Correlations in the T Cell Response to Altered Peptide Ligands

The vertebrate immune system is a wonder of modern evolution. Occasionally, however, correlations within the immune system lead to inappropriate recruitment of preexisting T cells against novel viral diseases. We present a random energy theory for the correlations in the naive and memory T cell immune responses. The non-linear susceptibility of the random energy model to structural changes captures the correlations in the immune response to mutated antigens. We show how the sequence-level diversity of the T cell repertoire drives the dynamics of the immune response against mutated viral antigens.Comment: 21 pages; 6 figures; to appear in Physica

Estimating the Precursor Frequency of Naive Antigen-specific CD8 T Cells

The constraint of fitting a diverse repertoire of antigen specificities in a limited total population of lymphocytes results in the frequency of naive cells specific for any given antigen (defined as the precursor frequency) being below the limit of detection by direct measurement. We have estimated this precursor frequency by titrating a known quantity of antigen-specific cells into naive recipients. Adoptive transfer of naive antigen-specific T cell receptor transgenic cells into syngeneic nontransgenic recipients, followed by stimulation with specific antigen, results in activation and expansion of both donor and endogenous antigen-specific cells in a dose-dependent manner. The precursor frequency is equal to the number of transferred cells when the transgenic and endogenous responses are of equal magnitude. Using this method we have estimated the precursor frequency of naive CD8 T cells specific for the H-2Db–restricted GP33–41 epitope of LCMV to be 1 in 2 × 105. Thus, in an uninfected mouse containing ∼2-4 × 107 naive CD8 T cells we estimate there to be 100–200 epitope-specific cells. After LCMV infection these 100–200 GP33-specific naive CD8 T cells divide >14 times in 1 wk to reach a total of ∼107 cells. Approximately 5% of these activated GP33-specific effector CD8 T cells survive to generate a memory pool consisting of ∼5 × 105 cells. Thus, an acute LCMV infection results in a >1,000-fold increase in precursor frequency of DbGP33-specific CD8 T cells from 2 × 102 naive cells in uninfected mice to 5 × 105 memory cells in immunized mice

The application of real-time PCR to the analysis of T cell repertoires

The diversity of T-cell populations is determined by the spectrum of antigen-specific T-cell receptors (TCRs) that are heterodimers of α and β subunits encoded by rearranged combinations of variable (AV and BV), joining (AJ and BJ), and constant region genes (AC and BC). We have developed a novel approach for analysis of β transcript diversity in mice with a real-time PCR-based method that uses a matrix of BV- and BJ-specific primers to amplify 240 distinct BV–BJ combinations. Defined endpoints (Ct values) and dissociation curves are generated for each BV–BJ combination and the Ct values are consolidated in a matrix that characterizes the β transcript diversity of each RNA sample. Relative diversities of BV–BJ combinations in individual RNA samples are further described by estimates of scaled entropy. A skin allograft system was used to demonstrate that dissection of repertoires into 240 BV–BJ combinations increases efficiency of identifying and sequencing β transcripts that are overrepresented at inflammatory sites. These BV–BJ matrices should generate greater investigation in laboratory and clinical settings due to increased throughput, resolution and identification of overrepresented TCR transcripts

Proteomic screen defines the hepatocyte nuclear factor 1α-binding partners and identifies HMGB1 as a new cofactor of HNF1α

Hepatocyte nuclear factor (HNF)-1α is one of the liver-enriched transcription factors involved in many tissue-specific expressions of hepatic genes. The molecular mechanisms for determining HNF1α-mediated transactivation have not been explained fully. To identify unknown proteins that interact with HNF1α, we developed a co-IP-MS strategy to search HNF1α interactions, and high mobility group protein-B1 (HMGB1), a chromosomal protein, was identified as a novel HNF1α-interacting protein. In vitro glutathione S-transferase pull-down and in vivo co-immunoprecipitation studies confirmed an interaction between HMGB1 and HNF1α. The protein–protein interaction was mediated through the HMG box domains of HMGB1 and the homeodomain of HNF1α. Furthermore, electrophoretic mobility shift assay and chromatin-immunoprecipitation assay demonstrated that HMGB1 was recruited to endogenous HNF1α-responsive promoters and enhanced HNF1α binding to its cognate DNA sequences. Moreover, luciferase reporter analyses showed that HMGB1 potentiated the transcriptional activities of HNF1α in cultured cells, and downregulation of HMGB1 by RNA interference specifically affected the HNF1α-dependent gene expression in HepG2 cell. Taken together, these findings raise the intriguing possibility that HMGB1 is a new cofactor of HNF1α and participates in HNF1α-mediated transcription regulation through protein–protein interaction