scholarly journals Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity

2017 ◽  
Author(s):  
Michal Bassani-Sternberg ◽  
Chloé Chong ◽  
Philippe Guillaume ◽  
Marthe Solleder ◽  
HuiSong Pak ◽  
...  
Keyword(s):  
A Priori ◽  
Human Leukocyte ◽  
Binding Specificity ◽  
Binding Assays ◽  
Sequencing Data ◽  
Protein Structure Analysis ◽  
Binding Motifs ◽  
Leukocyte Antigen ◽  
Underlying Mechanisms

AbstractThe precise identification of Human Leukocyte Antigen class I (HLA-I) binding motifs plays a central role in our ability to understand and predict (neo-)antigen presentation in infectious diseases and cancer. Here, by exploiting co-occurrence of HLA-I alleles across ten newly generated as well as forty public HLA peptidomics datasets comprising more than 115,000 unique peptides, we show that we can rapidly and accurately identify many HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. Our approach recapitulates and refines known motifs for 43 of the most frequent alleles, uncovers new motifs for 9 alleles that up to now had less than five known ligands and provides a scalable framework to incorporate additional HLA peptidomics studies in the future. The refined motifs improve neo-antigen and cancer testis antigen predictions, indicating that unbiased HLA peptidomics data are ideal for in silico predictions of neo-antigens from tumor exome sequencing data. The new motifs further reveal allosteric modulation of the binding specificity of HLA-I alleles and we unravel the underlying mechanisms by protein structure analysis, mutagenesis and in vitro binding assays.

scholarly journals In silico tools for accurate HLA and KIR inference from clinical sequencing data empower immunogenetics on individual-patient and population scales

2020 ◽  
Author(s):  
Jieming Chen ◽  
Shravan Madireddi ◽  
Deepti Nagarkar ◽  
Maciej Migdal ◽  
Jason Vander Heiden ◽  
...  
Keyword(s):  
In Silico ◽  
Killer Cell ◽  
Human Leukocyte ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Leukocyte Antigen ◽  
Whole Exome ◽  
Whole Exome Sequencing Data ◽  
Kir Gene

Abstract Immunogenetic variation in humans is important in research, clinical diagnosis and increasingly a target for therapeutic intervention. Two highly polymorphic loci play critical roles, namely the human leukocyte antigen (HLA) system, which is the human version of the major histocompatibility complex (MHC), and the Killer-cell immunoglobulin-like receptors (KIR) that are relevant for responses of natural killer (NK) and some subsets of T cells. Their accurate classification has typically required the use of dedicated biological specimens and a combination of in vitro and in silico efforts. Increased availability of next generation sequencing data has led to the development of ancillary computational solutions. Here, we report an evaluation of recently published algorithms to computationally infer complex immunogenetic variation in the form of HLA alleles and KIR haplotypes from whole-genome or whole-exome sequencing data. For both HLA allele and KIR gene typing, we identified tools that yielded >97% overall accuracy for four-digit HLA types, and >99% overall accuracy for KIR gene presence, suggesting the readiness of in silico solutions for use in clinical and high-throughput research settings.

scholarly journals In silico tools for accurate HLA and KIR inference from clinical sequencing data empower immunogenetics on individual-patient and population scales

2020 ◽  
Author(s):  
Jieming Chen ◽  
Shravan Madireddi ◽  
Deepti Nagarkar ◽  
Maciej Migdal ◽  
Jason Vander Heiden ◽  
...  
Keyword(s):  
In Silico ◽  
Killer Cell ◽  
Human Leukocyte ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Leukocyte Antigen ◽  
Whole Exome ◽  
Whole Exome Sequencing Data ◽  
Kir Gene

Immunogenetic variation in humans is important in research, clinical diagnosis and increasingly a target for therapeutic intervention. Two highly polymorphic loci play critical roles, namely the human leukocyte antigen (HLA) system, which is the human version of the major histocompatibility complex (MHC), and the Killer-cell immunoglobulin-like receptors (KIR) that are relevant for responses of Natural Killer (NK) cells and some subsets of T cells. Their accurate classification has typically required the use of dedicated biological specimens and a combination of in vitro and in silico efforts. Increased availability of next generation sequencing data has led to the development of ancillary computational solutions. Here, we report an evaluation of recently published algorithms to computationally infer complex immunogenetic variation in the form of HLA alleles and KIR haplotypes from whole-genome or whole-exome sequencing data. For both HLA allele and KIR gene typing, we identified tools that yielded >97% overall accuracy for 4-digit HLA types, and >99% overall accuracy for KIR gene presence, suggesting the readiness of in silico solutions for use in clinical and high-throughput research settings.

scholarly journals Quality Verification with a Cluster−Controlled Manufacturing System to Generate Monocyte−Derived Dendritic Cells

Vaccines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 533
Author(s):  
Haruhiko Kawaguchi ◽  
Takuya Sakamoto ◽  
Terutsugu Koya ◽  
Misa Togi ◽  
Ippei Date ◽  
...  
Keyword(s):  
Cell Culture ◽  
Dna Microarrays ◽  
Cluster Formation ◽  
Human Leukocyte ◽  
Leukocyte Antigen ◽  
Dc Vaccines ◽  
Survival Gene ◽  
Adherent Culture ◽  
Ctl Induction

Dendritic cell (DC) vaccines for cancer immunotherapy have been actively developed to improve clinical efficacy. In our previous report, monocyte−derived DCs induced by interleukin (IL)−4 with a low−adherence dish (low−adherent IL-4−DCs: la−IL-4−DCs) improved the yield and viability, as well as relatively prolonged survival in vitro, compared to IL-4−DCs developed using an adherent culture protocol. However, la−IL-4−DCs exhibit remarkable cluster formation and display heterogeneous immature phenotypes. Therefore, cluster formation in la−IL-4−DCs needs to be optimized for the clinical development of DC vaccines. In this study, we examined the effects of cluster control in the generation of mature IL-4−DCs, using cell culture vessels and measuring spheroid formation, survival, cytokine secretion, and gene expression of IL-4−DCs. Mature IL-4−DCs in cell culture vessels (cluster−controlled IL-4−DCs: cc−IL-4−DCs) displayed increased levels of CD80, CD86, and CD40 compared with that of la−IL-4−DCs. cc−IL-4−DCs induced antigen−specific cytotoxic T lymphocytes (CTLs) with a human leukocyte antigen (HLA)−restricted melanoma antigen recognized by T cells 1 (MART−1) peptide. Additionally, cc−IL-4−DCs produced higher levels of IFN−γ, possessing the CTL induction. Furthermore, DNA microarrays revealed the upregulation of BCL2A1, a pro−survival gene. According to these findings, the cc−IL-4−DCs are useful for generating homogeneous and functional IL-4−DCs that would be expected to promote long−lasting effects in DC vaccines.

scholarly journals Human Herpesvirus 6A and 6B inhibit in vitro angiogenesis by induction of Human Leukocyte Antigen G

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Roberta Rizzo ◽  
Maria D’Accolti ◽  
Daria Bortolotti ◽  
Francesca Caccuri ◽  
Arnaldo Caruso ◽  
...  
Keyword(s):  
Human Leukocyte Antigen ◽  
Human Herpesvirus ◽  
Human Leukocyte ◽  
Leukocyte Antigen ◽  
Human Leukocyte Antigen G ◽  
In Vitro Angiogenesis

scholarly journals Targeting human leukocyte antigen G with chimeric antigen receptors of natural killer cells convert immunosuppression to ablate solid tumors

2021 ◽  
Vol 9 (10) ◽  
pp. e003050
Author(s):  
Chia-Ing Jan ◽  
Shi-Wei Huang ◽  
Peter Canoll ◽  
Jeffrey N Bruce ◽  
Yu-Chuan Lin ◽  
...  
Keyword(s):  
Human Leukocyte Antigen ◽  
Natural Killer ◽  
Solid Tumors ◽  
Low Dose ◽  
Human Leukocyte ◽  
Leukocyte Antigen ◽  
Low Dose Chemotherapy ◽  
Human Leukocyte Antigen G

BackgroundImmunotherapy against solid tumors has long been hampered by the development of immunosuppressive tumor microenvironment, and the lack of a specific tumor-associated antigen that could be targeted in different kinds of solid tumors. Human leukocyte antigen G (HLA-G) is an immune checkpoint protein (ICP) that is neoexpressed in most tumor cells as a way to evade immune attack and has been recently demonstrated as a useful target for chimeric antigen receptor (CAR)-T therapy of leukemia by in vitro studies. Here, we design and test for targeting HLA-G in solid tumors using a CAR strategy.MethodsWe developed a novel CAR strategy using natural killer (NK) cell as effector cells, featuring enhanced cytolytic effect via DAP12-based intracellular signal amplification. A single-chain variable fragment (scFv) against HLA-G is designed as the targeting moiety, and the construct is tested both in vitro and in vivo on four different solid tumor models. We also evaluated the synergy of this anti-HLA-G CAR-NK strategy with low-dose chemotherapy as combination therapy.ResultsHLA-G CAR-transduced NK cells present effective cytolysis of breast, brain, pancreatic, and ovarian cancer cells in vitro, as well as reduced xenograft tumor growth with extended median survival in orthotopic mouse models. In tumor coculture assays, the anti-HLA-G scFv moiety promotes Syk/Zap70 activation of NK cells, suggesting reversal of the HLA-G-mediated immunosuppression and hence restoration of native NK cytolytic functions. Tumor expression of HLA-G can be further induced using low-dose chemotherapy, which when combined with anti-HLA-G CAR-NK results in extensive tumor ablation both in vitro and in vivo. This upregulation of tumor HLA-G involves inhibition of DNMT1 and demethylation of transporter associated with antigen processing 1 promoter.ConclusionsOur novel CAR-NK strategy exploits the dual nature of HLA-G as both a tumor-associated neoantigen and an ICP to counteract tumor spread. Further ablation of tumors can be boosted when combined with administration of chemotherapeutic agents in clinical use. The readiness of this novel strategy envisions a wide applicability in treating solid tumors.

scholarly journals T cells targeted to TdT kill leukemic lymphoblasts while sparing normal lymphocytes

2021 ◽  
Author(s):  
Muhammad Ali ◽  
Eirini Giannakopoulou ◽  
Yingqian Li ◽  
Madeleine Lehander ◽  
Stina Virding Culleton ◽  
...  
Keyword(s):  
T Cells ◽  
B Cell ◽  
Lymphoblastic Leukemia ◽  
Human Leukocyte ◽  
Terminal Deoxynucleotidyl Transferase ◽  
Antigen Receptors ◽  
Leukocyte Antigen ◽  
Intracellular Targets

AbstractUnlike chimeric antigen receptors, T-cell receptors (TCRs) can recognize intracellular targets presented on human leukocyte antigen (HLA) molecules. Here we demonstrate that T cells expressing TCRs specific for peptides from the intracellular lymphoid-specific enzyme terminal deoxynucleotidyl transferase (TdT), presented in the context of HLA-A*02:01, specifically eliminate primary acute lymphoblastic leukemia (ALL) cells of T- and B-cell origin in vitro and in three mouse models of disseminated B-ALL. By contrast, the treatment spares normal peripheral T- and B-cell repertoires and normal myeloid cells in vitro, and in vivo in humanized mice. TdT is an attractive cancer target as it is highly and homogeneously expressed in 80–94% of B- and T-ALLs, but only transiently expressed during normal lymphoid differentiation, limiting on-target toxicity of TdT-specific T cells. TCR-modified T cells targeting TdT may be a promising immunotherapy for B-ALL and T-ALL that preserves normal lymphocytes.

scholarly journals arcasHLA: high resolution HLA typing from RNA seq

2018 ◽  
Author(s):  
Rose Orenbuch ◽  
Ioan Filip ◽  
Devon Comito ◽  
Jeffrey Shaman ◽  
Itsik Pe'er ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Mhc Class Ii ◽  
Critical Role ◽  
Human Leukocyte ◽  
Hla Typing ◽  
Sequencing Data ◽  
Leukocyte Antigen ◽  
Hla Genes ◽  
Biological Dataset ◽  
High Level

Human leukocyte antigen (HLA) locus makes up the major compatibility complex (MHC) and plays a critical role in host response to disease, including cancers and autoimmune disorders. In the clinical setting, HLA typing is necessary for determining tissue compatibility. Recent improvements in the quality and accessibility of next-generation sequencing have made HLA typing from standard short-read data practical. However, this task remains challenging given the high level of polymorphism and homology between the HLA genes. HLA typing from RNA sequencing is further complicated by post-transcriptional splicing and bias due to amplification. Here, we present arcasHLA: a fast and accurate in silico tool that infers HLA genotypes from RNA sequencing data. Our tool outperforms established tools on the gold-standard benchmark dataset for HLA typing in terms of both accuracy and speed, with an accuracy rate of 100% at two field precision for MHC class I genes, and over 99.7% for MHC class II. Importantly, arcasHLA takes as its input pre-aligned BAM files, and outputs three-field resolution for all HLA genes in less than 2 minutes. Finally, we discuss evaluate the performance of our tool on a new biological dataset of 447 single-end total RNA samples from nasopharyngeal swabs, and establish the applicability of arcasHLA in metatranscriptome studies. arcasHLA is available at https://github.com/RabadanLab/arcasHLA.

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