Data integration from two microarray platforms identifies bi-allelic genetic inactivation of RIC8A in a breast cancer cell line

Background: Using array comparative genomic hybridization (aCGH), a large number of deleted genomic regions have been identified in human cancers. However, subsequent efforts to identify target genes selected for inactivation in these regions have often been challenging. Methods: We integrated here genome-wide copy number data with gene expression data and non-sense mediated mRNA decay rates in breast cancer cell lines to prioritize gene candidates that are likely to be tumour suppressor genes inactivated by bi-allelic genetic events. The candidates were sequenced to identify potential mutations. Results: This integrated genomic approach led to the identification of RIC8A at 11p15 as a putative candidate target gene for the genomic deletion in the ZR-75-1 breast cancer cell line. We identified a truncating mutation in this cell line, leading to loss of expression and rapid decay of the transcript. We screened 127 breast cancers for RIC8A mutations, but did not find any pathogenic mutations. No promoter hypermethylation in these tumours was detected either. However, analysis of gene expression data from breast tumours identified a small group of aggressive tumours that displayed low levels of RIC8A transcripts. qRT-PCR analysis of 38 breast tumours showed a strong association between low RIC8A expression and the presence of TP53 mutations (P = 0.006). Conclusion: We demonstrate a data integration strategy leading to the identification of RIC8A as a gene undergoing a classical double-hit genetic inactivation in a breast cancer cell line, as well as in vivo evidence of loss of RIC8A expression in a subgroup of aggressive TP53 mutant breast cancers.

Array-based gene expression, CGH and tissue data defines a 12q24 gain in neuroblastic tumors with prognostic implication

Neuroblastoma has successfully served as a model system for the identification of neuroectoderm-derived oncogenes. However, in spite of various efforts, only a few clinically useful prognostic markers have been found. Here, we present a framework, which integrates DNA, RNA and tissue data to identify and prioritize genetic events that represent clinically relevant new therapeutic targets and prognostic biomarkers for neuroblastoma.

Integrin trafficking regulated by Rab21 is necessary for cytokinesis

Adherent cells undergo remarkable changes in shape during cell division. However, the functional interplay between cell adhesion turnover and the mitotic machinery is poorly understood. The endo/exocytic trafficking of integrins is regulated by the small GTPase Rab21, which associates with several integrin alpha subunits. Here, we show that targeted trafficking of integrins to and from the cleavage furrow is required for successful cytokinesis, and that this is regulated by Rab21. Rab21 activity, integrin-Rab21 association, and integrin endocytosis are all necessary for normal cytokinesis, which becomes impaired when integrin-mediated adhesion at the cleavage furrow fails. We also describe a chromosomal deletion and loss of Rab21 gene expression in human cancer, which leads to the accumulation of multinucleate cells. Importantly, reintroduction of Rab21 rescued this phenotype. In conclusion, Rab21-regulated integrin trafficking is essential for normal cell division, and its defects may contribute to multinucleation and genomic instability, which are hallmarks of cancer.

Studies of the Human Transcriptome

Gene expression is one of the most critical factors influencing the phenotype of a cell. As a result of several technological advances, measuring gene expression levels has become one of the most common molecular biological measurements to study the behaviour of cells. The scientific community has produced enormous and constantly increasing collection of gene expression data from various human cells both from healthy and pathological conditions. However, while each of these studies is informative and enlighting in its own context and research setup, diverging methods and terminologies make it very challenging to integrate existing gene expression data to a more comprehensive view of human transcriptome function. On the other hand, bioinformatic science advances only through data integration and synthesis. The aim of this study was to develop biological and mathematical methods to overcome these challenges and to construct an integrated database of human transcriptome as well as to demonstrate its usage. Methods developed in this study can be divided in two distinct parts. First, the biological and medical annotation of the existing gene expression measurements needed to be encoded by systematic vocabularies. There was no single existing biomedical ontology or vocabulary suitable for this purpose. Thus, new annotation terminology was developed as a part of this work. Second part was to develop mathematical methods correcting the noise and systematic differences/errors in the data caused by various array generations. Additionally, there was a need to develop suitable computational methods for sample collection and archiving, unique sample identification, database structures, data retrieval and visualization. Bioinformatic methods were developed to analyze gene expression levels and putative functional associations of human genes by using the integrated gene expression data. Also a method to interpret individual gene expression profiles across all the healthy and pathological tissues of the reference database was developed. As a result of this work 9783 human gene expression samples measured by Affymetrix microarrays were integrated to form a unique human transcriptome resource GeneSapiens. This makes it possible to analyse expression levels of 17330 genes across 175 types of healthy and pathological human tissues. Application of this resource to interpret individual gene expression measurements allowed identification of tissue of origin with 92.0% accuracy among 44 healthy tissue types. Systematic analysis of transcriptional activity levels of 459 kinase genes was performed across 44 healthy and 55 pathological tissue types and a genome wide analysis of kinase gene co-expression networks was done. This analysis revealed biologically and medically interesting data on putative kinase gene functions in health and disease. Finally, we developed a method for alignment of gene expression profiles (AGEP) to perform analysis for individual patient samples to pinpoint gene- and pathway-specific changes in the test sample in relation to the reference transcriptome database. We also showed how large-scale gene expression data resources can be used to quantitatively characterize changes in the transcriptomic program of differentiating stem cells. Taken together, these studies indicate the power of systematic bioinformatic analyses to infer biological and medical insights from existing published datasets as well as to facilitate the interpretation of new molecular profiling data from individual patients.

Arabidopsis GRI is involved in the regulation of cell death induced by extracellular ROS

Reactive oxygen species (ROS) have important functions in plant stress responses and development. In plants, ozone and pathogen infection induce an extracellular oxidative burst that is involved in the regulation of cell death. However, very little is known about how plants can perceive ROS and regulate the initiation and the containment of cell death. We have identified an Arabidopsis thaliana protein, GRIM REAPER (GRI), that is involved in the regulation of cell death induced by extracellular ROS. Plants with an insertion in GRI display an ozone-sensitive phenotype. GRI is an Arabidopsis ortholog of the tobacco flower-specific Stig1 gene. The GRI protein appears to be processed in leaves with a release of an N-terminal fragment of the protein. Infiltration of the N-terminal fragment of the GRI protein into leaves caused cell death in a superoxide-and salicylic acid-dependent manner. Analysis of the extracellular GRI protein yields information on how plants can initiate ROS-induced cell death during stress response and development.