Construction of predictive promoter models on the example of antibacterial response of human epithelial cells

Abstract

Background: Binding of a bacteria to a eukaryotic cell triggers a complex network of interactions in and between both cells. P. aeruginosa is a pathogen that causes acute and chronic lung infections by interacting with the pulmonary epithelial cells. We use this example for examining the ways of triggering the response of the eukaryotic cell(s), leading us to a better understanding of the details of the inflammatory process in general.

Results: Considering a set of genes co-expressed during the antibacterial response of human lung epithelial cells, we constructed a promoter model for the search of additional target genes potentially involved in the same cell response. The model construction is based on the consideration of pair-wise combinations of transcription factor binding sites (TFBS). It has been shown that the antibacterial response of human epithelial cells is triggered by at least two distinct pathways. We therefore supposed that there are two subsets of promoters activated by each of them. Optimally, they should be "complementary" in the sense of appearing in complementary subsets of the (+)-training set. We developed the concept of complementary pairs, i.e., two mutually exclusive pairs of TFBS, each of which should be found in one of the two complementary subsets.

Conclusions: We suggest a simple, but exhaustive method for searching for TFBS pairs which characterize the whole (+)-training set, as well as for complementary pairs. Applying this method, we came up with a promoter model of antibacterial response genes that consists of one TFBS pair which should be found in the whole training set and four complementary pairs. We applied this model to screening of 13,000 upstream regions of human genes and identified 430 new target genes which are potentially involved in antibacterial defense mechanisms.

Figure 1

Algorithm of the search for common pairs using seed sets. Step 1. Selection of a "seed" set. Step 2. Identification of all pairs in the "seed" set; only those, which are found in 100% of the "seed" sequences, are taken into further consideration. Step 3. Search for the selected pairs in the whole (+)-training set. Step 4. Only those which are found in more than 80% of sequences of the (+)-training set are taken for into the further consideration. Step 5. Search for the "survived" pairs in the negative training set. Only those which are present in less than 40% of sequences are left. Step 6. The list of the common pairs is ready for the next analysis.

Figure 2

Complementary pairs A, B, C and D are transcription factor binding sites, which form two sorts of pairs (A-B and C-D). These pairs are complementary in the sense of occurring in complementary subsets of the whole set.

Figure 3

Pair classes When grouping different combinations of transcription factor binding sites according to mutual orientation, we allow inversions of the whole module. This gives rise to a total of three classes as shown.

Figure 4

Algorithm of the search for complementary pairs using "seed" sets Step 1. Selection of a "seed" set; Step 2. Selection of complementary pairs in the human "seed"; every combination is checked in the (-) training set and only those, which are found in less than 40% of sequences, are taken into further consideration. Step 3. Selection of complementary pairs in the "seed" of orthologs or in the joint "human + orthologs" "seed". (Step 2 may be omitted and substituted by Step 3) Step 4. Search for the selected pairs in the whole (+)-training set. After that the final choice is made.

Figure 5

Seven pairs, which are combined in four complementary combinations, and the results of their simultaneous application Each of the complementary pairs searches for nearly the same portion of the training set, while in the negative training set their intersection appears to be very small. Here, only those pairs are shown that have been chosen for the final model, but there were several more, which searched for the same subset of the training set and gave altogether 1,7% in the negative training set. Note that the circles are not exactly drawn to scale.