Background: Clostridium difficile infection (CDI) is the most common hospital-associated infection in the US. However, asymptomatic carriage is common, and use of microbial nucleic acid amplification tests have been suggested to lead to over-diagnosis. Host factors play an important role in pathogenesis and disease outcome in CDI, and characterization of these responses could uncover potential host biomarkers to complement existing microbial-based diagnostics.
Methods: We collected fecal samples from patients with CDI diagnosed by both positive nucleic acid amplification and toxin enzyme immunoassay. We extracted RNA and quantified a human gene (gamma actin 1, ACTG1) as well as bacterial 16S rRNA by quantitative reverse-transcription PCR to assess sample composition. Human mRNA was profiles using an amplicon-based system (AmpliSeq Transcriptome kit). We compared the fecal host mRNA transcript expression profiles of patients with CDI to controls with diarrhea of other causes.
Results: We found the ACTG1/16S rRNA ratio to be highly correlated with next-generation sequencing quality as measure by percent reads on target (Fig 1). Patients with CDI (n=9) and non-CDI (n=3) diarrhea could be differentiated based on their fecal mRNA expression profiles using principal component analysis (Fig 2). Among the most differentially expressed genes (Fig 3) were the upregulation of genes related to immune response (IL23A, IL34) and actin-cytoskeleton function (TNNT1, MYL4, SMTN, MYBPC3, all adjusted p-values <1x10-3), as well as downregulation of NMRAL1 (a negative regulator of NF_B).
Conclusion: In this proof-of-concept study, we used host fecal transcriptomic analysis for non-invasive profiling of the human mucosal immune response in CDI. We identified differentially expressed genes related to immune response and actin-cytoskeleton function that are consistent with published studies in animal and cell culture models. This demonstrates the potential of fecal transcriptomics to uncover host-based biomarkers for enteric infections.
Fig 1. Correlation of ACTG1/16S ratio with % reads on target.
Fig 2. Principal component analysis of the top 50 differentially expressed genes.
Fig 3. Heat map of the top 50 differentially expressed genes.
J. Rychert, None
M. Graves, None
K. Edes, None
B. K. Lopansri, None
D. Leung, None