The transcriptome of the fetal inflammatory response syndrome

Abstract

Problem: The fetal inflammatory response syndrome (FIRS) is considered the counterpart of the systemic inflammatory response syndrome (SIRS), but similarities in their regulatory mechanisms are unclear. This study characterizes the fetal mRNA transcriptome of peripheral leukocytes to identify key biological processes and pathways involved in FIRS.

Method of study: Umbilical cord blood from preterm neonates with FIRS (funisitis, plasma IL-6 >11 pg/mL; n = 10) and neonates with no evidence of inflammation (n = 10) was collected at birth. Results Microarray analysis of leukocyte RNA revealed differential expression of 541 unique genes, changes confirmed by qRT-PCR for 41 or 44 genes tested. Similar to SIRS and sepsis, ontological and pathway analyses yielded significant enrichment of biological processes including antigen processing and presentation, immune response, and processes critical to cellular metabolism.

Results: are comparable with microarray studies of endotoxin challenge models and pediatric sepsis, identifying 25 genes across all studies.

Conclusion: This study is the first to profile genome-wide expression in FIRS, which demonstrates a substantial degree of similarity with SIRS despite differences in fetal and adult immune systems.

Figure 1. Genes differentially expressed in FIRS

Cord blood RNA from preterm neonates with (n=10) and without (n=7) FIRS was analyzed using the Illumina microarray platform. The color scale represents normalized log2 gene expression levels; data are 0 centered by rows (genes) and sorted as a function of the t-scores between the two groups. Displayed are the 296 genes that were significantly increased in leukocytes from neonates with FIRS and the 252 that were decreased (False discovery rate <0.05).

Figure 2. Principal Component Analysis of gene expression data

The intensity of 26,000 well annotated probes on the Illumina arrays were used to compute the main directions of variability within the data (principal components). This unsupervised analysis demonstrates that gene expression values can be used to delineate the two groups of samples. More details on this representation can be found elsewhere (Machine Learning and Its Applications to Biology, Tarca AL, Carey VJ, Chen Xw, Romero R, Draghici S, PLoS Comput Biol, 3(6): e116 doi:10.1371/journal.pcbi.0030116.)

Figure 3. Confirmation of differential gene expression by qRT-PCR

mRNA abundance was assessed in cord blood RNA from neonates with and without FIRS (n=10/group). Altered abundance of genes within ontological categories of immune response and inflammation (A), MHC II receptor activity (B), carbohydrate metabolism (C) and signal transduction (D) was confirmed using qRT-PCR. Box-plots include 50% of the data with the middle line showing the median value; P<0.05 for all genes shown when modeled using GEE. Depending on the statistical method used, 93% to 95% of the genes tested confirmed the change observed by microarray analysis.

Figure 3. Confirmation of differential gene expression by qRT-PCR

mRNA abundance was assessed in cord blood RNA from neonates with and without FIRS (n=10/group). Altered abundance of genes within ontological categories of immune response and inflammation (A), MHC II receptor activity (B), carbohydrate metabolism (C) and signal transduction (D) was confirmed using qRT-PCR. Box-plots include 50% of the data with the middle line showing the median value; P<0.05 for all genes shown when modeled using GEE. Depending on the statistical method used, 93% to 95% of the genes tested confirmed the change observed by microarray analysis.

Figure 3. Confirmation of differential gene expression by qRT-PCR

mRNA abundance was assessed in cord blood RNA from neonates with and without FIRS (n=10/group). Altered abundance of genes within ontological categories of immune response and inflammation (A), MHC II receptor activity (B), carbohydrate metabolism (C) and signal transduction (D) was confirmed using qRT-PCR. Box-plots include 50% of the data with the middle line showing the median value; P<0.05 for all genes shown when modeled using GEE. Depending on the statistical method used, 93% to 95% of the genes tested confirmed the change observed by microarray analysis.

Figure 3. Confirmation of differential gene expression by qRT-PCR

mRNA abundance was assessed in cord blood RNA from neonates with and without FIRS (n=10/group). Altered abundance of genes within ontological categories of immune response and inflammation (A), MHC II receptor activity (B), carbohydrate metabolism (C) and signal transduction (D) was confirmed using qRT-PCR. Box-plots include 50% of the data with the middle line showing the median value; P<0.05 for all genes shown when modeled using GEE. Depending on the statistical method used, 93% to 95% of the genes tested confirmed the change observed by microarray analysis.