Postgenomics: Proteomics and Bioinformatics in Cancer Research

Now that the human genome is completed, the characterization of the proteins encoded by the sequence remains a challenging task. The study of the complete protein complement of the genome, the “proteome,” referred to as proteomics, will be essential if new therapeutic drugs and new disease biomarkers for early diagnosis are to be developed. Research efforts are already underway to develop the technology necessary to compare the specific protein profiles of diseased versus nondiseased states. These technologies provide a wealth of information and rapidly generate large quantities of data. Processing the large amounts of data will lead to useful predictive mathematical descriptions of biological systems which will permit rapid identification of novel therapeutic targets and identification of metabolic disorders. Here, we present an overview of the current status and future research approaches in defining the cancer cell's proteome in combination with different bioinformatics and computational biology tools toward a better understanding of health and disease

Functional Clustering Algorithm for High-Dimensional Proteomics Data

Clustering proteomics data is a challenging problem for any traditional clustering algorithm. Usually, the number of samples is largely smaller than the number of protein peaks. The use of a clustering algorithm which does not take into consideration the number of features of variables (here the number of peaks) is needed. An innovative hierarchical clustering algorithm may be a good approach. We propose here a new dissimilarity measure for the hierarchical clustering combined with a functional data analysis. We present a specific application of functional data analysis (FDA) to a high-throughput proteomics study. The high performance of the proposed algorithm is compared to two popular dissimilarity measures in the clustering of normal and human T-cell leukemia virus type 1 (HTLV-1)-infected patients samples

Retrotransposition-Competent Human LINE-1 Induces Apoptosis in Cancer Cells With Intact p53

Retrotransposition of human LINE-1 (L1) element, a major representative non-LTR retrotransposon in the human genome, is known to be a source of insertional mutagenesis. However, nothing is known about effects of L1 retrotransposition on cell growth and differentiation. To investigate the potential for such biological effects and the impact that human L1 retrotransposition has upon cancer cell growth, we examined a panel of human L1 transformed cell lines following a complete retrotransposition process. The results demonstrated that transposition of L1 leads to the activation of the p53-mediated apoptotic pathway in human cancer cells that possess a wild-type p53. In addition, we found that inactivation of p53 in cells, where L1 was undergoing retrotransposition, inhibited the induction of apoptosis. This suggests an association between active retrotransposition and a competent p53 response in which induction of apoptosis is a major outcome. These data are consistent with a model in which human retrotransposition is sensed by the cell as a “genetic damaging event” and that massive retrotransposition triggers signaling pathways resulting in apoptosis

Coevolution Analysis of HIV-1 Envelope Glycoprotein Complex

The HIV-1 Env spike is the main protein complex that facilitates HIV-1 entry into CD4+ host cells. HIV-1 entry is a multistep process that is not yet completely understood. This process involves several protein-protein interactions between HIV-1 Env and a variety of host cell receptors along with many conformational changes within the spike. HIV-1 Env developed due to high mutation rates and plasticity escape strategies from immense immune pressure and entry inhibitors. We applied a coevolution and residue-residue contact detecting method to identify coevolution patterns within HIV-1 Env protein sequences representing all group M subtypes. We identified 424 coevolving residue pairs within HIV-1 Env. The majority of predicted pairs are residue-residue contacts and are proximal in 3D structure. Furthermore, many of the detected pairs have functional implications due to contributions in either CD4 or coreceptor binding, or variable loop, gp120-gp41, and interdomain interactions. This study provides a new dimension of information in HIV research. The identified residue couplings may not only be important in assisting gp120 and gp41 coordinate structure prediction, but also in designing new and effective entry inhibitors that incorporate mutation patterns of HIV-1 Env