17 research outputs found
Unique reporter-based sensor platforms to monitor signalling in cells
Introduction: In recent years much progress has been made in the development of tools for systems biology to study the levels of mRNA and protein, and their interactions within cells. However, few multiplexed methodologies are available to study cell signalling directly at the transcription factor level.
<p/>Methods: Here we describe a sensitive, plasmid-based RNA reporter methodology to study transcription factor activation in mammalian cells, and apply this technology to profiling 60 transcription factors in parallel. The methodology uses two robust and easily accessible detection platforms; quantitative real-time PCR for quantitative analysis and DNA microarrays for parallel, higher throughput analysis.
<p/>Findings: We test the specificity of the detection platforms with ten inducers and independently validate the transcription factor activation.
<p/>Conclusions: We report a methodology for the multiplexed study of transcription factor activation in mammalian cells that is direct and not theoretically limited by the number of available reporters
2DâEM clustering approach for high-dimensional data through folding feature vectors
Abstract Background Clustering methods are becoming widely utilized in biomedical research where the volume and complexity of data is rapidly increasing. Unsupervised clustering of patient information can reveal distinct phenotype groups with different underlying mechanism, risk prognosis and treatment response. However, biological datasets are usually characterized by a combination of low sample number and very high dimensionality, something that is not adequately addressed by current algorithms. While the performance of the methods is satisfactory for low dimensional data, increasing number of features results in either deterioration of accuracy or inability to cluster. To tackle these challenges, new methodologies designed specifically for such data are needed. Results We present 2DâEM, a clustering algorithm approach designed for small sample size and high-dimensional datasets. To employ information corresponding to data distribution and facilitate visualization, the sample is folded into its two-dimension (2D) matrix form (or feature matrix). The maximum likelihood estimate is then estimated using a modified expectation-maximization (EM) algorithm. The 2DâEM methodology was benchmarked against several existing clustering methods using 6 medically-relevant transcriptome datasets. The percentage improvement of Rand score and adjusted Rand index compared to the best performing alternative method is up to 21.9% and 155.6%, respectively. To present the general utility of the 2DâEM method we also employed 2 methylome datasets, again showing superior performance relative to established methods. Conclusions The 2DâEM algorithm was able to reproduce the groups in transcriptome and methylome data with high accuracy. This build confidence in the methods ability to uncover novel disease subtypes in new datasets. The design of 2DâEM algorithm enables it to handle a diverse set of challenging biomedical dataset and cluster with higher accuracy than established methods. MATLAB implementation of the tool can be freely accessed online ( http://www.riken.jp/en/research/labs/ims/med_sci_math or http://www.alok-ai-lab.com /)
The siRNA Non-seed Region and Its Target Sequences Are Auxiliary Determinants of Off-Target Effects
<div><p>RNA interference (RNAi) is a powerful tool for post-transcriptional gene silencing. However, the siRNA guide strand may bind unintended off-target transcripts via partial sequence complementarity by a mechanism closely mirroring micro RNA (miRNA) silencing. To better understand these off-target effects, we investigated the correlation between sequence features within various subsections of siRNA guide strands, and its corresponding target sequences, with off-target activities. Our results confirm previous reports that strength of base-pairing in the siRNA seed region is the primary factor determining the efficiency of off-target silencing. However, the degree of downregulation of off-target transcripts with shared seed sequence is not necessarily similar, suggesting that there are additional auxiliary factors that influence the silencing potential. Here, we demonstrate that both the melting temperature (Tm) in a subsection of siRNA non-seed region, and the GC contents of its corresponding target sequences, are negatively correlated with the efficiency of off-target effect. Analysis of experimentally validated miRNA targets demonstrated a similar trend, indicating a putative conserved mechanistic feature of seed region-dependent targeting mechanism. These observations may prove useful as parameters for off-target prediction algorithms and improve siRNA âspecificityâ design rules.</p></div
Analysis of off-target effects based on sequence similarity between an siRNA non-seed region and its corresponding target sequences.
<p><b>(A, B)</b> Cumulative distribution of off-target transcripts grouped by their non-seed base-pairing (Fig 4A; siVIM-270 off-target effects, 2AU (70), 1AU (52), None (27), 1GC (133), 2GC (151), Fig 4B; siVIM-805 off-target effects, 2AU (55), 1AU (52), None (20), 1GC (52), 2GC (46)). Off-target transcripts with more than 2AU or 2GC match were omitted due to their low number. <b>(C, D)</b> The average GC contents for non-seed region (positions 8â15) were calculated for each group of off-target transcripts. <b>(E, F)</b> The cumulative distribution of off-target transcripts of siVIM-270 with 1GC match (133 transcripts)(E) and 2GC matches (151 transcripts)(F), were sub-divided based on their GC contents at positions 8â15. âLowâ subgroups have GC content lower than the average while âHighâ subgroups have GC content higher than the average.</p
Seed and non-seed region-dependent off-target effect analyses for siVIM-270 and siVIM-805.
<p><b>(A, B)</b> Expression profiles of off-target genes with 3âUTR sequences that perfectly match the corresponding siRNA seed region (i.e. off-targets) were compared to genes without such sequence. <b>(C, D)</b> The correlation between GC content in all the subsections within mRNA targets of siRNA non-seed region (8â21) and fold change of off-target effect was calculated in a similar manner to that shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004656#pcbi.1004656.g002" target="_blank">Fig 2</a>. The correlation coefficient was grouped using quantiles as boundary values and target position corresponds to the numbering shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004656#pcbi.1004656.g001" target="_blank">Fig 1</a>. GC contents in the non-seed regions at positions 8â15 showed the highest correlation with off-target effect (Fig 3C; siVIM-270, r = 0.22, p-value = 1.13E-12, Fig 3D; siVIM-805, r = 0.15, p-value = 1.57E-05). <b>(E, F)</b> Off-target transcripts for siVIM-270 and siVIM-805 were divided into four groups defined by the number of GC nucleotides in their non-seed regions (positions 8â15). Quantiles were used as the boundary values for classification; âLowâ (GC content < 3), âMediumâ (GC content = 3), âHighâ (GC content = 4) and âVery Highâ (GC content â„ 5)(out of a total of 8). The number of off-target genes was 1065 for siVIM-270 (E) and 823 for siVIM-805 (F).</p
Correlation between <i>T</i><sub>m</sub> values of sequence subsections within siRNA duplexes and the corresponding off-target silencing efficiency.
<p>The start of the subsection within a duplex is plotted on the Y axis (âStart Positionâ) whereas the end of the subsection is plotted on the X axis (âEnd Positionâ). The position numbering mirrors that used in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004656#pcbi.1004656.g001" target="_blank">Fig 1</a>. The analysis was performed separately for each siRNA concentrationâ<b>(A)</b> 0.05, <b>(B)</b> 0.5, <b>(C)</b> 5 and <b>(D)</b> 50 nM. The siRNA sequences used in the analysis, together with corresponding knockdown percentages, are listed in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004656#pcbi.1004656.s001" target="_blank">S1 Table</a>.</p
Contribution of miRNA non-seed region to gene silencing.
<p>The list of putative targets perfectly matching the miRNA seed region (positions 2â8) was intersected with a list of experimentally validated miRNA targets [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004656#pcbi.1004656.ref023" target="_blank">23</a>]. Genes present on both lists were placed in the âValidated Targetsâ group, while genes predicted to interact with miRNA but which have not been experimentally confirmed, were placed in âRemaining Genesâ group. The GC content in the positions 8â15 was calculated for both groups and compared. The difference was calculated by subtracting values of the âremainingâ group from the values in the âvalidatedâ group.</p
Analysis of induction in cadmium chloride-treated cells transfected with TFBS-UR plasmids.
<p>HEK293 cells transfected with a plasmid pool, that included the plasmids listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050521#pone.0050521.s003" target="_blank">Table S2</a> and pRL-SV40 and were subsequently treated with cadmium. (A) Microarray-based detection of TF derived activation of UR expression. (B) qPCR-based detection of TF-derived activation of UR expression. Values are presented as log2 treatments of the fold induction of the TFBS-directed UR expression after treatment with the inducer of interest. The grey bar represents treatment-independent changes in the system. TFBS marked with * represent treatment-dependent effects on the TF library. Numerical data is presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050521#pone.0050521.s004" target="_blank">Table S3</a>. A statistical analysis of the qPCR assay data is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050521#pone-0050521-g003" target="_blank">Figure 3</a>.</p