Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering

NLRP10 maintains epidermal homeostasis by promoting keratinocyte survival and P63-dependent differentiation and barrier function

Atopic dermatitis (AD) is a common chronic inflammatory skin disorder characterized by disrupted epidermal barrier function and aberrant immune responses. Despite recent developments in new therapeutics for AD, there is still a large unmet medical need for disease management due to the complex and multifactorial nature of AD. Recent genome-wide association studies (GWAS) have identified NLRP10 as a susceptible gene for AD but the physiological role of NLRP10 in skin homeostasis and AD remains unknown. Here we show that NLRP10 is downregulated in AD skin samples. Using an air-lift human skin equivalent culture, we demonstrate that NLRP10 promotes keratinocyte survival and is required for epidermal differentiation and barrier function. Mechanistically, NLRP10 limits cell death by preventing the recruitment of caspase-8 to the death inducing signaling complex (DISC) and by inhibiting its subsequent activation. NLRP10 also stabilizes p63, the master regulator of keratinocyte differentiation, to drive proper keratinocyte differentiation and to reinforce the barrier function. Our findings underscore NLRP10 as a key player in atopic dermatitis pathogenesis, highlighting NLRP10 as a potential target for therapeutic intervention to restore skin barrier function and homeostasis in AD.

Genome-wide p63-Target Gene Analyses Reveal TAp63/NRF2-Dependent Oxidative Stress Responses

The p53 family member TP63 encodes two sets of N-terminal isoforms, TAp63 and ΔNp63 isoforms. They each regulate diverse biological functions in epidermal morphogenesis and in cancer. In the skin, where their activities have been extensively characterized, TAp63 prevents premature aging by regulating the quiescence and genomic stability of stem cells required for wound healing and hair regeneration, while ΔNp63 controls maintenance and terminal differentiation of epidermal basal cells. This functional diversity is surprising given that these isoforms share a high degree of similarity, including an identical sequence for a DNA-binding domain. To understand the mechanisms of the transcriptional programs regulated by each p63 isoform and leading to diverse biological functions, we performed genome-wide analyses using p63 isoform-specific chromatin immunoprecipitation, RNA sequencing, and metabolomics of TAp63-/- and ΔNp63-/- mouse epidermal cells. Our data indicate that TAp63 and ΔNp63 physically and functionally interact with distinct transcription factors for the downstream regulation of their target genes, thus ultimately leading to the regulation of unique transcriptional programs and biological processes. Our findings unveil novel transcriptomes regulated by the p63 isoforms to control diverse biological functions, including the cooperation between TAp63 and NRF2 in the modulation of metabolic pathways and response to oxidative stress providing a mechanistic explanation for the TAp63 knock out phenotypes.

SIGNIFICANCE

The p63 isoforms, TAp63 and ΔNp63, control epithelial morphogenesis and tumorigenesis through the interaction with distinct transcription factors and the subsequent regulation of unique transcriptional programs.

ΔNp63α-mediated epigenetic regulation in keratinocyte senescence

Keratinocyte senescence contributes to skin ageing and epidermal dysfunction. According to the existing knowledge, the transcription factor ΔNp63α plays pivotal roles in differentiation and proliferation of keratinocytes. It is traditionally accepted that ΔNp63α exerts its functions via binding to promoter regions to activate or repress gene transcription. However, accumulating evidence demonstrates that ΔNp63α can bind to elements away from promoter regions of its target genes, mediating epigenetic regulation. On the other hand, several epigenetic alterations, including DNA methylation, histone modification and variation, chromatin remodelling, as well as enhancer-promoter looping, are found to be related to cell senescence. To systematically elucidate how ΔNp63α affects keratinocyte senescence via epigenetic regulation, we comprehensively compiled the literatures on the roles of ΔNp63α in keratinocyte senescence, epigenetics in cellular senescence, and the relation between ΔNp63α-mediated epigenetic regulation and keratinocyte senescence. Based on the published data, we conclude that ΔNp63α mediates epigenetic regulation via multiple mechanisms: recruiting epigenetic enzymes to modify DNA or histones, coordinating chromatin remodelling complexes (CRCs) or regulating their expression, and mediating enhancer-promoter looping. Consequently, the expression of genes related to cell cycle is modulated, and proliferation of keratinocytes and renewal of stem cells are maintained, by ΔNp63α. During skin inflammaging, the decline of ΔNp63α may lead to epigenetic dysregulation, resultantly deteriorating keratinocyte senescence.

Isoform-Specific Roles of Mutant p63 in Human Diseases

Simple Summary

The protein p63 belongs to the family of the p53 tumor suppressor. Mouse models have, however, shown that it is not a classical tumor suppressor but instead involved in developmental processes. Mutations in the p63 gene cause several developmental defects in human patients characterized by limb deformation, cleft lip/palate, and ectodermal dysplasia due to p63’s role as a master regulator of epidermal development. In addition, p63 plays a key role as a quality control factor in oocytes and p63 mutations can result either in compromised genetic quality control or premature cell death of all oocytes.

Abstract

The p63 gene encodes a master regulator of epidermal commitment, development, and differentiation. Heterozygous mutations in the DNA binding domain cause Ectrodactyly, Ectodermal Dysplasia, characterized by limb deformation, cleft lip/palate, and ectodermal dysplasia while mutations in in the C-terminal domain of the α-isoform cause Ankyloblepharon-Ectodermal defects-Cleft lip/palate (AEC) syndrome, a life-threatening disorder characterized by skin fragility, severe, long-lasting skin erosions, and cleft lip/palate. The molecular disease mechanisms of these syndromes have recently become elucidated and have enhanced our understanding of the role of p63 in epidermal development. Here we review the molecular cause and functional consequences of these p63-mutations for skin development and discuss the consequences of p63 mutations for female fertility.

The Impact of Notch Signaling for Carcinogenesis and Progression of Nonmelanoma Skin Cancer: Lessons Learned from Cancer Stem Cells, Tumor Angiogenesis, and Beyond

Since many decades, nonmelanoma skin cancer (NMSCs) is the most common malignancy worldwide. Basal cell carcinomas (BCC) and squamous cell carcinomas (SCC) are the major types of NMSCs, representing approximately 70% and 25% of these neoplasias, respectively. Because of their continuously rising incidence rates, NMSCs represent a constantly increasing global challenge for healthcare, although they are in most cases nonlethal and curable (e.g., by surgery). While at present, carcinogenesis of NMSC is still not fully understood, the relevance of genetic and molecular alterations in several pathways, including evolutionary highly conserved Notch signaling, has now been shown convincingly. The Notch pathway, which was first developed during evolution in metazoans and that was first discovered in fruit flies (Drosophila melanogaster), governs cell fate decisions and many other fundamental processes that are of high relevance not only for embryonic development, but also for initiation, promotion, and progression of cancer. Choosing NMSC as a model, we give in this review a brief overview on the interaction of Notch signaling with important oncogenic and tumor suppressor pathways and on its role for several hallmarks of carcinogenesis and cancer progression, including the regulation of cancer stem cells, tumor angiogenesis, and senescence.

Master regulatory role of p63 in epidermal development and disease

The transcription factor p63 is a master regulator of epidermal development. Mutations in p63 give rise to human developmental diseases that often manifest epidermal defects. In this review, we summarize major p63 isoforms identified so far and p63 mutation-associated human diseases that show epidermal defects. We discuss key roles of p63 in epidermal keratinocyte proliferation and differentiation, emphasizing its master regulatory control of the gene expression pattern and epigenetic landscape that define epidermal fate. We subsequently review the essential function of p63 during epidermal commitment and transdifferentiation towards epithelial lineages, highlighting the notion that p63 is the guardian of the epithelial lineage. Finally, we discuss current therapeutic development strategies for p63 mutation-associated diseases. Our review proposes future directions for dissecting p63-controlled mechanisms in normal and diseased epidermal development and for developing therapeutic options.

Analysis of time-course microarray data: Comparison of common tools

High-throughput time-series data have a special value for studying the dynamism of biological systems. However, the interpretation of such complex data can be challenging. The aim of this study was to compare common algorithms recently developed for the detection of differentially expressed genes in time-course microarray data. Using different measures such as sensitivity, specificity, predictive values, and related signaling pathways, we found that limma, timecourse, and gprege have reasonably good performance for the analysis of datasets in which only test group is followed over time. However, limma has the additional advantage of being able to report significance cut off, making it a more practical tool. In addition, limma and TTCA can be satisfactorily used for datasets with time-series data for all experimental groups. These findings may assist investigators to select appropriate tools for the detection of differentially expressed genes as an initial step in the interpretation of time-course big data.

p63 exerts spatio-temporal control of palatal epithelial cell fate to prevent cleft palate

Cleft palate is a common congenital disorder that affects up to 1 in 2500 live births and results in considerable morbidity to affected individuals and their families. The aetiology of cleft palate is complex with both genetic and environmental factors implicated. Mutations in the transcription factor p63 are one of the major individual causes of cleft palate; however, the gene regulatory networks in which p63 functions remain only partially characterized. Our findings demonstrate that p63 functions as an essential regulatory molecule in the spatio-temporal control of palatal epithelial cell fate to ensure appropriate fusion of the palatal shelves. Initially, p63 induces periderm formation and controls its subsequent maintenance to prevent premature adhesion between adhesion-competent, intra-oral epithelia. Subsequently, TGFβ3-induced down-regulation of p63 in the medial edge epithelia of the palatal shelves is a pre-requisite for palatal fusion by facilitating periderm migration from, and reducing the proliferative potential of, the midline epithelial seam thereby preventing cleft palate.

Cleft palate is a serious congenital condition which affects approximately 1 in every 2500 births. Cleft palate occurs when the palatal shelves fail to grow, adhere or fuse during development. Mutations in the gene encoding the transcription factor p63 result in cleft palate in humans and mice. However, the role of p63 and how it controls the network of genes to regulate palate development is not well understood.In this study, we demonstrate that p63 controls the spatio-temporal regulation of palatal epithelial cell fate to ensure appropriate palatal adhesion: p63 induces the formation of a flattened layer of epithelial (periderm) cells and controls its subsequent maintenance. We also demonstrate that TGFβ3-induced, down-regulation of p63 in the medial edge epithelial cells, through which the palatal shelves adhere and fuse, controls Jag2-induced periderm migration to the oral and nasal epithelial triangles. In addition, p63 plays a central role in maintaining the proliferative potential of the basal layer of the medial edge epithelia. Our study provides significant new insights into the mechanisms that regulate development of the palate by establishing the role of p63 in governing the fate of the midline epithelial cells.

TIGERi: modeling and visualizing the responses to perturbation of a transcription factor network

BACKGROUND

Transcription factor (TF) networks play a key role in controlling the transfer of genetic information from gene to mRNA. Much progress has been made on understanding and reverse-engineering TF network topologies using a range of experimental and theoretical methodologies. Less work has focused on using these models to examine how TF networks respond to changes in the cellular environment.

METHODS

In this paper, we have developed a simple, pragmatic methodology, TIGERi (Transcription-factor-activity Illustrator for Global Explanation of Regulatory interaction), to model the response of an inferred TF network to changes in cellular environment. The methodology was tested using publicly available data comparing gene expression profiles of a mouse p38α (Mapk14) knock-out line to the original wild-type.

RESULTS

Using the model, we have examined changes in the TF network resulting from the presence or absence of p38α. A part of this network was confirmed by experimental work in the original paper. Additional relationships were identified by our analysis, for example between p38α and HNF3, and between p38α and SOX9, and these are strongly supported by published evidence. FXR and MYC were also discovered in our analysis as two novel links of p38α. To provide a computational methodology to the biomedical communities that has more user-friendly interface, we also developed a standalone GUI (graphical user interface) software for TIGERi and it is freely available at https://github.com/namshik/tigeri/ .

CONCLUSIONS

We therefore believe that our computational approach can identify new members of networks and new interactions between members that are supported by published data but have not been integrated into the existing network models. Moreover, ones who want to analyze their own data with TIGERi could use the software without any command line experience. This work could therefore accelerate researches in transcriptional gene regulation in higher eukaryotes.

Recurrent neural network based hybrid model for reconstructing gene regulatory network

One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model.

Foxn1 regulates key target genes essential for T cell development in postnatal thymic epithelial cells

Thymic epithelial cell differentiation, growth and function depend on the expression of the transcription factor Foxn1, however its target genes have never been physically identified. Using novel static and inducible genetic model systems and chromatin studies, we provide now a genome wide map of direct Foxn1 target genes for postnatal thymic epithelia and define the Foxn1 binding motif. We detail the function of Foxn1 in these cells and demonstrate that in addition to the transcriptional control of genes involved in the attraction and lineage commitment of T cell precursors, Foxn1 regulates the expression of genes involved in antigen processing and thymocyte selection. Thus, critical events in thymic lympho-stromal cross-talk and T cell selection are indispensably choreographed by Foxn1.

Understanding transcriptional regulatory networks using computational models

Transcriptional regulatory networks (TRNs) encode instructions for animal development and physiological responses. Recent advances in genomic technologies and computational modeling have revolutionized our ability to construct models of TRNs. Here, we survey current computational methods for inferring TRN models using genome-scale data. We discuss their advantages and limitations. We summarize representative TRNs constructed using genome-scale data in both normal and disease development. We discuss lessons learned about the structure/function relationship of TRNs, based on examining various large-scale TRN models. Finally, we outline some open questions regarding TRNs, including how to improve model accuracy by integrating complementary data types, how to infer condition-specific TRNs, and how to compare TRNs across conditions and species in order to understand their structure/function relationship.

Transcription factor p63 bookmarks and regulates dynamic enhancers during epidermal differentiation

The transcription factor p63 plays a pivotal role in keratinocyte proliferation and differentiation in the epidermis. However, how p63 regulates epidermal genes during differentiation is not yet clear. Using epigenome profiling of differentiating human primary epidermal keratinocytes, we characterized a catalog of dynamically regulated genes and p63-bound regulatory elements that are relevant for epithelial development and related diseases. p63-bound regulatory elements occur as single or clustered enhancers, and remarkably, only a subset is active as defined by the co-presence of the active enhancer mark histone modification H3K27ac in epidermal keratinocytes. We show that the dynamics of gene expression correlates with the activity of p63-bound enhancers rather than with p63 binding itself. The activity of p63-bound enhancers is likely determined by other transcription factors that cooperate with p63. Our data show that inactive p63-bound enhancers in epidermal keratinocytes may be active during the development of other epithelial-related structures such as limbs and suggest that p63 bookmarks genomic loci during the commitment of the epithelial lineage and regulates genes through temporal- and spatial-specific active enhancers.

Gene regulatory mechanisms orchestrated by p63 in epithelial development and related disorders

The transcription factor p63 belongs to the p53 family and is a key regulator in epithelial commitment and development. Mutations in p63 give rise to several epithelial related disorders with defects in skin, limb and orofacial structures. Since the discovery of p63, efforts have been made to identify its target genes using individual gene approaches and to understand p63 function in normal epithelial development and related diseases. Recent genome-wide approaches have identified tens of thousands of potential p63-regulated target genes and regulatory elements, and reshaped the concept of gene regulation orchestrated by p63. These data also provide insights into p63-related disease mechanisms. In this review, we discuss the regulatory role of p63 in normal and diseased epithelial development in light of these novel findings. We also propose future perspectives for dissecting the molecular mechanism of p63-mediated epithelial development and related disorders as well as for potential therapeutic strategies.

Epidermal cell junctions and their regulation by p63 in health and disease

As the outermost tissue of the body, the epidermis is the first physical barrier for any pressure, stress or trauma. Several specialized cell-matrix and cell-cell adhesion structures, together with an intracellular network of dedicated intermediate filaments, are required to confer critical resilience to mechanical stress. The transcription factor p63 is a master regulator of gene expression in the epidermis and in other stratified epithelia. It has been extensively demonstrated that p63 positively controls a large number of tissue-specific genes, including those encoding a large fraction of tissue-restricted cell adhesion molecules. Consistent with p63 functions in cell adhesion and in epidermal differentiation, heterozygous mutations clustered mainly in the p63 C-terminus are causative of AEC syndrome, an autosomal dominant disorder characterized by cleft palate, ankyloblepharon and ectodermal dysplasia associated with severe skin erosions, bleeding and infections. The molecular basis of skin erosions in AEC patients is not fully understood, although defects in desmosomes and in other cell junctions are likely to be involved. Here, we provide an extensive review of the different epidermal cell junctions that cooperate to withstand mechanical stress and on the mechanisms by which p63 regulates gene expression of their components in healthy skin and in AEC syndrome. Collectively, advancement in understanding the molecular mechanisms by which epidermal cell junctions precisely exert their functions and how p63 orchestrates their coordinated expression, will ultimately lead to insight into developing future strategies for the treatment of AEC syndrome and more in generally for diseases that share an overlapping phenotype.

A composite enhancer regulates p63 gene expression in epidermal morphogenesis and in keratinocyte differentiation by multiple mechanisms

p63 is a crucial regulator of epidermal development, but its transcriptional control has remained elusive. Here, we report the identification of a long-range enhancer (p63LRE) that is composed of two evolutionary conserved modules (C38 and C40), acting in concert to control tissue- and layer-specific expression of the p63 gene. Both modules are in an open and active chromatin state in human and mouse keratinocytes and in embryonic epidermis, and are strongly bound by p63. p63LRE activity is dependent on p63 expression in embryonic skin, and also in the commitment of human induced pluripotent stem cells toward an epithelial cell fate. A search for other transcription factors involved in p63LRE regulation revealed that the CAAT enhancer binding proteins Cebpa and Cebpb and the POU domain-containing protein Pou3f1 repress p63 expression during keratinocyte differentiation by binding the p63LRE enhancer. Collectively, our data indicate that p63LRE is composed of additive and partly redundant enhancer modules that act to direct robust p63 expression selectively in the basal layer of the epidermis.

Gene differential coexpression analysis based on biweight correlation and maximum clique

Differential coexpression analysis usually requires the definition of 'distance' or 'similarity' between measured datasets. Until now, the most common choice is Pearson correlation coefficient. However, Pearson correlation coefficient is sensitive to outliers. Biweight midcorrelation is considered to be a good alternative to Pearson correlation since it is more robust to outliers. In this paper, we introduce to use Biweight Midcorrelation to measure 'similarity' between gene expression profiles, and provide a new approach for gene differential coexpression analysis. Firstly, we calculate the biweight midcorrelation coefficients between all gene pairs. Then, we filter out non-informative correlation pairs using the 'half-thresholding' strategy and calculate the differential coexpression value of gene, The experimental results on simulated data show that the new approach performed better than three previously published differential coexpression analysis (DCEA) methods. Moreover, we use the maximum clique analysis to gene subset included genes identified by our approach and previously reported T2D-related genes, many additional discoveries can be found through our method.

Crosstalk among p53 family members in cutaneous carcinoma

Cutaneous squamous cell carcinoma (cSCC) is the second most common human cancer with a frequency increasing worldwide. The risk of developing cSCC has been strongly associated with chronic sun exposure, especially in light skin people. The aim of this viewpoint is to discuss the contribution of the tumor suppressor p53 and its homologues p63 and p73 in the formation and progression of cSCC. Mutations in the p53 gene are early and frequent events in skin carcinogenesis mainly as a consequence of UV light exposure, often followed by loss of function of the second allele. Although rarely mutated in cancer, p63 and p73 play key roles in human cancers, with their truncated isoforms lacking the N-terminal transactivating domain (∆N) being often upregulated as compared to normal tissues. ∆Np63 is abundantly expressed in cSCC, and it is likely to favour tumor initiation and progression. The function of p73 in cSCC is more enigmatic and awaits further studies. Interestingly, an intimate interplay exists between both p53 and p63, and the Notch signalling pathway, often inactivated in cSCC. Here, we summarize our current knowledge about the biological activities of p53 family members in cSCC and propose that integration of their signalling with Notch is key to cSCC formation and progression.

CaSPIAN: a causal compressive sensing algorithm for discovering directed interactions in gene networks

We introduce a novel algorithm for inference of causal gene interactions, termed CaSPIAN (Causal Subspace Pursuit for Inference and Analysis of Networks), which is based on coupling compressive sensing and Granger causality techniques. The core of the approach is to discover sparse linear dependencies between shifted time series of gene expressions using a sequential list-version of the subspace pursuit reconstruction algorithm and to estimate the direction of gene interactions via Granger-type elimination. The method is conceptually simple and computationally efficient, and it allows for dealing with noisy measurements. Its performance as a stand-alone platform without biological side-information was tested on simulated networks, on the synthetic IRMA network in Saccharomyces cerevisiae, and on data pertaining to the human HeLa cell network and the SOS network in E. coli. The results produced by CaSPIAN are compared to the results of several related algorithms, demonstrating significant improvements in inference accuracy of documented interactions. These findings highlight the importance of Granger causality techniques for reducing the number of false-positives, as well as the influence of noise and sampling period on the accuracy of the estimates. In addition, the performance of the method was tested in conjunction with biological side information of the form of sparse “scaffold networks”, to which new edges were added using available RNA-seq or microarray data. These biological priors aid in increasing the sensitivity and precision of the algorithm in the small sample regime.

Network based elucidation of drug response: from modulators to targets

: Network-based drug discovery aims at harnessing the power of networks to investigate the mechanism of action of existing drugs, or new molecules, in order to identify innovative therapeutic treatments. In this review, we describe some of the most recent advances in the field of network pharmacology, starting with approaches relying on computational models of transcriptional networks, then moving to protein and signaling network models and concluding with "drug networks". These networks are derived from different sources of experimental data, or literature-based analysis, and provide a complementary view of drug mode of action. Molecular and drug networks are powerful integrated computational and experimental approaches that will likely speed up and improve the drug discovery process, once fully integrated into the academic and industrial drug discovery pipeline.

Reprogramming of lysosomal gene expression by interleukin-4 and Stat6

Background

Lysosomes play important roles in multiple aspects of physiology, but the problem of how the transcription of lysosomal genes is coordinated remains incompletely understood. The goal of this study was to illuminate the physiological contexts in which lysosomal genes are coordinately regulated and to identify transcription factors involved in this control.

Results

As transcription factors and their target genes are often co-regulated, we performed meta-analyses of array-based expression data to identify regulators whose mRNA profiles are highly correlated with those of a core set of lysosomal genes. Among the ~50 transcription factors that rank highest by this measure, 65% are involved in differentiation or development, and 22% have been implicated in interferon signaling. The most strongly correlated candidate was Stat6, a factor commonly activated by interleukin-4 (IL-4) or IL-13. Publicly available chromatin immunoprecipitation (ChIP) data from alternatively activated mouse macrophages show that lysosomal genes are overrepresented among Stat6-bound targets. Quantification of RNA from wild-type and Stat6-deficient cells indicates that Stat6 promotes the expression of over 100 lysosomal genes, including hydrolases, subunits of the vacuolar H⁺ ATPase and trafficking factors. While IL-4 inhibits and activates different sets of lysosomal genes, Stat6 mediates only the activating effects of IL-4, by promoting increased expression and by neutralizing undefined inhibitory signals induced by IL-4.

Conclusions

The current data establish Stat6 as a broadly acting regulator of lysosomal gene expression in mouse macrophages. Other regulators whose expression correlates with lysosomal genes suggest that lysosome function is frequently re-programmed during differentiation, development and interferon signaling.

Insulin/IGF-1 controls epidermal morphogenesis via regulation of FoxO-mediated p63 inhibition

The multilayered epidermis is established through a stratification program, which is accompanied by a shift from symmetric toward asymmetric divisions (ACD), a process under tight control of the transcription factor p63. However, the physiological signals regulating p63 activity in epidermal morphogenesis remain ill defined. Here, we reveal a role for insulin/IGF-1 signaling (IIS) in the regulation of p63 activity. Loss of epidermal IIS leads to a biased loss of ACD, resulting in impaired stratification. Upon loss of IIS, FoxO transcription factors are retained in the nucleus, where they bind and inhibit p63-regulated transcription. This is reversed by small interfering RNA-mediated knockdown of FoxOs. Accordingly, transgenic expression of a constitutive nuclear FoxO variant in the epidermis abrogates ACD and inhibits p63-regulated transcription and stratification. Collectively, the present study reveals a critical role for IIS-dependent control of p63 activity in coordination of ACD and stratification during epithelial morphogenesis.

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Epidermal insulin/IGF-1 signaling (IIS) regulates asymmetric cell division and mitosis

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IIS-controlled FoxOs bind p63 and negatively regulate p63 transcriptional activity

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Constitutive nuclear FoxO disturbs epidermal stratification

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The IIS/FoxO/p63 pathway is a major mechanism controlling epidermal stratification

Evaluating influence of microRNA in reconstructing gene regulatory networks

Gene regulatory network (GRN) consists of interactions between transcription factors (TFs) and target genes (TGs). Recently, it has been observed that micro RNAs (miRNAs) play a significant part in genetic interactions. However, current microarray technologies do not capture miRNA expression levels. To overcome this, we propose a new technique to reverse engineer GRN from the available partial microarray data which contains expression levels of TFs and TGs only. Using S-System model, the approach is adapted to cope with the unavailability of information about the expression levels of miRNAs. The versatile Differential Evolutionary algorithm is used for optimization and parameter estimation. Experimental studies on four in silico networks, and a real network of Saccharomyces cerevisiae called IRMA network, show significant improvement compared to traditional S-System approach.

FGFR2 signaling underlies p63 oncogenic function in squamous cell carcinoma

Oncogenic transcription factors drive many human cancers, yet identifying and therapeutically targeting the resulting deregulated pathways has proven difficult. Squamous cell carcinoma (SCC) is a common and lethal human cancer, and relatively little progress has been made in improving outcomes for SCC due to a poor understanding of its underlying molecular pathogenesis. While SCCs typically lack somatic oncogene-activating mutations, they exhibit frequent overexpression of the p53-related transcription factor p63. We developed an in vivo murine tumor model to investigate the function and key transcriptional programs of p63 in SCC. Here, we show that established SCCs are exquisitely dependent on p63, as acute genetic ablation of p63 in advanced, invasive SCC induced rapid and dramatic apoptosis and tumor regression. In vivo genome-wide gene expression analysis identified a tumor-survival program involving p63-regulated FGFR2 signaling that was activated by ligand emanating from abundant tumor-associated stroma. Correspondingly, we demonstrate the therapeutic efficacy of extinguishing this signaling axis in endogenous SCCs using the clinical FGFR2 inhibitor AZD4547. Collectively, these results reveal an unanticipated role for p63-driven paracrine FGFR2 signaling as an addicting pathway in human cancer and suggest a new approach for the treatment of SCC.

Incorporating time-delays in S-System model for reverse engineering genetic networks

Background

In any gene regulatory network (GRN), the complex interactions occurring amongst transcription factors and target genes can be either instantaneous or time-delayed. However, many existing modeling approaches currently applied for inferring GRNs are unable to represent both these interactions simultaneously. As a result, all these approaches cannot detect important interactions of the other type. S-System model, a differential equation based approach which has been increasingly applied for modeling GRNs, also suffers from this limitation. In fact, all S-System based existing modeling approaches have been designed to capture only instantaneous interactions, and are unable to infer time-delayed interactions.

Results

In this paper, we propose a novel Time-Delayed S-System (TDSS) model which uses a set of delay differential equations to represent the system dynamics. The ability to incorporate time-delay parameters in the proposed S-System model enables simultaneous modeling of both instantaneous and time-delayed interactions. Furthermore, the delay parameters are not limited to just positive integer values (corresponding to time stamps in the data), but can also take fractional values. Moreover, we also propose a new criterion for model evaluation exploiting the sparse and scale-free nature of GRNs to effectively narrow down the search space, which not only reduces the computation time significantly but also improves model accuracy. The evaluation criterion systematically adapts the max-min in-degrees and also systematically balances the effect of network accuracy and complexity during optimization.

Conclusion

The four well-known performance measures applied to the experimental studies on synthetic networks with various time-delayed regulations clearly demonstrate that the proposed method can capture both instantaneous and delayed interactions correctly with high precision. The experiments carried out on two well-known real-life networks, namely IRMA and SOS DNA repair network in Escherichia coli show a significant improvement compared with other state-of-the-art approaches for GRN modeling.

p63 control of desmosome gene expression and adhesion is compromised in AEC syndrome

Ankyloblepharon, ectodermal defects, cleft lip/palate (AEC) syndrome is a rare autosomal dominant disorder caused by mutations in the p63 gene, essential for embryonic development of stratified epithelia. The most severe cutaneous manifestation of this disorder is the long-lasting skin fragility associated with severe skin erosions after birth. Using a knock-in mouse model for AEC syndrome, we found that skin fragility was associated with microscopic blistering between the basal and suprabasal compartments of the epidermis and reduced desmosomal contacts. Expression of desmosomal cadherins and desmoplakin was strongly reduced in AEC mutant keratinocytes and in newborn epidermis. A similar impairment in desmosome gene expression was observed in human keratinocytes isolated from AEC patients, in p63-depleted keratinocytes and in p63 null embryonic skin, indicating that p63 mutations causative of AEC syndrome have a dominant-negative effect on the wild-type p63 protein. Among the desmosomal components, desmocollin 3, desmoplakin and desmoglein 1 were the most significantly reduced by mutant p63 both at the RNA and protein levels. Chromatin immunoprecipitation experiments and transactivation assays revealed that p63 controls these genes at the transcriptional level. Consistent with reduced desmosome function, AEC mutant and p63-deficient keratinocytes had an impaired ability to withstand mechanical stress, which was alleviated by epidermal growth factor receptor inhibitors known to stabilize desmosomes. Our study reveals that p63 is a crucial regulator of a subset of desmosomal genes and that this function is impaired in AEC syndrome. Reduced mechanical strength resulting from p63 mutations can be alleviated pharmacologically by increasing desmosome adhesion with possible therapeutic implications.

Genome-wide analysis of p63 binding sites identifies AP-2 factors as co-regulators of epidermal differentiation

The p63 transcription factor (TP63) is critical in development, growth and differentiation of stratifying epithelia. This is highlighted by the severity of congenital abnormalities caused by TP63 mutations in humans, the dramatic phenotypes in knockout mice and de-regulation of TP63 expression in neoplasia altering the tumour suppressive roles of the TP53 family. In order to define the normal role played by TP63 and provide the basis for better understanding how this network is perturbed in disease, we used chromatin immunoprecipitation combined with massively parallel sequencing (ChIP-seq) to identify >7500 high-confidence TP63-binding regions across the entire genome, in primary human neonatal foreskin keratinocytes (HFKs). Using integrative strategies, we demonstrate that only a subset of these sites are bound by TP53 in response to DNA damage. We identify a role for TP63 in transcriptional regulation of multiple genes genetically linked to cleft palate and identify AP-2alpha (TFAP2A) as a co-regulator of a subset of these genes. We further demonstrate that AP-2gamma (TFAP2C) can bind a subset of these regions and that acute depletion of either TFAP2A or TFAP2C alone is sufficient to reduce terminal differentiation of organotypic epidermal skin equivalents, indicating overlapping physiological functions with TP63.

Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison

BACKGROUND

Complete transcriptional regulatory network inference is a huge challenge because of the complexity of the network and sparsity of available data. One approach to make it more manageable is to focus on the inference of context-specific networks involving a few interacting transcription factors (TFs) and all of their target genes.

RESULTS

We present a computational framework for Bayesian statistical inference of target genes of multiple interacting TFs from high-throughput gene expression time-series data. We use ordinary differential equation models that describe transcription of target genes taking into account combinatorial regulation. The method consists of a training and a prediction phase. During the training phase we infer the unobserved TF protein concentrations on a subnetwork of approximately known regulatory structure. During the prediction phase we apply Bayesian model selection on a genome-wide scale and score all alternative regulatory structures for each target gene. We use our methodology to identify targets of five TFs regulating Drosophila melanogaster mesoderm development. We find that confident predicted links between TFs and targets are significantly enriched for supporting ChIP-chip binding events and annotated TF-gene interations. Our method statistically significantly outperforms existing alternatives.

CONCLUSIONS

Our results show that it is possible to infer regulatory links between multiple interacting TFs and their target genes even from a single relatively short time series and in presence of unmodelled confounders and unreliable prior knowledge on training network connectivity. Introducing data from several different experimental perturbations significantly increases the accuracy.

Mutations in AEC syndrome skin reveal a role for p63 in basement membrane adhesion, skin barrier integrity and hair follicle biology

BACKGROUND

AEC (ankyloblepharon-ectodermal defects-clefting) syndrome is an autosomal dominant ectodermal dysplasia disorder caused by mutations in the transcription factor p63. Clinically, the skin is dry and often fragile; other features can include partial eyelid fusion (ankyloblepharon), hypodontia, orofacial clefting, sparse hair or alopecia, and nail dystrophy.

OBJECTIVES

To investigate how p63 gene mutations affect gene and protein expression in AEC syndrome skin.

METHODS

We performed microarray analysis on samples of intact and eroded AEC syndrome skin compared with control skin. Changes were verified by quantitative real-time reverse transcription-polymerase chain reaction and, for basal keratinocyte-associated genes, by immunohistochemistry and analysis of microdissected skin.

RESULTS

We identified significant upregulation of six genes and downregulation of 69 genes in AEC syndrome skin, with the main changes in genes implicated in epidermal adhesion, skin barrier formation and hair follicle biology. There was reduced expression of genes encoding the basement membrane proteins FRAS1 and collagen VII, as well as the skin barrier-associated small proline-rich proteins 1A and 4, late cornified envelope protein 5A, hornerin, and lipid transporters including ALOX15B. Reduced expression of the hair-associated keratins 25, 27, 31, 33B, 34, 35, 81 and 85 was also noted. We also confirmed similar alterations in gene expression for 26 of the 75 genes in eroded AEC scalp skin.

CONCLUSIONS

This study identifies specific changes in skin structural biology and signalling pathways that result from mutant p63 and provides new molecular insight into the AEC syndrome phenotype.

Id proteins synchronize stemness and anchorage to the niche of neural stem cells

Stem-cell functions require activation of stem-cell-intrinsic transcriptional programs and extracellular interaction with a niche microenvironment. How the transcriptional machinery controls residency of stem cells in the niche is unknown. Here we show that Id proteins coordinate stem-cell activities with anchorage of neural stem cells (NSCs) to the niche. Conditional inactivation of three Id genes in NSCs triggered detachment of embryonic and postnatal NSCs from the ventricular and vascular niche, respectively. The interrogation of the gene modules directly targeted by Id deletion in NSCs revealed that Id proteins repress bHLH-mediated activation of Rap1GAP, thus serving to maintain the GTPase activity of RAP1, a key mediator of cell adhesion. Preventing the elevation of the Rap1GAP level countered the consequences of Id loss on NSC-niche interaction and stem-cell identity. Thus, by preserving anchorage of NSCs to the extracellular environment, Id activity synchronizes NSC functions to residency in the specialized niche.

Mutant p63 causes defective expansion of ectodermal progenitor cells and impaired FGF signalling in AEC syndrome

Ankyloblepharon-ectodermal defects-cleft lip/palate (AEC) syndrome, which is characterized by cleft palate and severe defects of the skin, is an autosomal dominant disorder caused by mutations in the gene encoding transcription factor p63. Here, we report the generation of a knock-in mouse model for AEC syndrome (p63(+/L514F) ) that recapitulates the human disorder. The AEC mutation exerts a selective dominant-negative function on wild-type p63 by affecting progenitor cell expansion during ectodermal development leading to a defective epidermal stem cell compartment. These phenotypes are associated with impairment of fibroblast growth factor (FGF) signalling resulting from reduced expression of Fgfr2 and Fgfr3, direct p63 target genes. In parallel, a defective stem cell compartment is observed in humans affected by AEC syndrome and in Fgfr2b(-/-) mice. Restoring Fgfr2b expression in p63(+/L514F) epithelial cells by treatment with FGF7 reactivates downstream mitogen-activated protein kinase signalling and cell proliferation. These findings establish a functional link between FGF signalling and p63 in the expansion of epithelial progenitor cells and provide mechanistic insights into the pathogenesis of AEC syndrome.

Genome-wide dissection of posttranscriptional and posttranslational interactions

Transcriptional interactions in the cell are modulated by a variety of posttranscriptional and posttranslational mechanisms that make them highly dependent on the molecular context of the specific cell. These include, among others, microRNA-mediated control of transcription factor (TF) mRNA translation and degradation, transcription factor activation by phosphorylation and acetylation, formation of active complexes with one or more cofactors, and mRNA/protein degradation and stabilization processes. Thus, the ability of a transcription factor to regulate its targets depends on a variety of genetic and epigenetic mechanisms, resulting in highly context-dependent regulatory networks. In this chapter, we introduce a step-by-step guide on how to use the MINDy systems biology algorithm (Modulator Inference by Network Dynamics) that we recently developed, for the genome-wide, context-specific identification of posttranscriptional and posttranslational modulators of transcription factor activity.

Gene network inference and visualization tools for biologists: application to new human transcriptome datasets

Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions.

Transcriptional gene network inference from a massive dataset elucidates transcriptome organization and gene function

We collected a massive and heterogeneous dataset of 20 255 gene expression profiles (GEPs) from a variety of human samples and experimental conditions, as well as 8895 GEPs from mouse samples. We developed a mutual information (MI) reverse-engineering approach to quantify the extent to which the mRNA levels of two genes are related to each other across the dataset. The resulting networks consist of 4 817 629 connections among 20 255 transcripts in human and 14 461 095 connections among 45 101 transcripts in mouse, with a inter-species conservation of 12%. The inferred connections were compared against known interactions to assess their biological significance. We experimentally validated a subset of not previously described protein–protein interactions. We discovered co-expressed modules within the networks, consisting of genes strongly connected to each other, which carry out specific biological functions, and tend to be in physical proximity at the chromatin level in the nucleus. We show that the network can be used to predict the biological function and subcellular localization of a protein, and to elucidate the function of a disease gene. We experimentally verified that granulin precursor (GRN) gene, whose mutations cause frontotemporal lobar degeneration, is involved in lysosome function. We have developed an online tool to explore the human and mouse gene networks.

Probabilistic polynomial dynamical systems for reverse engineering of gene regulatory networks

Elucidating the structure and/or dynamics of gene regulatory networks from experimental data is a major goal of systems biology. Stochastic models have the potential to absorb noise, account for un-certainty, and help avoid data overfitting. Within the frame work of probabilistic polynomial dynamical systems, we present an algorithm for the reverse engineering of any gene regulatory network as a discrete, probabilistic polynomial dynamical system. The resulting stochastic model is assembled from all minimal models in the model space and the probability assignment is based on partitioning the model space according to the likeliness with which a minimal model explains the observed data. We used this method to identify stochastic models for two published synthetic network models. In both cases, the generated model retains the key features of the original model and compares favorably to the resulting models from other algorithms.

A simple approach to ranking differentially expressed gene expression time courses through Gaussian process regression

BACKGROUND

The analysis of gene expression from time series underpins many biological studies. Two basic forms of analysis recur for data of this type: removing inactive (quiet) genes from the study and determining which genes are differentially expressed. Often these analysis stages are applied disregarding the fact that the data is drawn from a time series. In this paper we propose a simple model for accounting for the underlying temporal nature of the data based on a Gaussian process.

RESULTS

We review Gaussian process (GP) regression for estimating the continuous trajectories underlying in gene expression time-series. We present a simple approach which can be used to filter quiet genes, or for the case of time series in the form of expression ratios, quantify differential expression. We assess via ROC curves the rankings produced by our regression framework and compare them to a recently proposed hierarchical Bayesian model for the analysis of gene expression time-series (BATS). We compare on both simulated and experimental data showing that the proposed approach considerably outperforms the current state of the art.

CONCLUSIONS

Gaussian processes offer an attractive trade-off between efficiency and usability for the analysis of microarray time series. The Gaussian process framework offers a natural way of handling biological replicates and missing values and provides confidence intervals along the estimated curves of gene expression. Therefore, we believe Gaussian processes should be a standard tool in the analysis of gene expression time series.

Insights into global mechanisms and disease by gene expression profiling

Transcriptomics has played an essential role as proof of concept in the development of experimental and bioinformatics approaches for the generation and analysis of Omics data. We are giving an introduction on how large-scale technologies for gene expression profiling, especially microarrays, have changed the view from studying single molecular events to a systems level view of global mechanisms in a cell, the biological processes, and their pathological mutations. The main platforms available for gene expression profiling (from microarrays to RNA-seq) are presented and the general concepts that need to be taken into account for proper data analysis in order to extract objective and general conclusions from transcriptomics experiments are introduced. We also describe the available main bioinformatics resources used for this purpose.

C/EBPδ gene targets in human keratinocytes

C/EBPs are a family of B-Zip transcription factors -TFs- involved in the regulation of differentiation in several tissues. The two most studied members -C/EBPα and C/EBPβ- play important roles in skin homeostasis and their ablation reveals cells with stem cells signatures. Much less is known about C/EBPδ which is highly expressed in the granular layer of interfollicular epidermis and is a direct target of p63, the master regular of multilayered epithelia. We identified C/EBPδ target genes in human primary keratinocytes by ChIP on chip and profiling of cells functionally inactivated with siRNA. Categorization suggests a role in differentiation and control of cell-cycle, particularly of G2/M genes. Among positively controlled targets are numerous genes involved in barrier function. Functional inactivation of C/EBPδ as well as overexpressions of two TF targets -MafB and SOX2- affect expression of markers of keratinocyte differentiation. We performed IHC on skin tumor tissue arrays: expression of C/EBPδ is lost in Basal Cell Carcinomas, but a majority of Squamous Cell Carcinomas showed elevated levels of the protein. Our data indicate that C/EBPδ plays a role in late stages of keratinocyte differentiation.

Genome-wide profiling of p63 DNA-binding sites identifies an element that regulates gene expression during limb development in the 7q21 SHFM1 locus

Heterozygous mutations in p63 are associated with split hand/foot malformations (SHFM), orofacial clefting, and ectodermal abnormalities. Elucidation of the p63 gene network that includes target genes and regulatory elements may reveal new genes for other malformation disorders. We performed genome-wide DNA–binding profiling by chromatin immunoprecipitation (ChIP), followed by deep sequencing (ChIP–seq) in primary human keratinocytes, and identified potential target genes and regulatory elements controlled by p63. We show that p63 binds to an enhancer element in the SHFM1 locus on chromosome 7q and that this element controls expression of DLX6 and possibly DLX5, both of which are important for limb development. A unique micro-deletion including this enhancer element, but not the DLX5/DLX6 genes, was identified in a patient with SHFM. Our study strongly indicates disruption of a non-coding cis-regulatory element located more than 250 kb from the DLX5/DLX6 genes as a novel disease mechanism in SHFM1. These data provide a proof-of-concept that the catalogue of p63 binding sites identified in this study may be of relevance to the studies of SHFM and other congenital malformations that resemble the p63-associated phenotypes.

Mammalian embryonic development requires precise control of gene expression in the right place at the right time. One level of control of gene expression is through cis-regulatory elements controlled by transcription factors. Deregulation of gene expression by mutations in such cis-regulatory elements has been described in developmental disorders. Heterozygous mutations in the transcription factor p63 are found in patients with limb malformations, cleft lip/palate, and defects in skin and other epidermal appendages, through disruption of normal ectodermal development during embryogenesis. We reasoned that the identification of target genes and cis-regulatory elements controlled by p63 would provide candidate genes for defects arising from abnormally regulated ectodermal development. To test our hypothesis, we carried out a genome-wide binding site analysis and identified a large number of target genes and regulatory elements regulated by p63. We further showed that one of these regulatory elements controls expression of DLX6 and possibly DLX5 in the apical ectodermal ridge in the developing limbs. Loss of this element through a micro-deletion was associated with split hand foot malformation (SHFM1). The list of p63 binding sites provides a resource for the identification of mutations that cause ectodermal dysplasias and malformations in humans.

Estimating sparse gene regulatory networks using a bayesian linear regression

In this paper, we propose a gene regulatory network (GRN) estimation method, which assumes that such networks are typically sparse, using time-series microarray datasets. We represent the regulatory relationships between the genes using weights, with the "net" regulation influence on a gene's expression being the summation of the independent regulatory inputs. We estimate the weights using a Bayesian linear regression method for sparse parameter vectors. We apply our proposed method to the extraction of differential gene expression software selected genes of a human buffy-coat microarray expression profile dataset of ventilator-associated pneumonia (VAP), and compare the estimation result with the GRNs estimated using both a correlation coefficient method and a database-based method ingenuity pathway analysis. A biological analysis of the resulting consensus network that is derived using the GRNs, estimated with both our and the correlation-coefficient methods results in four biologically meaningful subnetworks. Also, our method performs either better than or competitively with the existing well-established GRN estimation methods. Moreover, it performs comparatively with respect to: 1) the ground-truth GRNs for the in silico 50- and 100-gene datasets reported recently in the DREAM3 challenge and 2) the GRN estimated using a mutual information-based method for the top-ranked Bayesian analysis of time series (a Bayesian user-friendly software for analyzing time-series microarray experiments) selected genes of the VAP dataset.

Model-based method for transcription factor target identification with limited data

We present a computational method for identifying potential targets of a transcription factor (TF) using wild-type gene expression time series data. For each putative target gene we fit a simple differential equation model of transcriptional regulation, and the model likelihood serves as a score to rank targets. The expression profile of the TF is modeled as a sample from a Gaussian process prior distribution that is integrated out using a nonparametric Bayesian procedure. This results in a parsimonious model with relatively few parameters that can be applied to short time series datasets without noticeable overfitting. We assess our method using genome-wide chromatin immunoprecipitation (ChIP-chip) and loss-of-function mutant expression data for two TFs, Twist, and Mef2, controlling mesoderm development in Drosophila. Lists of top-ranked genes identified by our method are significantly enriched for genes close to bound regions identified in the ChIP-chip data and for genes that are differentially expressed in loss-of-function mutants. Targets of Twist display diverse expression profiles, and in this case a model-based approach performs significantly better than scoring based on correlation with TF expression. Our approach is found to be comparable or superior to ranking based on mutant differential expression scores. Also, we show how integrating complementary wild-type spatial expression data can further improve target ranking performance.

p63 directly induces expression of Alox12, a regulator of epidermal barrier formation

Epidermal development and differentiation are tightly controlled processes that culminate in the formation of the epidermal barrier. A critical regulator of different stages of epidermal development and differentiation is the transcription factor p63. More specifically, we previously demonstrated elsewhere that p63 is required for both the commitment to stratification and the commitment to terminal differentiation. We now demonstrate that DeltaNp63alpha, the predominantly expressed p63 isoform in postnatal epidermis, also plays a role in the final stages of epidermal differentiation, namely the formation of the epidermal barrier. We found that DeltaNp63alpha contributes to epidermal barrier formation by directly inducing expression of ALOX12, a lipoxygenase which contributes to epidermal barrier function. Our data demonstrate that DeltaNp63alpha directly interacts with the promoter of Alox12 in the developing epidermis. Furthermore, we found that the induction of Alox12 expression by DeltaNp63alpha depends on intact p63 binding sites in the Alox12 promoter. Finally, we found that DeltaNp63alpha can induce Alox12 expression only in differentiating keratinocytes, consistent with the role of ALOX12 in epidermal barrier formation.

Prediction and experimental validation of novel STAT3 target genes in human cancer cells

The comprehensive identification of functional transcription factor binding sites (TFBSs) is an important step in understanding complex transcriptional regulatory networks. This study presents a motif-based comparative approach, STAT-Finder, for identifying functional DNA binding sites of STAT3 transcription factor. STAT-Finder combines STAT-Scanner, which was designed to predict functional STAT TFBSs with improved sensitivity, and a motif-based alignment to minimize false positive prediction rates. Using two reference sets containing promoter sequences of known STAT3 target genes, STAT-Finder identified functional STAT3 TFBSs with enhanced prediction efficiency and sensitivity relative to other conventional TFBS prediction tools. In addition, STAT-Finder identified novel STAT3 target genes among a group of genes that are over-expressed in human cancer cells. The binding of STAT3 to the predicted TFBSs was also experimentally confirmed through chromatin immunoprecipitation. Our proposed method provides a systematic approach to the prediction of functional TFBSs that can be applied to other TFs.

p63 Suppresses non-epidermal lineage markers in a bone morphogenetic protein-dependent manner via repression of Smad7

p63, a p53 family member, plays an essential role in epidermal development by regulating its transcriptional program. Here we report a previously uncovered role of p63 in controlling bone morphogenetic protein (BMP) signaling, which is required for maintaining low expression levels of several non-epidermal genes. p63 represses transcription of the inhibitory Smad7 and activates Bmp7, thereby sustaining BMP signaling. In the absence of p63, compromised BMP signaling leads to inappropriate non-epidermal gene expression in postnatal mouse keratinocytes and in embryonic epidermis. Reactivation of BMP signaling by Smad7 knockdown and/or, to a lesser extent, by BMP treatment suppresses expression of non-epidermal genes in the absence of p63. Canonical BMP/Smad signaling is essential for control of non-epidermal genes as use of a specific inhibitor, or simultaneous knockdown of Smad1 and Smad5 counteract suppression of non-epidermal genes. Our data indicate that p63 prevents ectopic expression of non-epidermal genes by a mechanism involving Smad7 repression and, to a lesser extent, Bmp7 induction, with consequent enhancement of BMP/Smad signaling.

Computational methods for discovering gene networks from expression data

Designing and conducting experiments are routine practices for modern biologists. The real challenge, especially in the post-genome era, usually comes not from acquiring data, but from subsequent activities such as data processing, analysis, knowledge generation and gaining insight into the research question of interest. The approach of inferring gene regulatory networks (GRNs) has been flourishing for many years, and new methods from mathematics, information science, engineering and social sciences have been applied. We review different kinds of computational methods biologists use to infer networks of varying levels of accuracy and complexity. The primary concern of biologists is how to translate the inferred network into hypotheses that can be tested with real-life experiments. Taking the biologists' viewpoint, we scrutinized several methods for predicting GRNs in mammalian cells, and more importantly show how the power of different knowledge databases of different types can be used to identify modules and subnetworks, thereby reducing complexity and facilitating the generation of testable hypotheses.

Reverse engineering and verification of gene networks: principles, assumptions, and limitations of present methods and future perspectives

Reverse engineering of gene networks aims at revealing the structure of the gene regulation network in a biological system by reasoning backward directly from experimental data. Many methods have recently been proposed for reverse engineering of gene networks by using gene transcript expression data measured by microarray. Whereas the potentials of the methods have been well demonstrated, the assumptions and limitations behind them are often not clearly stated or not well understood. In this review, we first briefly explain the principles of the major methods, identify the assumptions behind them and pinpoint the limitations and possible pitfalls in applying them to real biological questions. With regard to applications, we then discuss challenges in the experimental verification of gene networks generated from reverse engineering methods. We further propose an optimal experimental design for allocating sampling schedule and possible strategies for reducing the limitations of some of the current reverse engineering methods. Finally, we examine the perspectives for the development of reverse engineering and urge the need to move from revealing network structure to the dynamics of biological systems.

Systems and Synthetic biology: tackling genetic networks and complex diseases

In the era of post-genomic research two new disciplines, Systems and Synthetic biology, act in a complementary way to shed light on the ever-increasing amount of data produced by novel high-throughput techniques. Systems biology aims at developing a formal understanding of biological processes through the development of quantitative mathematical models (bottom-up approach) and of 'reverse engineering' (top-down approach), whose aim is to infer the interactions among genes and proteins from experimental observations (gene regulatory networks). Synthetic biology on the other hand uses mathematical models to design novel biological 'circuits' (synthetic networks) able to perform specific tasks (for example, periodic expression of a gene of interest), or able to change the behavior of a biological process in a desired way (for example, modify metabolism to produce a specific compound of interest). The use of a pioneering approach that combines biology and engineering, to describe and/or invent new behaviors, could represent a valuable resource for studying complex diseases and design novel therapies. The identification of regulatory networks will help in identifying hundreds of genes that are responsible for most genetic diseases and that could serve as a starting point for therapeutic intervention. Here we present some of the main genetics and medical applications of these two emerging fields.

An active role of the DeltaN isoform of p63 in regulating basal keratin genes K5 and K14 and directing epidermal cell fate

Background

One major defining characteristic of the basal keratinocytes of the stratified epithelium is the expression of the keratin genes K5 and K14. The temporal and spatial expression of these two genes is usually tightly and coordinately regulated at the transcriptional level. This ensures the obligate pairing of K5 and K14 proteins to generate an intermediate filament (IF) network that is essential for the structure and function of the proliferative keratinocytes. Our previous studies have shown that the basal-keratinocyte restricted transcription factor p63 is a direct regulator of K14 gene.

Methodology/Principal Findings

Here we provide evidence that p63, specifically the ΔN isoform also regulates the expression of the K5 gene by binding to a conserved enhancer within the 5′ upstream region. By using specific antibodies against ΔNp63, we show a concordance in the expression between basal keratins and ΔNp63 proteins but not the TAp63 isoforms during early embryonic skin development. We demonstrate, that contrary to a previous report, transgenic mice expressing ΔNp63 in lung epithelium exhibit squamous metaplasia with de novo induction of K5 and K14 as well as transdifferentiation to the epidermal cell lineage. Interestingly, the in vivo epidermal inductive properties of ΔNp63 do not require the C-terminal SAM domain. Finally, we show that ΔNp63 alone can restore the expression of the basal keratins and reinitiate the failed epidermal differentiation program in the skin of p63 null animals.

Significance

ΔNp63 is a critical mediator of keratinocyte stratification program and directly regulates the basal keratin genes.