Animal models of enteroaggregative Escherichia coli infection

Abstract

Enteroaggregative Escherichia coli (EAEC) has been acknowledged as an emerging cause of gastroenteritis worldwide for over two decades. Epidemiologists are revealing the role of EAEC in diarrheal outbreaks as a more common occurrence than ever suggested before. EAEC induced diarrhea is most commonly associated with travelers, children and immunocompromised individuals however its afflictions are not limited to any particular demographic. Many attributes have been discovered and characterized surrounding the capability of EAEC to provoke a potent pro-inflammatory immune response, however cellular and molecular mechanisms underlying initiation, progression and outcomes are largely unknown. This limited understanding can be attributed to heterogeneity in strains and the lack of adequate animal models. This review aims to summarize current knowledge about EAEC etiology, pathogenesis and clinical manifestation. Additionally, current animal models and their limitations will be discussed along with the value of applying systems-wide approaches such as computational modeling to study host-EAEC interactions.

Keywords: EAEC pathogenesis; Th17; animal model; computational modeling; enteroaggregative E. coli.

Figure 1. Enteroaggregative Escherichia coli (EAEC) pathogenesis and host response at the colonic mucosa. The clinical manifestation of EAEC infection is the outcome resulting from complex host-pathogen-microbiota interactions regulated at a molecular level. EAEC attach and aggregate on colonic epithelial cells in a stacked brick pattern by means of AAF fimbria and the secreted protein encoded by aap known as dispersin. EAEC form a thick biofilm enabling protection against host or interventional antimicrobial responses. FliC surface flagella are then recognized by TLR5 receptors expressed on the apical surface of enterocytes. Bacterial-epithelial cell contact triggers a cascade of events activating NFκB and MAPK pathways that result in the upregulation of proinflammatory cytokines IL-8, TNFα and CCL20 responsible for recruiting dendritic cells and neutrophils to the site of infection. Small red spheres underneath the colonic epithelial layer portray the chemokine gradient indicative of inflammation. EAEC harbors the transcriptional regulator AggR responsible for the expression of virulence factors including Pic, Pet, EAST-1, aap and ShET1 portrayed in the amplified image of the bacteria. EAST-1 toxin binds to extracellular guanylate cyclase (GC) on enterocytes and stimulates overproduction of intracellular cyclicGMP (cGMP) ultimately impairing Na/Cl transport. This causes water to be secreted from the enterocyte and contributes to the watery diarrhea seen in infected individuals. ShET1 is also proposed to affect intracellular cGMP levels however much of the biochemistry surrounding this enterotoxin remains unknown. Pet enters the cell via clathrin-mediated endocytosis and is translocated into the cytosol after being transferred from the Golgi complex to the endoplasmic reticulum through retrograde trafficking. In the cytosol, Pet cleaves the actin-binding protein α-Fodrin inducing cytotoxic disruption of the cytoskeleton. Systemic administration of PPAR γ antagonist GW9662 to malnourished EAEC infected hosts enhances an upregulation of inflammatory gene expression and potentiates a beneficial early T helper 17 (Th17) response that successfully facilitates neutrophil recruitment and antimicrobial production that clears the infection and ameliorates disease. A healthy enterocyte is depicted on the far right cohabitating peacefully with the beneficial microflora.

Figure 2. Modeling immune responses to EAEC. The EAEC T cell differentiation model network created in CellDesigner using systems biology markup language (A) was linked to COPASI software for the calibration. A calibrated wild type system was created using in house data from malnourished EAEC strain JM221 infected mice (C).Asterisks (*) indicate statistical significance compared with uninfected mice and the number symbol (#) represents statistical difference compared with infected wild type mice (p < 0.05). Modulating the parameters regulating T cell differentiation into separate phenotypes simulated PPARγ deficiency: Th1 and Th17 cell differentiation was enhanced equally while Treg differentiation was decreased in an equal magnitude. Bacterial clearance predicted in silico mimicked EAEC quantification in feces from infected mice (B). In silico simulations of a time course infection over 14 d were performed using COPASI. The wild type system (D) portrays immunodeficiency while the PPARγ deficient system (E) predicted enhanced effector responses.