Periodic Reporting for period 2 - VIROINF (Understanding (harmful) virus-host interactions by linking virology and bioinformatics)
Reporting period: 2022-09-01 to 2024-08-31
The VIROINF project addressed the critical need for advanced bioinformatics tools in virus research, particularly those that enable real-time application in understanding virus-host interactions. As viral threats evolved, it became increasingly important to have research methodologies that integrated computational and experimental approaches to support proactive and effective responses. The VIROINF consortium was established to meet this challenge by fostering collaboration between theoretical and practical virology, allowing for the rapid translation of bioinformatics developments into virology labs and vice versa.
The project’s main scientific objectives included modeling virus-host interactions (WP1) and virus evolution within hosts (WP2), supported by work packages on training (WP3), outreach (WP4), management (WP5), and ethics (WP8). Each WP involved early-stage researchers (ESRs), who presented their progress at multiple project meetings, facilitating ongoing adaptation of project goals based on developments.
Despite challenges due to COVID-19, which delayed recruitment and restricted lab access, VIROINF successfully achieved its primary milestones. These included developing tools for virus identification, host prediction, and modeling virus evolution, all of which provided valuable contributions to both academic and applied virology fields. By advancing generalized bioinformatics methods and promoting interdisciplinary knowledge exchange, VIROINF built a stronger foundation for combating viral diseases, offering immediate insights and long-term benefits to society.
The project’s main scientific objectives included modeling virus-host interactions (WP1) and virus evolution within hosts (WP2), supported by work packages on training (WP3), outreach (WP4), management (WP5), and ethics (WP8). Each WP involved early-stage researchers (ESRs), who presented their progress at multiple project meetings, facilitating ongoing adaptation of project goals based on developments.
Despite challenges due to COVID-19, which delayed recruitment and restricted lab access, VIROINF successfully achieved its primary milestones. These included developing tools for virus identification, host prediction, and modeling virus evolution, all of which provided valuable contributions to both academic and applied virology fields. By advancing generalized bioinformatics methods and promoting interdisciplinary knowledge exchange, VIROINF built a stronger foundation for combating viral diseases, offering immediate insights and long-term benefits to society.
The first objective of Work Package 1, "Modelling Virus-Host Interactions," focused on developing bioinformatics tools to understand virus-host dynamics, particularly identifying viruses, predicting hosts, understanding virus-host interactions, and establishing viral products to counteract harmful hosts.
In virus identification, ESR11 produced honey bee gut sequencing datasets, with ESR12 designing a machine-learning algorithm that improved viral genome recovery. ESR11 also used flow cytometry for viral particle quantification, and ESR12 developed a deep learning pipeline that identified significant phage diversity within the SHIP dataset.
Host prediction efforts led by ESR5 and ESR10 focused on linking phages with bacterial or archaeal hosts. ESR5’s viral tagging approach in anaerobic environments identified phage-host pairs linked to disease, while ESR10’s EvoMIL tool extracted viral protein features relevant to host association. Collaborative efforts highlighted viral proteins linked to host specificity.
To explore virus-host interactions, ESR13 developed iPHoP, an integrative tool predicting phage-host relationships and gene transfer patterns in Crassvirales. ESR6 analyzed host modulation during cytomegalovirus infection, developing methods to track infection in single-cell RNA sequencing data.
WP1.5 aimed to establish viral products that inhibit host pathogenicity. ESRs analyzed viral proteins in the honey bee dataset, identifying candidates with therapeutic potential by focusing on domains potentially interacting with host immune responses.
In Work Package 2, WP2.1 focused on experimentally validating bioinformatics predictions. ESRs optimized viral tagging and high-throughput sorting, establishing an in vitro setup to test infection efficiency, which validated phage-host predictions and informed phage therapy applications.
WP2.2 advanced understanding of phage-host interactions. Utilizing EvoMIL and iPHoP, ESRs characterized viral proteins predicted to interact with bacterial receptors. Experimental assays confirmed protein-protein interactions, providing insights into phage biology and infection efficiency.
In virus identification, ESR11 produced honey bee gut sequencing datasets, with ESR12 designing a machine-learning algorithm that improved viral genome recovery. ESR11 also used flow cytometry for viral particle quantification, and ESR12 developed a deep learning pipeline that identified significant phage diversity within the SHIP dataset.
Host prediction efforts led by ESR5 and ESR10 focused on linking phages with bacterial or archaeal hosts. ESR5’s viral tagging approach in anaerobic environments identified phage-host pairs linked to disease, while ESR10’s EvoMIL tool extracted viral protein features relevant to host association. Collaborative efforts highlighted viral proteins linked to host specificity.
To explore virus-host interactions, ESR13 developed iPHoP, an integrative tool predicting phage-host relationships and gene transfer patterns in Crassvirales. ESR6 analyzed host modulation during cytomegalovirus infection, developing methods to track infection in single-cell RNA sequencing data.
WP1.5 aimed to establish viral products that inhibit host pathogenicity. ESRs analyzed viral proteins in the honey bee dataset, identifying candidates with therapeutic potential by focusing on domains potentially interacting with host immune responses.
In Work Package 2, WP2.1 focused on experimentally validating bioinformatics predictions. ESRs optimized viral tagging and high-throughput sorting, establishing an in vitro setup to test infection efficiency, which validated phage-host predictions and informed phage therapy applications.
WP2.2 advanced understanding of phage-host interactions. Utilizing EvoMIL and iPHoP, ESRs characterized viral proteins predicted to interact with bacterial receptors. Experimental assays confirmed protein-protein interactions, providing insights into phage biology and infection efficiency.
This project has progressed beyond the state of the art by advancing a cohort of early-stage researchers (ESRs) trained to bridge the fields of virology and bioinformatics, strengthening their employability and career prospects across academia and industry. Each ESR was supported by two local supervisors, one focusing on virology and the other on bioinformatics, allowing them to develop expertise in both disciplines. Regular network-wide seminars, international conference participation, secondments, and specialized workshops provided them with extensive opportunities to deepen their knowledge, broaden their professional networks, and gain hands-on experience in a multidisciplinary environment. The strong collaboration within this group of young scientists has cultivated a unique blend of skills, equipping them to evaluate, critique, and innovate within both fields.
The ESRs gained the ability to translate between bioinformatics and virology has set them apart in a competitive job market, demonstrating the added value of their dual-discipline expertise. The interdisciplinary nature of this training has also equipped bioinformaticians to apply scientific methods effectively and enabled virologists to develop mechanistic bioinformatics solutions to complex biological questions. As a result, these ESRs have laid a foundation for the burgeoning field of virus bioinformatics, poised to inspire future researchers and contribute to a growing demand for interdisciplinary scientists.
The socio-economic and societal impacts of VIROINF are substantial. The project has pioneered subsequent virus-bioinformatics initiatives and fostered significant grant proposals across Europe, including successful pre-applications and funded projects. ESRs and their mentors have influenced new research areas and established collaborations that transcend borders, contributing to grants like the ANR grant “RISKEVOL,” the SNF grant WISE, and several DFG grants. The project has thus not only enhanced the ESRs’ professional trajectories but has also advanced virus-bioinformatics research globally, creating a legacy of knowledge transfer, resource sharing, and innovative partnerships expected to benefit the scientific community and society at large.
The ESRs gained the ability to translate between bioinformatics and virology has set them apart in a competitive job market, demonstrating the added value of their dual-discipline expertise. The interdisciplinary nature of this training has also equipped bioinformaticians to apply scientific methods effectively and enabled virologists to develop mechanistic bioinformatics solutions to complex biological questions. As a result, these ESRs have laid a foundation for the burgeoning field of virus bioinformatics, poised to inspire future researchers and contribute to a growing demand for interdisciplinary scientists.
The socio-economic and societal impacts of VIROINF are substantial. The project has pioneered subsequent virus-bioinformatics initiatives and fostered significant grant proposals across Europe, including successful pre-applications and funded projects. ESRs and their mentors have influenced new research areas and established collaborations that transcend borders, contributing to grants like the ANR grant “RISKEVOL,” the SNF grant WISE, and several DFG grants. The project has thus not only enhanced the ESRs’ professional trajectories but has also advanced virus-bioinformatics research globally, creating a legacy of knowledge transfer, resource sharing, and innovative partnerships expected to benefit the scientific community and society at large.