Proteome-wide analysis of a malaria vaccine study reveals personalized humoral immune profiles in Tanzanian adults

  1. Flavia Camponovo
  2. Joseph J Campo
  3. Timothy Q Le
  4. Amit Oberai
  5. Christopher Hung
  6. Jozelyn V Pablo
  7. Andy A Teng
  8. Xiaowu Liang
  9. B Kim Lee Sim
  10. Said Jongo
  11. Salim Abdulla
  12. Marcel Tanner
  13. Stephen L Hoffman
  14. Claudia Daubenberger
  15. Melissa A Penny  Is a corresponding author
  1. Swiss Tropical and Public Health Institute, Switzerland
  2. Antigen Discovery Inc, United States
  3. Sanaria Inc, United States
  4. Ifakara Health Institute, United Republic of Tanzania
  5. 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

  1. Flavia Camponovo

    Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland
    Competing interests
    No competing interests declared.
  2. Joseph J Campo

    Antigen Discovery Inc, Irvine, United States
    Competing interests
    Joseph J Campo, is an employee of Antigen Discovery, Inc.
  3. Timothy Q Le

    Antigen Discovery Inc, Irvine, United States
    Competing interests
    Timothy Q Le, is an employee of Antigen Discovery, Inc.
  4. Amit Oberai

    Antigen Discovery Inc, Irvine, United States
    Competing interests
    Amit Oberai, is an employee of Antigen Discovery, Inc.
  5. Christopher Hung

    Antigen Discovery Inc, Irvine, United States
    Competing interests
    Christopher Hung, is an employee of Antigen Discovery, Inc.
  6. Jozelyn V Pablo

    Antigen Discovery Inc, Irvine, United States
    Competing interests
    Jozelyn V Pablo, is an employee of Antigen Discovery, Inc.
  7. Andy A Teng

    Antigen Discovery Inc, Irvine, United States
    Competing interests
    Andy A Teng, is an employee of Antigen Discovery, Inc.
  8. Xiaowu Liang

    Antigen Discovery Inc, Irvine, United States
    Competing interests
    Xiaowu Liang, is an employee of Antigen Discovery, Inc.
  9. B Kim Lee Sim

    Process Development and Manufacturing, Sanaria Inc, Rockville, United States
    Competing interests
    B Kim Lee Sim, This author is affiliated with Sanaria Inc. Sanaria manufactured PfSPZ Vaccine and PfSPZ Challenge.
  10. Said Jongo

    Interventions and Clinical Trials Unit, Bagamoyo Branch, Ifakara Health Institute, Dar es Salam, United Republic of Tanzania
    Competing interests
    No competing interests declared.
  11. Salim Abdulla

    Interventions and Clinical Trials Unit, Bagamoyo Branch, Ifakara Health Institute, Dar es Salam, United Republic of Tanzania
    Competing interests
    No competing interests declared.
  12. Marcel Tanner

    Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland
    Competing interests
    No competing interests declared.
  13. Stephen L Hoffman

    Process Development and Manufacturing, Sanaria Inc, Rockville, United States
    Competing interests
    Stephen L Hoffman, This author is employed by Sanaria. Sanaria Inc. manufactured PfSPZ Vaccine and PfSPZ Challenge. Thus, all authors associated with Sanaria have potential conflicts of interest..
  14. Claudia Daubenberger

    University of Basel, Basel, Switzerland
    Competing interests
    No competing interests declared.
  15. Melissa A Penny

    Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland
    For correspondence
    melissa.penny@unibas.ch
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4972-593X

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

  1. Urszula Krzych, Walter Reed Army Institute of Research, United States

Version history

  1. Received: October 26, 2019
  2. Accepted: July 10, 2020
  3. Accepted Manuscript published: July 14, 2020 (version 1)
  4. 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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  1. Flavia Camponovo
  2. Joseph J Campo
  3. Timothy Q Le
  4. Amit Oberai
  5. Christopher Hung
  6. Jozelyn V Pablo
  7. Andy A Teng
  8. Xiaowu Liang
  9. B Kim Lee Sim
  10. Said Jongo
  11. Salim Abdulla
  12. Marcel Tanner
  13. Stephen L Hoffman
  14. Claudia Daubenberger
  15. Melissa A Penny
(2020)
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

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    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.

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    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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