Specific impact of β5t on proteasome subunit composition in cortical thymic epithelial cells

Abstract

β5t is a cortical thymic epithelial cell (cTEC)-specific component of the thymoproteasome, which is essential for the optimal production of functionally competent CD8⁺ T cells. Our recent analysis showed a specific impact of β5t on proteasome subunit composition in cTECs, supporting the possibility that the thymoproteasome optimizes CD8⁺ T cell development through the production of MHC-I-associated unique self-peptides in cTECs. However, a recent article reports that β5t regulates the expression of hundreds of cTEC genes and affects both CD4⁺ and CD8⁺ thymocytes by causing oxidative stress in thymocytes. The authors further analyze our published data and describe that they confirm their conclusions. Here, we examine the issues that they raise and conclude that, rather than regulating hundreds of genes in cTECs, β5t has a highly specific impact in cTECs on proteasome subunit composition. This Matters Arising Response article addresses the Apavaloaei et al. (2021) Matters Arising paper, published concurrently in Cell Reports.

Keywords: Cortical thymic epithelial cell; Positive selection; Proteasome; Psmb11; T cell development; Thymoproteasome; Thymus; β5t.

Figure 1.. Quality of isolated cTEC samples

(A and B) Abundance (means and SEMs, n = 3) of Dll4 mRNA in transcriptomic data obtained from Apavaloaei et al. (2019) (A) and Ohigashi et al. (2019) (B). The data from the 2 studies were analyzed in parallel by using the CLC Genomics Workbench (QIAGEN). Read counts were normalized to counts in cTEC samples. ***p < 0.001 by unpaired Student’s t test. (C) Fold change in the abundance of Psmb11, Prss16, Krt8, and Dll4 mRNAs between cTEC and mTEC samples in transcriptomic data by Apavaloaei et al. (2019) and Ohigashi et al. (2019).

Figure 2.. β5t does not regulate the expression of many genes in cTECs

(A) Number of genes that were different in abundance between β5t-deficient cTECs and control cTECs, among 89 differentially expressed genes extracted by Apavaloaei et al. (2021). Shown are the parallel re-analyses of the results described in Apavaloaei et al. (2021, Figure 2B and Table S2) based on the data published by Apavaloaei et al. (2021) (top) and by Ohigashi et al. (2019) (bottom). (B) Venn diagrams showing the overlap among the numbers of differentially expressed genes in transcriptomic data by Apavaloaei and colleagues (blue), our B6 background data (purple), and our K5D1 background data (yellow). Results of the parallel re-analysis of the data described in Apavaloaei et al. (2021, Tables S2 and S3) are shown. (C) Quantitative RT-PCR analysis of mRNA expression levels (means and SEMs, n = 3) of indicated genes relative to Gapdh level in β5t-Het and β5t-KO cTECs. *p < 0.05; NS, not significant, by unpaired Student’s t test.

Figure 3.. Proteomic impact on proteasome subunit composition in β5t-deficient cTECs

(A) Thirty-nine proteins that were significantly (p < 0.05) altered in abundance between K5D1 cTECs and K5D1-β5t-deficient cTECs in our TMT-based quantification, among 415 differentially expressed proteins that Apavaloaei et al. (2021) extracted from our label-free proteomic analysis. Eight core particle components and six regulatory particle components of proteasomes are highlighted in bold letters. (B) Gene Ontology enrichment analysis of proteins shown in (A). The numbers in parentheses indicate the number of categorized proteins.