3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Enhancing immune responses of ESC-based TAA cancer vaccines with a novel OMV delivery system

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Embryonic stem cell (ESC)-derived epitopes can act as therapeutic tumor vaccines against different types of tumors Jin (Adv Healthc Mater 2023). However, these epitopes have poor immunogenicity and stimulate insufficient CD8 + T cell responses, which motivated us to develop a new method to deliver and enhance their effectiveness. Bacterial outer membrane vesicles (OMVs) can serve as immunoadjuvants and act as a delivery vector for tumor antigens. In the current study, we engineered a new OMV platform for the co-delivery of ESC-derived tumor antigens and immune checkpoint inhibitors (PD-L1 antibody). An engineered Staphylococcal Protein A (SpA) was created to non-specifically bind to anti-PD-L1 antibody. SpyCatcher (SpC) and SpA were fused into the cell outer membrane protein OmpA to capture SpyTag-attached peptides and PD-L1 antibody, respectively. The modified OMV was able to efficiently conjugate with ESC-derived TAAs and PD-L1 antibody (SpC-OMVs + SpT-peptides + anti-PD-L1), increasing the residence time of TAAs in the body. The results showed that the combination therapy of ESC-based TAAs and PD-L1 antibody delivered by OMV had significant inhibitory effects in mouse tumor model. Specifically, it was effective in reducing tumor growth by enhancing IFN-γ-CD8 + T cell responses and increasing the number of CD8 + memory cells and antigen-specific T cells. Overall, the new OMV delivery system is a versatile platform that can enhance the immune responses of ESC-based TAA cancer vaccines.

          Graphical Abstract

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12951-023-02273-8.

          Related collections

          Most cited references38

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference

            We introduce Salmon, a method for quantifying transcript abundance from RNA-seq reads that is accurate and fast. Salmon is the first transcriptome-wide quantifier to correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              clusterProfiler 4.0: A universal enrichment tool for interpreting omics data

              Summary Functional enrichment analysis is pivotal for interpreting high-throughput omics data in life science. It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible. To meet these requirements, we present here an updated version of our popular Bioconductor package, clusterProfiler 4.0. This package has been enhanced considerably compared with its original version published 9 years ago. The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases. It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization. Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists. We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms.
                Bookmark

                Author and article information

                Contributors
                jdhuang@hku.hk
                Journal
                J Nanobiotechnology
                J Nanobiotechnology
                Journal of Nanobiotechnology
                BioMed Central (London )
                1477-3155
                3 January 2024
                3 January 2024
                2024
                : 22
                : 15
                Affiliations
                [1 ]GRID grid.9227.e, ISNI 0000000119573309, Chinese Academy of Sciences (CAS) Key Laboratory of Quantitative Engineering Biology, Shenzhen Institutes of Advanced Technology, , Shenzhen Institute of Synthetic Biology, Chinese Academy of Sciences, ; Shenzhen, China
                [2 ]School of Biomedical Sciences, Faculty of Medicine, Li Ka Shing, The University of Hong Kong, ( https://ror.org/02zhqgq86) Pokfulam, Hong Kong SAR, China
                [3 ]Department of Clinical Oncology, Shenzhen Key Laboratory for Cancer Metastasis and Personalized Therapy, The University of Hong Kong-Shenzhen Hospital, ( https://ror.org/047w7d678) Shenzhen, China
                [4 ]Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen University, ( https://ror.org/0064kty71) Guangzhou, 510120 China
                Article
                2273
                10.1186/s12951-023-02273-8
                10763241
                d39c51e3-a59c-4ba2-96e9-c9ee58ff0004
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 9 October 2023
                : 14 December 2023
                Funding
                Funded by: National Key Research and Development Program of China
                Award ID: 2018YFA0903000
                Award Recipient :
                Funded by: Natural Science Foundation of China
                Award ID: 32101225
                Award ID: 82003259
                Award Recipient :
                Funded by: Guangdong Science and Technology Department
                Award ID: (2020B1212030004, 2020B151520007
                Award ID: (2020B1212030004, 2020B151520007
                Award Recipient :
                Funded by: Natural Science Foundation of Guangdong Province
                Award ID: 2023A1515012795
                Award Recipient :
                Funded by: Shenzhen Science and Technology Innovation Commission
                Award ID: ZDSYS20210623091811035
                Award Recipient :
                Funded by: L & T Charitable Foundation and the Program for Guangdong Introducing Innovative and Entrepreneurial Teams
                Award ID: 2019BT02Y198
                Award Recipient :
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Biotechnology
                embryonic stem cell,epitopes,tumor immunity,omvs,vaccines
                Biotechnology
                embryonic stem cell, epitopes, tumor immunity, omvs, vaccines

                Comments

                Comment on this article