The mRNA vaccines for SARS-CoV-2 have demonstrated efficacy and immunogenicity in the real-world setting. However, most of the research on vaccine immunogenicity has been centered on characterizing the antibody response, with limited exploration into the persistence of spike-specific memory B cells. Here we monitored the durability of the memory B cell response up to 9 months post-vaccination, and characterized the trajectory of spike-specific B cell phenotypes in healthy individuals who received two doses of the BNT162b2 vaccine. To profile the spike-specific B cell response, we applied the tSNE and Cytotree automated approaches. Spike-specific IgA⁺ and IgG⁺ plasmablasts and IgA⁺ activated cells were observed 7 days after the second dose and disappeared 3 months later, while subsets of spike-specific IgG⁺ resting memory B cells became predominant 9 months after vaccination, and they were capable of differentiating into spike-specific IgG secreting cells when restimulated in vitro. Other subsets of spike-specific B cells, such as IgM⁺ or unswitched IgM⁺IgD⁺ or IgG⁺ double negative/atypical cells, were also elicited by the BNT162b2 vaccine and persisted up to month 9. The analysis of circulating spike-specific IgG, IgA, and IgM was in line with the plasmablasts observed. The longitudinal analysis of the antigen-specific B cell response elicited by mRNA-based vaccines provides valuable insights into our understanding of the immunogenicity of this novel vaccine platform destined for future widespread use, and it can help in guiding future decisions and vaccination schedules.

Background The spread of SARS-CoV-2 Omicron variant and its subvariants, highly transmissible but responsible of milder disease, has increased unreported infection cases. Identifying unaware infected individuals is crucial for estimating the true prevalence of infection and evaluating the breadth of hybrid immunity. In this study, this challenge was addressed by applying several Machine Learning approaches. Methods A group of 116 participants, vaccinated against SARS-CoV-2, was enrolled in the IMMUNO_COV study at Siena University Hospital, Italy. Blood samples were collected before and six months after third vaccine dose. Machine Learning analysis, involving dimensionality reduction techniques, unsupervised clustering methods and classification models, were applied to serological data including antibody responses specific for wild type SARS-CoV-2 strain as well as Delta, Omicron BA.1 and Omicron BA.2 variants. Spike- and nucleocapsid-specific B cells were also assessed in each participant. Results Using dimensionality reduction and unsupervised clustering, participants are grouped into high- and low-responders, with infected participants mainly distributed within the high-responders. Implementation of a consensus-based approach, including k-NN, RF, and SVM models, identifies 14 participants unaware of previous infection. Their immunological profile, characterized by strong spike- and nucleocapsid-specific humoral and B cell responses, significantly differs from that of non-infected participants. Conclusions Machine Learning approaches are applied to identify participants unaware of prior infection and to dissect their hybrid immunity profiles. Based on serological data, this cost-effective method can be a valuable tool for estimating the true prevalence of infection, improving comprehension of immune responses elicited by vaccination alone or combined with infection, and tailoring public health interventions.

The mRNA vaccines for SARS-CoV-2 have demonstrated efficacy and immunogenicity in the re-al-world setting. However, most of the research on vaccine immunogenicity has been centered on characterizing the antibody response, with limited exploration into the persistence of spike-specific memory B cell response. Here we monitored the durability of the memory B cell response and characterize the trajectory of spike-specific B cell phenotypes in healthy individuals who have received two doses of the BNT162b2 vaccine up to 9 months post-vaccination. To profile the spike-specific B cell response we applied the tSNE and Cytotree automated approaches. Our data showed the induction of spike-specific IgA+ and IgG+ plasmablasts and IgA+ activated cells 7 days after the second dose which disappeared 3 months later, while subsets of spike-specific IgG+ resting memory B cells became predominant 9 months after vaccination, and they were capable to differentiate into spike-specific IgG secreting cells when in vitro restimulated. Other subsets of spike-specific B cells, such as IgM+ or unswitched IgM+IgD+ or IgG+ double negative/atypical cells, were also elicited by the BNT162b2 vaccine and persisted up to month 9. The analysis of circulating spike-specific IgG, IgA and IgM was in line with the plasmablasts observed. The longitudinal analysis of the antigen-specific B cell response elicited by mRNA-based vaccines provides valuable insights into our understanding of the immunogenicity of this novel vaccine platform destined to a future widespread use, and it is crucial in guiding future decisions and vaccination schedules.

Long-term persistence of spike-specific memory B cells (MBC) upon mRNA SARS-CoV-2 vaccination remains poorly explored in people living with HIV (PLWH). We

Summary: Malaria caused by Plasmodium falciparum ( Pf ) compromise innate immunity, yet the underlying mechanisms remain elusive. The immune dysregulation caused by the parasite may lead to bacterial superinfections and increase mortality. We reveal that Pf exploits extracellular vesicles (EVs) secreted by infected red blood cells (iRBC-EVs) to deliver host-derived miR451a to human neutrophils, impairing their antimicrobial defences. Neutrophil phagocytosis of iRBC-EVs suppresses reactive oxygen species (ROS) production and compromised microbicidal activity against Salmonella typhimurium . Microfluidic assays show that miR451a transfer significantly disrupts neutrophil chemotaxis and swarming upon microbial challenge. Transcriptomic profiling indicates that EVs and miR451a reprogram neutrophil gene expression, notably upregulating ferroptosis-related genes, suggesting a role in further impairing immune responses. We have uncovered a novel mechanism of iRBC-EVs-induced neutrophil immune suppression and provide insights into increased susceptibility to bacterial superinfections in malaria. These findings have implications for therapeutic strategies aimed at mitigating bacterial superinfections and sepsis in malaria-endemic regions.

Murine infection models are essential for investigating host–pathogen interactions, disease progression, vaccine-induced immune responses and protection. Early identification of disease trajectories is important for both animal monitoring and high-quality biological sample collection. Although clinical and microbiological readouts such as body-weight changes, clinical scores, and circulating bacterial burden are routinely used to monitor disease progression, individual parameters often fail to capture short-term disease trajectory when analysed separately. Here, four longitudinal clinical and minimally invasive readouts (cumulative weight loss, day-to-day weight change, clinical score and blood bacterial load) were integrated using Machine Learning approaches to assess short-term disease trajectory and the occurrence of a fatal outcome within the subsequent three days. Two independently conducted Salmonella enterica serovar Typhimurium murine infection studies were analysed, with the first one used for model development and internal testing and the second one for independent external validation. Four classifiers (Random Forest, Support Vector Machine, XGBoost, and penalized logistic regression) alongside three ensemble strategies (majority voting, weighted voting, and stacked meta-learner) were evaluated in this experimental context. In the internal test set, all models achieved high performance, while in the independent external validation dataset, the stacked ensemble outperformed all other models achieving an accuracy of 0.96 and a specificity of 0.75. Although evaluated in a relatively small cohort using a single mouse genetic background and one Salmonella enterica serovar Typhimurium strain, these results indicate that the proposed Machine Learning framework, based on readily obtainable and minimally invasive longitudinal readouts, can serve as a valuable tool to anticipate short-term disease trajectory, support disease monitoring and improve experimental planning.