Sequential gene regulatory events leading to glucocorticoid-evoked apoptosis of CEM human leukemic cells:interactions of MAPK, MYC and glucocorticoid pathways.

Gene expression responses to glucocorticoid (GC) in the hours preceding onset of apoptosis were compared in three clones of human acute lymphoblastic leukemia CEM cells. Between 2 and 20h, all three clones showed increasing numbers of responding genes. Each clone had many unique responses, but the two responsive clones showed a group of responding genes in common, different from the resistant clone. MYC levels and the balance of activities between the three major groups of MAPKs are known important regulators of glucocorticoid-driven apoptosis in several lymphoid cell systems. Common to the two sensitive clones were changed transcript levels from genes that decrease amounts or activity of anti-apoptotic ERK/MAPK1 and JNK2/MAPK9, or of genes that increase activity of pro-apoptotic p38/MAPK14. Down-regulation of MYC and several MYC-regulated genes relevant to MAPKs also occurred in both sensitive clones. Transcriptomine comparisons revealed probable NOTCH-GC crosstalk in these cells.

Genotyping of Bartonella bacteria and their animal hosts: current status and perspectives.

Growing evidence demonstrates that bacterial species diversity is substantial, and many of these species are pathogenic in some contexts or hosts. At the same time, laboratories and museums have collected valuable animal tissue and ectoparasite samples that may contain substantial novel information on bacterial prevalence and diversity. However, the identification of bacterial species is challenging, partly due to the difficulty in culturing many microbes and the reliance on molecular data. Although the genomics revolution will surely add to our knowledge of bacterial systematics, these approaches are not accessible to all researchers and rely predominantly on cultured isolates. Thus, there is a need for comprehensive molecular analyses capable of accurately genotyping bacteria from animal tissues or ectoparasites using common methods that will facilitate large-scale comparisons of species diversity and prevalence. To illustrate the challenges of genotyping bacteria, we focus on the genus Bartonella, vector-borne bacteria common in mammals. We highlight the value and limitations of commonly used techniques for genotyping bartonellae and make recommendations for researchers interested in studying the diversity of these bacteria in various samples. Our recommendations could be applicable to many bacterial taxa (with some modifications) and could lead to a more complete understanding of bacterial species diversity.

PIQA: pipeline for Illumina G1 genome analyzer data quality assessment.

SUMMARY: PIQA is a quality analysis pipeline designed to examine genomic reads produced by Next Generation Sequencing technology (Illumina G1 Genome Analyzer). A short statistical summary, as well as tile-by-tile and cycle-by-cycle graphical representation of clusters density, quality scores and nucleotide frequencies allow easy identification of various technical problems including defective tiles, mistakes in sample/library preparations and abnormalities in the frequencies of appearance of sequenced genomic reads. PIQA is written in the R statistical programming language and is compatible with bustard, fastq and scarf Illumina G1 Genome Analyzer data formats. AVAILABILITY: The PIQA pipeline, installation instructions and examples are available at the supplementary web site (http://bioinfo.uh.edu/PIQA).

PIDA:A new algorithm for pattern identification.

Algorithms for motif identification in sequence space have predominately been focused on recognizing patterns of a fixed length containing regions of perfect conservation with possible regions of unconstrained sequence. Such motifs can be found in everything from proteins with distinct active sites to non-coding RNAs with specific structural elements that are necessary to maintain functionality. In the event that an insertion/deletion has occurred within an unconstrained portion of the pattern, it is possible that the pattern retains its functionality. In such a case the length of the pattern is now variable and may be overlooked when utilizing existing motif detection methods. The Pattern Island Detection Algorithm (PIDA) presented here has been developed to recognize patterns that have occurrences of varying length within sequences of any size alphabet. PIDA works by identifying all regions of perfect conservation (for lengths longer than a user-specified threshold), and then builds those conservation "islands" into fixed-length patterns. Next the algorithm modifies these fixed-length patterns by identifying additional (and different) islands that can be incorporated into each pattern through insertions/deletions within the "water" separating the islands. To provide some benchmarks for this analysis, PIDA was used to search for patterns within randomly generated sequences as well as sequences known to contain conserved patterns. For each of the patterns found, the statistical significance is calculated based upon the pattern's likelihood to appear by chance, thus providing a means to determine those patterns which are likely to have a functional role. The PIDA approach to motif finding is designed to perform best when searching for patterns of variable length although it is also able to identify patterns of a fixed length. PIDA has been created to be as generally applicable as possible since there are a variety of sequence problems of this type. The algorithm was implemented in C++ and is freely available upon request from the authors.

Identification of genes leading to glucocorticoid-induced leukemic cell death.

Glucocorticoidal steroids (GC) are capable of causing apoptotic death of many varieties of lymphoid cells; consequently, GC are used in therapy for many lymphoid malignancies. Gene transcription in the GC-treated cells is required for subsequent apoptosis, but only a few of the actual genes involved have been identified. We employed gene microarray analysis to find the network of genes involved in GC-evoked cell death, using three clones derived from the CEM lymphoid leukemia cell line. Clone C1-15 was resistant to GC-evoked apoptosis, although not necessarily to GC-induced gene transcription; the other two underwent apoptosis in the presence of GC. Clone C7-14 was subcloned from the apoptosis-sensitive parental C7 clone to establish karyotypic uniformity. The second sensitive clone, C1-6, was a spontaneous revertant from parental resistant clone C1. A period of > or = 24 h in the constant presence of receptor-occupying concentrations of synthetic GC dexamethasone (Dex) was necessary for apoptosis to begin. To identify the steps leading to this dramatic event, we identified the changes in gene expression in the 20-h period preceding the onset of overt apoptosis. Cells in the log phase of growth were treated with 10(-6) M Dex, and 2-20 h later, mRNA was prepared and analyzed using the Affymetrix HG_U95Av2 chip, containing probes for about 12,600 genes. Of these, approximately 6,000 were expressed above background. Comparisons of the basal and expressed genes in the three clones led to several conclusions: The Dex-sensitive clones shared the regulation of a limited set of genes. The apoptosis-resistant clone C1-15 showed Dex effects on a largely different set of genes. Promoter analysis of the regulated genes suggested that primary gene targets for GC often lack a classic GC response element.