An insertional mutagenesis screen identifies genes that cooperate with Mll-AF9 in a murine leukemogenesis model.

Patients with a t(9;11) translocation (MLL-AF9) develop acute myeloid leukemia (AML), and while in mice the expression of this fusion oncogene also results in the development of myeloid leukemia, it is with long latency. To identify mutations that cooperate with Mll-AF9, we infected neonatal wild-type (WT) or Mll-AF9 mice with a murine leukemia virus (MuLV). MuLV-infected Mll-AF9 mice succumbed to disease significantly faster than controls presenting predominantly with myeloid leukemia while infected WT animals developed predominantly lymphoid leukemia. We identified 88 candidate cancer genes near common sites of proviral insertion. Analysis of transcript levels revealed significantly elevated expression of Mn1, and a trend toward increased expression of Bcl11a and Fosb in Mll-AF9 murine leukemia samples with proviral insertions proximal to these genes. Accordingly, FOSB and BCL11A were also overexpressed in human AML harboring MLL gene translocations. FOSB was revealed to be essential for growth in mouse and human myeloid leukemia cells using shRNA lentiviral vectors in vitro. Importantly, MN1 cooperated with Mll-AF9 in leukemogenesis in an in vivo BM viral transduction and transplantation assay. Together, our data identified genes that define transcription factor networks and important genetic pathways acting during progression of leukemia induced by MLL fusion oncogenes.

Evaluation of a new high-dimensional miRNA profiling platform.

BACKGROUND: MicroRNAs (miRNAs) are a class of approximately 22 nucleotide long, widely expressed RNA molecules that play important regulatory roles in eukaryotes. To investigate miRNA function, it is essential that methods to quantify their expression levels be available. METHODS: We evaluated a new miRNA profiling platform that utilizes Illumina's existing robust DASL chemistry as the basis for the assay. Using total RNA from five colon cancer patients and four cell lines, we evaluated the reproducibility of miRNA expression levels across replicates and with varying amounts of input RNA. The beta test version was comprised of 735 miRNA targets of Illumina's miRNA profiling application. RESULTS: Reproducibility between sample replicates within a plate was good (Spearman's correlation 0.91 to 0.98) as was the plate-to-plate reproducibility replicates run on different days (Spearman's correlation 0.84 to 0.98). To determine whether quality data could be obtained from a broad range of input RNA, data obtained from amounts ranging from 25 ng to 800 ng were compared to those obtained at 200 ng. No effect across the range of RNA input was observed. CONCLUSION: These results indicate that very small amounts of starting material are sufficient to allow sensitive miRNA profiling using the Illumina miRNA high-dimensional platform. Nonlinear biases were observed between replicates, indicating the need for abundance-dependent normalization. Overall, the performance characteristics of the Illumina miRNA profiling system were excellent.

Gene profile analysis of osteoblast genes differentially regulated by histone deacetylase inhibitors.

BACKGROUND: Osteoblast differentiation requires the coordinated stepwise expression of multiple genes. Histone deacetylase inhibitors (HDIs) accelerate the osteoblast differentiation process by blocking the activity of histone deacetylases (HDACs), which alter gene expression by modifying chromatin structure. We previously demonstrated that HDIs and HDAC3 shRNAs accelerate matrix mineralization and the expression of osteoblast maturation genes (e.g. alkaline phosphatase, osteocalcin). Identifying other genes that are differentially regulated by HDIs might identify new pathways that contribute to osteoblast differentiation. RESULTS: To identify other osteoblast genes that are altered early by HDIs, we incubated MC3T3-E1 preosteoblasts with HDIs (trichostatin A, MS-275, or valproic acid) for 18 hours in osteogenic conditions. The promotion of osteoblast differentiation by HDIs in this experiment was confirmed by osteogenic assays. Gene expression profiles relative to vehicle-treated cells were assessed by microarray analysis with Affymetrix GeneChip 430 2.0 arrays. The regulation of several genes by HDIs in MC3T3-E1 cells and primary osteoblasts was verified by quantitative real-time PCR. Nine genes were differentially regulated by at least two-fold after exposure to each of the three HDIs and six were verified by PCR in osteoblasts. Four of the verified genes (solute carrier family 9 isoform 3 regulator 1 (Slc9a3r1), sorbitol dehydrogenase 1, a kinase anchor protein, and glutathione S-transferase alpha 4) were induced. Two genes (proteasome subunit, beta type 10 and adaptor-related protein complex AP-4 sigma 1) were suppressed. We also identified eight growth factors and growth factor receptor genes that are significantly altered by each of the HDIs, including Frizzled related proteins 1 and 4, which modulate the Wnt signaling pathway. CONCLUSION: This study identifies osteoblast genes that are regulated early by HDIs and indicates pathways that might promote osteoblast maturation following HDI exposure. One gene whose upregulation following HDI treatment is consistent with this notion is Slc9a3r1. Also known as NHERF1, Slc9a3r1 is required for optimal bone density. Similarly, the regulation of Wnt receptor genes indicates that this crucial pathway in osteoblast development is also affected by HDIs. These data support the hypothesis that HDIs regulate the expression of genes that promote osteoblast differentiation and maturation.

Optimization of laser capture microdissection and RNA amplification for gene expression profiling of prostate cancer.

BACKGROUND: To discover prostate cancer biomarkers, we profiled gene expression in benign and malignant cells laser capture microdissected (LCM) from prostate tissues and metastatic prostatic adenocarcinomas. Here we present methods developed, optimized, and validated to obtain high quality gene expression data. RESULTS: RNase inhibitor was included in solutions used to stain frozen tissue sections for LCM, which improved RNA quality significantly. Quantitative PCR assays, requiring minimal amounts of LCM RNA, were developed to determine RNA quality and concentration. SuperScript II reverse transcriptase was replaced with SuperScript III, and SpeedVac concentration was eliminated to optimize linear amplification. The GeneChip(R) IVT labeling kit was used rather than the Enzo BioArray HighYield RNA transcript labeling kit since side-by-side comparisons indicated high-end signal saturation with the latter. We obtained 72 mug of labeled complementary RNA on average after linear amplification of about 2 ng of total RNA. CONCLUSION: Unsupervised clustering placed 5/5 normal and 2/2 benign prostatic hyperplasia cases in one group, 5/7 Gleason pattern 3 cases in another group, and the remaining 2/7 pattern 3 cases in a third group with 8/8 Gleason pattern 5 cases and 3/3 metastatic prostatic adenocarcinomas. Differential expression of alpha-methylacyl coenzyme A racemase (AMACR) and hepsin was confirmed using quantitative PCR.

Databases and information integration for the Medicago truncatula genome and transcriptome.

An international consortium is sequencing the euchromatic genespace of Medicago truncatula. Extensive bioinformatic and database resources support the marker-anchored bacterial artificial chromosome (BAC) sequencing strategy. Existing physical and genetic maps and deep BAC-end sequencing help to guide the sequencing effort, while EST databases provide essential resources for genome annotation as well as transcriptome characterization and microarray design. Finished BAC sequences are joined into overlapping sequence assemblies and undergo an automated annotation process that integrates ab initio predictions with EST, protein, and other recognizable features. Because of the sequencing project's international and collaborative nature, data production, storage, and visualization tools are broadly distributed. This paper describes databases and Web resources for the project, which provide support for physical and genetic maps, genome sequence assembly, gene prediction, and integration of EST data. A central project Web site at medicago.org/genome provides access to genome viewers and other resources project-wide, including an Ensembl implementation at medicago.org, physical map and marker resources at mtgenome.ucdavis.edu, and genome viewers at the University of Oklahoma (www.genome.ou.edu), the Institute for Genomic Research (www.tigr.org), and Munich Information for Protein Sequences Center (mips.gsf.de).

MtDB: a database for personalized data mining of the model legume Medicago truncatula transcriptome.

In order to identify the genes and gene functions that underlie key aspects of legume biology, researchers have selected the cool season legume Medicago truncatula (Mt) as a model system for legume research. A set of >170 000 Mt ESTs has been assembled based on in-depth sampling from various developmental stages and pathogen-challenged tissues. MtDB is a relational database that integrates Mt transcriptome data and provides a wide range of user-defined data mining options. The database is interrogated through a series of interfaces with 58 options grouped into two filters. In addition, the user can select and compare unigene sets generated by different assemblers: Phrap, Cap3 and Cap4. Sequence identifiers from all public Mt sites (e.g. IDs from GenBank, CCGB, TIGR, NCGR, INRA) are fully cross-referenced to facilitate comparisons between different sites, and hypertext links to the appropriate database records are provided for all queries' results. MtDB's goal is to provide researchers with the means to quickly and independently identify sequences that match specific research interests based on user-defined criteria. The underlying database and query software have been designed for ease of updates and portability to other model organisms. Public access to the database is at http://www.medicago.org/MtDB.