Telomerase reverse transcriptase regulates microRNAs.

MicroRNAs are small non-coding RNAs that inhibit the translation of target mRNAs. In humans, most microRNAs are transcribed by RNA polymerase II as long primary transcripts and processed by sequential cleavage of the two RNase III enzymes, DROSHA and DICER, into precursor and mature microRNAs, respectively. Although the fundamental functions of microRNAs in RNA silencing have been gradually uncovered, less is known about the regulatory mechanisms of microRNA expression. Here, we report that telomerase reverse transcriptase (TERT) extensively affects the expression levels of mature microRNAs. Deep sequencing-based screens of short RNA populations revealed that the suppression of TERT resulted in the downregulation of microRNAs expressed in THP-1 cells and HeLa cells. Primary and precursor microRNA levels were also reduced under the suppression of TERT. Similar results were obtained with the suppression of either BRG1 (also called SMARCA4) or nucleostemin, which are proteins interacting with TERT and functioning beyond telomeres. These results suggest that TERT regulates microRNAs at the very early phases in their biogenesis, presumably through non-telomerase mechanism(s).

CpG site-specific alteration of hydroxymethylcytosine to methylcytosine beyond DNA replication.

Hydroxymethylcytosines (hmC), one of several reported cytosine modifications, was recently found to be enriched in embryonic stem cells and neuronal cells, and thought to play an important role in regulating gene expression and cell specification. However, unlike methylcytosines (mC), the fate of hmC beyond DNA replication is not well understood. Here, to monitor the status of hmC during DNA replication, we prepared a stable episomal vector-based monitoring system called MoCEV in 293T cells. The MoCEV system containing fully hydroxymethylated-cytosine fragments revealed a significant modification towards mC after several rounds of DNA replication. Strikingly this modification was specifically observed at the CpG sites (71.9% of cytosines), whereas only 1.1% of modified cytosines were detected at the non-CpG sites. Since the unmodified MoCEV did not undergo any DNA methylation during cell division, the results strongly suggest that somatic cells undergo hmC to mC specifically at the CpG sites during cell division.

Empirically controlled mapping of the Caenorhabditis elegans protein-protein interactome network.

To provide accurate biological hypotheses and elucidate global properties of cellular networks, systematic identification of protein-protein interactions must meet high quality standards.We present an expanded C. elegans protein-protein interaction network, or 'interactome' map, derived from testing a matrix of approximately 10,000 x approximately 10,000 proteins using a highly specific, high-throughput yeast two-hybrid system. Through a new empirical quality control framework, we show that the resulting data set (Worm Interactome 2007, or WI-2007) was similar in quality to low-throughput data curated from the literature. We filtered previous interaction data sets and integrated them with WI-2007 to generate a high-confidence consolidated map (Worm Interactome version 8, or WI8). This work allowed us to estimate the size of the worm interactome at approximately 116,000 interactions. Comparison with other types of functional genomic data shows the complementarity of distinct experimental approaches in predicting different functional relationships between genes or proteins

An empirical framework for binary interactome mapping.

Several attempts have been made to systematically map protein-protein interaction, or 'interactome', networks. However, it remains difficult to assess the quality and coverage of existing data sets. Here we describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human proteins are more precise than literature-curated interactions supported by a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human interactome. We estimate that the human interactome contains approximately 130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the Human Genome Project, estimates of protein interaction data quality and interactome size are crucial to establish the magnitude of the task of comprehensive human interactome mapping and to elucidate a path toward this goal.

Towards a proteome-scale map of the human protein-protein interaction network.

Systematic mapping of protein-protein interactions, or 'interactome' mapping, was initiated in model organisms, starting with defined biological processes and then expanding to the scale of the proteome. Although far from complete, such maps have revealed global topological and dynamic features of interactome networks that relate to known biological properties, suggesting that a human interactome map will provide insight into development and disease mechanisms at a systems level. Here we describe an initial version of a proteome-scale map of human binary protein-protein interactions. Using a stringent, high-throughput yeast two-hybrid system, we tested pairwise interactions among the products of approximately 8,100 currently available Gateway-cloned open reading frames and detected approximately 2,800 interactions. This data set, called CCSB-HI1, has a verification rate of approximately 78% as revealed by an independent co-affinity purification assay, and correlates significantly with other biological attributes. The CCSB-HI1 data set increases by approximately 70% the set of available binary interactions within the tested space and reveals more than 300 new connections to over 100 disease-associated proteins. This work represents an important step towards a systematic and comprehensive human interactome project.

FANTOM5 CAGE profiles of human and mouse samples.

In the FANTOM5 project, transcription initiation events across the human and mouse genomes were mapped at a single base-pair resolution and their frequencies were monitored by CAGE (Cap Analysis of Gene Expression) coupled with single-molecule sequencing. Approximately three thousands of samples, consisting of a variety of primary cells, tissues, cell lines, and time series samples during cell activation and development, were subjected to a uniform pipeline of CAGE data production. The analysis pipeline started by measuring RNA extracts to assess their quality, and continued to CAGE library production by using a robotic or a manual workflow, single molecule sequencing, and computational processing to generate frequencies of transcription initiation. Resulting data represents the consequence of transcriptional regulation in each analyzed state of mammalian cells. Non-overlapping peaks over the CAGE profiles, approximately 200,000 and 150,000 peaks for the human and mouse genomes, were identified and annotated to provide precise location of known promoters as well as novel ones, and to quantify their activities.

Study of copper electroless displacement by Ag+; prospecting for multilayers elaboration.

The copper electroless displacement by silver has been studied in the absence of cyanide ions. The investigations have been carried out using a quartz crystal microbalance, and it was shown that the electroless displacement was plainly diffusion-controlled during 1 min. The electrochemical deposition of Cu-Ag multilayers has been achieved using two methods: by displacement reaction and by alternated applied potentials.

The discovery of I-BET726 (GSK1324726A), a potent tetrahydroquinoline ApoA1 up-regulator and selective BET bromodomain inhibitor.

Through their function as epigenetic readers of the histone code, the BET family of bromodomain-containing proteins regulate expression of multiple genes of therapeutic relevance, including those involved in tumor cell growth and inflammation. BET bromodomain inhibitors have profound antiproliferative and anti-inflammatory effects which translate into efficacy in oncology and inflammation models, and the first compounds have now progressed into clinical trials. The exciting biology of the BETs has led to great interest in the discovery of novel inhibitor classes. Here we describe the identification of a novel tetrahydroquinoline series through up-regulation of apolipoprotein A1 and the optimization into potent compounds active in murine models of septic shock and neuroblastoma. At the molecular level, these effects are produced by inhibition of BET bromodomains. X-ray crystallography reveals the interactions explaining the structure-activity relationships of binding. The resulting lead molecule, I-BET726, represents a new, potent, and selective class of tetrahydroquinoline-based BET inhibitors.

Reconstruction of monocyte transcriptional regulatory network accompanies monocytic functions in human fibroblasts.

Transcriptional regulatory networks (TRN) control the underlying mechanisms behind cellular functions and they are defined by a set of core transcription factors regulating cascades of peripheral genes. Here we report SPI1, CEBPA, MNDA and IRF8 as core transcription factors of monocyte TRN and demonstrate functional inductions of phagocytosis, inflammatory response and chemotaxis activities in human dermal fibroblasts. The Gene Ontology and KEGG pathway analyses also revealed notable representation of genes involved in immune response and endocytosis in fibroblasts. Moreover, monocyte TRN-inducers triggered multiple monocyte-specific genes based on the transcription factor motif response analysis and suggest that complex cellular TRNs are uniquely amenable to elicit cell-specific functions in unrelated cell types.

The combination of gene perturbation assay and ChIP-chip reveals functional direct target genes for IRF8 in THP-1 cells.

Gene regulatory networks in living cells are controlled by the interaction of multiple cell type-specific transcription regulators with DNA binding sites in target genes. Interferon regulatory factor 8 (IRF8), also known as interferon consensus sequence binding protein (ICSBP), is a transcription factor expressed predominantly in myeloid and lymphoid cell lineages. To find the functional direct target genes of IRF8, the gene expression profiles of siRNA knockdown samples and genome-wide binding locations by ChIP-chip were analyzed in THP-1 myelomonocytic leukemia cells. Consequently, 84 genes were identified as functional direct targets. The ETS family transcription factor PU.1, also known as SPI1, binds to IRF8 and regulates basal transcription in macrophages. Using the same approach, we identified 53 direct target genes of PU.1; these overlapped with 19 IRF8 targets. These 19 genes included key molecules of IFN signaling such as OAS1 and IRF9, but excluded other IFN-related genes amongst the IRF8 functional direct target genes. We suggest that IRF8 and PU.1 can have both combined, and independent actions on different promoters in myeloid cells.

Regulatory interdependence of myeloid transcription factors revealed by Matrix RNAi analysis.

BACKGROUND: With the move towards systems biology, we need sensitive and reliable ways to determine the relationships between transcription factors and their target genes. In this paper we analyze the regulatory relationships between 78 myeloid transcription factors and their coding genes by using the matrix RNAi system in which a set of transcription factor genes are individually knocked down and the resultant expression perturbation is quantified. RESULTS: Using small interfering RNAs we knocked down the 78 transcription factor genes in monocytic THP-1 cells and monitored the perturbation of the expression of the same 78 transcription factors and 13 other transcription factor genes as well as 5 non-transcription factor genes by quantitative real-time RT-PCR, thereby building a 78 x 96 matrix of perturbation and measurement. This approach identified 876 cases where knockdown of one transcription factor significantly affected the expression of another (from a potential 7,488 combinations). Our study also revealed cell-type-specific transcriptional regulatory networks in two different cell types. CONCLUSIONS: By considering whether the targets of a given transcription factor are naturally up- or downregulated during phorbol 12-myristate 13-acetate-induced differentiation, we could classify these edges as pro-differentiative (229), anti-differentiative (76) or neither (571) using expression profiling data obtained in the FANTOM4 study. This classification analysis suggested that several factors could be involved in monocytic differentiation, while others such as MYB and the leukemogenic fusion MLL-MLLT3 could help to maintain the initial undifferentiated state by repressing the expression of pro-differentiative factors or maintaining expression of anti-differentiative factors.

High-quality binary protein interaction map of the yeast interactome network.

Current yeast interactome network maps contain several hundred molecular complexes with limited and somewhat controversial representation of direct binary interactions. We carried out a comparative quality assessment of current yeast interactome data sets, demonstrating that high-throughput yeast two-hybrid (Y2H) screening provides high-quality binary interaction information. Because a large fraction of the yeast binary interactome remains to be mapped, we developed an empirically controlled mapping framework to produce a "second-generation" high-quality, high-throughput Y2H data set covering approximately 20% of all yeast binary interactions. Both Y2H and affinity purification followed by mass spectrometry (AP/MS) data are of equally high quality but of a fundamentally different and complementary nature, resulting in networks with different topological and biological properties. Compared to co-complex interactome models, this binary map is enriched for transient signaling interactions and intercomplex connections with a highly significant clustering between essential proteins. Rather than correlating with essentiality, protein connectivity correlates with genetic pleiotropy.