Biology subjects

Polansky, J. K.

Publications and source records attributed to Polansky, J. K..

3 recordsLinked to original sources

bioRxiv2024

Gene editing of CD3 epsilon gene to redirect regulatory T cells for adoptive T cell transfer

I.Adoptive transfer of regulatory T cells (Tregs) is a promising strategy to combat immunopathologies in transplantation and autoimmune diseases. Antigen-specific Tregs are more effective in modulating undesired immune reactions, but their low frequency in peripheral blood poses challenges for manufacturing and their clinical application. Chimeric antigen receptors (CARs) have been used to redirect the specificity of Tregs, employing retroviral vectors. However, retroviral gene transfer is costly, time consuming, and raises safety issues. Here, we explored non-viral gene editing to redirect Tregs with CARs, using HLA-A2-specific constructs for proof-of-concept studies in transplantation models. We introduce a virus-free CRISPR-Cas12a approach to integrate an antigen-binding domain into the CD3 epsilon (CD3{varepsilon}) gene, generating Tregs expressing a T cell receptor fusion construct (TruC). These CD3{varepsilon}-TruC Tregs exhibit potent antigen-dependent activation while maintaining responsiveness to TCR/CD3 stimulation. This enables preferential enrichment of TruC-redirected Tregs via repetitive CD3/CD28-stimulation in a GMP-compatible expansion system. Non-viral gene edited CD3{varepsilon}-TruC Tregs retained their phenotypic, epigenetic, and functional identity. In a humanized mouse model, HLA-A2-specific CD3{varepsilon}-TruC Tregs demonstrate superior protection of allogeneic HLA-A2+ skin grafts from rejection compared to polyclonal Tregs. This approach provides a pathway for developing clinical-grade CD3{varepsilon}-TruC-based Treg cell products for transplantation immunotherapy and other immunopathologies.

immunology↗

bioRxiv2023

Matching or genetic engineering of HLA Class I and II facilitates successful allogeneic 'off-the-shelf' regulatory T cell therapy

The potential to harness regulatory T cells (Tregs) for the treatment of autoimmune diseases and transplant rejection has been restricted by several barriers: donor variability, manufacturing complications, and time-consuming expansion processes. These issues further complicate the use of autologous Tregs during acute disease phases or when Tregs are low in number or dysfunctional. Here we explore the potential of off-the-shelf allogeneic Tregs, from healthy donors or universal sources, to provide a more practical solution. We discover that the efficacy of these cells is undermined by the recipients immune response, and that that rigorous matching of HLA classes I and II overcomes this barrier. Importantly, genetically manipulating HLA expression enables the use of unmatched allogeneic Tregs with in vivo efficacy. Our findings underscore the transformative potential of HLA-engineered Tregs, offering a novel, ready-to-use therapeutic avenue for treating a wide array of inflammatory diseases. One-Sentence SummaryMatching or engineering of HLA-I and HLA-II facilitates allogeneic off-the-shelf regulatory T cells for immunoregulation.

immunology↗

bioRxiv2016

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq datasets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

bioinformatics↗