General Aim

The general aim of this research is the investigation of gene regulatory networks of innate immune response as well as the cellular immune response arising during the interaction of P. aeruginosa with lung epithelium. Promoter model construction is a way to utilize information about coexpressed genes, which becomes more and more available with the results of microarray experiments. In this work we consider the interaction of P. aeruginosa with lung epithelial cells in terms of transcription regulation of the eukaryotic cells? response. 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 [1,2]. We use this example for understanding 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. The other important innate defense mechanism is represented by neutrophils, which are the first line of defense against invading microorganisms. This mechanism depends on the ability of neutrophils to ingest and subsequently eliminate pathogens.

Promoter model of antibacterial response: The considered scheme of interactions.

After adhesion of P. aeruginosa to the epithelial cells, the response of these cells is triggered by at least two distinct agents: bacterial lipopolysaccharides [3] or bacterial pilins or flaggelins [4]. Both pathways lead to the activation of transcription factor NF-kB which is well-known to participate in these kinds of reactions. In this work we searched for additional transcription factors that may cooperate with NF-kB thus complementing and/or specifying its effects.


Promoter model of antibacterial response: Sequence sets and TFs

(+)Training set (34 sequences)

The training set comprises:

(a) Promoter regions of human genes which have been shown by microarray analysis to be affected in epithelial cells after interaction of P. aeruginosa [5],

(b) Promoters of human genes the participation of which in the same response has been shown by other methods.

(c) Orthologous mouse promoters

(-)Training set (2080 sequences)

The control set was composed from arbitrarily chosen 5?-upstream sequences derived from the TRANSGENOME information resource of annotated human genome features [6]. The set was cleaned from all genes which potentially could be involved in the same or similar response

Defining the set of transcription factors (potential participants of the model)

In the search of the TFs potentially relevant for this type of reaction, we combined two approaches:

(a) biological (experimental) knowledge: some of the factors either have been reported to participate in this very process (like NF-kB, AP-1), or in similar processes or processes of the same type occurring in related (epithelial) cells under similar conditions (antimicrobial defence, innate immunity) (C/EBP, Elk-1);

Identification of pairs

We considered all the coordinates of all potential TF binding sites found by Match? [7] for each transcription factor (TF) of the model. These data were organised in tables for each sequence. Further on, we considered all possible combinations of the coordinates, thus revealing all possible pairs in the sequence.


Promoter model of antibacterial response: Partial results

IL8 interleukin 8

PKC, protein kinase C, alpha binding protein; SCYB6

STAT1 signal transducer and activator of transcription 1

small inducible cytokine subfamily B (Cys-X-Cys)

TAF2F TATA box binding protein (TBP)-associated factor

SCYA23 small inducible cytokine subfamily A (Cys-Cys)

NFYA nuclear transcription factor Y, alpha

leukocyte immunoglobulin-like receptor,

EHF ets homologous factor;

G protein-coupled receptor slt

zinc finger protein-like 1;

regulator of G-protein signalling 3

ELK3, ETS-domain protein

TRAF1 TNF receptor-associated factor1

GTF2A2 general transcription factor IIA

eNOS interacting protein;

TCF2 transcription factor 2, hepatic; LF-B3

GCA granalcin, EF-hand calcium binding protein;

CREB/ATF familiy transcription factor

MAP2K1IP1 mitogen-activated protein kinase kinase 1 interacting protein 1

POU domain, class 2, transcription factor 2

CAM kinase, calcium/calmodulin-dependent protein kinase truncated calcium binding protein
RAB32, member RAS oncogene family

POU domain, class 1, transcription factor 1

RAS p21 protein activator

NFKB1 nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 (p105)

APOCI aplipoprotein C-I

defensin, beta2

IRAK1 interleukin-1, receptor-associated kinase 1

cytochrome P450,

EGF epidermal growth factor (beta-urogastrone) 6-sialytransferase alpha2

NFKBIB nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, beta

SLG sialic acid-binding immunoglobin-like lectin-like

Table 1. Most interesting potential target genes, identified by applying the promoter model.



Shelest E., Kel A.E.,Gößling E., Wingender E.Prediction of potential C/EBP/NF-kappaB composite elements using matrix-based search methods. In silico biology, 2003, 3, 0007


Shelest K.,Kel-Margoulis O., Kel A., Wingender E.: Bioinformatics representation of cellular responses to bacterial infection.Cell signaling, transcription and translation as theraupeutic targets.30.01-2.02.2002, Luxembourg


Constructing a promoter model for antibacterial response of lung epithelial cells. Gordon Research Conference ?Bioinformatics: from predictive models to inference?. 24-29.08.2003, Oxford UK.


Shelest K., Sauer T., Wingender E.: Regulatory networks of antibacterial response. Symposium NGFN, 18-20.09.2003, Tuebingen, Germany.



Funded by the Federal Ministry of Education and Research (BMBF; 031U110A)