An experimental and theoretical study of T cell differentiation in response to mixed-input conditions reveals that cells can tune between Th1 and Th2 states through a continuum of mixed phenotypes.

The tRNA pool determines the efficiency, throughput, and accuracy of translation. Previous studies have identified dynamic changes in the tRNA supply and mRNA demand during cancerous proliferation. Yet, dynamic changes may occur also during physiologically normal proliferation, and these are less characterized. We examined the tRNA and mRNA pools of Tcells during their vigorous proliferation and differentiation upon triggering of the T cell antigen receptor. We observe a global signature of switch in demand for codon at the early proliferation phase of the response, accompanied by corresponding changes in tRNA expression levels. In the later phase, upon differentiation of the T cells, the response of the tRNA pool is relaxed back to basal level, potentially restraining excessive proliferation. Sequencing of tRNAs allowed us to also evaluate their diverse base-modifications. We found that two types of tRNA modifications, Wybutosine and ms 2 t6A, are reduced dramatically during T-cell activation. These modifications occur in the anti-codon loops of two tRNAs that decode "slippery codons", that are prone to ribosomal frameshifting. Attenuation of these frameshift-protective modifications is expected to increase proteome-wide frameshifting during T-cell proliferation. Indeed, human cell lines deleted of a Wybutosine writer showed increased ribosomal frameshifting, as detected with a reporter that consists of a critical frameshifting site taken from the HIV gag-pol slippery codon motif. These results may explain HIV's specificity to proliferating T-Cells since it requires ribosomal frameshift exactly on this codon for infection. The changes in tRNA expression and modifications uncover a new layer of translation regulation during T-cell proliferation and exposes a potential trade-off between cellular growth and translation fidelity.The tRNA pool decodes genetic information during translation. As such, it is subject to intricate physiological regulation in all species, across different physiological conditions. Here we show for the first time a program that governs the tRNA pool and its interaction with the transcriptome upon a physiological cellular proliferation-T-cells activation. We found that upon antigenic activation of T-cells, their tRNA and mRNA pools undergo coordinated and complementary changes, which relex when cells reduces their proliferation rate. We also found a reduction in two particular tRNA modifications that have a role in governing translation fidelity and frameshift prevention. This exposes a vulnerability in activated T-cells that may be utilized by HIV for its replication.

T helper cell differentiation is influenced by the spectrum of cytokines in the cells' milieu. In-vivo, cells are expected to sense a complex cytokine environment. Therefore, we aimed to study differentiation under mixed input conditions. By exposing cells to varied combinations of cytokines, we mapped the "decision space" of the differentiation process. Naïve CD4+ T cells were exposed to all 64 binary combinations of 6 cytokines, which induce the differentiation of the main T helper subsets: Th1, Th2, Th17 and iTreg. We examined the differentiation outcome in terms of cytokine-production pattern and expression of lineage-specifying transcription factors, by multi-color flow cytometry. Our data indicate that mixed conditions result in mixed phenotypes with cells expressing markers of opposing subsets simultaneously. Clustering and principal component analysis revealed up to eight different Th subsets, some of which are in accordance with known states. We also identified a hierarchical organization of input signals in their influence on driving differentiation into those subsets. Our results demonstrate flexibility and heterogeneity in Th cell differentiation. Under uncertain conditions, rather than following a hard, binary decision, cells reflect uncertainty through a biased stochastic decision process. This strategy allows flexibility and plasticity of the response at the single-cell level, while maintaining an average, possibly optimized response at the population level.