The scale and breadth of the data collection is what sets this study apart. Rather than measuring a handful of immune markers, the team assembled four complementary omics layers from the same longitudinal samples: transcriptomics, which captures gene expression patterns in circulating immune cells; metabolomics, which profiles the small molecules that fuel and regulate cellular chemistry; lipidomics, which maps the fat-based signaling molecules and membrane components that shift during inflammation; and cytokine measurements, which quantify the soluble messengers that immune cells use to communicate. Integrating these layers allowed the investigators to move beyond the question of whether a vaccine produces antibodies and toward a systems-level picture of how vaccination reorganizes host physiology. This approach, often called systems vaccinology, treats the immune response not as an isolated event but as a coordinated, body-wide program of gene regulation, metabolism and cellular signaling.

The headline finding is that the two vaccine platforms produce pronounced and persistent differences in their multiomic fingerprints. BNT162b2 induced far more profound changes across the omics landscape than CoronaVac, with particularly marked alterations in adaptive immunity and in metabolomic function. That is consistent with the mechanistic distinction between the platforms: an mRNA vaccine delivers genetic instructions that cause the recipient’s own cells to manufacture the SARS-CoV-2 spike protein, triggering a potent innate sensing response and driving strong T-cell help and B-cell maturation. An inactivated virus vaccine, by contrast, presents a killed, non-replicating virus that primarily stimulates antibody production through more contained innate signaling. The molecular consequences of those different entry points into the immune system, the study shows, ripple outward into gene expression programs and metabolic pathways that remain measurable for months.

Perhaps the most striking observation is durability. At six months after the second dose, recipients of BNT162b2 still displayed a durable immunological imprint, characterized by persistent modifications in immune gene expression and ongoing metabolic reprogramming. In other words, the mRNA vaccine did not simply provoke a transient burst of activity that faded back to baseline; it left a lasting signature in the transcriptome and metabolome of vaccinated individuals. The authors link this prolonged imprint to the vaccine’s known immunogenicity and to the durability of protection it confers. CoronaVac recipients, meanwhile, showed a more subdued and less persistent omics response, which aligns with clinical observations that inactivated-virus primary series generally elicit lower and more rapidly waning neutralizing antibody levels than mRNA vaccines, particularly against evolving variants of concern.

One of the study’s technical contributions lies in its analysis of crosstalk between omics layers. The researchers found substantially greater interplay between transcriptomic, metabolomic and lipidomic features in the BNT162b2 group, suggesting that the mRNA platform engages a more integrated, multi-system response. Crosstalk of this kind matters because immune outcomes rarely depend on a single molecule. Gene expression changes drive metabolic shifts; metabolites in turn feed back on signaling pathways that regulate cytokine production and lymphocyte differentiation. A vaccine that coordinates these layers coherently may be more effective at shepherding naive immune cells through the full program of activation, clonal expansion, germinal center reaction and memory formation. The enhanced crosstalk observed after BNT162b2 vaccination, the authors argue, is related to the platform’s stronger immunogenicity and longer-lasting protection.

Beyond describing group-level differences, the team asked whether a person’s baseline molecular state—measured before any vaccination—could predict how well they would respond to a given vaccine. Using predictive modeling on the multiomics data, they found that pre-vaccination profiles could forecast vaccine-specific performance with area under the curve values of 0.73 to 0.79. In practical terms, that means the metabolic and transcriptional state of an individual before vaccination carries meaningful information about the strength and character of the immune response they will mount. An AUC in this range is well above chance and, while not yet accurate enough for standalone clinical decision-making, it demonstrates that the concept of predicting vaccine responsiveness from a baseline blood draw is scientifically viable. It also hints at why vaccine responses vary so widely between individuals: differences in baseline metabolism, immune tone and inflammatory state shape how the same vaccine is interpreted by different bodies.

The study’s setting gives it particular epidemiological weight. Hong Kong was one of the few places where mRNA and inactivated-virus vaccines were deployed side by side in large numbers during the primary vaccination campaign, creating a natural comparative cohort that would have been difficult to assemble elsewhere. Because both groups lived through the same pandemic conditions, were exposed to the same circulating variants and were recruited through the same health system, confounding from environment and exposure history is reduced. The 553-participant cohort, with samples spanning half a year, therefore provides an unusually clean head-to-head comparison of the two dominant vaccine platforms used across Asia and much of the world, where CoronaVac and similar inactivated vaccines reached billions of arms.

The implications extend beyond COVID-19. The finding that mRNA vaccination produces a prolonged metabolic reprogramming and persistent immune gene modification adds to a growing body of literature on trained immunity, the concept that innate immune cells undergo long-term epigenetic and metabolic rewiring after stimulation, altering their responsiveness to future challenges. If mRNA vaccines induce a durable trained-immunity-like state, that could partly explain their robust performance and could inform the design of future mRNA vaccines against influenza, respiratory syncytial virus, cancer and other targets. Conversely, understanding the molecular reasons why inactivated vaccines elicit narrower and less durable signatures could guide adjuvant choices or prime-boost regimens that combine platforms to broaden and prolong protection.

The authors are explicit about the translational direction of the work. They advocate integrating multiomics data to tailor vaccination strategies to individual immune profiles, with the goal of enhancing public health outcomes. In a future shaped by this kind of systems vaccinology, a pre-vaccination blood test could, in principle, identify who is likely to respond poorly to a standard regimen and who might benefit from an alternative platform, an earlier booster or an adjusted dose. The predictive models reported here, with their moderate but real discriminatory power, are early proof of concept for that vision. Before such tests reach the clinic, they would need validation in independent cohorts, standardization of assays across laboratories and demonstration that acting on a prediction actually improves outcomes. The cost of multiomics profiling, while falling, also remains a barrier to population-scale deployment.

Limitations should temper interpretation of the results. The study captured the primary two-dose series and did not follow participants through boosters or breakthrough infections, both of which reshape the omics landscape further. Multiomics datasets are high-dimensional, and associations between molecular signatures and protection, however biologically plausible, are correlational rather than proof of mechanism. The published version of the article is also subject to final editorial processing, as it was shared early under Springer Nature’s open-access policy with a permanent DOI. Even so, the study stands as a landmark demonstration that two vaccines against the same pathogen, both highly effective at reducing severe disease, inscribe fundamentally different and enduring patterns on human biology. As vaccine development accelerates for emerging pathogens, the lesson is clear: the platform matters not only for how strong the response is, but for what kind of molecular memory it leaves behind.

Subject of Research: Longitudinal multiomic comparison of host immune responses to mRNA and inactivated virus COVID-19 vaccines

Article Title: Differential prolonged multiomic responses to mRNA and inactivated virus COVID-19 vaccines

Article References: Tun, H. M., Zhao, S., Chen, C., Chen, Y., Peng, Y., Lv, H., Zhu, J., Tang, Y. S., Chan, K. K. P., Ip, B. Y., Sun, Y., Liu, X., Yiu, K., Hui, D. S. C., & Mok, C. K. P. (2026). Differential prolonged multiomic responses to mRNA and inactivated virus COVID-19 vaccines. Cellular and Molecular Life Sciences. https://doi.org/10.1007/s00018-026-06455-z

Image Credits: AI Generated

DOI: 10.1007/s00018-026-06455-z

Keywords: COVID-19 vaccines, mRNA vaccine, BNT162b2, CoronaVac, inactivated virus vaccine, multiomics, transcriptomics, metabolomics, lipidomics, cytokines, systems vaccinology, immune durability