Automated Glyco-Analysis of Cancer Related Proteins

癌症相关蛋白的自动糖分析

• 批准号: 7140158
• 负责人: Lewis K. Pannell
• 金额: $ 14.26万
• 依托单位: UNIVERSITY OF SOUTH ALABAMA
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-18 至 2008-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7140158
• 关键词: biomarker; cancer information system; cell surface receptors; computer program /software; glycoprotein biosynthesis; glycoprotein structure; glycosylation; mass spectrometry; membrane proteins; neoplastic growth; posttranslational modifications; proteomics; technology /technique development

DESCRIPTION (provided by applicant): The quote that "aberrant glycosylation is the hallmark of cancer cells" is reflected in numerous reports in the literature documenting changes in glycosylation on specific membrane proteins in cancer cells relative to normal cells. These changes have been shown to be involved in the release of cancer cells into the extracellular matrix and in the formation of metastasis. Glycosylated proteins represent a huge, almost untapped source of biomarkers, considering the wealth of evidence documenting their significance in cancer. Unique glycoforms could be used for diagnostic purposes, to target drugs at cancer cells, and for the development of immunotherapy. Despite the evolution of new mass spectrometry based methods for protein analysis, few of these involve the determination of post-translational modifications, especially glycosylation. As routine methods (e.g., MS/MS based sequencing methods) yield little light on glycosylated peptides, this proteomics research facility has established a new approach to automatically identify glycan structures on pure proteins from commercial or recombinant sources. It involves the acquisition of molecular weight only spectra and the detection of the glycosylation patterns using accurately determined mass gaps between the various glycoforms. The presence of multiple glycoforms is used to enhance the analysis rather than to confuse it. The approach has been shown to be reliable and extremely fast (taking less than one second) at identifying and characterizing such sites, including in proteins with highly complex glycosylation patterns. The aim of this proposal is to prove its utility in cancer where changes in glycosylation changes are interlaced with the progression of the disease. It will concentrate on both the cell surface proteins and those secreted from cells. Data will be compared to previously published reports where available. The glycans on previously uncharacterized proteins will be established and validated against the best hand interpreted results. The long-term aim is to make glycoanalyses routine to all cancer investigators, and the software integral to the approach will be made publicly available on a www site. This will represent the first step, with the glyco-analysis of full proteomes being an ultimate objective.

描述（由申请人提供）：在文献中，许多报道记录了相对于正常细胞中癌细胞中特定膜蛋白的糖基化变化的许多报道，“异常糖基化是癌细胞的标志”的引用。 这些变化已显示与癌细胞释放到细胞外基质和转移的形成中有关。考虑到记录其在癌症中的意义的大量证据，糖基化蛋白代表了一个巨大的，几乎未开发的生物标志物来源。 独特的糖型可用于诊断目的，靶向癌细胞的药物以及免疫疗法的发展。 尽管基于蛋白质分析的新型质谱法的发展，但其中很少有涉及翻译后修饰的确定，尤其是糖基化。 由于常规方法（例如，基于MS/MS的测序方法）几乎没有对糖基化肽的启示，因此该蛋白质组学研究机构已建立了一种新方法，可以自动从商业或重组来源鉴定有关纯蛋白质的聚糖结构。 它涉及仅分子量量光谱的获取，并使用各种糖基型之间的准确确定的质量间隙检测糖基化模式。 多种糖基型的存在用于增强分析而不是混淆。 该方法已被证明是可靠的，并且在识别和表征此类位点的表征（包括具有高度复杂的糖基化模式的蛋白质）方面非常快速（不到一秒钟）。 该提案的目的是证明其在癌症中的效用，在糖基化变化的变化与疾病的进展相交。 它将集中在细胞表面蛋白和细胞分泌的细胞表面蛋白上。 数据将与可用的先前发布的报告进行比较。 以前未表征的蛋白质上的聚糖将被建立并根据最佳手动解释结果进行验证。 长期的目的是为所有癌症研究人员制作甘油菜的常规，并且该方法不可或缺的软件将在www网站上公开提供。 这将代表第一步，全蛋白质组的聚糖分析是一个最终目标。

项目成果

Large isoform of MRJ (DNAJB6) reduces malignant activity of breast cancer.

• DOI: 10.1186/bcr1874
• 发表时间: 2008
• 期刊: BREAST CANCER RESEARCH
• 影响因子: 7.4
• 作者: Mitra, Aparna;Fillmore, Rebecca A.;Metge, Brandon J.;Rajesh, Mathur;Xi, Yaguang;King, Judy;Ju, Jingfang;Pannell, Lewis;Shevde, Lalita A.;Samant, Rajeev S.
• 通讯作者: Samant, Rajeev S.