Dynamic changes in tRNA modifications and abundance during T cell activation.

The tRNA pool determines the efficiency, throughput, and accuracy of translation. Previous studies have identified dynamic changes in the tRNA (transfer RNA) supply and mRNA (messenger RNA) demand during cancerous proliferation. Yet dynamic changes may also occur during physiologically normal proliferation, and these are less well characterized. We examined the tRNA and mRNA pools of T cells during their vigorous proliferation and differentiation upon triggering their antigen receptor. We observed a global signature of switch in demand for codons at the early proliferation phase of the response, accompanied by corresponding changes in tRNA expression levels. In the later phase, upon differentiation, the response of the tRNA pool relaxed back to the basal level, potentially restraining excessive proliferation. Sequencing of tRNAs allowed us to evaluate their diverse base-modifications. We found that two types of tRNA modifications, wybutosine and ms2t6A, are reduced dramatically during T cell activation. These modifications occur in the anticodon loops of two tRNAs that decode "slippery codons," which are prone to ribosomal frameshifting. Attenuation of these frameshift-protective modifications is expected to increase the potential for proteome-wide frameshifting during T cell proliferation. Indeed, human cell lines deleted of a wybutosine writer showed increased ribosomal frameshifting, as detected with an HIV gag-pol frameshifting site reporter. These results may explain HIV's specific tropism toward proliferating T cells since it requires ribosomal frameshift exactly on the corresponding codon for infection. The changes in tRNA expression and modifications uncover a layer of translation regulation during T cell proliferation and expose a potential tradeoff between cellular growth and translation fidelity.

Aging promotes reorganization of the CD4 T cell landscape toward extreme regulatory and effector phenotypes.

Age-associated changes in CD4 T-cell functionality have been linked to chronic inflammation and decreased immunity. However, a detailed characterization of CD4 T cell phenotypes that could explain these dysregulated functional properties is lacking. We used single-cell RNA sequencing and multidimensional protein analyses to profile thousands of CD4 T cells obtained from young and old mice. We found that the landscape of CD4 T cell subsets differs markedly between young and old mice, such that three cell subsets-exhausted, cytotoxic, and activated regulatory T cells (aTregs)-appear rarely in young mice but gradually accumulate with age. Most unexpected were the extreme pro- and anti-inflammatory phenotypes of cytotoxic CD4 T cells and aTregs, respectively. These findings provide a comprehensive view of the dynamic reorganization of the CD4 T cell milieu with age and illuminate dominant subsets associated with chronic inflammation and immunity decline, suggesting new therapeutic avenues for age-related diseases.

Diverse continuum of CD4+ T-cell states is determined by hierarchical additive integration of cytokine signals.

During cell differentiation, progenitor cells integrate signals from their environment that guide their development into specialized phenotypes. The ways by which cells respond to complex signal combinations remain difficult to analyze and model. To gain additional insight into signal integration, we systematically mapped the response of CD4+ T cells to a large number of input cytokine combinations that drive their differentiation. We find that, in response to varied input combinations, cells differentiate into a continuum of cell fates as opposed to a limited number of discrete phenotypes. Input cytokines hierarchically influence the cell population, with TGFß being most dominant followed by IL-6 and IL-4. Mathematical modeling explains these results using additive signal integration within hierarchical groups of input cytokine combinations and correctly predicts cell population response to new input conditions. These findings suggest that complex cellular responses can be effectively described using a segmented linear approach, providing a framework for prediction of cellular responses to new cytokine combinations and doses, with implications to fine-tuned immunotherapies.