TNIP1 reduction of HSPA6 gene expression occurs in promoter regions lacking binding sites for known TNIP1-repressed transcription factors

Abstract Background In silico analysis of transcription start sites, promoter regions, transcription factors and their binding sites, and CpG islands for the Trametes hirsuta strain 072 genome were performed to understand the regulation mechanisms of gene expression and its genetic variations in the genomes. Therefore, a computational survey was carried out for the Trametes hirsuta strain 072 genome with the open reading frames from the National Center for Biotechnology Information database. Seventeen functional sequences were used to analyze promoter regions and their regulatory elements. Result The present study revealed that 94% of Trametes hirsuta strain 072 genes contained more than two TSSs. Among these identified TSSs, a TSS with the highest predictive score was considered to determine a promoter region of the genes. Moreover, a total of five common candidate motifs such as MotI, MotII, MotIII, MotIV, and MotV were identified. Among these motifs, motif IV was investigated as the common promoter motif for 41.17% of genes that serve as binding sites for transcription factors (TFs) involved in the expression regulation of Trametes hirsuta strain 072 genes. Motif IV was also compared to registered motifs in publically available databases to see if they are similar to known regulatory motifs for TF using TOMTOM web server. Hence, it was revealed that MotIV might serve as the binding site mainly for the leucine zipper TF gene family to regulate a gene expression of Trametes hirsuta strain 072. Regarding CpG island determination, it was concluded that there is no CpG island in both promoter and gene body regions of the Trametes hirsuta strain 072 genome. Conclusions This study provides a better insight into further molecular characterization which aimed to efficiently exploit a white rot fungus, Trametes hirsuta strain 072, for several biotechnological applications aimed to revitalize a severely contaminated environment.

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Identification of NFI-binding sites and cloning of NFI-cDNAs suggest a regulatory role for NFI transcription factors in olfactory neuron gene expression

Molecular Brain Research ◽

10.1016/s0169-328x(99)00210-7 ◽

1999 ◽

Vol 72 (1) ◽

pp. 65-79 ◽

Cited By ~ 25

Author(s):

Hans Baumeister ◽

Richard M. Gronostajski ◽

Gary E. Lyons ◽

Frank L. Margolis

Keyword(s):

Gene Expression ◽

Transcription Factors ◽

Binding Sites ◽

Regulatory Role ◽

Olfactory Neuron

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The Expression Signature of M3 AML Suggests Direct and Indirect Effects of PML-RARA on Target Genes.

Blood ◽

10.1182/blood.v110.11.719.719 ◽

2007 ◽

Vol 110 (11) ◽

pp. 719-719 ◽

Cited By ~ 1

Author(s):

Jacqueline E. Payton ◽

Nicole R. Grieselhuber ◽

Li-Wei Chang ◽

Mark A. Murakami ◽

Wenlin Yuan ◽

...

Keyword(s):

Gene Expression ◽

Transcription Factors ◽

Cell Lines ◽

Binding Sites ◽

Expression Profiles ◽

Gene Expression Signature ◽

Myeloid Cell ◽

Expression Signature ◽

Signature Genes ◽

Cell Fractions

Abstract In order to better understand the pathogenesis of acute promyelocytic leukemia (APL, FAB M3), we sought to determine its gene expression signature by comparing the expression profiles of 14 APL samples to that of other AML subtypes (M0, M1, M2, M4, n=62) and to fractionated normal whole bone marrow cells (CD34 cells, promyelocytes, PMNs, n=5 each). We used ANOVA and SAM (Significance Analysis of Microarrays) to select genes that were highly expressed in APL cells and that displayed low to no expression in other AML subtypes. The APL signature was then further refined by filtering genes whose expression in APL was not significantly different from that of normal promyelocytes, yielding 1121 annotated genes that reliably distinguish APL from the other FAB subtypes using unsupervised hierarchical clustering, both in training and validation datasets. Fold change differences in expression between M3 and other AML FAB classes were striking, for example: GABRE 35.4, HGF 21.3, ANXA8 21.3, PTPRG 16.9, PTGDS 12.1, PPARG 11.1, STAB1 9.8. A large proportion of the APL versus other FAB dysregulome was recapitulated when we compared APL expression to that of the normal pattern of myeloid development. We identified 733 annotated genes with significantly different expression in APL versus normal myeloid cell fractions. These dysregulated genes were assigned to 4 classes: persistently expressed CD34 cell-specific genes, repressed promyelocyte-specific genes, prematurely expressed neutrophil-specific genes and genes with high expression in APL and low/no expression in normal myeloid cell fractions. Expression differences in several of the most dysregulated genes were validated by qRT-PCR. We then examined the expression of the APL signature genes in myeloid cell lines and tumors from a murine APL model. The bona fide M3 signature was not apparent in resting NB4 cells (which contain t(15;17), and which express PML-RARA), nor in PR-9 cells following Zn induction of PML-RARA expression, suggesting that neither cell line accurately models the gene expression signature of primary APL cells. Most of the nodal genes of the mCG-PML-RARA murine APL dysregulome (Yuan, et al, 2007) are similarly dysregulated in human M3 cells; however, the human and mouse dysregulomes do not completely coincide. Finally, we have begun investigating which APL signature genes are direct transcriptional targets of PML-RARA. The promoters of the APL signature genes were analyzed for the presence of known PML-RARA binding sites using multiple computational methods. The analyses demonstrated that several transcription factors (EBF3, TWIST1, SIX3, PPARG) have putative retinoic acid response elements (RAREs) in their upstream regulatory regions. Additionally, we examined the promoters of some of the most upregulated genes (HGF, PTGDS, STAB1) for known consensus sites of these transcription factors, and found that all have putative binding sites for at least one. These results suggest that PML-RARA may initiate a transcriptional cascade that relies not only on its own activity, but also on the actions of downstream transcription factors. In summary, our studies indicate that primary APL cells have a gene expression signature that is consistent and highly reproducible, but different from commonly used human APL cell lines and a mouse model of APL. The molecular mechanisms that govern this unique signature are currently under investigation.

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Promoter-Wide Transcriptional Deregulation In Waldenstrom Macroglobulinemia

Blood ◽

10.1182/blood.v116.21.3620.3620 ◽

2010 ◽

Vol 116 (21) ◽

pp. 3620-3620

Author(s):

Yang Liu ◽

Min Ni ◽

Aldo M. Roccaro ◽

Xavier Leleu ◽

Yong Zhang ◽

...

Keyword(s):

Gene Expression ◽

Bone Marrow ◽

Board Of Directors ◽

Binding Sites ◽

Research Funding ◽

Enrichment Analysis ◽

Promoter Regions ◽

Advisory Committees ◽

Pol Ii ◽

Normal Controls

Abstract Abstract 3620 Introduction: Waldenstrom macroglobulinemia (WM) is a rare indolent non-Hodgkin lymphoma, characterized by bone marrow infiltration of clonal lymphoplasmacytic cells. Despite recent advances in understanding the pathogenesis of this disease, the molecular basis of WM etiology has not been clearly defined. We therefore performed genome-wide analysis of RNA polymerase II (pol II) binding sites and gene expression profiling in primary WM cells in order to comprehensively define the aberrant transcriptional regulation and related genes in WM. Methods: Primary CD19+ bone marrow derived WM cells and normal primary bone marrow were used. Genomic DNA was extracted using genome isolation kit (QIAGEN) after cross linking. All the DNA samples were sent for Chip assay and human promoter 1.0R array (Genepathway Inc.) which comprised of over 4.6 million probes tiled through over 25.500 human promoter regions. Each promoter region covers approximately 7.6kb upstream through 2.45kb downstream of the transcription start sites. For over 1,300 cancer associated genes, coverage of promoter regions was expanded to additional genomic content; for selected genes total coverage spans from 10kb upstream through 2.45kb downstream of transcription start sites. The published gene expression datasets (GDS2643) which included 10 CD19+ B cell from bone marrow of 10 WM patients and 8 normal controls was analyzed by d-chip software and normalized to normal control. The motif analysis was performed using Cistrome online tools from the Dana Farber Cancer Institute. The gene sets enrichment analysis (GSEA) was performed using GSEA online software from Broad institute. Results: A total of 13,546 high-confidence pol II sites were identified in WM samples and share a small percentage of overlap (11.5%) with the binding sites identified in normal controls. Combining the expression microarray data of WM patient samples and normal controls, we demonstrated a significant correlation between high levels of gene expression and enriched promoter binding of pol II. Notably, we also observed that the WM-unique pol II binding sites are localized in the promoters of 5,556 genes which are involved in important signaling pathways, such as Jak/STAT and MAPK pathways by applying gene set enrichment analysis (GSEA). Interestingly, we found that STAT, FOXO and IRF family binding sites motifs were enriched in the pol II-bound promoter region of IL-6 which plays a crucial role in cell proliferation and survival of WM cells. Moreover, the CpG island associated c-fos promoter was enriched for Pol II binding as compared to the normal control. Conclusion: The presence of increased Pol II binding and the identification of transcription factor motifs in the promoters of key oncogenes may lead to a better understanding of WM. Our findings suggest that altered transcriptional regulation may play an important role in the pathogenesis of WM. In addition, this study will provide novel insights into the molecular mechanism of WM etiology, and may lead to discovery of novel diagnostic molecular biomarkers and therapeutic targets for WM. Disclosures: Leleu: Celgene: Consultancy, Research Funding; Janssen Cilag: Consultancy, Research Funding; Leo Pharma: Consultancy; Amgen: Consultancy; Chugai: Research Funding; Roche: Consultancy, Research Funding; Novartis: Consultancy, Research Funding. Ghobrial:Celgene: Membership on an entity's Board of Directors or advisory committees; Millennium: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees.

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Identifying Transcription Regulatory Elements in the Human and Mouse Genomes Using Tissue-specific Gene Expression Profiles

Journal of Integrative Bioinformatics ◽

10.1515/jib-2007-59 ◽

2007 ◽

Vol 4 (2) ◽

pp. 1-23

Author(s):

Amitava Karmaker ◽

Kihoon Yoon ◽

Mark Doderer ◽

Russell Kruzelock ◽

Stephen Kwek

Keyword(s):

Gene Expression ◽

Transcription Factor ◽

Transcription Factors ◽

Binding Sites ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Regulatory Elements ◽

Specific Gene ◽

Tissue Specific Gene ◽

Human And Mouse

Summary Revealing the complex interaction between trans- and cis-regulatory elements and identifying these potential binding sites are fundamental problems in understanding gene expression. The progresses in ChIP-chip technology facilitate identifying DNA sequences that are recognized by a specific transcription factor. However, protein-DNA binding is a necessary, but not sufficient, condition for transcription regulation. We need to demonstrate that their gene expression levels are correlated to further confirm regulatory relationship. Here, instead of using a linear correlation coefficient, we used a non-linear function that seems to better capture possible regulatory relationships. By analyzing tissue-specific gene expression profiles of human and mouse, we delineate a list of pairs of transcription factor and gene with highly correlated expression levels, which may have regulatory relationships. Using two closely-related species (human and mouse), we perform comparative genome analysis to cross-validate the quality of our prediction. Our findings are confirmed by matching publicly available TFBS databases (like TRANFAC and ConSite) and by reviewing biological literature. For example, according to our analysis, 80% and 85.71% of the targets genes associated with E2F5 and RELB transcription factors have the corresponding known binding sites. We also substantiated our results on some oncogenes with the biomedical literature. Moreover, we performed further analysis on them and found that BCR and DEK may be regulated by some common transcription factors. Similar results for BTG1, FCGR2B and LCK genes were also reported.

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Analysis of Activator and Repressor Functions Reveals the Requirements for Transcriptional Control by LuxR, the Master Regulator of Quorum Sensing in Vibrio harveyi

mBio ◽

10.1128/mbio.00378-13 ◽

2013 ◽

Vol 4 (4) ◽

Cited By ~ 43

Author(s):

Julia C. van Kessel ◽

Luke E. Ulrich ◽

Igor B. Zhulin ◽

Bonnie L. Bassler

Keyword(s):

Gene Expression ◽

Transcription Factors ◽

Dna Binding ◽

Quorum Sensing ◽

Binding Site ◽

Binding Sites ◽

Vibrio Harveyi ◽

Communication Process ◽

Nucleotide Sequencing ◽

Collective Behaviors

ABSTRACT LuxR-type transcription factors are the master regulators of quorum sensing in vibrios. LuxR proteins are unique members of the TetR superfamily of transcription factors because they activate and repress large regulons of genes. Here, we used chromatin immunoprecipitation and nucleotide sequencing (ChIP-seq) to identify LuxR binding sites in the Vibrio harveyi genome. Bioinformatics analyses showed that the LuxR consensus binding site at repressed promoters is a symmetric palindrome, whereas at activated promoters it is asymmetric and contains only half of the palindrome. Using a genetic screen, we isolated LuxR mutants that separated activation and repression functions at representative promoters. These LuxR mutants exhibit sequence-specific DNA binding defects that restrict activation or repression activity to subsets of target promoters. Altering the LuxR DNA binding site sequence to one more closely resembling the ideal LuxR consensus motif can restore in vivo function to a LuxR mutant. This study provides a mechanistic understanding of how a single protein can recognize a variety of binding sites to differentially regulate gene expression. IMPORTANCE Bacteria use the cell-cell communication process called quorum sensing to regulate collective behaviors. In vibrios, LuxR-type transcription factors control the quorum-sensing gene expression cascade. LuxR-type proteins are structural homologs of TetR-type transcription factors. LuxR proteins were assumed to function analogously to TetR proteins, which typically bind to a single conserved binding site to repress transcription of one or two genes. We find here that unlike TetR proteins, LuxR acts a global regulator, directly binding upstream of and controlling more than 100 genes. Again unlike TetR, LuxR functions as both an activator and a repressor, and these two activities can be separated by mutagenesis. Finally, the consensus binding motifs driving LuxR-activated and -repressed genes are distinct. This work shows that LuxR, although structurally similar to TetR, has evolved unique features enabling it to differentially control a large regulon of genes in response to quorum-sensing cues.

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8-OxoG in GC-rich Sp1 binding sites enhances gene transcription during adipose tissue development in juvenile mice

10.1101/538967 ◽

2019 ◽

Author(s):

Jong Woo Park ◽

Young In Han ◽

Tae Min Kim ◽

Su Cheong Yeom ◽

Jaeku Kang ◽

...

Keyword(s):

Gene Expression ◽

Gene Regulation ◽

Adipose Tissue ◽

Binding Sites ◽

Gene Promoter ◽

Tissue Development ◽

Promoter Regions ◽

Dna Lesion ◽

Estrogen Response ◽

Adipose Tissue Development

ABSTRACTThe oxidation of guanine to 8-oxoguanine (8-oxoG) is the most common type of oxidative DNA lesion. There is a growing body of evidence indicating that 8-oxoG is not only pre-mutagenic, but also plays an essential role in modulating gene expression along with its cognate repair proteins. In this study, we investigated the relationship between 8-oxoG formed under intrinsic oxidative stress conditions and gene expression in adipose and lung tissues of juvenile mice. We observed that transcriptional activity and the number of active genes were significantly correlated with the distribution of 8-oxoG in gene promoter regions, as determined by reverse-phase liquid chromatography/mass spectrometry (RP-LC/MS), and 8-oxoG and RNA sequencing. Gene regulation by 8-oxoG was not associated with the degree of 8-oxoG formation. Instead, genes with GC-rich transcription factor binding sites in their promoters became more active with increasing 8-oxoG abundance as also demonstrated by specificity protein 1 (Sp1)- and estrogen response element (ERE)-luciferase assays in human embryonic kidney (HEK293T) cells. These results indicate that the occurrence of 8-oxoG in GC-rich Sp1 binding sites is important for gene regulation during adipose tissue development.

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HCK Transcription Is Regulated By AP1, NF-Kb and STAT3 Transcription Factors in MYD88 Mutated WM and ABC-DLBCL Cells

Blood ◽

10.1182/blood.v128.22.2931.2931 ◽

2016 ◽

Vol 128 (22) ◽

pp. 2931-2931

Author(s):

Xia Liu ◽

Jiaji G Chen ◽

Jie Chen ◽

Lian Xu ◽

Nicholas Tsakmaklis ◽

...

Keyword(s):

Gene Expression ◽

Transcription Factors ◽

Cell Lines ◽

Binding Sites ◽

Research Funding ◽

Promoter Activity ◽

Promoter Sequence ◽

Wild Type ◽

Mutation Status ◽

Bcr Signaling

Abstract Hematopoietic cell kinase (HCK) is a member of the SRC family of tyrosine kinases (SFKs). HCK transcription is aberrantly upregulated in Waldenström's Macroglobulinemia (WM) and Activated B-cell (ABC) subtype Diffuse Large B-cell Lymphoma (DLBCL) in response to activating mutations in MYD88 (Yang et al, Blood 2016). To clarify the mechanism responsible for the aberrant upregulation of HCK transcription inMYD88 mutated cells, we analyzed the promoter sequence of HCK using PROMO and identified consensus binding sites for transcription factors (AP1, NF-kB, STAT3, and IRF1) that are regulated by mutated MYD88 (Ngo et al, Nature 2011; Treon et al, NEJM 2012; Yang et al, Blood 2013; Juilland et al, Blood 2016; Yang et al, Blood 2016). We performed Chromatin Immuno-precipitation (ChIP) assays using ChIP grade antibodies to JunB, c-Jun, NF-kB-p65, STAT3 and IRF1 in MYD88 mutated WM (BCWM.1, MWCL-1) and ABC DLBCL (TMD-8, HBL-1, OCI-Ly3) cells that highly express HCK transcripts, as well as wild type MYD88 expressing GCB DLBCL (OCI-Ly7, OCI-Ly19) cells that show low HCK transcription. Following ChIP, a HCK promoter specific quantitative PCR assay was used to detect HCK promoter sequences. These studies showed that JunB, NF-kB-p65 and STAT3 bound more robustly to the HCK promoter in MYD88 mutated WM and ABC-DLBCL cells versus MYD88 wild type GCB DLBCL cell lines, while c-Jun bound more abundantly to the HCK promoter sequence in all DLBCL cell lines, regardless of MYD88 mutation status. In contrast c-Jun binding was low in MYD88 mutated WM cells. IRF1 binding to the HCK promoter was similar in all cell lines, regardless of the MYD88 mutation status. To further investigate HCK regulation, we developed an HCK promoter driven luciferase reporter vector (WT) with mutated AP-1 binding (AP1-mu-1~6), NF-kB binding (NF-kB-mu-1~5), and STAT3 binding (STAT3-mu) sites and investigated their impact on HCK promoter activity in MYD88 mutated BCWM.1 cells. We observed that mutation of AP1-mu-1,4,5,6; NF-kB-mu-1,4,5, as well as STAT3-mu binding sites greatly reduced HCK promoter activity, thereby supporting a role for AP-1, NF-kB and STAT3 transcription factors in HCK gene expression in MYD88 mutated cells. To further clarify the importance of these transcription factors in aberrant HCK gene expression in MYD88 mutated cells, we treated BCWM.1, MWCL-1, TMD-8 and HBL-1 cells with the AP-1 inhibitor SR 11302; NF-kB inhibitor QNZ; and the STAT3 inhibitor STA-21. Treatment of cells for 2 hours with SR 11302, QNZ, and STA-21 at sub-EC50 concentrations resulted in decreased HCK expression in MYD88 mutated all cell lines. Lastly, we investigated the contribution of BCR signaling to HCK transcription. BCWM.1, MWCL-1, TMD-8, and HBL-1 cells were treated with the Syk kinase inhibitor R406, and HCK transcription levels were then assessed. Differences in HCK expression were observed between MYD88 mutated WM and ABC DLBCL cells following R406, supporting a contributing role for BCR signaling in ABC DLBCL but not WM cells to HCK expression. Our data provide critical new insights into HCK regulation, and a framework for targeting pro-survival HCK signaling in WM and ABC DLBCL cells dependent on activating MYD88 mutations. Disclosures Castillo: Biogen: Consultancy; Otsuka: Consultancy; Millennium: Research Funding; Janssen: Honoraria; Abbvie: Research Funding; Pharmacyclics: Honoraria. Treon:Janssen: Consultancy; Pharmacyclics: Consultancy, Research Funding.

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target: an R package to predict combined function of transcription factors

F1000Research ◽

10.12688/f1000research.52173.2 ◽

2021 ◽

Vol 10 ◽

pp. 344

Author(s):

Mahmoud Ahmed ◽

Deok Ryong Kim

Keyword(s):

Gene Expression ◽

Transcription Factor ◽

Transcription Factors ◽

Binding Sites ◽

R Package ◽

Expression Data ◽

Bioconductor Package ◽

Chip Experiment ◽

Binding Data ◽

Two Factors

Researchers use ChIP binding data to identify potential transcription factor binding sites. Similarly, they use gene expression data from sequencing or microarrays to quantify the effect of the factor overexpression or knockdown on its targets. Therefore, the integration of the binding and expression data can be used to improve the understanding of a transcription factor function. Here, we implemented the binding and expression target analysis (BETA) in an R/Bioconductor package. This algorithm ranks the targets based on the distances of their assigned peaks from the factor ChIP experiment and the signed statistics from gene expression profiling with factor perturbation. We further extend BETA to integrate two sets of data from two factors to predict their targets and their combined functions. In this article, we briefly describe the workings of the algorithm and provide a workflow with a real dataset for using it. The gene targets and the aggregate functions of transcription factors YY1 and YY2 in HeLa cells were identified. Using the same datasets, we identified the shared targets of the two factors, which were found to be, on average, more cooperatively regulated.

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