Innovating virology: from empirical verification to hypothesis exploration

Akio Adachi^*Yuma InamotoQuoc-Khanh TranBao Quoc LeNaoya DoiTakaaki KomaMasako Nomaguchi

• Department of Microbiology, Tokushima University, Tokushima, Tokushima, Japan

In recent years, we have been quite focused on HIV-1 adaptation/evolution research and its related investigations (Nomaguchi et al., 2013a(Nomaguchi et al., ,c, 2014a(Nomaguchi et al., , 2016(Nomaguchi et al., , 2017b;;Doi et al., 2019b;Koma et al., 2021aKoma et al., , 2023b)). The basis for this mindset/approach toward virus research could be our keen scientific interest in viral fundamental property and our sense of mission against pathogenic viruses. Viruses will mutate and diverge to survive their hostile fluxing environments. Naturally, we virologists wish to understand biological and molecular bases for virus changeability as viral foundational characteristics to cope with the virus concerned. Ideally, we can predict how viruses change well in advance before they alter. This is a very critical issue especially in the case of viruses that are pathogenic and highly transmissible among human/animal populations. So far, unfortunately, we can just recognize virus mutations/variations in hindsight.It may be needless to mention here, the typical approaches used in authentic molecular virology, i.e., building themes/goals individually depending on experimental observations/data and/or published scientific results etc. (Fig. 1) will stay highly valid, always being essential not only for modern virology but also for science in general. It is critically important for science in every field to demonstrate, prove, or substantiate all the hypotheses presented. The only drawback there, therefore, is its low-throughput nature. Since a topic/theme in experimental virology is based on the theory/experience of the researcher involved (Fig. 1), it is quite likely to be unremarkable, not to be so ingenious. Also, brainstorming the relevant projects is quite limited by the knowledge base of the research team. We today's virologists have to address or overcome this rather technical issue (but may presently represent an essential matter) to further empower the science and scientific activity of contemporary virology. As emphasized in our previous short articles (Adachi, 2021;Adachi et al., 2024), it is vital for current virology to perform high-throughput, data-driven studies (Fig. 1) in order to effectively tackle a wide variety of research themes on virtually all species-derived viruses (Adachi, 2020). Completely different from traditional authentic virus studies the way to investigate, data-oriented virological studies are data first (the sheer scale of a variety of data) and set themes/projects that transcend human intelligence (Fig. 1). Therefore, the projects presented could be exquisitely unique and could go beyond boundaries. The main issue in those studies probably is how to collect and use the big data, and the data sets (various databases) themselves. For this reason, in the data-driven studies, characteristically analytical and large-throughput methods such as the next generation sequencing, CRISPR-based technologies, single-molecule imaging, spatial biology techniques, artificial intelligence, machine learning, and also various algorithms, inevitably have been utilized. Although we have lately incorporated some notion and methodologies of the computational science into our own studies (Nomaguchi et al., 2013a(Nomaguchi et al., ,b,c, 2014b(Nomaguchi et al., , 2017b;;Yokoyama et al., 2016: Doi et al., 2019c;Koma et al., 2019Koma et al., , 2021bKoma et al., , 2025b)), we have not yet done the data science investigation in a proper sense. In the past decade, however, the concept that the data science is crucial for virus research appears to have been widely and deeply accepted in the virology research community and numbers of relevant papers (below, quoted with a particular attention to the articles published in 2025) have been published (Kao et al., 2014;Sharma et al., 2015;Rocklöv et al., 2017;Dolan et al., 2018;Villarreal andWitzany, 2018, 2021;Bos et al., 2021;Carlson et al., 2021;Blassel et al., 2021;Goettsch et al., 2021;Knyazev et al., 2021;McLeish et al., 2021;Wray and Whitmore, 2022;Markov et al., 2023;Cortes-Azuero et al., 2024;Holmes et al., 2024;Mifsud et al., 2024;Prosser et al., 2024;Dhakal et al., 2025;Duy and Srisongkram, 2024;Girault et al., 2025;Guerrero et al., 2025;Hao et al., 2025;Ibrahim, 2025;Idisi et al., 2025;Lammi et al., 2025;Li et al., 2025;Lin et al., 2025;Mandal et al., 2025;Meijers et al., 2025;Neuman et al., 2025;Pradeesh and Mani, 2025;Teoh et al., 2025;Zhao J et al., 2025;Zhao HN et al., 2025;Zhang Y and Ghahramani et al., 2025;Zhang Y and Han et al., 2025;Zhuang et al., 2025; editorial articles in Science (https://www.science.org/doi/10.1126/science.aee5227) and Nat Rev Microbiol. (https://doi.org/10.1038/s41579-025-01263-x). Thus, it might not be necessary for us to further highlight this issue here. Nevertheless, as a research group that is deeply ingrained with the importance of virological relevance and significance through a wide variety of experimental studies as stated above, we believe it is worth describing how crucial for today's virology the hypothesisexplorative data-oriented investigations are. Innovation of classical virology is urgently required in the era of global infectious diseases.It is essential for the data-oriented virology to specify and refine key data sources (the target data sets/databases) as described above. In a sense, HIV-1/AIDS is the ultimate object of this kind of study. HIV-1 is highly mutable/adaptable not only in infected cells but also in infected human individuals, quite efficiently escaping from human immunity/anti-HIV-1 drugs. So far, unfortunately, the mutations are unpredictable like the cases of other viruses with a highly mutable nature. HIV-1 finally persists in infected humans in an ineradicable manner. The molecular base(s) for this fundamental property of HIV-1 is not yet fully elucidated. It is a pressing practical issue to remove the persistent HIV-1 proviruses within infected humans. As for the AIDS/AIDS-related diseases, numerous factors are believed to be associated with them, whereas definite answers are not yet obtained. Many researchers are actually struggling to solve how HIV-1 behaves in individuals and causes the diseases. In this regard, excellent databases (sequence, structure of viral RNAs/proteins, immunology, cohort, omics and so on) relevant to these issues are available for HIV-1. In addition, plenty of experimental systems have been developed and available. Therefore, some specific experts can readily design data-oriented studies on HIV-1. In fact, a number of related articles have already been published (Liang et al., 2021;Jakubik and Feuerriegel, 2022;Young et al., 2023;Ge et al., 2024;Almarashi, 2025;Jin and Zhang, 2025).Of course, in reality, many challenges will lie ahead of us. Traditional authentic virologists are apt to be unfamiliar with how to handle huge amounts of data accumulated, whereas data scientists might not be so conscientious about the virological significance of new experimental results obtained from data-driven studies. Thus, it is desirable that researchers on both sides collaborate to yield outstanding results with strong biological relevance. If not, each study group that aims to perform empirical verifications of the data-oriented hypotheses may need to have an expert(s) strong in both research styles within the team. It appears that such human resources are not so common yet in the research field of virology. Skilled experienced researchers who are proficient in data-driven virology as well as experimental virology are definitely required. It may be necessary to actively nurture such experts in an organized systemic way in the field of virology. We may need to incorporate a new "strategic virology" course into the education system at various steps (university, graduate school, and postdoctoral programs). Cultivating next-generation talents represents an urgent issue for us virologists. Our conclusion here is quite straightforward and clear-cut, and can be summarized only in one sentence: we virologists need to find or identify research topics (themes) not only by orthodox empirical strategy but also by high throughput data-oriented tactic (Fig. 1). We virologists thus can proceed and develop virology truly beneficial to humanity and surrounding environments. It is easier said than done and should be really challenging, though, if we virologists consider the current status of the academic communities and surrounding circumstances. Unfortunately, except for some special experts, data scientists are not familiar with the analytical virus research and we typical virologists are unfamiliar with the data science. Also, the research environment appears to be insufficient for most areas/countries. Therefore, researchers of various expertise have to work in a very close and tight partnership. Meticulous and multifaceted efforts are needed to achieve significant and valuable results in both basic and applied research fields of virology. Adachi A. 2020. Grand challenge in human/animal virology: unseen, smallest replicative entities shape the whole globe.