Posted in Resource

Systems virology: host-directed approaches to viral pathogenesis and drug targeting

Article summary

Systems virology can identify gene expression signatures that are predictive of viral pathogenesis and vaccine efficacy, insights into how viruses disrupt cellular metabolism, and the mapping of virus–host interactomes. In these recent years, the field has progressed from genomics-based approaches to measurements of proteins and metabolites, and has also embraced the analysis of host genetic variation as a means to better understand disease processes. The authors highlight some important considerations to harness important information from systems virology.

They highlight that the field must move beyond just the listing of molecules that are differentially expressed on viral infection. Instead, the relationships between key molecules will have to be better defined. Such relationships may be cause-and-effect relationships, the result of co-expression, or due to genetic or direct physical interactions. Network modelling and analysis can help explore the relationships among molecules and the structure and organization of these relationships to better predict the behaviour of the network or system.

Finally, it will also be necessary to consider nonlinear relationships such as how the network functions over time. This is particularly true in light of evidence that the magnitude and timing of the host response to respiratory viruses are crucial determinants of the eventual disease outcome.

New inspiration and motivation for us

Human variation can be frustrating to work with, but we believe that it can provide opportunities for us to understand disease processes better. Approaching the research question systematically with the right exploratory data analysis tools will be critical to identify outliers and confounding factors. In addition, we can supplement our findings with curated databases to investigate baseline individual omics signature profile that influence host responses to viruses.

To manage human variation, setting the appropriate thresholds will be important. In many cases, when the cutoffs are too stringent, a lot of information may be lost, creating voids in data analysis. Hence, besides using differentially expressed genes to characterise human responses, these analyses should be supplemented with GSEA pre-rank analysis to confirm that the assigned cutoffs are appropriate.

The ingenuity pathway analysis provides some insights into the relationship between genes, but their relationship with viruses can be difficult to ascertain as the information is not consolidated and accessible. We believe we can do more in this area.

Our experience with human responses to viruses also highlight the importance of magnitude and time in data analysis. To capture both linear and non-linear relationships across time, we believe that extraction and analysis of differential gene expression (EDGE) with self-organising maps (SOM) analysis will remain relevant to capture all these events. Readers can read our newly published article published in EBioMedicine to find out how we used EDGE and SOM to characterise the dynamic transcriptomic profile in severe COVID-19 patients.

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