analysis of cooperating TFs
5.3. Identification of inter- and intra-regional cooperating TFs in the context of inflammatory response in lung tissue
In this section, I applied the pointwise mutual information approach for the identification of intra-cooperating TFs as well as the multivariate mutual information based approach for the identification of intra-regional TF-cooperations to the same dataset, in order to demon-strate the mutual complementarity of the two methods. For this aim I chose a data set provided by the ExITox project. The ExITox project (FKZ 031L0120C) investigates the molecular changes in lung tissue in response to the inhalation of toxic substances (see http://genexplain.com/exitox-ii/for details). The underlying data set comprises 36 differentially expressed genes (DEGs) in response to butanol exposure.
For each gene, I took the promoter region -1000bp to +100bp relative to the TSS as promoter sequence under study. Further, I determined the potentially regulating enhancer regions for each gene by taking all enhancer regions provided by ENCODE that have a distance of at most 2 Mbp up- or downstream from the TSS of the gene and which have a length of at least 300bps. In total, I identified 1036 enhancer regions that take part in 2212 promoter-enhancer interactions (PEIs).
I applied my approach for the identification of intra-regional sequence-set specific TF coop-erations to the enhancer sequences and to the set of promoter sequences, respectively. Fur-ther, I determined all potential inter-regional TFBS associations in the entire PEI sequence-set using the second approach. Finally, I end up with three TF cooperation networks, each for one analysis and summarized the networks in Table 5.25.
Table 5.25.: Summary of the cooperation networks based on the intra- and inter-regional analyses. The edges refer to identified cooperations and the nodes to the TFBSs.
For the inter-regional cooperation network, I further distinguished between TFBSs in en-hancer (enh.) and promoter (prom.) sequences.
Intra-regional Inter-regional
Promoters Enhancers PEIs
Edges 36 44 170
Nodes 30 25 126 (51 enh. and 75 prom.)
In order to evaluate the performance of the approaches in the biological perspective, I deter-mined the hub nodes for each TF cooperation network (see Table 5.26) and analyzed them according to their biological function by paying special attention to inflammatory processes in the lung.
Table 5.26.: Hub nodes for the inter-and intra regional cooperating TFBS network.
The identified inter-regional hub nodes stem from the same network but are classified in enhancer and promoter TFBSs. The intra-regional hubs are taken from the network of enhancer and promoter sequence analysis, respectively.
Intra-regional Inter-regional
TFBS promoter TFBS enhancer TFBS promoter TFBS enhancer
V$BRCA_01 V$CTF_01 V$NF1_Q6 V$AP1_Q6_02
V$PAX_Q6 V$GKLF_Q4 V$MEF2A_Q6 V$MAF_Q6_01
V$CTF_01 V$BEN_01 V$P53DECAMER_Q2 V$MUSCLEINI_B
V$SRY_Q6 V$DMRT4_01 V$ETS_Q6
V$CHCH_01 V$MAFA_Q4
In the analysis for the identification of intra-regional TF cooperations specific for the pro-moter sequences, the most frequently represented TFBSs are V$BRCA_01, V$PAX_Q6 and V$CTF_01. V$BRCA_01 is bound by transcription factor BRCA1 that is involved in apoptosis and heat shock response [134, 135]. V$PAX_Q6 is bound by paired box transcrip-tion factors such as PAX1 and PAX5 that are both involved in the immune system [136, 137]
and PAX2 which acts in cell proliferation and antiapoptosis [138, 139, 140, 141, 142].
V$CTF_01 is bound by factors of the SMAD-family whose members act in response to TGF-β, a cytokine, involved in fibrotic processes [143].
Regarding the identified intra-regional enhancer sequence-set specific TF cooperation net-work, one highly connected node is V$GKLF_Q4, which is bound by Krüppel-like factor 4 (KLF4), a factor that induces inflammation and apoptosis [144, 145, 146, 147, 148, 149, 150] and has been identified to attenuate lung fibrosis [151].
For the network of inter-regional TF cooperations, I differentiated between hub nodes that are related to TFBSs in enhancer and those in promoter sequences. Regarding the TFBSs in the promoter regions, V$NF1_Q6 is highly connected. V$NF1_Q6 is bound by factors such as NFIA and NFIB. NFIA acts in Notch signalling [152] and is linked to asthma plus rhinitis phenotype [153], whereas NFIB is involved in small cell lung cancer [154].
Another important binding site is V$MEF2A_Q6 bound by MEF2A, a factor upregulated in small-cell lung carcinoma [155]. The related factor MEF2D, which has a nearly identical DNA-binding domain and therefore an identical or very similar DNA-binding specificity,
Figure5.16.:Networkofinter-andintra-regionalcooperatingTFs.Thenetworkshowstheaggregationofallthreecoopera- tionnetworks.TheTFBSsrelatedtopromoterregionsarecoloredredandthoserelatedtoenhancerregionsaremarkedingreen. Theedgesrefertopredictedinter-regionalcooperations(yellow)andsequence-setspecificintra-regionalTFcooperationsfor theenhancersequences(blue)andthepromotersequences(darkred).
has been identified to be upregulated in lung inflammation and the resulting development of lung cancer [155].
Regarding the highly connected TFBSs in the enhancer sequences, V$AP1_Q6_02 is a highly connected node. V$AP1_Q6_02 is bound by AP1 which might be activated by the development of oxidant/antioxidant imbalance in lung inflammation [156] and the inhibi-tion of AP1 leads to the attenuainhibi-tion of lung inflammainhibi-tion [157]. Another highly connected binding site is V$MAF_Q6_01 that is bound by MAF, a factor involved in toll-like receptor signaling and is also involved in immunity [158, 159, 160, 161, 162]. Further, the binding site V$ETS_Q6 is bound by ETS1 or ETS2. While ETS1 acts in apoptosis and cytokine secretion, ETS2 has been identified as a putative biomarker for progression of chronic ob-structive pulmonary disease [163].
Table 5.27.: TFBSs identified in the analysis for inter-regional and intra-regional TF cooperations.
TFBSs promoter TFBSs enhancer
V$CHCH_01 V$BRCA_01
V$SOX10_Q6_01 V$SMAD4_Q6_01 V$HOXC13_01 V$LEF1_Q5_01
V$CPBP_Q6 V$PAX_Q6
V$BRCA_01 V$IK_Q5
V$CMYB_Q5 V$SRY_Q6
V$IK_Q5 V$PAX_Q6 V$CEBPA_Q6
V$HDX_01 V$AP1_Q6_02
V$ING4_01 V$CTF_01 V$CDPCR1_01
There are several TFBSs involved in identified pairs of the inter- and intra-regional TF co-operation analysis. These TFBSs act as linking nodes between the inter-and intra-regional cooperation networks (see Figure 5.16 and Table 5.27). One of these linking TFBSs is V$CPBP_Q6 that is bound by KLF6, a factor involved in the activation of TGF-β in the cellular response to the human respiratory syncytial virus [164]. Another linking TFBS is V$CMYB_Q5 bound by v-MYB which is among others involved in idiopathic pulmonary fibrosis [165]. Further, binding site V$ING4_01 is bound by ING4 which is involved in cell proliferation and apoptosis and in lung carcinomas [166, 167]. Finally, V$CDPCR1_01
bound by CUX1 appears to be a linking node between inter-and intra-regional TF coopera-tion networks. CUX1 acts in lung development, immune response and is downregulated in interstitial fibrosis [168, 169, 170, 171, 172, 173].
The complementary usage of the two approaches provides a more extensive insight in the underlying regulatory mechanisms in the cell, in contrast to the single analyses by joining the underlying TF cooperation networks. Thereby, TFs not conspicuous in the single anal-ysis excel as linking nodes between the corresponding networks and, thus, can be identified as important factors in the regulatory processes of inflammatory response in the cell.
In this chapter, I will discuss the methods established in this thesis as well as the corre-sponding results. First of all I will discuss the determination of intra-regional cooperating transcription factors based on the co-occurrence of their binding sites by using pointwise mutual information. Second, I will consider the identification of associated TFBSs between enhancers and their related promoters, referred to as inter-regional cooperations, by using and comparing different multivariate mutual information measures. In the last section of the chapter, I will discuss the complementary usage of these two applications based on the analyzed inflammation linked gene set.