The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists

The DAVID gene functional classification tool uses a novel fuzzy clustering algorithm to condense a list of genes or associated biological terms into organized classes of related genes or biology, called biological modules

An Integrated Genetic-Genomic Approach for the Identification of Novel Cancer Loci in Mice Sensitized to c-Myc–Induced Apoptosis

Deregulated c-Myc is associated with a wide range of human cancers. In many cell types, overexpression of c-Myc potently promotes cell growth and proliferation concomitant with the induction of apoptosis. Secondary genetic events that shift this balance either by increasing growth and proliferation or limiting apoptosis are likely to cooperate with c-Myc in tumorigenesis. Here, the authors have performed large-scale insertional mutagenesis in Eµ-c-myc mice that, through mdm2 loss of function mutations, are sensitized to apoptosis. The authors chose to use this genetic background based on the hypothesis that the high level of apoptosis induced by c-Myc overexpression in MDM2-deficient mice would act as a rate-limiting barrier for lymphoma development. As a result, it was predicted that the spectrum of retroviral insertions would be shifted toward loci that harbor antiapoptotic genes. Nine novel common insertion sites (CISs) specific to mice with this sensitized genetic background were identified, suggesting the presence of novel antiapoptotic cancer genes. Moreover, cross-comparing the data to the Retroviral Tagged Cancer Gene Database, the authors identified an additional 23 novel CISs. Here, evidence is presented that 2 genes, ppp1r16b and hdac6, identified at CISs, are bona fide cellular oncogenes. This study highlights the power of combining unique sensitized genetic backgrounds with large-scale mutagenesis as an approach for identifying novel cancer genes

Evaluation of a Single-Platform Technology for Lymphocyte Immunophenotyping▿

An accurate and reproducible CD4 count is a fundamental clinical tool for monitoring and treating human immunodeficiency virus infection and its complications. Two methods exist for calculating absolute CD4 counts: dual-platform technology (DPT) and single-platform technology (SPT). Numerous studies have documented the unacceptably wide range of variation in absolute CD4 counts between laboratories. SPT was introduced in 1996 to reduce the interlaboratory variation in absolute CD4 counts. The aim of this study was to compare DPT with the BD Biosciences Trucount method (an SPT method). Both the percentages of CD4 (r = 0.986; P = 0.0541) and the absolute CD4 counts (r = 0.960; P = 0.0001) had very good correlation between the two methods. However, poor correlation was observed for the CD8+ RO− (r = 0.314; P = 0.0002), CD8+ DR+ (r = 0.666; P = 0.0138), CD3+ CD38+ (r = 0.8000; P = 0.0004), CD3+ CD25+ (r = 0.464; P = 0.0082), and CD4+ CD38+ (r = 0.357; P = 0.0127) measurements

A hypothetical example of detecting gene-gene functional relationships by statistics

The all-redundant and structured terms are broken into 'independent' terms in a flat linear collection. Each gene associates with some of the annotation term collection so that a gene-annotation matrix can be built in a binary format, where 1 represents a positive match for the particular gene-term and 0 represents the unknown. Thus, each gene has a unique profile of annotation terms represented by a combination of 1 s and 0 s. For a particular example of genes and , a contingency table was constructed for statistics calculation. The higher score (0.66) indicates that genes and are in considerable agreement, more so than by random chance. By flipping the table 90 degrees, the score of term-term can be achieved, based on the agreement of common genes (not shown). For more information see Additional data files 11 and 12.<p><b>Copyright information:</b></p><p>Taken from "The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists"</p><p>http://genomebiology.com/2007/8/9/R183</p><p>Genome Biology 2007;8(9):R183-R183.</p><p>Published online 4 Sep 2007</p><p>PMCID:PMC2375021.</p><p></p

The gene-gene functional relationship can be specifically detected by statistics

scores were calculated for all possible combinations of human gene-gene pairs (approximately 300 million). Only gene-gene pairs with a higher number of annotation terms in common possibly have good values. The box plot consists of the smallest and largest observations at the two end points (95% confidence interval), as well as a box from the 1st to 3rd quartiles. The blue and red lines represent median and mean observations, respectively. scores were calculated for all possible human gene-gene pairs, gene-gene pairs with randomized annotation terms, all collected protein-protein interacting pairs, and all 'chemokine' gene pairs, respectively. The distributions of those scores from protein-protein interacting pairs (pink) and 'chemokine' gene pairs (light blue) significantly shift to the high value end compared to human total (blue); conversely, the score distribution (yellow) of gene pairs with randomized annotation terms remains in the lower value end below 0.35. Interestingly, for the human genome (blue), over 50% of the scores equal 0 (no detectable relationships) and >95% are lower than 0.35. Altogether, this indicates that statistics can specifically detect the gene-gene functional relationships.<p><b>Copyright information:</b></p><p>Taken from "The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists"</p><p>http://genomebiology.com/2007/8/9/R183</p><p>Genome Biology 2007;8(9):R183-R183.</p><p>Published online 4 Sep 2007</p><p>PMCID:PMC2375021.</p><p></p

Sustained high-level polyclonal hematopoietic marking and transgene expression 4 years after autologous transplantation of rhesus macaques with SIV lentiviral vector–transduced CD34+ cells

We previously reported that lentiviral vectors derived from the simian immunodeficiency virus (SIV) were efficient at transducing rhesus hematopoietic repopulating cells. To evaluate the persistence of vector-containing and -expressing cells long term, and the safety implications of SIV lentiviral vector–mediated gene transfer, we followed 3 rhesus macaques for more than 4 years after transplantation with transduced CD34+ cells. All 3 animals demonstrated significant vector marking and expression of the GFP transgene in T cells, B cells, and granulocytes, with mean GFP+ levels of 6.7% (range, 3.3%-13.0%), 7.4% (4.2%-13.4%), and 5.6% (3.1%-10.5%), respectively. There was no vector silencing in hematopoietic cells over time. Vector insertion site analysis of granulocytes demonstrated sustained highly polyclonal reconstitution, with no evidence for progression to oligoclonality. A significant number of clones were found to contribute at both 1-year and 3- or 4-year time points. No vector integrations were detected in the MDS1/EVI1 region, in contrast to our previous findings with a γ-retroviral vector. These data show that lentiviral vectors can mediate stable and efficient long-term expression in the progeny of transduced hematopoietic stem cells, with an integration profile that may be safer than that of standard Moloney murine leukemia virus (MLV)–derived retroviral vectors