Back

A post-translational regulatory map of chronic antigen-driven human T cell dysfunction.

Kojima, H.; Wayne, C. R.; Somarribas Patterson, L. F.; Sanford, H.; Chen, T.-J.; Lin, Y.-H.; Schoenfeld, J. D.; McGary, L. H. F.; Chen, Y.-T.; Kropp, K. N.; Zhang, B.; Rahman, J.; Zhang, T. L.; Ropek, N.; Roberts, C.; Ai, Y.; Menon, K.; Hakimi, A. A.; Lyu, J.; Klebanoff, C. A.; Abdel-Wahab, O.; Vardhana, S. A.; Vinogradova, E. V.

2026-03-06 immunology
10.64898/2026.03.04.709614 bioRxiv
Show abstract

T cells exposed to persistent antigen in the context of chronic viral infections or cancer lose self-renewal and cytotoxic capacity. Several transcriptional, epigenetic, and metabolic drivers of this process have been identified. However, the post-transcriptional regulatory mechanisms influencing the proteome of dysfunctional T cells are not well understood. Here we present a time-resolved molecular landscape of human T cells during the development of chronic antigen-driven dysfunction. Persistent T cell receptor stimulation significantly remodeled the proteome, including changes in canonical T cell exhaustion-associated proteins and proteins related to mitochondrial function, redox homeostasis, nucleotide metabolism, and cell-cycle progression. Dysfunctional T cells displayed activation of stress response pathways that were recapitulated in vivo; targeting these pathways altered the cytotoxic capacity of T cells during persistent tumor exposure. Our comprehensive proteomic resource reveals unique post-transcriptional changes in dysfunctional T cells and lays the groundwork for novel cysteine-directed therapeutics to enhance cancer immunotherapy.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Cell
○ 431 papers in training set
Top 0.1%
14.8%
Similar papers in this journal
2
Nature Immunology
○ 79 papers in training set
Top 0.1%
12.6%
Similar papers in this journal
3
Cell Reports
○ 1498 papers in training set
Top 1%
11.7%
Similar papers in this journal
4
Nature Communications
○ 5641 papers in training set
Top 19%
9.6%
Similar papers in this journal
5
Immunity
○ 67 papers in training set
Top 0.2%
6.1%
Similar papers in this journal
50% of probability mass above
6
Cell Metabolism
○ 57 papers in training set
Top 0.3%
4.8%
7
eLife
● 5828 papers in training set
Top 26%
4.8%
8
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 17%
3.2%
9
Science Advances
● 1243 papers in training set
Top 13%
2.7%
10
Nature Metabolism
○ 69 papers in training set
Top 0.7%
2.7%
11
Science Immunology
● 88 papers in training set
Top 1%
2.3%
12
Molecular Cell
○ 350 papers in training set
Top 3%
2.1%
13
Cell Reports Medicine
○ 153 papers in training set
Top 2%
1.9%
14
Nature
○ 645 papers in training set
Top 7%
1.7%
15
Science
● 477 papers in training set
Top 7%
1.1%
16
iScience
○ 1154 papers in training set
Top 30%
1.0%
17
Cell Systems
○ 201 papers in training set
Top 4%
1.0%
18
Nature Methods
○ 385 papers in training set
Top 6%
1.0%
19
Cancer Discovery
● 66 papers in training set
Top 2%
0.8%
20
JCI Insight
● 277 papers in training set
Top 8%
0.8%
21
Science Translational Medicine
● 127 papers in training set
Top 4%
0.6%
22
Journal of Experimental Medicine
● 119 papers in training set
Top 4%
0.6%
23
Developmental Cell
○ 196 papers in training set
Top 5%
0.6%
24
Nature Biotechnology
○ 172 papers in training set
Top 5%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.