Identification of differences in gene expression implicated in the Adherent-Invasive Escherichia coli phenotype during in vitro infection of intestinal epithelial cells

Abstract

Introduction: Adherent-invasive Escherichia coli (AIEC) is strongly associated with the pathogenesis of Crohn's disease (CD). However, no molecular markers currently exist for AIEC identification. This study aimed to identify differentially expressed genes (DEGs) between AIEC and non-AIEC strains that may contribute to AIEC pathogenicity and to evaluate their utility as molecular markers.

Methods: Comparative transcriptomics was performed on two closely related AIEC/non-AIEC strain pairs during Intestine-407 cell infection. DEGs were quantified by RT-qPCR in the same RNA extracts, as well as in 14 AIEC and 23 non-AIEC strains to validate the results across a diverse strain collection. Binary logistical regression was performed to identify DEGs whose quantification could be used as AIEC biomarkers.

Results: Comparative transcriptomics revealed 67 differences in expression between the two phenotypes in the strain pairs, 50 of which (81.97%) were corroborated by RT-qPCR. When explored in the whole strain collection, 29 DEGs were differentially expressed between AIEC and non-AIEC phenotypes (p-value < 0.042), and 42 genes between the supernatant fraction of infected cell cultures and the cellular fraction containing adhered and intracellular bacteria (p-value < 0.049). Notably, six DEGs detected in the strain collection were implicated in arginine biosynthesis and five in colanic acid synthesis. Furthermore, two biomarkers based on wzb and cueR gene expression were proposed with an accuracy of ≥ 85% in our strain collection.

Discussion: This is the first transcriptomic study conducted using AIEC-infected cell cultures. We have identified several genes that may be involved in AIEC pathogenicity, two of which are putative biomarkers for identification.

Keywords: Adherent-invasive Escherichia coli; Crohn’s disease; Intestine-407; arginine biosynthesis; biomarker; colanic acid biosynthesis; comparative transcriptomics.

Figure 1

Differentially expressed genes (DEGs) displayed in comparative transcriptomics between AIEC and non-AIEC strain pairs during I-407 infection (p-value< 0.050). Total fragments per kilobase of transcript per million mapped reads (FPKM) and log2 fold change are shown. Symbols point out genes found in two or more comparisons. Genes that presented an over-expression in AIEC are shown in black, while genes that showed an under-expression are shown in grey. (A) DEGs found in the supernatant (SN) fraction of the strain pairs corresponding to mRNA. (B) DEGs found in the fraction containing infected cells with adherent and/or intracellular bacteria (A/I) of the strain pairs, corresponding to mRNA. (C) DEGs found in the SN fraction of the strain pairs correspond to sRNA and/or tRNA. (D) DEGs found in the A/I fraction of the strain pairs that correspond to sRNA and/or tRNA.

Figure 2

Predicted function of the differentially expressed genes (DEGs) in each comparison distributed in eight functional categories. The number of DEGs in each category is shown for the SN and A/I fractions of the two strain pairs. Genes that presented an over-expression in AIEC are shown in black, while genes that showed an under-expression are shown in grey. The DEGs of this figure are listed in Supplementary Table 2 .

Figure 3

Correlation between log2 fold change values obtained in comparative transcriptomics and log2 RTA values obtained by RT-qPCR (ρ = 0.677, p<0.001). Amplification of the DEGs with RT-qPCR was done in the same samples that were sequenced. Genes located in quadrant I revealed an under-expression in AIEC in both techniques, whereas the genes in quadrant III revealed an over-expression (n = 50). Quadrants II and IV (in grey) contain genes with discrepancies between the two techniques (n = 11).

Figure 4

Percentage of strains amplified in a collection of clinical isolates for each DEG. Only those DEGs with statistically significant differences between AIEC and non-AIEC strains are shown. *p-value<0.05; **p-value<0.01. Genes that presented an over-expression in AIEC are shown in black, while genes that showed an under-expression are shown in grey.

Figure 5

Percentage of genes corroborated for the different groups of samples performed with RT-qPCR compared with the initial results obtained by RNA-Seq. The number of DEGs is indicated on each occasion.

Figure 6

Boxplot of the genes that could predict AIEC phenotype with an accuracy >85% by binary logistic regression. Two (0414 and 2505) were selected as putative biomarkers, and 0420 was dismissed due to high dispersion. Log2 RTA values are represented for all the samples used in this study, classified by phenotype.