» Articles » PMID: 35403441

Lymphocytic Thyroiditis Transcriptomic Profiles Support the Role of Checkpoint Pathways and B Cells in Pathogenesis

Overview
Journal Thyroid
Date 2022 Apr 11
PMID 35403441
Authors
Affiliations
Soon will be listed here.
Abstract

Autoimmune thyroid diseases are the most common types of autoimmune diseases, but their physiopathology is still relatively unexplored. Genotype-tissue expression (GTEx) is a publicly available repository containing RNAseq data, including profiles from thyroid. Approximately 14.8% of these glands were affected by focal lymphocytic thyroiditis and 6.3% were annotated as Hashimoto. We interrogated these data to improve the characterization of infiltrating cells and to identify new molecular pathways active in autoimmune thyroiditis. Histological GTEx images of 336 thyroid samples were classified into three categories, that is, non-infiltrated thyroid, small focal infiltrated thyroid, and extensive lymphoid infiltrated thyroid. Differentially expressed genes among these categories were identified and subjected to pathway enrichment analysis accordingly. CIBERSORTx deconvolution was used to characterize infiltrating cells. As expected, most of the transcriptional changes were dependent on tissue infiltration. Upregulated genes in tissues include-in addition to lineage-specific B and T cell genes-a broad representation of inhibitory immune checkpoint receptors expressed by B and T lymphocytes. CIBERSORTx analysis identified 22 types of infiltrating cells showed that T cells predominate 3:1 over B cells in glands with small infiltrates, only by 1.7:1 in those with large infiltrates. Follicular helper and memory CD4 T cells were significantly more abundant in glands with large infiltrates ( < 0.0001), but the most prominent finding in these glands was an almost sixfold increase in the number of naive B cells ( < 0.0001). A predominance of M2 macrophages over M1 and M0 macrophages was observed in the three gland categories ( < 0.001). Analysis of transcriptomic RNA-seq profiles constitutes a rich source of information for the analysis of autoimmune tissues. High-resolution transcriptomic data analysis of thyroid glands indicates the following: (a) in all infiltrated glands, active autoimmune response coexists with suppressor counteracting mechanisms involving several inhibitory checkpoint receptor pairs, (b) glands with small infiltrates contain an unexpected relatively high proportion of B lymphocytes, and (c) in highly infiltrated glands, there is a distinct transcriptomic signature of active tertiary lymphoid organs. These results support the concept that the autoimmune response is amplified in the thyroid tissue.

Citing Articles

Identification of BTK as an immune-related biomarker for Hashimoto's thyroiditis by integrated bioinformatic analysis.

Liu Y, Zhu Z, Xu Q, Xu J, Xing J, Wang S BMC Immunol. 2025; 26(1):11.

PMID: 40022006 PMC: 11869739. DOI: 10.1186/s12865-025-00691-x.


T-cell exhaustion-related genes in Graves' disease: a comprehensive genome mapping analysis.

Jiang Z, Cai H, Lin Y, Lin R, Chen L, Huang H Front Endocrinol (Lausanne). 2024; 15:1364782.

PMID: 39239096 PMC: 11374593. DOI: 10.3389/fendo.2024.1364782.


Clinical and molecular impact of concurrent thyroid autoimmune disease and thyroid cancer: From the bench to bedside.

Valsecchi V, Betoni F, Ward L, Cunha L Rev Endocr Metab Disord. 2023; 25(1):5-17.

PMID: 37889392 DOI: 10.1007/s11154-023-09846-w.


The Immune Landscape of Papillary Thyroid Cancer in the Context of Autoimmune Thyroiditis.

Pani F, Caria P, Yasuda Y, Makoto M, Mariotti S, Leenhardt L Cancers (Basel). 2022; 14(17).

PMID: 36077831 PMC: 9454449. DOI: 10.3390/cancers14174287.

References
1.
Wherry E . T cell exhaustion. Nat Immunol. 2011; 12(6):492-9. DOI: 10.1038/ni.2035. View

2.
Rostamzadeh D, Dabbaghmanesh M, Shabani M, Hosseini A, Amirghofran Z . Expression Profile of Human Fc Receptor-Like 1, 2, and 4 Molecules in Peripheral Blood Mononuclear Cells of Patients with Hashimoto's Thyroiditis and Graves' Disease. Horm Metab Res. 2015; 47(9):693-8. DOI: 10.1055/s-0035-1545280. View

3.
Newman A, Liu C, Green M, Gentles A, Feng W, Xu Y . Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015; 12(5):453-7. PMC: 4739640. DOI: 10.1038/nmeth.3337. View

4.
Murphy K, Nelson C, Sedy J . Balancing co-stimulation and inhibition with BTLA and HVEM. Nat Rev Immunol. 2006; 6(9):671-81. DOI: 10.1038/nri1917. View

5.
Chen B, Khodadoust M, Liu C, Newman A, Alizadeh A . Profiling Tumor Infiltrating Immune Cells with CIBERSORT. Methods Mol Biol. 2018; 1711:243-259. PMC: 5895181. DOI: 10.1007/978-1-4939-7493-1_12. View