Transcriptomic profiling of blood from autoimmune hepatitis patients reveals potential mechanisms with implications for management

Abstract

Autoimmune hepatitis (AIH) is a poorly understood, chronic disease, for which corticosteroids are still the mainstay of therapy and most patients undergo liver biopsy to obtain a diagnosis. We aimed to determine if there was a transcriptomic signature of AIH in the peripheral blood and investigate underlying biologic pathways revealed by gene expression analysis. Whole blood RNA from 75 AIH patients and 25 healthy volunteers was extracted and sequenced. Differential gene expression analysis revealed 249 genes that were significantly differentially expressed in AIH patients compared to controls. Using a random forest algorithm, we determined that less than 10 genes were sufficient to differentiate the two groups in our cohort. Interferon signaling was more active in AIH samples compared to controls, regardless of treatment status. Pegivirus sequences were detected in five AIH samples and 1 healthy sample. The gene expression data and clinical metadata were used to determine 12 genes that were significantly associated with advanced fibrosis in AIH. AIH patients with a partial response to therapy demonstrated decreased evidence of a CD8+ T cell gene expression signal. These findings represent progress in understanding a disease in need of better tests, therapies, and biomarkers.

Fig 1

A. Schematic of sample processing for library preparation of whole blood for AIH cohort. B. Heat map displaying gene counts (variance stabilizing transformation, see DESeq2) of the top 1000 genes with highest variance amongst samples. Samples are clustered on the x-axis and protein coding genes are clustered on the y-axis using a Ward D2 method. Relevant metadata included patient status, steroid exposure, and fibrosis groups to classify samples. C. PCA plot of variance stabilizing transformed gene counts colored by sex of patient sample. D. Volcano plot of differential gene expression in DESeq2, showing genes with P < 0.05 and log fold change > 1 in red, and genes with only log fold change > 1 in blue, and genes with only P < 0.05 in grey.

Fig 2

A. Top ten most significant canonical pathways related to differential gene expression of AIH patients compared to healthy controls. B. kmer-based phylogenetic analysis performed with the IDSeq platform shows relationships between four assembled pegivirus genomes from the GRACE cohort and their closest related publicly available pegivirus NCBI reference genomes, with patient metadata annotated.

Fig 3

A. WGCNA module trait graph, with patient metadata on the x-axis and generated gene modules on the y-axis. B. Interconnectivity plot for gene module 9 identified by WGCNA. C. Heatmap displaying gene counts (variance stabilizing transformation, see DESeq2) of the top 12 hub genes identified from interconnectivity metrics as described in Methods. Samples are clustered on the x-axis using a Ward D2 method and color annotated with cirrhosis status and treatment at sample collection.

Fig 4

A. CIBERSORT data deconvoluting absolute CD8+ T-cell counts from bulk RNA-seq, with significant differences in counts between healthy patients and those with a partial response to treatment and between those with a complete compared to a partial response to treatment. B. CIBERSORT data deconvoluting absolute CD8+ T-cell counts from bulk RNA-seq, removing steroid patients and showing a significant difference between those with a complete compared to a partial response to treatment.