Early over expression of messenger RNA for multiple genes, including insulin, in the Pancreatic Lymph Nodes of NOD mice is associated with Islet Autoimmunity

Abstract

Background: Autoimmune diabetes (T1D) onset is preceded by a long inflammatory process directed against the insulin-secreting beta cells of the pancreas. Deciphering the early autoimmune mechanisms represents a challenge due to the absence of clinical signs at early disease stages. The aim of this study was to identify genes implicated in the early steps of the autoimmune process, prior to inflammation, in T1D. We have previously established that insulin autoantibodies (E-IAA) predict early diabetes onset delineating an early phenotypic check point (window 1) in disease pathogenesis. We used this sub-phenotype and applied differential gene expression analysis in the pancreatic lymph nodes (PLN) of 5 weeks old Non Obese Diabetic (NOD) mice differing solely upon the presence or absence of E-IAA. Analysis of gene expression profiles has the potential to provide a global understanding of the disease and to generate novel hypothesis concerning the initiation of the autoimmune process.

Methods: Animals have been screened weekly for the presence of E-IAA between 3 and 5 weeks of age. E-IAA positive or negative NOD mice at least twice were selected and RNAs isolated from the PLN were used for microarray analysis. Comparison of transcriptional profiles between positive and negative animals and functional annotations of the resulting differentially expressed genes, using software together with manual literature data mining, have been performed.

Results: The expression of 165 genes was modulated between E-IAA positive and negative PLN. In particular, genes coding for insulin and for proteins known to be implicated in tissue remodelling and Th1 immunity have been found to be highly differentially expressed. Forty one genes showed over 5 fold differences between the two sets of samples and 30 code for extracellular proteins. This class of proteins represents potential diagnostic markers and drug targets for T1D.

Conclusion: Our data strongly suggest that the immune related mechanisms taking place at this early age in the PLN, correlate with homeostatic changes influencing tissue integrity of the adjacent pancreatic tissue. Functional analysis of the identified genes suggested that similar mechanisms might be operating during pre-inflammatory processes deployed in tissues i) hosting parasitic microorganisms and ii) experiencing unrestricted invasion by tumour cells.

Figure 1

Correlation of gene expression between E-IAA positive and negative PLN. Scatter plot representation of all MU74Av2 array elements (GS6.0 software), after hybridization with the RNA probes. A subset of elements that are distinct between the two arrays and which deviate the most in signal intensity is depicted by the colour codes: blue for the highly expressed in the E-IAA positive PLNs, red for the highly expressed in the E-IAA negative samples and yellow for the probe sets that do not show statistical significant changes between the two sets of samples in both X and Y "fluorescence intensities".

Figure 2

Hierarchical clustering representation of differentially expressed probe sets between E-IAA positive and negative PLN samples. Log₂ transformed data from 177 probe sets are represented in a matrix format wherein each row displays expression results for a single gene across the arrays and each column shows the relative expression levels for all the genes in each sample. Red represents relative expression greater than the median expression level across all samples, and green represents an expression level lower than the median expression level. The colour intensity represents the magnitude of the deviation from the median. The dendrogram at the left lists the genes and provides a measure of the relatedness of their expression profile in each sample. *Sample A36.4 corresponds to E-IAA negative phenotype despite its clustering according gene expression with the E-IAA positive samples (see also Table 1).

Figure 3

Validation of expression patterns of selected genes differentially expressed in the PLN of NOD mice. A and B: Reg2 and Reg3a genes (A: Real Time PCR and B: arrays row data). C and D: Ins1 and Ins2 genes (C: Real Time PCR; D: arrays row data). Samples are PLN RNA from E-IAApos mice (A9.6 & A15.4) and from E-IAA neg mice (A12.2 & A8.1). E, F &G. Staining of histological sections with anti-Insulin antibodies, from pancreas (E), PLN (F) and Inguinal lymph nodes (G) from NOD mouse at 5 weeks.

Figure 4

Functional annotation for up- and down-regulated genes. Functional categories distribution are according to GO and PANTHER annotations. a. Cellular component, b. Molecular function and c. Biological process. Significance for functional categories retained was 8.3E-19<p < 0.02 (* P-values ≤ 0.05). The gene list and corresponding P-values from each category are represented on Additional File 5.

Figure 5

Genome image view of genes identified in the PLN transcriptome. Probe sets taken in consideration are the same as for hierarchical clustering (see Fig 2). In this "Genome view" genes in the gene list are coloured in black small vertical bars while the other genes are coloured in light-gray. The transcription starting site is used for gene position. The significant gene stretches are outlined in blue boxes and shorter stretches, when they exist, are contained within the longer ones that are indicated (methodology used is as described in ).

Figure 6

Heat Map of signal intensities of gene expression patterns. Genes found to be highly expressed in the E-IAA positive (yellow) negative (red) PLN according to functional annotations, as described on Table 3. The dendrogram, on the top of the figure, represents unsupervised hierarchical clustering of the samples, according to gene expression patterns. *Sample A36.4 corresponds to E-IAA negative phenotype despite its clustering according gene expression with the E-IAA positive samples (see also Table 1). Functional annotations have been ordered manually.