Michael T. White

The bacterium Chlamydia trachomatis causes three distinct conditions – urogenital chlamydia, the eye disease trachoma, and lymphogranuloma venereum. Use of serological testing for exposure to C. trachomatis for evaluating ocular disease risk factors and population-based surveillance has expanded over the past decade. We developed chimeric monoclonal antibodies to the C. trachomatis antigen Pgp3 as standardized reagents for Pgp3 serological assays. Here we use two anti-Pgp3 monoclonals (1G1 and 1C3) to conduct an interlaboratory comparison of Pgp3 Luminex-based multiplex bead serological assays in 4 different laboratories (each with their own protocol) with 5 operators using 3 different instrument types. Antibodies spotted onto filter paper were stable for two weeks at room temperature. Accuracy and precision were evaluated at 3 concentrations for each monoclonal (high, medium, and low). Accuracy within 20% was achieved for 11/21 conditions for 1G1 and 18/21 conditions for 1C3; the high concentration resulted in the poorest accuracy. Precision was within 10% for all low and medium concentrations and was above 10% for several conditions at the high concentration. The fluorescence values differed between instruments but showed no difference when extrapolated to concentrations using a standard curve. These data suggest that 1C3 may be the more reliable standard reagent and that the Pgp3 multiplex assays are highly robust when comparing different instruments and assay protocols.

Arboviruses such as dengue virus (DENV) and chikungunya virus (CHIKV) pose major global health threats. To support serological surveillance and study cross-reactivity, an in-house multiplex bead-based immunoassay was developed to measure IgG responses against a wide panel of orthoflavivirus and alphavirus antigens. Samples from Senegal, France, the Philippines, and Bangladesh enabled analyses to be carried out in endemic and non-endemic regions. IgG kinetics following DENV1 and CHIKV infections revealed virus-specific temporal profiles and identified antigens, such as CHIKV E2 and VLP, as well as DENV1 EDIII and SNAP-tagged EDIII, as promising markers for serosurveillance. A two-step Random Forest model was used to classify PRNT-positive samples and predict PRNT titers. CHIKV showed a strong correlation between IgG and PRNT titers (R² = 0.71), while DENV1 showed weaker performance (R² = 0.42). These results support the use of multiplex serology for arbovirus surveillance and highlight the limitations of using binding antibodies to predict neutralisation, particularly for orthoflaviviruses.

The advent of multiplexing technologies, allowing antibodies to hundreds of antigens to be measured in a single test, has led to enormous increases in the amount of data generated by serological surveys. New modelling methods are required to exploit this data. This study extends serocatalytic models to consider up to three antibody responses targeting the same pathogen simultaneously. These models were fitted to data from cross-sectional serological surveys of Plasmodium falciparum malaria in the Senegalese villages of Dielmo and Ndiop, and model predictions were validated against 22 years of longitudinal epidemiological data. The most accurate reconstruction of historical clinical incidence of P. falciparum was provided by a combination of antibodies to Apical Membrane Antigen 1 ( Pf AMA1) and Glutamate-Rich Protein ( Pf GlurpR2). This model estimated a 76% (95% CrI: 61% - 86%) drop in transmission in 2004 (95% CrI: 2001 – 2008) coinciding with changing anti-malarial treatment. Multiplex serocatalytic models provided more accurate estimates of past clinical incidence than singleplex serocatalytic models, with the most accurate multiplex model ( Pf AMA1 + Pf GlurpR2) outperforming all singleplex models (KruskalWallis p < 0.01). Finally, models with three antigens did not provide more accurate estimation than models with two antigens.

The complement system is a major effector system of the humoral immune response, but its capacity to cooperate with antibodies to inhibit the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a host remains poorly characterized. Here, we combined authentic virus neutralization, antibody engineering, and single viral particle analysis by flow virometry to show that complement enhances antibody neutralization of SARS-CoV-2. Addition of complement increased neutralization titers of sera from convalescent individuals ( n = 14), vaccinated individuals ( n = 15), and individuals with hybrid immunity ( n = 15) against D614G, BA.1, XBB.1.5, and JN.1 variants by up to 42-fold. D614G and BA.1 displayed a high level of neutralization, which was only slightly improved by adding complement. Conversely, neutralization of XBB.1.5 and JN.1 was weak but strongly enhanced by complement. Among the 32 JN.1 nonneutralizers, 22 regained detectable neutralization in the presence of complement. The enhancement mainly relied on cross-reactive anti-S2 antibodies, but monoclonal antibodies targeting the receptor binding domain and N-terminal domain could also be augmented. Viral inhibition required antibody Fc hexamerization, C1q recruitment, and C3 deposition, but it was independent of the membrane attack complex formed by C9. Consistently, complement components were deposited at the surfaces of viral particles with no detectable virolysis. In sera from individuals with severe COVID-19, both complement-mediated neutralization and anti-S2 antibody levels were associated with improved survival. Overall, our data show that complement potentiates the ability of antispike antibodies to neutralize immune-evasive SARS-CoV-2 variants.

Background: Reliable inference of Plasmodium vivax recurrence states — relapse, recrudescence and reinfection (the “3Rs”) — improves estimates of antimalarial efficacy. The R package Pv3Rs features a Bayesian model designed for P. vivax molecular correction, i.e., using parasite genetic data to infer recurrence states. The model is an extension of a prototype built to analyse microsatellite data from the Vivax History (VHX) and Best Primaquine Dose (BPD) trials. Methods: We re-analysed data from 212 VHX and BPD trial participants (493 recurrences) using Pv3Rs, comparing results with those from the prototype and with genetic relatedness estimated using Dcifer, a tool for estimating relatedness based on identity-by-descent. Posterior recurrence state probabilities were computed using both uniform and time-to-event priors: artificial but equal prior probabilities facilitate posterior interpretation, while time-to-event priors leverage all available information and enable re-computation of failure rates. Relatedness estimates were used to identify and correct instances of model misspecification. Results: The Pv3Rs model generated posterior probabilities for all recurrences and was able to jointly model data on all episodes per participant for 89% of participants, compared with 73% using the prototype. Recurrence state probabilities were broadly consistent across methods, though the Pv3Rs model elevated reinfection probabilities slightly. Relatedness estimates exposed various outliers consistent with half-sibling parasites and/or genotyping errors. Outlier correction impacted some per-participant failure probabilities, but reinfection-adjusted radical-cure failure rates of high-dose primaquine remained near 3%, in line with previous findings. Conclusion: Re-analysis of VHX and BPD P. vivax genetic data restates earlier reinfection-adjusted efficacy estimates. It demonstrates the increased computational capability and misspecification sensitivity of Pv3Rs, highlighting a need for careful analyses. Using relatedness-based diagnostics alongside model-based inference, we were able to harness the advantages of model-based inference and provide a framework for future P. vivax molecular correction.

Human immune responses vary across individuals due to both genetic and environmental factors. We previously established the Milieu Intérieur cohort to define boundaries of healthy immune variation and identify their determinants. To evaluate how immune responses change over time and test whether immune states can predict future disease, we conducted a 10-year follow-up of the cohort. Here we show widespread changes in humoral responses to pathogens over this period, including unexpected seroreversion of cytomegalovirus (CMV) status, despite a general age-related increase in CMV antibodies. We also investigated whether immune profiles at initial recruitment are associated with disease development a decade later. Strikingly, whole blood transcriptional responses to Staphylococcus aureus and Candida albicans stimulation were predictive of subsequent infectious disease. Collectively, the Milieu Intérieur longitudinal study represents a valuable resource for evaluating immune aging and may help identify predictors of adverse health outcomes.

Accurate serological tools are essential for monitoring the transmission of arboviruses with pandemic potential, yet cross-reactivity between closely related viruses hampers diagnostics and surveillance. Here, we develop a high-throughput multiplex serological assay to quantify antibody responses to 28 antigens from nine arboviruses (dengue, Zika, yellow fever, West Nile, Usutu, Japanese encephalitis, chikungunya (CHIKV), Mayaro (MAYV), and O'nyong-nyong virus) and apply it to over 4000 samples from epidemiologically distinct sites on four continents. We implement a flexible analytical method based on Bayesian finite mixture models and Receiver Operating Characteristic analysis to evaluate assay performance and define seropositivity thresholds. As a case study, we resolve cross-reactive and virus-specific responses for CHIKV and the emerging MAYV by combining competitive immunoassays with mathematical modelling of multiplex serological and epidemiological data. This approach yields cross-reactivity-adjusted estimates of local transmission dynamics, in agreement with existing epidemiological evidence, and reveals that CHIKV is more prone to induce cross-reactive antibody responses than MAYV. Our results demonstrate the power of combining multiplex serology with experimental validation and modelling to disentangle exposure histories in the face of serological cross-reactivity. This integrative approach holds promise for improving arbovirus surveillance, particularly in settings with overlapping transmission of multiple viruses and limited diagnostic capacity.

: The Mandoto District in the central highlands of Madagascar experiences year-round transmission of Plasmodium vivax ( P. vivax ) and Plasmodium falciparum ( P. falciparum ). Monthly malaria case data from 27 health centers across Mandoto between 2019 and 2024 were analyzed alongside meteorological data to understand transmission dynamics and forecast potential influences of climate change using descriptive, cross-correlation, and seasonal autoregressive integrated moving average forecast models. Over a period of 6 years, 276,318 rapid diagnostic tests (RDTs) were performed, yielding a 39.6% positivity rate, totaling 109,428 malaria cases. After 2021, when multispecies RDTs became available, 71.5% of cases were attributed to P. falciparum , and 28.5% were attributed to P. vivax . Both species were co-endemic across all health centers, with the western region experiencing a higher transmission risk. Malaria cases peaked in January, with a second peak from April to June after the rainy season, and declined between July and September. Precipitation and temperature effectively revealed the seasonality of malaria dynamics, thereby improving model accuracy. Plasmodium falciparum exhibited stronger associations with precipitation and temperature variability. The present study highlights that combining time-series modeling with precipitation and temperature data can help predict malaria cases and support timely planning and resource allocation.

Summary: A Plasmodium vivax recurrence can be caused by activation of dormant liver-stage hypnozoites (relapse), failure to successfully treat a blood-stage infection (recrudescence), or a new mosquito inoculation (reinfection) (Pv3Rs). Inference of the cause of recurrence is important, especially in clinical trials, where molecular correction is used to improve estimates of antimalarial treatment efficacy. Pv3Rs implements statistical inference of P. vivax recurrence states (recrudescence, relapse, reinfection) from P. vivax genetic data. Under various simplifying assumptions, we model genetic relationships between parasites within and across infections to compute the posterior probabilities of recurrence states. Pv3Rs is applicable to a variety of genetic data types, including length polymorphic loci and amplicon sequencing data. Availability and implementation: Pv3Rs is freely available on CRAN under the GNU license. Its CRAN DOI is 10.32614/CRAN.package.Pv3Rs. Source code and installation instructions can be found at https://cran.r-project.org/web/packages/Pv3Rs/index.html.