Transcription factor functionality and transcription regulatory networks

Analysis of the interaction of a non-canonical twin half-site of Cyclic AMP-Response Element (CRE) with CRE-binding protein

Differential regulation of a gene having either canonical or non-canonical cyclic AMP response element (CRE) in its promoter is primarily accomplished by its interactions with CREB (cAMP-response element binding protein). The present study aims to delineate the mechanism of the CREB-CRE interactions at the Oncostatin-M (osm) promoter by in vitro and in silico approaches. The non-canonical CREosm consists of two half-CREs separated by a short intervening sequence of 9 base pairs. In this study, in vitro binding assays revealed that out of the two CRE half-sites, the right half-CRE was indispensable for binding of CREB, while the left sequence showed weaker binding ability and specificity. Genome-wide modeling and high throughput free energy calculations for the energy-minimized models containing CREB-CREosm revealed that there was no difference in the binding of CREB to the right half of CREosm site when compared to the entire CREosm. These results were in accordance with the in vitro studies, confirming the indispensable role of the right half-CREosm site in stable complex formation with the CREB protein. Additionally, conversion of the right half-CREosm site to a canonical CRE palindrome showed stronger CREB binding, irrespective of the presence or absence of the left CRE sequence. Thus, the present study establishes an interesting insight into the interaction of CREB with a CRE variant located at the far end of a TATA-less promoter of a cytokine-encoding gene, which in turn could be involved in the regulation of transcription under specific conditions.

PITX1 Is a Regulator of TERT Expression in Prostate Cancer with Prognostic Power

Simple Summary

Most prostate cancer is of an indolent form and is curable. However, some prostate cancer belongs to rather aggressive subtypes leading to metastasis and death, and immediate therapy is mandatory. However, for these, the therapeutic options are highly invasive, such as radical prostatectomy, radiation or brachytherapy. Hence, a precise diagnosis of these tumor subtypes is needed, and the thus far applied diagnostic means are insufficient for this. Besides this, for their endless cell divisions, prostate cancer cells need the enzyme telomerase to elongate their telomeres (chromatin endings). In this study, we developed a gene regulatory model based on large data from transcription profiles from prostate cancer and chromatin-immuno-precipitation studies. We identified the developmental regulator PITX1 regulating telomerase. Besides observing experimental evidence of PITX1′s functional role in telomerase regulation, we also found PITX1 serving as a prognostic marker, as concluded from an analysis of more than 15,000 prostate cancer samples.

Abstract

The current risk stratification in prostate cancer (PCa) is frequently insufficient to adequately predict disease development and outcome. One hallmark of cancer is telomere maintenance. For telomere maintenance, PCa cells exclusively employ telomerase, making it essential for this cancer entity. However, TERT, the catalytic protein component of the reverse transcriptase telomerase, itself does not suit as a prognostic marker for prostate cancer as it is rather low expressed. We investigated if, instead of TERT, transcription factors regulating TERT may suit as prognostic markers. To identify transcription factors regulating TERT, we developed and applied a new gene regulatory modeling strategy to a comprehensive transcriptome dataset of 445 primary PCa. Six transcription factors were predicted as TERT regulators, and most prominently, the developmental morphogenic factor PITX1. PITX1 expression positively correlated with telomere staining intensity in PCa tumor samples. Functional assays and chromatin immune-precipitation showed that PITX1 activates TERT expression in PCa cells. Clinically, we observed that PITX1 is an excellent prognostic marker, as concluded from an analysis of more than 15,000 PCa samples. PITX1 expression in tumor samples associated with (i) increased Ki67 expression indicating increased tumor growth, (ii) a worse prognosis, and (iii) correlated with telomere length.

C-myc promotes miR-92a-2-5p transcription in rat ovarian granulosa cells after cadmium exposure

Cadmium (Cd) can induce ovarian injury by microRNAs (miRNAs), however, the molecular mechanism of miRNAs after Cd exposure have not known. In this study, 56-day-old adult female Sprague-Dawley (SD) rats were injection with PMSG, after 48 h, ovarian granulosa cells (GCs) were extracted and cultured for 24 h, then treated with 0, 2.5, 5, 10 and 20 μM Cd for 24 h. The results showed that expression levels of miR-92a-2-5p (upregulated) and Bcl2 (downregulated) changed significantly after Cd exposure. The messenger RNA (mRNA) and protein expression levels of DNMT1, DNMT3A, and DNMT3B had changed, but no obvious differences were found in miR-92a-2-5p single site methylation. The transcription factors C-MYC (upregulated), E2F1 (downregulated), and SP1 (downregulated), which target miRNAs significantly changed after exposure to Cd. The human ovarian GC tumor line (COV434) was used to knocked down C-myc, and the expression of miR-92a-2-5p was downregulated in the COV434-C-myc + 10 μM Cd group compared with COV434 cells. The N6-methyladenosine (m6A) methylation modification levels of long noncoding RNA (lncRNA) MT1JP and lncRNA CDKN2B-AS, which regulate miR-92a-2-5p were detected. In the 10 μM Cd group, m6A methylation levels at MT1JP-84, CDKN2B-AS-257, and CDKN2B-AS-329 were reduced. In summary, after Cd exposure, expression of miR-92a-2-5p, which targets the antiapoptotic gene Bcl2, was upregulated, which may be primarily related to upregulation of C-myc. MiR-92a-2-5p promoter DNA methylation may has no obvious effect on miR-92a-2-5p. Otherwise, the role of m6A methylation modified lncRNA MT1JP and lncRNA CDKN2B-AS in the regulation of miR-92a-2-5p needs further study.

Exploring a Drosophila Transcription Factor Interaction Network to Identify Cis-Regulatory Modules

Multiple transcription factors (TFs) bind to specific sites in the genome and interact among themselves to form the cis-regulatory modules (CRMs). They are essential in modulating the expression of genes, and it is important to study this interplay to understand gene regulation. In the present study, we integrated experimentally identified TF binding sites collected from published studies with computationally predicted TF binding sites to identify Drosophila CRMs. Along with the detection of the previously known CRMs, this approach identified novel protein combinations. We determined high-occupancy target sites, where a large number of TFs bind. Investigating these sites revealed that Giant, Dichaete, and Knirp are highly enriched in these locations. A common TAG team motif was observed at these sites, which might play a role in recruiting other TFs. While comparing the binding sites at distal and proximal promoters, we found that certain regulatory TFs, such as Zelda, were highly enriched in enhancers. Our study has shown that, from the information available concerning the TF binding sites, the real CRMs could be predicted accurately and efficiently. Although we only may claim co-occurrence of these proteins in this study, it may actually point to their interaction (as known interaction proteins typically co-occur together). Such an integrative approach can, therefore, help us to provide a better understanding of the interplay among the factors, even though further experimental verification is required.

TGFB1-Mediated Gliosis in Multiple Sclerosis Spinal Cords Is Favored by the Regionalized Expression of HOXA5 and the Age-Dependent Decline in Androgen Receptor Ligands

In multiple sclerosis (MS) patients with a progressive form of the disease, spinal cord (SC) functions slowly deteriorate beyond age 40. We previously showed that in the SC of these patients, large areas of incomplete demyelination extend distance away from plaque borders and are characterized by a unique progliotic TGFB1 (Transforming Growth Factor Beta 1) genomic signature. Here, we attempted to determine whether region- and age-specific physiological parameters could promote the progression of SC periplaques in MS patients beyond age 40. An analysis of transcriptomics databases showed that, under physiological conditions, a set of 10 homeobox (HOX) genes are highly significantly overexpressed in the human SC as compared to distinct brain regions. Among these HOX genes, a survey of the human proteome showed that only HOXA5 encodes a protein which interacts with a member of the TGF-beta signaling pathway, namely SMAD1 (SMAD family member 1). Moreover, HOXA5 was previously found to promote the TGF-beta pathway. Interestingly, SMAD1 is also a protein partner of the androgen receptor (AR) and an unsupervised analysis of gene ontology terms indicates that the AR pathway antagonizes the TGF-beta/SMAD pathway. Retrieval of promoter analysis data further confirmed that AR negatively regulates the transcription of several members of the TGF-beta/SMAD pathway. On this basis, we propose that in progressive MS patients, the physiological SC overexpression of HOXA5 combined with the age-dependent decline in AR ligands may favor the slow progression of TGFB1-mediated gliosis. Potential therapeutic implications are discussed.

Designing Conservation Relations in Layered Synthetic Biomolecular Networks

In Synthetic Biology, biomolecular networks are designed and constructed to perform specified tasks. Design strategies for these networks tend to center on tuning the parameters of mathematical models to achieve a specified behavior, and implementing these parameters experimentally. This design strategy often assumes a fixed network structure that defines the possible behaviors, which may be too restrictive for our purposes. This paper investigates the extent to which the state space of a synthetic network can also be designed and shaped by parametric tuning. We exploit timescale separation to implement new, nonlinear, tunable conservation relations that hold for all times beyond a fast transient. We demonstrate an application of this design strategy by flexibly constraining the possible behaviors of a gene regulatory network through the design of fast protein interactions.

Transcription factor networks in Drosophila melanogaster

Specific cellular fates and functions depend on differential gene expression, which occurs primarily at the transcriptional level and is controlled by complex regulatory networks of transcription factors (TFs). TFs act through combinatorial interactions with other TFs, cofactors, and chromatin-remodeling proteins. Here, we define protein-protein interactions using a coaffinity purification/mass spectrometry method and study 459 Drosophila melanogaster transcription-related factors, representing approximately half of the established catalog of TFs. We probe this network in vivo, demonstrating functional interactions for many interacting proteins, and test the predictive value of our data set. Building on these analyses, we combine regulatory network inference models with physical interactions to define an integrated network that connects combinatorial TF protein interactions to the transcriptional regulatory network of the cell. We use this integrated network as a tool to connect the functional network of genetic modifiers related to mastermind, a transcriptional cofactor of the Notch pathway.

Ethylene-responsive element-binding factor 5, ERF5, is involved in chitin-induced innate immunity response

Our recent work demonstrated that chitin treatment modulated the expression of 118 transcription factor (TF) genes in Arabidopsis. To investigate the potential roles of these TF in chitin signaling and plant defense, we initiated an interaction study among these TF proteins, as well as two chitin-activated mitogen-activated protein kinases (MPK3 and MPK6), using a yeast two-hybrid system. This study revealed interactions among the following proteins: three ethylene-responsive element-binding factors (ERF), five WRKY transcription factors, one scarecrow-like (SCL), and the two MPK, in addition to many other interactions, reflecting a complex TF interaction network. Most of these interactions were subsequently validated by other methods, such as pull-down and in planta bimolecular fluorescence complementation assays. The key node ERF5 was shown to interact with multiple proteins in the network, such as ERF6, ERF8, and SCL13, as well as MPK3 and MPK6. Interestingly, ERF5 appeared to negatively regulate chitin signaling and plant defense against the fungal pathogen Alternaria brassicicola and positively regulate salicylic acid signaling and plant defense against the bacterial pathogen Pseudomonas syringae pv. tomato DC3000. Therefore, ERF5 may play an important role in plant innate immunity, likely through coordinating chitin and other defense pathways in plants in response to different pathogens.

Using a structural and logics systems approach to infer bHLH-DNA binding specificity determinants

Numerous efforts are underway to determine gene regulatory networks that describe physical relationships between transcription factors (TFs) and their target DNA sequences. Members of paralogous TF families typically recognize similar DNA sequences. Knowledge of the molecular determinants of protein–DNA recognition by paralogous TFs is of central importance for understanding how small differences in DNA specificities can dictate target gene selection. Previously, we determined the in vitro DNA binding specificities of 19 Caenorhabditis elegans basic helix-loop-helix (bHLH) dimers using protein binding microarrays. These TFs bind E-box (CANNTG) and E-box-like sequences. Here, we combine these data with logics, bHLH–DNA co-crystal structures and computational modeling to infer which bHLH monomer can interact with which CAN E-box half-site and we identify a critical residue in the protein that dictates this specificity. Validation experiments using mutant bHLH proteins provide support for our inferences. Our study provides insights into the mechanisms of DNA recognition by bHLH dimers as well as a blueprint for system-level studies of the DNA binding determinants of other TF families in different model organisms and humans.

Tissue-specific regulation of mouse microRNA genes in endoderm-derived tissues

MicroRNAs fine-tune the activity of hundreds of protein-coding genes. The identification of tissue-specific microRNAs and their promoters has been constrained by the limited sensitivity of prior microRNA quantification methods. Here, we determine the entire microRNAome of three endoderm-derived tissues, liver, jejunum and pancreas, using ultra-high throughput sequencing. Although many microRNA genes are expressed at comparable levels, 162 microRNAs exhibited striking tissue-specificity. After mapping the putative promoters for these microRNA genes using H3K4me3 histone occupancy, we analyzed the regulatory modules of 63 microRNAs differentially expressed between liver and jejunum or pancreas. We determined that the same transcriptional regulatory mechanisms govern tissue-specific gene expression of both mRNA and microRNA encoding genes in mammals.

Using the Tet-On system to develop a procedure for extracting transcription factor activation dynamics

The regulation of gene expression by transcription factors through different expression and activation dynamics is an important aspect of genomics and systems biology. Reporter systems using green fluorescent protein (GFP) or luciferase are often used to infer transcription factor dynamics. We recently used an inverse problem solution of GFP reporter profiles to demonstrate that the activation dynamics of a model transcription actor (NF-kappaB) can be reconstructed from GFP data. This approach assumes that the general nature of the transcription factor dynamics is known; however, it is non-trivial to determine the exact nature of the transcription factor dynamics as it often depends upon the cell type and the stimulus used to activate the transcription factor. This, in turn, limits the determination of accurate transcription factor dynamics from reporter data, especially since the model used for solution of an inverse problem needs to be verified. To address this point, we developed a reporter cell line for expressing GFP using an inducible, artificial transcription factor (tTA) and minimal promoter system. The artificial transcription factor can be activated independent of the cellular regulatory machinery through addition of doxycycline. This allows us to directly control the dynamics of the artificial transcription factor, and thereby, develop a model describing its activation dynamics from reporter data. Our experimental data and model predictions are in good agreement, and illustrate the utility of our approach. Future work will focus on using the developed approach, i.e. solution of an inverse problem involving the model describing expression of GFP, to extract the dynamics of transcription factors that are currently uncharacterized.

Gene-centered regulatory networks

Differential gene expression plays a critical role in the development and physiology of multicellular organisms. At a 'systems level' (e.g. at the level of a tissue, organ or whole organism), this process can be studied using gene regulatory network (GRN) models that capture physical and regulatory interactions between genes and their regulators. In the past years, significant progress has been made toward the mapping of GRNs using a variety of experimental and computational approaches. Here, we will discuss gene-centered approaches that we employed to characterize GRNs and describe insights that we have obtained into the global design principles of gene regulation in complex metazoan systems.

A multiparameter network reveals extensive divergence between C. elegans bHLH transcription factors

Differences in expression, protein interactions, and DNA binding of paralogous transcription factors ("TF parameters") are thought to be important determinants of regulatory and biological specificity. However, both the extent of TF divergence and the relative contribution of individual TF parameters remain undetermined. We comprehensively identify dimerization partners, spatiotemporal expression patterns, and DNA-binding specificities for the C. elegans bHLH family of TFs, and model these data into an integrated network. This network displays both specificity and promiscuity, as some bHLH proteins, DNA sequences, and tissues are highly connected, whereas others are not. By comparing all bHLH TFs, we find extensive divergence and that all three parameters contribute equally to bHLH divergence. Our approach provides a framework for examining divergence for other protein families in C. elegans and in other complex multicellular organisms, including humans. Cross-species comparisons of integrated networks may provide further insights into molecular features underlying protein family evolution. For a video summary of this article, see the PaperFlick file available with the online Supplemental Data.

The interplay between transcription factors and microRNAs in genome-scale regulatory networks

Metazoan genomes contain thousands of protein-coding and non-coding RNA genes, most of which are differentially expressed, i.e., at different locations, at different times during development, or in response to environmental signals. Differential gene expression is achieved through complex regulatory networks that are controlled in part by two types of trans-regulators: transcription factors (TFs) and microRNAs (miRNAs). TFs bind to cis-regulatory DNA elements that are often located in or near their target genes, while miRNAs hybridize to cis-regulatory RNA elements mostly located in the 3' untranslated region of their target mRNAs. Here, we describe how these trans-regulators interact with each other in the context of gene regulatory networks to coordinate gene expression at the genome-scale level, and discuss future challenges of integrating these networks with other types of functional networks.

Integrated modeling and experimental approach for determining transcription factor profiles from fluorescent reporter data

Background

The development of quantitative models of signal transduction, as well as parameter estimation to improve existing models, depends on the ability to obtain quantitative information about various proteins that are part of the signaling pathway. However, commonly-used measurement techniques such as Western blots and mobility shift assays provide only qualitative or semi-quantitative data which cannot be used for estimating parameters. Thus there is a clear need for techniques that enable quantitative determination of signal transduction intermediates.

Results

This paper presents an integrated modeling and experimental approach for quantitatively determining transcription factor profiles from green fluorescent protein (GFP) reporter data. The technique consists of three steps: (1) creating data sets for green fluorescent reporter systems upon stimulation, (2) analyzing the fluorescence images to determine fluorescence intensity profiles using principal component analysis (PCA) and K-means clustering, and (3) computing the transcription factor concentration from the fluorescence intensity profiles by inverting a model describing transcription, translation, and activation of green fluorescent proteins.

We have used this technique to quantitatively characterize activation of the transcription factor NF-κB by the cytokine TNF-α. In addition, we have applied the quantitative NF-κB profiles obtained from our technique to develop a model for TNF-α signal transduction where the parameters were estimated from the obtained data.

Conclusion

The technique presented here for computing transcription factor profiles from fluorescence microscopy images of reporter cells generated quantitative data on the magnitude and dynamics of NF-κB activation by TNF-α. The obtained results are in good agreement with qualitative descriptions of NF-κB activation as well as semi-quantitative experimental data from the literature. The profiles computed from the experimental data have been used to re-estimate parameters for a NF-κB model and the results of additional experiments are predicted very well by the model with the new parameter values. While the presented approach has been applied to NF-κB and TNF-α signaling, it can be used to determine the profile of any transcription factor as long as GFP reporter fluorescent profiles are available.