Integrative modeling of transcriptional regulatory networks in head and neck cancer

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1 Integrative modeling of transcriptional regulatory networks in head and neck cancer Bin Yan 1*, Huai Li, Zhong Chen 3, Jiaofang Shao 1, Ming Zhan 4* 1 Department of Biology, Hong Kong Baptist University, Kln, Hong Kong; RRB, NIA, National Institutes of Health, Baltimore, MD, USA; 3 Head and Neck Surgery Branch, NIDCD, National Institutes of Health, Bethesda, MD, USA ; 4 Department of Systems Medicine and Bioengineering, Methodist Hospital Research Institute, Houston, TX, USA. * Contact author, BY: bin1999@hkbu.edu.hk, MZ: mzhan@tmhs.org Abstract p53 is the most mutated tumor suppressor gene in cancers, which are usually inflammatory with aberrant NF-κB activation. However, how NF-κB family members and p53 interact to globally regulate genes expression is not yet fully understood. Using head and neck squamous cell carcinoma (HNSCC) lines as the model system, we developed a novel integrative model based on Regulatory Component Analysis, which combined mrna expression profile with transcription factor and microrna binding for integrated analyses through matrix decomposition. We observed that the majority of p53 targets are also co-regulated by NF-κB in p53 wild-type or mutant subset of HNSCC cells. We further constructed regulatory networks of NF-κB, p53 and micrornas 1 and 34s. Our results unraveled the cross-regulations among NFκB, p53, and micrornas, provided an insight into understanding of underlying regulatory mechanisms, and showed an efficient approach to inferring the regulatory programs in these datasets. Keywords: Regulatory networks, Integrative modeling, NFκB, p53, Head and neck cancer 1 Introduction Reconstruction and modeling of gene regulatory networks are one of main challenges in computational biology. Various mathematical algorithms or computational methods have been developed for integrative analysis of microarray and transcription factor (TF) binding data for unraveling transcriptional regulatory modules. Several matrix decomposition methods, such as PSMF, ModulePro, NMF, have been recently presented for regulatory network reconstruction based on the constraints of sparseness, non-negativeness, or partial network connectivity information [1-3]. Although all these methods show an improved result in uncovering biologically meaningful regulatory networks than the decomposition methods without the constraints, they were conducted separately, and no integrative framework has been utilized that brings the sparseness and pre-knowledge of regulatortarget interactions together during matrix decomposition [1,, 4]. Here, we devised a new methodology, based on Regulatory Component Analysis (RCA), for inferring regulatory gene networks and uncovering transcriptional modules. The RCA-based model performs matrix decomposition under the joint constraints of sparseness and partial information of TF-target connectivity, and allows an integrated analysis of gene expression profile and regulator binding data. We used the new method in studies of the head and neck squamous cell carcinoma (HNSCC). HNSCC is one of the most common human cancer worldwide. The development of HNSCC is associated with alterations in expression of a large set of genes, which could be underlined by shared TFs or regulators of key regulatory mechanisms that control transcriptional regulatory networks. Among these TFs, NF-κB has been demonstrated to play a central role in the control of gene expression that mediates cellular proliferation, apoptosis, angiogenesis, immune and proinflammatory responses, and therapeutic resistance [5, 6]. In a systems biology study, we defined 748 NF-κB target genes and their functional associations using an integrative model COGRIM in HNSCC, thus proposing that NF-κB is one of the critical regulatory determinants of expression of multiple gene programs, interacting pathways and malignant phenotypes [7]. Followed by that study, some challenging questions would be further asked with regard to NF-κB regulatory mechanisms. For example, whether NF-κB functions are affected by other TFs or regulators? If so, how NF-κB interacts with these TFs or regulators to modulate the gene programs of HNSCC? As a tumor suppresser, p53 is implicated as a master regulator of apoptosis, cell cycle and DNA repair, etc. Mutations of TP53 have been observed in near half cases for all types of human cancer, including HNSCC. In previous studies, tumor suppresser TF p53 was reported to also regulate NF-κB target genes [8-10]. However, the molecular basis underlying their interactions has not been adequately understood in HNSCC. In addition to p53, other cancer-related TFs such as AP1, STAT3, EGR1, CEBPB and SP1 were reported to be involved in

2 complex regulatory systems of NF-κB [11-14]. Moreover, micrornas are proposed to play as co-regulators that involve in modulation of gene expression at posttranscriptional level. Therefore, genome-wide investigation of significant interactions between NF-κB and p53 or other regulators would enhance our understanding of transcriptional regulatory mechanisms associated with diverse HNSCC phenotypes. In this study, we applied a newly developed method to identify transcriptional regulatory programs by combining TF and microrna binding information with expression profiling in HNSCC cells with the wild type (wt) p53-deficient and the mutant (mt) p53 status. Our studies demonstrated that two master TFs NF-κB and p53 have a wide impact on expression profile of gene programs in the tumor cells. Furthermore, our results revealed that NF-κB, p53 and the micrornas may form concerted regulatory modules for contributing to the gene programs in both wt p53-deficient and mt p53 phenotypes. Methods.1 Microarray dataset The gene expression data was collected from GEO database with an accession number GSE The microarray data were generated from two subgroups: mt p53 and wt p53- deficient HNSCC cell lines [7, 15]. The microarray data of differentially expressed genes satisfying.0 fold and above change were used for the RCA-based analyses. L M and Z R such that the square error (Euclidean distance) function: E( Y, Z) = X YZ (1) is minimized under a desired degree of sparseness on the mixing matrix Y. We defined a sparseness measure S( y l ) based on the relationship between the L 1 norm and the L norm [16]: S( y l ) = N N N y i= 1 il i= 1 N 1 where y l ] superscript "T" means "transpose". The L 1 norm y and the L norm y l = y il () T = [ y1 l yl y is the lth column of Y, the Nl y l were defined as N = l 1 i = 1 N i = y 1 il l 1 y y and, respectively. The sparseness evaluates to one if and only if y contains a single non-zero element, l and takes a value of zero if and only if all elements are equal. Here, there are two interpretations of the decomposition X YZ. First, the rows of Z represent the expression profiles of the L latent variables across samples. Second, the rows of Z can be viewed as the activity profiles of the L regulators. Thus, we can cluster genes based on corresponding non-zero coefficients of Y, which represent gene regulatory programs, i.e. transcriptional modules that are co-regulated by the L regulators. il. TF and microrna binding data NF-κB and p53 binding data were extracted from available sources: 1) NF-κB and p53 websites ) previous publications. The binding information of other TFs (AP1, EGR1, CEBPB, STAT3, and SP1) and micrornas was gained from 1) website /gsea/msigdb/; ) curated from previous publications..3 RCA-based method The method was performing matrix decomposition under the joint constraints of sparseness and partial information of TF-target connectivity. The method allows an integrated analysis of gene expression profiles with binding data of a set of regulators, including TFs, micrornas, etc. The RCA-based method (see Figure 1) is a network structure-driven model for inferring gene regulatory networks and uncovering transcriptional modules. Given a N M microarray data matrix X R with the sample size M N L and the numbers of genes N, our aim is to find Y R Figure 1. Overview of the Regulatory Component Analysis (RCA)-based method. R: regulators, such as transcription factors (TFs) and micrornas. TG: target genes. TSS: transcriptional start site.

3 We devised an iterated learning algorithm that is capable of combining constraints of sparseness and limited information of regulator-target binding. The sparseness was used as a statistical parameter for modeling the regulatory components of regulators and their targets. The learning procedure was based on a projected gradient descent approach with sparseness constraints. The output (Y matrix) of the RCA procedure provided quantitative relationships between regulators (such as TFs and micrornas in this study) and every gene from microarray dataset. The non-zero values stand for regulatory interactions or components which can be used to estimate how possible a gene is regulated by the regulators or whether a gene is target of the regulators. 3 Results 3.1 The RCA-based approach In this study, we sought to unravel both TF- and microrna-mediated regulatory gene networks responsible for the malignant phenotypes of HNSCC, and the analytic strategy is depicted in Figure 1. The integrative model exhibited several advantages in comparison with the COGRIM method, that was previously used in the same HNSCC gene profiling [7]. To justify our new method, we compared the results derived by using the RCA-based method in current study with those by COGRIM previously. The comparison was based on a Gene Ontology (GO) analysis of the target genes of the three NF-κB subunits, RelA, NFκB1 and crel, predicted by using RCA and COGRIM, respectively, based on the same microarray dataset. We assessed the functional relevance of GO biological processes based on the enrichment analysis by Fisher's exact tests. Table 1 shows the statistical enrichment of biological processes among the target genes identified by the two methods. The enrichment level was calculated by transforming the enrichment P values after FDR correction to negative log10 values and averaged over all biological processes with corrected P<0.05. Overall, our RCA-method showed advantages than COGRIM, where the averaged P values of FDR values were lower than COGRIM in both wt p53-deficient and the mt p53 datasets. 3. Prediction of HNSCC-specific target genes of TFs Next, we intent to identify TF and microrna regulatory modules controlling different gene expression programs in both malignant subgroups. Our analysis identified 48 and 418 target genes of NF-κB, and putative 169 and 81 p53 target genes in the wt p53-deficient and mt p53 HNSCC cells, respectively. Then significant overlaps of target genes between NF-κB and p53 was detected (Figure ), that all p53 target genes predicted in the mt p53 cells overlapped with NF-κB targets, whereas such overlap in the wt p53-deficient cells was 60% (overlapping P value = ). On the other hand, we noted that the fraction of the NF-κB target genes that overlapped with p53 targets seemed different between the wt and mt p53 subgroups. Among the total NF-κB target genes, 41% overlapped with the p53 targets in the wt p53-deficient, which was greater than those in the mt p53 (19%). This difference is mainly due to their different fractions observed in the underexpressed gene subsets (61% vs. 16%). We did not find such a difference in the overexpressed gene subsets (8% vs. 4%). Our analyses provide a set of common genes co-regulated by the two master TFs in HNSCC. Table 1. Comparison based on GO functional enrichment HNSCC TFs FDR by different methods type RCA COGRIM wt p53- RelA deficient NFκB crel Average of TFs mt p53 RelA NFκB crel Average of TFs The enrichment level was calculated by transforming enrichment P values averaged over all GO processes with False Discovery Rate (FDR) corrected P<0.05. Figure. Overlaps between target genes of NF-κB and p53 in wt p53- deficient HNSCC cells of overexpressed genes (A) and underexpressed genes (C), and mt p53 HNSCC cells of overexpressed genes (B) and underexpressed genes (D) Additional TFs (AP1, EGR1, CEBPB, STAT3, and SP1) were also previously implicated as important regulators in the tumorigenesis. To identify regulatory programs co-regulated by NF-κB and p53 with these TFs,

4 we first constructed two networks linking each TFs and their putative target genes predicted by the RCA-based method for the two tumor subgroups. Totally 98 and 3 genes were identified as targets of at least two TFs in the wt p53-deficient and the mt p53 cells, respectively (data not shown). Next, we identified two regulatory programs consisting of genes putatively co-targeted by all the seven TFs (Figure 3). The programs of the wt p53-deficient comprised 37 genes, where 17 and 1 genes are consistent with known NF-κB and p53 targets based on previous publications, respectively. The percentage of known NFκB and p53 target genes in the program was greater than their total prediction (i.e. all of their predicted NF-κB or p53 target genes), where NF-κB is 46% vs. 19 % and p53 is 34% vs. 14%. Similarly, 39 genes (including 1 known NF-κB and 18 known p53 ones) formed the regulatory programs of the mt p53. The prediction of the known target genes in the network was also relatively accurate by compared with the total prediction for NF-κB (31% vs. 15 %) and p53 (46% vs. 9%). We further found that most genes in the TF regulatory programs were functionally classified to GO biological processes adhesion, angiogenesis, apoptosis, cell cycle, inflammatory and immune responses, proteolysis, regulation of transcription, etc. genes overlapped with NF-κB ones in the wt p53-deficient and mt p53 cells, respectively, suggesting more interaction between mir34s and NF-κB in gene regulation of mt p53 tumor cells. By the contrast, we did not observe such a difference of overlapping between mir1 and NF-κB target gene sets (51-55 % in the wt and mt p53 subgroups). We then constructed two regulatory networks of NFκB, p53 and mir 1 or mir34s (Figure 4). The network of the wt p53-deficient comprised 49 common target genes of NF-κB, p53, mir1 or mir34ac_449, respectively. Relatively, the network of the mt p53 was composed of a small number of 1 genes including 7 common targets of the two micrornas. Even though most of common targets in the networks were underexpressed, we still detected several overexpressed ones, for example, IL6 and ELF3 (inflammatory), PTGES (proliferation), and CASP4 (apoptosis) in the wt p53-deficient, and MMP1 (proteolysis) and PTK (angiogenesis and migration) in the mt p53 (Figure 4). This analysis highlights a considerable interaction of regulatory programs among NF-κB, p53 and the two micrornas. Figure 3. Gene programs co-regulated by all seven TFs (NF-κB, p53, AP1, CEBPB, EGR1, SP1, and STAT3) in wt and mt p53 HNSCC cells. Genes in underlined, bold and bold-underlined refer to known targets of NF-κB, p53 and NF-κB/p53, respectively. refer to genes differentially over- and underexpressed, respectively 3.3 microrna target genes and their interaction with TFs We applied the RCA-based approach to analyze target genes of micrornas. Since oncogenic mir1 and tumor suppresser mir34s have been studied for their relationships with the p53 pathway [17, 18], we concentrated on their interaction with the NF-κB regulatory network. mir34ac_449 was used to represent mir34s because both micrornas mir34ac and mir449 share the same binding motif consensus from the available website (see method). In our analysis, 3% and 7% of the mir34ac_449 target Figure 4. Regulatory gene networks of NF-κB, p53 and micrornas 1 and 34s in HNSCC cells. Every node represents a common target gene of NF-κB, p53, mir1 or mir34ac_449, and was annotated to processes with different colors. (A) the wt p53-deficient. (B) the mt p53.

5 4 Discussion Our integrative modeling is RCA-based and can capture the sparse structure existing in gene expression data for unraveling transcriptional regulatory networks. The efficiency of the RCA-based method is supported by its applicability in prediction of NF-κB targets, in comparison with the analysis by other methods. We compared the RCA-based method with a similar method, COGRIM. Among the NF-κB targets predicted by our method, 19% (in the wt p53-deficient) and 15% (in the mt p53) are consistent with known ones published previously. But the known NF-κB genes predicted by COGRIM only reaches to 10% of the total prediction [7]. More importantly, the NF-κB genes predicted by the RCA-based method are more functionally relevant than those by COGRIM (Table 1). Our method improved the efficiency and accuracy to indentify regulatory associations between NF-κB and their targets. The identified NF-κB genes by the newly developed method are highly associated with biological processes, suggesting that they are biologically more meaningful than those by other methods. A previous study has confirmed NF-κB function in the tumor cells with both wt and mt p53 status [19]. By promoter analysis, p53 and NF-κB were shown to play a reciprocal role in the two distinct over-expressed gene clusters of HNSCC [10, 15]. In the present study, we demonstrate a significant intersection of p53 and NF-κB regulated genes in HNSCC. However, the p53 and NF-κB interaction is different in the gene subsets underexpressed in the wt or mt p53 cells. In the wt p53-deficient cells, the two TFs can jointly regulate over 60% of the underexpressed genes. In contrast, all p53 targets were putatively regulated by NF-κB in the mt p53 cells (Figure ). This observation strongly suggests that a tight cooperation between NF-κB family members and p53 controls the p53 network in the mt p53 cells, but to a less extent affects the p53 network of the wt p53-defieient cells. Moreover, our analyses showed a more accurate prediction of known NF-κB and p53 target genes in the regulatory programs of the seven TFs (Figure 3) in comparison with those in the total prediction, Such predicted programs from both computational and literature search suggest that the malignant progression of HNSCC is likely as a result of co-regulation by a combinatorial cooperation of NF-κB, p53, and the other five TFs. Identification of TF-microRNA modules enhanced our understanding of complex transcriptional regulatory architectures in cancer cells. In this study, our results support that both mir1 and mir34s likely participate in transcriptional control of gene expression by NF-κB and p53. Their functions may contribute to the progression or suppression of HNSCC cells. Several common genes were downregulated by NF-κB, p53 and the two micrornas in both wt and mt p53 cells (Figure 4), such as ITGA3 and LAMA3 (adhesion), SERPINE1 (angiogenesis), and PLAU (proteolysis). These are suggested to favor cancer metastasis [0, 1]. The micrornas likely cooperate with p53 and NF-κB to inhibit their expression so that repressing tumor progression of HNSCC. In contrast, several overexpressed genes in the networks may promote tumorigenesis of the wt p53-deficient cells by alterations in gene expression associated with inflammatory, proliferation, apoptosis and other processes, such as IL6, ELF3, PTGES, and CASP4, or trigger metastatic processes of the mt p53 cells. 5 Conclusions In summary, our results provide a general view of cross-regulatory relationships among NF-κB, p53 and the micrornas in different malignant phenotypes. To our knowledge, this is the first investigation of TF-microRNA regulatory interactions by modeling diverse data sources and integrating constraints of sparseness in HNSCC. Within experimental validation of predicted microrna targets, it would help in understanding of TF-microRNA regulatory mechanisms responsible for different cancer phenotypes and heterogeneity of HNSCC. Also, successful application of the RCA-based method in HNSCC showed it could serve as a useful approach to study on regulatory networks of regulators in other complex biological systems. 6 References [1] Dueck D, Morris QD, Frey BJ: Multi-way clustering of microarray data using probabilistic sparse matrix factorization. Bioinformatics 005, 1 Suppl 1:i144-i151. [] Li H, Sun Y, Zhan M: The discovery of transcriptional modules by a two-stage matrix decomposition approach. 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