Robust multi-tissue gene panel for cancer detection.

BACKGROUND: We have identified a set of genes whose relative mRNA expression levels in various solid tumors can be used to robustly distinguish cancer from matching normal tissue. Our current feature set consists of 113 gene probes for 104 unique genes, originally identified as differentially expressed in solid primary tumors in microarray data on Affymetrix HG-U133A platform in five tissue types: breast, colon, lung, prostate and ovary. For each dataset, we first identified a set of genes significantly differentially expressed in tumor vs. normal tissue at p-value = 0.05 using an experimentally derived error model. Our common cancer gene panel is the intersection of these sets of significantly dysregulated genes and can distinguish tumors from normal tissue on all these five tissue types. METHODS: Frozen tumor specimens were obtained from two commercial vendors Clinomics (Pittsfield, MA) and Asterand (Detroit, MI). Biotinylated targets were prepared using published methods (Affymetrix, CA) and hybridized to Affymetrix U133A GeneChips (Affymetrix, CA). Expression values for each gene were calculated using Affymetrix GeneChip analysis software MAS 5.0. We then used a software package called Genes@Work for differential expression discovery, and SVM light linear kernel for building classification models. RESULTS: We validate the predictability of this gene list on several publicly available data sets generated on the same platform. Of note, when analysing the lung cancer data set of Spira et al, using an SVM linear kernel classifier, our gene panel had 94.7% leave-one-out accuracy compared to 87.8% using the gene panel in the original paper. In addition, we performed high-throughput validation on the Dana Farber Cancer Institute GCOD database and several GEO datasets. CONCLUSIONS: Our result showed the potential for this panel as a robust classification tool for multiple tumor types on the Affymetrix platform, as well as other whole genome arrays. Apart from possible use in diagnosis of early tumorigenesis, some other potential uses of our methodology and gene panel would be in assisting pathologists in diagnosis of pre-cancerous lesions, determining tumor boundaries, assessing levels of contamination in cell populations in vitro and identifying transformations in cell cultures after multiple passages. Moreover, based on the robustness of this gene panel in identifying normal vs. tumor, mislabelled or misinterpreted samples can be pinpointed with high confidence.

Quantitative genome-scale analysis of protein localization in an asymmetric bacterium.

Despite the importance of subcellular localization for cellular activities, the lack of high-throughput, high-resolution imaging and quantitation methodologies has limited genomic localization analysis to a small number of archival studies focused on C-terminal fluorescent protein fusions. Here, we develop a high-throughput pipeline for generating, imaging, and quantitating fluorescent protein fusions that we use for the quantitative genomic assessment of the distributions of both N- and C-terminal fluorescent protein fusions. We identify nearly 300 localized Caulobacter crescentus proteins, up to 10-fold more than were previously characterized. The localized proteins tend to be involved in spatially or temporally dynamic processes and proteins that function together and often localize together as well. The distributions of the localized proteins were quantitated by using our recently described projected system of internal coordinates from interpolated contours (PSICIC) image analysis toolkit, leading to the identification of cellular regions that are over- or under-enriched in localized proteins and of potential differences in the mechanisms that target proteins to different subcellular destinations. The Caulobacter localizome data thus represent a resource for studying both global properties of protein localization and specific protein functions, whereas the localization analysis pipeline is a methodological resource that can be readily applied to other systems.

The Vibrio harveyi master quorum-sensing regulator, LuxR, a TetR-type protein is both an activator and a repressor: DNA recognition and binding specificity at target promoters.

Quorum sensing is the process of cell-to-cell communication by which bacteria communicate via secreted signal molecules called autoinducers. As cell population density increases, the accumulation of autoinducers leads to co-ordinated changes in gene expression across the bacterial community. The marine bacterium, Vibrio harveyi, uses three autoinducers to achieve intra-species, intra-genera and inter-species cell-cell communication. The detection of these autoinducers ultimately leads to the production of LuxR, the quorum-sensing master regulator that controls expression of the genes in the quorum-sensing regulon. LuxR is a member of the TetR protein superfamily; however, unlike other TetR repressors that typically repress their own gene expression and that of an adjacent operon, LuxR is capable of activating and repressing a large number of genes. Here, we used protein binding microarrays and a two-layered bioinformatics approach to show that LuxR binds a 21 bp consensus operator with dyad symmetry. In vitro and in vivo analyses of two promoters directly regulated by LuxR allowed us to identify those bases that are critical for LuxR binding. Together, the in silico and biochemical results enabled us to scan the genome and identify novel targets of LuxR in V. harveyi and thus expand the understanding of the quorum-sensing regulon.