Expression of <scp>HLA</scp>‐<scp>DQA</scp>1 and <scp>HLA</scp>‐<scp>DQB</scp>1 genes in B lymphocytes, monocytes and whole blood

Zajacova, Marta; Kotrbová-Kozak, Anna; Černá, Marie

doi:10.1111/iji.12367

Cited by 12 publications

(9 citation statements)

References 31 publications

(54 reference statements)

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“…However, alleles in the rhesus macaque with significant cell type specific expression were found. Some HLA-C alleles with consistently higher expression have been found by qPCR (Bettens et al 2014); this has also been observed for some alleles of the class II genes HLA-DQB1 and HLA-DQA1 (Zajacova et al 2018).…”

Section: Introductionmentioning

confidence: 73%

scHLAcount: Allele-specific HLA expression from single-cell gene expression data

Darby

Stubbington

Marks

et al. 2019

Preprint

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AbstractStudies in bulk RNA sequencing data suggest cell-type and allele-specific expression of the human leukocyte antigen (HLA) genes. These loci are extremely diverse and they function as part of the major histocompatibility complex (MHC) which is responsible for antigen presentation. Mutation and or misregulation of expression of HLA genes has implications in diseases, especially cancer. Immune responses to tumor cells can be evaded through HLA loss of function. However, bulk RNA-seq does not fully disentangle cell type specificity and allelic expression. Here we present scHLAcount, a workflow for computing allele-specific molecule counts of the HLA genes in single cells an individualized reference. We demonstrate that scHLAcount can be used to find cell-type specific allelic expression of HLA genes in blood cells, and detect different allelic expression patterns between tumor and normal cells in patient biopsies. scHLAcount is available at https://github.com/10XGenomics/scHLAcount.

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Section: Introductionmentioning

confidence: 73%

scHLAcount: Allele-specific HLA expression from single-cell gene expression data

Darby

Stubbington

Marks

et al. 2019

Preprint

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“…We previously reported on the interallelic differences of HLA-DQA1 and HLA-DQB1 expression in monocytes and B-lymphocytes [ 30 ]. Intriguingly, it was noted that HLA-DQA1*01 and HLA-DQB1*06 showed low expression levels compared to other alleles in monocytes and B-lymphocytes.…”

Section: Resultsmentioning

confidence: 99%

The Immunological Epigenetic Landscape of the Human Life Trajectory

et al. 2022

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Adaptive immunity changes over an individual’s lifetime, maturing by adulthood and diminishing with old age. Epigenetic mechanisms involving DNA and histone methylation form the molecular basis of immunological memory during lymphocyte development. Monocytes alter their function to convey immune tolerance, yet the epigenetic influences at play remain to be fully understood in the context of lifespan. This study of a healthy genetically homogenous cohort of children, adults and seniors sought to decipher the epigenetic dynamics in B-lymphocytes and monocytes. Variable global cytosine methylation within retro-transposable LINE-1 repeats was noted in monocytes compared to B-lymphocytes across age groups. The expression of the human leukocyte antigen (HLA)-DQ alpha chain gene HLA-DQA1*01 revealed significantly reduced levels in monocytes in all ages relative to B-lymphocytes, as well as between lifespan groups. High melting point analysis and bisulfite sequencing of the HLA-DQA1*01 promoter in monocytes highlighted variable cytosine methylation in children and seniors but greater stability at this locus in adults. Further epigenetic evaluation revealed higher histone lysine 27 trimethylation in monocytes from this adult group. Chromatin immunoprecipitation and RNA pulldown demonstrated association with a novel lncRNA TINA with structurally conserved similarities to the previously recognized epigenetic modifier PARTICLE. Seeking to interpret the epigenetic immunological landscape across three representative age groups, this study focused on HLA-DQA1*01 to expose cytosine and histone methylation alterations and their association with the non-coding transcriptome. Such insights unveil previously unknown complex epigenetic layers, orchestrating the strength and weakening of adaptive immunity with the progression of life.

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“…In current study, differential expression of HLA-DQA1 gene alleles was analyzed in three different cell populations isolated from peripheral blood B lymphocytes, monocytes, and whole-blood cells. DQA1*03 alleles were among the most expressed in all cell types, whereas DQA1*05 alleles were least expressed in whole blood and monocytes and among the most expressed in B cells ( 54 ). It was reported ( 55 ) that TNFSF13 could support leukemia cell proliferation in an NF-κB-dependent manner by binding TNFRSF17 and suppressed apoptosis.…”

Section: Discussionmentioning

confidence: 99%

Using Machine Learning Modeling to Explore New Immune-Related Prognostic Markers in Non-Small Cell Lung Cancer

Wang

et al. 2020

Front. Oncol.

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Objective To find new immune-related prognostic markers for non-small cell lung cancer (NSCLC). Methods We found GSE14814 is related to NSCLC in GEO database. The non-small cell lung cancer observation (NSCLC-OBS) group was evaluated for immunity and divided into high and low groups for differential gene screening according to the score of immune evaluation. A single factor COX regression analysis was performed to select the genes related to prognosis. A prognostic model was constructed by machine learning, and test whether the model has a test efficacy for prognosis. A chip-in-chip non-small cell lung cancer chemotherapy (NSCLC-ACT) sample was used as a validation dataset for the same validation and prognostic analysis of the model. The coexpression genes of hub genes were obtained by pearson analysis and gene enrichment, function enrichment and protein interaction analysis. The tumor samples of patients with different clinical stages were detected by immunohistochemistry and the expression difference of prognostic genes in tumor tissues of patients with different stages was compared. Results By screening, we found that LYN, C3, COPG2IT1, HLA.DQA1, and TNFRSF17 is closely related to prognosis. After machine learning, we constructed the immune prognosis model from these 5 genes, and the model AUC values were greater than 0.9 at three time periods of 1, 3, and 5 years; the total survival period of the low-risk group was significantly better than that of the high-risk group. The results of prognosis analysis in ACT samples were consistent with OBS groups. The coexpression genes are mainly involved B cell receptor signaling pathway and are mainly enriched in apoptotic cell clearance. Prognostic key genes are highly correlated with PDCD1, PDCD1LG2, LAG3, and CTLA4 immune checkpoints. The immunohistochemical results showed that the expression of COPG2IT1 and HLA.DQA1 in stage III increased significantly and the expression of LYN, C3, and TNFRSF17 in stage III decreased significantly compared with that of stage I. The experimental results are consistent with the previous analysis. Conclusion LYN, C3, COPG2IT1, LA.DQA1, and NFRSF17 may be new immune markers to judge the prognosis of patients with non-small cell lung cancer.

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Expression of HLA‐DQA1 and HLA‐DQB1 genes in B lymphocytes, monocytes and whole blood

Cited by 12 publications

References 31 publications

scHLAcount: Allele-specific HLA expression from single-cell gene expression data

scHLAcount: Allele-specific HLA expression from single-cell gene expression data

The Immunological Epigenetic Landscape of the Human Life Trajectory

Using Machine Learning Modeling to Explore New Immune-Related Prognostic Markers in Non-Small Cell Lung Cancer

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