Abstract
The ability to simultaneously characterize the bacterial and host expression programs during infection would facilitate a comprehensive understanding of pathogen-host interactions. Although RNA sequencing (RNA-seq) has greatly advanced our ability to study the transcriptomes of prokaryotes and eukaryotes separately, limitations in existing protocols for the generation and analysis of RNA-seq data have hindered simultaneous profiling of host and bacterial pathogen transcripts from the same sample. Here we provide a detailed protocol for simultaneous analysis of host and bacterial transcripts by RNA-seq. Importantly, this protocol details the steps required for efficient host and bacteria lysis, barcoding of samples, technical advances in sample preparation for low-yield sample inputs and a computational pipeline for analysis of both mammalian and microbial reads from mixed host-pathogen RNA-seq data. Sample preparation takes 3 d from cultured cells to pooled libraries. Data analysis takes an additional day. Compared with previous methods, the protocol detailed here provides a sensitive, facile and generalizable approach that is suitable for large-scale studies and will enable the field to obtain in-depth analysis of host-pathogen interactions in infection models.
Highlights
Intracellular bacterial pathogens, such as Mycobacterium tuberculosis, Salmonella enterica, Legionella pneumophilia, and Neisseria gonorrhea, spend a significant portion of their life-cycle surviving and replicating within host cells
We provide a protocol that overcomes these limitations and allows inference of inter-species gene regulatory networks based on simultaneous gene expression data from samples of host cells infected with pathogenic bacteria[9]
We found that infection of bone marrow macrophages with Salmonella at high Muliplicity Of Infection (MOI), host transcriptional responses are dominated by the response to extracellular ligand[9, 18]
Summary
Intracellular bacterial pathogens, such as Mycobacterium tuberculosis, Salmonella enterica, Legionella pneumophilia, and Neisseria gonorrhea, spend a significant portion of their life-cycle surviving and replicating within host cells. Even with accurate alignment of reads to their cognate reference sequences, the low read counts for bacterial genes makes it difficult to ascertain the statistical significance of their differential abundance among samples To address these challenges, all reads derived from our host-pathogen RNA-seq data are aligned to a composite host and pathogen reference sequence database and host and bacterial transcripts are quantified separately to account for differences in pathogen burdens and variations in cell isolation efficiencies. All reads derived from our host-pathogen RNA-seq data are aligned to a composite host and pathogen reference sequence database and host and bacterial transcripts are quantified separately to account for differences in pathogen burdens and variations in cell isolation efficiencies These data are mined for coordinated changes in the abundances of functionally related and/or transcriptionally coregulated genes, enabling identification of biologically relevant transcriptional changes even when the differential expression of individual genes does not achieve statistical significance due to low read counts.
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