MicroRNAs Modulate Hematopoietic Lineage Differentiation

MicroRNAs (miRNAs), an abundant class of ~22 nucleotide non-coding RNAs, are thought to play an important regulatory role in animal and plant development at the posttranscriptional level. Many miRNAs cloned from mouse bone marrow cells are differentially regulated in various hematopoietic lineages, suggesting that they might influence hematopoietic lineage differentiation. Some human miRNAs are linked to leukemias: the miR-15a/miR-16 locus is frequently deleted or down-regulated in patients with B-cell chronic lymphocytic leukemia and miR-142 is at a translocation site found in a case of aggressive B-cell leukemia. miR-181, a miRNA upregulated only in the B cell lineage of mouse bone marrow cells, promotes B cell differentiation and inhibits production of CD8⁺ T cells when expressed in hematopoietic stem/progenitor cells. In contrast miR-142s inhibits production of both CD4⁺ and CD8⁺ T cells and does not affect B cells. Collectively, these results indicate that microRNAs are components of the molecular circuitry controlling mouse hematopoiesis and suggest that other microRNAs have similar regulatory roles during other facets of vertebrate development.

Selecting Relevant Genes with a Spectral Approach

Array technologies have made it possible to record simultaneously the expression pattern of thousands of genes. A fundamental problem in the analysis of gene expression data is the identification of highly relevant genes that either discriminate between phenotypic labels or are important with respect to the cellular process studied in the experiment: for example cell cycle or heat shock in yeast experiments, chemical or genetic perturbations of mammalian cell lines, and genes involved in class discovery for human tumors. In this paper we focus on the task of unsupervised gene selection. The problem of selecting a small subset of genes is particularly challenging as the datasets involved are typically characterized by a very small sample size ?? the order of few tens of tissue samples ??d by a very large feature space as the number of genes tend to be in the high thousands. We propose a model independent approach which scores candidate gene selections using spectral properties of the candidate affinity matrix. The algorithm is very straightforward to implement yet contains a number of remarkable properties which guarantee consistent sparse selections. To illustrate the value of our approach we applied our algorithm on five different datasets. The first consists of time course data from four well studied Hematopoietic cell lines (HL-60, Jurkat, NB4, and U937). The other four datasets include three well studied treatment outcomes (large cell lymphoma, childhood medulloblastomas, breast tumors) and one unpublished dataset (lymph status). We compared our approach both with other unsupervised methods (SOM,PCA,GS) and with supervised methods (SNR,RMB,RFE). The results clearly show that our approach considerably outperforms all the other unsupervised approaches in our study, is competitive with supervised methods and in some case even outperforms supervised approaches.