Helminth exposure and immune response to the two-dose heterologous Ad26.ZEBOV, MVA-BN-Filo Ebola vaccine regimen.

BACKGROUND: The exposure to parasites may influence the immune response to vaccines in endemic African countries. In this study, we aimed to assess the association between helminth exposure to the most prevalent parasitic infections, schistosomiasis, soil transmitted helminths infection and filariasis, and the Ebola virus glycoprotein (EBOV GP) antibody concentration in response to vaccination with the Ad26.ZEBOV, MVA-BN-Filo vaccine regimen in African and European participants using samples obtained from three international clinical trials. METHODS/PRINCIPAL FINDINGS: We conducted a study in a subset of participants in the EBL2001, EBL2002 and EBL3001 clinical trials that evaluated the Ad26.ZEBOV, MVA-BN-Filo vaccine regimen against EVD in children, adolescents and adults from the United Kingdom, France, Burkina Faso, Cote d'Ivoire, Kenya, Uganda and Sierra Leone. Immune markers of helminth exposure at baseline were evaluated by ELISA with three commercial kits which detect IgG antibodies against schistosome, filarial and Strongyloides antigens. Luminex technology was used to measure inflammatory and activation markers, and Th1/Th2/Th17 cytokines at baseline. The association between binding IgG antibodies specific to EBOV GP (measured on day 21 post-dose 2 and on Day 365 after the first dose respectively), and helminth exposure at baseline was evaluated using a multivariable linear regression model adjusted for age and study group. Seventy-eight (21.3%) of the 367 participants included in the study had at least one helminth positive ELISA test at baseline, with differences of prevalence between studies and an increased prevalence with age. The most frequently detected antibodies were those to Schistosoma mansoni (10.9%), followed by Acanthocheilonema viteae (9%) and then Strongyloides ratti (7.9%). Among the 41 immunological analytes tested, five were significantly (p < .003) lower in participants with at least one positive helminth ELISA test result: CCL2/MCP1, FGFbasic, IL-7, IL-13 and CCL11/Eotaxin compared to participants with negative helminth ELISA tests. No significant association was found with EBOV-GP specific antibody concentration at 21 days post-dose 2, or at 365 days post-dose 1, adjusted for age group, study, and the presence of any helminth antibodies at baseline. CONCLUSIONS/SIGNIFICANCE: No clear association was found between immune markers of helminth exposure as measured by ELISA and post-vaccination response to the Ebola Ad26.ZEBOV/ MVA-BN-Filo vaccine regimen. TRIAL REGISTRATION: NCT02416453, NCT02564523, NCT02509494. ClinicalTrials.gov.

A vaccine targeting antigen-presenting cells through CD40 induces protective immunity against Nipah disease.

Nipah virus (NiV) has been recently ranked by the World Health Organization as being among the top eight emerging pathogens likely to cause major epidemics, whereas no therapeutics or vaccines have yet been approved. We report a method to deliver immunogenic epitopes from NiV through the targeting of the CD40 receptor of antigen-presenting cells by fusing a selected humanized anti-CD40 monoclonal antibody to the Nipah glycoprotein with conserved NiV fusion and nucleocapsid peptides. In the African green monkey model, CD40.NiV induces specific immunoglobulin A (IgA) and IgG as well as cross-neutralizing responses against circulating NiV strains and Hendra virus and T cell responses. Challenge experiments using a NiV-B strain demonstrate the high protective efficacy of the vaccine, with all vaccinated animals surviving and showing no significant clinical signs or virus replication, suggesting that the CD40.NiV vaccine conferred sterilizing immunity. Overall, results obtained with the CD40.NiV vaccine are highly promising in terms of the breadth and efficacy against NiV.

Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report.

In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness.

Estimating the population effectiveness of interventions against COVID-19 in France: A modelling study.

BACKGROUND: Non-pharmaceutical interventions (NPIs) and vaccines have been widely used to manage the COVID-19 pandemic. However, uncertainty persists regarding the effectiveness of these interventions due to data quality issues, methodological challenges, and differing contextual factors. Accurate estimation of their effects is crucial for future epidemic preparedness. METHODS: To address this, we developed a population-based mechanistic model that includes the impact of NPIs and vaccines on SARS-CoV-2 transmission and hospitalization rates. Our statistical approach estimated all parameters in one step, accurately propagating uncertainty. We fitted the model to comprehensive epidemiological data in France from March 2020 to October 2021. With the same model, we simulated scenarios of vaccine rollout. RESULTS: The first lockdown was the most effective, reducing transmission by 84 % (95 % confidence interval (CI) 83-85). Subsequent lockdowns had diminished effectiveness (reduction of 74 % (69-77) and 11 % (9-18), respectively). A 6 pm curfew was more effective than one at 8 pm (68 % (66-69) vs. 48 % (45-49) reduction), while school closures reduced transmission by 15 % (12-18). In a scenario without vaccines before November 2021, we predicted 159,000 or 168 % (95 % prediction interval (PI) 70-315) more deaths and 1,488,000 or 300 % (133-492) more hospitalizations. If a vaccine had been available after 100 days, over 71,000 deaths (16,507-204,249) and 384,000 (88,579-1,020,386) hospitalizations could have been averted. CONCLUSION: Our results highlight the substantial impact of NPIs, including lockdowns and curfews, in controlling the COVID-19 pandemic. We also demonstrate the value of the 100 days objective of the Coalition for Epidemic Preparedness Innovations (CEPI) initiative for vaccine availability.

Prediction of long-term humoral response induced by the two-dose heterologous Ad26.ZEBOV, MVA-BN-Filo vaccine against Ebola.

The persistence of the long-term immune response induced by the heterologous Ad26.ZEBOV, MVA-BN-Filo two-dose vaccination regimen against Ebola has been investigated in several clinical trials. Longitudinal data on IgG-binding antibody concentrations were analyzed from 487 participants enrolled in six Phase I and Phase II clinical trials conducted by the EBOVAC1 and EBOVAC2 consortia. A model based on ordinary differential equations describing the dynamics of antibodies and short- and long-lived antibody-secreting cells (ASCs) was used to model the humoral response from 7 days after the second vaccination to a follow-up period of 2 years. Using a population-based approach, we first assessed the robustness of the model, which was originally estimated based on Phase I data, against all data. Then we assessed the longevity of the humoral response and identified factors that influence these dynamics. We estimated a half-life of the long-lived ASC of at least 15 years and found an influence of geographic region, sex, and age on the humoral response dynamics, with longer antibody persistence in Europeans and women and higher production of antibodies in younger participants.

Safety and immunogenicity of the two-dose heterologous Ad26.ZEBOV and MVA-BN-Filo Ebola vaccine regimen in infants: a phase 2, randomised, double-blind, active-controlled trial in Guinea and Sierra Leone.

BACKGROUND: This study assessed the safety and immunogenicity of the Ad26.ZEBOV and MVA-BN-Filo Ebola virus (EBOV) vaccine regimen in infants aged 4-11 months in Guinea and Sierra Leone. METHODS: In this phase 2, randomised, double-blind, active-controlled trial, we randomly assigned healthy infants (1:1 in a sentinel cohort, 5:2 for the remaining infants via an interactive web response system) to receive Ad26.ZEBOV followed by MVA-BN-Filo (Ebola vaccine group) or two doses of meningococcal quadrivalent conjugate vaccine (control group) administered 56 days apart. Infants were recruited at two sites in west Africa: Conakry, Guinea, and Kambia, Sierra Leone. All infants received the meningococcal vaccine 8 months after being randomly assigned. The primary objective was safety. The secondary objective was immunogenicity, measured as EBOV glycoprotein-binding antibody concentration 21 days post-dose 2, using the Filovirus Animal Non-Clinical Group ELISA. This study is registered with ClinicalTrials.gov (NCT03929757) and the Pan African Clinical Trials Registry (PACTR201905827924069). FINDINGS: From Aug 20 to Nov 29, 2019, 142 infants were screened and 108 were randomly assigned (Ebola vaccine n=75; control n=33). The most common solicited local adverse event was injection-site pain (Ebola vaccine 15 [20%] of 75; control four [12%] of 33). The most common solicited systemic adverse events with the Ebola vaccine were irritability (26 [35%] of 75), decreased appetite (18 [24%] of 75), pyrexia (16 [21%] of 75), and decreased activity (15 [20%] of 75). In the control group, ten (30%) of 33 had irritability, seven (21%) of 33 had decreased appetite, three (9%) of 33 had pyrexia, and five (15%) of 33 had decreased activity. The frequency of unsolicited adverse events was 83% (62 of 75 infants) in the Ebola vaccine group and 85% (28 of 33 infants) in the control group. No serious adverse events were vaccine-related. In the Ebola vaccine group, EBOV glycoprotein-binding antibody geometric mean concentrations (GMCs) at 21 days post-dose 2 were 27 700 ELISA units (EU)/mL (95% CI 20 477-37 470) in infants aged 4-8 months and 20 481 EU/mL (15 325-27 372) in infants aged 9-11 months. The responder rate was 100% (74 of 74 responded). In the control group, GMCs for both age groups were less than the lower limit of quantification and the responder rate was 3% (one of 33 responded). INTERPRETATION: Ad26.ZEBOV and MVA-BN-Filo was well tolerated and induced strong humoral responses in infants younger than 1 year. There were no safety concerns related to vaccination. FUNDING: Janssen Vaccines & Prevention and Innovative Medicines Initiative 2 Joint Undertaking. TRANSLATION: For the French translation of the abstract see Supplementary Materials section.

Identification of early gene expression profiles associated with long-lasting antibody responses to the Ebola vaccine Ad26.ZEBOV/MVA-BN-Filo.

Ebola virus disease is a severe hemorrhagic fever with a high fatality rate. We investigate transcriptome profiles at 3 h, 1 day, and 7 days after vaccination with Ad26.ZEBOV and MVA-BN-Filo. 3 h after Ad26.ZEBOV injection, we observe an increase in genes related to antigen presentation, sensing, and T and B cell receptors. The highest response occurs 1 day after Ad26.ZEBOV injection, with an increase of the gene expression of interferon-induced antiviral molecules, monocyte activation, and sensing receptors. This response is regulated by the HESX1, ATF3, ANKRD22, and ETV7 transcription factors. A plasma cell signature is observed on day 7 post-Ad26.ZEBOV vaccination, with an increase of CD138, MZB1, CD38, CD79A, and immunoglobulin genes. We have identified early expressed genes correlated with the magnitude of the antibody response 21 days after the MVA-BN-Filo and 364 days after Ad26.ZEBOV vaccinations. Our results provide early gene signatures that correlate with vaccine-induced Ebola virus glycoprotein-specific antibodies.

Modeling the kinetics of the neutralizing antibody response against SARS-CoV-2 variants after several administrations of Bnt162b2.

Because SARS-CoV-2 constantly mutates to escape from the immune response, there is a reduction of neutralizing capacity of antibodies initially targeting the historical strain against emerging Variants of Concern (VoC)s. That is why the measure of the protection conferred by vaccination cannot solely rely on the antibody levels, but also requires to measure their neutralization capacity. Here we used a mathematical model to follow the humoral response in 26 individuals that received up to three vaccination doses of Bnt162b2 vaccine, and for whom both anti-S IgG and neutralization capacity was measured longitudinally against all main VoCs. Our model could identify two independent mechanisms that led to a marked increase in measured humoral response over the successive vaccination doses. In addition to the already known increase in IgG levels after each dose, we identified that the neutralization capacity was significantly increased after the third vaccine administration against all VoCs, despite large inter-individual variability. Consequently, the model projects that the mean duration of detectable neutralizing capacity against non-Omicron VoC is between 348 days (Beta variant, 95% Prediction Intervals PI [307; 389]) and 587 days (Alpha variant, 95% PI [537; 636]). Despite the low neutralization levels after three doses, the mean duration of detectable neutralizing capacity against Omicron variants varies between 173 days (BA.5 variant, 95% PI [142; 200]) and 256 days (BA.1 variant, 95% PI [227; 286]). Our model shows the benefit of incorporating the neutralization capacity in the follow-up of patients to better inform on their level of protection against the different SARS-CoV-2 variants. Trial registration: This clinical trial is registered with ClinicalTrials.gov, Trial IDs NCT04750720 and NCT05315583.

Development and external validation of a prediction model for the transition from mild to moderate or severe form of COVID-19.

OBJECTIVES: COVID-19 pandemic seems to be under control. However, despite the vaccines, 5 to 10% of the patients with mild disease develop moderate to critical forms with potential lethal evolution. In addition to assess lung infection spread, chest CT helps to detect complications. Developing a prediction model to identify at-risk patients of worsening from mild COVID-19 combining simple clinical and biological parameters with qualitative or quantitative data using CT would be relevant to organizing optimal patient management. METHODS: Four French hospitals were used for model training and internal validation. External validation was conducted in two independent hospitals. We used easy-to-obtain clinical (age, gender, smoking, symptoms' onset, cardiovascular comorbidities, diabetes, chronic respiratory diseases, immunosuppression) and biological parameters (lymphocytes, CRP) with qualitative or quantitative data (including radiomics) from the initial CT in mild COVID-19 patients. RESULTS: Qualitative CT scan with clinical and biological parameters can predict which patients with an initial mild presentation would develop a moderate to critical form of COVID-19, with a c-index of 0.70 (95% CI 0.63; 0.77). CT scan quantification improved the performance of the prediction up to 0.73 (95% CI 0.67; 0.79) and radiomics up to 0.77 (95% CI 0.71; 0.83). Results were similar in both validation cohorts, considering CT scans with or without injection. CONCLUSION: Adding CT scan quantification or radiomics to simple clinical and biological parameters can better predict which patients with an initial mild COVID-19 would worsen than qualitative analyses alone. This tool could help to the fair use of healthcare resources and to screen patients for potential new drugs to prevent a pejorative evolution of COVID-19. CLINICAL TRIAL REGISTRATION: NCT04481620. CLINICAL RELEVANCE STATEMENT: CT scan quantification or radiomics analysis is superior to qualitative analysis, when used with simple clinical and biological parameters, to determine which patients with an initial mild presentation of COVID-19 would worsen to a moderate to critical form. KEY POINTS: • Qualitative CT scan analyses with simple clinical and biological parameters can predict which patients with an initial mild COVID-19 and respiratory symptoms would worsen with a c-index of 0.70. • Adding CT scan quantification improves the performance of the clinical prediction model to an AUC of 0.73. • Radiomics analyses slightly improve the performance of the model to a c-index of 0.77.

Assessment of circulating blood lymphocytes in adult patients on rituximab to treat immune thrombocytopenia: Circulating number of NK cells is associated with the response at 6 months.

Immune thrombocytopenia (ITP) is defined by a low platelet count that can trigger potentially life-threatening haemorrhages. Three-quarters of adult patients exhibit persistent or chronic disease and require second-line treatments. Among these, rituximab, an anti-CD20 antibody, has yielded valuable results, with global responses in 60% of patients at 6 months and complete responses in 30% at 5 years. Factors predictive of response to ITP therapy would help physicians choose optimal treatments. We retrospectively analysed clinical courses, biological markers and blood lymphocyte subset numbers of 72 patients on rituximab to treat persistent/chronic ITP followed-up in our department between 2007 and 2021, divided into three groups according to the platelet count at 6 months: complete, partial or no response. Among all studied parameters, a low number of CD3- CD16+ CD56+ circulating NK cells was associated with the complete response to rituximab. We also found that, after rituximab therapy, complete responders exhibited increased NK and decreased activated CD8+ T cell percentages. These results emphasize that the role played by NK cells in ITP remains incompletely known but that factors predictive of response to rituximab can be easily derived using blood lymphocyte subset data.

Immune response of a two-dose heterologous Ebola vaccine regimen: summary of three African clinical trials using a single validated Filovirus Animal Nonclinical Group enzyme-linked immunosorbent assay in a single accredited laboratory.

BACKGROUND: This analysis evaluated the immune response to the two-dose, heterologous Ad26.ZEBOV, MVA-BN-Filo Ebola virus vaccine regimen, administered 56-days apart, from multiple African sites based on results from one analytic laboratory. METHODS: Immunogenicity across three trials (EBL2002, EBL2004/PREVAC, EBL3001) conducted in East and West Africa is summarised. Vaccine-induced Ebola glycoprotein-binding antibody concentrations were analysed by Q2 Solutions laboratory at baseline, 21 days (EBL2002 and EBL3001) or 28 days (EBL2004) post-dose 2 (regimen completion), and 12 months post-dose 1 using the validated Filovirus Animal Nonclinical Group Ebola glycoprotein enzyme-linked immunosorbent assay (ELISA). Responders were defined as those with a >2.5-fold increase from baseline or the lower limit of quantification (LLOQ) if 

On the Choice of Longitudinal Models for the Analysis of Antitumor Efficacy in Mouse Clinical Trials of Patient-derived Xenograft Models.

In translational oncology research, the patient-derived xenograft (PDX) model and its use in mouse clinical trials (MCT) are increasingly described. This involves transplanting a human tumor into a mouse and studying its evolution during follow-up or until death. A MCT contains several PDXs in which several mice are randomized to different treatment arms. Our aim was to compare longitudinal modeling of tumor growth using mixed and joint models. Mixed and joint models were compared in a real MCT (N = 225 mice) to estimate the effect of a chemotherapy and a simulation study. Mixed models assume that death is predictable by observed tumor volumes (data missing at random, MAR) while the joint models assume that death depends on nonobserved tumor volumes (data missing not at random, MNAR). In the real dataset, of 103 deaths, 97 mice were sacrificed when reaching a predetermined tumor size (MAR data). Joint and mixed model estimates of tumor growth slopes differed significantly [0.24 (0.13;0.36)log(mm3)/week for mixed model vs. -0.02 [-0.16;0.11] for joint model]. By disrupting the MAR process of mice deaths (inducing MNAR process), the estimate of the joint model was 0.24 [0.04;0.45], close to mixed model estimation for the original dataset. The simulation results confirmed the bias in the slope estimate from the joint model. Using a MCT example, we show that joint model can provide biased estimates under MAR mechanisms of dropout. We thus recommend to carefully choose the statistical model according to nature of mice deaths. Significance: This work brings new arguments to a controversy on the correct choice of statistical modeling methods for the analysis of MCTs. We conclude that mixed models are more robust than joint models.

Impact of non-pharmaceutical interventions, weather, vaccination, and variants on COVID-19 transmission across departments in France.

BACKGROUND: Multiple factors shape the temporal dynamics of the COVID-19 pandemic. Quantifying their relative contributions is key to guide future control strategies. Our objective was to disentangle the individual effects of non-pharmaceutical interventions (NPIs), weather, vaccination, and variants of concern (VOC) on local SARS-CoV-2 transmission. METHODS: We developed a log-linear model for the weekly reproduction number (R) of hospital admissions in 92 French metropolitan departments. We leveraged (i) the homogeneity in data collection and NPI definitions across departments, (ii) the spatial heterogeneity in the timing of NPIs, and (iii) an extensive observation period (14 months) covering different weather conditions, VOC proportions, and vaccine coverage levels. FINDINGS: Three lockdowns reduced R by 72.7% (95% CI 71.3-74.1), 70.4% (69.2-71.6) and 60.7% (56.4-64.5), respectively. Curfews implemented at 6/7 pm and 8/9 pm reduced R by 34.3% (27.9-40.2) and 18.9% (12.04-25.3), respectively. School closures reduced R by only 4.9% (2.0-7.8). We estimated that vaccination of the entire population would have reduced R by 71.7% (56.4-81.6), whereas the emergence of VOC (mainly Alpha during the study period) increased transmission by 44.6% (36.1-53.6) compared with the historical variant. Winter weather conditions (lower temperature and absolute humidity) increased R by 42.2% (37.3-47.3) compared to summer weather conditions. Additionally, we explored counterfactual scenarios (absence of VOC or vaccination) to assess their impact on hospital admissions. INTERPRETATION: Our study demonstrates the strong effectiveness of NPIs and vaccination and quantifies the role of weather while adjusting for other confounders. It highlights the importance of retrospective evaluation of interventions to inform future decision-making.

Predicting COVID-19 positivity and hospitalization with multi-scale graph neural networks.

The pandemic of COVID-19 is undoubtedly one of the biggest challenges for modern healthcare. In order to analyze the spatio-temporal aspects of the spread of COVID-19, technology has helped us to track, identify and store information regarding positivity and hospitalization, across different levels of municipal entities. In this work, we present a method for predicting the number of positive and hospitalized cases via a novel multi-scale graph neural network, integrating information from fine-scale geographical zones of a few thousand inhabitants. By leveraging population mobility data and other features, the model utilizes message passing to model interaction between areas. Our proposed model manages to outperform baselines and deep learning models, presenting low errors in both prediction tasks. We specifically point out the importance of our contribution in predicting hospitalization since hospitals became critical infrastructure during the pandemic. To the best of our knowledge, this is the first work to exploit high-resolution spatio-temporal data in a multi-scale manner, incorporating additional knowledge, such as vaccination rates and population mobility data. We believe that our method may improve future estimations of positivity and hospitalization, which is crucial for healthcare planning.

Neutrophil Activation and Immune Thrombosis Profiles Persist in Convalescent COVID-19.

PURPOSE: Following a severe COVID-19 infection, a proportion of individuals develop prolonged symptoms. We investigated the immunological dysfunction that underlies the persistence of symptoms months after the resolution of acute COVID-19. METHODS: We analyzed cytokines, cell phenotypes, SARS-CoV-2 spike-specific and neutralizing antibodies, and whole blood gene expression profiles in convalescent severe COVID-19 patients 1, 3, and 6 months following hospital discharge. RESULTS: We observed persistent abnormalities until month 6 marked by (i) high serum levels of monocyte/macrophage and endothelial activation markers, chemotaxis, and hematopoietic cytokines; (ii) a high frequency of central memory CD4+ and effector CD8+ T cells; (iii) a decrease in anti-SARS-CoV-2 spike and neutralizing antibodies; and (iv) an upregulation of genes related to platelet, neutrophil activation, erythrocytes, myeloid cell differentiation, and RUNX1 signaling. We identified a "core gene signature" associated with a history of thrombotic events, with upregulation of a set of genes involved in neutrophil activation, platelet, hematopoiesis, and blood coagulation. CONCLUSION: The lack of restoration of gene expression to a normal profile after up to 6 months of follow-up, even in asymptomatic patients who experienced severe COVID-19, signals the need to carefully extend their clinical follow-up and propose preventive measures.

Using a population-based Kalman estimator to model the COVID-19 epidemic in France: estimating associations between disease transmission and non-pharmaceutical interventions.

In response to the COVID-19 pandemic caused by SARS-CoV-2, governments have adopted a wide range of non-pharmaceutical interventions (NPI). These include stringent measures such as strict lockdowns, closing schools, bars and restaurants, curfews, and barrier gestures such as mask-wearing and social distancing. Deciphering the effectiveness of each NPI is critical to responding to future waves and outbreaks. To this end, we first develop a dynamic model of the French COVID-19 epidemics over a one-year period. We rely on a global extended Susceptible-Infectious-Recovered (SIR) mechanistic model of infection that includes a dynamic transmission rate over time. Multilevel data across French regions are integrated using random effects on the parameters of the mechanistic model, boosting statistical power by multiplying integrated observation series. We estimate the parameters using a new population-based statistical approach based on a Kalman filter, used for the first time in analysing real-world data. We then fit the estimated time-varying transmission rate using a regression model that depends on the NPIs while accounting for vaccination coverage, the occurrence of variants of concern (VoC), and seasonal weather conditions. We show that all NPIs considered have an independent significant association with transmission rates. In addition, we show a strong association between weather conditions that reduces transmission in summer, and we also estimate increased transmissibility of VoC.

Erratum: CD177, a specific marker of neutrophil activation, is associated with coronavirus disease 2019 severity and death.

[This corrects the article DOI: 10.1016/j.isci.2021.102711.].