Ae. aegypti genes. We have confirmed by RT-PCR that zygotic transcription starts as early as 2–3 hours. A degenerate motif VBRGGTA was found to be overrepresented in the upstream sequences of the identified EZGs using a motif identification software called SCOPE. We find evidence for homology between this motif and the TAGteam motif found in Drosophila that has been implicated in EZG activation. A 38 bp sequence in the proximal upstream sequence of a kinesin light chain EZG (KLC2.1) contains two copies of the mosquito motif. This sequence was shown to support EZG transcription by luciferase reporter assays performed on injected early embryos, and confers early zygotic activity to a heterologous promoter from a divergent mosquito species. The results of these studies are consistent with the model of early zygotic genome activation via transcriptional activators, similar to what has been found recently in Drosophila.During early embryogenesis the zygotic genome is transcriptionally silent and all mRNAs present are of maternal origin. The maternal-zygotic transition marks the time over which embryogenesis changes its dependence from maternal RNAs to zygotically transcribed RNAs. Here we present the first systematic investigation of early zygotic genes (EZGs) in a mosquito species and focus on genes involved in the onset of transcription during 2–4 hr. We used transcriptome sequencing to identify the “pure”  EZGs by analyzing transcripts from four embryonic time ranges of 0–2, 2–4, 4–8, and 8–12 hr, which includes the time of cellular blastoderm formation and up to the start of gastrulation. Blast of 16,789 annotated transcripts vs. the transcriptome reads revealed evidence for 63 (P<0.001) and 143 (P<0.05) nonmaternally derived transcripts having a significant increase in expression at 2–4 hr. One third of the 63 EZG transcripts do not have predicted introns compared to 10% of all  Ae. aegypti, Drosophila early development is much faster with initiation of the syncytial blastoderm and initiation of blastoderm cellularization occurring at 1.5 hr and about 2.5 hr, respectively The maternal-zygotic transition is the period in embryogenesis when maternal RNAs and proteins are degraded and activation of the zygotic genome occurs with nascent RNA transcription Historical models for generalized transcriptional silencing mechanisms of the zygotic genome involve chromatin-mediated repression, transcriptional machinery deficiency, and transcriptional repression or abortion by rapid cell cycles Ae. aegypti), we focus mostly on those involved at the onset of transcription during 2–4 hr. Furthermore, we only consider those genes which do not have any maternally-derived transcripts. Using high-throughput transcriptome sequencing (RNA-Seq) combined with bioinformatic methods, we have covered time points from 0 to 12 hr, up to the start of gastrulation. Before this work, cis-regulatory elements involved in zygotic genome activation in mosquitoes had not been found. We have identified a motif that is at least partially responsible for the activation of EZGs and find support for homology between this motif and the Drosophila TAGteam motif.One purpose of this research was to study the activation and expression of EZGs, as relatively little is known in mosquitoes as previous studies have focused on expression more broadly over development Medea) gene-drive system, as has been engineered in Drosophila Medea is comprised of two basic components as part of the same transgenic cassette, a maternally-expressed toxin for which all eggs are exposed, and a zygotically-expressed antidote that will rescue those offspring which inherit the Medea cassette. The maternal toxin can be a miRNA that targets the 5′ untranslated region (5′UTR) of a maternal-effect transcript that is critical for embryonic development. The zygotically-expressed antidote from the Medea cassette that encodes for the same product as the targeted maternal-effect gene has a unique 5′UTR that is insensitive to the toxin. Therefore, offspring from a heterozygous Medea-carrying female will not survive unless they have inherited at least one allele of the Medea cassette from either parent. Thus Medea can spread relatively quickly in a population Medea in mosquitoes and use it to drive pathogen-resistant genes into mosquito populations. In this study, we have identified several candidate genes that may facilitate development of promoters for early zygotic-specific expression of transgenic antidotes for a Medea gene-drive system.Another purpose of this study was to identify EZGs that may contribute promoters for zygotic-specific expression of antidote genes for a mosquito maternal-effect dominant embryonic arrest . Expected product sizes amplified in Transcriptome sequencing of four embryonic time points  was performed and described previously To identify the “pure” EZG set from the transcriptome data, we first applied two filtering criteria: transcripts could not be present in the 0–2 hr time range and must be present at least in the 2–4 hr time range. Presence at 0–2 hr indicates a maternal transcript as zygotic transcription is not expected at this time see . To be iAe. aegypti EZG intron content by comparing them to genes expressed at 4–8 hr, 8–12 hr, and to all Ae. aegypti genes from the VectorBase AaegL1.1 database . The difference between the 4–8 hr genes and all genes was also highly significant (P<0.0001). We observed a similar trend with respect to the mean number of introns per gene, EZGs having 1.78 introns per gene compared to 3.04 for all genes (not shown). Therefore we find evidence of selection against intron presence in EZGs.In Drosophila it was reported that activation of the zygotic genome starts with the transcription of about 59 genes at mitotic cycles 10–11, 70% of which do not have introns and encode small proteins database . As descTo identify a potential motif involved in early zygotic transcription, the upstream sequences of EZGs were analyzed using the web-based Suite for Computational Identification of Promoter Elements (SCOPE) As a control, upstream sequences from 3 sets of 63 randomly chosen genes (File S3) were analyzed by SCOPE. An additional control was performed in a similar manner using datasets comprising transcripts first showing up significantly (P<0.001) at 4–8 hr and 8–12 hr. As before, 200–2500 bp upstream sequence was used as the input for SCOPE. No motif similar to VBRGGTA was identified in these datasets (not shown). Additionally, when VBRGGTA was used as a query to search these data sets using SCOPE, the significance score was negative for all searches performed (not shown).Previous studies in Drosophila had identified the EZG TAGteam motif, CAGGTAG being the most overrepresented sequence In order to establish significance of alignment between the mosquito and Drosophila motifs, the STAMP program was used KLC2.1 and its paralog KLC2.2 that are expressed exclusively in the early embryo (Biedler and Tu 2010). KLC2.1 and KLC2.2 are 12th and 13th in our list of most highly expressed EZGs (see File S1). We performed 5′RACE and identified the transcription start site (TSS) starting from the “T” in the last base of the arthropod initiator sequence “TCAGT” KLC2.1 TSS. The slightly less degenerate VYRGGTA motif overlaps 4 of the 5 VBRGGTA motifs, including the two proximal sites investigated below. The 300 bp upstream sequences of KLC2.1 and KLC2.2 have 94% nt identity and the VBRGGTA and VYRGGTA sites are conserved.We reported the discovery of an early zygotic kinesin light chain gene KLC2.1 and KLC2.1 mentioned above and their identical expression profiles by RT-PCR KLC2.1. Initial experiments with 12 deletion series constructs of this 1 kb sequence identified the most significant loss of activity occurring when a 38 bp fragment containing 2 VBRGGTA motifs was removed (not shown). These 2 VBRGGTA motifs are the closest to the TSS, within 86 bp (VYRGGTA overlaps these 2 VBRGGTA motifs). Experiments focusing on this 38 bp fragment are presented here  promoter AgCecA upstream sequence. An earlier experiment that tested individual mutants of each of the 4 GGTA motifs for activity showed the greatest reduction in activity when the 1st and 4th GGTAs were mutated  and 143 (P<0.05) transcripts had a significant increase in expression at 2–4 hr. Certainly some transcripts not having a significant change in expression according to DEGseq may indeed be biologically relevant. Additionally, many annotated transcripts have only one or a few hits in the transcriptome data so determining their significance is difficult. We must add that slight variations in temperature and embryo collection times could affect the RNA populations in the samples.We have presented the first genome-wide study of EZGs in a mosquito species and have identified the EZGs of D. melanogaster snail is found in the list of 1,529, and while it has only 1 read hit at 2–4 hr, it has 91 and 159 hits in the 4–8 hr and 8–12 hr samples, respectively.It is possible that some of the detected 1,529 transcripts that had low expression could result from background expression. However, many of these with very low relative expression at 2–4 hr have a trend of increasing expression in each of the 3 samples covering 2–12 hrs, and a significant change in expression between two or more samples after 4 hr (see File S1). Some are predicted homologs to known Drosophila genes with developmental roles. For example, an ortholog (AAEL008336) of Ae. aegypti according to VectorBase but these orthologs are not expressed until 4–8 hr or 8–12 hr in Ae. aegypti (not shown). Drosophila development occurs at a much faster rate compared to Ae. aegypti and 2–4 hr in Ae. aegypti is relatively very early in Drosophila development which can partly explain this observation. Currently we are working on a detailed evolutionary analysis of these genes.It is interesting that many of the 61 EZGs do not have any clear homologs in non-mosquito species. Our preliminary analysis shows that many of the 61 EZGs are mosquito-specific. Approximately one fourth of the developmental genes identified in Drosophila listed in two studies Varied expression profiles were observed from our data (see File S1) and it will be interesting to investigate the responsible regulatory mechanisms. Transcriptional activators as well as factors involved in transcript destabilization will likely be revealed. Some transcripts exhibiting a transient profile were present at only 2–4 hr or only in the 2–4 hr and 4–8 hr samples. Some transcripts appeared to have a bimodal profile being present at 2–4 hr and 8–12 hr, but not at 4–8 hr. In addition to the EZG group (transcripts first detected at 2–4 hr), there were transcripts first detected at either 4–8 hr or 8–12 hr. Further investigation will be needed to verify these expression profiles. We now have more transcriptome data sets for other developmental time points and tissues that will allow further analysis of gene expression in a broader context.Ae. aegypti EZGs. Our experiments show that a 38 bp fragment containing multiple motifs both enhances early zygotic transcription and is capable of conferring early zygotic transcription to a heterologous promoter in Ae. aegypti embryos. This activity presumably results from the binding of a positively-acting transcription factor(s). We cannot rule out the potential influence of sequence flanking the motifs in the 38 bp fragment.We have discovered a motif that is significantly overrepresented in the upstream sequences of An. stephensi, a very divergent mosquito species relative to Ae. aegypti. The top-scoring motif identified was VGGTAV and it is present in all sequences (not shown).Our findings are important as it is the second demonstration of such a motif in an insect species that is involved in early zygotic genome activation. It is interesting that we find evidence for homology between the mosquito EZG motif VBRGGTA and the Drosophila TAGteam motif. We used a common method (SCOPE) to identify high scoring and highly represented motifs in EZG datasets from these two divergent species. Importantly, the mosquito motif aligns against TAGteam motifs with considerably low E values . Additiohttp://metazoa.ensembl.org/Drosophila_melanogaster/Info/Index) Ae. aegypti, one of which is a predicted one-to-one ortholog (AAEL008722) (File S7). One-to-one orthologs are also predicted for two other mosquito species, C. quinquefasciatus and An. gambiae. The gene tree reveals a complex evolutionary history for this gene family. Similarity in the amino acid alignment is primarily restricted to the zinc finger domains (not shown). Drosophila Zelda is transcribed maternally. However, AAEL008722 shows up first in the 4–8 hr sample (see File S1). Therefore, although it is possible that AAEL008722 functions as an EZG activator, it does not reason that it is responsible for activation of EZGs transcribed at 2–4 hr. We are currently working toward identifying the factor(s) that bind to the Ae. aegypti VBRGGTA motif.We can speculate that there may be an EZG transcriptional activator in mosquitoes that is homologous to the Drosophila Zelda. A gene tree from Ensembl . We are now working on transgenesis experiments to establish the in vivo zygotic-specific nature of these promoters.Engineering of a Ae. aegypti transcriptome sequencing (RNA-Seq) was performed on 4 embryonic samples of 0–2, 2–4, 4–8, and 8–12 hr using the Illumina platform. To establish relative transcript level in each sample, 16,789 annotated transcript queries from the VectorBase AaegL1.1 database (http://vectorbase.org/) based on the Ae. aegypti genome http://www.ncbi.nlm.nih.gov/geo/) with accession GSE34480.Illumina transcriptome sequencing, mosquito rearing, embryo collection, RNA Isolation, RT-PCR, embryo injection, and luciferase assay were described previously Ae. aegypti and Drosophila. In Drosophila, zygotic transcription was found to start with a group of 59 genes beginning at cycles 10–11 at 1.5 hr Ae. aegypti, pole cells form at about 3 hr Ae. aegypti are generally well conserved but occurs at a faster rate in Drosophila An. gambiae and Drosophila Our choice of 0–2 hr embryo as a time of transcriptional quiescence was based on previously published developmental facts for Ae. aegypti embryos at one-hour intervals from zero to seven hours, using 25 cycles for PCR. To test for genomic DNA contamination, primers designed for ribosomal protein S7 were used with samples prepared as others for reverse transcription but without reverse transcriptase, obtaining a negative result. Primers used to amplify the transcripts with IDs AAEL008851, AAEL001543, and AAEL008722 were forward primer GACGGATGGGATATCAATACC (Tm = 61.7) and reverse primer AACATTTAAGGAACGTGGCAC (Tm-62.6), forward primer ACCAATTCCACTAAGGACCAG (Tm = 61.7) and reverse primer GTTCAACTAAGCTTCGTTCTTCTAG (Tm = 60.5), forward primer AACTGCACTGCCTGTAACAAG (61.9) and reverse primer GAATCTACGGATGTGCTATCATATC (Tm = 61.4), respectively. PCR cycling parameters included an initial denaturation of 94 C for 3 m followed by 25 cycles of 94 C for 30 s, 59 C for 30 s, and 72 C for 40 s, and a final step of 72 C for 5 m. Expected product sizes were approximately 500 bp.RT-PCR was performed on RNA isolated from https://www.vbi.vt.edu/dac/).A DEGseq method in the DEGseq package Ae. aegypti genes was obtained from VectorBase (vectorbase.org). Because genes may have multiple associated transcripts, genes were scored for presence/absence of introns according to their cognate transcripts. Transcripts were assigned as either having at least one intron or no introns, using predictions according to VectorBase annotations of the AaegL1.2 database. P-values were determined by the Chi-squared test.Four data sets were used to compare the number of genes without introns. EZG, 4–8 hr, and 8–12 hr gene sets were selected as described above. The data set comprising all http://genie.dartmouth.edu/scope/). SCOPE was used to identify potential motifs involved in EZG transcription. Twenty-five hundred bp upstream sequence relative to the TSS for each of the 61 EZG genes (63 transcripts) was retrieved from VectorBase (Vectorbase.org). Because of incomplete annotation in VectorBase, some retrieved upstream sequences start from the start codon and not from the TSS. Analysis was performed with the “fixed” search option and with Ae. aegypti as the target genome, using 2500, 1500, 1000, 800, 400, and 200 bp upstream sequence as these are all the options available by SCOPE. SCOPE output shows the motif along with the Count , Sig Value , Coverage (the percentage of sequences in the query sequences that contain at least once motif occurrence), and Algorithm . Control motif searches were performed with 3 sets of 61 randomly chosen genes that were obtained using an in-house Perl script. As an additional control, the upstream sequences of genes that had transcripts showing first presence at 4–8 hr and 8–12 hr (P<0.001) were used as for motif search and analysis as described above. All control data set sequences were retrieved using Vectorbase and their transcript IDs can be found in File S3.The Suite for Computational Identification of Promoter Elements (SCOPE) is a parameter-free program designed to identify cis sequences in a set of co-regulated genes (SCOPE was used to identify TAGteam EZG motifs in data sets from previous Drosophila studies http://www.benoslab.pitt.edu/stamp/) was used to establish significance of alignments between mosquito and Drosophila EZG motifs. The PWMs generated by SCOPE for the most significant motifs identified in the Ae. aegypti and Drosophila The STAMP program (AgCecA promoter KLC2.1 F1R3/F8R3 and the pGL3-AgCecA plasmid.Different regions of 1 kb promoter sequence from the EZG AaKLC2.1 (gene ID: AAEL011410) Experimental and control plasmids were injected at a concentration ratio 5∶1, respectively.Renilla luciferase reporter pRL-null (Promega) containing the D. melanogaster actin promoter was used as an injection control. P-values were calculated by a one-tailed, TypeII T-test.Dual luciferase assays were performed at 5 hr post-oviposition using lysates from injected embryos to investigate the promoter of the previously described kinesin light chain gene AAEL011410 . The File S1Ae. aegypti transcripts, DEGseq output with p-values and q-values.Blast hit counts for (XLSX)Click here for additional data file.File S2Numbers of genes with and without introns.(XLSX)Click here for additional data file.File S3Transcript IDs for 2–4, 4–8, 8–12 hr groups (P<0.001) by DEGseq, and IDs for random sequence datasets.(XLSX)Click here for additional data file.File S4Tabulated summary of SCOPE output.(XLSX)Click here for additional data file.File S5PWMs used in STAMP.(DOCX)Click here for additional data file.File S6Luciferase assay results for other EZG promoters.(PPTX)Click here for additional data file.File S7Phylogeny of Zelda homologs from mosquito and Drosophila.(PDF)Click here for additional data file.


Cellular signal transduction generally involves cascades of post-translational protein modifications that rapidly catalyze changes in protein-DNA interactions and gene expression. High-throughput measurements are improving our ability to study each of these stages individually, but do not capture the connections between them. Here we present an approach for building a network of physical links among these data that can be used to prioritize targets for pharmacological intervention. Our method recovers the critical missing links between proteomic and transcriptional data by relating changes in chromatin accessibility to changes in expression and then uses these links to connect proteomic and transcriptome data. We applied our approach to integrate epigenomic, phosphoproteomic and transcriptome changes induced by the variant III mutation of the epidermal growth factor receptor (EGFRvIII) in a cell line model of glioblastoma multiforme (GBM). To test the relevance of the network, we used small molecules to target highly connected nodes implicated by the network model that were not detected by the experimental data in isolation and we found that a large fraction of these agents alter cell viability. Among these are two compounds, ICG-001, targeting CREB binding protein (CREBBP), and PKF118–310, targeting β-catenin (CTNNB1), which have not been tested previously for effectiveness against GBM. At the level of transcriptional regulation, we used chromatin immunoprecipitation sequencing (ChIP-Seq) to experimentally determine the genome-wide binding locations of p300, a transcriptional co-regulator highly connected in the network. Analysis of p300 target genes suggested its role in tumorigenesis. We propose that this general method, in which experimental measurements are used as constraints for building regulatory networks from the interactome while taking into account noise and missing data, should be applicable to a wide range of high-throughput datasets. The ways in which cells respond to changes in their environment are controlled by networks of physical links among the proteins and genes. The initial signal of a change in conditions rapidly passes through these networks from the cytoplasm to the nucleus, where it can lead to long-term alterations in cellular behavior by controlling the expression of genes. These cascades of signaling events underlie many normal biological processes. As a result, being able to map out how these networks change in disease can provide critical insights for new approaches to treatment. We present a computational method for reconstructing these networks by finding links between the rapid short-term changes in proteins and the longer-term changes in gene regulation. This method brings together systematic measurements of protein signaling, genome organization and transcription in the context of protein-protein and protein-DNA interactions. When used to analyze datasets from an oncogene expressing cell line model of human glioblastoma, our approach identifies key nodes that affect cell survival and functional transcriptional regulators. First, we used DNaseI-hypersensitivity site sequencing (DNase-Seq) Beginning with an interactome network in which the reliability of each interaction is weighted by experimental evidence, we find an optimal subnetwork of the most reliable interactions that include a subset of the phosphorylation events and the transcriptional regulators, with preferences given to phosphorylation events that undergo large changes and transcriptional regulators that show strong activities. An important aspect of the PCST algorithm is that it is able to naturally account for missing data and false positives. In particular, since the input experimental data do not capture every relevant protein, we allow the network to include proteins that were not explicitly measured in the proteomic or transcriptomic assays. In addition, to account for false positives in the data, we allow the algorithm to exclude experimentally determined proteins and genes that are not connected with high confidence interactions. As a result, the final networks are compact, enriched for functionally relevant proteins and the most reliable interactions that include these proteins, and can be used to guide subsequent experiments. To further account for possible noise in the input datasets, we merge an optimal PCST solution with a set of suboptimal solutions to obtain a robust final network see .Here we apply our integrated approach to prioritize experiments for probing the signaling pathways downstream of a mutant epidermal growth factor receptor (EGFR) in glioblastoma multiforme (GBM). The variant III mutant of EGFR (EGFRvIII) is the most common deletion mutant of EGFR in human cancer in vivoTo understand the signaling pathways downstream of EGFRvIII, we used as a model two cell lines derived from human U87MG glioblastoma cells that were engineered to express high levels of EGFRvIII  or a mutant form of the receptor with an inactive kinase (U87DK) In order to uncover links between the phosphorylation and transcriptional changes, we identified a set of transcriptional regulators that were most likely to be differentially active in the two cell types and responsible for the transcriptional changes. Our approach integrates sequence information with epigenomic and expression differences between the cell lines. We collected DNase-Seq data in each cell line and found 12,807 regions that showed quantitative changes in hypersensitivity. About 68% of these sites were hypersensitive in only one condition, while the rest were hypersensitive to different extents in the two cell lines. We then scanned these differentially hypersensitive regions for matches to known DNA binding motifs to compute affinity scores of the motifs in these regions. To select a subset of these motifs most likely to drive expression differences, we scored each motif using a univariate regression model relating its affinity scores in the differentially hypersensitive regions to differential mRNA expression of genes within 40 kb With this approach, we identified 185 significant motifs that mapped to 297 proteins in the interactome see . These pThe interactome we used was derived from iRefIndex The PCST network constructed from the U87 EGFRvIII dataset consists of 199 edges and 172 nodes, of which 65 are phoshoprotein termini and 63 are transcription factor termini . It giveIntriguingly, the network includes two subnetworks containing neurological processes previously linked to GBM: synaptic transmission and axon guidance. The neurotransmitter glutamate, which mediates synaptic transmission through the N-methyl-D-aspartate glutamate receptors 2A and 2B (GRIN2A and GRIN2B) that appear in the PCST network, can promote glioma cell growth Our approach also recovered a number of transcriptional regulators already known to be involved in EGFRvIII signaling. For example, our network identifies the transcription factor signal transducer and activator of transcription 3 (STAT3), which has been previously implicated in EGFRvIII induced transformation The PCST solution network was constructed by finding a small number of proteins that directly or indirectly interact with the set of termini in order to explain the measured changes between U87H and U87DK cells. Compared to networks constructed from proteins that directly interact with the set of phosphorylated proteins, transcription factor candidates or both (“nearest neighbor” networks), the number of nodes in the PCST solution network is about 4% to 7% of these nearest neighbor networks . To systin vivo.Since GBM is a heterogeneous disease where the EGFRvIII mutation is among the most common mutation of EGFR To determine if the transcription factors in the PCST solution contribute to the transcriptional response to EGFRvIII mutation in human patients, we used publicly available TCGA GBM data The EGFRvIII up-regulated genes in the TCGA samples are more strongly enriched  for targets of the transcription factors identified in the U87H cells (U87H TF) than the EGFRvIII down-regulated genes  a quantitative score that measures how closely connected they were to the nodes in the PCST in the interactome  and (2) higher than the GI50 values (the concentration that inhibit growth by 50%) in the NCI60 panel of cancer lines as reported by the In Vitro Cell Line Screening Project (IVCLSP) of the National Cancer Institute Developmental Therapeutics Program (http://dtp.nci.nih.gov/). By contrast, the doses we used to target the highest-ranked nodes were within the range of GI50 values  . With th0 values . TherefoTo further understand the behavior of these compounds on the U87 cells, we fit the measured dose responses to a four-parameter log-logistic (4-PL) function. For compounds that could be fit to the 4-PL model (lack-of-fit test p>0.05), we compared parameters of the fitted curves between U87H and U87DK cells . CompounSince transcriptional regulation is the first step that defines the long-term behavior of the cell including tumorigenesis, we sought to identify the transcriptional regulators responding to oncogenic signaling. In addition to several transcription factors known to be induced by EGFRvIII, our PCST network also included novel putative transcriptional regulators. Using the same scoring procedure as for ranking targets for small molecules , we seleWe therefore performed a ChIP-Seq experiment in the U87H cells to identify the genes targeted by p300. 60.8 million uniquely mapped reads of 36 bp were obtained . Peak cap300 does not directly bind DNA but is instead recruited to its targets by sequence-specific DNA-binding proteins. Therefore, the PCST algorithm selected it solely by virtue of its protein-protein interactions. Nevertheless, if the network is correct, we should be able to detect the consequences of its recruitment to the DNA. Indeed, we found evidence that p300 is actively involved in chromatin remodeling of these cells: p300 binding sites are significantly enriched in regions that increase in hypersensitivity in the U87H cells compared to U87DK cells p<1E-23; , suggestOur networks also included the DNA-binding proteins that recruit p300 to its targets. To test these predictions, we examined the sequence motifs present in the highest confidence p300 targets, defined as those regions that were present in the ChIP-Seq data and showed a change in DNaseI hypersensitivity between the cell lines. We used cross-validation to rank motifs by their ability to predict which differentially hypersensitive sequences were p300 targets see . Of the Gene Ontology enrichment analysis of the p300 targets identified by ChIP-Seq revealed the potentially important role of this protein in regulating the transcriptional consequences of the EGFRvIII mutation , specifiWe have shown that the PCST solution provided an integrated view of the biological processes in the EGFRvIII network leading to directly testable predictions. Our analysis revealed proteins whose activities we subsequently targeted with small-molecule inhibitors to block the growth of tumor cell lines. In addition, the PCST solution network identified transcriptional regulators enriched at hypersensitive sites. Many of the proteins we targeted did not appear in the phosphoproteomic and transcriptional profiling data but were selected among thousands of other proteins in the interactome graph that interact directly or indirectly with the hits from the experiments. In particular, a network of direct interactors of the phosphorylated proteins contains 2,554 nodes. In the absence of a network optimization approach, it would be extremely difficult to prioritize experiments. Using the PCST approach, we were able to integrate the phosphoproteomic data with additional transcriptional data and provide a ranked list of proteins for experiments. Targets far from nodes in the PCST were less likely to exert differential cytotoxic effects in response to the oncogenic mutation, and in the cases in which targeting these nodes were cytotoxic, their effects tended to be weaker. Therefore, our approach provides a powerful way to prioritize targets based on experimental datasets that represent different aspects of the cell state such as protein signaling, chromatin conformation, and transcription output.Our network-based approach was also able to identify transcriptional regulators that could not be found by other methods. Standard promoter analysis of the differential expressed genes in the current datasets yielded little information about potential DNA binding proteins besides the cell cycle regulator E2F and a zinc finger protein , whereasAt the genome-wide level, p300 targets were found to be enriched in genes involved in the response to hormone. Little is known about the role of p300 in regulating hormone response genes transcriptionally. However, it is known that p300 associates with multiple nuclear hormone receptor proteins and functions as a co-regulator The sequence specific factors responsible for p300 recruitment to EMT-related genes remain to be found. C/EBP-β (CEBPB) and STAT3 have previously been shown to synergistically induce mesenchymal transformation of glioma cells We tested the effects of seven compounds targeting high-ranked nodes predicted by the PCST solution, and six of these resulted in significant reduction in cell viability. These compounds represent both known and novel therapeutic agents for GBM . Of thesOur experiments also illustrated that the selective estrogen receptor modulator tamoxifen reduced cell viability in both cell types but more potently in the EGFRvIII-expressing cells. Epidemiologic data on the effect of steroid hormone in the etiology of glioma are ambiguous  did show a modest difference in cytotoxicity between DK and H cells. Such false-negatives arise because they are part of biological responses that are not connected to the available data, either because the interactome is incomplete or because these targets are functioning in biological processes that have little in common with the proteomic data on which we based our study.The proteomic data selectively identified phosphorylated tyrosine residues, which are relatively rare compared to phosphorylated serine and threonine Escherichia coliOur models, which treat transcriptional changes as downstream of proteomic changes, focused on identifying the differences between two cell types at steady state. In dynamic systems, it will be important to include feedback as the transcriptional changes also lead to changes at the proteomic level. Although differential equations provide a natural way to describe such feedback, these approaches are limited to relatively small systems. For example, a comprehensive differential-equations-based transcriptional and translational network for 2 humidified atmosphere at 37°C. Expression of EGFRvIII and DK receptors were selected by 400 mg/mL G418 . To enhance cell attachment, tissue culture vessels with the Corning CellBIND surface (Corning) were used.The human glioblastoma cell lines U87MG expressing high levels of EGFRvIII  and a kinase dead mutant of EGFRvIII  were generous gifts from Dr. Paul Huang and Dr. Forest White at MIT. Cells were cultured in complete media  supplemented with 10% fetal bovine serum, 100 units/mL penicillin, 100 mg/mL streptomycin (Invitrogen), 4 mM L-glutamine) and in a 95% air/5% COTotal RNA was prepared from the U87MG derived cell lines by the RNeasy Plus Mini Kit (Qiagen) and quantified on the Affymetrix Human Genome U133 Plus 2.0 arrays. Labeling, hybridization, washing and staining were performed following the standard Affymetrix GeneChip protocol. The arrays were hybridized in an Affymetrix GeneChip Hybridization Oven 640 at 45°C at 60 rpm for 16 hours, washed and stained in Affymetrix Fluidics Station 450, and scanned with Affymetrix GeneChip Scanner 3000 7G. Two biological replicates were done for each cell line. The intensity values were normalized using the GC Robust Multi-array Average (gcrma) package The U87DK and U87H cells were seeded in parental media (complete media without G418). After 24 hours, the cells were washed gently with phosphate buffered saline (PBS) and cultured in serum free media for 24 hours. Nuclei extraction and DNaseI digestion followed published protocol The U87H cells were seeded in media without G418. After 24 hours, the cells were washed gently with PBS and cultured in serum free media for 24 hours. Crosslinking and cell lysis were done as previously described 4,000 cells in 100 µL of parental media were seeded per well in a 96-well CellBIND clear plate. Twenty-four hours later, the medium was aspirated, each well was washed with 150 µL of PBS, and 100 µL of fresh serum-free media (DMEM with no phenol red) containing the indicated concentrations of drugs was added. Six to eight within-day biological replicates were performed for at least three between-day biological replicates for each treatment of each cell line. To make stock solutions of small molecule drugs, dasatinib, rapamycin, paclitaxel (LC Labs), ICG-001, SAHA, SB-431542 , 17-AAG (AG Scientific), PKF118–310, SB-505124 (Sigma), D4476, and harmine  were dissolved in dimethyl sulfoxide (DMSO), 4-OHT (Sigma) in pure ethanol and PDTC (Sigma) in PBS. All stock solutions were stored in the dark at −20°C and diluted to the desired concentration in cell culture media immediately prior to treatment. After 72 hours of drug treatment, cell viability was measured by the WST-1 reagent (Roche Applied Science). 10 µL of WST-1 was added to each well, the plates were incubated at 37°C for four hours and absorbance at 450 nm was measured by Varioskan Flash Multimode Reader (Thermo Scientific). Raw signals were normalized by a linear mixed-effects model (see below) to eliminate between-day batch effects. Relative viability values were computed as the ratios between the normalized signals of drug-treated cells and the corresponding vehicle control cells. Curve fitting to the four-parameter log-logistic model and statistical tests of fitted parameter values were performed using the R package drc To eliminate batch effects between plates and experiments performed on different days, a linear mixed-effects model was fitted to the viability measurements from the vehicle control wells on each plate: signal = grand mean+cell line+day of the experiment+plate+residual error, where cell line was classified as a fixed effect term and day of experiment and plate were classified as random effect terms. Model fitting was performed by the R lme4 package We used the Goemans-Williamson formulation of the PCST problem. Given an undirected graph To allow the possibility of multiple alternative pathways that may connect the data together despite weaker interaction evidence, we report a composite network  that repWe ran PSCIQUIC query We defined two kinds of penalties for proteins in the interaction graph. At the signaling level, we used fold changes in the phosphoproteomics mass-spectrometry (MS) data, and at the level of transcriptional regulation, we used a t-statistic derived from DNase-Seq and mRNA expression data representing our confidence that the transcription factor binds regulatory sites and influences expression. These penalties are described in detail below.Quantitative phosphotyrosine proteomic data on the U87H and U87DK cells were published previously http://may2009.archive.ensembl.org and We derived the penalty values for transcription factors in the protein interaction network from the inferred activity of these transcription factors in inducing changes of mRNA expression. Specifically, we used a regression method to find the correlation between the differential mRNA expression and the sequence specific transcription factor binding motifs in nearby differentially accessible chromatin regions. Filtering the limma analysis results by a maximum p-value of 0.001 adjusted by the Benjamini and Hochberg method We considered the magnitude of node penalties defined above as a measure for the activities of the protein termini revealed by the experimental data. Since the PCST algorithm minimizes penalty values on termini excluded from the final network, it will preferentially include proteins with larger penalty values, i.e., bigger fold changes in phosphorylation or stronger association of the transcription factor motif to changes in target gene expression. In the absence of formal statistical model for the likelihood of these data, we tested the sensitivity of the PCST solution in The parameter olutions . We founA complete list of tyrosine phosphorylated peptides was downloaded from the supplementary data of For each node in the interactome, either inside or outside of the PCST solution, we found the edges connecting this node to the nodes in the PCST solution and summed up the confidence scores on these edges (https://tcga-data.nci.nih.gov/tcga/). The U87 cell line is reported to contain wild-type p53 and deletion of p16 Level 3 exon array data of the TCGA GBM project Separately, for each transcription factor that has a motif in TRANSFAC, we used the MATCH program We searched for sequence motifs that could predict which of the regions that were more hypersensitive in U87H than in U87DK cells were bound by p300. Differentially hypersensitive regions overlapping with p300 bound regions in U87H from ChIP-Seq were considered positive and the rest were negative. We then computed matches to the sequence motifs in the TRANSFAC vertebrate non-redundant set in all the positive and negative regions. The motif match scores were used in a feature selection procedure by Wilcoxon test in five-fold cross validation to rank these motifs by their ability to classify the positive and negative regions. To compute the significance of the p300 interactors inside the PCST to those outside of the PCST, the top 20 motifs were selected in each iteration of the cross-validation. Those correspond to proteins that interact with p300 were recorded and enrichment p-values were computed by Fisher exact test.The raw data for ChIP-Seq, DNase-Seq and microarray experiments have been submitted to GEO under accession number GSE36902.Figure S1Correlation is poor between changes in tyrosine phosphorylation on proteins and changes in transcript level of the corresponding mRNA observed in the U87DK and U87H cells.(PDF)Click here for additional data file.Figure S2Hierarchical clustering by exon array gene expression of TCGA GBM patient samples with wild-type p53 and deleted p16. The set of differentially expressed genes  were the input to the clustering. Each row in the heatmap is a gene and each column is one sample. EGFRvIII status of a sample is indicated by the color code at the top of the heatmap.(PDF)Click here for additional data file.Figure S3Distribution of GI50 values (the concentration that inhibit growth by 50%) of the NCI60 panel of cell lines for compounds that were used in this study and also tested in the In Vitro Cell Line Screening Project (IVCLSP) of the Developmental Therapeutics Program at the National Cancer Institute. Paclitaxel and rapamycin were tested more than once in the IVCLSP at the indicated maximum concentrations. The vertical dashed lines mark the concentrations of the compounds for the viability data presented in (PDF)Click here for additional data file.Figure S4Assessment of multiple methods for identification of differential DNaseI hypersensitive regions. A. Scatter plot of log2 read counts from the U87DK and U87H cells inside the DNaseI hypersensitive regions that were called by MACS to be differentially hypersensitive (p<1E-06) and otherwise. B Same scatter plot as in A, but the differential hypersensitive regions were determined by the edgeR method (PDF)Click here for additional data file.Figure S5The PCST solution was robust to noise in node penalties and edge costs. The node penalties used to find the U87 PCST network were multiplied by a random factor that is normally distributed with mean 1 and a specific level of standard deviation  and a new PCST network was found for this set of randomized node penalties. For each level of the standard deviation, 100 such PCST solutions were obtained and compared to the original PCST. Randomization on edge costs were performed similarly. A. The mean and standard error for the percentage of nodes in the original PCST that appear in the randomized PCST. 80% of the nodes in the original PCST still appear in the randomization solutions even at 50% noise. B. The mean and standard error of the Jaccard Index (size of intersection divided by size of union) comparing the PCST solutions from randomization to the original PCST. The solution was more sensitive to changes in edge cost values but still showed Jaccard index about 0.75 at 20% noise. At much higher noise levels, the randomization solutions went through other nodes in the interactome, indicating that optimization on the edge costs indeed controlled the nodes included in the PCST solution.(PDF)Click here for additional data file.Figure S6Plot of efficiency ratio  for different sizes of the PCST solutions obtained from the indicated β values. The efficiency ratio was relatively stable for β values between 40 and 100. Within this range, the rate of increase of efficiency ratio with respect to network size is the slightly largest between β of 50 and 60. The PCST presented in (PDF)Click here for additional data file.Table S1List of differentially expressed genes between the U87H and U87DK cells. Columns are: Affymetrix probe ID, official gene symbol, gene name, UniGene ID, (remaining columns are output from limma) log base 2 fold change of the probe in U87H cells relative to U87DK cells, average log base 2 expression of the probe over all arrays, moderated t-statistic, raw p-value, adjusted p-value, log-odds that the gene is differentially expressed.(TXT)Click here for additional data file.Table S2Significantly enriched motifs in the proximal promoter of genes differentially expressed between U87H and U87DK. The differentially expressed genes were analyzed by the motif enrichment function of Expander (TXT)Click here for additional data file.Table S3Illumina sequencing statistics of the DNase-Seq and p300 ChIP-Seq samples. Aligned unique: reads that are uniquely aligned to the genome. Aligned repeat: reads that are aligned to more than one locations in the genome. Aligned none: reads that are not aligned to any location in the genome.(TXT)Click here for additional data file.Table S4Ranking of motifs by how well the motif scores can classify hypersensitive regions that overlap with p300 bound regions from those that do not overlap with p300 bound regions. For each iteration in five-fold cross-validation, the top 20 motifs out of a total of 151 are listed. Columns are: iteration number in five-fold cross-validation, rank of motif, TRANSFAC ID of motif, whether the motif is mapped to a p300 interacting protein inside the PCST , whether the motif is mapped to a p300 interacting protein outside of the PCST .(TXT)Click here for additional data file.Table S5Enriched GO categories of p300 bound genes in GM12878 cells and HepG2 cells. Files containing p300 bound peaks detected by ChIP-Seq were downloaded from the UCSC Genome Browser and represented the ENCODE Jan 2010 Freeze for GM12878 and July 2009 Freeze for HepG2. Mapping peaks to genes and GO analysis were performed using the same parameters as for the U87H p300 ChIP-Seq dataset.(TXT)Click here for additional data file.


FTO) is the first gene associated with body mass index (BMI) and risk for diabetes. FTO is highly expressed in the brain and pancreas, and is involved in regulating dietary intake and energy expenditure. To investigate the transcriptional regulation of FTO expression, we created 5′-deletion constructs of the FTO promoter to determine which transcription factors are most relevant to FTO expression. The presence of an activation region at −201/+34 was confirmed by luciferase activity analysis. A potential Foxa2  binding site and an upstream stimulatory factor (USF)-binding site was identified in the −100 bp fragment upstream of the transcription start site (TSS). Furthermore, using mutagenesis, we identified the Foxa2 binding sequence (−26/−14) as a negative regulatory element to the activity of the human FTO promoter. The USF binding site did not affect the FTO promoter activity. Chromatin immunoprecipitation (ChIP) assays were performed to confirm Foxa2 binding to the FTO promoter. Overexpression of Foxa2 in HEK 293 cells significantly down-regulated FTO promoter activity and expression. Conversely, knockdown of Foxa2 by siRNA significantly up-regulated FTO expression. These findings suggest that Foxa2 negatively regulates the basal transcription and expression of the human FTO gene.Fat mass and obesity associated gene ( FTO) was discovered in a genome-wide association study (GWAS) for obesity (or obesity-related traits) FTO polymorphisms  and obesity risk in 41,734 cases and 69,837 controls from 59 studies and concluded that the SNPs account for the risk of obesity to various degrees FTO polymorphisms are closely related to the risk and outcome of insulin resistance and diabetes FTO SNPs are significantly associated with breast cancer and endometrial cancer as well FTO mRNA and protein levels in muscle tissue of patients with type 2 diabetes, resulting in altered insulin signaling, and increased lipogenesis and reactive oxygen species levels (ROS). Other data support a potential role for FTO in oxidative metabolism, lipogenesis and oxidative stress in muscle Overweight and obesity are independent risk factors for type 2 diabetes, cancer, and coronary artery disease, posing serious threats to public health FTO expression will be important in elucidating the role of FTO in the regulation of energy balance. It may also point to new therapeutic strategies for inhibiting or activating FTO activity in patients with obesity or diabetes. In order to explore the mechanisms of its transcriptional regulation, we identified and characterized the promoter of the human FTO gene. By generating a series of 5′ deletions, we revealed that the core promoter is located within -201 relative to the transcription start site (TSS). Using the promoter screening method, three Sp1, one Foxa2 and a USF binding site were selected for further study. Luciferase reporter studies were carried out to investigate the functions of these factors in regulating FTO promoter activity in Hela and HEK 293 cells. We found that two Sp1 sites in the region (-201/101) can strongly transactivate the FTO promoter. Surprisingly, results from fluorescence measurements showed that mutation of the Foxa2 binding site increased the promoter activity of FTO, while the USF binding site did not affect the FTO promoter activity. Electrophoretic mobility shift assay (EMSA) and Chromatin immunoprecipitation (ChIP) analysis demonstrated that Foxa2 associates with the binding sites of the FTO gene in vitro and in vivo. Overexpression of Foxa2 decreased the level of FTO, whereas knockdown of Foxa2 by siRNA led to increased FTO expression. These results suggest that the Foxa2 transcription factor binding sequence AGCCTGTTTGCTC (−26/−14) is the negative regulatory element of FTO.Understanding the transcriptional regulation of FTO gene expression is frequently detected FTO. The transcription factor Foxa2 may play a vital role in regulating the balance of food intake and other biological actions.The Foxa2 transcription factor is essential for glucose and lipid homeostasis, especially for insulin secretion from β cells The human embryonic kidney cell line HEK 293 and the human cervical carcinoma cell line Hela were obtained from Invitrogen and maintained in DMEM (Invitrogen) supplemented with 10% fetal bovine serum and 1% antibiotic-antimycotic agents.FTO promoter. An approximately 2.0 kb KpnI/BglII fragment (−2028 to +34) which contains the 5′-flanking region of the FTO gene was isolated by PCR amplification from HEK 293 cells DNA, and then ligated into the KpnI/BglII-digested pGL3-Basic vector (Promega). The resulting vector allows for the monitoring of the level of gene expression by the firefly luciferase gene under the control of this promoter fragment. The resulting plasmid (pGL3-2k) was used as a template to synthesize a series of deletion reporter gene constructs. PCR was performed using sets of oligonucleotide primers specific for the human FTO gene sequence, of which the forward primer was the KpnI-site-linked, and the reverse primer was a BglII-site-linked (PCR was used to generate 5′ stepwise deletion constructs of the e-linked . All pla4) were seeded onto 24-well plates, cultured overnight, and cotransfected with 800 ng of the pGL3 vector reporter construct and 200 ng of pRL-TK (Promega) as a transfection efficiency control. For all experiments, cells were cultured for 24 h after transfection, and cell lysates were measured using the Dual Luciferase Reporter Assay System Kit (Promega). Luminescence was determined in a Modulus luminometer (Turner Biosystems) after addition of substrate to allow adequate mixing. Relative firefly luciferase activities (RLU) were calculated by normalizing transfection efficiencies with the Renilla luciferase activity. All the data shown in this study were obtained from at least three independent experiments.For assaying luciferase expression, HEK 293 and Hela cells (5 to 7.5×10http://mbs.cbrc.jp/research/db/TFSEARCH.html) and AliBaba 2.1 (http://www.gene-regulation.com).To identify transcriptional regulatory sequences and potential transcription factor binding sites on the putative promoter regions. We analyzed the 5′-flanking region from 240 bp upstream of the transcription start site with TFsearch  with the pGL3-100 plasmid as the template. The mutagenesis primers designed for the mutations were as follows (the mutated sequences are underlined): mu-USF-Forward: 5′-CTCTAG CCTGTTTGCTCGCG-3′; Foxa2 mutant GACCGGCTCGCG-3′5′-CTCTAGCC (mutated bases shown as underline). Nuclear extracts were prepared from cultured HEK 293 cells using NE-PER nuclear and cytoplasmic extraction reagent kit . The protein concentrations were determined using the Bradford protein assay kit  following the manufacturer’s protocol. EMSA was conducted using a Light Shift Chemiluminescent EMSA kit  as recommended by the manufacturer’s instructions.For the gel shift assay, double-strand DNA oligonucleotide probes were synthesized with a biotin label at the 5′ end . The probes used included the following double-stranded oligonucleotides: Foxa2 wild type Nuclear extracts containing 15 ug of protein were incubated with the poly[d(I-C)], the binding buffer and oligonucleotide probes for 20 minutes at 25°C. In the competition experiments, mutant probe or 100-fold excess of cold oligonucleotides were pre-incubated with the nuclear extracts for 10 minutes before the labeled probes were added to the reaction. The antibody for Foxa2  were used to carry out super-EMSA.5′-GCTAGCTACCGTTGCTATAGC-3′) and reverse (5′-CTGGAA GAGCGTAGTCCGCT-3′) primers were designed to amplify the FTO promoter region (−142 to +24 nucleotides in relation to the transcription start site). Amplification of the input chromatin before immunoprecipitation at a dilution of 1∶50 was used as a positive control. The PCR products were analyzed on a 2% agarose gel and quantified by densitometry using a Fluor’s fluorimeter and Quantity One software (Bio-rad).ChIP assays were performed according to the manufacturer’s instructions . The DNA/protein complex were immunoprecipitated using a Foxa2 antibody (Santa Cruz) and the DNA was purified using gel exclusion columns. The purified ChIP DNA fragment was subjected to semiquantitative PCR analysis . Specific forward  were used in transient transfection. Total RNA was isolated 24 hour later and analyzed by RT-PCR. For western blotting experiments, lysates were obtained from cells cultured for 48 hours in 6-well plates.5′-ACTTGGCTCCCTTATCTGACC and FTO-Reverse, 5′-TGTGCAGTGTGAGAAAGGCTT; GAPDH-Forward, 5′-AGGAC TCATGTCCATGCCAT-3′ and GAPDH-Reverse, 5′-ACCCTGTTGCTGTAGCCAAACells were transfected as described above for the luciferase assay. Total RNA was isolated according to the standard TRIZOL (Invitrogen) method. First-strand cDNA was synthesized from 1 µg of total RNA using M-MLV reverse transcriptase (Promega). Real time PCR was performed in ABI7500 (Applied Biosystems) using SYBR Green (Takara) using. Negative control reactions contained water instead of cDNA and were included in each run to ensure absence of contamination. Primers used were as follows: FTO-Forward, Cells were harvested by scraping, washed in ice-cold PBS and lysed with 200 ul of lysis buffer containing a protease inhibitor cocktail (Roche). Protein concentrations were determined using the Bio-Rad Protein Assay (BioRad). Protein samples were prepared with 5×SDS sample buffer and loaded at 20 ug of protein per lane for SDS-PAGE. Western blot was performed with FTO (Abcam) and Foxa2 (Santa Sruz) antibodies, followed by goat anti-mouse IgG conjugated with HRP. GAPDH was detected as loading control. Chemoluminescence signals from three independent western analyses were quantified using an ECL imager, and analyzed using Quantity One software (BioRad).t-test with Bonferroni correction as appropriate, using Microsoft Excel.Data are presented as means±SEM. Comparisons between means were performed by unpaired two-tailed Student’s FTO gene expression, a nested series of 5′ truncated portions of the promoter were created extending up to 2.0 kb from the transcription start site of the human FTO gene. These constructs were transiently transfected in HEK 293 and Hela cells, and promoter activity was assessed by measuring luciferase activity.To localize the active promoter regions and to determine the important transcription factors regulating human FTO gene promoter which is located within the −201 bp before the transcription start site (TSS). Similar profiles of luciferase expression upon transfection with the reporter constructs were observed in both cell lines.As shown in Based on the above findings, we analyzed the 5′-flanking region between 240 bp upstream to the transcription start site. Three Sp1 transcription factor binding sites are located between –201 and –100. A putative USF-binding site and a Foxa2 (HNF-3b) binding site were identified within the 100 bp before the TSS .FTO gene, mutants of the putative binding sites were constructed by site-directed mutagenesis. HEK 293 and Hela cells were transiently transfected with the mutant constructs. From these experiments we confirmed that the two Sp1 sites are essential for maintaining the basal transcriptional activity (data not shown). To further determine the function of the USF and Foxa2 binding sites, we measured the luciferase activities of the mutants pGL3-100/mu-USF, pGL3-100/mu-Foxa2 and pGL3-100/mu-USF/Foxa2 (P<0.01). In contrast, pGL3-100/mu-USF luciferase activity was not significantly different from pGL3-100. Furthermore, the level of luciferase activity of pGL3-100/mu-USF/Foxa2 matched that of pGL3-100/mu-Foxa2.To investigate the role of the binding sites of transcription factors in the regulation of the SF/Foxa2 . It was FTO promoter.To explore the effect of Foxa2 on pGL3-100 luciferase activity, Foxa2 expression vector pHD-Foxa2 or a Foxa2 siRNA was transfected into HEK 293 and Hela cells . We deteFTO expression level, a Foxa2 expression vector or a Foxa2 siRNA was transfected into HEK 293 cells. FTO mRNA and protein levels were detected by qRT-PCR or Western blotting, respectively. As illustrated in FTO transcript level by 60% and FTO protein level by 58.4%. In contrast, RNAi-mediated knockdown of Foxa2 resulted in a 1.5-fold more FTO mRNA level than the negative control siRNA and a concomitant increase of 45.5% in the FTO protein level.To further explore the role of Foxa2 in regulating FTO gene in vitro. A biotin-labeled 20-nt fragment (−30 nt to −11 nt) containing the Foxa2 binding sites of the FTO promoter used in this assay. In competitive binding assays, unlabeled nucleotide sequence (cold WT probe) and a mutant probe with 5 bp nucleotide substitutions in the motif was used as competitor. To figure out whether the binding of DNA-protein complex is specifical, super-EMSA experiments were undertaken using the anti-Foxa2 antibody. Competition studies using mutanted EMSA probes showed that the bands of DNA-protein complexes were vanished in lane 3 and 4. Additionally, these shifted bands could be eliminated by the 100-fold excess of cold probe . For the super-EMSA studies shown in the last two lanes, the complex was super shifted by antibody when incubated with nuclear extracts  were included. As can be seen in FTO promoter region.To further determine whether Foxa2 binds to the FTO was first discovered in a genome-wide association study (GWAS) for type 2 diabetes. In a number of studies conducted on multiple populations and different age groups, SNPs in the first intron of FTO were correlated with body weight and food intake Fto−/− mice show normal food intake but have an elevated metabolic rate FTO promoter to gain a better understanding of its transcriptional regulation.FTO gene expression, we characterized the promoter region of FTO. Using 5′-deletion luciferase reporter constructs of the FTO promoter, we found the core promoter to be located in the −201 bp before the TSS. Bioinformatics analysis of the promoter sequence identified putative Foxa2 binding sites. Data from both overexpression and siRNA knockdown experiments suggest that Foxa2 is a negative regulatory element capable of inhibiting FTO promoter activity and expression level. EMSA and ChIP assays further demonstrate that Foxa2 binds to a conserved motif in the FTO promoter in vitro and in vivo.To understand the regulatory mechanisms controlling in vitro differentiated primary human preadipocytes and in Simpson-Golabi-Behmel syndrome (SGBS) preadipocytes, FTO expression is downregulated during adipogenic differentiation. These findings are consistent with our results, in that FTO is a target gene of Foxa2.Foxa2 is well known to be a critical transcription factor regulating glycogen and lipid metabolism. Recently, it was shown that the activation of Foxa2 is implicated in the regulation of several genes involved in liver and pancreas development FTO promoter and repress its transcription. Homeostasis within the organism is maintained through cross-talk among signaling networks. Indeed, the balance of food intake and other biological actions rely on such mechanism. Subtle regulation in the expression levels and balance among the genes in the “feeding center” would affect = food consumption, metabolism and insulin sensitivity. The siRNA-mediated functional knockdown of Foxa2 increased the transcriptional activity of FTO, which makes Foxa2 an interesting target in the therapy to improve the energy metabolism of overweight and obese people.Furthermore, Foxa2 binds to melanin-concentrating hormone (MCH) and orexin promoters and stimulates their expression FTO. Stratigopoulos. G et al. discovered that the SNP rs8050136 within the FTO intron affects the binding of isoforms of transcription factor Cut-like Homeobox 1 (CUX1) with its regulatory site, resulting in the altered expression level of FTOThere is only one seminal study so far on the transcriptional regulation of FTO gene promoter is located within the −200 bp upstream of the TSS. We presented the first data demonstrating that Foxa2 is a key regulator of the human FTO gene; it down-regulates the promoter activity and expression of FTO. It is likely that Foxa2 may be involved in the regulation of diet intake and metabolic functions. FTO can act as a crucial factor in accommodating metabolic signals, adapting behavior and physiological responses.In conclusion, the core promoter region involved in the regulation of the human


Mitosis is accompanied by dramatic changes in chromatin organization and nuclear architecture. Transcription halts globally and most sequence-specific transcription factors and co-factors are ejected from mitotic chromatin. How then does the cell maintain its transcriptional identity throughout the cell division cycle? It has become clear that not all traces of active transcription and gene repression are erased within mitotic chromatin. Many histone modifications are stable or only partially diminished throughout mitosis. In addition, some sequence-specific DNA binding factors have emerged that remain bound to select sites within mitotic chromatin, raising the possibility that they function to transmit regulatory information through the transcriptionally silent mitotic phase, a concept that has been termed “mitotic bookmarking.” Here we review recent approaches to studying potential bookmarking factors with regards to their mitotic partitioning, and summarize emerging ideas concerning the in vivo functions of mitotically bound nuclear factors. Mitosis imposes dramatic and dynamic changes on nuclear organization and gene expression in eukaryotic cells. In metazoans, the nuclear envelope temporarily disintegrates, most nuclear structures are transiently dissolved or rearranged, and nuclear transcription by all three polymerases is globally arrested. Silencing of gene expression is accompanied by the separation of most transcriptional regulators from mitotic chromatin. Following chromosome segregation and re-formation of the nuclear envelope, bulk transcription resumes in the daughter cells that ultimately re-acquire gene expression patterns that are often similar to or indistinguishable from those of the mother cell. It has been widely assumed that these transitions present a problem for the cell’s maintenance of transcriptional identity, prompting investigations into mechanisms that ensure rapid and faithful restoration of gene expression upon re-entry into the G1 phase of the cell cycle. Such mechanisms fall under the category of epigenetics in the stricter sense of the word by providing a cellular memory function throughout the cell division cycle . TherefoSuch considerations led to the exploration of diverse mechanisms of gene bookmarking in mitosis to prevent potentially detrimental alterations in gene expression upon re-entry into G1 . These iA different perspective on the effects of mitosis on gene expression comes from the notion that genome-wide perturbations in transcription factor occupancy might facilitate changes in cell fate by allowing the reshaping of transcription programs. An impressive example is the observation that zygotes are capable of reprogramming somatic nuclei only after recipient cells have been arrested in mitosis , suggestIn this article instead of providing an extension of previous comprehensive reviews of known factors and histone marks that persist on mitotic chromatin, we aim to highlight recent technical and conceptual developments that approach questions of mitotic bookmarking.Here we provide a brief review of frequently used approaches to study potential mitotic memory mechanisms, since inconsistencies or controversies in the literature can be rooted in different methodologies used. Immunofluorescence (IF) microscopy is commonly employed to globally survey mitotic partitioning of nuclear factors and persistence of histone marks. An advantage of IF is that it detects endogenous proteins in their natural context, but it requires suitable antibodies for which the epitope is not occluded by chromatin compaction during mitosis, by mitosis-specific post-translational modifications, or because of fixation. These problems can be sidestepped by live cell-imaging with ectopically expressed fluorophore-tagged molecules. However, overexpression might lead to shifts in mitotic occupancy patterns. The monitoring of histone marks in living cells is more challenging, although strategies have been developed using fluorescence resonance energy transfer (FRET)-based indicator molecules that specifically interact with a given histone mark ,15. GiveTo localize nuclear factors or histone marks on specific genomic sites in mitosis, chromatin immunoprecipitation (ChIP) can be used conventionally or in combination with high throughput sequencing. Concerns about epitope recognition in mitotic cells are similar to those described for IF. The preparation of pure mitotic cells for ChIP is essential and has been aided by the recent development of effective protocols that use antibodies against phosphorylated histone H3 serine 10, a modification globally enriched during mitosis in all cell types, for fluorescence activated cell sorting (FACS) ,17. ThisThe combinatorial use of IF, live cell imaging, and ChIP does not only serve to corroborate key results, but can also uncover new concepts. For example, the hematopoietic transcription factor GATA1 globally separates from mitotic chromosomes as revealed by IF ,18. HoweAdditional strategies to assess mitotic chromatin binding involve the fractionation of mitotic chromosomes followed by western blotting  or unbiThe methods mentioned so far are useful tools for untangling the protein composition and post-translational modifications of mitotic chromosomes. However, the mere presence of a factor or histone mark on mitotic chromatin is insufficient to establish a functional role in mitotic memory. In several studies select mitotically retained factors were depleted by RNAi, leading to a delay in postmitotic target gene reactivation ,20,23-26et al. devised a strategy to deplete GATA1 selectively in mitosis [In an attempt to determine a mitotic bookmarking function for GATA1 Kadauke  mitosis . GATA1 w mitosis . It shouFinally, it is worth noting that mitotic partitioning of nuclear factors and histone marks can vary between cell types and might be influenced by whether primary or transformed cells are used . It is Finally, transcription factors both influence and are influenced by their chromatin environment. The dynamics of histone modifications during mitosis and possible roles in bookmarking are discussed elsewhere .Mechanisms that allow dispersal of nuclear factors and modulate histone modifications during mitosis have been extensively studied. Distinct kinetics of dissociation and re-association of factors during mitosis as well as varying stabilities of histone marks argue that multiple mechanisms control mitotic partitioning. Nevertheless, common themes have emerged that apply to broad classes of molecules, including dynamic phosphorylation of histones and nuclear factors. We will not discuss these mechanisms here as they have been thoughtfully covered in several recent reviews ,10,28. IAlthough global occupancy of transcription factors is sometimes reduced during mitosis ,19-21, iThe observed transcription factor occupancy patterns typically fall into distinct classes. At some sites nuclear factors are retained at normal levels while at others binding is diminished, which might be a result of fewer molecules per occupied site or fewer alleles being bound in heterogenous cell populations. In addition, new sites might be occupied by nuclear factors only in mitosis, thus signaling a shift in binding properties ,19,35. WDNA-binding factors generally occupy sites in a sequence-specific fashion, and variation in sequence can affect binding dynamics. Comparative genome-wide occupancy profiles of sequence-specific transcription factors in interphase and mitosis are now beginning to be determined ,20,21. DD. melanogaster GAGA transcription factor shifts from its interphase binding sites to centromeric alpha-satellite GA repeats [n repeats in intergenic regions remote from any annotated genes [An emerging theme of mitosis-specific occupancy involves repetitive DNA sequence elements. During mitosis the  repeats ,36. More repeats . The majed genes . Why woued genes . It is aSeveral features of chromatin such as DNase hypersensitivity, various modifications of DNA and histones, and histone variants persist at least in part through mitosis while others are dynamic throughout the cell cycle -47. It sHowever, some features of chromatin structure influence transcription factor binding profiles during mitosis. FoxA1 binding in mitosis seems to favor sites of high nucleosome density, consistent with its strong affinity for nucleosomes . It has n repeats (see previous section).The post-translational histone modifications examined so far fail to clearly distinguish interphase-only from persistent transcription factor binding sites ,20, althHox locus, even though the polycomb target mark H3K27me3 is present at this locus throughout mitosis [The polycomb protein PSC is partially maintained on chromatin in mitosis but not at the prototypic PSC target  mitosis . Notablyin vivo will be an important question for the future. It is notable that PP2A can also reverse the mitotic inactivation of the SWI/SNF chromatin remodeling complex [Dynamic phosphorylation of histones, transcription factors, and the mitotic condensation machinery control transitions through mitosis ,28, and  complex , but sinAcetylation of histones is at least partially maintained during mitosis . ReadersTranscription co-regulators are typically loaded onto chromatin via DNA binding proteins. It is therefore expected that mitotic partitioning of the former mirrors that of the latter. Correspondingly, the majority of cofactors are separated from mitotic chromosomes Table . A notabFor over half a century it has been known that transcription is globally silenced during mitosis ,115, yetTo what extent mitotically stable features of chromatin are required for the propagation of transcriptional information and maintenance is still mostly subject to speculation but has moved closer within our reach. Important related questions to be addressed include whether there is a direct role for mitosis in facilitating changes in transcription patterns to allow cell fate decisions during development, and whether alleviation of bookmarking facilitates cellular reprogramming or lineage switching. Finally, it will be important to explore whether perturbation of mitotic bookmarking accounts for developmental disorders or malignancies.ASH2L: Absent, small, or homeotic-like protein; BET: bromodomain-ET; BRD: bromodomain-containing protein; ChIP: chromatin immunoprecipitation; FACS: fluorescence-activated cell sorting; FoxA1: forkhead box A1; FOG 1: Friend of GATA 1; FRET: fluorescence resonance energy transfer; GATA1: GATA binding factor 1; HDAC1: Histone deacetylase 1; IF: immunofluorescence; P-TEFb: positive transcription elongation factor b; PSC: posterior sex combs; RbBP5: Retinoblastoma-binding protein 5; SWI/SNF: switch/sucrose nonfermentable; TAL: transactivator-like; TAL1: T-cell acute lymphocytic leukemia protein 1; TBP: TATA-binding protein; TLE: Transducin-like enhancer protein 1The authors declare that they have no competing interests.SK and GAB drafted and edited the manuscript. Both authors have read and approved the final manuscript.


As a result, we obtained a more comprehensive view on the complexity and plasticity of the prokaryotic genome biology. Listeria monocytogenes was utilized as a model system for intracellular pathogenic bacteria in several studies, which revealed the presence of about 180 regulatory RNAs in the listerial genome. A regulatory role of non-coding RNAs in survival, virulence and adaptation mechanisms of L. monocytogenes was confirmed in subsequent experiments, thus, providing insight into a multifaceted modulatory function of RNA/mRNA interference. In this review, we discuss the identification of regulatory RNAs by high-throughput techniques and in their functional role in L. monocytogenes.Bacterial regulatory non-coding RNAs control numerous mRNA targets that direct a plethora of biological processes, such as the adaption to environmental changes, growth and virulence. Recently developed high-throughput techniques, such as genomic tiling arrays and RNA-Seq have allowed investigating prokaryotic  Its genome bears a low G + C DNA content and is closely related to the genus of Streptococcus, Staphylococcus, Enterococcus and Clostridium. Eight different species have been described for the Listeria genus, including Listeria monocytogenes, Listeria ivanovii, Listeria innocua, Listeria welshimeri, Listeria seeligeri, Listeria grayi, Listeria marthii and the recently isolated Listeria rocourtiae [courtiae –4.L. monocytogenes cause infections in humans and animals, such as meningitis and septicaemia that are associated with a high mortality rate up to 30%, despite antibiotic therapy [L. monocytogenes is the ability to actively induce its own phagocytosis by host cells mediated by internalins (inl) and escape from the phagosom/autophagosom using the virulence factors listerolysin (LLO) or ActA for actin polymerization. Once located in the host cytosol, it replicates and induces actin-based motion allowing the pathogen to move in infected cells and spread to adjacent cells [inlA-C and hpt [L. monocytogenes are clustered in a ∼9 kb chromosomal region that is controlled by the principal virulence regulator PrfA [ therapy . A chara and hpt , the majtor PrfA ,3,7.L. monocytogenes has served as a model pathogen for the investigation of host-pathogen interactions and the immune response following infection intracellular bacteria and is now one of the best characterized microorganisms worldwide. Using recently developed high-throughput techniques, such as tiling arrays and next-generation DNA sequencing, which allows high-throughput RNA analysis through cDNA sequencing (RNA-Seq), several studies uncovered numerous non-coding RNAs in the listerial genome that were expressed under different conditions and introduce a new chapter of L. monocytogenes as an indispensable model organism for the investigation of prokaryotic transcriptomics [iptomics –10.Escherichia coli [Salmonella typhimurium [Pseudomonas aeruginosa [Streptococcus pyogenes [Streptococcus pneumoniae [Staphylococcus aureus [Three major studies detected 180 regulatory RNAs that are almost exclusively non-coding . Discovehia coli ,12, Salmhimurium ,14, Pseuruginosa , Streptopyogenes –18, Streeumoniae  and Staps aureus  extendeds aureus .L. monocytogenes. In general, interference of regulatory RNA with mRNA leads to destabilization and degradation of the target mRNA [trans-acting sRNAs encoded in intergenic regions (IGRs) at a chromosomal locus distal to the target transcript, (ii) anti-sense RNA (asRNA) encoded on the opposite strand of the open reading frame (ORF) or overlapping 5′ or 3′ untranslated region (UTR), and (iii) cis-regulatory RNAs, including riboswitches and 5′UTRs located upstream of a target gene with possible overlap with the ORF [trans-acting sRNA (discussed below), have recently been described and highlight an unexpected level of complexity of prokaryotic transcriptional regulation [Non-coding RNAs control the fate of mRNA through different mechanisms and thereby influence several biological processes and virulence in get mRNA . Interac the ORF . Howevergulation .L. monocytogenes, their role in virulence and adaptation to environmental changes predominantly identified by genomewide approaches.In this paper, we give an overview on non-coding RNAs in 2.L. monocytogenes adjusts to physiological and hostile environmental changes. For instance, previous studies investigated the intracellular expression signature and found that Listeria induces several metabolic and virulence genes that in concert allow survival and proliferation within host cells [B) characterized several stress response genes that are important for intracellular survival of the pathogen [Gene expression analyses using microarrays provided us with comprehensive information on how st cells ,26. Furtpathogen ,28. HoweTwo innovative techniques, genomic tiling arrays and RNA-Seq have revolutionized the field of RNA research, because they provide an uninterrupted picture of the whole transcriptome, including non-coding RNAs ,13,29,30L. monocytogenes has been identified by Barry et al. in 1999 [The first sRNA in  in 1999 . Subsequ in 1999 –34.et al. provided the first genome-wide study on the identification of regulatory RNA candidates expressed in L. monocytogenes and its deletion mutants under various physiological growth conditions in human blood, the intestinal lumen and other organs in mice [L. innocua. These data was largely confirmed by a following study using RNA-Seq by Oliver et al. who found 67 non-coding RNAs, of which 7 have not been described previously [trans-acting sRNA, sbrE was found to be directly regulated by σB. However, due to the RNA-Seq protocol, regulatory RNAs previously described to be coded anti-sense were not covered. In a more recent study, however, Hain and colleges provided the so far most detailed picture on sRNAs in L. monocytogenes using RNA-Seq that also allowed for directional reads [et al. [L. monocytogenes revealed that only 33% of predicted sRNAs overlapped with the RNA-Seq data [Using whole genome tiling arrays, Toledo-Arana  in mice . They ideviously . One traal reads . 150 smaal reads  and 69% al reads , respect [et al. . FurtherSeq data ,35.3.L. monocytogenes survives at a broad range of temperatures (0–45 °C) and pH (4.5–9), at high salt concentrations (10% NaCl) and also in most hostile environment, such as the phagocytic vacuole and within infected macrophages [rophages ,3,10. Forophages .4.L. monocytogenes and affect virulence and growth at the same time.5′UTRs are defined as the mRNA region starting at the promoter and ends with the start codon of the ORF. Since the transcription of an ORF may begin at different promoters, the length of the 5′UTR of a protein coding transcript may vary in length. 5′UTRs act as sensors for temperature and metabolites in , the master virulence regulator in L. monocytogenes, is controlled by a thermosensor that is located 116 nucleotides 5′UTR upstream of the coding mRNA sequence [prfA. An increase in temperature to 37 °C leads to formation of an alternative secondary structure that uncovers the SD site and allows for translation of the prfA transcript. Subsequently, PrfA induces expression of e.g. the major virulence factors LLO and ActA.PrfAsequence . At low L. monocytogenes [lmo0798). When present in abundance, lysine binds to the LysRS and stabilizes its tertiary structure leading to the formation of a Rho-independent transcriptional terminator (RITT) within the LysRS that prevents the expression of the downstream gene. RITT are strong secondary RNA structures that destabilize the binding of the RNA-polymerase to target DNA and thus stop transcription of downstream genes. In the absence of lysine, an anti-terminator structure is formed within the LysRS, which then allows for transcription of the lysine transporter gene. Interestingly, Toledo-Arana et al. discovered that LysRS regulates expression of the upstream protein coding gene lmo0799, thus, functions as a 3′UTR regulator as well [lmo0799 lacks an intrinsic 3′end terminator structure other than the LysRS that forms the RITT in lysine rich conditions. In contrast, lysine depletion leads to generation of a long transcript consisting of lmo0799-LysRS-lmo0798 [L. monocytogenes, including the cobalamin, T-box, M-box and S-adenosylmethionine binding (SAM) riboswitches [trans-acting sRNA [lmo2419, lmo2418 and lmo2417 that are involved in the cysteine or methionine metabolism. Strikingly, SreA also diffuses and binds to the 5′UTR of the prfA mRNA preventing its translation. Because the 5′UTR thermosensor of prfA exhibits a closed stem-loop structure at low temperatures, thus hiding the SD site, binding of SreA to prfA predominantly occurred at 37 °C, when the prfA thermosensor exhibits its open structure and allows SreA to target the SD region. These findings outline the complexity of the prokaryotic regulation by non-coding RNAs that are rather multifunctional and emphasize the potential flexibility of how bacteria control the expression of essential virulence genes.Metabolite-binding 5′UTRs that act as sensors for a substrate are also known as riboswitches. These RNA control elements possess an aptamer, an RNA region that is specific to a metabolite . Binding of the metabolite leads to an alteration of the riboswitch structure and results in transcription termination of the downstream gene. Several putative riboswitches have been identified for ytogenes ,10,24,37Listeria. In contrast, mutation of the FMN riboswitch allowed the pathogen to proliferate in the presence of roseoflavin. In conclusion, this study provides evidence that the FMN riboswitch directly controls bacterial growth.Furthermore, a regulatory role in the connection between nutrient availability and growth was recently shown for the flavin mononucleotide (FMN) riboswitch . Additio5.Trans-acting small non-coding RNAs (sRNAs) are encoded in intergenic regions (IGR) distal to the target gene. To date, 101 sRNAs (including SreA) have been identified in L. monocyotenes, predominantly by using whole genome tiling-arrays and RNA-Seq [trans-acting sRNAs [ RNA-Seq –10. Howeng sRNAs . The entng sRNAs . With a rliB, rli31, rli33-1, rli38 and rli50 were shown to have a role in virulence and growth of L. monocytogenes [rli31, these sRNAs are absent in L. innocua and other apathogenic strains of Listeria. A rli38 knockout strain displayed attenuated growth in several mouse organs [rli31, Δrli33-1 and Δrli50*  had decreased survival rates in infected murine macrophages, mouse organs and in an invertebrate infection model, when compared to the wild type strain, indicating an important role in virulence [rliB displayed affected virulence, although it colonized the liver faster than the wild type strain [Although functional data on sRNAs is limited, recently some sRNAs, including ytogenes ,10. Excee organs . Δrli31,e strain .Listeriae grown in blood, five of these sRNAs (LhrC1-5) displayed expression patterns similar to that of the virulence gene locus and virulence genes vip (lmo0320), uhpT and inlC, suggesting a possible role in virulence regulation [29 sRNAs were shown to be exclusively transcribed by bacteria within infected host cells, indicating a role in response to intracellular stress . InteresThe complexity of regulatory networks by sRNAs is increased by potential interactions between sRNAs . Conside6.Anti-sense RNAs (asRNAs) are encoded on the complementary DNA strand to the target transcript. Among asRNAs, different types and mechanisms of action can be distinguished depending on the localization on the opposite strand: (i) asRNAs that cover one or more ORFs without overlap to the flanking regions and (ii) 5′ or 3′UTRs of an mRNA overlapping with the mRNA encoded on the opposite strand.In the first case, promoter as well as terminator of the asRNA is located in a region between the ORFs located at the opposite strand. The length of asRNA substantially varies and may range from a few bases to kilobases. Certain asRNAs may target only the SD region of a transcript while others cover several ORFs.Overlapping 5′UTRs occur when the promoter of a certain gene is located within the targeted opposite divergent ORFs, but transcription of this gene ends upstream of the target ORF promoter. Overlapping 3′UTRs are produced when the promoter of the asRNA lies downstream of the target ORF terminator region and the transcription of the asRNA terminates within the ORF of the opposite strand.L. monocytogenes [anti2095 lies on the opposite strand of lmo2095 and covers, both lmo2096 and lmo2097, which encode for hypothetical proteins that may be implicated in carbohydrate metabolism. A similar organization was observed for anti2325 and anti2394 [More than 50 anti-sense regulatory RNAs and overlapping UTRs have been discovered in ytogenes ,10. Althytogenes . InteresL. monocytogenes harbours 13 overlapping 3′UTRs (anti0360, anti0945, anti0946, anti2260 [anti0111, anti0734, anti1909, anti1910, anti1980, anti1981, anti2044, anti2045 and anti2259 [anti0943, anti0977, anti2224 [anti0306, anti0647, anti0648 and mogR [mogR has been characterized in further detail. MogR is a transcriptional repressor of flagellum genes lmo0675, lmo0676 and lmo0677. mogR possesses two promoters, P1, which is localized 1697 nucleotides from the start codon and P2 at 45 nucleotides from the ATG. Transcription start at P1 generates a long overlapping 5′UTR that covers three genes required for flagellum synthesis in L. monocytogenes, thus decreasing pathogen motility. In line with this, overexpression of the long mogR transcript was shown to reduce the motility of L. monocytogenes. Interestingly, P1 also contains a σB box. A subsequent experiment with a σB isogenic deletion mutant showed that expression of the long anti-sense transcript starting at P1 was directly regulated by σB. Consequently, σB dependent mogR induction at P1 resulted in the generation of a long mogR transcript resulting in decreased motility of L. monocytogenes [Furthermore, the genome of anti2260  and anti7.trans-acting sRNAs with target mRNA often requires the RNA chaperone Hfq [L. monocytogenes, whereas Hfq is commonly indispensable for virulence in Gram-negative pathogens. However, to date, homologues of Hfq have been found in only half of the sequenced bacteria. Although Hfq was shown to be required for target interaction of several sRNAs, such as LhrA-C, others interact with the target mRNA independently from Hfq [Interaction of rone Hfq . This prfrom Hfq . Possiblfrom Hfq .B, while PrfA and Hfq had little or no effect on sRNA expression [Regulatory RNAs undergo transcriptional deregulation in a variety of conditions, most prominently in bacteria grown in blood and bacteria located in the intestinal lumen or within macrophages ,10. Tranpression ,9.8.Identification of a number of non-coding regulatory RNAs in many prokaryotic genomes has widened our view on the complexity of transcription regulation in bacteria. Recent developments in high-throughput techniques have been instrumental in this process. Tiling arrays and RNA-Seq have not only led to the detection of the vast majority of today’s known regulatory non-coding RNAs, but also channelled the discovery of new genes, the construction of the first operon map, the definition of untranslated regions and finally, the correction of previously annotated genes.Listeria, it becomes obvious that knowledge about the function of hundreds of regulatory RNAs and their target transcripts will have a remarkable impact on our understanding about several bacterial phenomena. For this process, L. monocytogenes serves as an unmatched model pathogen. Finally, regulatory RNAs may be attractive therapeutical targets to aim for with novel antibiotic treatments, such as PNAs [Although some insights have been obtained about how regulatory RNAs control mRNAs, precise functions and mechanisms remain largely unclear and need to be elucidated in further detail. Considering that only two years ago, the first sRNA has been described to have a role in virulence in  as PNAs ,40 or po