenge V2: Calling variants from short- and long-reads in difficult-to-map regions

The precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based and machine-learning methods scoring best for short-read and long-read datasets, respectively. New methods out-performed the 2016 Truth Challenge winners, and new machine-learning approaches combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.

bioinformatics↗

bioRxiv2019

dSNE: a visualization approach for use with decentralized data

Privacy concerns for rare disease data, institutional or IRB policies, access to local computational or storage resources or download capabilities are among the reasons that may preclude analyses that pool data to a single site. A growing number of multi-site projects and consortia were formed to function in the federated environment to conduct productive research under constraints of this kind. In this scenario, a quality control tool that visualizes decentralized data in its entirety via global aggregation of local computations is especially important, as it would allow the screening of samples that cannot be jointly evaluated otherwise. To solve this issue, we present two algorithms: decentralized data stochastic neighbor embedding, dSNE, and its differentially private counterpart, DP-dSNE. We leverage publicly available datasets to simultaneously map data samples located at different sites according to their similarities. Even though the data never leaves the individual sites, dSNE does not provide any formal privacy guarantees. To overcome that, we rely on differential privacy: a formal mathematical guarantee that protects individuals from being identified as contributors to a dataset. We implement DP-dSNE with AdaCliP, a method recently proposed to add less noise to the gradients per iteration. We introduce metrics for measuring the embedding quality and validate our algorithms on these metrics against their centralized counterpart on two toy datasets. Our validation on six multi-site neuroimaging datasets shows promising results for the quality control tasks of visualization and outlier detection, highlighting the potential of our private, decentralized visualization approach.

neuroscience↗