Variation in the 56-kDa type-specific antigen (TSA56) drives the evolution of Orientia tsutsugamushi (O. tsutsugamushi) strains, yet the influence of spatiotemporal factors, Leptotrombidium mites, point mutations, and recombination on genetic diversification remains unclear. This study explores variations in type I interferon-associated APOBEC expression and its association with TSA56 sequence variation during intracellular infection. The gene expression profiles of human monocytes, macrophages, and monocyte-derived dendritic cells infected with O. tsutsugamushi were comprehensively analysed as secondary data. This analysis included gene expression profiling, the identification of mutations, and an assessment of TSA56 antigenicity. Principal component analysis revealed distinct separation between infected and uninfected cells, whereas hierarchical clustering revealed specific patterns of gene regulation, with immune-related genes predominant among the top 30 upregulated genes. Notably, APOBEC3A and APOBEC3B were significantly upregulated in infected monocytes, which is consistent with their known involvement in innate immune responses and nucleic acid editing. The molecular functions associated with the upregulated genes included activities related to death receptors, chemokines, cytokine receptors, and catalytic functions. Deaminase-associated genes, such as ADA, AMPD3, APOBEC3A, APOBEC3B, APOBEC3G, and ZBP1, were substantially upregulated in infected immune cells and exhibited two distinct expression patterns. Furthermore, the levels of type I and III interferons (IFNs) were markedly increased, with coexpression patterns observed alongside deaminase-associated genes, which is consistent with coordinated immune responses during infection. Furthermore, analysis of the TSA56 transcriptome revealed a predominance of C > U single-nucleotide variants consistent with APOBEC-associated mutational signatures, suggesting a possible association with host deaminase activity during intracellular infection. Because this study is based on independent public transcriptomic datasets and computational sequence analyses, the findings should be interpreted as descriptive and hypothesis-generating rather than as mechanistic or predictive.
So-In et al. (Tue,) studied this question.