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Kwok, W. W.

Publications and source records attributed to Kwok, W. W..

5 recordsLinked to original sources
bioRxiv2026

DNA-barcode labelled MHCII multimers for detection of antigen-specific CD4 T cells across large libraries of epitopes

The role of antigen-specific T cells responding to antigen is a topic of intense studies, and critical for mechanistic insight of diseases and development of therapeutic strategies. Methods for broad-scale detection of antigen-specific CD4 T cells are lacking, while such methods have demonstrated great value in exploring CD8 T cell response in health and disease. Furthermore, major histocompatibility complex II (MHCII) assays are technically challenging due to high HLA diversity, lower binding affinities, low frequencies of ex vivo antigen-specific CD4 T cells and several bottlenecks in production and peptide exchange of MHCII monomers. Here we use peptide-loaded MHCII (pMHCII) proteins multimerized on a barcode- and fluorophore-labelled dextran backbone to provide a method for the detection of peptide-specific CD4 T cells by using a large display of MHCII-associated peptides. We have established a protocol for MHCII production and peptide-exchange suitable for the generation of large libraries of peptide-MHCII complexes. We validate the use of such pMHCII complexes in the form of barcode-labelled MHCII multimers to detect antigen-specific CD4 T cells. We demonstrate that we can identify antigen specific CD4 T cells, using these DNA barcoded peptide-MHCII multimer. The multimer bound CD4 T cells were selected based on the fluorochrome signal, and the co-attached DNA barcodes were hereafter amplified and used to identify the peptide-MHCII response/binding. In cases where the peptide-specific CD4 T cells frequencies are very low, we expanded the cell population with peptide-pools and in the presence of IL2. The given CD4 T cell populations hereby reach a cell number allowing for the DNA-barcoded pMHCII multimers to detect responses otherwise missed out. Applying this technology, we utilized a panel of 150 peptides derived from human cytomegalo virus (CMV), Epstein barr virus (EBV), Influenza (Flu), SARS CoV 2 and SARS CoV1, Hepatitis B virus (HBV), and Hepatitis C virus (HCV) loaded onto HLA-DRB1*01:01 and DRB1*04:01 to screen peripheral blood mononuclear cells (PBMC). We assessed ex vivo responses in 16 participants with HCV infection, and successfully detected naturally occurring viral-specific CD4 T cells at frequencies as low as 0.004% of total CD4 T cells. The low-frequency responses, identified via the barcode screen, were rigorously validated using individual fluorophore-labelled tetramer staining after a peptide-driven expansion in 15 participants. Furthermore, we assessed the recognition of novel HCV epitopes in 11 additional participants. Through this, we identified a total of 12 distinct HCV epitopes, including 9 that have not been previously utilized in assays to detect CD4 T cells. Overall, this barcoded-multimer platform provides a powerful tool for the large-scale discovery of class II epitopes and the broad profiling of CD4 T cell specificities. This method will allow for in-depth analyses of immune interactions, provide a better understanding of the antigen-driven associations between CD4 and CD8 T cell responses, and help dissect the complexities of CD4 T cell protection in HCV infection.

immunology↗
bioRxiv2025

Clonal analysis of SepSecS-specific CD4 T cells reveals a new HLA-DPA1*02:01/HLA-DPB1*01:01-restricted immunodominant epitope in autoimmune hepatitis

Autoreactive CD4 T cells, recognizing liver-self-antigens such as SepSecS, are main drivers of the chronic inflammatory response during autoimmune hepatitis (AIH). Previous studies have uncovered immunodominant SepSecS epitopes often associated with HLA-DRB1*03 or HLA-DRB1*04 restriction, two alleles enriched in AIH population. However, HLA restriction of numerous SepSecS epitopes remains incomplete and it is still unclear if immunodominant epitopes could be presented by non-HLA-DR molecules. Here, we investigated epitope recognition of SepSecS-specific TCRs isolated from AIH patients, and their HLA restriction by generating TCR hybridoma cell lines. Seventeen TCRs recognized eight SepSecS epitopes with four distinct HLA restrictions, including a novel HLA-DPA1*02:01/DPB1*01:01-restricted SepSecS epitope. TCR clustering analysis using GLIPH2 algorithm suggested that this epitope is recognized by multiple distinct TCRs in HLA-DPA1*02:01[~]HLA-DPB1*01:01 patients. Our study provides new insights into liver-self-antigen T cell reactivity during AIH, which could offer potential therapeutic strategies by targeting autoreactive CD4 T cells.

immunology↗
bioRxiv2020

Parabacteroides distasonis insulin B:9-23 epitope mimic stimulates insulin specific T-cells and enhances Type 1 Diabetes in NOD mice

Type 1 Diabetes (T1D) is an autoimmune disease characterized by the destruction of pancreatic {beta}-cells. One of the earliest aspects of this process is development of autoantibodies and T-cells directed at an epitope in the B-chain of insulin (insB:9-23). Analysis of microbial protein sequences with homology to insB:9-23 sequence revealed 17 peptides showing >50% identity to insB:9-23. Of these, one peptide, found in the normal human gut commensal Parabacteroides distasonis, activated both human T cell clones from T1D patients and T-cell hybridomas from non-obese diabetic (NOD) mice specific to insB:9-23. Immunization of NOD mice with P. distasonis insB:9-23 peptide mimic or insB:9-23 peptide verified immune cross-reactivity. Colonization of female NOD mice with P. distasonis accelerated the development of T1D, increasing macrophages, dendritic cells and destructive CD8+ T-cells, while decreasing FoxP3+ regulatory T-cells. Western blot analysis identified P. distasonis reacting antibodies in sera of NOD mice colonized with P. distasonis and human T1D patients. Furthermore, adoptive transfer of splenocytes from P. distasonis treated mice to NOD/SCID mice enhanced disease phenotype in the recipients. Finally, analysis of human infant gut microbiome data revealed that exposure of infants to P. distasonis may modulate disease pathogenesis. Taken together, these data demonstrate the potential role for an insB:9-23-mimimetic peptide from gut microbiota as a molecular trigger or modifier of T1D pathogenesis. SIGNIFICANCE STATEMENTIn Type 1 diabetes (T1D), immune cells destroy pancreatic {beta}-cells. The trigger of this response, however, is unknown. Some sequences (epitopes) in the insulin molecule form a major target for this autoimmune response. We have identified a sequence in a human gut bacterium that can mimetic this insulin epitope. Immune cells specific to insulin cross-react with this bacterial mimetic. Further, this bacterium can accelerate diabetes onset in a mouse model of T1D, inducing destructive and decreasing protective immune cells. We found this mimetic in the gut of children developing T1D. Furthermore, T1D patients have a stronger immune response to this bacterium compared to healthy individuals. Taken together, this bacterial mimetic in human gut has the potential to trigger/modify T1D onset.

immunology↗
bioRxiv2020

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders Expanding (T-REX) was created to identify changes in both very rare and common cells in diverse human immune monitoring settings. T-REX identified cells that were highly similar in phenotype and localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized reagents used to detect the rhinovirus-specific CD4+ cells, MHCII tetramers, were not used during unsupervised analysis and instead left out to serve as a test of whether T-REX identified biologically significant cells. In the rhinovirus challenge study, T-REX identified virus-specific CD4+ T cells based on these cells being a distinct phenotype that expanded by [≥]95% following infection. T-REX successfully identified hotspots containing virus-specific T cells using pairs of samples comparing Day 7 of infection to samples taken either prior to infection (Day 0) or after clearing the infection (Day 28). Mapping pairwise comparisons in samples according to both the direction and degree of change provided a framework to compare systems level immune changes during infectious disease or therapy response. This revealed that the magnitude and direction of systemic immune change in some COVID-19 patients was comparable to that of blast crisis acute myeloid leukemia patients undergoing induction chemotherapy and characterized the identity of the immune cells that changed the most. Other COVID-19 patients instead matched an immune trajectory like that of individuals with rhinovirus infection or melanoma patients receiving checkpoint inhibitor therapy. T-REX analysis of paired blood samples provides an approach to rapidly identify and characterize mechanistically significant cells and to place emerging diseases into a systems immunology context.

systems biology↗
bioRxiv2017

A systems approach to the characterization and classification of T-cell responses

Types of T-cell responses are categorized on the basis of a limited number of molecular markers selected using a priori knowledge about T-cell immunobiology. We sought to develop a novel systems-based approach for the creation of an unbiased framework enabling assessment of antigenic-peptide specific T-cell responses in vitro. A meta-analysis of transcriptome data from PBMCs stimulated with a wide range of peptides identified patterns of gene regulation that provided an unbiased classification of types of antigen-specific responses. Further analysis yielded new insight about the molecular processes engaged following antigenic stimulation. This led for instance to the identification of transcription factors not previously studied in the context of T-cell differentiation. Taken together this profiling approach can serve as a basis for the unbiased characterization of antigen-specific responses and as a foundation for the development of novel systems-based immune profiling assays.

genomics↗