dRNASb: a systems biology approach to decipher dynamics of host- pathogen interactions using temporal dual RNA-seq data

Infection triggers a dynamic cascade of reciprocal events between host and pathogen wherein the host activates complex mechanisms to recognise and kill pathogens while the pathogen often adjusts its virulence and fitness to avoid eradication by the host. The interaction between the pathogen and the host results in large -scale changes in gene expression in both organisms. Dual RNA- seq, the simultaneous detection of host and pathogen transcripts, has become a leading approach to unravelling complex molecular interactions between the host and the pathogen and is particularly informative for intracellular organisms. The amount of in vitro and in vivo dual RNA- seq data is rapidly growing, which demands computational pipelines to effectively analyse such data. In particular, holistic, systems-level, and temporal analyses of dual RNA- seq data are essential to enable further insights into the host-pathogen transcriptional dynamics and potential interactions. Here, we developed an integrative network-driven bioinformatics pipeline, dRNASb, a systems biology -based computational pipeline to analyse tempo-ral transcriptional clusters, incorporate molecular interaction networks (e.g. protein-protein interactions), identify topologically and functionally key transcripts in host and pathogen, and associate host and pathogen temporal transcriptome to decipher potential between-species interactions. The pipeline is applicable to various dual RNA- seq data from different species and experimental conditions. As a case study, we applied dRNASb to analyse temporal dual RNA- seq data of Salmonella-infected human cells, which enabled us to uncover genes contributing to the infection process and their potential functions and to iden-tify putative associations between host and pathogen genes during infection. Overall, dRNASb has the potential to identify key genes involved in bacterial growth or host defence mechanisms for future uses as therapeutic targets.