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1.  Correction: Ago1 Interacts with RNA Polymerase II and Binds to the Promoters of Actively Transcribed Genes in Human Cancer Cells 
PLoS Genetics  2014;10(1):10.1371/annotation/e7c49fcc-0bc2-4aa9-bbcf-f4b386eb2cd0.
doi:10.1371/annotation/e7c49fcc-0bc2-4aa9-bbcf-f4b386eb2cd0
PMCID: PMC3888203
2.  Ago1 Interacts with RNA Polymerase II and Binds to the Promoters of Actively Transcribed Genes in Human Cancer Cells 
PLoS Genetics  2013;9(9):e1003821.
Argonaute proteins are often credited for their cytoplasmic activities in which they function as central mediators of the RNAi platform and microRNA (miRNA)-mediated processes. They also facilitate heterochromatin formation and establishment of repressive epigenetic marks in the nucleus of fission yeast and plants. However, the nuclear functions of Ago proteins in mammalian cells remain elusive. In the present study, we combine ChIP-seq (chromatin immunoprecipitation coupled with massively parallel sequencing) with biochemical assays to show that nuclear Ago1 directly interacts with RNA Polymerase II and is widely associated with chromosomal loci throughout the genome with preferential enrichment in promoters of transcriptionally active genes. Additional analyses show that nuclear Ago1 regulates the expression of Ago1-bound genes that are implicated in oncogenic pathways including cell cycle progression, growth, and survival. Our findings reveal the first landscape of human Ago1-chromosomal interactions, which may play a role in the oncogenic transcriptional program of cancer cells.
Author Summary
Argonaute (Ago) proteins are an evolutionarily conserved family of proteins indispensable for a gene regulation mechanism known as RNA interference (RNAi) which is mediated by small RNA including microRNA (miRNA) and small interfering RNA (siRNA) and occurs mainly in the cytoplasm. In mammalian cells, however, the function of Agos in the nucleus is largely unknown despite a few examples in which Agos are shown to be involved in regulating gene transcription and alternative splicing. In this study, by taking a genome-wide approach, we found that human Ago1, but not Ago2, is pervasively associated with gene regulatory sequences known as promoter and interacts with the core component of the gene transcription machinery to exert positive impact on gene expression in cancer cells. Strikingly, the genes bound and regulated by Ago1 are mostly genes that stimulate cell growth and survival, and are known to be involved in the development of cancer. The findings from our study unveil an unexpected role of nuclear Ago1 in regulating gene expression which may be important both in normal cellular processes and in disease such as cancer.
doi:10.1371/journal.pgen.1003821
PMCID: PMC3784563  PMID: 24086155
3.  The unfolded protein response in fission yeast modulates stability of select mRNAs to maintain protein homeostasis 
eLife  2012;1:e00048.
The unfolded protein response (UPR) monitors the protein folding capacity of the endoplasmic reticulum (ER). In all organisms analyzed to date, the UPR drives transcriptional programs that allow cells to cope with ER stress. The non-conventional splicing of Hac1 (yeasts) and XBP1 (metazoans) mRNA, encoding orthologous UPR transcription activators, is conserved and dependent on Ire1, an ER membrane-resident kinase/endoribonuclease. We found that the fission yeast Schizosaccharomyces pombe lacks both a Hac1/XBP1 ortholog and a UPR-dependent-transcriptional-program. Instead, Ire1 initiates the selective decay of a subset of ER-localized-mRNAs that is required to survive ER stress. We identified Bip1 mRNA, encoding a major ER-chaperone, as the sole mRNA cleaved upon Ire1 activation that escapes decay. Instead, truncation of its 3′ UTR, including loss of its polyA tail, stabilized Bip1 mRNA, resulting in increased Bip1 translation. Thus, S. pombe uses a universally conserved stress-sensing machinery in novel ways to maintain homeostasis in the ER.
DOI: http://dx.doi.org/10.7554/eLife.00048.001
eLife digest
Protein folding—the process by which a sequence of amino acids adopts the precise shape that is needed to perform a specific biological function—is one of the most important processes in all of biology. Any sequence of amino acids has the potential to fold into a large number of different shapes, and misfolded proteins can lead to toxicity and other problems. For example, all cells rely on signaling proteins in the membranes that enclose them to monitor their environment so that they can adapt to changing conditions and, in multicellular organisms, communicate with neighboring cells: without properly folded signaling proteins, chaos would ensue. Moreover, many diseases—including diabetes, cancer, viral infection and neurodegenerative disease—have been linked to protein folding processes. It is not surprising, therefore, that cells have evolved elaborate mechanisms to exert exquisite quality control over protein folding.
One of these mechanisms, called the unfolded protein response (UPR), operates in a compartment within the cell known as the endoplasmic reticulum (ER). The ER is a labyrinthine network of tubes and sacs within all eukaryotic cells, and most proteins destined for the cell surface or outside the cell adopt their properly folded shapes within this compartment. If the ER does not have enough capacity to fold all of the proteins that are delivered there, the UPR switches on to increase the protein folding capacity, to expand the surface area and volume of the compartment, and to degrade misfolded proteins. If the UPR cannot adequately adjust the folding capacity of the ER to meet the demands of the cell, the UPR triggers a program that kills the cell to prevent putting the whole organism at risk.
Researchers have identified the cellular components that monitor the protein folding conditions inside the ER. All eukaryotic cells, from unicellular yeasts to mammalian cells, contain a highly conserved protein-folding sensor called Ire1. In all species analyzed to date, Ire1 is known to activate the UPR through an messenger RNA (mRNA) splicing mechanism. This splicing event provides the switch that drives a gene expression program in which the production of ER components is increased to boost the protein folding capacity of the compartment.
Kimmig, Diaz et al. now report the first instance of an organism in which the UPR does not involve mRNA splicing or the initiation of a gene expression program. Rather, the yeast Schizosaccharomyces pombe utilizes Ire1 to an entirely different end. The authors find that the activation of Ire1 in S. pombe leads to the selective decay of a specific class of mRNAs that all encode proteins entering the ER. Thus, rather than increasing the protein folding capacity of the ER when faced with an increased protein folding load, S. pombe cells correct the imbalance by decreasing the load.
The authors also show that a lone mRNA—the mRNA that encodes the molecular chaperone BiP, which is one of the major protein-folding components in the ER—uniquely escapes this decay. Rather than being degraded, Ire1 truncates BiP mRNA and renders it more stable. By studying the UPR in a divergent organism, the authors shed new light on the evolution of a universally important process and illustrate how conserved machinery has been repurposed.
DOI: http://dx.doi.org/10.7554/eLife.00048.002
doi:10.7554/eLife.00048
PMCID: PMC3470409  PMID: 23066505
Unfolded Protein Response; Ire1; selective mRNA decay; Bip1 mRNA stabilization; ER homeostasis; S. pombe
4.  De Novo Identification and Biophysical Characterization of Transcription Factor Binding Sites with Microfluidic Affinity Analysis 
Nature biotechnology  2010;28(9):970-975.
Gene expression is regulated in part by protein transcription factors (TFs) that bind target regulatory DNA sequences. Predicting DNA binding sites and affinities from transcription factor sequence or structure is difficult; therefore, experimental data are required to link TFs to target sequences. We present a microfluidics-based approach for de novo discovery and quantitative biophysical characterization of DNA target sequences. We validated our technique by measuring sequence preferences for 28 S. cerevisiae TFs with a variety of DNA binding domains, including several that have proven difficult to study via other techniques. For each TF, we measured relative binding affinities to oligonucleotides covering all possible 8-bp DNA sequences to create a comprehensive map of sequence preferences; for 4 TFs, we also determined absolute affinities. We anticipate that these data and future use of this technique will provide information essential for understanding TF specificity, improving identification of regulatory sites, and reconstructing regulatory interactions.
doi:10.1038/nbt.1675
PMCID: PMC2937095  PMID: 20802496
5.  Epistatic relationships reveal the functional organization of yeast transcription factors 
A comprehensive quantitative genetic interaction map, or E-MAP, has provided a global view of the functional interdependencies between the components of the transcriptional apparatus in budding yeast.Transcription factors that display aggravating/negative genetic interactions regulate gene expression in an independent rather than coordinated manner.Parallel/compensating relationships between regulators often characterize transcriptional circuits.
Genetic interactions identify the functional interdependencies between genes (Guarente, 1993). They can be either positive (i.e. alleviating) or negative (i.e. aggravating) in nature corresponding to cases where the double mutant grows better or worse, respectively, then expected from growth of the corresponding single mutants (Beyer et al, 2007). Negative genetic interactions between non-essential genes often identify factors involved in parallel pathways, whereas positive ones often correspond to cases where the corresponding proteins are working in the same pathway and/or are physically associated (Beltrao et al, 2010). The epistatic miniarray profile (E-MAP) approach (Schuldiner et al, 2005), which quantitatively and comprehensively identifies both positive and negative genetic interactions on a logically selected set of genes, was used in this study in S. cerevisiae to genetically interrogate the set of 151 sequence-specific transcription factors (STFs) as well as 172 components of the general transcriptional machinery (GTFs).
We found a higher propensity of the group of STFs to strongly genetically interact with GTFs than with themselves (Figure 1A and B). However, within the set of STF–STF genetic interactions, there was a significant enrichment of negative over positive genetic interactions (Figure 1A and C), suggesting that parallel/compensating relationships, rather than linear pathways, predominate within the set of STFs. These genetic trends are in stark contrast to what was previously observed with factors involved in regulating signaling (e.g. kinases and phosphatases), which were significantly enriched in positive over negative genetic interactions (Fiedler et al, 2009).
In addition to providing an overview of the global relationships among TFs, the fine structure of the E-MAP can be used to address the nature of the regulatory architecture controlling individual genes. A variety of regulatory patterns have been described that serve the differing functional requirements of various biological processes (Istrail and Davidson, 2005). Our E-MAP identified several examples of the regulatory relationships between transcription factors, including (1) one TF acting as a repressor of another TF (e.g. Gal80 acting as a repressor of Gal4, the activator of the GAL genes); (2) two TFs acting redundantly to regulate a set of genes (e.g. Gln3 and Gat1, two GATA family activators involved in regulating nitrogen catabolite repression (NCR)); and (3) two TFs regulating genes in a coordinated manner (e.g. Hac1 working with the HDAC complex Rpd3C(L) to regulate expression of early meiotic genes).
Given the complex structures of promoters (Zhu and Zhang, 1999; Chin et al, 2005) and the possible types of regulatory logic (Buchler et al, 2003), we wanted to identify the types of logic that are used in nature. We explored this by combining our genetic interaction data with the information about the network connections between STFs and their targets. By initially focusing on pairs of STFs that share a set of targets defined by the genome-wide binding studies (Harbison et al, 2004; MacIsaac et al, 2006), a total of 110 STF gene pairs were identified that have statistically significant target overlap with a P-value <0.005, whereas 49 pairs have significant overlap at a more stringent cutoff (P<10−7). Several examples were examined in more detail by quantitative growth assay in liquid culture and gene expression profiling of the TF-deletion mutants. In each case, the growth rate of one of the single-deletion mutants is significantly reduced (i.e. ‘the major regulator'), whereas the growth rate of the other single-deletion mutant is similar to that of the wild type (i.e. ‘the minor regulator'). In the absence of the major regulator, the deletion of the minor regulator leads to a more severe growth defect, resulting in a negative genetic interaction (Figure 5A). We examined the response of common target genes of two pairs of TFs (Swi4-Skn7 and Gcr2-Tye7) and found an enrichment of common target genes displaying ‘OR' but not ‘AND' behavior, in the simplified language of Boolean logic. Further examination of the targets revealed that many of them are induced/repressed more by the double deletion than each of the single deletions (Figure 5D). Collectively, these results suggest that frequently TF pairs with negative interactions regulate the transcription of their common target genes in a redundant manner.
The regulation of gene expression is, in large part, mediated by interplay between the general transcription factors (GTFs) that function to bring about the expression of many genes and site-specific DNA-binding transcription factors (STFs). Here, quantitative genetic profiling using the epistatic miniarray profile (E-MAP) approach allowed us to measure 48 391 pairwise genetic interactions, both negative (aggravating) and positive (alleviating), between and among genes encoding STFs and GTFs in Saccharomyces cerevisiae. This allowed us to both reconstruct regulatory models for specific subsets of transcription factors and identify global epistatic patterns. Overall, there was a much stronger preference for negative relative to positive genetic interactions among STFs than there was among GTFs. Negative genetic interactions, which often identify factors working in non-essential, redundant pathways, were also enriched for pairs of STFs that co-regulate similar sets of genes. Microarray analysis demonstrated that pairs of STFs that display negative genetic interactions regulate gene expression in an independent rather than coordinated manner. Collectively, these data suggest that parallel/compensating relationships between regulators, rather than linear pathways, often characterize transcriptional circuits.
doi:10.1038/msb.2010.77
PMCID: PMC2990640  PMID: 20959818
genetic interaction; regulatory network; transcription factor; transcription regulation
6.  fREDUCE: Detection of degenerate regulatory elements using correlation with expression 
BMC Bioinformatics  2007;8:399.
Background
The precision of transcriptional regulation is made possible by the specificity of physical interactions between transcription factors and their cognate binding sites on DNA. A major challenge is to decipher transcription factor binding sites from sequence and functional genomic data using computational means. While current methods can detect strong binding sites, they are less sensitive to degenerate motifs.
Results
We present fREDUCE, a computational method specialized for the detection of weak or degenerate binding motifs from gene expression or ChIP-chip data. fREDUCE is built upon the widely applied program REDUCE, which elicits motifs by global statistical correlation of motif counts with expression data. fREDUCE introduces several algorithmic refinements that allow efficient exhaustive searches of oligonucleotides with a specified number of degenerate IUPAC symbols. On yeast ChIP-chip benchmarks, fREDUCE correctly identified motifs and their degeneracies with accuracies greater than its predecessor REDUCE as well as other known motif-finding programs. We have also used fREDUCE to make novel motif predictions for transcription factors with poorly characterized binding sites.
Conclusion
We demonstrate that fREDUCE is a valuable tool for the prediction of degenerate transcription factor binding sites, especially from array datasets with weak signals that may elude other motif detection methods.
doi:10.1186/1471-2105-8-399
PMCID: PMC2174516  PMID: 17941998
7.  An approach to identify over-represented cis-elements in related sequences 
Nucleic Acids Research  2003;31(7):1995-2005.
Computational identification of transcription factor binding sites is an important research area of computational biology. Positional weight matrix (PWM) is a model to describe the sequence pattern of binding sites. Usually, transcription factor binding sites prediction methods based on PWMs require user-defined thresholds. The arbitrary threshold and also the relatively low specificity of the algorithm prevent the result of such an analysis from being properly interpreted. In this study, a method was developed to identify over-represented cis-elements with PWM-based similarity scores. Three sets of closely related promoters were analyzed, and only over- represented motifs with high PWM similarity scores were reported. The thresholds to evaluate the similarity scores to the PWMs of putative transcription factors binding sites can also be automatically determined during the analysis, which can also be used in further research with the same PWMs. The online program is available on the website: http://www.bioinfo.tsinghua.edu.cn/∼zhengjsh/OTFBS/.
PMCID: PMC152803  PMID: 12655017

Results 1-7 (7)