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.

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 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 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 Plant Phytochromes and their Phosphorylation

2019 ◽  
Vol 20 (14) ◽  
pp. 3450 ◽  
Author(s):  
Quyen T. N. Hoang ◽  
Yun-Jeong Han ◽  
Jeong-Il Kim
Keyword(s):  
Transcription Factors ◽  
Protein Kinase ◽  
Protein Kinases ◽  
Molecular Mechanisms ◽  
Kinase Activity ◽  
Protein Phosphatases ◽  
The Other ◽  
Light Signaling ◽  
Biochemical Mechanism ◽  
Reversible Phosphorylation

Extensive research over several decades in plant light signaling mediated by photoreceptors has identified the molecular mechanisms for how phytochromes regulate photomorphogenic development, which includes degradation of phytochrome-interacting factors (PIFs) and inactivation of COP1-SPA complexes with the accumulation of master transcription factors for photomorphogenesis, such as HY5. However, the initial biochemical mechanism for the function of phytochromes has not been fully elucidated. Plant phytochromes have long been known as phosphoproteins, and a few protein phosphatases that directly interact with and dephosphorylate phytochromes have been identified. However, there is no report thus far of a protein kinase that acts on phytochromes. On the other hand, plant phytochromes have been suggested as autophosphorylating serine/threonine protein kinases, proposing that the kinase activity might be important for their functions. Indeed, the autophosphorylation of phytochromes has been reported to play an important role in the regulation of plant light signaling. More recently, evidence that phytochromes function as protein kinases in plant light signaling has been provided using phytochrome mutants displaying reduced kinase activities. In this review, we highlight recent advances in the reversible phosphorylation of phytochromes and their functions as protein kinases in plant light signaling.

scholarly journals Post-inference Methods of Prior Knowledge Incorporation in Gene Regulatory Network Inference

2017 ◽  
Author(s):  
Ajay Nair ◽  
Madhu Chetty
Keyword(s):  
Gene Expression ◽  
Prior Knowledge ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Expression Data ◽  
Inference Method ◽  
Regulatory Interactions ◽  
A Cell ◽  
Gene Regulatory ◽  
Inference Methods

AbstractThe regulatory interactions in a cell control cellular response to environmental and genetic perturbations. Gene regulatory network (GRN) inference from high-throughput gene expression data helps to identify unknown regulatory interactions in a cell. One of the main challenges in the GRN inference is to identify complex biological interactions from the limited information contained in the gene expression data. Using prior biological knowledge, in addition to the gene expression data, is a common method to overcome this challenge. However, only a few GRN inference methods can inherently incorporate the prior knowledge and these methods are also not among the best-ranked in benchmarking studies.We propose to incorporate the prior knowledge after the GRN inference so that any inference method can be used. Two algorithms have been developed and tested on the well studied Escherichia coli, yeast, and realistic in silico networks. Their accuracy is higher than the best-ranking method in the latest community-wide benchmarking study. Further, one of the algorithms identifies and removes wrong interactions predicted by the inference methods. With half of the available prior knowledge of interactions, around 970 additional correct edges were obtained and 1300 wrong interactions were removed. Moreover, the limitation that only a few GRN inference methods can incorporate the prior knowledge is overcome. Therefore, a post-inference method of incorporating the prior knowledge improves accuracy, removes wrong edges, and overcomes the limitation of GRN inference methods.

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 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 Ca2+ Microdomains, Calcineurin and the Regulation of Gene Transcription

Cells ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 875
Author(s):  
Gerald Thiel ◽  
Tobias Schmidt ◽  
Oliver G. Rössler
Keyword(s):  
Transcription Factors ◽  
Protein Kinase ◽  
Protein Kinases ◽  
Gene Transcription ◽  
Intracellular Signaling ◽  
Second Messengers ◽  
Major Function ◽  
Surface Receptors ◽  
Dependent Protein

Ca2+ ions function as second messengers regulating many intracellular events, including neurotransmitter release, exocytosis, muscle contraction, metabolism and gene transcription. Cells of a multicellular organism express a variety of cell-surface receptors and channels that trigger an increase of the intracellular Ca2+ concentration upon stimulation. The elevated Ca2+ concentration is not uniformly distributed within the cytoplasm but is organized in subcellular microdomains with high and low concentrations of Ca2+ at different locations in the cell. Ca2+ ions are stored and released by intracellular organelles that change the concentration and distribution of Ca2+ ions. A major function of the rise in intracellular Ca2+ is the change of the genetic expression pattern of the cell via the activation of Ca2+-responsive transcription factors. It has been proposed that Ca2+-responsive transcription factors are differently affected by a rise in cytoplasmic versus nuclear Ca2+. Moreover, it has been suggested that the mode of entry determines whether an influx of Ca2+ leads to the stimulation of gene transcription. A rise in cytoplasmic Ca2+ induces an intracellular signaling cascade, involving the activation of the Ca2+/calmodulin-dependent protein phosphatase calcineurin and various protein kinases (protein kinase C, extracellular signal-regulated protein kinase, Ca2+/calmodulin-dependent protein kinases). In this review article, we discuss the concept of gene regulation via elevated Ca2+ concentration in the cytoplasm and the nucleus, the role of Ca2+ entry and the role of enzymes as signal transducers. We give particular emphasis to the regulation of gene transcription by calcineurin, linking protein dephosphorylation with Ca2+ signaling and gene expression.

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