Immunological and transcriptomic profile of chimeric live-attenuated Zika vaccine linked to protection in non-human primates

Domain: biology
Published: 2025-12-13
Editorial outcome: minor_revision

Abstract
Zika virus (ZIKV) is typically mild in humans but can cause severe congenital defects when contracted during pregnancy. Chimeric live-attenuated vaccine candidate YF-ZIK previously showed protective efficacy against lethal infection and developmental abnormalities in mice after a single dose. Here we demonstrate that YF-ZIK is safe, induces antiviral immunity, and protects rhesus macaques against high-dose experimental challenge. A single subcutaneous dose elicits neutralizing antibodies within 7–14 days, boosted by a second dose at 4 weeks. Passive serum transfer protects AG129 mice, supporting antibodies as a correlate of protection. YF-ZIK triggers balanced Th1/Th2 responses and a transcriptional profile resembling the licensed YF17D vaccine, involving multiple pathways favoring polyvalent immunity. Upon challenge, vaccinated macaques show no detectable viral RNA nor seroconversion to anti-ZIKV NS1 antibodies, suggesting sterilizing immunity. Systems analysis identifies TNFRSF17 as predictor of antibody responses to YF-ZIK, and GNAS and CD207 (Langerin) as potentially linked to clinical outcomes. The favorable preclinical safety, immunogenicity, and efficacy of YF-ZIK justify its future evaluation in humans.

Introduction
Zika virus (ZIKV) is a mosquito-borne Orthoflavivirus, belonging to a genus that also contains other important human pathogens like the yellow fever virus (YFV), dengue virus (DENV), Japanese encephalitis virus (JEV), West Nile virus (WNV), and tick-borne encephalitis virus. Alike other orthoflaviviruses, ZIKV has a ~11 kb-long, single-stranded RNA genome containing one open-reading frame, encoding for three structural proteins, namely capsid, precursor membrane (prM), the envelope (E), and several non-structural (NS1-5) proteins1. Until 2007, ZIKV circulated only in Africa and Asia, sporadically causing self-limited asymptomatic infections in humans. However, from 2007 to 2016, ZIKV explosively spread to the Asian-Pacific and Americas, causing more than 700.000 documented infections in humans. Unexpectedly, during the last epidemic, ZIKV infections were associated with severe complications including a high rate of fetal microcephaly, congenital malformations in infants born to mothers infected during pregnancy, and neuromuscular disorders, namely Guillain-Barré syndrome, in adults2,3. Despite the significant decline in reported cases after 2016, the high chance of recurring outbreaks and transmission to previously spared regions by global travel and expansion of mosquito vectors urge the development of safe and effective ZIKV vaccines3,4.

In response to the ZIKV emergency, multiple vaccine candidates have been developed employing different technologies, including DNA, messenger RNA, recombinant protein, inactivated whole virus, virus-like particle, non-replicating, and live-attenuated viral vector vaccines. Several that proved safe and effective in mice and non-human primates (NHPs) have entered clinical trials, but none have been approved yet for use in humans3,4. Full clinical development has been impeded by a rapid decline in Zika cases after the 2015/2016 pandemic. Though ZIKV transmission continues across multiple regions5, setting up field studies for formal vaccine efficacy evaluation remain challenging due to limited surveillance and the impossibility to predict local outbreaks6. Future vaccine development may therefore need to consider alterative pathways towards licensure based on immunological correlates of protection, alike in the past for YFV and JEV vaccines7. All the more, understanding ZIKV pathogenesis and immunology in step-up models such as NHP is crucial to define relevant parameters that need to be considered for future clinical trial design.

The mechanisms conferring protective immunity against ZIKV are not fully understood. Virus-specific neutralizing antibodies (NAbs) targeting the structural proteins, especially E, are considered the major correlate of protection against ZIKV and other Orthoflavivirus infections7,8. Seminal studies testing several vaccine platforms, including purified inactivated, DNA, and adenovirus vector-based vaccine candidates have established that ZIKV-specific serum NAbs, either induced by active immunization or instilled by passive transfer of purified immunoglobulin, can protect from ZIKV infection in both mouse and NHP models9–12, in line with the widely accepted concept of seroprotection. Thereby, specific NAbs above a certain threshold protected from viremia, scored as suppression of viral RNA below RT-qPCR detection limits9–11. However, depending on the respective animal model employed, on the viral dose and route of inoculation used for challenge, as well as on the methodology used for antibody detection, thus determined critical NAb titres varied widely; ranging from about 1:100 to 1:1000 (ref. 13). Notably, lower NAb levels were required to prevent viremia in semi-permissive BALB/c mice challenged with a low (102 plaque forming units, PFU) virus dose of ZIKV9,10 than to protect readily susceptible rhesus macaques challenged with a moderately higher dose (103 PFU)10,11. In line, in these models also a potent monoclonal NAb could protect from viremia in a dose-dependent manner14. By contrast, highly susceptible IFN-α/β and γ receptor-deficient (AG129) mice15,16 could only partially be protected from lethal infection by NAbs, even when treated with high amounts of purified, fully functional IgG and despite use of a very low challenge dose12. Careful inspection further showed that abolition of viremia is not more than a proxy for a certain degree of protection. In fact, also macaques that stayed non-viremic following challenge showed later a raise in NAbs to saturating levels, whereby titres increased markedly by up to two orders of magnitude compared to post-vaccination levels10. Such a massive recall response suggests that humoral immunity was incomplete and not sterilizing.

Due to technical challenges and lack of reagents, the role of T cell responses has rarely been studied in NHPs, yet more extensively in mice. Intriguingly, while generally robust in mice, vaccine-induced T cell responses remained relatively weak in NHPs, hardly exceeding 100 Spot Forming Units/106 PBMC (~0.01% of ZIKV E-specific T cells) by IFN-γ ELISpot9,10. Nevertheless, depletion and adoptive transfer experiments in mice suggest a complementary role of T cells in mediating vigorous and durable protection against ZIKV infection and disease17–21. Therefore, an ideal ZIKV vaccine should induce both strong humoral and cell-mediated immune (CMI) responses3,4,22,23.

Live-attenuated vaccines, such as the yellow fever vaccine YF17D, are known for their outstanding immunogenicity profile, durable protective immunity, and efficacy after single vaccination without the need for an adjuvant. With more than 1 billion administered doses, YF17D itself is recognized as one of the most effective human vaccines ever developed24–26. Consequently, there is a long history of developing live-attenuated chimeric YF17D-based Orthoflavivirus vaccines, exemplified by the licensed JEV (Imojev®) and DENV (Dengvaxia®) vaccines27,28. Generally, single-dose YF17D vaccination is efficient to induce vigorous innate, long-lasting humoral and balanced Th1/Th2 cellular responses that possibly warrant life-long protection in most vaccinees. Mechanistically, the early, broad, and unique transcriptional profile elicited by YF17D is considered critical to orchestrating a markedly polyvalent and durable immunity. Therefore, new vaccines recapitulating the favourable immunogenicity profile of YF17D are more likely to succeed in human clinical trials25,26,29–33.

In response to the ZIKV outbreak of 2015/16, we developed a YF17D-based live-attenuated Zika vaccine candidate (YF-ZIK) by replacing the antigenic surface glycoproteins prM/E and the C anchor domain of YF17D with a prototypic Asian ZIKV isolate (Fig. 1a). YF-ZIK was shown to be highly attenuated and safe as demonstrated by smaller plaque phenotype, poor replication in mosquito cells, and no neurovirulence in suckling mice. Single-dose YF-ZIK immunization rapidly elicited protective immunity and conferred within 7–12 days vigorous protection against both lethal ZIKV infection and congenital malformations in mice22.

In this study, we demonstrate the safety, immunogenicity, and protective efficacy of YF-ZIK in a stringent non-human primate ZIKV challenge model. A single dose induces rapid seroconversion in macaques, generating high-titer ZIKV-specific NAbs that may confer sterilizing immunity, with responses further enhanced by a booster dose. Transcriptomic profiling reveals a balanced Th1/Th2 response and a signature closely resembling that of the parental YF17D vaccine, involving multiple pathways linked to broad antiviral immunity. Systems-level analysis identifies TNFRSF17 as a correlate of antibody responses, and GNAS and CD207 (Langerin) as predictive biomarkers of vaccine efficacy against high-dose viral challenge.

Results
Immunization and safety profile of YF-ZIK in macaques
NHPs exhibit key characteristics of ZIKV infection in humans, including rapid viremia, the presence of viral RNA in multiple organs and body fluids, and the development of NAbs and CMI responses; hence, NHPs represent a relevant model for the study of ZIKV pathogenesis and preclinical evaluation of potential interventions34,35. In a first step, we assessed the vaccine safety of our previously described chimeric yellow fever-Zika virus vaccine candidate YF-ZIK (Fig. 1a)22. To that end, six Orthoflavivirus-seronegative rhesus macaques (sex ratio 1:1) were immunized subcutaneously (s.c.) twice (at day 0 and day 28) with each 105 PFU of YF-ZIK, while four macaques received medium as sham controls (Fig. 1b). The vaccine was well tolerated as no signs of discomfort (normal behaviour and food intake), local reactogenicity (no swelling at the injection site or of draining lymph nodes), nor significant changes in weight and body temperature (Supplementary Fig. 1a,b) were observed in both groups. In addition, all hematological parameters tested during the immunization phase (blood cell counts and ratios, platelet counts, hemoglobin levels) remained within physiological ranges in both YF-ZIK-immunized and in sham-vaccinated animals (Supplementary Fig. 1c–l). Altogether, no adverse reactions were observed in rhesus macaques, confirming the previously observed favourable preclinical safety profile of YF-ZIK22,36.

YF-ZIK rapidly activates broad innate and adaptive transcriptional programs
As a benchmark of successful human viral vaccines, YF17D-induced immunity has been extensively studied. Data collectively suggest that YF17D induces a unique transcriptional master profile integrating multiple arms of the innate and adaptive immune system to develop broad, polyfunctional, and persistent immunity in humans24,26,30–33. However, despite serving as well-established viral vector, there is limited information about the molecular mechanisms by which YF17D-derived vaccines act and protect29,37. To fill this gap, we used nCounter digital transcriptomics for the profiling of whole blood samples collected both after prime (at day 2 and 7, as compared to day 0) and boost (at day 30 and 35, as compared to day 28) (Fig. 1b). More specifically, we analyzed the expression profile of >750 genes associated with vaccination (especially YF17D immunization in humans), immune response and viral infection26,30–33.

Our analysis revealed that YF-ZIK immunization rapidly induces a gene expression profile associated with activation of multiple pathways as early as 2 days after prime and then further pronounced and shaped after vaccine boost (Fig. 2a–e, see Source Data file for raw values and descriptive statistics).

More specifically, prime vaccination induced an early upregulation of CXCL10 (or IP-10; pro-inflammatory response to viral infection), EIF2AK2 (or PKR), OAS1, OAS2, MX1, MX2, IFIT2, STAT1, STAT2, IFIH1, IFI16, IFI44, and DDX58 (antiviral and IFN-related genes), CSF1R, CD163, CD68 and SIGLEC1 (related to monocytes/macrophages immunity), and CYBB and CD38 (related to adaptive immunity) within 2 days after immunization (Fig. 2a). Overall, YF-ZIK immunization in macaques appeared to induce an earlier peak in DEG (at day 2) than reported for YF17D immunization in humans, which peaks around day 7 after vaccination31. Upregulation of CXCL10 and OAS1 transcripts by YF-ZIK at day 2, genes commonly associated with successful immunization, was particularly fast, especially when contrasted with the delayed response observed with YF17D vaccination, where similar changes were not seen until day 21 (ref. 32). Downregulated DEG in the whole blood transcriptome included TLR9 (TLR signaling), TRAF1 and TRAF6 (related to antiviral immune responses), and CD59 (related to complement system) amongst others. A downregulation of TLR pathways and simultaneous upregulation of antiviral interferon signaling (Fig. 2b,e, Supplementary Fig. 2c,d, and Source Data) would align with RIG-I/DDX58 and MDA5/IFIH1 possibly acting as major cytosolic RNA sensors during early Orthoflavivirus infections. Likewise, a reduced enrichment of gene activation to adaptive immunity and T cell activation (LCP2 and KLRB1), FADD and CASP8 (related to cell apoptosis), GATA3 (related to Th2 immune responses) (Fig. 2b) may result from cell recruitment and migration from peripheral blood into draining lymph nodes in early response to YF-ZIK. The pronounced early transcriptional response was transient, with transcript levels and pathway signatures returning to baseline by day 7 post-vaccination, indicating a rapid contraction of the immune response following primary immunization (Supplementary Fig. 2a–c).

A booster dose of YF-ZIK administered at day 28 activated a significantly stronger and broader transcriptional response compared to the primary immunization. This included enhanced activation of DEG clusters that were already upregulated by prime vaccination, as well as an additional gene set associated with further maturation and shaping of the immune response (Fig. 2c–e and Supplementary Fig. 2c). DEG included, among others, the upregulation of canonical antiviral and IFN-related genes (OAS1-3, PML, IRF5, MX1, MX2, IFITM1, ISG20, EIF2AK2, IFI16, STAT2, and SOCS1), genes related to monocytes/macrophages immunity (SIGLEC1 and CD68), to interleukin signaling (IL1RN), to TLR signaling (TLR7, ELK1, and MEF2A), and complement activation (SERPING1). Besides a strong innate antiviral signal, thus identified DEG included key genes involved in humoral responses and upregulation of T cell activity (MR1 and CD4), cell proliferation (TNFSF13B and BST2), and apoptosis (TNFSF10) (Fig. 2c), indicative for a strong adaptive recall response.

In summary, though often measured at low amplitude and in many cases with limited statistical power due to the small number of samples suitable for analysis, our whole blood transcriptomic analyses suggest a rapid, canonical IFN-centered innate antiviral response and an early, concerted induction of adaptive immunity after prime vaccination, which is further enhanced by a booster dose (Fig. 2f).

YF-ZIK induces polyfunctional immunity, including a strong NAbs response as major correlate of protection
Next, we assessed the immunogenicity of YF-ZIK, focusing on the induction of polyfunctional immunity and the generation of NAbs, a key correlate of protection. The immunogenicity was evaluated following a two-dose regimen (Fig. 1b) analogous to the schedule recommended for the chimeric YF17D-based JEV vaccine (Imojev®). Primary YF17D vaccination may result in transient, low-level viremia (typically 3–9 days; <104 genome RNA copies/mL) in both humans and NHP38–41. After YF-ZIK prime immunization, viremia remained very low, exceeding quantification limits occasionally in only one macaque (R10128) (Supplementary Fig. 3a,b). Though rare, the detection of viral RNA in the bloodstream proves that live-attenuated YF-ZIK is capable of active replication, amplification, and systemic spread. Nevertheless, 3 out of 6 YF-ZIK-immunized macaques (50%) developed ZIKV-specific NAbs as early as 7 days after prime vaccination, with a log10 geometric mean titre (GMT) of 2.0, including previously mentioned individual R10128 (NAb titre: 2.5 log10).

At day 14, all animals (6/6) had seroconverted with a peak in NAbs at a log10 GMT of 3.2 (Fig. 3a and Source Data file). NAbs were absent in the sham group. NAb titres were determined by an in-house developed fluorescence-based reporter virus seroneutralization assay, which has been validated and shown to yield a comparable reliability and accuracy as a classical PRNT assay used as benchmark (Supplementary Fig. 3c). Notably, by day 28 post-prime vaccination, ZIKV-specific NAb titres (log10 GMT of 2.8) still exceeded established correlates of protection for several licensed Orthoflavivirus vaccines in humans (NAb titre > 1:10 or 1:40), as well as the more stringent threshold proposed for ZIKV (NAb titres > 1:25 to 1:100) derived from challenge studies in macaques9–11,42. This suggests that single-dose YF-ZIK vaccination might be sufficient for protection. As expected, a second YF-ZIK dose given at day 28 significantly boosted ZIKV-specific NAb levels, resulting in a marked 25-fold increase by day 35 (log10 GMT of 4.2) as compared to day 28 (prior to boost; log10 GMT of 2.8), persisting until day 56 (log10 GMT of 3.8) immediately before animals were challenged (Fig. 3a and Source Data file).

The early production of NAbs, which was further boosted in all animals within 7 days after the second dose, coincided with a rapid contraction of the immediate transcriptional response associated with IFN signaling and innate immune pathways (Fig. 2b, e and Supplementary Fig. 2a–d). This sequence of events following booster vaccinations suggests a functional relationship between the resolution of an early innate immune activation in response to the live vaccine virus and a rapid onset of a previously primed adaptive humoral response shortly thereafter. Notably, the raise in ZIKV-specific IgG levels measured in parallel to NAbs displayed only slightly delayed kinetics, indicating an early antibody class switch (Fig. 3b). In support for this, we found that transcript levels of activation-induced cytidine deaminase (AICDA), encoding for a key enzyme required for antibody diversification via somatic hypermutation and class switching from IgM to IgG, increased markedly, peaking at day 7 after prime vaccination and again 2 days after the booster dose (Fig. 3c).

To determine whether vaccine-elicited NAbs are sufficient for protection, we performed adoptive serum transfer in AG129 mice, a very sensitive and stringent mouse model for ZIKV challenge and infection studies15,22,43. AG129 mice are highly susceptible to infection, with as little as 1 PFU of ZIKV (MR766 strain) being sufficient to induce disease and full mortality (5/5) (Supplementary Fig. 3d, e). For this purpose, serum pools from YF-ZIK-immunized macaques (from day 21, 35, and 42 with log10 GMT of 3.7), or sham-vaccinated macaques were used. Antiserum transfer was well tolerated (Fig. 3e and Supplementary Fig. 3f, g) and resulted in uniformly high serum concentrations of ZIKV-specific NAbs (log10 GMT of 3.2) immediately after dosing. Titres declined rapidly to undetectable levels by day 14, consistent with the short half-life of NHP antibodies in mice2–4,23 (Supplementary Fig. 3h). Upon challenge with 10 PFU (i.e., a more than ten fold lethal dose) of ZIKV one day after serum transfer, all mice that received the serum from sham-vaccinated macaques developed severe disease and succumbed to infection (Fig. 3d, e). In contrast, 7 out of 9 mice that received serum from YF-ZIK-immunized macaques remained healthy and were fully protected, as confirmed by the absence of weight loss, clinical signs of disease, or mortality following MR766 challenge (Fig. 3e and Supplementary Fig. 3g). These data confirm the critical role of NAbs in conferring protection against lethal ZIKV infection. However, additional immune mechanisms triggered by YF-ZIK vaccination are be expected to contribute to vaccine efficacy2–4,23.

Systems vaccinology analyses suggest mechanistic similarities with YF17D vaccine
Several clinical trials have demonstrated the great complexity of molecular and cellular events that are triggered by the prototypic YF17D vaccine to induce long-lasting polyfunctional immunity. Thereby, the mounting of vigorous adaptive responses and immunological memory has been linked to a highly predictive transcriptional pattern early after vaccination within specific cell compartments26,30–33. To validate and translate our NHP data to the human model, we compared the gene expression profile triggered by YF-ZIK vaccination of macaques with what can be expected for YF17D vaccination following meta-analysis of available human data. Using UMAP analysis, we first plotted the YF-ZIK and YF17D specific expression profiles to the human blood transcriptome44 to demonstrate that both vaccines affected multiple leukocyte populations (amongst others, dendritic cells, macrophages and neutrophils; naive and memory B cell, plasmablasts and plasma cells; CD4+ T, and CD8+ T and NK T cells), following a comparable pattern (Fig. 4a and Supplementary Fig. 4a).

For quantitative comparison, publicly available early expressions data for YF17D (prime only: day 3 and day 7) were plotted over those gathered in this study for early timepoints after YF-ZIK administration (i.e., day 2 post-prime and day 2 post-boost) (Fig. 4b). The thus aligned DEGs revealed a strong overlap for both vaccines after prime (at day 2; 39 shared genes, enrichment p=0.0013) and boost (at day 30; 48 shared genes, enrichment p < 10−6) (Fig. 4b, c). This strong correlation does not appear to be markedly affected by differences in vaccine doses and experimental schedules, or their possible impact on DEG expression kinetics. By contrast, YF-ZIK and YF17D shared an upregulation of numerous canonical antiviral and IFN-related genes (e.g., EIF2AK2, OAS1, MX1, STAT1/2, ISG20, DDX58) (Fig. 4c and Source Data), genes related to T cell immunity, to TNF and chemokine signaling and to the involvement of macrophages and complement. However, a most striking overlap was observed for genes associated with humoral immunity, BCR signaling, and B cell proliferation (see also Fig. 7d–f below)26,31. An intriguingly similar yet enhanced transcriptional response triggered by a second dose of YF-ZIK shows that booster immunization remains feasible and may be beneficial to further increase ZIKV-specific NAb levels (Fig. 3a). Overall, our systems vaccinology analyses suggest a high similarity in the molecular mechanism of both vaccines, as well as the validity and translational value of data obtained in the macaque model.

Besides obvious commonalities, the comparison of YF-ZIK and YF17D also unveiled unique DEG sets. At day 2, YF-ZIK exclusively upregulated 26 genes that were not altered by YF17D at any timepoint. These included several IFN-stimulated genes such as SIGLEC1, IRF5, TMEM173 (the gene encoding IFN activator STING), HLA-DRA, and HLA-DQA1. Likewise, YF17D exclusively upregulated 45 genes, including several IFN-stimulated genes. Of interest, these genes were also upregulated by infection with unrelated viral pathogens such as SARS-CoV-2 in rhesus macaques and humans, as well as in multiple other animal species (ferrets, hamsters, and mice; Fig. 4c and Source Data). It is hence tempting to speculate whether the favorable safety profile of YF-ZIK (Supplementary Fig. 1), observed as a reduced neurovirulence in suckling mice and no lethality for AG129 mice in contrast to parental YF17D22,36,43, might be linked to the absence of any of these transcripts. The 16 genes exclusively downregulated by YF17D were not significantly enriched in any particular pathway.

Finally, for four genes, the two vaccines induced opposing expression patterns. SOCS1 (Suppressor Of Cytokine Signaling 1, an inhibitor of type I and III IFN signaling) and TICAM1 (TIR Domain Containing Adaptor Molecule 1, a TLR signaling adapter protein) (highlighted in Fig. 4b), and anti-apoptotic BLC2L1 (Bcl-2-Like Protein 1) were upregulated by YF17D and downregulated by YF-ZIK, while PTPRC (encoding pan-leukocyte marker CD45) was downregulated by YF17D and upregulated by YF-ZIK. An elevated CD45 and diminished BLC2L1 expression 2 days after YF-ZIK vaccination are in agreement with a simultaneous increase in monocytes/macrophages and a decrease in apoptosis (Fig. 2 and Supplementary Fig. 2), features not shared with YF17D26,32. Overall, however, transcriptomics analyses unveiled marked similarities rather than differences between chimeric YF-ZIK and its parent YF17D.

YF-ZIK immunization protects macaques against ZIKV infection
The magnitude and quality of YF-ZIK-induced humoral and cellular responses supported the hypothesis that vaccinated animals would be protected against ZIKV infection. To test this, we performed an experimental challenge, whereby all animals were intradermally (i.d.) injected with a high dose (105 PFU) of ZIKV DR/2016/PD1 strain eight weeks after priming (day 56, Fig. 1b). Consistent with previous reports34,35, ZIKV infection resulted in a high, protracted blood plasma viremia in the sham-immunized macaques (Fig. 5a and Supplementary Fig. 5a). The viremia rapidly increased with viral RNA detectable in 3/4 animals within 24 h after inoculation and peaked around 4 days post-challenge (log10 GMT of 5.7 viral genome copies, 95% CI 3.9–7.5). As expected for rhesus macaques, infection was mild (no signs of weight loss, nor fever) and self-limiting (Supplementary Fig. 5). Following induction of an original antiviral IFN-related and adaptive response (Supplementary Fig. 5a, b), viral clearance from plasma occurred within 10 days post-infection (Fig. 5a and Supplementary Fig. 5a), concomitant with seroconversion to ZIKV-specific NAb and anti-NS1 antibodies (Fig. 5b, c).

In contrast, YF-ZIK immunized macaques were protected against vigorous ZIKV challenge, as demonstrated by the absence of any detectable viral RNA in the blood plasma after challenge, except for a singular transient event in one individual (R13010), yet markedly lower and delayed in onset by about 5 days compared to sham-vaccinated animals (Fig. 5a and Supplementary Fig. 5a). Induction of ZIKV-specific anti-NS1 antibodies was assessed as a sensitive biomarker of ZIKV exposure. Compared to the high levels observed in sham-vaccinated macaques at day 70 (14 days after challenge), only a minimal increase of ZIKV NS1-specific IgG was detectable in YF-ZIK-immunized animals (Fig. 5b). Notably, no substantial increase in ZIKV-specific NAb could be observed after challenge in YF-ZIK-immunized macaques, suggesting that vaccination had already elicited saturating antibody levels, resulting in almost sterilizing immunity (Fig. 5c). Of note, the vaccine candidate proved safe as no enhancement of disease nor virus replication by pre-existing immunity was observed in vaccinated animals (Supplementary Fig. 5b, c). In summary, YF-ZIK immunization protected macaques against vigorous high-dose i.d. ZIKV infection.

Rapid recall of polyfunctional immune responses in YF-ZIK immunized macaques contribute to protection
To explore the molecular and cellular underpinnings of such a high degree of protection, we extend our transcriptional analysis to days 1 and 4 after challenge, compared to pre-challenge (day 56 after priming). The initial response to ZIKV challenge in sham-vaccinated macaques remained limited, both regarding the number and amplitude of affected DEG at day 57 (1 day after infection) without statistical significance (Supplementary Fig. 6; see Source Data file for raw values and descriptive statistics). Likewise, a slight yet marked upregulation of genes related to IFNs (including OASL, OAS1, OAS2, OAS3, MX1, MX2, DDX58, IRF7, IFIT1, IFIT2, IFIT3, IFI44, IFN3), metabolism (SLC2A1, CHIT1, ALOX15), host defense against infections and adaptive immunity (LTF, DEFA1, ICAM4) appeared delayed at day 60 (4 days after challenge) (Fig. 6a and Supplementary Fig. 6), in line with the capacity of ZIKV to antagonize antiviral host responses2–4,23.

In contrast, YF-ZIK immunized macaques showed a stronger, earlier, and broader transcriptional signature compared to the sham group; especially pronounced at day 60 (4 days after challenge; Fig. 6b and Supplementary Fig. 6; see Source Data file for raw values and descriptive statistics). While largely limited to a primary IFN response in sham animals, YF-ZIK immunized macaques showed a strong upregulation of a large number of genes related to the (re)activation of both the innate and adaptive arms of the immune system, suggestive for early involvement in vaccine-induced protection. These included the activation and modulation of TLR-mediated, interleukin-, Fc Receptor-, MAPK-, VEGF-, NF-kB-induced immune responses (such as IRAK1, MYD88, TRAF6, MAVS, TNFRSF14, IKBKE, etc.), macrophage involvement (SIGLEC1, CD68, and CSF3R), complement (SERPING1 and C1R), antigen presentation (HLA-DMB), T cell function (CXCR3, CXCR, 4 and CD4), and metabolism (CHIT1, PLA2G2A, and ALOX15) (Fig. 6b, c and Supplementary Fig. 6b, c). Collectively, this fast switch in the expression of multiple DEGs from several interlinked antiviral pathways upon virus exposure (Fig. 6d and Supplementary Fig. 6d) lays a molecular basis for vaccine-induced protection and rapid control of ZIKV infection in YF-ZIK immunized macaques.

Vaccine-induced transcriptional patterns predict immunity and clinical outcome
Finally, we used linear and logistic regression models and machine learning algorithms to identify gene signatures that could serve as predictors for successful vaccination and protective immunity against ZIKV infection in macaques. Herein, several biomarkers routinely measured in human cohort studies were considered, namely peak viral load (at day 60), overall viral load (AUC, area under the curve across all timepoints), and anti-NS1 antibody levels (as a measure of exposure to replicating ZIKV). Due to the limited number of samples available at day 0 and day 3, predictions were performed from digital transcriptomic profiles obtained at day 7 and day 28 (after prime), as well as day 30 and day 35 (after boost). After multiple uni- and multivariate regression models were tested, best-fitting univariate predictors for NAb levels and clinical outcomes were identified by linear regression.

Among all timepoints, gene expression profiles from day 30 (2 days after boost) generally provided the best predictive models. Demographic variables (age, sex, and weight of animals) were not further considered as they did not lead to any better predictions despite correlation to some serological and transcriptional parameters (Fig. 7 and Source Data).

The top 50 most predictive DEGs (quantified at day 30) for NAb levels at day 56 (prior to challenge) form a densely linked protein-protein interaction network centered around B cell activation including the highly connected canonical B cell marker CD19, B cell receptor-associated CD79A/CD79B and MS4A1 (CD20), B cell activation markers CD40 and CD86, as well as several B cell specific members from the TNF receptor superfamily, such as the two bona fide receptors for TNFSF13B/BAFF (B cell activating factor) called TNFRSF13C (BAFFR) and TNFRSF17 (B cell maturation antigen, BCMA) (Fig. 7a, b).

As shown in Fig. 7c, TNFSF13B/BAFF transcript levels were significantly increased after both prime and boost, which paralleled the previously observed immune pathway activation (Fig. 7a, b) and NAb kinetics (Fig. 3a), as well as an increased expression of its receptor TNFRSF17, reaching a plateau starting from day 7 after vaccination, like human YF17D data. In addition, TNFRSF17, together with FASLG (Fas Ligand), appeared to be the most predictive DEG for NAb production after YF-ZIK immunization in macaques (Fig. 7d). Notably, TNFRSF17 turned similarly out as the most accurate predictor for the YF17D-induced NAb response in humans with up to 100% accuracy26.

ZIKV-specific NAb alone may not be sufficient to confer full protection. Our transcriptional analysis revealed another two transcripts in peripheral blood to most accurately predict viral load at day 60, the peak of ZIKV infection in macaques, and overall viremia (AUC), respectively, being CD207 (Langerin), present on circulating conventional dendritic cells, and GNAS, a pleiotropic gene involved in anti-inflammatory cytokines production and GPCR signaling. Thereby, low expression of either DEG prior to ZIKV infection correlated with protection from virus replication (Fig. 7e and Supplementary Fig. 7a). High viral loads were similarly associated with an exhausted CD8+ T cell profile and high LAIR1 (inhibitory leukocyte-associated Ig-like receptor, CD305) expression (Fig. 7e and Supplementary Fig. 7a), further implicating CD8+ T cells as relevant mediators of protective immunity against ZIKV infection3,4,17,23,42,45.

ZIKV-specific NS1 antibody levels (at day 70) correlated strongly with peak viral loads (at day 60) (Fig. 5a, b). Seroconversion to anti-NS1 may hence serve as a sensitive marker for exposure to replicating ZIKV. Consequently, low transcript levels for CCL5/RANTES (leucocyte migration) and PDCD1 (encoding PD-1 indicative for inactive lymphocytes) in the blood prior to challenge translated into a barely detectable anti-ZIKV NS1 IgG response (Supplementary Fig. 7b) and, thus, protection in macaques imprinted by YF-ZIK vaccination. In contrast, extensive virus replication observed in sham-vaccinated animals may reflect delayed tissue recruitment and slow responsiveness of T cells in naïve subjects during an acute ZIKV infection.

Multiple studies highlight the importance of ZIKV-specific CMI responses for protective immunity2–4,17,23,42. Induction of a balanced (Th1/Th2/Treg) and polyfunctional (CD4+/CD8+) ZIKV-specific antiviral CMI response by YF-ZIK is corroborated by a comprehensive DEG and correlation analysis of transcriptional, virological, serological and cellular parameters comparing YF-ZIK and sham-vaccinated animals (summarized in Fig. 7f). For additional experimental evidence, intracellular cytokine staining (ICS) was performed on PBMCs collected at day 42; i.e., 14 days after administration of the booster dose (Fig. 1b). Upon stimulation with ZIKV E protein-derived peptides, ZIKV E-specific CD4+ T cells producing IFN-γ, IL-2 (promoting CMI responses; Th1) or IL-4 (promoting humoral responses; Th2), as well as CD8+ T cells producing IFN-γ or IL-2 could be identified in YF-ZIK immunized macaques (Supplementary Fig. 7d). Restricted in scope and depth by technical issues and by a limited number of specimens, our ICS analysis remains preliminary and merely suggestive (no statistical significance). Nevertheless, based on our experimental evidence, a balanced Th1/Th2 cellular response can be expected to complement humoral immunity through the antiviral activity of cytotoxic T cells and the provision of T cell help, thereby enhancing the quality and longevity of YF-ZIK-induced protection.

Taken together, protective immunity triggered by YF-ZIK appears to extend beyond a certain NAb level serving as proxy and serological correlate for successful immunization. We further demonstrate the translational relevance of our NHP data to humans and identified TNFRSF17 as a common predictor for NAbs for both YF17D and YF-ZIK. Several factors in peripheral blood such as CD207, GNAS, LIAR1, PDCD1, and CCL5 are inversely correlated with a protective cellular immune response and forecast an adverse infection outcome.

Methods
Cell culture and virus
Vero E6 (African green monkey kidney) and BHK-21J (baby hamster kidney) cells were provided by Dr. P. Bredenbeek and maintained in minimum essential medium (Gibco). Cells were maintained in Dulbecco’s modified Eagle medium (Gibco). All media were supplemented with 10% fetal bovine serum (FBS, Hyclone), 2 mM L-glutamine (Gibco), and 1% sodium bicarbonate (Gibco).

YF-ZIK vaccine stock, earlier called YF-ZIK prM/E, was generated as supernatant of infected Vero E6 cells22. ZIKV PD1 strain (ZIKV DR/2016/PD1) was kindly provided by Dr. L. Barzon, University of Padua, Italy, which was isolated from a traveler returning sick from the Dominican Republic86. ZIKV MR766 strain was obtained from the European Virus Archive (EVA) (Ref-SKU: 001v-EVA143). Virus titres expressed as plaque-forming units (PFU/mL) were determined as described before22. In brief, Vero E6 cells (1×10⁶/well) were seeded in 6-well plates overnight. After PBS washes, cells were incubated with serial virus dilutions for 1 h at 37°C. Post-infection, cells were washed and overlaid with 0.5% low melting agarose (Invitrogen) in Minimal Essential medium (MEM) (Gibco) with 2% FBS. After agar solidification, plates were incubated for 7 days at 37°C, then fixed with 4% formaldehyde and stained with 1% crystal violet. Plaques were counted manually and expressed as PFU/mL.

NHP study
All housing and animal procedures involving NHP took place at the BPRC, upon positive advice by the independent ethics committee (DEC-BPRC), under project license AVD502002016496 issued by the Dutch Central Committee for Animal Experiments and following approval of the detailed study protocol by the institutional animal welfare body. The NHP study was conducted on rhesus macaques (Macaca mulatta; 7 males, 3 females, approximately 4 years old with a body weight of at least 6 kg). All animals were pre-screened as negative for IgG antibodies against other related orthoflaviviruses (including ZIKV, DENV, JEV, and YFV) by ELISA. A group of six macaques was immunized twice in BSL3 facilities at Biomedical Primate Research Centre (BPRC, Netherlands) by subcutaneous injection of 105 PFU of YF-ZIK at day 0 and day 28, respectively. Another group of four macaques received MEM containing 2% FBS (referred to as sham-vaccinated) subcutaneously at the matched time point. Animals were randomly allocated to the experimental groups. All animals were monitored daily for clinical signs, appearance of injection site, and swelling of local draining lymph nodes by palpation. Blood draws, body weight, and temperature measurements were performed every 2 to 3 days during the first week after every vaccination and then once a week up to day 56. At day 56, all macaques were intradermally challenged with 105 PFU of ZIKV PD1 strain. After the challenge, macaques were followed daily for clinical signs including body temperature, body weight, food intake, dehydration, and abnormal behaviors. Blood draws were performed daily until day 70.

Hematology test
Hematology parameters were measured by the clinical laboratory at the BPRC with a Sysmex XT-2000iV Automated Hematology Analyzer (Sysmex Nederland B.V., Etten-Leur, The Netherlands).

Serum neutralizing test (SNT)
Quantification of NAb by SNT using ZIKV–mCherry reporter viruses has been described previously in detail52,87. In brief, serum dilutions were incubated with 10 TCID50 ZIKV–mCherry for 1 h at 37°C, after which serum–virus complexes were transferred for 72 h to 104 BHK-21J cells. The percentage of mCherry-expressing cells was quantified on a Cell Insight CX5/7 High Content Screening platform (Thermo Fischer Scientific) to determine neutralization titres, including half-maximal (SNT50) and 90% (SNT90) inhibitory concentration. Neutralization values were calculated by normalizing to virus-only (100%) and cell-only (0%) controls, followed by curve fitting in GraphPad Prism. Our SNT assay has been validated and showed strong correlation with the gold standard plaque reduction neutralization test (PRNT) (Supplementary Fig. 3c).

Flow cytometry and intracellular staining (ICS)
Cryopreserved peripheral blood mononuclear cells (PBMCs) were thawed at 37°C and 1×106 cells were cultured in RPMI 1640 with L-glutamine (Lonza), supplemented with 10% fetal calf serum (FCS), penicillin (100 units/ml; Continental Pharma), and streptomycin (100 μg/ml; Continental Pharma). Cells were stimulated with interleukin-2 (IL-2) (50 ng/ml; PeproTech), anti-human CD28 (Clone CD28.2, 1 μg/ml; BD Biosciences), and anti-human CD49d (Clone R1-2, 1 μg/ml; BD Biosciences) for 72 h at 37°C/5% CO2. To measure vaccine-specific T cell responses, cells were either or not stimulated with PepMix ZIKV E protein peptide (PM-ZIKV-E, 5 μM; JPT Peptide Technologies). As a positive control, cells were stimulated with PMA (50 ng/ml; Sigma-Aldrich) and ionomycin (500 ng/ml; Sigma-Aldrich). Four hours prior to staining, cells were incubated with Brefeldin A (GolgiPlug, 1:1000; BD Biosciences) and Monensin (GolgiStop, 1:1500; BD Biosciences). Subsequently, cells were incubated with FcR-blocking reagent (0.5 μl/well; Miltenyi Biotec) and labelled for extracellular markers, including with CD45 BV786 (Clone D058-1283), CD3 BUV395 (Clone SP34-2), CD4 BB515 (Clone L200) and CD8 PE (Clone SK1, BioLegend) antibody in FACS buffer (Ca2+/Mg2+-free PBS, supplemented with 2% FCS and 2 mM ethylenediaminetetraacetic acid (EDTA)). Dead cells were excluded using Zombie Aqua 516 viability dye (BioLegend). Intracellular staining with IFN-γ BV711 (Clone 4S.B3), IL-2 BV421 (Clone MQ1-17H12), and IL-4 APC (Clone 8D4-8) was performed with the Fixation and Permeabilization kit (BD Biosciences) according to the manufacturer’s protocol. Unless stated otherwise, antibodies were purchased from BD Biosciences. Flow cytometry was performed on the BD LSRFortessa X20, and data were analyzed with FlowJo (LLC, V10). The gating strategy is shown in Supplementary Fig. 7c. Frequencies of ZIKV-specific T cells were calculated by subtracting non-peptide stimulated samples from corresponding stimulated samples. Due to cryopreservation-related viability loss, PBMC samples from three YF-ZIK-immunized individuals showed insufficient viable cell counts for ICS and were therefore excluded from analysis.

AG129 mice
All mouse experiments were approved by the Ethical Committee of the Animal Research Center of KU Leuven (project number P100/2019) and conducted in full accordance with the Belgian guidelines for animal experimentation and the guidelines of the Federation of European Laboratory Animal Science Associations. 129/SvEv Ifnar1tm1Agt Ifngr1tm1Agt mice deficient in interferon-α/β and γ receptors (AG129 mice; B&K Universal, Marshall Bio resources, UK) were bred in-house at the Experimental Animal Facilities of KU Leuven and randomly assigned to study groups. Sex was not considered in the study design. Animals were housed in groups of up to n=5 in individually ventilated cages (IsoCage N Biocontainment System, Tecniplast) at a temperature of 21°C, humidity of 55%, and 12:12 dark/light cycles, with ad libitum access to food and water and cage enrichment (cardboard houses and wood blocks).

ZIKV MR766 titration and passive serum transfer experiment in mice
For ZIKV MR766 titration, 8–10 weeks old AG129 mice were injected intraperitoneally (i.p.) with 1 PFU, 10 PFU, 102 PFU, and 103 PFU of MR766 virus, or medium as control. Mice were monitored daily for changes in body weight and clinical signs of disease. Sick mice were euthanized based on morbidity criteria (hind limb paralysis, hunch posture, ruffled fur, and weakness) or if weight loss exceeded 25%.

For the serum transfer experiment, 8–10 weeks old AG129 mice received a single dose of 450 µl of immune serum pool from samples collected on days 21, 35, and 42 from YF-ZIK-immunized macaques (mean neutralizing titre of 5492), or from sham-vaccinated macaques by i.p. injection. Twenty-four hours after passive serum transfer, mice were infected with 10 PFU of ZIKV MR766 by i.p. injection (Fig. 3d). For survival analysis (Fig. 3e), results from two independent experiments were pooled from in total n=9 mice (4+5) receiving antiserum and n=10 mice (5+5) receiving sham serum. A separate group of AG129 mice (n=3) that received the serum pool from YF-ZIK-immunized macaques was not infected and bled weekly to determine NAbs kinetics after serum transfer (Supplementary Fig. 3h). Mice were monitored daily for weight change and bled weekly to determine the antibody titres. Sick mice were euthanized based on morbidity criteria (hind limb paralysis, hunch posture, ruffled fur, and weakness) or if weight loss exceeded 25%.

Quantitative ZIKV RNA analysis in plasma
ZIKV RNA in plasma samples from EDTA-treated blood was quantified using real-time PCR as described before86. Briefly, viral RNA was extracted by using the Qiagen QIAamp® Viral RNA Mini Kit. The cDNA synthesis and PCR amplification were performed using the Brilliant II QRT-PCR Core Reagent Kit, 1-Step kit (Agilent), and RNA quantification was performed on a Bio-Rad CFX Connect real-time system.

ZIKV NS1-specific antibody ELISA
The ZIKV (Suriname) NS1 recombinant protein (The Native antigen company, cat. no. ZIKVSU-NS1-100) was used at a concentration of 1 µg/ml in Dulbecco’s phosphate-buffered saline (DPBS, Gibco) to coat 96-well microplates overnight at room temperature (RT). The antigen-coated plates were washed three times with PBST-20 (0.05% Tween-20 in DPBS), blocked with 3% Bovine Serum Albumin (BSA) for 1 h and incubated with NHP serum (1:500 dilution in DPBS containing 0.5% BSA and 0.2% Tween-20) for another 30 min. Subsequently, the plates were incubated with a goat anti-monkey IgG, IgA, IgM (Fc Specific), secondary antibody conjugated with horseradish peroxidase (1:1000, Nordic-MUBio) for 30 min. Plates were washed (3–5 times) between incubations steps with PBST-20. The reaction was developed with tetramethylbenzidine liquid substrate system for ELISA (Sigma-Aldrich) for 10 min and stopped with 1 M hydrochloric acid (HCl). The absorbance at 450 nm was measured using SPARK Magellan plate reader (Tecan Life Technologies). All measurements were conducted in triplicates.

Transcriptional and systems immunology analyses
Whole blood samples were collected in PAXgene tubes (Qiagen) for RNA preservation. Digital transcriptomics (nCounter platform, NanoString Ltd.) was performed as previously described for other viral pathologies and their vaccine outcomes65,66,88,89. In brief, purified RNA samples were hybridized to unique capture/reporter pairs (50 bp each) targeting 770 transcripts (754 immune transcripts and 16 housekeeping genes present in the “Non-human Primate Immunology” Panel V2, as well as 6 positive and 8 negative control probes (all from NanoString). Results were sequentially corrected for background (negative control probes), technical variation (positive control probes) and RNA content (housekeeping genes), followed by differential expression, cell type deconvolution, pathway enrichment and network analysis using nSolver 4.0 (NanoString), MSigDb90, and STRING, in conjunction with publicly available bulk and single-cell transcriptomic datasets GEO and CZ CELLxGENE Discover (https://cellxgene.cziscience.com/)91,92. Resulting signature and cell type scores are the combined normalized total of all z-scores for all genes in a certain pathway or cell type. Genesets used to define individual pathways are listed in the section of the Source Data corresponding to Fig. 2. Data mining of demographic, serological, and transcriptomic data with machine learning algorithms for prediction (attribute selection, decision trees) was performed using Weka (version 3.8.4). Uniform Manifold Approximation and Projection (UMAP) and dot plot representations were performed in RStudio (version 2024.04.2+764), using the Seurat package (v5)93 and the Tabula Sapiens - Blood database44.

Statistics and reproducibility
GraphPad Prism Version 9 (GraphPad Software, Inc.) was used for all statistical analyses. One-way or two-way analysis of variance (ANOVA) with multiple comparisons was applied to analyze the time-series observations for independent groups. The nonparametric two-tailed Mann–Whitney U-test was used for the comparison between paired groups. Kruskal–Wallis test was used to analyze differences among more than two groups with a non-normal distribution and corrected for multiple comparisons using Dunn’s method. Univariate and multivariate linear and logistic regression models were tested in GraphPad Prism Version 9 and XL-STAT. No statistical method was used to predetermine sample size. Details of statistical analyses are specified in each figure legend. Values were considered significantly different at P < 0.05. All raw, individual-level data are presented in the Source Data file.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript reports continued studies by this group on their candidate Zika vaccine based on a chimera with a yellow fever (YF) 17D vaccine backbone with YF sequences swapped for Zika sequences (YF-ZIK). The mouse data has been published and looks promising, and in this manuscript they continue their studies by investigating the vaccine in non-human primates (NHPs) with incorporation of system vaccinology.\n\nThe manuscript is interesting but views everything very positively with the vaccine candidate while this reviewer believes that, while the results are very interesting, the conclusions and interpretation need to be toned down.\n\nThe introduction is weak on previous work on correlates of protection. Some is mentioned in the results and discussion, but a summary of the state-of-the-art of protection and neutralizing antibody titers in mouse and NHP models for Zika vaccine candidates would help readers understand where the data included in this paper sits with respect to other candidates. It is suggested this section includes text on different assays used by different groups and the limitations with making comparisons.\n\nThe manuscript uses log10 geometric neutralization titers. These are very difficult to interpret when a log scale is used. It is suggested arithmetic/real numbers are used with error bars and/or actual titers shown as, for example, Fig 2a has only lines. It is not possible to evaluate differences between animals.\n\nLines 146-160 describe the passive protection data where sera for immunized NHPs is administered to AG129 mice and then challenged with wild-type ZIKV. The authors claim the results are impressive, but the titer of neutralizing antibodies administered was over 1 in 1000 yet only 7/9 mice were protected. I would argue this result is not as good as what other groups have reported previously with lower doses of neutralizing antibodies. This raises the question is the mCherry neutralization assay used by the authors too sensitive. Have the authors compared their neutralization assay vs a classical plaque reduction neutralization assay? Also, since the data reported is 7/9 mice, has the experiment been repeated?\n\nThe investigation of anti-NS1 antibodies is very interesting but appears to be only binding antibodies. Were any functions of these antibodies, such as complement, investigated?\n\nThe systems vaccinology is interesting, but would not one expect there to be extensive similarly with 17D vaccine as this is the backbone of the Zika vaccine. Would not a focus on interpreting differences between the Zika vaccine and 17D be important in terms of interpretation. The animal numbers are small. Are there sufficient numbers for statistical analysis?\n\nLines 83-84 and 437-438: The authors need to expand their text on clinical trials to say that none have undergone phase III clinical trials as the low incidence of Zika disease means vaccine efficacy trials cannot be undertaken.\n\nLine 187: the early production of neutralizing antibodies is interesting. Is this IgM as with 17D vaccine or another isotype?\n\nLine 232: Is the limit of detection 10e4 copies of vRNA?\n\nLines 235-236: What does “very limited, if any, increase” mean? Either there is an increase or there is not.\n\nLine 356: Why “remarkably”? Was the NHP study repeated to confirm the data?", "reviewer_2": "1. Please provide the reason for only including a subset of YF-ZIK vaccinated macaques (n=3) in the ICS assay. Why weren’t all six vaccinated animals included?\n\n2. Along these lines, I am not sure the T cell responses can be described as robust as indicated in the Discussion. None of the T cell responses are significantly different from the sham controls (although this may be a result of low animal numbers). Moreover, the CD8+ T cell responses are not consistent for the three animals tested, and one animal appears to be a non-responder.\n\n3. Why is the data for Figure 3 not discussed until late in the manuscript? It may be beneficial to the reader to move the figure so that it follows Figure 6 and matches the text.\n\n4. In Figure 6B, please explain the rationale for comparing D30 YF-ZIK to D2 YF17D. Do you consider the priming YF17D vaccination to be functionally equivalent to the boosting YF-ZIK vaccination? Is this due to species differences or artifacts of the respective vaccines?\n\n5. In Figure 3E, IFN signaling increases at D2 following prime and boosting vaccinations, yet TLR signaling decreases after the priming vaccination. Is the IFN increase due to RIG-I or some other cytosolic RNA sensor?\n\n6. In line 422, I believe a word is missing after serially. Perhaps collected?", "reviewer_3": "In this manuscript, the authors extend their investigation to test the chimeric vaccine YF-ZIKV in macaques. While the clinical evidence supports that the yellow fever YF17D is safe and efficacious, it remains unclear whether YF-ZIKV behaves in the same way.\n\nBased on their data, I am not convinced that the YF-ZIKV stimulates a polyfunctional response, especially the cell-mediated responses. Also, based on current literature, most of the T cell epitopes are present in the NS genes, which means that it is very unlikely the YF-ZIKV will stimulate T cell responses as the NS genes belong to YF. Because of this, the data does not support their claims.\n\nIn addition, there are several other concerns that should be addressed:\n\n1. The authors did not show the viremia of YF-ZIKV in the animals. This is important because YF17D is known to induce detectable viremia, and it is this viremia that drives vaccine immunogenicity. Moreover, knowing the peak viremia will help in interpreting the transcriptomics data better.\n\n2. Figure 2D. The authors claim that they detected T cell responses. However, the data clearly shows that the T cell responses to ZIKV E peptides are not significantly increased. Thus, the data indicated that the mechanism of protection is primarily antibodies and not T cells. If this is indeed the case, then the authors should not claim that the vaccine can induce polyfunctional responses.\n\n3. Figure 3B and 3D: It is unclear how the heatmaps are generated. Based on the volcano plot, it is clear that the host responses are resolved at day 7. Yet, the colours indicated that the pathways are in red. There are also no mention of the methods employed to generate the heatmaps.\n\n4. Figure 3E: What is the pathway score based on? Is this based on a list of genes involved in the pathways or only DEGs?\n\n5. Figure 3: There is a lot of mention about DEGs, but the authors did not indicate the fold-change and p-value thresholds to indicate DEGs.\n\n6. Figure 3: Various critical genes involved in IFN/TLR/B cell related genes were mentioned but the authors did not plot the kinetics of these key genes to show their dynamics and extent of change.\n\n7. Figure 6: Public data used are either RNAseq or scRNAseq data. However, the nCounter was used for the transcriptomic analysis. How did the authors process the data so that nCounter and RNAseq data can be compared? Were the correlations based on only the DEGs?\n\n8. Since the primary mechanism for efficacy is likely neutralising antibodies, then the authors need to acknowledge the limitations, and highlight the potential risk of antibody-dependent enhancement."}, "author_response": "Reviewer #1 (Remarks to the Author):\nThis manuscript reports continued studies by this group on their candidate Zika\nvaccine based on a chimera with a yellow fever (YF) 17D vaccine backbone with YF\nsequences swapped for Zika sequences (YF-ZIK). The mouse data has been\npublished and looks promising, and in this manuscript they continue their studies by\ninvestigating the vaccine in non-human primates (NHPs) with incorporation of system\nvaccinology.\n1. The manuscript is interesting but views everything very positively with the vaccine\ncandidate while this reviewer believes that, while the results are very interesting,\nthe conclusions and interpretation need to be toned down.\nR1/ We appreciate this overall supportive assessment of our study and agree that\nsome conclusions drawn have been too strong. In the revised version, interpretations\nand wording have been toned down accordingly, as will become obvious from our\npoint-by-point responses.\nFor convenience of assessment, we add a marked-up version of our updated\nmanuscript with highlights in two font colors. Rearrangements comprising entire\nsections of old text (i.e., without major edits) are highlighted in LIGHT BLUE.\nAdditions, corrections and major changes are highlighted in RED.\n2. The introduction is weak on previous work on correlates of protection. Some is\nmentioned in the results and discussion, but a summary of the state-of-the-art of\nprotection and neutralizing antibody titres in mouse and NHP models for Zika\nvaccine candidates would help readers understand where the data included in this\npaper sits with respect to other candidates. It is suggested this section includes\ntext on different assays used by different groups and the limitations with making\ncomparisons.\nR2/ As suggested, the widely established concept of seroprotection has now been\nadded to the Introduction, citing seminal ZIKV studies in mice and NHP (new lines 95-\n115), yet also emphasizing that cell mediated immunity (CMI) to ZIKV is not irrelevant\nyet simply highly understudied in NHP (lines 116-123). This (carefully critical) review\nof published evidence for the – as we still think – often overemphasized role of NAbs\nfor protective immunity is complemented by a more in-depth comparison of our data\nwith those described in these seminal studies in the Discussion (lines 476-486).\nImportantly, despite high NAb serum titres may be sufficient, growing evidence,\nincluding from controlled human challenge trials (Kalimuddin et al. Nat Microbiol 2025\ndispensable mechanism for protection. Besides humoral responses, active\nimmunization induces innate and CMI responses. Hence, NAb titres should be\nconsidered rather as diagnostic biomarker for immunization (i.e., formal Correlate of\nProtection, CoP) rather than the mechanism of protection [See also discussion in\nBarrett JID 2020 PMID: 31545366; added as new REF# 8].\nFor discussion of the AG129 model for passive serum transfer studies, please see our\nresponse R4/ to Comment 4.\n3. The manuscript uses log10 geometric neutralization titres. These are very difficult\nto interpret when a log scale is used. It is suggested arithmetic/real numbers are\nused with error bars and/or actual titres shown as, for example, Fig 2a has only\nlines. It is not possible to evaluate differences between animals.\nR3/ In contrast to laboratory mice, NHPs are outbred and exhibit significantly higher\nvariability. We therefore decided to treat each animal individually, showing their\nseroconversion kinetics and NAb trajectories as individual lines. Log10 transformed\nneutralization titres (Supplementary Table 2) were used to visualize the relatively high\nor very high values observed in our study, especially in comparison to other reports\n(see also our R4b/ to Comment 4). Nevertheless, all original values are presented in\nthe now provided Source Data file.\n4. Lines 146-160 describe the passive protection data where sera for immunized\nNHPs is administered to AG129 mice and then challenged with wild-type ZIKV. The\nauthors claim the results are impressive, but the titre of neutralizing antibodies\nadministered was over 1 in 1000 yet only 7/9 mice were protected. I would argue\nthis result is not as good as what other groups have reported previously with lower\ndoses of neutralizing antibodies. This raises the question is the mCherry\nneutralization assay used by the authors too sensitive. Have the authors compared\ntheir neutralization assay vs a classical plaque reduction neutralization assay?\nAlso, since the data reported is 7/9 mice, has the experiment been repeated?\nR4a/ Regarding choice of AG129 mouse model: Different mouse models have been\nused for ZIKV serum transfer experiments, including BALB/c and AG129 mice. Earlier\nseminal studies focused on the use of immunocompetent BALB/c mice: Abbink et al.\n(PMID: 27492477; REF#9) transferred purified IgG from immunized monkeys into\nBALB/c mice and challenged them with the very low dose of 102 PFU ZIKV. Under\nthese conditions, 50% of mice were protected against RNAemia by a serum NAb titre\nof 1:100 or higher. Similar values have been reported by Larocca et al. (PMID:\n27355570; REF#13) using purified IgG from immunized BALB/c mice.\na mouse-adapted ZIKV strain to induce disease (PMID: 28148798), while clinical\nisolates of ZIKV do not cause any disease in this semi-permissive wild-type mouse\nmodel. In contrast, IFN-α/β and γ receptor-deficient (AG129) mice are highly sensitive\nto lethal infection. See also Supplementary Fig. 3d, e, where we confirm this\nextraordinary sensitivity with virtually any dose of ZIKV (down to one infectious unit, 1\nPFU) uniformly causing lethal infection.\nTo increase assay stringency for our serum adoptive transfer experiment, we hence\nchose AG129 mice, alike Maciejewski et al. 2020 (PMID: 32522807; REF# 12 cited as\none of the seminal studies). Notably, our non-purified NHP serum pool conferred\nsurvival in 7/9 AG129 mice (78% protection) and was, with this regard, more potent\nthan purified IgG enriched for highly functional NAb that provided only a 50% survival\nbenefit in the study by Maciejewski et al. 2020. Small additions have been added\nthroughout the main text (lines 95-115) to emphasize these peculiarities of the AG129\nmouse model, plus an extra paragraph in the new Limitations section (lines 558-588).\nR4b/ Regarding assay proficiency: For ZIKV NAbs quantification, we used an in-house\ndeveloped fluorescence-based seroneutralization assay (SNT ) that employs a\nFluo\nmCherry-tagged fully infectious ZIKV reporter virus; details are described in the M&M\n(lines 642-651). Our SNT approach for ZIKV is based on a methodology originally\nFluo\ndeveloped by us for the determination of YFV-specific NAbs. This methodology was\nintensively cross-validated regarding reliability and accuracy, benchmarking against (i)\nclassical PRNT assay results as well as (ii) data generated in two reference\nlaboratories (Rasulova et al. Microbiol Spectrum 2022 PMID: 35670599; # 76).\nFor further validation of the respective ZIKV NAb assay, we now compared our SNT\nassay with standard PRNT by using the same serum samples from YF-ZIK immunized\nmacaques in parallel in both assays (new Supplementary Fig. 3c).\nHereby, for both the SNT and PRNT\nFluo\nassays, each data point represents three\ntechnical replicates. Supplementary Fig. 3c\nshows a strong correlation between our\nSNT and PRNT (R=0.99, p-value=0.0079).\nFluo\nImportantly, our method of NAb quantification\ndoes not overestimate, but rather slightly\nunderestimates, NAb titres compared to\nPRNT titres as further discussed in the new\nLimitations section (lines 558-588).\nserum transfer experiments in AG129 mice. This has now been specified in the M&M\nsection in lines 697-699.\nIn our serum transfer experiment, we used pooled serum from YF-ZIK-immunized\nNHPs with a mean reciprocal neutralizing titre of 5492, (GMT of 3.7), which is\ncomparable to or not higher than titres used in similar studies. Additionally, we used a\nuniformly lethal challenge dose of ZIKV MR766 (i.e., 10 PFU possibly representing a\n10x LD ) which causes 100% mortality and severe neurotropic disease (including\n100\nhind limb paralysis, hunch posture, ruffled fur and weakness) in AG129 mice (see\nresponse R4a). Despite the stringency of this model, 7 out of 9 mice were fully\nprotected and survived such a harsh challenge without any signs of disease. By using\nthe stringent AG129 mouse model, our data suggest that both the quantity and quality\nof NAbs induced by YF-ZIK might alone be sufficient to protect against Zika virus\ninfection.\n5. The investigation of anti-NS1 antibodies is very interesting but appears to be only\nbinding antibodies. Were any functions of these antibodies, such as complement,\ninvestigated?\nR5/ NS1-specific antibody has been used as a sensitive\nbiomarker for early diagnosis of flavivirus infections. In our\ncorrelation analysis, the level of ZIKV NS1-specific antibody in\nthe serum also strongly correlates with the cumulative viral\nloads after ZIKV challenge (see inserted figure).\nHowever, NS1-specific antibodies have shown elusive roles in\nflaviviruses infection, from eliciting a protective immune\nresponse in infected hosts to being pathogenic in different\nstudies. The function of anti-NS1 antibodies indeed deserves\nmore investigation in the future. However, we trust exploring this is beyond the scope\nof the current study and manuscript (see Limitations, line 559-588).\n6. The systems vaccinology is interesting, but would not one expect there to be\nextensive similarly with 17D vaccine as this is the backbone of the Zika vaccine.\nWould not a focus on interpreting differences between the Zika vaccine and 17D\nbe important in terms of interpretation. The animal numbers are small. Are there\nsufficient numbers for statistical analysis?\nR6/ Based on our previous extensive studies on YF17D-vectored vaccine candidates,\nincluding those against ZIKV, SARS-CoV-2, Ebola and studies from other groups,\nthere are indeed strong similarities between YF17D-based vaccines and the YF17D\nvaccine (see our review by Sanchez-Felipe et al. EJI 2024; REF#37). It is generally\nassumed that YF17D derivatives inherit favourable features from the YF17D legacy\n4\nvaccine vector. Our first objective was to test this hypothesis. We are delighted to read\nthat for the reviewer we are “carrying coals to Newcastle”.\nThough seemingly trivial, to the best of our knowledge, this a priori assumption has\nnever been shown for any YF17D derivative in a step-up NHP model by systems\napproaches. While most published data have been generated in mice, our study might\nbe the first complete transcriptional analysis using a NHP model to understand the (i)\nearly (after prime, day 2 and 7), (ii) late (after boost, day 30 and 35) and (iii) post-\ninfection (day 57 and 60) immunity of such a representative YF17D-vectored vaccine.\nThese data could provide a more complete picture of how live-attenuated vaccines\nstimulate innate and adaptive immunity and shape the response to infection.\nIn our NHP study, we found that YF-ZIK induced, alike YF17D in humans, early and\nstrong antibody responses, (evidence for) a balanced T cell response, and activation\nof key immune pathways, such as IFNs, to mount prime immunity. Such a live-\nattenuated vaccine that recapitulates key features of YF17D will more likely confer\nlong-lasting immunity. In addition, our systems analysis indicates that some\ntranscriptional markers may serve as common predictors for both YF-ZIK and YF17D\nvaccine immunogenicity and efficacy, e.g. B-cell receptor TNFRSF17 as a predictor\nfor strong antibody responses. These transcriptional patterns identified in NHP might\ntranslate into relevant biomarkers and deserve further validation in future clinical trials.\nWe also agree with the reviewer that differences at the molecular level between the\ntwo vaccines (secondary objective) might be equally relevant as the thus identified\ncommonalities. We hence now also report some novel findings listed briefly below as\n(a)-(e). Limitations linked to these datasets are discussed in lines 562-569:\na) In general, YF-ZIK induced an earlier peak of DEG (at day 2) than reported for\nYF17D immunization in humans, which peaks around day 7 (PMID:19047440;\nPMID: 36316475); in lines 184-186.\nb) Besides obvious commonalities, the comparison of YF-ZIK and YF17D also\nunveiled unique DEG sets (Supplementary Table 3). At day 2, YF-ZIK exclusively\nupregulated 26 genes that were not regulated by YF17D at any time point. These\nincluded several IFN-stimulated genes such as SIGLEC1, IRF5, TMEM173 (the\ngene encoding IFN activator STING), HLA-DRA, and HLA-DQA1. Likewise,\nYF17D exclusively upregulated 45 genes, including several IFN-stimulated genes.\nOf interest, these genes were also upregulated by infection with unrelated viral\npathogens such as SARS-CoV-2 in rhesus macaques and humans, as well as in\nferrets, hamsters and mice. It is hence tempting to speculate whether the favorable\nsafety profile of YF-ZIK (Supplementary Fig. 1), observed before as reduced\nneurovirulence in suckling mice and survival of AG129 mice as compared to\nparental YF17D (REF #6, #18, #36), might be linked to the absence of any these\n5\ntranscripts. The 16 genes exclusively downregulated by YF17D were not\nsignificantly enriched in any particular pathway.\nFor four genes both vaccines showed an opposite behavior. SOCS1 (Suppressor\nOf Cytokine Signaling 1, an inhibitor of type I and II IFN signaling) and TICAM1\n(TIR Domain Containing Adaptor Molecule 1, a TLR signaling adapter protein;\nhighlighted in Fig. 4b), and anti-apoptotic BLC2L1 (Bcl-2-Like Protein 1) were\nupregulated by YF17D and downregulated by YF-ZIK, while PTPRC (encoding\npan-leukocyte marker CD45) was downregulated by YF17D and upregulated by\nYF-ZIK. An elevated CD45 and diminished BLC2L1 expression 2 days after YF-\nZIK vaccination are in agreement with a simultaneous increase in\nmonocytes/macrophages and a decrease in apoptosis (Fig. 2; Supplementary Fig.\n2), a feature not shared with YF17D (PMID: 19029902, PMID: 36316475); lines\n320-329.\nc) Several new transcriptional biomarkers defining infection outcome were identified;\nlines 550-556.\n7. Lines 83-84 and 437-438: The authors need to expand their text on clinical trials to\nsay that none have undergone phase III clinical trials as the low incidence of Zika\ndisease means vaccine efficacy trials cannot be undertaken.\nR7/ Added to Introduction as suggested (lines 84-91).\n8. Line 187: the early production of neutralizing antibodies is interesting. Is this IgM\nas with 17D vaccine or another isotype?\nR8/ Unfortunately, detection of ZIKV-specific IgM failed, partially due to a high assay\nbackground when using the available macaque-specific reagents (see Limitations,\nlines 562-566). In addition, rapid induction of high(er) affinity IgG may have\novershadowed low-affinity IgM. Data for IgG detection has now been added as new\nFig. 3b and lines 252-258.\nIgG titres were determined using pooled\nsera from YF-ZIK immunized macaques\nover time (each pool was tested in\nduplicate). Induction of both NAbs and IgG\nwas detected, as early as 7 days after YF-\nZIK immunization. Interestingly, NAbs\ntested in parallel showed very similar\nkinetics as IgG, suggestive for an early\nantibody class switch. This is further\nconfirmed by a change in IgG/NAbs ratios\ncomparing prime (70%) and boost immunization (98%). The relatively low IgG/NAbs\n6\nratio early during immunization can be interpreted as a higher prevalence of non-IgG\nNAbs, i.e. likely virus-neutralizing IgM. Accordingly, later during the course of\nimmunization NAb get dominated by IgG.\nFurthermore, our transcriptomic analyses underscore early class switching as soon as\nday 7. AICDA (Activation Induced Cytidine Deaminase), a gene specifically expressed\nand active in germinal center-like B cells, is involved in somatic hypermutation, gene\nconversion, and Ig class-switch recombination. We found that AICDA RNA levels were\nalmost undetectable before vaccination (as expected), peaked at d7 after vaccination\nand slowly declined thereafter, as shown in the new Fig. 3c.\n100\n)n\no\nis\ns\ne\nrp\nx\nDe 10\nC d\nIAe\nz\nila\nm\nro\nN\n(\n1\n0 2 7 28 30 35\nDays post vaccination\n9. Line 232: Is the limit of detection 10e4 copies of vRNA?\nR9/ No, LLOD is <102/mL (Fig. 5a). The value originally mentioned here (< 104/mL) is\nclearly above LLOD. We thought that it would be useful to mention that there was\ndetectable vRNA in at least one YF-ZIK-immunized macaque (R13010) at least at one\ntime point. This “break-through” viremia was thus (i) delayed, very short-lived and\nlower than the average viremia at any other time points (1-5 dpi; range 3.96 to 5.72\nlog Supplementary Fig. 5a in the sham group. To avoid any confusion, the mention\n10\nof the value has been removed. It can still be found in the Source Data table.\n10. Lines 235-236: What does “very limited, if any, increase” mean? Either there is an\nincrease or there is not.\nR10/ We removed ‘if any’.\n11. Line 356: Why “remarkably”? Was the NHP study repeated to confirm the data?\nR11/ We removed ‘remarkably’. Due to ethical considerations and unaffordable costs,\nthe NHP study cannot be repeated.\nSeminal ZIKV vaccine studies have used groups of n=4-5 inbred mice (PMID:\n27492477; PMID: 27355570) or NHPs (PMID: 32522807; PMID: 27492477) without\n7\nrepeats; in contrast to n=6 NHPs in our study. We leveraged on the to-be-expected\nvariability in NHPs regarding the different readouts for immunogenicity (innate,\nhumoral, CMI and transcriptional) to derive correlations beyond binary Y/N responses\n(Fig. 7 and Supplementary Fig. 7). Nevertheless, despite any possible variation,\nuniform global protection has been observed in YF-ZIK immunized animals (Fig. 5).\nNote: For convenience of assessment, we add a marked-up version of our updated\nmanuscript with highlights in two font colors. Rearrangements comprising entire\nsections of old text (i.e., without major edits) are highlighted in LIGHT BLUE.\nAdditions, corrections and major changes are highlighted in RED.\n1. Please provide the reason for only including a subset of YF-ZIK vaccinated\nmacaques (n=3) in the ICS assay. Why weren’t all six vaccinated animals included?\nR1/ Three PBMC samples were of very low quality due to issues during the freezing\nprocedure. These samples had low viable cell counts, did not meet quality criteria, and\nwere therefore excluded from the flow cytometry analysis. This shortcoming is now\nexplained in the M&M (lines 675-677) and new Limitations (lines 562-569) section.\n2. Along these lines, I am not sure the T cell responses can be described as robust\nas indicated in the Discussion. None of the T cell responses are significantly\ndifferent from the sham controls (although this may be a result of low animal\nnumbers). Moreover, the CD8+ T cell responses are not consistent for the three\nanimals tested, and one animal appears to be a non-responder.\nR2/ We agree with the reviewer that the limited number of animals in our experimental\ngroups (n=3; see R1) does not allow us to infer conclusions about the ‘robustness’ of\nthe cellular immune response. Furthermore, to compensate for poor preservation of\nAPCs in the cryopreserved PBMCs, stimulation of antigen-specific recall responses\nrequired massive co-stimulation (using a combination of IL-2, anti-CD28 and anti-\nCD49d for 72h; see M&M lines 654-659). This led to an overall high background in\nexperimental controls, both (i) PBMC from sham-vaccinated individuals and (ii) PBMC\nfrom both groups without peptide stimulation; further reducing the dynamic range and\nincreasing the experimental noise of the assay. As a consequence (small n; generally\nhigh background; reduced dynamic range), no formal statistical significance (using\ntwo-sided test) could be established as also indicated in Supplementary Fig. 7d. These\nshortcomings in CMI analysis are now further discussed in the new Limitations section\n(lines 562-569). The attribute ‘robust’ has been eliminated to tone our claims down\naccordingly.\nNonetheless, the reviewer may appreciate that ZIKV E-specific CD8+ T cells, as well\nas CD4+ T cells with both Th1 (IFN-γ) and Th2 (IL4) polarisations, can be clearly\ndetected. Lack of established benchmarks makes it difficult to assess the biological\nrelevance of our findings. Studies reporting ZIKV E-specific T cell responses in NHPs\nare scarce. Abbink et al. (Science 2016 PMID: 27492477; STM 2017 PMID: 29237759)\nreport T cell responses by IFN-γ ELISPOT in rhesus monkeys after a multi-dose\n9\nimmunization comparing a range of different ZIKV prM/E-containing vaccine\ncandidates. Generally, immunization led to the production of no more than 100 ZIKV\nE-specific SFU per 106 PBMCs (0.01%) as assessed by IFN-γ ELISpot. These seminal\nstudies are now explicitly mentioned in the extended Introduction (lines 116-123).\nPolyfunctionality (CD4+/CD8+; IL-2/IFN-γ/IL-4) has not been assessed in macaques\nbefore.\nBy contrast, we provide evidence for such a diversified CMI response, at least for a\nselection of YF-ZIK vaccinated macaques (see R1). In our case, ZIKV E-specific T\ncells, as assessed by ICS reach a 0.5-2 % range (max. 4%) in all tested subgroups\n(Supplementary Fig. 7d), which may suggest a stronger CMI response, though\nmethodologies are difficult to compare quantitatively.\nIn support of our ICS data, in our transcriptional analysis, we have observed a robust\nactivation of T cell-related immune genes and pathways, including:\n1) T cell activation (MR1, CD4, KLRB1, PSMB8, CD74, TAP2) (Fig. 2a, c).\n2) Leveraging publicly available single-cell RNAseq data (Tabula Sapiens), we\nfound that YF-ZIK and YF17D induced comparable gene expression across all\nmajor blood leukocyte populations (dendritic cells, macrophages, neutrophils;\nnaive and memory B cells, plasmablasts, plasma cells, and NK cells), including\nCD4+ T, and CD8+ T cells (Fig. 4a; Supplementary Fig. 4b).\n3) As shown in the new Fig. 7f, we found that ZIKV E-specific cellular responses\n(quantified by flow cytometry after stimulation with ZIKV E protein peptide pool)\nare significantly correlated to corresponding transcriptomic signatures: the level\nof ZIKV-stimulated CD4+IL4+ T-cells is significantly correlated with IL4 RNA\nlevel measured by nCounter (p=0.0067, R=0.89, Spearman), while ZIKV-\nstimulated CD4+ IL2+ T-cells were significantly correlated with FOXP3 RNA\n(p=0.0028, R=0.96), a proxy for Treg cell levels. Of note, RNA levels of IL13\nand IL4R were positively correlated with neutralizing antibody levels (p=0.0048\nand p=0.057, respectively) and negatively correlated with viral load (p=0.013\nand p=0.0095, respectively), underscoring a protective effect of YF-ZIKV-\ninduced Th2 signaling. From a mathematical point of view, we have more power\nfor these non-parametric (Spearman) correlations using a linear (rank-based)\nmodel with n=7, in contrast to the lack of power in a non-parametric Mann-\nWhitney analysis as shown in Supplementary Fig. 7d (which can only reach\nsignificance with n=12, i.e. with 6 animals in each experimental group).\ncell responses in NHPs and that T cell responses are significantly correlated to both\nantibody production (SNT90) and protection from ZIKV challenge (virus load at peak\nof viremia, VL d60).\n3. Why is the data for Figure 3 not discussed until late in the manuscript? It may be\nbeneficial to the reader to move the figure so that it follows Figure 6 and matches\nthe text.\nR3/ As suggested we reorganized the order in which data are presented. In the\nupdated version of the manuscript, data sets are lined up according to a less bumpy\nstoryline that now comprises three major parts: A-mounting of polyfunctional immunity\n(i-iii), B-infection and protection (iv-vi), and C-prediction of biomarkers/correlation\nanalysis; each aspect organized in series of main Figures and accompanying\nSupplementary Figures as follows:\n(i) new Fig. 2 (old Fig. 3) shows the transcriptional profile after YF-ZIK prime\nand boost; discussed in lines 496-506.\n(ii) new Fig. 3 (with parts from old Fig. 2 and new data) focuses on the kinetics,\nquantification and quality of the humoral immune response following\nvaccination.\n(iii) new Fig. 4 (formerly part old Fig. 6) compares the pattern and\ncompartmentalization of the transcriptional profiles seen for YF-ZIK in NHP\nand parental YF17D in humans\n(iv) new Fig. 5 (old Fig. 4) presents the virological and serological analysis of\nZIKV challenge in vaccinated NHP in comparison to sham controls\n11\n(v) new Fig. 6 (with parts from old Fig. 5) presents the transcriptional response\nto ZIKV challenge infection in vaccinated NHP in comparison to sham\ncontrols\n(vi) new Fig. 7 and new Supplementary Fig. 7 (with parts from old Fig. 5 and\nnew analyses) links DEG networks and the expression of particular marker\ngenes to explain and predict vaccine immunogenicity and efficacy.\nWe agree that such an order might be a more logical way to present our study.\n4. In Figure 6B, please explain the rationale for comparing D30 YF-ZIK to D2 YF17D.\nDo you consider the priming YF17D vaccination to be functionally equivalent to\nthe boosting YF-ZIK vaccination? Is this due to species differences or artifacts of\nthe respective vaccines?\nR4/ We would like to point out that this is probably a misunderstanding. Since in\noriginal Fig. 6b (now Fig. 4b) all comparisons were made regarding YF-ZIK D2: values\nfor YF-ZIK D2 on the X-axis, compared to those for YF17D at D3 and D7 (for YF17D\nno public D2 data are available) on the Y-axis. In the same graph we also plotted YF-\nZIK D2 vs YF-ZIK D30, which confirms that the overall transcriptional response to YF-\nZIK is similar between the prime and boost (lines 303-305). Thus, we do not make any\nclaims that the prime only YF17D vaccination might be functionally equivalent to the\nprime/boost YF-ZIK vaccination, especially because our 2-dose study design does not\nallow us to separate prime/boost response and protective efficacy upon ZIKV\nchallenge of our vaccinated animals. To clarify this, a respective disclaimer was added\n(lines 472-473).\nHowever, we have exhaustively tested all relevant YF-ZIK time points to all publicly\navailable time points for YF17D transcriptional responses (Supplementary Table 3),\nwhich shows similar fold-changes and significant overlaps at all time points except\nD14 for YF17D. As elaborated above in reply to Q6 from Reviewer 1, we have\ncompared both similarities (lines 283-307) and “exclusive” effects (lines 308-329) of\neach vaccine. Regarding possible differences arising from a comparison of data\ngenerated in humans versus NHP, we observed that YF17D exclusively up-regulated\n45 genes, including several IFN-stimulated genes. Of interest, these genes were\nsignificantly upregulated by infection with other unrelated viruses such as SARS-CoV-\n2 besides in macaques as well as in mice, hamsters, ferrets and humans (new\nSupplementary Table 3), thus arguing rather for vaccine differences rather than\nspecies differences.\n5. In Figure 3E, IFN signaling increases at D2 following prime and boosting\nvaccinations, yet TLR signaling decreases after the priming vaccination. Is the IFN\nincrease due to RIG-I or some other cytosolic RNA sensor?\nrespective signaling pathways. As indicated in Supplementary Table 1, 90 genes in\nour panel are associated to IFN signaling, and 69 genes with TLR signaling, with 23\ngenes overlapping between both pathways.\nAs shown in the Venn diagram (see below), 20 out of 55 genes (36%) upregulated at\nday 2 belong to the IFN signaling pathway (Supplementary Table S1), in contrast to\nonly one gene (ITGB2) in the TLR signaling pathway. In addition, 3 upregulated genes\n(IRF7, NFKBIA, APP) were shared between both pathways. These 20 IFN-related\ngenes include RIG-I (encoded by DDX58), as well as other key antiviral genes\n(EIF2AK2, STAT2, OAS3, OAS2, OAS, OASL, STAT1, MX1, MX2, IFIT3, IFIT2, JAK,\nJAK2, IFIH1, IFIT1, IRF5) and HLA class II encoding genes (HLA-DRA, HLA-DQA1,\nHLA-DRB1).\nAmong the 143 genes downregulated at day 2, 9 genes (6%) correspond exclusively\nto IFN signaling (IFNGR2, IFNAR1, B2M, TYK2, IFNA2, IFN-γ, IFNAR2, SOCS3,\nPTPN2) and 11 genes (8%) to TLR signaling (TLR4, TLR6, TLR9, TIRAP, MAPK,\nMAPK14, FOS, MAP2K4, MAPK8, JUN, BIRC2). Six downregulated genes (4%) were\nshared between both pathways (TRAF6, CHUK, FADD, MAPK3, CASP8, SOCS1).\nThus, our results are in agreement with RIG-I/DDX58 and MDA5/IFIH1 acting as major\ncytosolic RNA sensors in flavivirus infections. A brief mention of this observation is\nadded in lines 192-201.\nVenn diagram showing the overlap between\nup- and down-regulated genes by YF-ZIKV at\nday 2 with the IFN and TLR signaling\npathways as defined in nCounter analysis.\n6. In line 422, I believe a word is missing after serially. Perhaps collected?\nR6/ Yes, one word was missing, it should be serial samples.\nNote: For convenience of assessment, we add a marked-up version of our updated\nmanuscript with highlights in two font colors. Rearrangements comprising entire\nsections of old text (i.e., without major edits) are highlighted in LIGHT BLUE.\nAdditions, corrections and major changes are highlighted in RED.\nIn this manuscript, the authors extend their investigation to test the chimeric vaccine\nYF-ZIKV in macaques. While the clinical evidence supports that the yellow fever\nYF17D is safe and efficacious, it remains unclear whether YF-ZIKV behaves in the\nsame way. Based on their data, I am not convinced that the YF-ZIKV stimulates a\npolyfunctional response, especially the cell-mediated responses. Also, based on\ncurrent literature, most of the T cell epitopes are present in the NS genes, which means\nthat it is very unlikely the YF-ZIKV will stimulate T cell responses as the NS genes\nbelong to YF. Because of this, the data does not support their claim.\nIndeed, most T cell epitopes are present in NS genes as part of the YF17D vector;\nconsistent with their cytoplasmic exposure, access to the proteasome and processing\npathway towards MHC-I presentation (prM/E and NS1 reside in the ER lumen).\nHowever, growing evidence suggests that NS T cell responses also contribute to\nprotection. Recently, Kalimuddin et al. (PMID: 39794472) conducted a clinical trial\nusing YF17D and chimeric YF17D-Japanese encephalitis (IMOJEV) vaccine to\ndemonstrate that vaccine-induced T cell responses might be sufficient to contain a\nsystemic viral challenge, independent of neutralizing antibodies.\nRegarding the YF-ZIK induced polyfunctional cellular responses, please see our\nresponses to Reviewer 2, Q2/R2. Particularly, the transcriptional signatures observed\nboth (i) after prime and boost vaccination as well as (ii) after challenge infection clearly\nindicate a broad activation of T cell-related immune genes and pathways in a ZIKV\nspecific manner. This expression analysis is experimentally supported by ICS\nanalyses of peptide stimulated PBMC; revealing the induction of ZIKV prM/E specific\nCD4+ and CD8+ T cells expressing both Th1 (IFN-γ) and Th2 (IL-4) cytokines as\nunequivocal evidence for polyfunctionality.\nIn addition, there are several other concerns that should be addressed:\n1. The authors did not show the viremia of YF-ZIKV in the animals. This is important\nbecause YF17D is known to induce detectable viremia, and it is this viremia that\ndrives vaccine immunogenicity. Moreover, knowing the peak viremia will help in\ninterpreting the transcriptomics data better.\nR1/ Seroconversion rates for original YF17D have been reported to be almost 100%,\nirrespective of detectable virus RNA. By contrast, viremia has not consistently been\n14\ndetected between studies, with individual detection rates may have been maximally\n30-70%; often for a singular timepoint and at barely detectable viral RNA levels.\nHence, subclinical replication (rather than detectable viremia) is sufficient for\nimmunogenicity, and lack of detectable viremia does not defy vaccine efficacy. By\ncontrast, elevated levels and persistence of viremia (resulting from prolonged viral\nreplication and systemic spread) is frequently seen as a safety concern (PMID:\n25446819).\nNevertheless, the magnitude and duration of viremia following YF17D immunization\nhave been discussed as determinants of immunogenicity (PMID: 25713354). Where\ndetectable, viremia from YF17D vaccination has been shown to be transient and low\nin both humans (detectable between day 3 and 9 post-immunization, <104 genome\nRNA copies/mL peripheral blood/plasma) (PMID: 25713354, PMID: 39794472) and\nmacaques (detectable between day 3 and 7 post-immunization, <104 genome RNA\ncopies/mL peripheral blood/plasma) (PMID: 38567951, PMID: 20607226). Thereby\nviremia (i.e. viral replication) is believed to drive type I interferon (IFN) responses as\nwell as other innate immune responses that shape the development of adaptive\nimmunity.\nSeeing the high degree of attenuation for the YF17D-derived YF-ZIK candidate, both\nin vitro and in vivo (Kum et al. NPJ Vaccines. 2018 PMID: 30564463 REF# 22; Göbel\net al. Vaccines 2024 PMID: 39066393 REF#36, we did not expect strong viremia from\nYF-ZIK immunization, alike reported for other YF17D-derivatives IMOJEV and\nDENGVAXIA also induce (if at all) lower viremia in NHPs and humans (PMID:\n25446819).\nTo detect any viremia from YF-ZIK immunization, we followed a combined nested RT-\nPCR/qPCR approach adapted from a sensitive in-house methodology (validated by Li\net al. Mol Ther Methods Clin Dev. 2022 PMID: 35313504): We first ran RT-PCR with\nprimers targeting the YFV NS3 gene, followed by qPCR with primers and probes\ntargeting the same region. RNA extract from a serial dilution of YF-ZIK virus stock was\nused as a positive control and to determine the sensitivity (or LLOD) of the assay.\nThe results show that our assay is sensitive enough to detect vRNA until a 107 times\ndilution of the virus stock with a LLOD of 2.52 log10 RNA copies/mL (about 1 copy per\nqPCR reaction; new Supplementary Fig. 3a, b).\nWhen analyzing the NHP sera we only detect very transient and minimal viremia in\nmacaques after prime, either at day 2 or 7 (new Supplementary Fig. 3b).\nanimal was also among those 3/6 individuals (R13010, R13151 and R10128) that were\n“early responders” and seroconverted to high NAb titres (log10 GMT of 2.5) already 7\ndays post immunization. On the other hand, we did observe a pronounced activation\nof IFN signaling after both prime and boost (Fig. 2b, d), which is driven by (transient)\nvirus replication. Likewise, the absence of viremia further supports the excellent safety\nprofile of our ZIKV vaccine candidate.\nThe detection (new Supplementary Fig. 3a, b) and likely consequences of viremia are\nnow mentioned in lines 225-233 and in the new Limitations section.\n2. Figure 2D. The authors claim that they detected T cell responses. However, the\ndata clearly shows that the T cell responses to ZIKV E peptides are not\nsignificantly increased. Thus, the data indicated that the mechanism of protection\nis primarily antibodies and not T cells. If this is indeed the case, then the authors\nshould not claim that the vaccine can induce polyfunctional responses.\nR2/ Regarding questions on YF-ZIK induced polyfunctional cellular responses, please\nsee our responses to Reviewer 2, Q2/R2.\nWe agree that due to high background in non-stimulated PBMCs and the limited\nnumber of specimens of sufficiently high quality amenable for ICS analysis, formal\nsignificance is not reached. These data have now been moved to new Supplementary\nFig. 7d and more critically discussed in the new Limitations section (lines 558-588).\nImportantly, though more indirect, our in silico whole blood transcriptomics analysis of\nvaccinated animals after ZIKV challenge shows a clear pattern that can be\nunequivocal linked to a virus-specific T-cell recall response. The thus observed\ntranscriptional T-cell signature suggests CMI to be polyfunctional (CD4/CD8) and\nbalanced (Th1/Th2/Treg). Such CMI recall is fully in line (and in our view difficult to\nexplain without) priming/induction of decent T-cell memory during original vaccination\nand prior to first virus encounter (new Fig. 7f).\n16\n3. Figure 3B and 3D: It is unclear how the heatmaps are generated. Based on the\nvolcano plot, it is clear that the host responses are resolved at day 7. Yet, the\ncolours indicated that the pathways are in red. There are also no mention of the\nmethods employed to generate the heatmaps.\nR3/ We agree with the reviewer that, at first glance, the visual impressions of the\nVolcano plots and pathway heatmaps seem distinct. However, it should be noted that\neach pathway consist of multiple genes (as detailed below in reply to Q4, specified in\nSupplementary Table 1), with some genes being upregulated and others\ndownregulated within the same pathway; as a result, a net zero change at the pathway\nlevelis possible. In addition, heatmaps only provide an overall visual overview of all\ndata and are not a statistical tool: as indicated in Fig. 2, the colours are individually\nscaled per row (row minimum= white, row maximum= red), with each row representing\nan individual animal. Thus, if IFN signaling is strongly upregulated accross a large\nnumber of genes (e.g., at d2 after prime and boost), all other pathways will appear\nvisually downregulateddue to the effect of the scaling. Thus, colours in the heatmap\ngive a visual impression for each animal at each time point across all pathways,\nindependent of statistical significance across experimental groups.\n4. Figure 3E: What is the pathway score based on? Is this based on a list of genes\ninvolved in the pathways or only DEGs?\nR4/ Pathways are predefined by the nCounter design; the specific lists of genes in\neach pathway are detailed in Supplementary Table 1; referred to in M&M section\n(line 740).\n5. Figure 3: There is a lot of mention about DEGs, but the authors did not indicate\nthe fold-change and p-value thresholds to indicate DEGs.\nR5/ Due to the low number of animals in each experimental group, uncorrected p-\nvalues (p<0.05) were used for systems vaccinology analyses, to enhance discovery\nand to avoid false negatives (as discussed below). However, FDR-corrected p-values\nare shown by dotted lines in Volcano plots where appropriate (Figs. 2c and 6b). No\nfold-changes were pre-specified since we observed in this study as well as several\nprevious papers from our group (see below) that bona fide IFN-stimulated genes often\ndisplay a fold-change lower than 2 (which is “traditionally” used in transcriptomics,\nalthough this number is not evidence-based), even with significant p- and q-values.\n7. Figure 3: Various critical genes involved in IFN/TLR/B cell related genes were\nmentioned but the authors did not plot the kinetics of these key genes to show\ntheir dynamics and extent of change.\nchallenge outcome are shown in the figure below. Myeloid-expressed gene\nTNFSF13B/BAFF and its B-cell-specific receptor TNFRSF17 (plotted in Fig. 7c) follow\nsimilar kinetics as IFI44 (the strongest IFN-stimulated gene/ISG in the YF17D dataset)\nand CD74 (expressed in B-cells and other APC), respectively; increasing at d2 and\nfurther up at d30; or increasing at d2 and remaining on a higher plateau from d2 to\nd35. In contrast, CD207 and C8B increased at d2, peaked at d7 and then steadily\ndeclined until d35. CD8+ T-cell and NK cell-expressed CCL5 showed an exceptional\nkinetic profile: decreasing at d2 but remaining highly expressed from d7 to d35.\n100000\n10000\nn\no\nis 1000\ns\ne\nrp\nx\ne\nd\n100\ne\nz\nila\nm 10\nro\nN\n1\n0.1\nd0d2d7 d28 d30 d35d0d2d7 d28 d30 d35d0d2d7 d28 d30 d35d0d2d7 d28 d30 d35d0d2d7 d28 d30 d35\nCD207 C8B CCL5 IFI44 CD74\nFigure legend: Kinetics of major genes predictive of viral challenge outcome. Gene\nexpression quantified by digital transcriptomics over the course of vaccination (d0-\nd35), with prime at d0 and boost at d28.\n8. Figure 6: Public data used are either RNAseq or scRNAseq data. However, the\nnCounter was used for the transcriptomic analysis. How did the authors process\nthe data so that nCounter and RNAseq data can be compared?\nR7a/ Due to its digital nature (hybridization and direct counting without enzymatic\namplification), nCounter data (normalized for internal positive and negative controls,\nas well as housekeeping genes) represent an unbiased and robust quantification of\nthe transcriptome. Upon its inception (Geiss et al. Nature Biotechnology, 2008 PMID:\n18278033), comparison of the NanoString nCounter gene expression system with\nmicroarrays and TaqMan PCR demonstrated that the nCounter system is more\nsensitive than microarrays and similar in sensitivity to real-time PCR. During the last\ndecade, several papers from our group (Feys et al. Lancet Microbe. 2024 PMID:\n38280387; de Moraes et al. Front Microbiol. 2023 PMID: 37601355; Cuypers et al.\nNature Aging. 2023 PMID: 37217661; Assone et al. J Neuroinflammation. 2022 PMID:\n36482436; Feys et al. Lancet Respir Med. 2022 PMID: 36029799; Dirkx et al. Commun\nBiol. 2022 PMID: 35752645; Lloréns-Rico Nature Commun. 2021 PMID: 34716338;\nnCounter digital transcriptomics to both older technologies (microarrays and qPCR)\nand newer technologies such as RNAseq, single-cell RNAseq and spatial\ntranscriptomics. Moreover, two recent manuscripts have successfully used nCounter\nanalysis in macaque models of Ebola virus, in comparison to spatial transcriptomics\n(Wanninger et al. 2024 PMID: 39635525) and RNAseq, the latter being slightly less\nrobust then nCounter in this model (Speranza et al. 2017 PMID: 29116224).\nWere the correlations based on only the DEGs?\nAs shown in Fig. 7, a strong positive correlation (R>0.70, p<10-15) was found across\nall genes that classified as DEG in YF17D (d3 and d7) and their corresponding RNA\nlevels quantified by nCounter after YF-ZIKV “prime” vaccination (d2), i.e. without\nselection of being DEG upon YF-ZIKV vaccination, for the following reasons:\na. A low number of animals in our study limited the statistical power and hence a\nstringent selection of FDR-corrected p-values would result in a large number of\nfalse negatives.\nb. As outlined above, we and others have extensively cross-compared nCounter\ndata with other technologies using transcriptome-wide fold-changes as\nquantitative readout, which resulted in highly significant cross-platform and\ncross-species correlations (e.g. Dirkx et al. Commun Biol 2022 PMID:\n35752645).\n8. Since the primary mechanism for efficacy is likely neutralising antibodies, then the\nauthors need to acknowledge the limitations, and highlight the potential risk of\nantibody-dependent enhancement.\nThe risk of ADE and some considerations on how that risk could be mitigated during\nimplementation have been added to the discussion (lines 581-588).\n19"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have provided a very comprehensive rebuttal to the reviewers’ comments and this greatly improves the manuscript. There is one aspect that I do not agree with the authors, and I would suggest should be included in the limitations, namely the extensive comparisons with a “human dose of YF/17D vaccine”. The dose of the candidate Zika vaccine was 10e5 pfu whereas 17D vaccine is usually administered at a lower dose to vaccinees for all but one producer. The authors are encouraged to read the 17D vaccine dose-sparing papers for information that include the quantity of 17D virus in a dose and parameters of induction of antibodies post-vaccination, which do show some differences depending on the dose of vaccine and particular producer. Indeed, the Brazilian 17DD substrain has been compared at many different doses in volunteers. Thus, I do not believe the authors can directly compare the human 17D dose to the dose of candidate Zika vaccine as different quantities of virus are being compared. The authors may also wish to look how much virus is in a dose of Imojev.\n\nThe English language needs to be carefully reviewed.\n\nThe term “flavivirus” should be updated to “orthoflavivirus”\n\nLine 86: I would suggest that Zika virus is not found “globally”, and this statement should be tempered.", "reviewer_3": "The revised version of the manuscript provides a more balanced view of the results and findings. I have no further comments as my previous comments were adequately addressed.\n\nEditorial note: due to the absence of reviewer 2 , this reviewer was additionally asked to comment on the authors response to the prior comments of reviewer 2: Yes, I think the reviewer's comments were adequately addressed."}, "author_response": "RESPONSE TO REVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors have provided a very comprehensive rebuttal to the reviewers’ comments\nand this greatly improves the manuscript. There is one aspect that I do not agree with the\nauthors, and I would suggest should be included in the limitations, namely the extensive\ncomparisons with a “human dose of YF/17D vaccine”. The dose of the candidate Zika\nvaccine was 10e5 pfu whereas 17D vaccine is usually administered at a lower dose to\nvaccinees for all but one producer. The authors are encouraged to read the 17D vaccine\ndose-sparing papers for information that include the quantity of 17D virus in a dose and\nparameters of induction of antibodies post-vaccination, which do show some differences\ndepending on the dose of vaccine and particular producer. Indeed, the Brazilian 17DD\nsubstrain has been compared at many different doses in volunteers. Thus, I do not believe\nthe authors can directly compare the human 17D dose to the dose of candidate Zika\nvaccine as different quantities of virus are being compared. The authors may also wish to\nlook how much virus is in a dose of Imojev.\nR/ We fully agree that comparing YF17D in humans and our YF-ZIK vaccine candidate in\nNHP under different immunization dosages comes with limitations. We gratefully\nacknowledge the suggestion of the Reviewer hinting towards other (pre)clinical studies\nwhich are taken up and now discussed in lines 580-592 of an extended ‘Limitations’\nsection.\nAlso, there might be a misunderstanding, as we did not intend to compare a “human dose\nof YF17D vaccine” with our “macaque dose of YF-ZIK immunization”. In our manuscript,\nour comparison is made based on:\n1) Similar immunization schedule (prime and boost), as explained in lines 223-225:\n“The immunogenicity was evaluated following a two-dose regimen (Fig. 1b)\nanalogous to the schedule recommended for the chimeric YF17D-based JEV\nvaccine (Imojev®).”\n2) Our comparison mostly focuses on vaccine-induced transcriptional responses after\nprime and boost immunization (Fig. 2 and 4), instead of antibody and cellular\nresponses, as latter are more likely to be impacted by viral dosages and likewise\nreplication characteristics (see e.g. Monath et al. PNAS 2006).\n3) The public transcriptional data of human YF17D immunization were likely pooled\nfrom different YF17D strains and dosages (see e.g. Gaucher et al. JEM 2008 using\nboth YF-VAX and Stamaril), under the condition that YF17D immunization was\nconfirmed successful in recipients. We explained in lines 284-286: “To validate and\ntranslate our NHP data to the human model, we compared the gene expression\nprofile triggered by YF-ZIK vaccination of macaques with what can be expected for\nYF17D vaccination following meta-analysis of available human data.”\nWe trust that by clearly stating these caveats, our comparative meta-analyses will be\ninterpreted with appropriate caution.\nThe English language needs to be carefully reviewed.\nR/ Language reviewed and refined.\nThe term “flavivirus” should be updated to “orthoflavivirus”\nR/ Updated throughout the manuscript to ‘Orthoflavivirus’ (with capital and in italics) when\nreferring to the original Latin genus name, and ‘orthoflaviviruses’ when referring to several\nmembers from that genus.\nLine 86: I would suggest that Zika virus is not found “globally”, and this statement should\nbe tempered.\nR/ ‘Globally’ has been changed for ‘across multiple regions’ (line 78-79).\nReviewer #3 (Remarks to the Author):\nThe revised version of the manuscript provides a more balanced view of the results and\nfindings. I have no further comments as my previous comments were adequately\naddressed.\nR/ Thank you. Much appreciated.\nEditorial note: due to the absence of reviewer 2, this reviewer was additionally asked to\ncomment on the authors response to the prior comments of reviewer 2: Yes, I think the\nreviewer's comments were adequately addressed.\nR/ Many thanks for stepping in and the final assessment."}]

Decision letter