scholarly journals Transcription factors drive opposite relationships between gene age and tissue specificity in male and female Drosophila gonads

2020 ◽  
Author(s):  
Evan Witt ◽  
Nicolas Svetec ◽  
Sigi Benjamin ◽  
Li Zhao
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Tissue Specificity ◽  
Expression Data ◽  
Male And Female ◽  
Regulatory Changes ◽  
Trans Regulation ◽  
The Relationship ◽  
Gene Age

AbstractEvolutionarily young genes are usually preferentially expressed in the testis across species. While it is known that older genes are generally more broadly expressed than younger genes, the properties that shaped this pattern are unknown. Older genes may gain expression across other tissues uniformly, or faster in certain tissues than others. Using Drosophila gene expression data, we confirmed previous findings that younger genes are disproportionately testis-biased and older genes are disproportionately ovary-biased. We found that the relationship between gene age and expression is stronger in the ovary than any other tissue, and weakest in testis. We performed ATAC-seq on Drosophila testis and found that while genes of all ages are more likely to have open promoter chromatin in testis than in ovary, promoter chromatin alone does not explain the ovary-bias of older genes. Instead, we found that upstream transcription factor (TF) expression is highly predictive of gene expression in ovary, but not in testis. In ovary, TF expression is more predictive of gene expression than open promoter chromatin, whereas testis gene expression is similarly influenced by both TF expression and open promoter chromatin. We propose that the testis is uniquely able to expresses younger genes controlled by relatively few TFs, while older genes with more TF partners are broadly expressed with peak expression most likely in ovary. The testis allows widespread baseline expression that is relatively unresponsive to regulatory changes, whereas the ovary transcriptome is more responsive to trans-regulation and has a higher ceiling for gene expression.

scholarly journals Transcription Factors Drive Opposite Relationships between Gene Age and Tissue Specificity in Male and Female Drosophila Gonads

2021 ◽  
Author(s):  
Evan Witt ◽  
Nicolas Svetec ◽  
Sigi Benjamin ◽  
Li Zhao
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Tissue Specificity ◽  
Expression Data ◽  
Male And Female ◽  
Regulatory Changes ◽  
Drosophila Testis ◽  
Trans Regulation ◽  
The Relationship ◽  
Gene Age

Abstract Evolutionarily young genes are usually preferentially expressed in the testis across species. Although it is known that older genes are generally more broadly expressed than younger genes, the properties that shaped this pattern are unknown. Older genes may gain expression across other tissues uniformly, or faster in certain tissues than others. Using Drosophila gene expression data, we confirmed previous findings that younger genes are disproportionately testis biased and older genes are disproportionately ovary biased. We found that the relationship between gene age and expression is stronger in the ovary than any other tissue and weakest in testis. We performed ATAC-seq on Drosophila testis and found that although genes of all ages are more likely to have open promoter chromatin in testis than in ovary, promoter chromatin alone does not explain the ovary bias of older genes. Instead, we found that upstream transcription factor (TF) expression is highly predictive of gene expression in ovary but not in testis. In the ovary, TF expression is more predictive of gene expression than open promoter chromatin, whereas testis gene expression is similarly influenced by both TF expression and open promoter chromatin. We propose that the testis is uniquely able to express younger genes controlled by relatively few TFs, whereas older genes with more TF partners are broadly expressed with peak expression most likely in the ovary. The testis allows widespread baseline expression that is relatively unresponsive to regulatory changes, whereas the ovary transcriptome is more responsive to trans-regulation and has a higher ceiling for gene expression.

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

F1000Research ◽  
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.

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

F1000Research ◽  
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 ◽  
Factor Function

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 transcription 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 transcription factor ChIP experiment and the signed statistics from gene expression profiling with transcription factor perturbation. We further extend BETA to integrate two sets of data from two transcription 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 transcription factors, which were found to be, on average, more cooperatively regulated.

scholarly journals Integrating binding and expression data to predict transcription factors combined function

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Mahmoud Ahmed ◽  
Do Sik Min ◽  
Deok Ryong Kim
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Sites ◽  
Regulatory Region ◽  
Transcription Factor Binding ◽  
Expression Data ◽  
Dominant Type ◽  
Factor Binding ◽  
Two Factors

Abstract Background Transcription factor binding to the regulatory region of a gene induces or represses its gene expression. Transcription factors share their binding sites with other factors, co-factors and/or DNA-binding proteins. These proteins form complexes which bind to the DNA as one-units. The binding of two factors to a shared site does not always lead to a functional interaction. Results We propose a method to predict the combined functions of two factors using comparable binding and expression data (target). We based this method on binding and expression target analysis (BETA), which we re-implemented in R and extended for this purpose. target ranks the factor’s targets by importance and predicts the dominant type of interaction between two transcription factors. We applied the method to simulated and real datasets of transcription factor-binding sites and gene expression under perturbation of factors. We found that Yin Yang 1 transcription factor (YY1) and YY2 have antagonistic and independent regulatory targets in HeLa cells, but they may cooperate on a few shared targets. Conclusion We developed an R package and a web application to integrate binding (ChIP-seq) and expression (microarrays or RNA-seq) data to determine the cooperative or competitive combined function of two transcription factors.

scholarly journals Systematic inference of regulation by protein kinases finds surprising level of transcription factor deactivation

2021 ◽  
Author(s):  
Christian Degnbol Madsen ◽  
Jotun Hein ◽  
Christopher T. Workman
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Protein Kinase ◽  
Protein Kinases ◽  
Kinase Activity ◽  
Expression Data ◽  
Inference Method ◽  
Regulatory Interactions

AbstractGene expression is controlled by pathways of regulatory factors often involving the activity of protein kinases on transcription factor proteins. Despite this well established mechanism, the number of well described pathways that include the regulatory role of protein kinases on transcription factors is surprisingly scarce in eukaryotes.To address this, PhosTF was developed to infer functional regulatory interactions and pathways in both simulated and real biological networks, based on linear cyclic causal models with latent variables. GeneNetWeaverPhos, an extension of GeneNetWeaver, was developed to allow the simulation of perturbations in known networks that included the activity of protein kinases and phosphatases on gene regulation. Over 2000 genome-wide gene expression profiles, where the loss or gain of regulatory genes could be observed to perturb gene regulation, were then used to infer the existence of regulatory interactions, and their mode of regulation in the budding yeast Saccharomyces cerevisiae.Despite the additional complexity, our inference performed comparably to the best methods that inferred transcription factor regulation assessed in the DREAM4 challenge on similar simulated networks. Inference on integrated genome-scale data sets for yeast identified ∼8800 protein kinase/phosphatase-transcription factor interactions and ∼6500 interactions among protein kinases and/or phosphatases. Both types of regulatory predictions captured statistically significant numbers of known interactions of their type. Surprisingly, kinases and phosphatases regulated transcription factors by a negative mode or regulation (deactivation) in over 70% of the predictions.Author summaryIn this work we addressed the challenging problem of inferring regulation by protein kinases and phosphatases via their activity on transcription factors. Although many protein kinase activity predictors have been developed for classes of protein kinases on specific amino acids within target sequences, our approach (PhosTF) provides predictions of regulatory activity for specific protein kinases and phosphatases on specific transcription factors. Our inference approach achieves this using the functional output observed in gene expression data of gene knock out stains, along with known transcription factor regulatory interactions. We formulated and tested a model for inference of regulation as well as a model for simulation of genes expression, transcription and translation. The simulation was used for in-silico validation of the inference method, which performed comparably to the best performers on simpler inference problem in the DREAM4 competition. The inference method was then applied to yeast expression data, with significant validation by known kinase/phosphatase interactions. Over 15300 novel regulatory interactions were predicted, suggesting that kinase activity provided a surprising level of repression of gene expression, either through the deactivation of activators or the activation of repressors.

Subnuclear compartmentalization of sequence-specific transcription factors and regulation of eukaryotic gene expression

2005 ◽  
Vol 83 (4) ◽  
pp. 535-547 ◽  
Author(s):  
Gareth N Corry ◽  
D Alan Underhill
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Transcriptional Activation ◽  
Current Knowledge ◽  
Nuclear Architecture ◽  
Subnuclear Localization ◽  
Modular Structures

To date, the majority of the research regarding eukaryotic transcription factors has focused on characterizing their function primarily through in vitro methods. These studies have revealed that transcription factors are essentially modular structures, containing separate regions that participate in such activities as DNA binding, protein–protein interaction, and transcriptional activation or repression. To fully comprehend the behavior of a given transcription factor, however, these domains must be analyzed in the context of the entire protein, and in certain cases the context of a multiprotein complex. Furthermore, it must be appreciated that transcription factors function in the nucleus, where they must contend with a variety of factors, including the nuclear architecture, chromatin domains, chromosome territories, and cell-cycle-associated processes. Recent examinations of transcription factors in the nucleus have clarified the behavior of these proteins in vivo and have increased our understanding of how gene expression is regulated in eukaryotes. Here, we review the current knowledge regarding sequence-specific transcription factor compartmentalization within the nucleus and discuss its impact on the regulation of such processes as activation or repression of gene expression and interaction with coregulatory factors.Key words: transcription, subnuclear localization, chromatin, gene expression, nuclear architecture.

scholarly journals Endolysosomal Cation Channels and MITF in Melanocytes and Melanoma

Biomolecules ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1021
Author(s):  
Carla Abrahamian ◽  
Christian Grimm
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Transcription Factor ◽  
Convincing Evidence ◽  
Signaling Cascades ◽  
Cation Channels ◽  
Pore Channel ◽  
Different Types ◽  
Canonical Wnt ◽  
The Relationship

Microphthalmia-associated transcription factor (MITF) is the principal transcription factor regulating pivotal processes in melanoma cell development, growth, survival, proliferation, differentiation and invasion. In recent years, convincing evidence has been provided attesting key roles of endolysosomal cation channels, specifically TPCs and TRPMLs, in cancer, including breast cancer, glioblastoma, bladder cancer, hepatocellular carcinoma and melanoma. In this review, we provide a gene expression profile of these channels in different types of cancers and decipher their roles, in particular the roles of two-pore channel 2 (TPC2) and TRPML1 in melanocytes and melanoma. We specifically discuss the signaling cascades regulating MITF and the relationship between endolysosomal cation channels, MAPK, canonical Wnt/GSK3 pathways and MITF.

scholarly journals The AML-associated K313 mutation enhances C/EBPα activity by leading to C/EBPα overexpression

2021 ◽  
Vol 12 (7) ◽  
Author(s):  
Ian Edward Gentle ◽  
Isabel Moelter ◽  
Mohamed Tarek Badr ◽  
Konstanze Döhner ◽  
Michael Lübbert ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Culture ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Transcriptional Activity ◽  
Target Gene ◽  
Wild Type ◽  
Elevated Expression ◽  
Factor C ◽  
Acute Myeloid

AbstractMutations in the transcription factor C/EBPα are found in ~10% of all acute myeloid leukaemia (AML) cases but the contribution of these mutations to leukemogenesis is incompletely understood. We here use a mouse model of granulocyte progenitors expressing conditionally active HoxB8 to assess the cell biological and molecular activity of C/EBPα-mutations associated with human AML. Both N-terminal truncation and C-terminal AML-associated mutations of C/EBPα substantially altered differentiation of progenitors into mature neutrophils in cell culture. Closer analysis of the C/EBPα-K313-duplication showed expansion and prolonged survival of mutant C/EBPα-expressing granulocytes following adoptive transfer into mice. C/EBPα-protein containing the K313-mutation further showed strongly enhanced transcriptional activity compared with the wild-type protein at certain promoters. Analysis of differentially regulated genes in cells overexpressing C/EBPα-K313 indicates a strong correlation with genes regulated by C/EBPα. Analysis of transcription factor enrichment in the differentially regulated genes indicated a strong reliance of SPI1/PU.1, suggesting that despite reduced DNA binding, C/EBPα-K313 is active in regulating target gene expression and acts largely through a network of other transcription factors. Strikingly, the K313 mutation caused strongly elevated expression of C/EBPα-protein, which could also be seen in primary K313 mutated AML blasts, explaining the enhanced C/EBPα activity in K313-expressing cells.

scholarly journals Candida albicans Ferric Reductases Are Differentially Regulated in Response to Distinct Forms of Iron Limitation by the Rim101 and CBF Transcription Factors

2008 ◽  
Vol 7 (7) ◽  
pp. 1168-1179 ◽  
Author(s):  
Yong-Un Baek ◽  
Mingchun Li ◽  
Dana A. Davis
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Candida Albicans ◽  
Transcription Factors ◽  
Iron Acquisition ◽  
Mammalian Host ◽  
Regulatory Sequence ◽  
Ferric Reductase ◽  
Reductase Gene ◽  
Ferric Reductases

ABSTRACT Iron is an essential nutrient that is severely limited in the mammalian host. Candida albicans encodes a family of 15 putative ferric reductases, which are required for iron acquisition and utilization. Despite the central role of ferric reductases in iron acquisition and mobilization, relatively little is known about the regulatory networks that govern ferric reductase gene expression in C. albicans. Here we have demonstrated the differential regulation of two ferric reductases, FRE2 and FRP1, in response to distinct iron-limited environments. FRE2 and FRP1 are both induced in alkaline-pH environments directly by the Rim101 transcription factor. However, FRP1 but not FRE2 is also induced by iron chelation. We have identified a CCAAT motif as the critical regulatory sequence for chelator-mediated induction and have found that the CCAAT binding factor (CBF) is essential for FRP1 expression in iron-limited environments. We found that a hap5Δ/hap5Δ mutant, which disrupts the core DNA binding activity of CBF, is unable to grow under iron-limited conditions. C. albicans encodes three CBF-dependent transcription factors, and we identified the Hap43 protein as the CBF-dependent transcription factor required for iron-limited responses. These studies provide key insights into the regulation of ferric reductase gene expression in the fungal pathogen C. albicans.

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