miR-150 blocks MLL-AF9-associated leukemia through oncogene repression.

UNLABELLED: The microRNA miR-150, a critical regulator of hematopoiesis, is downregulated in mixed-lineage leukemia (MLL). In this study, miR-150 acts as a potent leukemic tumor suppressor by blocking the oncogenic properties of leukemic cells. By using MLL-AF9-transformed cells, we demonstrate that ectopic expression of miR-150 inhibits blast colony formation, cell growth, and increases apoptosis in vitro. More importantly, ectopic expression of miR-150 in MLL-AF9-transformed cells completely blocked the development of myeloid leukemia in transplanted mice. Furthermore, gene expression profiling revealed that miR-150 altered the expression levels of more than 30 "stem cell signature" genes and many others that are involved in critical cancer pathways. In addition to the known miR-150 target Myb, we also identified Cbl and Egr2 as bona fide targets and shRNA-mediated suppression of these genes recapitulated the pro-apoptotic effects observed in leukemic cells with miR-150 ectopic expression. In conclusion, we demonstrate that miR-150 is a potent leukemic tumor suppressor that regulates multiple oncogenes. IMPLICATIONS: These data establish new, key players for the development of therapeutic strategies to treat MLL-AF9-related leukemia.

Identification and characterization of CTRP9, a novel secreted glycoprotein, from adipose tissue that reduces serum glucose in mice and forms heterotrimers with adiponectin.

Adiponectin is a major insulin-sensitizing, multimeric hormone derived from adipose tissue that acts on muscle and liver to regulate whole-body glucose and lipid metabolism. Here, we describe a novel and highly conserved paralog of adiponectin designated as C1q/TNF-related protein (CTRP) 9. Of all the CTRP paralogs, CTRP9 shows the highest degree of amino acid identity to adiponectin in its globular C1q domain. CTRP9 is expressed predominantly in adipose tissue and females expresses higher levels of the transcript than males. Moreover, its expression levels in ob/ob mice changed in an age-dependent manner, with significant up-regulation in younger mice. CTRP9 is a secreted glycoprotein with multiple post-translational modifications in its collagen domain that include hydroxylated prolines and hydroxylated and glycosylated lysines. It is secreted as multimers (predominantly trimers) from transfected cells and circulates in the mouse serum with levels varying according to sex and metabolic state of mice. Furthermore, CTRP9 and adiponectin can be secreted as heterooligomers when cotransfected into mammalian cells, and in vivo, adiponectin/CTRP9 complexes can be reciprocally coimmunoprecipitated from the serum of adiponectin and CTRP9 transgenic mice. Biochemical analysis demonstrates that adiponectin and CTRP9 associate via their globular C1q domain, and this interaction does not require their conserved N-terminal cysteines or their collagen domains. Furthermore, we show that adiponectin and CTRP9 form heterotrimers. In cultured myotubes, CTRP9 specifically activates AMPK, Akt, and p44/42 MAPK signaling pathways. Adenovirus-mediated overexpression of CTRP9 in obese (ob/ob) mice significantly lowered serum glucose levels. Collectively, these results suggest that CTRP9 is a novel adipokine, and further study of CTRP9 will yield novel mechanistic insights into its physiological and metabolic function.

Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles.

BACKGROUND: Pancreatic cancer is the fourth leading cause of cancer death in the United States. Consequently, identification of clinically relevant biomarkers for the early detection of this cancer type is urgently needed. In recent years, proteomics profiling techniques combined with various data analysis methods have been successfully used to gain critical insights into processes and mechanisms underlying pathologic conditions, particularly as they relate to cancer. However, the high dimensionality of proteomics data combined with their relatively small sample sizes poses a significant challenge to current data mining methodology where many of the standard methods cannot be applied directly. Here, we propose a novel methodological framework using machine learning method, in which decision tree based classifier ensembles coupled with feature selection methods, is applied to proteomics data generated from premalignant pancreatic cancer. RESULTS: This study explores the utility of three different feature selection schemas (Student t test, Wilcoxon rank sum test and genetic algorithm) to reduce the high dimensionality of a pancreatic cancer proteomic dataset. Using the top features selected from each method, we compared the prediction performances of a single decision tree algorithm C4.5 with six different decision-tree based classifier ensembles (Random forest, Stacked generalization, Bagging, Adaboost, Logitboost and Multiboost). We show that ensemble classifiers always outperform single decision tree classifier in having greater accuracies and smaller prediction errors when applied to a pancreatic cancer proteomics dataset. CONCLUSION: In our cross validation framework, classifier ensembles generally have better classification accuracies compared to that of a single decision tree when applied to a pancreatic cancer proteomic dataset, thus suggesting its utility in future proteomics data analysis. Additionally, the use of feature selection method allows us to select biomarkers with potentially important roles in cancer development, therefore highlighting the validity of this method.

Protein coding gene nucleotide substitution pattern in the apicomplexan protozoa Cryptosporidium parvum and Cryptosporidium hominis.

Cryptosporidium parvum and C. hominis are related protozoan pathogens which infect the intestinal epithelium of humans and other vertebrates. To explore the evolution of these parasites, and identify genes under positive selection, we performed a pairwise whole-genome comparison between all orthologous protein coding genes in C. parvum and C. hominis. Genome-wide calculation of the ratio of nonsynonymous versus synonymous nucleotide substitutions (dN/dS) was performed to detect the impact of positive and purifying selection. Of 2465 pairs of orthologous genes, a total of 27 (1.1%) showed a high ratio of nonsynonymous substitutions, consistent with positive selection. A majority of these genes were annotated as hypothetical proteins. In addition, proteins with transmembrane and signal peptide domains are significantly more frequent in the high dN/dS group.

Angiopoietin-like proteins stimulate ex vivo expansion of hematopoietic stem cells.

Successful ex vivo expansion of hematopoietic stem cells (HSCs) would greatly benefit the treatment of disease and the understanding of crucial questions of stem cell biology. Here we show, using microarray studies, that the HSC-supportive mouse fetal liver CD3(+) cells specifically express the proteins angiopoietin-like 2 (Angptl2) and angiopoietin-like 3 (Angptl3). We observed a 24- or 30-fold net expansion of long-term HSCs by reconstitution analysis when we cultured highly enriched HSCs for 10 days in the presence of Angptl2 or Angptl3 together with saturating levels of other growth factors. The coiled-coil domain of Angptl2 was capable of stimulating expansion of HSCs. Furthermore, angiopoietin-like 5, angiopoietin-like 7 and microfibril-associated glycoprotein 4 also supported expansion of HSCs in culture.

Prediction of siRNA knockdown efficiency using artificial neural network models.

Selective knockdown of gene expression by short interference RNAs (siRNAs) has allowed rapid validation of gene functions and made possible a high throughput, genome scale approach to interrogate gene function. However, randomly designed siRNAs display different knockdown efficiencies of target genes. Hence, various prediction algorithms based on siRNA functionality have recently been constructed to increase the likelihood of selecting effective siRNAs, thereby reducing the experimental cost. Toward this end, we have trained three Back-propagation and Bayesian neural network models, previously not used in this context, to predict the knockdown efficiencies of 180 experimentally verified siRNAs on their corresponding target genes. Using our input coding based primarily on RNA structure thermodynamic parameters and cross-validation method, we showed that our neural network models outperformed most other methods and are comparable to the best predicting algorithm thus far published. Furthermore, our neural network models correctly classified 74% of all siRNAs into different efficiency categories; with a correlation coefficient of 0.43 and receiver operating characteristic curve score of 0.78, thus highlighting the potential utility of this method to complement other existing siRNA classification and prediction schemes.