When our body gets infected, a complex battle ensues between our immune system and the invading pathogen. This interaction involves both the host (us) trying to recognize and destroy the pathogen, and the pathogen trying to survive and multiply by evading our immune defenses. These interactions lead to significant changes in the activity levels of many genes in both the host and the pathogen.
To better understand these complex interactions, scientists use a technique called dual RNA sequencing (dual RNA-seq). This method allows them to simultaneously study the gene activity (transcripts) of both the host and the pathogen. Dual RNA-seq is particularly useful for studying intracellular organisms—those that live inside our cells—because it helps researchers see how these organisms interact with the host at the molecular level.
As the amount of dual RNA-seq data grows, researchers need effective ways to analyze it. This is where computational tools come into play. A new tool called dRNASb (dual RNA-seq systems biology) has been developed by researchers at University of New South Wales to help scientists make sense of this data.
How dRNASb Works
dRNASb is a bioinformatics pipeline—a series of computational steps—that helps analyze dual RNA-seq data. It does several important things:
Temporal Analysis: It examines how gene activity changes over time during an infection.
Molecular Interaction Networks: It integrates information about how proteins interact with each other, both within the host and the pathogen.
Key Transcripts Identification: It identifies which genes are most important for either the pathogen’s survival and growth or the host’s defense mechanisms.
Host-Pathogen Interactions: It looks for possible interactions between the host and pathogen at the gene level.
Flowchart of the bioinformatic pipeline for the dual RNA-seq analysis of a host – pathogen interaction
The pipeline starts with data pre-processing and differential gene expression analysis following by Fuzzy clustering to decipher coherent patterns of temporal gene expression profiles. The pathway enrichment analysis was applied using KEGG and GO annotations to identify functions overrepresented by temporal clusters in host and pathogen (based on Fisher’s exact test with hypergeometric null hypothesis). To explore relationship and potential physical or regulatory interactions among differentially expressed genes, the protein-protein interaction (PPI) for both species and regulatory networks for pathogen were retrieved from different datasets. The topological characteristics of the genes were then identified. Additionally, the gene co-expression networks were constructed to infer cross-species gene associations and then used to identify hubs, betweenness centrality, closeness centrality and modularity followed by functional analysis.
This comprehensive analysis allows researchers to get a detailed picture of the host-pathogen interaction.
Case Study: Salmonella Infection
To demonstrate how dRNASb works, scientists applied it to a case study involving human cells infected with Salmonella, a type of bacteria that causes food poisoning. By analyzing the dual RNA-seq data over time, they were able to uncover several important findings:
They identified specific genes in both the human cells and the Salmonella bacteria that are active during the infection process.
They discovered potential functions of these genes and how they might contribute to the infection.
They found possible associations between human and bacterial genes, which could point to new targets for therapies.
Potential Applications
The dRNASb pipeline can be used with various dual RNA-seq datasets from different species and experimental conditions. This flexibility means that it can help researchers study a wide range of infections and host-pathogen interactions. By identifying key genes and their functions, dRNASb could help in developing new treatments for infectious diseases.
Conclusion
The development of dRNASb represents a significant advancement in our ability to analyze dual RNA-seq data. By providing a detailed understanding of the gene activity changes and interactions between host and pathogen, dRNASb opens up new possibilities for discovering therapeutic targets and improving our ability to fight infections.
Availability – The pipeline code is available at the GitHub repository https://github.com/VafaeeLab/dRNASb.
Dinarvand M, Koch FC, Al Mouiee D, Vuong K, Vijayan A, Tanzim AF, Azad AKM, Penesyan A, Castaño-Rodríguez N, Vafaee F. (2024) dRNASb: a systems biology approach to decipher dynamics of host-pathogen interactions using temporal dual RNA-seq data. Microb Genom 8(9):mgen000862. [article]