Immunity to self co-generates regulatory T cells

Immune responses to self are kept in check by tolerance mechanisms, including suppression by regulatory T cells (Tregs). The defective generation of Tregs specific for self-antigens may lead to autoimmune disease. We identified a novel population of human CD4^+^ Tregs, characterized by high surface expression of CD52, which is co-generated in response to autoantigen. Blood CD4^+^CD52^hi^ T cells were generated preferentially in response to low-dose autoantigen and suppressed proliferation and interferon-[gamma] production by other T cells. Depletion of resting CD4^+^CD52^hi^ T cells enhanced the T-cell response to autoantigen. CD4^+^CD52^hi^ Tregs were neither derived from nor distinguished by markers of conventional resting CD4^+^CD25^+^ Tregs. In response to the pancreatic islet autoantigens glutamic acid decarboxylase, the generation of CD4^+^CD52^hi^ Tregs was impaired in individuals with and at-risk for type 1 diabetes, compared to healthy controls and individuals with type 2 diabetes. CD4^+^CD52^hi^ Tregs co-generated to self-antigen may therefore contribute to immune homeostasis and protect against autoimmune disease

Cladribine and Fludarabine Nucleoside Change the Levels of CD Antigens on B-Lymphoproliferative Disorders

The purine analogs, fludarabine nucleoside (FdA), and cladribine (CdA) (1 μM, 24 hours), significantly changed the levels of some surface antigens on the human B-cell lines MEC2 and Raji. Changes in the surface proteins were identified using a Cluster of Differentiation (CD) antibody microarray that captures live cells and confirmed by flow cytometry. For Raji cells, CdA up-regulated CD10, CD54, CD80, and CD86, with repression of CD22, while FdA up-regulated CD20, CD54, CD80, CD86 and CD95. For MEC2 cells, CdA up-regulated CD11a, CD20, CD43, CD45, CD52, CD54, CD62L, CD80, CD86, and CD95, but FdA had no effect. Up-regulation of particular CD antigens induced on a B-cell lymphoproliferative disorder by a purine analog could provide targets for therapeutic antibodies with synergistic cell killing

Longitudinal microarray analysis of cell surface antigens on peripheral blood mononuclear cells from HIV+ individuals on highly active antiretroviral therapy

<p>Abstract</p> <p>Background</p> <p>The efficacy of highly active antiretroviral therapy (HAART) determined by simultaneous monitoring over 100 cell-surface antigens overtime has not been attempted. We used an antibody microarray to analyze changes in the expression of 135 different cell-surface antigens overtime on PBMC from HIV+ patients on HAART. Two groups were chosen, one (n = 6) achieved sustainable response by maintaining below detectable plasma viremia and the other (n = 6) responded intermittently. Blood samples were collected over an average of 3 years and 5–8 time points were selected for microarray assay and statistical analysis.</p> <p>Results</p> <p>Significant trends over time were observed for the expression of 7 cell surface antigens (CD2, CD3epsilon, CD5, CD95, CD36, CD27 and CD28) for combined patient groups. Between groups, expression levels of 10 cell surface antigens (CD11a, CD29, CD38, CD45RO, CD52, CD56, CD57, CD62E, CD64 and CD33) were found to be differential. Expression levels of CD9, CD11a, CD27, CD28 and CD52, CD44, CD49d, CD49e, CD11c strongly correlated with CD4+ and CD8+ T cell counts, respectively.</p> <p>Conclusion</p> <p>Our findings not only detected markers that may have potential prognostic/diagnostic values in evaluating HAART efficacy, but also showed how density of cell surface antigens could be efficiently exploited in an array-like manner in relation to HAART and HIV-infection. The antigens identified in this study should be further investigated by other methods such as flow cytometry for confirmation as biological analysis of these antigens may help further clarify their role during HAART and HIV infection.</p

Cell surface markers in colorectal cancer prognosis

The classification of colorectal cancers (CRC) is currently based largely on histologically determined tumour characteristics, such as differentiation status and tumour stage, i.e., depth of tumour invasion, involvement of regional lymph nodes and the occurrence of metastatic spread to other organs. These are the conventional prognostic factors for patient survival and often determine the requirement for adjuvant therapy after surgical resection of the primary tumour. However, patients with the same CRC stage can have very different disease-related outcomes. For some, surgical removal of early-stage tumours leads to full recovery, while for others, disease recurrence and metastasis may occur regardless of adjuvant therapy. It is therefore important to understand the molecular processes that lead to disease progression and metastasis and to find more reliable prognostic markers and novel targets for therapy. This review focuses on cell surface proteins that correlate with tumour progression, metastasis and patient outcome, and discusses some of the challenges in finding prognostic protein markers in CRC

Colorectal cancer therapeutic antibodies

Colorectal cancer (CRC) may occur in the colon, rectum or appendix. It is the third most common form of cancer and the third leading cause of cancer-related death in the Western world. CRC is the fourth most common cancer in men and the third in women, though significant international variations in the distribution of CRC have been observed. World-wide, nearly 1.2 million new cases of CRC were diagnosed in 2007, resulting in about 630,000 deaths (8 percent of all cancer deaths). Many CRC are thought to arise from adenomatous polyps in the colon, which are usually benign, but may develop into cancer over time. Localized CRC is generally detected by colonoscopy. Therapy is usually through surgery, often followed by chemotherapy. Recently, antibody-based therapies have also been used

Antibody microarrays and multiplexing

This chapter presents a range of statistical methods for antibody microarray normalization and data analysis. Commonly used techniques for cluster generation, differential analysis, and classification are covered. The focus is on the implementation of each technique to the technology and its suitability in relation to sample types and experiment design