A rapid and sensitive assay for quantification of siRNA efficiency and specificity

RNA Interference has rapidly emerged as an efficient procedure for knocking down gene expression in model systems. However, cross-reactivity, whereby multiple genes may be simultaneously targeted by a single short interfering RNA (siRNA), can potentially jeopardize correct interpretation of gene function. As such, it is essential to test the specificity of a siRNA prior to a full phenotypic analysis. To this end, we have adapted a reporter-based assay harnessing the sensitivity of luciferase activity to provide a quantitative readout of relative RNAi efficacy and specificity. We have tested different siRNAs directed against Thymosin β4 (Tβ4); determined their effectiveness at silencing Tβ4 and have both excluded off-target silencing of the Tβ4 homologue Thymosin β10 (Tβ10) and demonstrated partial knockdown of Tβ10 despite significant (12/23; 52%) sequence mismatch. This assay system is applicable to any RNAi study where there is a risk of targeting homologous genes and to the monitoring of off-target effects at the genome level following microarray expression profiling

An algorithm for classifying tumors based on genomic aberrations and selecting representative tumor models

<p>Abstract</p> <p>Background</p> <p>Cancer is a heterogeneous disease caused by genomic aberrations and characterized by significant variability in clinical outcomes and response to therapies. Several subtypes of common cancers have been identified based on alterations of individual cancer genes, such as HER2, EGFR, and others. However, cancer is a complex disease driven by the interaction of multiple genes, so the copy number status of individual genes is not sufficient to define cancer subtypes and predict responses to treatments. A classification based on genome-wide copy number patterns would be better suited for this purpose.</p> <p>Method</p> <p>To develop a more comprehensive cancer taxonomy based on genome-wide patterns of copy number abnormalities, we designed an unsupervised classification algorithm that identifies genomic subgroups of tumors. This algorithm is based on a modified genomic Non-negative Matrix Factorization (gNMF) algorithm and includes several additional components, namely a pilot hierarchical clustering procedure to determine the number of clusters, a multiple random initiation scheme, a new stop criterion for the core gNMF, as well as a 10-fold cross-validation stability test for quality assessment.</p> <p>Result</p> <p>We applied our algorithm to identify genomic subgroups of three major cancer types: non-small cell lung carcinoma (NSCLC), colorectal cancer (CRC), and malignant melanoma. High-density SNP array datasets for patient tumors and established cell lines were used to define genomic subclasses of the diseases and identify cell lines representative of each genomic subtype. The algorithm was compared with several traditional clustering methods and showed improved performance. To validate our genomic taxonomy of NSCLC, we correlated the genomic classification with disease outcomes. Overall survival time and time to recurrence were shown to differ significantly between the genomic subtypes.</p> <p>Conclusions</p> <p>We developed an algorithm for cancer classification based on genome-wide patterns of copy number aberrations and demonstrated its superiority to existing clustering methods. The algorithm was applied to define genomic subgroups of three cancer types and identify cell lines representative of these subgroups. Our data enabled the assembly of representative cell line panels for testing drug candidates.</p

An accurate and interpretable model for siRNA efficacy prediction

BACKGROUND: The use of exogenous small interfering RNAs (siRNAs) for gene silencing has quickly become a widespread molecular tool providing a powerful means for gene functional study and new drug target identification. Although considerable progress has been made recently in understanding how the RNAi pathway mediates gene silencing, the design of potent siRNAs remains challenging. RESULTS: We propose a simple linear model combining basic features of siRNA sequences for siRNA efficacy prediction. Trained and tested on a large dataset of siRNA sequences made recently available, it performs as well as more complex state-of-the-art models in terms of potency prediction accuracy, with the advantage of being directly interpretable. The analysis of this linear model allows us to detect and quantify the effect of nucleotide preferences at particular positions, including previously known and new observations. We also detect and quantify a strong propensity of potent siRNAs to contain short asymmetric motifs in their sequence, and show that, surprisingly, these motifs alone contain at least as much relevant information for potency prediction as the nucleotide preferences for particular positions. CONCLUSION: The model proposed for prediction of siRNA potency is as accurate as a state-of-the-art nonlinear model and is easily interpretable in terms of biological features. It is freely available on the web a

Myosin II Motor Proteins with Different Functions Determine the Fate of Lamellipodia Extension during Cell Spreading

Non-muscle cells express multiple myosin-II motor proteins myosin IIA, myosin IIB and myosin IIC transcribed from different loci in the human genome. Due to a significant homology in their sequences, these ubiquitously expressed myosin II motor proteins are believed to have overlapping cellular functions, but the mechanistic details are not elucidated. The present study uncovered a mechanism that coordinates the distinctly localized myosin IIA and myosin IIB with unexpected opposite mechanical roles in maneuvering lamellipodia extension, a critical step in the initiation of cell invasion, spreading, and migration. Myosin IIB motor protein by localizing at the front drives lamellipodia extension during cell spreading. On the other hand, myosin IIA localizes next to myosin IIB and attenuates or retracts lamellipodia extension. Myosin IIA and IIB increase cell adhesion by regulating focal contacts formation in the spreading margins and central part of the spreading cell, respectively. Spreading cells expressing both myosin IIA and myosin IIB motor proteins display an organized actin network consisting of retrograde filaments, arcs and central filaments attached to focal contacts. This organized actin network especially arcs and focal contacts formation in the spreading margins were lost in myosin IIÂ cells. Surprisingly, myosin IIB̂ cells displayed long parallel actin filaments connected to focal contacts in the spreading margins. Thus, with different roles in the regulation of the actin network and focal contacts formation, both myosin IIA and IIB determine the fate of lamellipodia extension during cell spreading

Dissecting Oct3/4-Regulated Gene Networks in Embryonic Stem Cells by Expression Profiling

POU transcription factor Pou5f1 (Oct3/4) is required to maintain ES cells in an undifferentiated state. Here we show that global expression profiling of Oct3/4-manipulated ES cells delineates the downstream target genes of Oct3/4. Combined with data from genome-wide chromatin-immunoprecipitation (ChIP) assays, this analysis identifies not only primary downstream targets of Oct3/4, but also secondary or tertiary targets. Furthermore, the analysis also reveals that downstream target genes are regulated either positively or negatively by Oct3/4. Identification of a group of genes that show both activation and repression depending on Oct3/4 expression levels provides a possible mechanism for the requirement of appropriate Oct3/4 expression to maintain undifferentiated ES cells. As a proof-of-principle study, one of the downstream genes, Tcl1, has been analyzed in detail. We show that Oct3/4 binds to the promoter region of Tcl1 and activates its transcription. We also show that Tcl1 is involved in the regulation of proliferation, but not differentiation, in ES cells. These findings suggest that the global expression profiling of gene-manipulated ES cells can help to delineate the structure and dynamics of gene regulatory networks

RNA Interference Knockdown of hU2AF(35) Impairs Cell Cycle Progression and Modulates Alternative Splicing of Cdc25 Transcripts

U2AF is a heterodimeric splicing factor composed of a large (U2AF(65)) and a small (U2AF(35)) subunit. In humans, alternative splicing generates two U2AF(35) variants, U2AF(35)a and U2AF(35)b. Here, we used RNA interference to specifically ablate the expression of each isoform in HeLa cells. Our results show that knockdown of the major U2AF(35)a isoform reduced cell viability and impaired mitotic progression, leading to accumulation of cells in prometaphase. Microarray analysis revealed that knockdown of U2AF(35)a affected the expression level of ∼500 mRNAs, from which >90% were underrepresented relative to the control. Among mRNAs underrepresented in U2AF(35)a-depleted cells we identified an essential cell cycle gene, Cdc27, for which there was an increase in the ratio between unspliced and spliced RNA and a significant reduction in protein level. Furthermore, we show that depletion of either U2AF(35)a or U2AF(35)b altered the ratios of alternatively spliced isoforms of Cdc25B and Cdc25C transcripts. Taken together our results demonstrate that U2AF(35)a is essential for HeLa cell division and suggest a novel role for both U2AF(35) protein isoforms as regulators of alternative splicing of a specific subset of genes