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SignificanceWidely available accurate estimates of malaria exposure are essential for targeting and evaluation of public health interventions. Antibody responses to the malaria parasite can provide information on past exposure, but to date, most such measurements have been based on responses to a small number of parasite proteins chosen by convenience rather than utility and have not provided quantitative information on an individual’s exposure. Our results generated by screening hundreds of responses in children with known exposure histories indicate that responses to a few appropriately selected antigens can provide such information. This new approach can be transformed into high-throughput, low-cost, field-based assays useful for surveillance of malaria and has the potential to be translated into similar tools for other infectious diseases. Tools to reliably measure Plasmodium falciparum (Pf) exposure in individuals and communities are needed to guide and evaluate malaria control interventions. Serologic assays can potentially produce precise exposure estimates at low cost; however, current approaches based on responses to a few characterized antigens are not designed to estimate exposure in individuals. Pf-specific antibody responses differ by antigen, suggesting that selection of antigens with defined kinetic profiles will improve estimates of Pf exposure. To identify novel serologic biomarkers of malaria exposure, we evaluated responses to 856 Pf antigens by protein microarray in 186 Ugandan children, for whom detailed Pf exposure data were available. Using data-adaptive statistical methods, we identified combinations of antibody responses that maximized information on an individual’s recent exposure. Responses to three novel Pf antigens accurately classified whether an individual had been infected within the last 30, 90, or 365 d (cross-validated area under the curve = 0.86–0.93), whereas responses to six antigens accurately estimated an individual’s malaria incidence in the prior year. Cross-validated incidence predictions for individuals in different communities provided accurate stratification of exposure between populations and suggest that precise estimates of community exposure can be obtained from sampling a small subset of that community. In addition, serologic incidence predictions from cross-sectional samples characterized heterogeneity within a community similarly to 1 y of continuous passive surveillance. Development of simple ELISA-based assays derived from the successful selection strategy outlined here offers the potential to generate rich epidemiologic surveillance data that will be widely accessible to malaria control programs.

Schistosomiasis, caused mainly by S. mansoni , S. haematobium and S. japonicum, continues to be a serious tropical disease and public health problem resulting in an unacceptably high level of morbidity in countries where it is endemic. Praziquantel, the only drug currently available for treatment, is unable to kill developing schistosomes, it does not prevent re-infection and its continued extensive use may result in the future emergence of drug-resistant parasites. This scenario provides impetus for the development and deployment of anti-schistosome vaccines to be used as part of an integrated approach for the prevention, control and eventual elimination of schistosomiasis. This review considers the present status of candidate vaccines for schistosomiasis, and provides some insight on future vaccine discovery and design.

Cutaneous leishmaniasis (CL) is a skin disease caused by infection, for which no licensed human vaccine exists. Protective immunity is largely T cell-mediated and depends on antigen presentation by MHC molecules, yet the naturally presented epitopes during human disease remain poorly defined. To address this gap, we performed mass spectrometry-based immunopeptidomics on lesional biopsies from 27 Ethiopian CL patients spanning the full clinical spectrum. We newly identified 333 MHC-I and 247 MHC-II epitopes from 398 proteins, including 19 peptides and 51 antigens recurrently presented across patients of which several were also epitope-rich. Some peptides were also detected during early infection in a -infected THP-1 monocyte model, highlighting their relevance from disease onset. Notably, despite the broad predictive coverage of NetMHCpan and NetMHCIIpan, these tools missed 20-70% of the naturally presented epitopes while predicting millions of candidates, underscoring the limitations of prediction-only vaccine pipelines. This first comprehensive map of the immunopeptidome in human disease reveals conserved and prevalent antigens that can inform rational vaccine design and deepen our understanding of protective T cell responses in leishmaniasis.

Abs are central to malaria immunity, which is only acquired after years of exposure to Plasmodium falciparum (Pf). Despite the enormous worldwide burden of malaria, the targets of protective Abs and the basis of their inefficient acquisition are unknown. Addressing these knowledge gaps could accelerate malaria vaccine development. To this end, we developed a protein microarray containing ∼23% of the Pf 5,400-protein proteome and used this array to probe plasma from 220 individuals between the ages of 2–10 years and 18–25 years in Mali before and after the 6-month malaria season. Episodes of malaria were detected by passive surveillance over the 8-month study period. Ab reactivity to Pf proteins rose dramatically in children during the malaria season; however, most of this response appeared to be short-lived based on cross-sectional analysis before the malaria season, which revealed only modest incremental increases in Ab reactivity with age. Ab reactivities to 49 Pf proteins measured before the malaria season were significantly higher in 8–10-year-old children who were infected with Pf during the malaria season but did not experience malaria (n = 12) vs. those who experienced malaria (n = 29). This analysis also provided insight into patterns of Ab reactivity against Pf proteins based on the life cycle stage at which proteins are expressed, subcellular location, and other proteomic features. This approach, if validated in larger studies and in other epidemiological settings, could prove to be a useful strategy for better understanding fundamental properties of the human immune response to Pf and for identifying previously undescribed vaccine targets.

Despite the promising therapeutic effects of immune checkpoint blockade (ICB), most patients with solid tumors treated with anti-PD-1/PD-L1 monotherapy do not achieve objective responses, with most tumor regressions being partial rather than complete. It is hypothesized that the absence of pre-existing antitumor immunity and/or the presence of additional tumor immune suppressive factors at the tumor microenvironment are responsible for such therapeutic failures. It is therefore clear that in order to fully exploit the potential of PD-1 blockade therapy, antitumor immune response should be amplified, while tumor immune suppression should be further attenuated. Cancer vaccines may prime patients for treatments with ICB by inducing effective anti-tumor immunity, especially in patients lacking tumor-infiltrating T-cells. These \"non-inflamed\" non-permissive tumors that are resistant to ICB could be rendered sensitive and transformed into \"inflamed\" tumor by vaccination. In this article we describe a clinical study where we use pancreatic cancer as a model, and we hypothesize that effective vaccination in pancreatic cancer patients, along with interventions that can reprogram important immunosuppressive factors in the tumor microenvironment, can enhance tumor immune recognition, thus enhancing response to PD-1/PD-L1 blockade. We incorporate into the schedule of standard of care (SOC) chemotherapy adjuvant setting a vaccine platform comprised of autologous dendritic cells loaded with personalized neoantigen peptides (PEP-DC) identified through our own proteo-genomics antigen discovery pipeline. Furthermore, we add nivolumab, an antibody against PD-1, to boost and maintain the vaccine's effect. We also demonstrate the feasibility of identifying personalized neoantigens in three pancreatic ductal adenocarcinoma (PDAC) patients, and we describe their optimal incorporation into long peptides for manufacturing into vaccine products. We finally discuss the advantages as well as the scientific and logistic challenges of such an exploratory vaccine clinical trial, and we highlight its novelty.

Tuberculosis (TB), caused by (MTB), remains a significant global health concern. The existing vaccine, Bacillus Calmette-Guérin (BCG), provides inconsistent protection, highlighting the pressing need for a more effective vaccine. We aimed to identify novel antigens and assess their protective efficacy as TB vaccine candidates. Using immunopeptidomics, we identified 64 and 80 unique mycobacterial antigens derived from BCG and MTB, respectively. We prioritised antigens based on HLA allele coverage through an immunoinformatics approach. The candidates, , , and , delivered as DNA vaccines, were evaluated for efficacy in mice using the ex vivo Mycobacterial Growth Inhibition Assay (MGIA) and was identified as a promising candidate. In vivo murine challenge experiments confirmed the protective efficacy conferred by when formulated as recombinant protein with AS01™ or AddaS03™ adjuvants, compared to the naïve group. The immunogenic profiles of formulated in the two different adjuvants differed, with -AS01™ inducing antigen-specific IFN-γ, TNF-α, IL-2, IL-17, IgG1 and IgG2a-c, while -AddaS03™ induced TNF-α, IL-2, IL-17, IL-4, IgM, IgG1, IgG2b. Our findings highlight as a promising protective antigen for future TB vaccine development.

Identifying conserved, immunogenic proteins that confer protection against Streptococcus pneumoniae (pneumococcus) colonization could enable development of serotype-independent vaccines. In our controlled human infection model, no individual IgG or cytokine/chemokine response correlated significantly with protection against colonization with pneumococcus, suggesting that effective immunity reflects a coordinated, multi-antigen response. To capture these complex patterns, we trained independent Random Forest models on humoral and cellular datasets. The humoral model identified IgG responses to PdB, SP1069, and SP0899 as predictive of protection. The cellular model revealed that MCP-1 responses to SP1069 and SP0899, and IL-17A production in response to SP0648-3, were associated with protection. Elevated baseline IFN-γ, RANTES, and anti-protein IgG levels were linked to reduced colonization density. We highlight SP1069 and SP0899 as potential serotype-independent vaccine candidates and demonstrate the utility of machine learning to identify immune correlates of protection. [Display omitted] •Machine learning uncovers immune correlates of pneumococcal protection.•Antibodies to SP1069, PdB, and SP0899 predict protection from colonization.•MCP-1 responses to SP1069 and SP0899, and IL-17A production in response to SP0648-3, were associated with protection.•Elevated baseline IFN-γ, RANTES, and anti-protein IgG levels were linked to reduced colonization density.•Insights guide next-generation pneumococcal vaccine antigen selection.

Vaccination stands as one of the most transformative interventions in the history of human civilization. In medicine, vaccination stands as a cornerstone that has saved countless lives across generations. Nevertheless, conventional vaccine development remains encumbered by prolonged timelines, substantial financial investment, and high attrition rates particularly during late-stage clinical trials underscoring the urgent need for more efficient and systematic approaches. In recent years, artificial intelligence (AI) has emerged as a transformative force across the biomedical sciences, offering unprecedented computational capacity to process and interpret complex biological datasets. The convergence of AI with vaccinology represents a significant methodological advancement which has the potential to fundamentally redefine the vaccine development paradigm. AI integrates advances in machine learning, multi-omics data analysis, and high-performance computing to accelerate antigen discovery, epitope prediction, immunogen design, and clinical evaluation. This development represents a paradigm shift toward faster, more precise, and scalable strategies for vaccine development. This review critically examines the current landscape of AI applications in vaccine development, with particular emphasis on recent advancements, translational challenges, and the prospective role of AI in shaping the future of immunization science.

•Plasmodium genomic sequence can be mined to identify novel targets of antibody or T cell responses.•Several distinct platforms and approaches can be used.•Targets of high throughput screening can protect against parasite challenge. Until recently, malaria vaccine development efforts have focused almost exclusively on a handful of well characterized Plasmodium falciparum antigens. Despite dedicated work by many researchers on different continents spanning more than half a century, a successful malaria vaccine remains elusive. Sequencing of the P. falciparum genome has revealed more than five thousand genes, providing the foundation for systematic approaches to discover candidate vaccine antigens. We are taking advantage of this wealth of information to discover new antigens that may be more effective vaccine targets. Herein, we describe different approaches to large-scale screening of the P. falciparum genome to identify targets of either antibody responses or T cell responses using human specimens collected in Controlled Human Malaria Infections (CHMI) or under conditions of natural exposure in the field. These genome, proteome and transcriptome based approaches offer enormous potential for the development of an efficacious malaria vaccine.

Understanding the way in which the immune system responds to infection is central to the development of vaccines and many diagnostics. To provide insight into this area, we fabricated a protein microarray containing 1,205 Burkholderia pseudomallei proteins, probed it with 88 melioidosis patient sera, and identified 170 reactive antigens. This subset of antigens was printed on a smaller array and probed with a collection of 747 individual sera derived from 10 patient groups including melioidosis patients from Northeast Thailand and Singapore, patients with different infections, healthy individuals from the USA, and from endemic and nonendemic regions of Thailand. We identified 49 antigens that are significantly more reactive in melioidosis patients than healthy people and patients with other types of bacterial infections. We also identified 59 cross-reactive antigens that are equally reactive among all groups, including healthy controls from the USA. Using these results we were able to devise a test that can classify melioidosis positive and negative individuals with sensitivity and specificity of 95% and 83%, respectively, a significant improvement over currently available diagnostic assays. Half of the reactive antigens contained a predicted signal peptide sequence and were classified as outer membrane, surface structures or secreted molecules, and an additional 20% were associated with pathogenicity, adaptation or chaperones. These results show that microarrays allow a more comprehensive analysis of the immune response on an antigen-specific, patient-specific, and population-specific basis, can identify serodiagnostic antigens, and contribute to a more detailed understanding of immunogenicity to this pathogen.