Hybrid capture RNA-seq defines temporal gene expression in <i>Rickettsia</i>

species are obligate intracellular bacteria that must reside in a mammalian host or arthropod vector cell to survive. Although these bacteria transition between different intracellular environments during infection, they encode few putative transcription factors, and their gene regulatory networks are largely unknown. Because of their inextricable relationship with eukaryotic cells, transcriptional profiling of the pathogen is complicated by the abundance of contaminating host RNA, especially in infection conditions or stages where the bacterial burden is inherently low. Here, we employ a pathogen hybrid capture technique (PatH-Cap) to improve library preparation by enriching bacterial transcripts while depleting host and rRNA molecules. Using PatH-Cap, we explored transcriptional changes throughout the first 24 h of infection, including infection initiation-an infection stage that has been difficult to profile with standard library preparation methods. We then clustered genes based on their temporal trends, revealing cohorts of genes whose expression is up- or downregulated at different stages of infection. We also highlighted the diverse temporal expression trends of genes with known roles in growth and pathogenesis, including translation and cell division genes, secreted effectors, and secretion system components. Lastly, we identified 639 antisense RNA molecules, many of which also showed strong temporal trends. This work demonstrates that sensitive transcriptional profiling approaches like PatH-Cap hold great promise for dissecting gene expression networks driving infection in intracellular pathogens that have historically posed significant technical challenges. IMPORTANCE: transcription regulation, such as predicting operon structure and identifying putative antisense RNA transcripts. This work establishes the most comprehensive analysis of temporal rickettsia gene expression to date, providing an important foundation for further analysis. Future work can apply the methods described here to investigate gene expression changes across different genetic or environmental perturbations, cellular contexts, or disease models.