In silico identification of a core regulatory network of OCT4 in human embryonic stem cells using an integrated approach

<p>Abstract</p> <p>Background</p> <p>The transcription factor OCT4 is highly expressed in pluripotent embryonic stem cells which are derived from the inner cell mass of mammalian blastocysts. Pluripotency and self renewal are controlled by a transcription regulatory network governed by the transcription factors OCT4, SOX2 and NANOG. Recent studies on reprogramming somatic cells to induced pluripotent stem cells highlight OCT4 as a key regulator of pluripotency.</p> <p>Results</p> <p>We have carried out an integrated analysis of high-throughput data (ChIP-on-chip and RNAi experiments along with promoter sequence analysis of putative target genes) and identified a core OCT4 regulatory network in human embryonic stem cells consisting of 33 target genes. Enrichment analysis with these target genes revealed that this integrative analysis increases the functional information content by factors of 1.3 – 4.7 compared to the individual studies. In order to identify potential regulatory co-factors of OCT4, we performed a <it>de novo </it>motif analysis. In addition to known validated OCT4 motifs we obtained binding sites similar to motifs recognized by further regulators of pluripotency and development; e.g. the heterodimer of the transcription factors C-MYC and MAX, a prerequisite for C-MYC transcriptional activity that leads to cell growth and proliferation.</p> <p>Conclusion</p> <p>Our analysis shows how heterogeneous functional information can be integrated in order to reconstruct gene regulatory networks. As a test case we identified a core OCT4-regulated network that is important for the analysis of stem cell characteristics and cellular differentiation. Functional information is largely enriched using different experimental results. The <it>de novo </it>motif discovery identified well-known regulators closely connected to the OCT4 network as well as potential new regulators of pluripotency and differentiation. These results provide the basis for further targeted functional studies.</p

High Throughput Determination of TGFβ1/SMAD3 Targets in A549 Lung Epithelial Cells

Transforming growth factor beta 1 (TGFβ1) plays a major role in many lung diseases including lung cancer, pulmonary hypertension, and pulmonary fibrosis. TGFβ1 activates a signal transduction cascade that results in the transcriptional regulation of genes in the nucleus, primarily through the DNA-binding transcription factor SMAD3. The objective of this study is to identify genome-wide scale map of SMAD3 binding targets and the molecular pathways and networks affected by the TGFβ1/SMAD3 signaling in lung epithelial cells. We combined chromatin immunoprecipitation with human promoter region microarrays (ChIP-on-chip) along with gene expression microarrays to study global transcriptional regulation of the TGFβ1/SMAD3 pathway in human A549 alveolar epithelial cells. The molecular pathways and networks associated with TGFβ1/SMAD3 signaling were identified using computational approaches. Validation of selected target gene expression and direct binding of SMAD3 to promoters were performed by quantitative real time RT-PCR and electrophoretic mobility shift assay on A549 and human primary lung epithelial cells. Known TGFβ1 target genes such as SERPINE1, SMAD6, SMAD7, TGFB1 and LTBP3, were found in both ChIP-on-chip and gene expression analyses as well as some previously unrecognized targets such as FOXA2. SMAD3 binding of FOXA2 promoter and changed expression were confirmed. Computational approaches combining ChIP-on-chip and gene expression microarray revealed multiple target molecular pathways affected by the TGFβ1/SMAD3 signaling. Identification of global targets and molecular pathways and networks associated with TGFβ1/SMAD3 signaling allow for a better understanding of the mechanisms that determine epithelial cell phenotypes in fibrogenesis and carcinogenesis as does the discovery of the direct effect of TGFβ1 on FOXA2

Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density

MicroRNAs (miRNAs) are post-transcriptional regulators that bind to their target mRNAs through base complementarity. Predicting miRNA targets is a challenging task and various studies showed that existing algorithms suffer from high number of false predictions and low to moderate overlap in their predictions. Until recently, very few algorithms considered the dynamic nature of the interactions, including the effect of less specific interactions, the miRNA expression level, and the effect of combinatorial miRNA binding. Addressing these issues can result in a more accurate miRNA:mRNA modeling with many applications, including efficient miRNA-related SNP evaluation. We present a novel thermodynamic model based on the Fermi-Dirac equation that incorporates miRNA expression in the prediction of target occupancy and we show that it improves the performance of two popular single miRNA target finders. Modeling combinatorial miRNA targeting is a natural extension of this model. Two other algorithms show improved prediction efficiency when combinatorial binding models were considered. ComiR (Combinatorial miRNA targeting), a novel algorithm we developed, incorporates the improved predictions of the four target finders into a single probabilistic score using ensemble learning. Combining target scores of multiple miRNAs using ComiR improves predictions over the naïve method for target combination. ComiR scoring scheme can be used for identification of SNPs affecting miRNA binding. As proof of principle, ComiR identified rs17737058 as disruptive to the miR-488-5p:NCOA1 interaction, which we confirmed in vitro. We also found rs17737058 to be significantly associated with decreased bone mineral density (BMD) in two independent cohorts indicating that the miR-488-5p/NCOA1 regulatory axis is likely critical in maintaining BMD in women. With increasing availability of comprehensive high-throughput datasets from patients ComiR is expected to become an essential tool for miRNA-related studies. © 2012 Coronnello et al

Enhancing regeneration after acute kidney injury by promoting cellular dedifferentiation in zebrafish

Acute kidney injury (AKI) is a serious disorder for which there are limited treatment options. Following injury, native nephrons display limited regenerative capabilities, relying on the dedifferentiation and proliferation of renal tubular epithelial cells (RTECs) that survive the insult. Previously, we identified 4-(phenylthio)butanoic acid (PTBA), a histone deacetylase inhibitor (HDI), as an enhancer of renal recovery, and showed that PTBA treatment increased RTEC proliferation and reduced renal fibrosis. Here, we investigated the regenerative mechanisms of PTBA in zebrafish models of larval renal injury and adult cardiac injury. With respect to renal injury, we showed that delivery of PTBA using an esterified prodrug (UPHD25) increases the reactivation of the renal progenitor gene Pax2a, enhances dedifferentiation of RTECs, reduces Kidney injury molecule-1 (Kim-1) expression, and lowers the number of infiltrating macrophages. Further, we found that the effects of PTBA on RTEC proliferation depend upon retinoic acid signaling and demonstrate that the therapeutic properties of PTBA are not restricted to the kidney but also increase cardiomyocyte proliferation and decrease fibrosis following cardiac injury in adult zebrafish. These studies provide key mechanistic insights into how PTBA enhances tissue repair in models of acute injury and lay the groundwork for translating this novel HDI into the clinic. This article has an associated First Person interview with the joint first authors of the paper

The effect of Fermi-Dirac model in miRNA target prediction.

<p>(A) Overlap of predicted targets from PITA and miRanda using a naïve combination of energy scores. (B) Target overlap between PITA and miRanda using the Fermi-Dirac energy score combination. (C) Receiver-operating Characteristic (ROC) curves of PITA and miRanda predictions with naïve (solid lines) and Fermi-Dirac (broken lines) energy score combination. AUC: area under the curve. Positive and negative sets were derived from the Ago1 IP data (Materials and Methods).</p

Predicting efficiency of Drosophila-trained ComiR on various datasets.

<p>(A) Self-test on the Drosophila Ago1-IP dataset consist of Set I (positive examples) and equal number of negative examples (from Set IV). (B) Performance on an external Drosophila Ago1-IP dataset consisting of Set III (positive examples) and the remaining of Set IV (negative examples). This Drosophila dataset was not used in training ComiR. (C) SN <i>vs.</i> threshold on an external <i>C. elegans</i> AIN-IP dataset (not an ROC curve due to inability to define a negative dataset). (D) Performance on an external human PAR-CLIP dataset. In all cases, TargetScan was used without the evolutionary conservation feature resulting in a binary outcome. For the human dataset the reader can find a continuous TargetScan ROC curve in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002830#pcbi.1002830.s003" target="_blank">Figure S3B</a>, plotted using the <i>context score</i>.</p

<i>Comparison of SVM models for multiple miRNA targets</i>.

<p>Multiple miRNA target scores are combined using the naïve model (red dots) or the ComiR model (FD score or WSUM score). The comparison has been performed on the same datasets as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002830#pcbi-1002830-g003" target="_blank">Figure 3</a> with the exception of the <i>C. elegans</i> dataset, which has no proper ROC curve. Results are arranged by the difference the ComiR combination models offers over the naïve combination model. <i>P</i>: PITA, <i>M</i>: miRanda, <i>T</i>: TargetScan, <i>S</i>: mirSVR. AUC: area under the curve.</p

ComiR schema.

<p>ComiR schema.</p