Proteome-wide analysis of a malaria vaccine study reveals personalized humoral immune profiles in Tanzanian adults
- Swiss Tropical and Public Health Institute, Switzerland
- Antigen Discovery Inc, United States
- Sanaria Inc, United States
- Ifakara Health Institute, United Republic of Tanzania
- University of Basel, Switzerland
Abstract
Tanzanian adult male volunteers were immunized by direct venous inoculation with radiation-attenuated, aseptic, purified, cryopreserved Plasmodium falciparum (Pf) sporozoites (PfSPZ Vaccine) and protective efficacy assessed by homologous controlled human malaria infection (CHMI). Serum immunoglobulin G (IgG) responses were analyzed longitudinally using a Pf protein microarray covering 91% of the proteome, providing first insights into naturally acquired and PfSPZ Vaccine-induced whole parasite antibody profiles in malaria pre-exposed Africans. Immunoreactivity was identified against 2,239 functionally diverse Pf proteins, showing a wide breadth of humoral response. Antibody-based immune 'fingerprints' in these individuals indicated a strong person-specific immune response at baseline, with little changes in the overall humoral immunoreactivity pattern measured after immunization. The moderate increase in immunogenicity following immunization and the extensive and variable breadth of humoral immune response observed in the volunteers at baseline suggest that pre-exposure reduces vaccine-induced antigen reactivity in unanticipated ways.
Data availability
All data analyzed during this study are included in the manuscript and supporting files, or sited accordingly when published elsewhere
Article and author information
Author details
Funding
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (PP00P3_170702)
- Melissa A Penny
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Urszula Krzych, Walter Reed Army Institute of Research, United States
Version history
- Received: October 26, 2019
- Accepted: July 10, 2020
- Accepted Manuscript published: July 14, 2020 (version 1)
- Version of Record published: July 28, 2020 (version 2)
Copyright
© 2020, Camponovo et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Proteome-wide analysis of a malaria vaccine study reveals personalized humoral immune profiles in Tanzanian adults
eLife 9:e53080.
https://doi.org/10.7554/eLife.53080
Further reading
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- Epidemiology and Global Health
Background:
Machine learning (ML) techniques improve disease prediction by identifying the most relevant features in multidimensional data. We compared the accuracy of ML algorithms for predicting incident diabetic kidney disease (DKD).
Methods:
We utilized longitudinal data from 1365 Chinese, Malay, and Indian participants aged 40–80 y with diabetes but free of DKD who participated in the baseline and 6-year follow-up visit of the Singapore Epidemiology of Eye Diseases Study (2004–2017). Incident DKD (11.9%) was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 with at least 25% decrease in eGFR at follow-up from baseline. A total of 339 features, including participant characteristics, retinal imaging, and genetic and blood metabolites, were used as predictors. Performances of several ML models were compared to each other and to logistic regression (LR) model based on established features of DKD (age, sex, ethnicity, duration of diabetes, systolic blood pressure, HbA1c, and body mass index) using area under the receiver operating characteristic curve (AUC).
Results:
ML model Elastic Net (EN) had the best AUC (95% CI) of 0.851 (0.847–0.856), which was 7.0% relatively higher than by LR 0.795 (0.790–0.801). Sensitivity and specificity of EN were 88.2 and 65.9% vs. 73.0 and 72.8% by LR. The top 15 predictors included age, ethnicity, antidiabetic medication, hypertension, diabetic retinopathy, systolic blood pressure, HbA1c, eGFR, and metabolites related to lipids, lipoproteins, fatty acids, and ketone bodies.
Conclusions:
Our results showed that ML, together with feature selection, improves prediction accuracy of DKD risk in an asymptomatic stable population and identifies novel risk factors, including metabolites.
Funding:
This study was supported by the National Medical Research Council, NMRC/OFLCG/001/2017 and NMRC/HCSAINV/MOH-001019-00. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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- Epidemiology and Global Health
The NIH-funded RECOVER study is collecting clinical data on patients who experience a SARS-CoV-2 infection. As patient representatives of the RECOVER Initiative’s Mechanistic Pathways task force, we offer our perspectives on patient motivations for partnering with researchers to obtain results from mechanistic studies. We emphasize the challenges of balancing urgency with scientific rigor. We recognize the importance of such partnerships in addressing post-acute sequelae of SARS-CoV-2 infection (PASC), which includes ‘long COVID,’ through contrasting objective and subjective narratives. Long COVID’s prevalence served as a call to action for patients like us to become actively involved in efforts to understand our condition. Patient-centered and patient-partnered research informs the balance between urgency and robust mechanistic research. Results from collaborating on protocol design, diverse patient inclusion, and awareness of community concerns establish a new precedent in biomedical research study design. With a public health matter as pressing as the long-term complications that can emerge after SARS-CoV-2 infection, considerate and equitable stakeholder involvement is essential to guiding seminal research. Discussions in the RECOVER Mechanistic Pathways task force gave rise to this commentary as well as other review articles on the current scientific understanding of PASC mechanisms.
