Activity Levels Of Transcription Factors Research Articles

Roles of alternative splicing in modulating transcriptional regulation

BackgroundThe ability of a transcription factor to regulate its targets is modulated by a variety of genetic and epigenetic mechanisms. Alternative splicing can modulate gene function by adding or removing certain protein domains, and therefore affect the activity of protein. Reverse engineering of gene regulatory networks using gene expression profiles has proven valuable in dissecting the logical relationships among multiple proteins during the transcriptional regulation. However, it is unclear whether alternative splicing of certain proteins affects the activity of other transcription factors.ResultsIn order to investigate the roles of alternative splicing during transcriptional regulation, we constructed a statistical model to infer whether the alternative splicing events of modulator proteins can affect the ability of key transcription factors in regulating the expression levels of their transcriptional targets. We tested our strategy in KIRC (Kidney Renal Clear Cell Carcinoma) using the RNA-seq data downloaded from TCGA (the Cancer Genomic Atlas). We identified 828of modulation relationships between the splicing levels of modulator proteins and activity levels of transcription factors. For instance, we found that the activity levels of GR (glucocorticoid receptor) protein, a key transcription factor in kidney, can be influenced by the splicing status of multiple proteins, including TP53, MDM2 (mouse double minute 2 homolog), RBM14 (RNA-binding protein 14) and SLK (STE20 like kinase). The influenced GR-targets are enriched by key cancer-related pathways, including p53 signaling pathway, TR/RXR activation, CAR/RXR activation, G1/S checkpoint regulation pathway, and G2/M DNA damage checkpoint regulation pathway.ConclusionsOur analysis suggests, for the first time, that exon inclusion levels of certain regulatory proteins can affect the activities of many transcription factors. Such analysis can potentially unravel a novel mechanism of how splicing variation influences the cellular function and provide important insights for how dysregulation of splicing outcome can lead to various diseases.

Thermodynamics-based models of transcriptional regulation with gene sequence.

Quantitative models of gene regulatory activity have the potential to improve our mechanistic understanding of transcriptional regulation. However, the few models available today have been based on simplistic assumptions about the sequences being modeled or heuristic approximations of the underlying regulatory mechanisms. In this work, we have developed a thermodynamics-based model to predict gene expression driven by any DNA sequence. The proposed model relies on a continuous time, differential equation description of transcriptional dynamics. The sequence features of the promoter are exploited to derive the binding affinity which is derived based on statistical molecular thermodynamics. Experimental results show that the proposed model can effectively identify the activity levels of transcription factors and the regulatory parameters. Comparing with the previous models, the proposed model can reveal more biological sense.

Quantitative modeling of transcriptional regulatory networks by integrating multiple source of knowledge

A key challenge in the post genome era is to identify genome-wide transcriptional regulatory networks, which specify the interactions between transcription factors and their target genes. In this work, a regulatory model-based binding energy is proposed to quantify the transcriptional regulatory network. Multiple quantities, including binding affinity, regulatory efficiency, and the activity level of transcription factor (TF) are incorporated into a general learning model. The sequence features of the promoter are exploited to derive the binding energy. Comparing with the previous models that only employ microarray data, the proposed model can bridge the gap between the relative background frequency of the observed nucleotide and the gene's transcription rate. Experimental results show that the proposed model can effectively identify the parameters and the activity level of TF. Moreover, the kinetic parameters introduced in the proposed model can reveal more biological sense than some previous models can do.

Bayesian inference based modelling for gene transcriptional dynamics by integrating multiple source of knowledge

BackgroundA key challenge in the post genome era is to identify genome-wide transcriptional regulatory networks, which specify the interactions between transcription factors and their target genes. Numerous methods have been developed for reconstructing gene regulatory networks from expression data. However, most of them are based on coarse grained qualitative models, and cannot provide a quantitative view of regulatory systems.ResultsA binding affinity based regulatory model is proposed to quantify the transcriptional regulatory network. Multiple quantities, including binding affinity and the activity level of transcription factor (TF) are incorporated into a general learning model. The sequence features of the promoter and the possible occupancy of nucleosomes are exploited to estimate the binding probability of regulators. Comparing with the previous models that only employ microarray data, the proposed model can bridge the gap between the relative background frequency of the observed nucleotide and the gene's transcription rate.ConclusionsWe testify the proposed approach on two real-world microarray datasets. Experimental results show that the proposed model can effectively identify the parameters and the activity level of TF. Moreover, the kinetic parameters introduced in the proposed model can reveal more biological sense than previous models can do.

Detection of eQTL modules mediated by activity levels of transcription factors

Studies of gene expression quantitative trait loci (eQTL) in different organisms have shown the existence of eQTL hot spots: each being a small segment of DNA sequence that harbors the eQTL of a large number of genes. Two questions of great interest about eQTL hot spots arise: (1) which gene within the hot spot is responsible for the linkages, i.e. which gene is the quantitative trait gene (QTG)? (2) How does a QTG affect the expression levels of many genes linked to it? Answers to the first question can be offered by available biological evidence or by statistical methods. The second question is harder to address. One simple situation is that the QTG encodes a transcription factor (TF), which regulates the expression of genes linked to it. However, previous results have shown that TFs are not overrepresented in the eQTL hot spots. In this article, we consider the scenario that the propagation of genetic perturbation from a QTG to other linked genes is mediated by the TF activity. We develop a procedure to detect the eQTL modules (eQTL hot spots together with linked genes) that are compatible with this scenario. We first detect 27 eQTL modules from a yeast eQTL data, and estimate TF activity profiles using the method of Yu and Li (2005). Then likelihood ratio tests (LRTs) are conducted to find 760 relationships supporting the scenario of TF activity mediation: (DNA polymorphism --> cis-linked gene --> TF activity --> downstream linked gene). They are organized into 4 eQTL modules: an amino acid synthesis module featuring a cis-linked gene LEU2 and the mediating TF Leu3; a pheromone response module featuring a cis-linked gene GPA1 and the mediating TF Ste12; an energy-source control module featuring two cis-linked genes, GSY2 and HAP1, and the mediating TF Hap1; a mitotic exit module featuring four cis-linked genes, AMN1, CSH1, DEM1 and TOS1, and the mediating TF complex Ace2/Swi5. Gene Ontology is utilized to reveal interesting functional groups of the downstream genes in each module. Our methods are implemented in an R package: eqtl.TF, which includes source codes and relevant data. It can be freely downloaded at http://www.stat.ucla.edu/~sunwei/software.htm. http://www.stat.ucla.edu/~sunwei/yeast_eQTL_TF/supplementary.pdf.

Factor analysis for gene regulatory networks and transcription factor activity profiles.

BackgroundMost existing algorithms for the inference of the structure of gene regulatory networks from gene expression data assume that the activity levels of transcription factors (TFs) are proportional to their mRNA levels. This assumption is invalid for most biological systems. However, one might be able to reconstruct unobserved activity profiles of TFs from the expression profiles of target genes. A simple model is a two-layer network with unobserved TF variables in the first layer and observed gene expression variables in the second layer. TFs are connected to regulated genes by weighted edges. The weights, known as factor loadings, indicate the strength and direction of regulation. Of particular interest are methods that produce sparse networks, networks with few edges, since it is known that most genes are regulated by only a small number of TFs, and most TFs regulate only a small number of genes.ResultsIn this paper, we explore the performance of five factor analysis algorithms, Bayesian as well as classical, on problems with biological context using both simulated and real data. Factor analysis (FA) models are used in order to describe a larger number of observed variables by a smaller number of unobserved variables, the factors, whereby all correlation between observed variables is explained by common factors. Bayesian FA methods allow one to infer sparse networks by enforcing sparsity through priors. In contrast, in the classical FA, matrix rotation methods are used to enforce sparsity and thus to increase the interpretability of the inferred factor loadings matrix. However, we also show that Bayesian FA models that do not impose sparsity through the priors can still be used for the reconstruction of a gene regulatory network if applied in conjunction with matrix rotation methods. Finally, we show the added advantage of merging the information derived from all algorithms in order to obtain a combined result.ConclusionMost of the algorithms tested are successful in reconstructing the connectivity structure as well as the TF profiles. Moreover, we demonstrate that if the underlying network is sparse it is still possible to reconstruct hidden activity profiles of TFs to some degree without prior connectivity information.