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1.
Sci Transl Med ; 12(557)2020 08 19.
Article in English | MEDLINE | ID: mdl-32817364

ABSTRACT

Antigen-specific regulatory T cells (Tregs) engineered with chimeric antigen receptors (CARs) are a potent immunosuppressive cellular therapy in multiple disease models and could overcome shortcomings of polyclonal Treg therapy. CAR therapy was initially developed with conventional T cells, which have different signaling requirements than do Tregs To date, most of the CAR Treg studies used second-generation CARs, encoding a CD28 or 4-1BB co-receptor signaling domain and CD3ζ, but it was not known if this CAR design was optimal for Tregs Using a human leukocyte antigen-A2-specific CAR platform and human Tregs, we compared 10 CARs with different co-receptor signaling domains and systematically tested their function and CAR-stimulated gene expression profile. Tregs expressing a CAR encoding CD28wt were markedly superior to all other CARs tested in an in vivo model of graft-versus-host disease. In vitro assays revealed stable expression of Helios and an ability to suppress CD80 expression on dendritic cells as key in vitro predictors of in vivo function. This comprehensive study of CAR signaling domain variants in Tregs can be leveraged to optimize CAR design for use in antigen-specific Treg therapy.


Subject(s)
Receptors, Chimeric Antigen , CD28 Antigens , Humans , Immunotherapy, Adoptive , Receptors, Antigen, T-Cell/genetics , Signal Transduction , T-Lymphocytes, Regulatory
2.
BMC Bioinformatics ; 19(1): 303, 2018 Aug 22.
Article in English | MEDLINE | ID: mdl-30134911

ABSTRACT

BACKGROUND: Computational biology requires the reading and comprehension of biological data files. Plain-text formats such as SAM, VCF, GTF, PDB and FASTA, often contain critical information which is obfuscated by the data structure complexity. RESULTS: bioSyntax ( https://biosyntax.org/ ) is a freely available suite of biological syntax highlighting packages for vim, gedit, Sublime, VSCode, and less. bioSyntax improves the legibility of low-level biological data in the bioinformatics workspace. CONCLUSION: bioSyntax supports computational scientists in parsing and comprehending their data efficiently and thus can accelerate research output.


Subject(s)
Computational Biology , Software , Information Storage and Retrieval , Nucleotides/genetics , Sequence Alignment
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