MON-723 Identification of Thyrotrope Signature Genes and Regulatory Elements

Abstract Recent genome wide association studies have begun to identify loci that are risk factors for sporadic pituitary adenomas, but the genes associated with these loci are unknown. In general, ~90% of GWAS hits are in noncoding regions, making it difficult to transition from genetic mapping to a biological understanding of risk factors. Recent studies that identify enhancer regions by undertaking large scale functional genomic annotation of non-coding elements like Encyclopedia of DNA Elements (ENCODE) have begun to yield a better understanding of some complex diseases. Dense molecular profiling maps of the transcriptome and epigenome have been generated for more than 250 cell lines and 150 tissues, but pituitary cell lines or tissues were not included. Epigenetic and gene expression data are emerging for somatotropes, gonadotropes and corticotropes, but there is very little available data on thyrotropes. We identified the transcription factors and epigenetic changes in chromatin that are associated with differentiation of POU1F1-expressing progenitors into thyrotropes using cell lines that represent an early, undifferentiated Pou1f1 lineage progenitor (GHF-T1) and a committed thyrotrope (TαT1). TαT1 is an excellent cell line for this purpose because it responds to TRH, retinoids, and secretes TSH in response to diurnal cues. We have also used genetic labeling and fluorescence activated cell sorting to purify thyrotropes from pituitaries of young mice and analyzed gene expression using single cell transcriptomics. We used the Assay for TransposaseAccessible Chromatin with sequencing (ATACseq) and Cleavage Under Target and Release Using Nuclease (CUT&RUN) to identify POU1F1 binding sites and histone marks associated with active enhancers, H3K27Ac and H3K4Me1, or inactive regions, H3K27Me3, in GHF-T1 and TαT1 cells. We integrated DNA accessibility, histone modification patterns, transcription factor binding and RNA expression data to identify regulatory elements and candidate transcriptional regulators. We identified POU1F1 binding sites that were unique to each cell line. For example, POU1F1 binds sites in and around Cga and Tshb only in TαT1 cells and Twist1 and Gli3 only in GHFT1 cells. POU1F1 binding sites are commonly associated with bZIP factor consensus binding sites in GHFT1 cells and Helix-Turn-Helix or basic Helix-Loop-Helix in TαT1 cells, suggesting classes of transcription factors that may recruit POU1F1 to unique sites. We validated enhancer function of novel elements we mapped near Tshb, Gata2, and Pitx1 by transfection in TαT1 cells. Finally, we confirmed that an enhancer element near Tshb can drive expression in thyrotropes of transgenic mice. These data extend the ENCODE analysis to an organ that is critical for growth and metabolism. This information could be valuable for understanding pituitary development and disease pathogenesis.

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Multi-omic profiling of pituitary thyrotropic cells and progenitors

BMC Biology ◽

10.1186/s12915-021-01009-0 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Alexandre Z. Daly ◽

Lindsey A. Dudley ◽

Michael T. Peel ◽

Stephen A. Liebhaber ◽

Stephen C. J. Parker ◽

...

Keyword(s):

Transcription Factors ◽

Pituitary Gland ◽

Cell Lines ◽

Binding Sites ◽

Critical Factor ◽

Regulatory Elements ◽

Thyroid Stimulating Hormone ◽

Neuroendocrine Cells ◽

Bzip Transcription Factor ◽

Enhancer Element

Abstract Background The pituitary gland is a neuroendocrine organ containing diverse cell types specialized in secreting hormones that regulate physiology. Pituitary thyrotropes produce thyroid-stimulating hormone (TSH), a critical factor for growth and maintenance of metabolism. The transcription factors POU1F1 and GATA2 have been implicated in thyrotrope fate, but the transcriptomic and epigenomic landscapes of these neuroendocrine cells have not been characterized. The goal of this work was to discover transcriptional regulatory elements that drive thyrotrope fate. Results We identified the transcription factors and epigenomic changes in chromatin that are associated with differentiation of POU1F1-expressing progenitors into thyrotropes using cell lines that represent an undifferentiated Pou1f1 lineage progenitor (GHF-T1) and a committed thyrotrope line that produces TSH (TαT1). We compared RNA-seq, ATAC-seq, histone modification (H3K27Ac, H3K4Me1, and H3K27Me3), and POU1F1 binding in these cell lines. POU1F1 binding sites are commonly associated with bZIP transcription factor consensus binding sites in GHF-T1 cells and Helix-Turn-Helix (HTH) or basic Helix-Loop-Helix (bHLH) factors in TαT1 cells, suggesting that these classes of transcription factors may recruit or cooperate with POU1F1 binding at unique sites. We validated enhancer function of novel elements we mapped near Cga, Pitx1, Gata2, and Tshb by transfection in TαT1 cells. Finally, we confirmed that an enhancer element near Tshb can drive expression in thyrotropes of transgenic mice, and we demonstrate that GATA2 enhances Tshb expression through this element. Conclusion These results extend the ENCODE multi-omic profiling approach to the pituitary gland, which should be valuable for understanding pituitary development and disease pathogenesis. Graphical abstract

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Identification of pituitary thyrotrope signature genes and regulatory elements

10.1101/2020.08.05.238253 ◽

2020 ◽

Author(s):

Alexandre Z. Daly ◽

Lindsey A. Dudley ◽

Michael T. Peel ◽

Stephen A. Liebhaber ◽

Stephen C. J. Parker ◽

...

Keyword(s):

Transcription Factor ◽

Transcription Factors ◽

Cell Lines ◽

Binding Sites ◽

Critical Factor ◽

Regulatory Elements ◽

Thyroid Stimulating Hormone ◽

Bzip Transcription Factor ◽

Enhancer Element ◽

Transcriptional Regulatory Elements

AbstractPituitary thyrotropes are specialized cells that produce thyroid stimulating hormone (TSH), a critical factor for growth and maintenance of metabolism. The transcription factors POU1F1 and GATA2 have been implicated in thyrotrope fate and transcriptional regulation of the beta subunit of TSH, Tshb, but no transcriptomic or epigenomic analyses of these cells has been undertaken. The goal of this work was to discover key transcriptional regulatory elements that drive thyrotrope fate. We identified the transcription factors and epigenomic changes in chromatin that are associated with differentiation of POU1F1-expressing progenitors into thyrotropes, a process modeled by two cell lines: one that represents an early, undifferentiated Pou1f1 lineage progenitor (GHF-T1) and one that is a committed thyrotrope that produces TSH (TαT1). We generated and compared RNA-seq, ATAC-seq, histone modification (including H3K27Ac, H3K4Me1, and H3K27Me3), and transcription factor (POU1F1) binding in these two cell lines to identify regulatory elements and candidate transcriptional regulators. We identified POU1F1 binding sites that were unique to each cell line. POU1F1 binding sites are commonly associated with bZIP transcription factor consensus binding sites in GHF-T1 cells and Helix-Turn-Helix (HTH) or basic Helix-Loop-Helix (bHLH) factors in TαT1 cells, suggesting that these classes of transcription factors may recruit or cooperate with POU1F1 binding to unique sites. We validated enhancer function of novel elements we mapped near Cga, Pitx1, Gata2, and Tshb by transfection in TαT1 cells. Finally, we confirmed that an enhancer element near Tshb can drive expression in thyrotropes of transgenic mice, and we demonstrate that GATA2 enhances Tshb expression through this element. These results extend the ENCODE multi-omic profiling approach to an organ that is critical for growth and metabolism, which should be valuable for understanding pituitary development and disease pathogenesis.

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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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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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Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data

F1000Research ◽

10.12688/f1000research.10529.1 ◽

2016 ◽

Vol 5 ◽

pp. 2927 ◽

Cited By ~ 9

Author(s):

Linh Nguyen ◽

Cuong C Dang ◽

Pedro J. Ballester

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Cell Line ◽

Cell Lines ◽

Gene Expression Data ◽

Single Gene ◽

Cancer Cell Line ◽

Expression Data ◽

Gene Markers ◽

Pan Cancer

Background:Selected gene mutations are routinely used to guide the selection of cancer drugs for a given patient tumour. Large pharmacogenomic data sets were introduced to discover more of these single-gene markers of drug sensitivity. Very recently, machine learning regression has been used to investigate how well cancer cell line sensitivity to drugs is predicted depending on the type of molecular profile. The latter has revealed that gene expression data is the most predictive profile in the pan-cancer setting. However, no study to date has exploited GDSC data to systematically compare the performance of machine learning models based on multi-gene expression data against that of widely-used single-gene markers based on genomics data.Methods:Here we present this systematic comparison using Random Forest (RF) classifiers exploiting the expression levels of 13,321 genes and an average of 501 tested cell lines per drug. To account for time-dependent batch effects in IC50measurements, we employ independent test sets generated with more recent GDSC data than that used to train the predictors and show that this is a more realistic validation than K-fold cross-validation.Results and Discussion:Across 127 GDSC drugs, our results show that the single-gene markers unveiled by the MANOVA analysis tend to achieve higher precision than these RF-based multi-gene models, at the cost of generally having a poor recall (i.e. correctly detecting only a small part of the cell lines sensitive to the drug). Regarding overall classification performance, about two thirds of the drugs are better predicted by multi-gene RF classifiers. Among the drugs with the most predictive of these models, we found pyrimethamine, sunitinib and 17-AAG.Conclusions:We now know that this type of models can predictin vitrotumour response to these drugs. These models can thus be further investigated onin vivotumour models.

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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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Deep Mining of Natural Genetic Variation in Erythroid Cells Reveals New Insights about In Vivo Transcription Factor Binding and Chromatin Accessibility

Blood ◽

10.1182/blood.v128.22.3879.3879 ◽

2016 ◽

Vol 128 (22) ◽

pp. 3879-3879

Author(s):

Vivek Behera ◽

Perry Evans ◽

Carolyne J Face ◽

Laavanya Sankaranarayanan ◽

Gerd A. Blobel

Keyword(s):

Transcription Factor ◽

Genetic Variation ◽

Transcription Factors ◽

Cell Line ◽

Cell Lines ◽

Genetic Variants ◽

Binding Sites ◽

Chromatin Accessibility ◽

Erythroid Cell

Abstract Erythroid transcription factors (TFs) control gene expression programs, lineage decisions, and disease outcomes. How transcription factors contact DNA has been studied extensively in vitro, but in vivo binding characteristics are less well understood as they are influenced in a reciprocal manner by chromatin accessibility and neighboring transcription factors. Here, we present a comparative analysis approach that takes advantage of non-coding sequence variation between functionally equivalent erythroid cell lines to conduct an in-depth analysis of erythroid TF binding profiles and chromatin features. Specifically, we analyzed ChIP-seq datasets to identify millions of genetic non-coding variants between the mouse erythroleukemia cell line (MEL), a GATA1-inducible erythroid progenitor cell line (G1E-ER4), and primary murine erythroblast cells. We found that while these cell lines are highly positively correlated in chromatin features, larger differences in TF binding intensity are correlated with higher degrees of genetic variation between cell lines. We next examined discriminatory genetic variants between the cell lines that are located in ChIP-seq peaks of the erythroid transcription factor GATA1. Hundreds of such variants fall within GATA1 motifs. Differential GATA1 binding intensities associated with the variants revealed nucleotide positions that contribute most to in vivo GATA1 chromatin occupancy and identified which alternative nucleotides are most likely to disrupt binding. Notably, this additional information about GATA1's in vivo nucleotide binding preferences improved prediction of GATA1 binding sites genome-wide. We applied similar approaches to determine the bp-resolution in vivo binding preferences of TAL1/SCL and CTCF. We additionally identified thousands of discriminatory genetic variants within GATA1 sites that fall outside canonical GATA elements but within binding sites of other known TFs. Association of these variants with differential GATA1 binding intensities revealed that the hematopoietic transcription factors TAL1/SCL and KLF1 positively regulate GATA1 chromatin occupancy. Strikingly, we identified a number of motifs not previously implicated in cooperating with GATA1 that positively impact GATA1 chromatin binding. Notably, we also defined motifs associated with negative regulation of GATA1 chromatin occupancy. Applying a similar analysis to TAL1/SCL and CTCF revealed additional motifs involved in regulating the chromatin occupancy of these TFs. Finally, we associated discriminatory genetic variation between erythroid cell lines with large changes in sub-kb-scale DNase hypersensitivity. We found that single base pair substitutions within or near a number of erythroid TF motifs, including that for the RUNX family of nuclear factors, are strongly associated with changes in chromatin accessibility. Our findings use novel methods in comparative ChIP-seq and DNase-seq analysis to reveal new insights about the genetic basis for erythroid TF chromatin occupancy and chromatin accessibility. Disclosures No relevant conflicts of interest to declare.

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Differentially expressed Maf family transcription factors, c-Maf and MafA, activate glucagon and insulin gene expression in pancreatic islet alpha- and beta-cells

Journal of Molecular Endocrinology ◽

10.1677/jme.0.0320009 ◽

2004 ◽

Vol 32 (1) ◽

pp. 9-20 ◽

Cited By ~ 53

Author(s):

K Kataoka ◽

S Shioda ◽

K Ando ◽

K Sakagami ◽

H Handa ◽

...

Keyword(s):

Gene Expression ◽

Transcription Factors ◽

Cell Line ◽

Beta Cells ◽

Cell Lines ◽

Promoter Activity ◽

Insulin Gene ◽

Insulinoma Cell ◽

Insulin Gene Expression ◽

Glucagon And Insulin

A basic-leucine zipper transcription factor, MafA, was recently identified as one of the most important transactivators of insulin gene expression. This protein controls the glucose-regulated and pancreatic beta-cell-specific expression of the insulin gene through a cis-regulatory element called RIPE3b/MARE (Maf-recognition element). Here, we show that MafA expression is restricted to beta-cells of pancreatic islets in vivo and in insulinoma cell lines. We also demonstrate that c-Maf, another member of the Maf family of transcription factors, is expressed in islet alpha-cells and in a glucagonoma cell line (alphaTC1), but not in gamma- and delta-cells. An insulinoma cell line, betaTC6, also expressed c-Maf, albeit at a low level. Chromatin immunoprecipitation assays demonstrated that Maf proteins associate with insulin and glucagon promoters in beta- and alpha-cell lines, respectively. c-Maf protein stimulated glucagon promoter activity in a transient luciferase assay, and activation of the glucagon promoter by c-Maf was more efficient than by the other alpha-cell-enriched transcription factors, Cdx2, Pax6, and Isl-1. Furthermore, inhibition of c-Maf expression in alphaTC1 cells by specific short hairpin RNA resulted in marked reduction of the glucagon promoter activity. Thus, c-Maf and MafA are differentially expressed in alpha- and beta-cells where they regulate glucagon and insulin gene expression, respectively.

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Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data

10.1101/095224 ◽

2016 ◽

Author(s):

Linh C. Nguyen ◽

Cuong C. Dang ◽

Pedro J. Ballester

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Cell Line ◽

Cell Lines ◽

Gene Expression Data ◽

Single Gene ◽

Cancer Cell Line ◽

Expression Data ◽

Gene Markers ◽

Pan Cancer

AbstractSelected gene mutations are routinely used to guide the selection of cancer drugs for a given patient tumour. Large pharmacogenomic data sets were introduced to discover more of these single-gene markers of drug sensitivity. Very recently, machine learning regression has been used to investigate how well cancer cell line sensitivity to drugs is predicted depending on the type of molecular profile. The latter has revealed that gene expression data is the most predictive profile in the pan-cancer setting. However, no study to date has exploited GDSC data to systematically compare the performance of machine learning models based on multi-gene expression data against that of widely-used single-gene markers based on genomics data.Here we present this systematic comparison using Random Forest (RF) classifiers exploiting the expression levels of 13,321 genes and an average of 501 tested cell lines per drug. To account for time-dependent batch effects in IC50measurements, we employ independent test sets generated with more recent GDSC data than that used to train the predictors and show that this is a more realistic validation than K-fold cross-validation. Across 127 GDSC drugs, our results show that the single-gene markers unveiled by the MANOVA analysis tend to achieve higher precision than these RF-based multi-gene models, at the cost of generally having a poor recall (i.e. correctly detecting only a small part of the cell lines sensitive to the drug). Regarding overall classification performance, about two thirds of the drugs are better predicted by multi-gene RF classifiers. Among the drugs with the most predictive of these models, we found pyrimethamine, sunitinib and 17-AAG.

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