T cells scan the surface of antigen presenting cells (APCs) to detect foreign peptides on major histocompatibility complex (MHC). Recognition takes place via the binding of T cell receptor (TCR) to peptide-MHC (pMHC) and the transduction of downstream signaling cascades. To induce a proper immune response, it is essential that T cells are activated when they bind to foreign peptides and not when confronted with self-peptides. Although T cells that have high affinity to self-peptides are deleted during development in the thymus, T cells in the periphery should still discriminate foreign peptides with high affinity to its TCR from the many and abundant low affinity self peptides. Failures in this discrimination result in either infection or autoimmune diseases.
     Many models have been suggested to explain this, however, it is not clear that how peptide discrimination by TCRs is disturbed in the background of many low affinity self-peptides that could antagonize signaling. Here we simulate the TCR signaling model developed by Francois et al. 2013 with low affinity ligands, and quantify the ability of ligand discrimination using mutual information. The mutual information indicates the maximum number of input signal values that a signaling pathway can reliably resolve. The results show that ligand discrimination works only in the presence of antagonists with very low affinity, which suggests that negative selection of immature T cells should be very strict. We will also discuss how the selection of immature T cells with different strength of TCR signaling results in different T cell differentiation.

     Many traits of individuals are controlled by genetic basis, as well as by the environment. Despite intense research over the last several decades, genotype-phenotype relationships remain largely unclear. However, with the accumulation of large numbers of individual genome sequences due to the rapid development of sequencing technology, this is beginning to change. A Genome-Wide Association Study (GWAS) is a method for identifying the genetic basis of quantitative traits by exploring associations between SNPs and phenotypic variations within a population. Extension of GWAS models makes it possible to flexibly estimate the effects of genetics, the environment, and their interaction. Using these extended GWAS models and natural populations of the model plant Arabidopsis thaliana, we are exploring the genetic architectures underlying complex quantitative traits. In this seminar, I will introduce two examples of GWAS applications from our previous studies. The first example concerns the genetic and environmental interactions (G x E) that control a life-history trait, flowering time (Sasaki et al., 2015). Although the environmental response is a major factor of adaptation, the genetic basis of this response is largely unknown. Using GWAS, we screened for environment-dependent genetic effects on flowering time phenotypes and identified small fractions of the genome that explained almost all the flowering time variation. The second example concerns the genetic control of an epigenetic mark, DNA methylation variation (Kawakatsu et al., 2016). DNA methylation plays an important role in various biological systems, including silencing of transposons and imprinting, and large natural variation in methylation has been reported. In this study, we identified major alleles controlling this phenotypic variation in two key DNA methylation pathways and found the geographic cline.

References
Sasaki, E. et al., "Missing" G x E Variation Controls Flowering Time in Arabidopsis thaliana. PLoS Genet., 11(10):e1005597 (2015)
Kawakatsu T., et al., Epigenetic Diversity in a Global Collection of Arabidopsis thaliana Accessions. Cell, 166:492-505 (2016)