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Updated: Dec 2, 2025
Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions
Published on: October 20, 2020
Modelling of pathogen-host systems using deeper ORF annotations and transcriptomics to inform proteomics analyses
Sebastien Leblanc1,2, Marie A Brunet1,2
1Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, Québec, Canada.
Abstract:
The Zika virus is a flavivirus that can cause fulminant outbreaks and lead to Guillain-Barré syndrome, microcephaly and fetal demise. Like other flaviviruses, the Zika virus is transmitted by mosquitoes and provokes neurological disorders. Despite its risk to public health, no antiviral nor vaccine are currently available. In the recent years, several studies have set to identify human host proteins interacting with Zika viral proteins to better understand its pathogenicity. Yet these studies used standard human protein sequence databases. Such databases rely on genome annotations, which enforce a minimal open reading frame (ORF) length criterion. An ever-increasing number of studies have demonstrated the shortcomings of such annotation, which overlooks thousands of functional ORFs. Here we show that the use of a customized database including currently non-annotated proteins led to the identification of 4 alternative proteins as interactors of the viral capsid and NS4A proteins. Furthermore, 12 alternative proteins were identified in the proteome profiling of Zika infected monocytes, one of which was significantly up-regulated. This study presents a computational framework for the re-analysis of proteomics datasets to better investigate the viral-host protein interplays upon infection with the Zika virus.
Insights
This study reveals novel Zika virus interactions by using a customized protein database, identifying previously undiscovered host proteins crucial for understanding Zika virus pathogenicity and developing potential therapeutics.
Area of Science:
- Virology
- Proteomics
- Bioinformatics
Background:
- Zika virus (ZIKV) outbreaks cause severe neurological disorders like microcephaly and Guillain-Barré syndrome.
- Current treatments are limited, necessitating research into ZIKV pathogenicity and host interactions.
- Standard protein databases overlook functional proteins due to annotation criteria, potentially missing key viral interactors.
Purpose of the Study:
- To identify novel human host proteins interacting with Zika virus proteins.
- To explore the impact of non-annotated proteins on understanding viral pathogenicity.
- To develop a computational framework for re-analyzing proteomics data in ZIKV infections.
Main Methods:
- Utilized a customized human protein sequence database excluding minimal open reading frame (ORF) length criteria.
- Performed protein-protein interaction analysis with ZIKV capsid and NS4A proteins.
- Conducted proteome profiling of ZIKV-infected monocytes.
Main Results:
- Identified 4 novel alternative protein interactors for ZIKV capsid and NS4A proteins.
- Discovered 12 alternative proteins in ZIKV-infected monocytes, with one significantly upregulated.
- Demonstrated the value of non-annotated proteins in uncovering viral-host interactions.
Conclusions:
- Customized protein databases enhance the identification of ZIKV-host protein interactions.
- This approach reveals new potential targets for therapeutic interventions against Zika virus.
- The proposed computational framework aids in re-analyzing proteomics data for infectious diseases.
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