Improved carrier injection in GaN-based VCSEL via AlGaN/GaN multiple quantum barrier electron blocking layer.

In this report, the improved lasing performance of the III-nitride based vertical-cavity surface-emitting laser (VCSEL) has been demonstrated by replacing the bulk AlGaN electron blocking layer (EBL) in the conventional VCSEL structure with an AlGaN/GaN multiple quantum barrier (MQB) EBL. The output power can be enhanced up to three times from 0.3 mW to 0.9 mW. In addition, the threshold current density of the fabricated device with the MQB-EBL was reduced from 12 kA/cm2 (9.5 mA) to 10.6 kA/cm2 (8.5 mA) compared with the use of the bulk AlGaN EBL. Theoretical calculation results suggest that the improved carrier injection efficiency can be mainly attributed to the partial release of the strain and the effect of quantum interference by using the MQB structure, hence increasing the effective barrier height of the conduction band.

Identification of candidate T-cell epitopes and molecular mimics in chronic Lyme disease.

Elucidating the cellular immune response to infectious agents is a prerequisite for understanding disease pathogenesis and designing effective vaccines. In the identification of microbial T-cell epitopes, the availability of purified or recombinant bacterial proteins has been a chief limiting factor. In chronic infectious diseases such as Lyme disease, immune-mediated damage may add to the effects of direct infection by means of molecular mimicry to tissue autoantigens. Here, we describe a new method to effectively identify both microbial epitopes and candidate autoantigens. The approach combines data acquisition by positional scanning peptide combinatorial libraries and biometric data analysis by generation of scoring matrices. In a patient with chronic neuroborreliosis, we show that this strategy leads to the identification of potentially relevant T-cell targets derived from both Borrelia burgdorferi and the host. We also found that the antigen specificity of a single T-cell clone can be degenerate and yet the clone can preferentially recognize different peptides derived from the same organism, thus demonstrating that flexibility in T-cell recognition does not preclude specificity. This approach has potential applications in the identification of ligands in infectious diseases, tumors and autoimmune diseases.

Expression profiling identifies responder and non-responder phenotypes to interferon-beta in multiple sclerosis.

Autoimmune diseases such as multiple sclerosis are characterized by complex genetic traits and pathomechanisms that translate into clinical heterogeneity. This wide heterogeneity of multiple sclerosis as well as different biological responses to immunomodulatory drugs can be expected to contribute to differential treatment responses. Strategies that dissect the relationship between the treatment response and the biological characteristics in individual patients are valuable not only as a clinical tool, but also in leading to a better understanding of the disease. Here we address the in vitro and ex vivo RNA expression profile under one approved therapy of multiple sclerosis, interferon-beta (IFN-beta, Betaseron), by cDNA microarrays and demonstrate that non-responder and responder phenotypes to IFN-beta as assessed by longitudinal gadolinium-enhanced MRI scans and clinical disease activity differ in their ex vivo gene expression profile. These findings will help to better elucidate the mechanism of action of IFN-beta in relation to different disease patterns and eventually lead to optimized therapy.

Minimal peptide length requirements for CD4(+) T cell clones--implications for molecular mimicry and T cell survival.

CD4(+) T lymphocytes usually recognize peptides of 12-16 amino acids in the context of HLA class II molecules. We have recently used synthetic peptide combinatorial libraries to dissect in detail antigen recognition by autoreactive CD4(+) T cell clones (TCC). The results of these studies demonstrated that antigen recognition by T cells is highly degenerate and that many cross-reactive ligands can be defined, some of which much more potent than the selecting autoantigen. Based on these observations, we examined the response of a myelin basic protein-specific HLA class II-restricted CD4(+) TCC to truncation variants of optimal ligands. Surprisingly, pentapeptides, tetrapeptides and even tripeptides derived from different segments of the optimal ligands were recognized by the TCC, and some were even more potent than the selecting autoantigen. In addition, these peptides enhanced the survival of the TCC at low concentration. The relevance of this finding was supported by the generation of pentapeptide-specific CD4(+) TCC from peripheral blood lymphocytes. These observations not only change existing views on the length requirements for activation of CD4(+) HLA class II-restricted T cells, but also extend our knowledge about the flexibility of TCR recognition and the potential for cross-reactivity in the immune system.

Combinatorial peptide libraries and biometric score matrices permit the quantitative analysis of specific and degenerate interactions between clonotypic TCR and MHC peptide ligands.

The interaction of TCRs with MHC peptide ligands can be highly flexible, so that many different peptides are recognized by the same TCR in the context of a single restriction element. We provide a quantitative description of such interactions, which allows the identification of T cell epitopes and molecular mimics. The response of T cell clones to positional scanning synthetic combinatorial libraries is analyzed with a mathematical approach that is based on a model of independent contribution of individual amino acids to peptide Ag recognition. This biometric analysis compares the information derived from these libraries composed of trillions of decapeptides with all the millions of decapeptides contained in a protein database to rank and predict the most stimulatory peptides for a given T cell clone. We demonstrate the predictive power of the novel strategy and show that, together with gene expression profiling by cDNA microarrays, it leads to the identification of novel candidate autoantigens in the inflammatory autoimmune disease, multiple sclerosis.