Abdelali Haoudi

Human LINE-1 retrotransposon induces DNA damage and apoptosis in cancer cells

BackgroundLong interspersed nuclear elements (LINEs), Alu and endogenous retroviruses (ERVs) make up some 45% of human DNA. LINE-1 also called L1, is the most common family of non-LTR retrotransposons in the human genome and comprises about 17% of the genome. L1 elements require the integration into chromosomal target sites using L1-encoded endonuclease which creates staggering DNA breaks allowing the newly transposed L1 copies to integrate into the genome. L1 expression and retrotransposition in cancer cells might cause transcriptional deregulation, insertional mutations, DNA breaks, and an increased frequency of recombinations, contributing to genome instability. There is however little evidence on the mechanism of L1-induced genetic instability and its impact on cancer cell growth and proliferation.ResultsWe report that L1 has genome-destabilizing effects indicated by an accumulation of γ-H2AX foci, an early response to DNA strand breaks, in association with an abnormal cell cycle progression through a G2/M accumulation and an induction of apoptosis in breast cancer cells. In addition, we found that adjuvant L1 activation may lead to supra-additive killing when combined with radiation by enhancing the radiation lethality through induction of apoptosis that we have detected through Bax activation.ConclusionL1 retrotransposition is sensed as a DNA damaging event through the creation DNA breaks involving L1-encoded endonuclease. The apparent synergistic interaction between L1 activation and radiation can further be utilized for targeted induction of cancer cell death. Thus, the role of retrotransoposons in general, and of L1 in particular, in DNA damage and repair assumes larger significance both for the understanding of mutagenicity and, potentially, for the control of cell proliferation and apoptosis.

Human T-cell Leukemia Virus-I Tax Oncoprotein Functionally Targets a Subnuclear Complex Involved in Cellular DNA Damage-Response

The virally encoded oncoprotein Tax has been implicated in HTLV-1-mediated cellular transformation. The exact mechanism by which this protein contributes to the oncogenic process is not known. However, it has been hypothesized that Tax induces genomic instability via repression of cellular DNA repair. We examined the effect of de novo Tax expression upon the cell cycle, because appropriate activation of cell cycle checkpoints is essential to a robust damage-repair response. Upon induction of tax expression, Jurkat T-cells displayed a pronounced accumulation in G2/M that was reversible by caffeine. We examined the G2-specific checkpoint signaling response in these cells and found activation of the ATM/chk2-mediated pathway, whereas the ATR/chk1-mediated response was unaffected. Immunoprecipitation with anti-chk2 antibody results in co-precipitation of Tax demonstrating a direct interaction of Tax with a chk2-containing complex. We also show that Tax targets a discrete nuclear site and co-localizes with chk2 and not chk1. This nuclear site, previously identified as Tax Speckled Structures (TSS), also contains the early damage response factor 53BP1. The recruitment of 53BP1 to TSS is dependent upon ATM signaling and requires expression of Tax. Specifically, Tax expression induces redistribution of diffuse nuclear 53BP1 to the TSS foci. Taken together these data suggest that the TSS describe a unique nuclear site involved in DNA damage recognition, repair response, and cell cycle checkpoint activation. We suggest that association of Tax with this multifunctional subnuclear site results in disruption of a subset of the site-specific activities and contributes to cellular genomic instability.

HTLV-1 Tax oncoprotein subverts the cellular DNA damage response via binding to DNA-dependent protein kinase.

Human T-cell leukemia virus type-1 is the causative agent for adult T-cell leukemia. Previous research has established that the viral oncoprotein Tax mediates the transformation process by impairing cell cycle control and cellular response to DNA damage. We showed previously that Tax sequesters huChk2 within chromatin and impairs the response to ionizing radiation. Here we demonstrate that DNA-dependent protein kinase (DNA-PK) is a member of the Tax·Chk2 nuclear complex. The catalytic subunit, DNA-PKcs, and the regulatory subunit, Ku70, were present. Tax-containing nuclear extracts showed increased DNA-PK activity, and specific inhibition of DNA-PK prevented Tax-induced activation of Chk2 kinase activity. Expression of Tax induced foci formation and phosphorylation of H2AX. However, Tax-induced constitutive signaling of the DNA-PK pathway impaired cellular response to new damage, as reflected in suppression of ionizing radiation-induced DNA-PK phosphorylation and γH2AX stabilization. Tax co-localized with phospho-DNA-PK into nuclear speckles and a nuclear excluded Tax mutant sequestered endogenous phospho-DNA-PK into the cytoplasm, suggesting that Tax interaction with DNA-PK is an initiating event. We also describe a novel interaction between DNA-PK and Chk2 that requires Tax. We propose that Tax binds to and stabilizes a protein complex with DNA-PK and Chk2, resulting in a saturation of DNA-PK-mediated damage repair response.

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.

Bioinformatics and data mining in proteomics.

Proteomic studies involve the identification as well as qualitative and quantitative comparison of proteins expressed under different conditions, and elucidation of their properties and functions, usually in a large-scale, high-throughput format. The high dimensionality of data generated from these studies will require the development of improved bioinformatics tools and data-mining approaches for efficient and accurate data analysis of biological specimens from healthy and diseased individuals. Mining large proteomics data sets provides a better understanding of the complexities between the normal and abnormal cell proteome of various biological systems, including environmental hazards, infectious agents (bioterrorism) and cancers. This review will shed light on recent developments in bioinformatics and data-mining approaches, and their limitations when applied to proteomics data sets, in order to strengthen the interdependence between proteomic technologies and bioinformatics tools.

A novel approach for clustering proteomics data using Bayesian fast Fourier transform

MOTIVATION Bioinformatics clustering tools are useful at all levels of proteomic data analysis. Proteomics studies can provide a wealth of information and rapidly generate large quantities of data from the analysis of biological specimens. The high dimensionality of data generated from these studies requires the development of improved bioinformatics tools for efficient and accurate data analyses. For proteome profiling of a particular system or organism, a number of specialized software tools are needed. Indeed, significant advances in the informatics and software tools necessary to support the analysis and management of these massive amounts of data are needed. Clustering algorithms based on probabilistic and Bayesian models provide an alternative to heuristic algorithms. The number of clusters (diseased and non-diseased groups) is reduced to the choice of the number of components of a mixture of underlying probability. The Bayesian approach is a tool for including information from the data to the analysis. It offers an estimation of the uncertainties of the data and the parameters involved. RESULTS We present novel algorithms that can organize, cluster and derive meaningful patterns of expression from large-scaled proteomics experiments. We processed raw data using a graphical-based algorithm by transforming it from a real space data-expression to a complex space data-expression using discrete Fourier transformation; then we used a thresholding approach to denoise and reduce the length of each spectrum. Bayesian clustering was applied to the reconstructed data. In comparison with several other algorithms used in this study including K-means, (Kohonen self-organizing map (SOM), and linear discriminant analysis, the Bayesian-Fourier model-based approach displayed superior performances consistently, in selecting the correct model and the number of clusters, thus providing a novel approach for accurate diagnosis of the disease. Using this approach, we were able to successfully denoise proteomic spectra and reach up to a 99% total reduction of the number of peaks compared to the original data. In addition, the Bayesian-based approach generated a better classification rate in comparison with other classification algorithms. This new finding will allow us to apply the Fourier transformation for the selection of the protein profile for each sample, and to develop a novel bioinformatic strategy based on Bayesian clustering for biomarker discovery and optimal diagnosis.

Control of telomere elongation and telomeric silencing in Drosophila melanogaster.

Chromosome length in Drosophilais maintained by the targeted transposition of two families of non-LTR retrotransposons, HeT-Aand TART. Although the rate of transposition to telomeres is sufficient to counterbalance loss from the chromosome ends due to incomplete DNA replication, transposition as a mechanism for elongating chromosome ends raises the possibility of damaged or deleted telomeres, because of its stochastic nature. Recent evidence suggests that HeT-Atransposition is controlled at the levels of transcription and reverse transcription. HeT-Atranscription is found primarily in mitotically active cells, and transcription of a w+reporter gene inserted into the 2L telomere increases when the homologous telomere is partially or completely deleted. The terminal HeT-Aarray may be important as a positive regulator of this activity in cis, and the subterminal satellite appears to be an important negative regulator in cis. A third chromosome modifier has been identified that increases the level of reverse transcriptase activity on a HeT-A RNA template and greatly increases the transposition of HeT-A. Thus, the host appears to play a role in transposition of these elements. Taken together, these results suggest that control of HeT-Atransposition is more complex than previously thought.

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 cells proteome in combination with different bioinformatics and computational biology tools toward a better understanding of health and disease.

Data mining in genomics and proteomics.

There is no doubt that both computational biology and bioinformatics, and the interface of computer science and biology in general, are central to the future of biological research. The disciplines span a process that begins with data collection, analysis, classification, and integration, and ends with interpretation, modeling, visualization, and prediction. Data mining plays a role in the middle of this process. Overall, the focus is on identifying opportunities and developing computational solutions (including algorithms, models, tools, and databases) that can be used for experimental design, data analysis and interpretation, and hypothesis generation. Data mining is the search for hidden trends within large sets of data. Data mining approaches are needed at all levels of genomics and proteomics analyses. These studies can provide a wealth of information and rapidly generate large quantities of data from the analysis of biological specimens from healthy and diseased tissues. The high dimensionality of data generated from these studies will require the development of improved bioinformatics and computational biology tools for efficient and accurate data analyses. This issue of the Journal of Biomedicine and Biotechnology consists of seventeen papers that describe different applications of data mining to both genomics and proteomics studies in yeast, and plant and human cells and tissues. Papers by Bensmail et al, Ghosh and Chinnaiyan, and Mao et al present different classification and clustering approaches for disease biomarkers discovery. Genomics and proteomics studies have shown great promises and have been applied to studies aiming at generating expression profiles and elucidating expression networks in different organisms as shown in the papers by Samsa et al, Mungur et al, Liu et al, Baldwin et al, and Joy et al. Data mining in genomics and proteomics studies reveals new regulatory pathways and mechanisms in different health and disease conditions as presented by Wren and Garner, and provides comparative sequence analysis approaches as presented by Gambin and Otto and Gao et al. Those studies have also provided approaches for subcellular localization of proteins suggesting that such approaches can produce an objective systematics for protein location and provide an important starting point for discovering sequence motifs that determine localization as presented by Chen and Murphy. Chen et al studied the performance of five nonparameteric tests to select genes and proved that the popular F test does not perform well on gene expression data since the heterogeneity behavior assumption is the most dominant in the gene expression data. Corder et al explored a statistical approach called grade of membership (GOM) and proved that brain hypoperfusion contributes to dementia, possibly to Alzheimers disease (AD) pathogenesis, and raises the possibility that the APOE ϵ4 allele contributes directly to heart value and myocardial damage. Hand and Heard present in their review article various tools for finding relevant subgroups in gene expression data. Alkharouf et al conduct an OLAP cube (online analytical processing) to mine a time series experiment designed to identify genes associated with resistance of soybean to the soybean cyst nematode, which is a devastating pest of soybean. Brylinski et al created a sequence-to-structure library based on the complete PDB database. Then an early-stage folding conformation and information entropy were used for structure analysis and classification. Whilst postgenomic science is producing vast data torrents, it is well known that data do not equal knowledge and so the extraction of the most meaningful parts of these data is key to the generation of useful new knowledge. More sophisticated data mining strategies are needed for mining such high-dimensional data to generate useful relationships, rules, and predictions.

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.