A novel microarray approach reveals new tissue-specific signatures of known and predicted mammalian microRNAs.

Microarrays to examine the global expression levels of microRNAs (miRNAs) in a systematic in-parallel manner have become important tools to help unravel the functions of miRNAs and to understand their roles in RNA-based regulation and their implications in human diseases. We have established a novel miRNA-specific microarray platform that enables the simultaneous expression analysis of both known and predicted miRNAs obtained from human or mouse origin. Chemically modified 2'-O-(2-methoxyethyl)-(MOE) oligoribonucleotide probes were arrayed onto Evanescent Resonance (ER) microchips by robotic spotting. Supplementing the complementary probes against miRNAs with carefully designed mismatch controls allowed for accurate sequence-specific determination of miRNA expression profiles obtained from a panel of mouse tissues. This revealed new expression signatures of known miRNAs as well as of novel miRNAs previously predicted using bioinformatic methods. Systematic confirmation of the array data with northern blotting and, in particular, real-time PCR suggests that the described microarray platform is a powerful tool to analyze miRNA expression patterns with rapid throughput and high fidelity.

Design of a genome-wide siRNA library using an artificial neural network.

The largest gene knock-down experiments performed to date have used multiple short interfering/short hairpin (si/sh)RNAs per gene. To overcome this burden for design of a genome-wide siRNA library, we used the Stuttgart Neural Net Simulator to train algorithms on a data set of 2,182 randomly selected siRNAs targeted to 34 mRNA species, assayed through a high-throughput fluorescent reporter gene system. The algorithm, (BIOPREDsi), reliably predicted activity of 249 siRNAs of an independent test set (Pearson coefficient r = 0.66) and siRNAs targeting endogenous genes at mRNA and protein levels. Neural networks trained on a complementary 21-nucleotide (nt) guide sequence were superior to those trained on a 19-nt sequence. BIOPREDsi was used in the design of a genome-wide siRNA collection with two potent siRNAs per gene. When this collection of 50,000 siRNAs was used to identify genes involved in the cellular response to hypoxia, two of the most potent hits were the key hypoxia transcription factors HIF1A and ARNT.

Whole genome functional analysis identifies novel components required for mitotic spindle integrity in human cells.

BACKGROUND: The mitotic spindle is a complex mechanical apparatus required for accurate segregation of sister chromosomes during mitosis. We designed a genetic screen using automated microscopy to discover factors essential for mitotic progression. Using a RNA interference library of 49,164 double-stranded RNAs targeting 23,835 human genes, we performed a loss of function screen to look for small interfering RNAs that arrest cells in metaphase. RESULTS: Here we report the identification of genes that, when suppressed, result in structural defects in the mitotic spindle leading to bent, twisted, monopolar, or multipolar spindles, and cause cell cycle arrest. We further describe a novel analysis methodology for large-scale RNA interference datasets that relies on supervised clustering of these genes based on Gene Ontology, protein families, tissue expression, and protein-protein interactions. CONCLUSION: This approach was utilized to classify functionally the identified genes in discrete mitotic processes. We confirmed the identity for a subset of these genes and examined more closely their mechanical role in spindle architecture.

Genome-wide functional analysis of human cell-cycle regulators.

Human cells have evolved complex signaling networks to coordinate the cell cycle. A detailed understanding of the global regulation of this fundamental process requires comprehensive identification of the genes and pathways involved in the various stages of cell-cycle progression. To this end, we report a genome-wide analysis of the human cell cycle, cell size, and proliferation by targeting >95% of the protein-coding genes in the human genome using small interfering RNAs (siRNAs). Analysis of >2 million images, acquired by quantitative fluorescence microscopy, showed that depletion of 1,152 genes strongly affected cell-cycle progression. These genes clustered into eight distinct phenotypic categories based on phase of arrest, nuclear area, and nuclear morphology. Phase-specific networks were built by interrogating knowledge-based and physical interaction databases with identified genes. Genome-wide analysis of cell-cycle regulators revealed a number of kinase, phosphatase, and proteolytic proteins and also suggests that processes thought to regulate G(1)-S phase progression like receptor-mediated signaling, nutrient status, and translation also play important roles in the regulation of G(2)/M phase transition. Moreover, 15 genes that are integral to TNF/NF-kappaB signaling were found to regulate G(2)/M, a previously unanticipated role for this pathway. These analyses provide systems-level insight into both known and novel genes as well as pathways that regulate cell-cycle progression, a number of which may provide new therapeutic approaches for the treatment of cancer.

Post-translational modification detection using metastable ions in reflector matrix-assisted laser desorption/ionization-time of flight mass spectrometry.

In addition to protein identification, characterization of post-translational modifications (PTMs) is an essential task in proteomics. PTMs represent the major reason for the variety of protein isoforms and they can influence protein structure and function. Upon matrix-assisted laser desorption/ionization (MALDI) most post-translationally modified peptides form a fraction of labile molecular ions, which lose PTM-specific residues only after acceleration. Compared to fully accelerated ions these fragment ions are defocused and show in reflector mass spectra reduced resolution. A short time Fourier transform using a Hanning window function now uses this difference in resolution to detect the metastable fragments. Its application over the whole mass range yields frequency distributions and amplitudes as a function of mass, where an increased low frequency proportion is highly indicative for metastable fragments. Applications on the detection of metastable losses originating from carboxamidomethylated cysteines, oxidized methionines, phosphorylated and glycosylated amino acid residues are presented. The metastable loss of mercaptoacetamide detected with this procedure represents a new feature and its integration in search algorithms will improve the specificity of MALDI peptide mass fingerprinting.