Transcriptional correlates of malaria in RTS,S/AS01-vaccinated African children: a matched case–control study

  1. Gemma Moncunill  Is a corresponding author
  2. Jason Carnes
  3. William Chad Young
  4. Lindsay Carpp
  5. Stephen De Rosa
  6. Joseph J Campo
  7. Augusto Nhabomba
  8. Maxmillian Mpina
  9. Chenjerai Jairoce
  10. Greg Finak
  11. Paige Haas
  12. Carl Muriel
  13. Phu Van
  14. Héctor Sanz
  15. Sheetij Dutta
  16. Benjamin Mordmüller
  17. Selidji T Agnandji
  18. Núria Díez-Padrisa
  19. Nana Aba Williams
  20. John J Aponte
  21. Clarissa Valim
  22. Daniel E Neafsey
  23. Claudia Daubenberger
  24. M Juliana McElrath
  25. Carlota Dobaño
  26. Ken Stuart
  27. Raphael Gottardo  Is a corresponding author
  1. ISGlobal, Hospital Clínic - Universitat de Barcelona, Spain
  2. CIBER de Enfermedades Infecciosas, Spain
  3. Center for Global Infectious Disease Research, Seattle Children's Research Institute, United States
  4. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, United States
  5. Antigen Discovery Inc, United States
  6. Centro de Investigação em Saúde de Manhiça (CISM), Rua 12, Cambeve, Vila de Manhiça, Mozambique
  7. Ifakara Health Institute. Bagamoyo Research and Training Centre, United Republic of Tanzania
  8. Walter Reed Army Institute of Research (WRAIR), United States
  9. Institute of Tropical Medicine and German Center for Infection Research, Germany
  10. Centre de Recherches Médicales de Lambaréné (CERMEL), BP 242, Gabon
  11. Department of Global Health, Boston University School of Public Health, United States
  12. Broad Institute of Massachusetts Institute of Technology and Harvard, United States
  13. Harvard T.H. Chan School of Public Health, United States
  14. Swiss Tropical and Public Health Institute, Switzerland
  15. University of Basel, Switzerland
  16. Departments of Laboratory Medicine and Medicine, University of Washington, United States
  17. Department of Pediatrics, University of Washington, United States
  18. Department of Global Health, University of Washington, United States
  19. University of Lausanne and Centre Hospitalier Universitaire Vaudois, Switzerland

Abstract

Background:

In a phase 3 trial in African infants and children, the RTS,S/AS01 vaccine (GSK) showed moderate efficacy against clinical malaria. We sought to further understand RTS,S/AS01-induced immune responses associated with vaccine protection.

Methods:

Applying the blood transcriptional module (BTM) framework, we characterized the transcriptomic response to RTS,S/AS01 vaccination in antigen-stimulated (and vehicle control) peripheral blood mononuclear cells sampled from a subset of trial participants at baseline and month 3 (1-month post-third dose). Using a matched case–control study design, we evaluated which of these ‘RTS,S/AS01 signature BTMs’ associated with malaria case status in RTS,S/AS01 vaccinees. Antigen-specific T-cell responses were analyzed by flow cytometry. We also performed a cross-study correlates analysis where we assessed the generalizability of our findings across three controlled human malaria infection studies of healthy, malaria-naive adult RTS,S/AS01 recipients.

Results:

RTS,S/AS01 vaccination was associated with downregulation of B-cell and monocyte-related BTMs and upregulation of T-cell-related BTMs, as well as higher month 3 (vs. baseline) circumsporozoite protein-specific CD4+ T-cell responses. There were few RTS,S/AS01-associated BTMs whose month 3 levels correlated with malaria risk. In contrast, baseline levels of BTMs associated with dendritic cells and with monocytes (among others) correlated with malaria risk. The baseline dendritic cell- and monocyte-related BTM correlations with malaria risk appeared to generalize to healthy, malaria-naive adults.

Conclusions:

A prevaccination transcriptomic signature associates with malaria in RTS,S/AS01-vaccinated African children, and elements of this signature may be broadly generalizable. The consistent presence of monocyte-related modules suggests that certain monocyte subsets may inhibit protective RTS,S/AS01-induced responses.

Funding:

Funding was obtained from the NIH-NIAID (R01AI095789), NIH-NIAID (U19AI128914), PATH Malaria Vaccine Initiative (MVI), and Ministerio de Economía y Competitividad (Instituto de Salud Carlos III, PI11/00423 and PI14/01422). The RNA-seq project has been funded in whole or in part with Federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under grant number U19AI110818 to the Broad Institute. This study was also supported by the Vaccine Statistical Support (Bill and Melinda Gates Foundation award INV-008576/OPP1154739 to R.G.). C.D. was the recipient of a Ramon y Cajal Contract from the Ministerio de Economía y Competitividad (RYC-2008-02631). G.M. was the recipient of a Sara Borrell–ISCIII fellowship (CD010/00156) and work was performed with the support of Department of Health, Catalan Government grant (SLT006/17/00109). This research is part of the ISGlobal’s Program on the Molecular Mechanisms of Malaria which is partially supported by the Fundación Ramón Areces and we acknowledge support from the Spanish Ministry of Science and Innovation through the ‘Centro de Excelencia Severo Ochoa 2019–2023’ Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program.

Introduction

Malaria remains a serious public health problem, with an estimated 241 million cases and 627,000 related deaths in 2020 (World Health Organization, 2021a). Despite the strides that interventions such as long-lasting insecticide-treated bed nets, improved vector control and diagnostic tests, and mass antimalarial drug administration campaigns have made toward reducing malaria-related morbidity and mortality (Yang et al., 2018; Eisele, 2019), there is a critical need for an effective malaria vaccine (Healer et al., 2017; Beeson et al., 2019).

The RTS,S/AS01 malaria vaccine targets the pre-erythrocytic stage of the parasite life cycle and has been designed to elicit strong humoral and cellular immune responses against the Plasmodium falciparum circumsporozoite protein (CSP) (Hoffman et al., 2015). This recombinant vaccine consists of a protein containing multiple immunodominant NANP repeats and the carboxy terminus of CSP fused to hepatitis B virus surface antigen (HBs) formulated in the AS01 adjuvant (Gordon et al., 1995).

In a phase 3 trial in 15,459 African infants and children (ClinicalTrials.gov NCT00866619) (Agnandji et al., 2011; RTS,S Clinical Trials Partnership, 2012; RTS,S Clinical Trials Partnership, 2014; RTS,S Clinical Trials Partnership, 2015), RTS,S/AS01 demonstrated 56% vaccine efficacy (VE) against clinical malaria (follow-up time: 12 month post-last dose) in children aged 5–17 months at enrollment and 31% in infants aged 6–12 weeks at enrollment. In 2015, RTS,S/AS01 became the first malaria vaccine to receive a positive opinion by the European Medicines Agency under Article 58 (Hawkes, 2015), and it was recommended by the World Health Organization (WHO) for a malaria vaccine pilot implementation program in Ghana, Malawi, and Kenya that started in 2019 (World Health Organization, 2019). Evidence gathered so far from this program led to the recent WHO recommendation for a wider use of this first malaria vaccine in African children at risk (World Health Organization, 2021b).

A critical limitation of the RTS,S vaccine is that VE is moderate (lower in infants than children) and wanes substantially within the first 18 months (RTS,S Clinical Trials Partnership, 2014). The identification of immune correlates of protection could help guide iterative vaccine improvements and expedite vaccine evaluation. Excellent work has been done on elucidating correlates of RTS,S/AS01-mediated protection in healthy, malaria-naive adults using the controlled human malaria infection (CHMI) model (Ockenhouse et al., 2015; Chaudhury et al., 2016; Kazmin et al., 2017; Du et al., 2020; Pallikkuth et al., 2020; Suscovich et al., 2020; Dennison et al., 2021), and cohort studies in African infants and children have implicated vaccine-induced anti-CSP antibodies (Dobaño et al., 2019a; Dobaño et al., 2019b; Ubillos et al., 2018), as well as CSP-specific Th1 cytokines (Moncunill et al., 2017b), and CD4+ T cells (albeit to a lesser extent) in protection in this population (reviewed in Moris et al., 2018).

The MAL067 study, an ancillary study to the RTS,S/AS01 phase 3 trial, was conducted to address key knowledge gaps of RTS,S-induced immune responses and their correlation with protection against natural exposure in the field. Using RNA-sequencing (RNA-seq) data from antigen- or vehicle-stimulated peripheral blood mononuclear cell (PBMC) obtained at baseline and 1-month postfinal primary vaccination dose from infants and children enrolled in Bagamoyo, Tanzania and Manhiça, Mozambique, we aimed to identify baseline and/or RTS,S/AS01-induced signatures associated with clinical malaria risk. Postvaccination anti-CSP antibody levels, cytokine profiles, and T-cell responses, the latter of which were additionally assessed in samples from participants enrolled in Lambaréné, Gabon, were also examined as correlates of clinical malaria and/or of RTS,S/AS01-induced transcriptional responses.

The major finding of our study is that prevaccination expression of immune-related blood transcriptional modules (BTMs), including BTMs related to dendritic cells and monocytes, correlated positively with malaria risk in RTS,S/AS01-vaccinated African children; moreover, the dendritic cell- and monocyte-related elements of this signature appeared to generalize to malaria-naive RTS,S/AS01-vaccinated healthy adults.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Antibodyanti-CD4, clone SK3 (mouse monoclonal)BDCat# 563,5501.5 µl/50 µl staining volume; doi:10.1002/cyto.a.22580
Antibodyanti-CD19, clone SJ25C1 (mouse monoclonal)BDCat# 564,3031 µl/50 µl staining volume; doi:10.1002/cyto.a.22580
Antibodyanti-CD25, clone M-A251 (mouse monoclonal)BDCat# 562,4425 µl/50 µl staining volume; doi:10.1002/cyto.a.22580
Antibodyanti-HLA-DR, clone B169414 (mouse monoclonal)BioLegendCat# 307,6370.625 µl/50 µl staining volume; doi:10.1002/cyto.a.22580
Antibodyanti-CD56, clone HCD56 (mouse monoclonal)BioLegendCat# 318,3340.625 µl/50 µl staining volume; doi:10.1002/cyto.a.22580
Antibodyanti-CD45RA, clone HI100 (mouse monoclonal)BioLegendCat# 304,1350.625 µl/50 µl staining volume; doi:10.1002/cyto.a.22580
Antibodyanti-CD14, clone MφP9 (mouse monoclonal)BDCat# 563,3730.2 µl/50 µl staining volume; doi:10.1002/cyto.a.22580
Antibodyanti-CCR7, clone G043H7 (mouse monoclonal)BioLegendCat# 353,2294 µl/50 µl staining volume; doi:10.1002/cyto.a.22580
Antibodyanti-CD57, clone NK-1 (mouse monoclonal)BDCat# 555,6195 µl/50 µl staining volume; doi:10.1002/cyto.a.22580
Antibodyanti-CD8, clone SK1 (mouse monoclonal)BDCat# 341,0512 µl/50 µl staining volume; doi:10.1002/cyto.a.22580
Antibodyanti-Vδ2 TCR, clone B6 (mouse monoclonal)BioLegendCat# 331,4080.156 µl/50 µl staining volume; doi:10.1002/cyto.a.22580
Antibodyanti-CD3, clone UCHT1 (mouse monoclonal)Beckman CoulterCat# IM2705U1 µl/50 µl staining volume; doi:10.1002/cyto.a.22580
Antibodyanti-CD38, clone HIT2 (mouse monoclonal)BDCat# 555,46110 µl/50 µl staining volume; doi:10.1002/cyto.a.22580
Antibodyanti-γ/δ TCR, clone 11F2 (mouse monoclonal)BDCat# 655,4341.25 µl/50 µl staining volume; doi:10.1002/cyto.a.22580
Antibodyanti-CD127, clone A019D5 (mouse monoclonal)BioLegendCat# 351,3150.2 µl/50 µl staining volume; doi:10.1002/cyto.a.22580
Antibodyanti-NKG2C, clone 134,591 (mouse monoclonal)R&D SystemsCat# FAB138N1.25 µl/50 µl staining volume; doi:10.1002/cyto.a.22580
Antibodyanti-CD16, clone 3G8 (mouse monoclonal)BDCat# 557,7580.312 µl/50 µl staining volume; doi:10.1002/cyto.a.22580
Antibodyanti-CD14, clone M5E2 (mouse monoclonal)BioLegendCat# 301,842Fluorochrome: BV510 (detected in the same channel as AViD)
Antibodyanti-CD56, clone NCAM16.2 (mouse monoclonal)BDCat# 564,447Fluorochrome: BUV737
Antibodyanti-CD3, clone UCHT1 (mouse monoclonal)BioLegendCat# 300,436Fluorochrome: BV570
Antibodyanti-CD8, clone RPA-T8 (mouse monoclonal)BDCat# 563,821Fluorochrome: BV650
Antibodyanti-CD45RA, clone HI100 (mouse monoclonal)BDCat# 560,674Fluorochrome: APC-H7
Antibodyanti-CXCR5, clone J252D4 (mouse monoclonal)BioLegendCat# 356,928Fluorochrome: PE-Dazzle594
Antibodyanti-PD-1, clone eBioJ105 (mouse monoclonal)eBioscienceCat# 25-2799-42Fluorochrome: PE-Cy7
Antibodyanti-IFN-γ, clone B27 (mouse monoclonal)BDCat# 560,371Fluorochrome: V450
Antibodyanti-IL-2, clone MQ1-17H12 (rat monoclonal)BDCat# 559,334Fluorochrome: PE
Antibodyanti-IL-4, clone MP4-25D2 (rat monoclonal)BioLegendCat# 500,822Fluorochrome: PerCP-Cy5.5
Antibodyanti-IL-13, clone JES10-5A2 (rat monoclonal)BDCat# 564,288Fluorochrome: BV711
Antibodyanti-IL-21, clone 3A3-N2 (mouse monoclonal)Miltenyi BiotecCat# 130-120-702Fluorochrome: APC
Antibodyanti-TNF-α, clone mAb11 (mouse monoclonal)eBioscienceCat# 11-7349-82Fluorochrome: FITC
Antibodyanti-CD40L, clone 24–31 (mouse monoclonal) (mouse monoclonal)BioLegendCat# 310,825Fluorochrome: BV605
Antibodyanti-Granzyme B, clone GB11 (mouse monoclonal)BDCat# 560,213Fluorochrome: Alx700
Chemical compound, drugBD FACS Lyse Solution, 10×BDCat #349,202doi:10.1002/cyto.a.22590 VC
Chemical compound, drugBD FACS Perm II, 10×BDCat #340,973doi:10.1002/cyto.a.22590VC
Chemical compound, drugBrefeldin ASigma Chemical Co.Cat #B-7651Final concentration of 10 µg/mldoi:10.1002/cyto.a.22590 VC
Chemical compound, drugCD28/49d (BD Biosciences)BDCat #347,690Final concentration of 1 µg/mldoi:10.1002/cyto.a.22590 VC
Chemical compound, drugGolgi Stop containing monensinBDCat #554,724doi:10.1002/cyto.a.22590 VC
Peptide, recombinant proteinRecombinant AMA1WRAIRFVO strain, GMP produced in E. coli
Peptide, recombinant proteinCSP peptide pooldoi: 10.3389/fimmu.2017.01008Biosynthan (RNA-sequencing stimulations) and Biosynthesis (ICS stimulations)
Peptide, recombinant proteinHBS peptide pooldoi: 10.3389/fimmu.2017.01008Biosynthan (RNAseq stimulations) and Biosynthesis (ICS stimulations)
Sequence-based reagentUniversal adapter
E5V6NEXT: 5′-iCiGiCACACTCT
TTCCCTACACGACGCrGrGrG-3′
Integrated DNA TechnologiesiC: iso-dC, iG: iso-dG, rG: RNA G
Sequence-based reagentBarcoded adapter E3V6NEXT:
5′-/5Biosg/ACACTCTTTCCCT
ACACGACGCTCTTCCGATC
T[BC6]N10T30VN-3′
Integrated DNA Technologies5Biosg = 5′ biotin, [BC6] = 6 bp barcode specific to each cell/well, N10 = unique molecular identifiers, 10 bp
Sequence-based reagent
SINGV6 primer: 5′-/5Biosg/ACACTC
TTTCCCTACACGACGC-3′
Integrated DNA Technologies
Sequence-based reagentP5NEXTPT5 primer: 5′-
AATGATACGGCGACC
ACCGAGATCTACACT
CTTTCCCTACACGAC
GCTCTTCC*G*A*T*C*T-3′
Integrated DNA Technologies* = phosphorothioate bonds
Chemical compound, drugSEBSigma Chemical Co.Cat #S4881
Commercial assay or kitSV96 Total RNA Isolation SystemPromegaCat# Z3500
Commercial assay or kitDNA Clean & Concentrator-5 columnZymo ResearchCat# D4004
Commercial assay or kitAdvantage 2 Polymerase MixTakara BioCat# 639,202
Commercial assay or kitdsDNA HS AssayLife TechnologiesCat# Q32851
Commercial assay or kitNextera XT library preparation kitIlluminaCat# FC-131–1096
Commercial assay or kitQIAquick Gel Extraction KitQiagenCat# 28706 × 4
Chemical compound, drugDMSOSigmaCat# D2650
Software, algorithmRThe R FoundationR version 4.0.4 (2021-02-15)
Software, algorithmBurrows-Wheeler Aligner (BWA)https://sourceforge.net/projects/bio-bwa/BWA Aln version 0.7.10
Software, algorithmFlowJoBD Life SciencesFlowJo version 9.9 Tree Star
OtherRLT bufferQiagenCat# 79,216
OtherRNA protectQiagenCat# 76,104
Other96-Well V-bottomed plateKisker, AttendBioCat# G096-VB
OtherAdhesive foilKisker, AttendBioCat# G071-P
OtherAviDInvitrogenCat# L349570.5 µl reagent/50 µl staining volumedoi:10.1002/cyto.a.22590 VC
OtherMaxima H Minus Reverse TranscriptaseThermo ScientificCat# EP0751
OtherExonuclease INew England BioLabsCat# M0293S
OtherAgencourt AMPure XP magnetic beadsBeckman CoulterCat# A638810.6×
OtherE-Gel EX Gel, 2%Thermo FisherCat# G401002
OtherRNA 6000 Pico ChipAgilentCat# 5067-1513

MAL067 trial

Request a detailed protocol

During the MAL055 phase 3 study (ClinicalTrials.gov identifier NCT00866619; RTS,S Clinical Trials Partnership, 2015), infants (6–12 weeks) and children (5–17 months) received RTS,S/AS01 or comparator (rabies vaccine for children; meningococcal C conjugate vaccine for infants), with injections given at month 0 (baseline), month 1, and month 2 (Figure 1). MAL067 was a multicenter immunology ancillary study nested within MAL055 and selection of participants for MAL067 is described in Moncunill et al., 2017b. Of the seven trial sites included in MAL067, three research centers desired and had the required facilities already established to participate in the cellular component of the MAL067 immunology study: Ifakara Health Institute and Bagamoyo Research and Training Centre (IHI-BRTC in Tanzania), Centre de Recherches Médicales de Lambaréné, Albert Schweitzer Hospital (CERMEL, Gabon), and Manhiça Health Research Center, Fundação Manhiça (FM-CISM, Mozambique). PBMC samples were collected at baseline (only children) and again at month 3 (1-month post-third dose) at the three sites. The present study analyzes PBMC data from children in Bagamoyo and from infants and children in Manhiça, as well as from infants and children in Lambaréné (only intracellular cytokine staining [ICS]/immunophenotyping for the latter). All participants met criteria for the modified according-to-protocol (ATP) cohort of the phase 3 trial, from whom we collected PBMC for cellular determinations, and from whom we had available stimulated cells collected. The ATP cohort of the MAL067 immunology study was defined similar to the ATP cohort of the MAL055 clinical trial and is described in detail in Moncunill et al., 2017b. In Manhiça all children and infants from one of the recruiting peripheral health posts (Palmeira neighborhood) were included after ethical approvals for the MAL067 immunology study were obtained. After collecting the target sample size of 292 samples in children and infants, we stopped performing fresh stimulation in Manhiça, since per protocol, samples were dedicated to a different study involving B cells. In Bagamoyo, the first 400 children recruited in the phase 3 trial after obtaining ethical approval were included (infants were not). In Lambaréné, after ethical approval, the first 200 volunteers recruited in the phase 3 trial in each age cohort were included. Recruitment period started on 06/08/2009 and ended on 28/01/2011 in Manhiça, 17/09/2009 to 25/02/2010 in Bagamoyo and 22/07/2009 to 31/01/2011 in Lambaréné. PBMC were collected at month 0 before vaccination and approximately 30 days after the third dose of vaccine (month 3) in children and only at month 3 in infants. After cryopreserving 5 × 106 PBMC in liquid nitrogen, the remaining cells were used for fresh antigen stimulations onsite and the cell pellets were collected. Therefore, only vaccinees with enough PBMC for cryopreservation and additional stimulations (at least 6.6 × 106 PBMC) were included in this study for RNA-seq.

Schematic showing vaccination and sampling schedule.

Participants received RTS,S/AS01 (or comparator) at months 0, 1, and 2; peripheral blood mononuclear cells (PBMCs) were collected for fresh stimulations and RNA-sequencing and for cryopreservation at months 0 and 3 (1-month postfinal primary vaccination dose). Stim, stimulation.

As malaria transmission intensity at the these three sites was low/moderate (RTS,S Clinical Trials Partnership, 2015), we used a case–control design for the study instead of a cohort design. Sample sizes were based on availability of samples and malaria cases. We used all samples available from malaria cases and selected 2–4 matched controls for each case for RTS,S recipients. In selecting controls, we prioritized participants who had samples at both months 0 and 3 and in whom the complete set of antigen stimulations was conducted.

Case–control definitions

Request a detailed protocol

Cases were defined as participants who had any episode of clinical malaria (fever >37.5°C with any parasitemia by blood smear) in the 12 months of follow-up after month 3.5, identified by passive case detection (participants who sought care at a health facility) during all phase 3 trial follow-up. Data were collected during the clinical trial. Controls were participants who did not have any clinical malaria case during the 12 months of follow-up. Controls were matched to cases based on site, age group, and time of vaccination and follow-up. Supplementary file 1 provides further information on participant characteristics, including select demographics, clinical characteristics, and case–control matching.

PBMC collection and antigen stimulation

Request a detailed protocol

PBMC collection and stimulation with dimethyl sulfoxide (DMSO, vehicle control), apical membrane antigen (AMA1, recombinant protein, FVO strain), CSP (peptide pools), or HBS (peptide pools) is described in Moncunill et al., 2017b. Peptides’ sequences are detailed in Moncunill et al., 2017a. DMSO (Cat# D2650, Sigma) was used at a final dilution of 1/322, the same concentration of DMSO as used for the CSP peptide pool. For stimulation before RNA extraction, 4 × 105 freshly isolated PBMC seeded in duplicates were rested for 12 hr and then incubated 12 hr at 37°C with 1 μg/ml antigens in 96-well plates. Plates were then centrifuged for 5 min at 250 × g at room temperature and cell pellet duplicates were resuspended and pooled in RLT buffer (Cat# 1053393, Qiagen) at Bagamoyo or RNAprotect (Cat# 76526, Qiagen) at Manhiça, transferred into a 96-well V-bottomed plate (Cat# G096-VB; Kisker, AttendBio) and sealed with adhesive foil (Cat# G071-P; Kisker, Attend Bio) and cryopreserved at −80°C until RNA extraction.

For stimulation before ICS, cryopreserved PBMC were thawed and then rested in a 37°C, 5% CO2 incubator overnight. PBMC were stimulated for 6 hr with the same peptide pools as above, DMSO (vehicle control), and Staphylococcal enterotoxin B (SEB, Sigma Chemical Co.; Cat #S4881) as a positive control.

Flow immunophenotyping

Request a detailed protocol

Leftover cryopreserved PBMC thawed for ICS (0.5–1 × 106 cells) were used for leukocyte phenotyping. The flow cytometry panel and staining protocol used are described in Moncunill et al., 2014 and in the Key Resources Table. Data were acquired using a BD LSR II flow cytometer (BD Biosciences) directly from 96-well plates using a high throughput sampler. Flow cytometry analysis was performed using FlowJo software (Version 9.9 Tree Star). The gating strategy was performed as in Moncunill et al., 2014.

Intracellular cytokine staining

Request a detailed protocol

Antigen- or vehicle-stimulated PBMC were stained using a flow cytometry panel and protocol previously described (Moncunill et al., 2017a; Moncunill et al., 2015) with the additional marker IL-13. Antibody details can be found in the Key Resources Table. Data were acquired and analyzed as above. Poor quality samples were filtered using two standard criteria: (1) samples with high background (vehicle nonstimulated sample) magnitude was >10% and (2) samples with less than 20,000 CD4 T cells. No subjects were flagged as high background and 85 were flagged as having low T-cell counts. These were removed from the analysis.

RNA isolation and sequencing

Request a detailed protocol

RNA was extracted at the Center for Global Infectious Disease Research, Seattle Children's Research Institute ( Seattle) using the Promega SV96 Total RNA Isolation kit (Cat# Z3500, Promega) following the manufacturer’s protocol. Samples kept in RNAprotect were centrifuged at 4000 × g for 7 min at 4°C, cell pellets were resuspended in 150 μl RLT buffer, and 150 μl of 70% ethanol was added prior to processing with the SV96 Total RNA Isolation kit. RNAs were eluted with 100 μl nuclease free water. Each 96-well extraction batch was spot checked by Bioanalyzer using an RNA 6000 Pico chip (Cat# 5067-1513, Agilent) and had an average RIN score of 7.4. RNA samples were distributed in 384-well plates for library preparation. Samples from the same individuals were in the same plate and key study variables (vaccine, site, and cases–controls) were checked for balance across plates to avoid batch effects.

An optimized version of Digital Gene Expression (DGE) was used, based on the Single Cell Barcoding and Sequencing method described by Soumillon et al., 2014 but further reducing the reverse transcriptase reaction volume. In brief, poly(A)+ mRNA from antigen-stimulated PBMCs was linked to unique molecular identifiers (UMIs) using a template-switching reverse transcriptase (Maxima H Minus Reverse Transcriptase, Cat# EP0751, Thermo Scientific), a universal adapter, and a barcoded adapter (see the Key Resources Table). Then, cDNA from multiple cells was pooled, purified, and concentrated using a DNA Clean & Concentrator-5 column (Cat# D4004, Zymo Research), and treated with Exonuclease I (Cat# M0293S, New England BioLabs). The pooled cDNA was then amplified by single primer PCR using the Advantage 2 Polymerase Mix (Cat# 639202, Takara Bio) and primer SINGV6 (Key Resources Table) and prepped for multiplexed sequencing using a transposon-based fragmentation method (Adey et al., 2010), enriching for 3′ ends and preserving strand information. Full-length cDNAs were purified with Agencourt AMPure XP magnetic beads (Cat# A63881, 0.6×, Beckman Coulter) and quantified on the Qubit 2.0 Fluorometer (Life Technologies) using the dsDNA HS Assay (Cat# Q32851, Life Technologies). Full-length cDNA was then used with an Nextera XT library preparation kit (Cat# FC-131-1096, Illumina) according to the manufacturer’s protocol, except that the i5 primer was replaced by the P5NEXTPT5 primer (see the Key Resources Table). The resulting library was again purified with Agencourt AMPure XP magnetic beads before size selection (300–800 bp) on an E-Gel EX Gel, 2% (Cat# G401002, Thermo Fisher), purification using the QIAquick Gel Extraction Kit (Cat# 28706 × 4, Qiagen) and quantification using the dsDNA HS Assay. Libraries were sequenced at the Broad Institute on Illumina HiSeq paired-end flow cells using an Illumina NextSeq instrument.

Antibody data analyzed for correlations with BTM expression

Request a detailed protocol

NANP-, HBS-, and C-terminal domain of CSP (C-term)-specific antibody data from previous studies were analyzed for correlations with BTM expression as described below. IgG titers (EU/ml) against NANP and against HBS were obtained from the MAL055 trial database (Agnandji et al., 2011; RTS,S Clinical Trials Partnership, 2012; RTS,S Clinical Trials Partnership, 2014; RTS,S Clinical Trials Partnership, 2015). IgG concentrations (EU/ml) against NANP and C-term were measured by ELISA at IAVI-HIL (Dobaño et al., 2019a). IgG and IgM levels (Median Fluorescence Intensity, MFI) against NANP, C-terminal CSP, and HBS together with 35 RTS,S/AS01 vaccine-unrelated malaria antigens were measured by Luminex technology (Dobaño et al., 2019b; Ubillos et al., 2018).

Data processing and statistical analysis

Request a detailed protocol

Preprocessing: Preprocessing of RNAseq data was done by Broad Technology Labs. In brief, reads were aligned using BWA Aln version 0.7.10 using UCSC RefSeq (Human 19) with mitochondrial genes added. Quantified samples were then quality controlled using mapping summary statistics to remove low quality samples based on predetermined minimum values for the total number of mapped reads, percent of mapped reads mapped to the human genome, etc. Downstream analysis was applied only to reads that mapped uniquely to a UMI and only mapped to isoforms of the same gene (UMI.unq).

Normalization: The TMM normalization method (Robinson and Oshlack, 2010) was applied to account for differing number of read counts and to address unwanted technical variation. The voom transformation (Law et al., 2014) from the limma R package (Smyth, 2004) was applied to standardize and appropriately weight the data for use in linear models.

Quality control: In a pilot study, we found that sample libraries that exhibit less than 75,000 total RNAseq reads per sample were of low quality. Thus, such libraries were removed from the study. Genes that had less than 20 samples (around 10%) with read counts greater than 5 were also removed. Multidimensional scaling (MDS) plots as implemented in the plotMDS function of the limma package were used to visualize variability across samples and identify potential sources of variability (batch effects such as total number of reads) or patterns of biological interest (association within experimental factors).

Differential expression: Differential expression was assessed using module-based (using voom and camera [Wu and Smyth, 2012]) approaches as implemented in the limma package. Camera, combined with voom, is one of the few gene set enrichment analysis methods that can properly account for intergene correlation in RNA-seq data. Specifically, camera estimates the variance inflation factor for the gene expression that results from intergene correlation in the data and incorporates it into test procedures to control the apparent false discovery rate (FDR). This step is important since significant correlation is expected among genes in the same module. Inference was based on p values adjusted for multiple testing by controlling the FDR with the Benjamini–Hochberg (Benjamini and Hochberg, 1995) method. Differential expression was used to downselect modules constituting the PBMC transcriptional response to RTS,S/AS01 vaccination comparing RTS,S/AS01 vaccinees with comparator at month 3 and pre- and post-RTS,S/AS01E vaccination in children (months 3 vs. 0).

BTM analysis: BTMs used were from Li et al., 2014. Resulting p values across BTMs (within stimulation condition) were adjusted for multiple testing with a FDR cutoff of 0.2. Only these significant BTMs were tested as candidate immune correlates.

Analysis of antigen (Ag)-specific T-cell transcriptional responses: When analyzing Ag-specific T cells, vehicle-only stimulations (DMSO) were used to determine the effect of Ag stimulation over vehicle stimulation for each PBMC sample. The comparison was performed using the limma package (Ritchie et al., 2015) in R as follows: stimulation*vaccine, where stimulation = (HBS, AMA1, CSP) vs. vehicle and vaccine = RTS,S/AS01 vs. Comparator. Quantitative variables were modeled at such, except for age, which was categorized as infant vs. child. Participants with missing data only for certain stimulation were included in the analysis and only the available samples were modeled (no imputation of data was performed).

Equations used were: Figure 2A, vehicle: equation = ~plate + total_reads + age + vaccine; CSP, HBS, AMA1: equation = ~plate + total_reads + age + stimulation*vaccine + (1|pid). Figure 2B: vehicle: equation = ~plate + total_reads + age + visit + (1|pid); CSP, HBS, AMA1: equation = ~plate + total_reads + age + visit*stimulation + (1|pid), where pid is the patient identifier, modeled as a random effect, and total_reads is the number of sequence reads per sample.

Transcriptional responses and antigen-specific transcriptional responses at 1-month postfinal dose associated with RTS,S/AS01 vaccination.

(A) Comparison 1: month 3 (M3) peripheral blood mononuclear cells (PBMC), RTS,S/AS01 vs. comparator; (B) Comparison 2: M3 PBMC vs. month 0 (M0) PBMC, RTS,S/AS01 recipients only. Cell color intensity represents the significance of the difference in the relevant comparison, expressed as signed log10 false discovery rate (FDR); blood transcriptional modules (BTMs) with significantly different expression (FDR ≤0.2) between the two compared groups are outlined in black. |FDR| < 0.2 (*), <0.05 (**), <0.01 (***). Red, higher expression in RTS,S/AS01 recipients vs. comparator recipients at M3 (Comparison 1) or higher expression in RTS,S/AS01 recipients at M3 vs. M0 (Comparison 2); blue, lower expression in RTS,S/AS01 recipients vs. comparator recipients at M3 (Comparison 1) or lower expression in RTS,S/AS01 recipients at M3 vs. M0 (Comparison 2). High-level BTM annotation groups are shown in the left-most color bar. Numbers of participants in each analysis are: (A) Vehicle: 348 (131 comparator, 217 RTS,S/AS01), CSP: 355 (135 comparator, 220 RTS,S/AS01), HBS: 353 (132 comparator, 221 RTS,S/AS01), and AMA1: 351 (132 comparator, 219 RTS,S/AS01). (B) Vehicle: 221, CSP: 224 (221 vehicle, 219 CSP), HBS: 225 (221 vehicle, 211 HBS), AMA1: 223 (221 vehicle, 195 AMA1). Numbers include participants not part of the case–control cohort, and thus exceed the numbers in Table 1. Each ‘vehicle’ column displays the vaccine effect in vehicle; each ‘stimulation’ column displays the vaccine effect for that stimulation compared to vehicle, that is adjusted for vehicle. Detailed equations are given in Methods.

Figure 2—source data 1

List of blood transcriptional modules (BTMs), p values, and false discovery rates (FDRs) for Comparison 1 (RTS,S/AS01 vs. comparator recipients at month 3).

https://cdn.elifesciences.org/articles/70393/elife-70393-fig2-data1-v1.csv
Figure 2—source data 2

List of blood transcriptional modules (BTMs), p values, and false discovery rates (FDRs) for Comparison 2 (RTS,S/AS01 recipients at months 3 vs. 0).

https://cdn.elifesciences.org/articles/70393/elife-70393-fig2-data2-v1.csv

BTM correlations with immunogenicity: For each module, a score was calculated for each RTS,S recipient at months 3 and 0 based on the average normalized expression level of all genes in the modules, on the log scale. Spearman’s rank correlation was used to assess association between gene expression, antibody and cellular responses. Each correlation was tested (Spearman correlation test) and a p value was obtained. p values were adjusted within each response (across all gene sets); significance was defined as an adjusted p value ≤0.2.

Correlates analysis: We identified BTMs significantly associated with protection using the limma package. All analyses controlled for plate, total reads, and age (as described above). This model was applied to each BTM and stimulation condition identified in the downselection process. Resulting p values were adjusted for multiple testing with an FDR cutoff of 0.2.

Equations used were: Figure 3, vehicle: equation = ~plate + total_reads + age + case; CSP, HBS, AMA1: equation = ~plate + total_reads + age + stimulation*case + (1|pid).

Figure 3 with 1 supplement see all
Associations of month 3 levels of RTS,S/AS01 signature blood transcriptional modules (BTMs) with malaria case status in RTS,S/AS01 recipients.

Heatmap showing downselected signature BTMs (Comparison 1) with significantly different expression (false discovery rate [FDR] ≤0.2) in month 3 peripheral blood mononuclear cells (PBMC) from RTS,S/AS01 malaria cases vs. nonmalaria controls, in at least one stimulation condition. Cell color intensity represents the significance of the difference in the relevant comparison, expressed as signed log10 FDR; BTMs with significantly different expression in the comparison are outlined in black. |FDR| < 0.2 (*), <0.05 (**), <0.01 (***). Red, higher expression in RTS,S/AS01 cases vs. controls; blue, lower expression in RTS,S/AS01 cases vs. controls. High-level BTM annotation groups are shown in the left-most color bar. Numbers of participants in each analysis are: vehicle: 122, CSP: 123 (122 vehicle, 122 CSP), HBS: 123 (122 vehicle, 115 HBS), AMA1: 123 (122 vehicle, 97 AMA1). The ‘vehicle’ column displays the vaccine effect in vehicle; each ‘stimulation’ column displays the vaccine effect for that stimulation compared to vehicle, that is adjusted for vehicle. Detailed equations are given in Methods.

Figure 3—source data 1

List of blood transcriptional modules (BTMs), p values, and false discovery rates (FDRs) for the comparison of RTS,S/AS01 cases vs. controls at month 3, within each stimulation condition.

https://cdn.elifesciences.org/articles/70393/elife-70393-fig3-data1-v1.csv

Cross-study correlates analysis: For the cross-study correlates analysis, BTMs were downselected based on month 3 or 0 (as appropriate) data from MAL067 (vehicle-stimulated PBMC only). In brief, month 3 or 0 data for every BTM were tested and FDR adjustment was done across all BTMs. Only those BTMs with FDR < 0.2 in MAL067 were examined as potential correlates of challenge outcome in the CHMI studies, with FDR adjustment performed within each study. The three CHMI studies used in the cross-study immune correlates were: WRAIR 1032 (NCT00075049), which randomly assigned participants to receive RTS,S/AS02A or RTS,S/AS01B at months 0, 1, and 2 (Kester et al., 2009) MAL068 (NCT01366534), which randomly assigned participants to receive Ad35.CS.01 at month 0 followed by RTS,S/AS01B at months 1 and 2 (heterologous prime–boost) or RTS,S/AS01B at months 0, 1, and 2 (Ockenhouse et al., 2015) and MAL071 (NCT01857869), which randomly assigned participants to receive a full dose of RTS,S/AS01B at months 0, 1, and 2 or a full dose of RTS,S/AS01B at months 0 and 1, followed by a fractional dose at month 7 (Regules et al., 2016). Microarray data from WRAIR 1032 were analyzed by Vahey et al., 2010, microarray data from MAL068 were analyzed by Kazmin et al., 2017, and RNA-seq data from MAL068 and MAL071 were analyzed by Du et al., 2020.

Equations used were: MAL067: equation = ~plate + total_reads + age + case; WRAIR 1032, MAL068 RRR, and MAL071 RRR: equation = ~age + infection, MAL067: equation = ~plate + total_reads + age + case; WRAIR 1032, MAL068 RRR, and MAL071 RRR: equation = ~age + infection.

Results

Study population and sample collection scheme

PBMC RNA-seq data from a total of 360 participants were analyzed (Table 1). For the immunogenicity analysis, 360 participants (225 RTS,S recipients and 135 comparator recipients) were analyzed. For the case–control analysis, baseline RNA-seq data were available for 38 recipients (9 cases and 29 controls) and 19 comparator recipients (5 cases and 14 controls), all of whom were children since baseline samples were not collected from infants. Month 3 RNA-seq data were available for 123 RTS,S/AS01 recipients (31 cases and 92 controls) and 73 comparator recipients (23 cases and 50 controls). All (100%) of the participants in Bagamoyo for whom month 3 RNA-seq data were available were children, whereas nearly all (94.9%) in Manhiça were infants.

Supplementary file 2 provides similar information for participants for whom immunophenotyping/ICS data were analyzed.

Table 1
Numbers, age group, and case–control status of participants by site for whom peripheral blood mononuclear cells (PBMC) RNA-seq data were available at months 0 and/or 3.
Month 0
Cases (n = 14)Controls (n = 43)Not included in the case–control (n = 70)
BagomoyoManhiçaBagomoyoManhiçaBagomoyoManhiça
InfantsChildrenInfantsChildrenInfantsChildrenInfantsChildrenInfantsChildrenInfantsChildren
RTS,S/AS01 (n = 88)09000290004109
Comparator (n = 39)05000120201901
Month 3
Cases (n = 54)Controls (n = 142)Not included in the case–control (n = 161)
BagomoyoManhiçaBagomoyoManhiçaBagomoyoManhiça
InfantsChildrenInfantsChildrenInfantsChildrenInfantsChildrenInfantsChildrenInfantsChildren
RTS,S/AS01 (n = 222)0191200514100562815
Comparator (n = 135)01661030164029294

RTS,S/AS01 vaccination is associated with month 3 downregulation of B-cell- and monocyte-related BTMs, along with upregulation of T-cell-related BTMs

The transcriptional response to RTS,S/AS01 vaccination was assessed in control-stimulated PBMC as well as Ag-stimulated PBMC. Through this approach, we hypothesized that we would see recall responses of Ag-specific T cells activated in vitro, as well as responses of other cell types to the secreted cytokines/chemokines. Of note, the sampling schedule at MAL067 was designed for evaluation of acquired immune responses to the vaccine and not ex vivo responses. Our motivation was that in healthy, malaria-naive adults, the transcriptional response to RTS,S/AS01 has been shown to largely wane by week 3 postfinal dose (Kazmin et al., 2017), implying that the majority of the RTS,S/AS01-induced transcriptional changes in this study likely preceded the month 3 sample collection. Three antigens were chosen for stimulation: CSP (peptides covering the CSP region of RTS,S that encodes B- and T-cell epitopes), HBS (peptides covering the HBS, also included in the RTS,S vaccine), and AMA1 (a highly immunogenic antigen expressed briefly on hepatocyte-invading P. falciparum sporozoites and predominantly on red blood cell-invading P. falciparum merozoites, not present in the RTS,S vaccine; included to analyze naturally acquired immunity responses).

Two comparisons were done to characterize the transcriptional response to RTS,S/AS01 vaccination: Comparison 1: comparing gene expression in month 3 samples from RTS,S/AS01 vs. comparator recipients (month 3 RTS,S/AS01 vs. comparator); and Comparison 2: comparing gene expression in months 3 vs. 0 from RTS,S/AS01 recipients (RTS,S/AS01 months 3 vs. 0). Each comparison has its own advantages: Comparison 1allows the identification of RTS,S/AS01-specific responses while taking into account other environmental factors to which the children are exposed, such as malaria exposure (albeit malaria transmission intensity was low during the study at both sites [RTS,S Clinical Trials Partnership, 2015]). Moreover, the very young ages of the trial participants mean that RTS,S/AS01-induced changes may be confounded with normal developmental changes in participant immune systems, further underscoring the value of Comparison 1, as it does not involve comparison across two different timepoints. On the other side, an advantage of Comparison 2is that it takes into consideration each participant’s intrinsic baseline gene expression. Comparison 1uses data from both infants and children, whereas Comparison 2can only yield insight into RTS,S/AS01 responses in children (as baseline samples were not collected from infants).

A BTM-based approach was taken to reduce dimensionality, avoid paying a high penalty for multiple testing, and aid results interpretability. For Comparison 1, there were 68 significantly differentially expressed (FDR cutoff ≤0.2) BTMs across all antigen stimulation conditions (Figure 2, Figure 2—source data 1). The majority (53)of these BTMs were in vehicle-treated PBMCs, with the most common categories being B cells (6 BTMs) and T cells (11 BTMs). Counter to our initial expectations, no significant correlations were identified in the CSP-stimulated cells adjusted by vehicle stimulation. This result is potentially explained by the low frequency of CSP-specific T cells in RTS,S/AS01 vaccinees (e.g., on average, <0.10% of all CD4+ T cells [Moncunill et al., 2017a]). In AMA1-stimulated cells adjusted by vehicle stimulation, some correlate BTMs associated with RTS,S/AS01 vaccination were shared with vehicle-treated cells (related to e.g. mitochondria, transcription, and translation), while distinct correlate BTMs were also identified (related to e.g. the cell cycle, dendritic cells, and the nuclear pore). It is possible that the latter finding reflects differences in Ag-specific responses vs. nonspecific responses in vehicle, as the AMA1-stimulated PBMC analysis was adjusted by vehicle stimulation. However, we favor the hypothesis that cytokines/chemokines released from activated T cells in malaria-exposed children and their effects on other PBMC may underlie this difference. Alternatively, AMA1 may be eliciting an innate response (Bueno et al., 2008).

For Comparison 2, only RTS,S/AS01 recipients for whom months 0 and 3 samples were available were included in the analysis (i.e., children only). There were a larger number (131) of significantly differentially expressed (FDR cutoff ≤0.2) BTMs across all stimulation conditions; again, the majority (90) were found in vehicle-stimulated PBMC (Figure 2, Figure 2—source data 2). In vehicle-stimulated PBMC, antiviral/interferon (IFN)-related BTMs were consistently upregulated at months 3 vs. 0, while monocyte- and antigen presentation-related BTMs were consistently downregulated. Similar to Comparison 1, the antigen stimulation results adjusted by vehicle stimulation shared little overlap with the vehicle stimulation results.

Monocyte-related RTS,S/AS01 signature BTMs associate with clinical malaria risk

To preserve statistical power in the immune correlates analysis, only BTMs differentially expressed after RTS,S/AS01 vaccination according to Comparison 1 (any stimulation) were down selected. We define these 68 BTMs as the ‘RTS,S/AS01 signature BTMs’ (Supplementary file 3). We next investigated if any of the RTS,S/AS01 signature BTMs were associated with clinical malaria case status in RTS,S/AS01 recipients, by comparing expression of the signature BTMs in cases vs. controls, within each stimulation condition.

In vehicle-stimulated PBMC, seven BTMs were significantly differently expressed in RTS,S/AS01 cases vs. controls (Figure 3, Figure 3—source data 1). Three were associated with risk (‘Enriched in myeloid cells and monocytes [M81]’, ‘Enriched in monocytes (II) [M11.0]’, and ‘Myeloid cell enriched receptors and transporters [M4.3]’), while four were associated with protection (‘Respiratory electron transport chain [mitochondrion] [M219]’, ‘Respiratory electron transport chain [mitochondrion] [M238]’, ‘spliceosome [M250]’, and ‘mitosis [TF motif CCAATNNSNNNGCG] [M169]’). The association of monocyte-related BTMs with risk is consistent with studies reporting a positive correlation between monocyte/lymphocyte ratio and clinical malaria risk and/or severity (Antwi-Baffour et al., 2018; Warimwe et al., 2013b).

The antigen-specific transcriptional modules associated with clinical malaria risk differed from those seen in vehicle-stimulated PBMC. In CSP- and HBS-stimulated cells adjusted by vehicle stimulation, there were 0 and 1 correlate BTMs, respectively. In AMA1-stimulated cells adjusted by vehicle stimulation, distinct and opposite correlations were seen, for example correlation with protection for ‘enriched in activated dendritic cells/monocytes (M64)’, ‘myeloid cell enriched receptors and transporters (M4.3)’, and ‘enriched in monocytes (II) (M11.0)’.

An analogous analysis, using the same downselected BTMs, was performed on comparator recipients. For all seven BTMs whose levels in vehicle-stimulated PBMC associated either directly or inversely with risk in RTS,S/AS01 recipients (Figure 3), significant correlations were observed in the opposite direction in comparator recipients (Figure 3—figure supplement 1, Figure 3—figure supplement 1—source data 1), suggesting that positive correlations of the three monocyte-related BTMs with risk and inverse correlations of the mitochondria-related BTMs with risk are specific to RTS,S/AS01 recipients.

RTS,S/AS01 vaccination elicits polyfunctional CSP-specific CD4+ T-cell responses that do not correlate with malaria risk

In addition to transcriptional changes, our group has shown previously that RTS,S/AS01 vaccination elicits vaccine-specific antibody and cellular responses in African infants and children (e.g., Dobaño et al., 2019a; Ubillos et al., 2018; Moncunill et al., 2017a). The polyfunctionality score is a summary measure that encapsulates a participant’s entire Ag-specific T-cell response after vaccination (Lin et al., 2015). Using data from a pilot study of 179 children (none of whom was a malaria case) at the Manhiça and Bagamoyo sites, Moncunill et al. previously showed that MAL067 RTS,S/AS01 recipients have higher month 3 CSP-specific and HBS-specific CD4+ T-cell polyfunctionality scores than comparator recipients (Moncunill et al., 2017a). Consistent with this finding, we report that average CSP-specific CD4+ T-cell polyfunctionality score is higher at month 3 vs. baseline in RTS,S/AS01 vaccine recipients (Figure 4A). The few high responders at baseline can likely be attributed to prior malaria exposure. However, there was no difference in magnitude (frequency of CD4+ T-cell expressing IL-2 or TNF or CD154) at month 3 vs. baseline in RTS,S/AS01 vaccine recipients (Figure 4B), nor was there a difference in average month 3 CSP-specific T-cell response polyfunctionality or magnitude between RTS,S/AS01 cases vs. controls (Figure 4C, D).

RTS,S/AS01 vaccination elicits circumsporozoite protein (CSP)-specific polyfunctional T-cell responses that do not correlate with clinical malaria risk.

Boxplots show (A) polyfunctionality score and (B) magnitude (% CD4+ T cells expressing IL2 or TNF-α or CD154) of CSP-specific CD4+ T-cell responses in RTS,S/AS01 recipients as assessed by intracellular cytokine staining of peripheral blood mononuclear cells (PBMC) collected at month 0 (M0) or at month 3 (M3). Each dot represents a single participant. Data plotted include all available months 0 and 3 samples, that is paired months 0–3 samples were not required for plotting. (C) Polyfunctionality score and (D) magnitude of CSP-specific CD4+ T-cell responses in RTS,S/AS01 vaccine recipients at month 3, stratified by case–control status. In panels A and B, p values were obtained using a mixed-effects model with participant as a random effect. In panels C and D, p values were obtained using a mixed-effects model with match_id as a random effect. Number of participants in each panel is: (A) 213 (73 M0 and 182 M3), (B) 194 (61 M0, 175 M3), (C) 37 cases and 145 controls, and (D) 36 cases and 139 controls.

Month 3 levels of RTS,S/AS01 signature BTMs tend to correlate directly with month 3 IgM antibody responses and inversely with month 3 IgG responses

We next investigated whether month 3 levels of the RTS,S/AS01 signature BTMs were associated with month 3 humoral immune responses in RTS,S/AS01 vaccinees. In vehicle-treated PBMC, both positive and negative associations were seen for multiple antibody variables across functional categories (Figure 5, Figure 5—source data 1) but mainly against nonvaccine antigens. Month 3 IgM antibodies against LSA1, MSP1 Block 2 (MAD20 strain), and MSP6 tended to correlate with month 3 levels of DC-, inflammatory/TLR/chemokine-, and monocyte-related BTMs (among others). In contrast, month 3 IgG antibodies against AMA1 (strains 3D7 and FVO) tended to correlate inversely with month 3 levels of DC- and monocyte-related BTMs, among others. These associations were not seen in comparator recipients (Figure 5—figure supplement 1), suggesting specificity to RTS,S/AS01 receipt, although we note that sample size is smaller which would have reduced statistical power to detect differences. Month 3 levels of cellular variables assessed by polychromatic flow cytometry did not correlate significantly with the month 3 level of any BTM.

Figure 5 with 1 supplement see all
Correlations of month 3 transcriptional and adaptive responses in RTS,S/AS01 vaccine recipients.

Heatmap showing correlations between month 3 levels of RTS,S/AS01 signature blood transcriptional modules (BTMs) in vehicle-treated peripheral blood mononuclear cells (PBMC) and month 3 antibody responses. Cell color intensity represents the strength of the correlation; BTM/response pairs with significant correlations (false discovery rate [FDR] ≤0.2) are outlined in black. Cell color represents correlation direction: red, positive correlation; blue, negative correlation. High-level BTM annotation groups are shown in the left-most color bar. Number of participants: 30–42.

Figure 5—source data 1

List of blood transcriptional modules (BTMs) whose month 3 levels in vehicle-treated peripheral blood mononuclear cells (PBMC) correlated significantly with at least one month 3 adaptive response variable in RTS,S/AS01 vaccinees, along with variable details, p value, and false discovery rate (FDR) results.

https://cdn.elifesciences.org/articles/70393/elife-70393-fig5-data1-v1.csv

Cross-study immune correlates analysis reveals a mostly consistent association in RTS,S/AS01 vaccinees between baseline expression of DC- and monocyte-related BTMs and risk

An important question is whether the results of our analysis of the MAL067 trial, which was conducted in African infants and children in malaria-endemic areas, are generally translatable to other study populations. PBMC transcriptomic data are available for at least three different CHMI studies conducted in healthy, malaria-naive adults in the United States. We performed a cross-study immune correlates analysis where we examined whether the BTMs associated with clinical malaria risk in MAL067 showed similar associations with challenge outcome in each of the three CHMI studies described in Methods: WRAIR 1032, MAL068, and MAL071. Importantly, all these trials share a common vaccine arm: one full dose of RTS,S/AS01B at months 0, 1, and 2 (referred to as the ‘RRR’ arm). Due to differences in sampling schedules, and the presence of the CHMI challenge (which would complicate results interpretation), we could not compare the exact same month 3 timepoint across studies. We chose instead to compare 21 days post-third dose in MAL068 and in MAL071, that is of day of challenge, and 14 days post-third dose in WRAIR 1032, that is just before or on day of challenge. We refer to these slightly different postvaccination timepoints as ‘month 3’ for simplicity. The month 3 cross-study correlates analysis included BTMs whose month 3 levels (in vehicle-stimulated PBMC) associated with clinical malaria risk in MAL067 RTS,S/AS01E recipients (Figure 3, Figure 3—source data 1) and is shown in Figure 6A. No BTM was consistently associated with malaria risk (or nonprotection) across all four studies. The most consistent result was for the monocyte-related BTM ‘enriched in monocytes (II) (M11.0)’, whose month 3 expression was significantly associated with risk in two of the three CHMI studies (Figure 6A, Figure 6—source data 1).

Figure 6 with 10 supplements see all
Associations of (A) month 3 or (B) month 0 levels of downselected blood transcriptional modules (BTMs) with malaria case status RTS,S/AS01 vaccine recipients across studies sharing a common months 0, 1, and 2 RTS,S/AS01 arm.

(A) Heatmap showing the difference in month 3 peripheral blood mononuclear cell (PBMC) BTM expression between RTS,S/AS01 cases vs. controls, in each of three controlled human malaria infection (CHMI) studies, of the seven BTMs whose month 3 levels in vehicle-stimulated PBMC associated with malaria case status in MAL067 (Figure 3). ‘Month 3’ = 21-day postfinal dose in MAL068 and MAL071, and 14-day postfinal dose in WRAIR 1032. BTMs with significantly different expression (false discovery rate [FDR] ≤0.2, with adjustment done across the five BTMs) are outlined in black. |FDR| < 0.2 (*), <0.05 (**), <0.01 (***). (B) Heatmap showing the 45 BTMs whose month 0 levels showed significantly different expression in MAL067 RTS,S/AS01 malaria cases vs. nonmalaria controls. These 45 BTMs were also examined as potential correlates of challenge outcome in each of the 3 CHMI studies. Significantly different expression is defined as FDR ≤0.2, with adjustment across the 45 BTMs. All data shown are from participants who received the same vaccine regimen: a dose of RTS,S/AS01 at months 0, 1, and 2. Cell color intensity represents the significance of the difference in the case vs. control comparison, expressed as signed log10 FDR; BTMs with significantly different expression (FDR ≤0.2) between the two compared groups are outlined in black. |FDR| < 0.2 (*), <0.05 (**), <0.01 (***). Red, higher expression in RTS,S/AS01 cases vs. controls; blue, lower expression in RTS,S/AS01 cases vs. controls. High-level BTM annotation groups are shown in the left-most color bar. Numbers of participants in each analysis are: (A) MAL067, 122; WRAIR 1032, 39; MAL068 RRR, 21; MAL071 RRR, 16. (B) MAL067, 37; WRAIR 1032, 39; MAL068 RRR, 21; MAL071 RRR, 16. Detailed equations are given in Methods.

Figure 6—source data 1

List of the seven blood transcriptional modules (BTMs) whose month 3 levels had significantly different expression in RTS,S/AS01 cases vs. controls in MAL067, along with p values and false discovery rate (FDR) results when testing these seven BTMs for significantly different expression in cases vs. controls in the WRAIR 1032, MAL068 RRR, and MAL071 RRR studies.

https://cdn.elifesciences.org/articles/70393/elife-70393-fig6-data1-v1.csv
Figure 6—source data 2

List of the 45 blood transcriptional modules (BTMs) whose month 0 levels had significantly different expression in RTS,S/AS01 cases vs. controls in MAL067, along with p values and false discovery rate (FDR) results when testing these 45 BTMs for significantly different expression in cases vs. controls in the WRAIR 1032, MAL068 RRR, and MAL071 RRR studies.

https://cdn.elifesciences.org/articles/70393/elife-70393-fig6-data2-v1.csv

We next performed the baseline correlates analysis of MAL067 (left-most column, Figure 6B). Compared to the results from the month 3 analysis (7 BTMs), the baseline correlates analysis of MAL067 revealed a larger number (45)of BTMs, spanning many functional categories, whose month 0 levels in vehicle-stimulated PBMC nearly all associated with clinical malaria risk in RTS,S/AS01 recipients (Figure 6B, Figure 6—source data 2). The BTM with the most significant association with risk was ‘enriched in monocytes (II) (M11.0)’ (FDR = 1.80E−14), followed by ‘inflammatory response (M33)’ (FDR = 2.45E−07) and ‘resting dendritic cell surface signature (S10)’ (FDR = 6.03E−07). Only one BTM, ‘cell cycle and transcription (M4.0)’, was significantly associated with risk across all four studies. Of the 335 genes in this module, 130 were also present in 1 or more of the 6 ‘monocyte-related’ BTMs shown in Figure 6 (297 genes total across all 6 BTMs), suggesting that the ‘cell cycle’ and ‘monocyte’ results may be picking up the same signal.

Comparing across studies, a fair degree of overlap was seen between the MAL067 associations and the CHMI associations. MAL067 and WRAIR 1032 shared the most BTMs significantly associated with risk (29 BTMs); of these, 12 were also associated with risk in MAL068 RRR. BTMs related to dendritic cells and to monocytes were most consistently associated with risk across these three studies (‘resting dendritic cell surface signature [S10]’, ‘DC surface signature [S5]’, ‘enriched in dendritic cells [M168]’, ‘enriched in monocytes [I] [M4.15]’, ‘enriched in monocytes [II] [M11.0]’, ‘enriched in monocytes [IV] [M118.0]’, and ‘monocyte surface signature [S4]’, significantly correlated with risk in all three studies).

To gain insight into specific module-member genes that may be involved in the RTS,S/AS01 baseline risk signature, we performed the same analysis on the gene level, that is examined associations with clinical malaria risk for each of the constituent genes in the 45 BTMs shown in Figure 6. Figure 6—figure supplements 18 show the gene-level association results within the eight BTMs that were significantly associated with clinical malaria risk in MAL067 and at least two of the three CHMI studies, and had at least one gene in MAL067 that was significantly associated with risk (these eight correspond to M4.0, S10, S5, M168, M4.3, M11.0, M4.15, and S4). Within MAL067, 35 unique genes were shown to significantly associate with malaria risk (Supplementary file 4); 9 of these genes (CCNF, MK167, KIF18A, NPL, RBM47, CFD, MAFB, IL13RA1, and CCR1) also had significant association with nonprotection in one of the CHMI studies. Although no individual gene was significantly associated with risk across >two studies, many showed consistent effect (direction and magnitude) across three studies. This further supports our choice to focus on modules instead of individual genes as GSEA increases power to detect more subtle but coordinated changes in gene expression data that would be missed otherwise. For this same reason, GSEA has been shown to enhance cross-study comparisons (Subramanian et al., 2005).

Baseline transcriptional associations with month 3 adaptive responses are presented in Figure 6—figure supplement 9. The baseline expression of each of 52 BTMs, spanning a range of functional categories, was significantly and positively correlated with CSP-specific CD4+ T-cell polyfunctionality score and the baseline expression of each of 17 BTMs was also significantly and positively correlated with HBS-specific CD8+ T-cell polyfunctionality score. No significant associations were seen with any antibody responses.

The finding that monocyte-related BTMs were expressed significantly higher in RTS,S/AS01 cases vs. controls at month 3 in three of the four studies examined (Figure 6A) and at month 0 in three of the four studies examined (Figure 6B) suggested that circulating monocyte frequencies may be higher in cases vs. controls at these two timepoints. To investigate this hypothesis, PBMC from RTS,S/AS01 recipients were assessed by immunophenotyping and flow cytometry. As shown in Figure 6—figure supplement 10A, the analysis revealed no significant difference in monocyte frequency in cases vs. controls, at either month 3 or 0. At month 3, both the inflammatory monocyte frequency and inflammatory monocyte/lymphocyte ratio tended to be higher in cases than in controls; however, these differences were not significant (Figure 6—figure supplement 10B, C). Thus, these findings do not support that the upregulation of monocyte-related genes in PBMC from cases (vs. controls) is due to higher frequencies of circulating monocytes. A potential explanation for why we identified monocyte-related BTMs in our transcriptional signature of risk yet did not see an association of baseline monocyte frequency, inflammatory frequency, or inflammatory monocyte/lymphocyte ratio with risk is that the baseline monocyte transcriptional signature of risk reflects expression changes in the existing circulating monocyte population, rather than an expansion in the circulating monocyte population.

Discussion

Our main finding is the identification of a baseline BTM signature that associates with clinical malaria risk in RTS,S/AS01-vaccinated African children. In a cross-study comparison, much of this baseline risk signature – specifically, dendritic cell- and monocyte-related BTMs – was also recapitulated in two of the three CHMI studies in healthy, malaria-naive adults. Our finding fits into a growing body of evidence that baseline immune status can influence vaccine responses (Tsang et al., 2020). Fourati et al. showed that higher baseline inflammation (as assessed by transcriptomic profiling and flow cytometric analysis of immune cell subset frequencies) was associated with poor antibody response to the hepatitis B vaccine (Fourati et al., 2016). Tsang et al. showed that baseline interferon signaling was robustly correlated with maximum fold change (postinfluenza vaccination to baseline) in influenza-specific antibody titer (Tsang et al., 2014). The HIPC Consortium identified a baseline inflammatory gene signature that was associated with higher antibody responses to influenza vaccine in younger participants, yet lower antibody responses in older participants (HIPC-CHI Signatures Project Team and HIPC-I Consortium, 2017). Kotliarov et al. identified baseline transcriptional signatures that predicted antibody responses to the live attenuated yellow fever vaccine and to the trivalent inactivated influenza vaccine (Kotliarov et al., 2020). Moreover, Hill et al. reported that increased baseline frequencies of plasmablasts and of circulating T follicular helper cells were associated with higher post-RTS,S/AS01 vaccination antibody titers (Hill et al., 2020).

Two previous studies have reported a positive correlation between monocyte to lymphocyte (ML) ratio and clinical malaria risk and/or severity (Antwi-Baffour et al., 2018; Warimwe et al., 2013b), and a higher ML ratio has been reported to associate with lower VE of RTS,S (Warimwe et al., 2013a). Note that the ML ratio in these studies was based on lymphocyte count and monocyte count from a differential blood count performed using a Coulter Counter, and thus cannot inform on the gene expression profiles of the circulating monocytes, or on composition of circulating monocyte subsets. Based on previous evidence in various mouse models of viral infection that inflammatory monocytes inhibit T-cell proliferation (Norris et al., 2013; Mitchell et al., 2012), T-cell activation (Mitchell et al., 2012), and B-cell responses (Sammicheli et al., 2016), Warimwe et al. proposed that inflammatory monocytes may inhibit RTS,S-induced protective adaptive responses. Our gene-level correlates analyses suggest an alternative hypothesis, however. With the caveat that the gene-level analyses were performed post hoc, high baseline expression of STAB1 (which is present in DC-, monocyte-, and cell cycle-related modules) was found to positively associate with malaria risk (Figure 6—figure supplements 1, 2 and 6). STAB1 encodes stabilin-1 (also called Clever-1), a transmembrane glycoprotein scavenger receptor that links extracellular signals to intracellular vesicle trafficking pathways (Kzhyshkowska et al., 2006). Stabilin-1high monocytes show downregulation of proinflammatory genes, and T cells cocultured with stabilin-1high monocytes showed decreased antigen recall, suggesting that monocyte stabilin-1 suppresses T-cell activation (Palani et al., 2016). Thus, one possibility is that stabilin-1high immunosuppressive monocytes circulating at baseline could decrease protective RTS,S-induced T-cell responses, or inhibit another aspect of adaptive immunity. Single-cell transcriptomic profiling of PBMC or purified monocyte subsets in future RTS,S trials in African children in malaria-endemic areas could help test this hypothesis.

The RTS,S/AS01 vaccine is adjuvanted with AS01, a liposome-based adjuvant containing 3-O-desacyl-monophosphoryl lipid A (MPL) and the saponin QS-21 (Didierlaurent et al., 2017). MPL activates the innate immune response by stimulation of Toll-like receptor 4 (TLR4) (Baldridge et al., 2004); thus, another interesting finding of the gene-level analyses is the significant association of baseline TLR4 expression with risk (Figure 6—figure supplements 1, 6 and 8). As TLR4 is expressed predominantly on monocytes (Vaure and Liu, 2014; Hornung et al., 2002) out of all the PBMC constituent cell types, this likely reflects an association of high baseline monocyte TLR4 expression with risk. This finding was unexpected, as we have previously hypothesized that increasing TLR expression and/or signaling may help augment RTS,S/AS01 VE (Moncunill et al., 2020).

We have also previously reported that interferon, NF-κB, TLR, and monocyte-related BTMs were associated with protection in children and infants in the RTS,S/AS01 phase 3 trial (Andersen-Nissen et al., 2021). While the latter appears to contradict the identification of monocyte-related BTMs in the baseline risk signature identified in the present study, key differences between the two studies can account for this apparent discrepancy. The main difference is that in the present study we identified a baseline signature in vehicle-treated PBMC that associated directly with malaria risk, whereas we found hardly any associations when analyzing antigen-specific transcriptional responses. In contrast, in our previous study (Moncunill et al., 2020), we only assessed in vitro recall responses with antigen stimulation of samples obtained at 1 month after the third vaccination, correcting for background responses for each individual. Thus, the association of expression of monocyte-related BTMs with protection was observed after analyzing gene expression levels in CSP-stimulated, background-corrected PBMC. In the case of the vaccine-nonspecific antigen AMA1, we also observed a general pattern of inverse correlations with risk in vehicle-treated vs. AMA-1 stimulated PBMC (Figure 3). Another potential reason for why no BTMs were found to associate with the response to RTS,S/AS01 vaccination or with protection when analyzing CSP-stimulated PBMC is that all PBMC were stimulated on site for 12 hr (this stimulation time was chosen based on the kinetics of the IFN-γ transcriptional response) and then cryopreserved. Thus, we were unable to detect earlier transient responses that had already resolved by 12 hr, as well as more delayed response that had not yet initiated by 12 hr, if such responses occurred.

It is perhaps counterintuitive – considering that the RTS,S/AS01 vaccine does not contain AMA1 – that we observed a small number of BTMs associated with the response to RTS,S/AS01 vaccination and with clinical malaria risk when analyzing AMA1-stimulated PBMC. To explain this result, we refer the reader to our previous work that showed that RTS,S/AS01 vaccination alters antibody responses to antigens not contained in the RTS,S/AS01 vaccine (Dobaño et al., 2019b). RTS,S/AS01 recipients received partial protection from the RTS,S/AS01 vaccine, leading possibly to decreased P. falciparum parasite load and/or exposure (infection). We hypothesize that the AMA1 stimulation activated T cells that had been previously primed by prior exposure to P. falciparum and that RTS,S/AS01 recipients had fewer primed T cells due to decreased P. falciparum infection (via partial RTS,S/AS01 protection), providing a potential explanation for the transcriptional differences in AMA1-stimulated PBMC between RTS,S/AS01 vs. comparator recipients.

Compared to the 45 BTMs whose baseline levels significantly associated with clinical malaria risk in RTS,S/AS01-vaccinated African children, fewer BTMs (seven) had levels at 1-month postfinal RTS,S/AS01 dose that significantly associated with clinical malaria risk. Moreover, if a more stringent FDR cutoff had been used (i.e., 5%), six of these seven BTMs would not have been identified. Thus, it is entirely possible that, at 1-month postfinal RTS,S/AS01 dose, there is no circulating immune transcriptomic signature predictive of risk. Such a conclusion would not be surprising, given that in malaria-naive adults, the transcriptional response to the third RTS,S/AS01 dose has been shown to peak at day 1 postinjection, with some decline by day 6 and approximately 90% of the response having waned by day 21 (Kazmin et al., 2017). Therefore, it is likely that the sampling scheme in this study (1-month postfinal dose) misses the majority of the transcriptional response to RTS,S/AS01. Future studies with dense PBMC sampling during the transcriptional peak of the vaccine-induced response could be useful for further investigating RTS,S transcriptional immune correlates.

Additional limitations of our study include the following: first, PBMCs were stimulated on site and then frozen. As each site performed the procedure separately, this renders our data susceptible to batch effects. However, a standardized SOP and shared reagents were used, decreasing the possibility of such effects. Moreover, an advantage of onsite stimulation of fresh PBMC is that it avoids the decrease in cell viability, and potential loss of detection of Ag-specific cells, that may have occurred if PBMC had been frozen, thawed, and then stimulated at a central location. Second, there was confounding between age and location. As all infants were from Manhiça and the majority of children were from Bagamoyo, it was not possible to examine the impact of age or clinical trial site on RTS,S/AS01 transcriptional response. Third, we do not know whether the controls were truly protected or whether they were never exposed to malaria in the first place. This limitation highlights the importance of our cross-study analysis, where all participants are exposed. Fourth, despite the relatively large size of the study, our statistical power was limited by the number of malaria cases with available samples; sampling additional controls would not have increased our statistical power. Fifth, as only patrolling cell subsets are present in PBMC, we were unable to detect potential signals from T cells, B cells, NK cells, and macrophages localized to an infection site including skin and liver or the immune memory compartment localized in secondary lymphoid organs. Finally, while it is not uncommon to use an FDR cutoff of 20% in high-dimensional immune correlates studies (e.g., Andersen-Nissen et al., 2021; Liu et al., 2021; Lu et al., 2021; Haynes et al., 2012; Fletcher et al., 2016; Young et al., 2021), our results should be interpreted with the requisite level of caution. However, we do note that many of our significant modules in the baseline risk analysis would have survived even lower FDR cutoffs (in many cases even a 1% cutoff), giving us a fair degree of confidence in our results. For example, of the seven monocyte-related BTMs whose baseline levels associated with risk, all would have survived a 5% FDR cutoff, and three even a 1% cutoff; likewise, of the four dendritic cell-related BTMs whose baseline levels associated with risk, all would have survived a 1% cutoff.

Despite these limitations, our study also has a number of strengths. For example, while excellent work has already been done to interrogate transcriptional responses to RTS,S/AS01 vaccination in healthy, malaria-naive adults (including densely sampled early postvaccination sampling timepoints to capture innate responses) and to identify molecular correlates of RTS,S/AS01-mediated protection against clinical malaria after CHMI in malaria-naive adults (Kazmin et al., 2017; Du et al., 2020; Vahey et al., 2010; van den Berg et al., 2017), in our study we examined transcriptional responses to RTS,S/AS01 vaccination in infants and children in malaria-endemic areas. This feature is a strength of our study, as (1) infants in particular have relatively immature immune systems (Simon et al., 2015), making it likely that infants (and younger children) mount different vaccine responses than adults (Pichichero, 2014) (2) infants and children are especially susceptible to malaria-related morbidity and mortality, making them the target population for this and other malaria vaccines; and (3) continual exposure to P. falciparum, as occurs in endemic areas, influences naturally acquired immunity, which in turn interacts with immunity conferred by RTS,S vaccination (Dobaño et al., 2019b). Related to this, another advantage of our study is the use of a comparator group which allows to discern the effect of the vaccine from environmental exposures including P. falciparum and age. As participants in the study are very young, significant development of their immune systems occurs throughout the duration of the study, meaning that such changes could potentially be confounded with vaccine-induced immune changes.

While it will be necessary to perform follow-up studies at more sites and with larger sample sizes to validate the baseline transcriptional signature associated with malaria risk identified here, our study suggests that innate immune cells may shape responses to RTS,S/AS01 and raises hypotheses for future testing related to monocytes and RTS,S/AS01-mediated protection.

Data availability

Sequencing data have been deposited in GEO under accession code GSE176156. Immune phenotyping and intracellular cytokine staining data used for the analysis are archived on ImmPort (https://immport.niaid.nih.gov/home). The analysis code can be found at https://github.com/william-c-young/mal067_paper (copy archived at swh:1:rev:1168525a46d42019c8ba39d1c440e92e2c33c596).

The following data sets were generated
The following previously published data sets were used
    1. Vahey M
    (2009) NCBI Gene Expression Omnibus
    ID GSE18323. Expression data from a malaria vaccine trial (HG-U133A2.0 and U133 Plus 2.0).
    1. Kazmn D
    2. Pulendran B
    (2017) NCBI Gene Expression Omnibus
    ID GSE89292. Systems analysis of protective immune responses to RTS,S malaria vaccination in humans.
    1. Du Y
    2. Zak DE
    3. Shankar S
    (2020) NCBI Gene Expression Omnibus
    ID GSE103401. Transcriptional responses to RTS,S standard regimen (RRR) and Ad35.CS+R,R vaccination in controlled human malaria infection study.
    1. Thompson E
    2. Du Y
    3. Zak DE
    4. Shankar S
    (2020) NCBI Gene Expression Omnibus
    ID GSE102288. Gene responses to RTS,S malaria vaccination in controlled human malaria study.
    1. Du Y
    2. Thompson E
    3. Zak DE
    4. Shankar S
    (2020) NCBI Gene Expression Omnibus
    ID GSE107672. Transcriptional responses to RTS,S induced vaccination in controlled human malaria infection studies.

References

    1. Agnandji ST
    2. Lell B
    3. Soulanoudjingar SS
    4. Fernandes JF
    5. Abossolo BP
    6. Conzelmann C
    7. Methogo B
    8. Doucka Y
    9. Flamen A
    10. Mordmüller B
    11. Issifou S
    12. Kremsner PG
    13. Sacarlal J
    14. Aide P
    15. Lanaspa M
    16. Aponte JJ
    17. Nhamuave A
    18. Quelhas D
    19. Bassat Q
    20. Mandjate S
    21. Macete E
    22. Alonso P
    23. Abdulla S
    24. Salim N
    25. Juma O
    26. Shomari M
    27. Shubis K
    28. Machera F
    29. Hamad AS
    30. Minja R
    31. Mtoro A
    32. Sykes A
    33. Ahmed S
    34. Urassa AM
    35. Ali AM
    36. Mwangoka G
    37. Tanner M
    38. Tinto H
    39. D’Alessandro U
    40. Sorgho H
    41. Valea I
    42. Tahita MC
    43. Kaboré W
    44. Ouédraogo S
    45. Sandrine Y
    46. Guiguemdé RT
    47. Ouédraogo JB
    48. Hamel MJ
    49. Kariuki S
    50. Odero C
    51. Oneko M
    52. Otieno K
    53. Awino N
    54. Omoto J
    55. Williamson J
    56. Muturi-Kioi V
    57. Laserson KF
    58. Slutsker L
    59. Otieno W
    60. Otieno L
    61. Nekoye O
    62. Gondi S
    63. Otieno A
    64. Ogutu B
    65. Wasuna R
    66. Owira V
    67. Jones D
    68. Onyango AA
    69. Njuguna P
    70. Chilengi R
    71. Akoo P
    72. Kerubo C
    73. Gitaka J
    74. Maingi C
    75. Lang T
    76. Olotu A
    77. Tsofa B
    78. Bejon P
    79. Peshu N
    80. Marsh K
    81. Owusu-Agyei S
    82. Asante KP
    83. Osei-Kwakye K
    84. Boahen O
    85. Ayamba S
    86. Kayan K
    87. Owusu-Ofori R
    88. Dosoo D
    89. Asante I
    90. Adjei G
    91. Adjei G
    92. Chandramohan D
    93. Greenwood B
    94. Lusingu J
    95. Gesase S
    96. Malabeja A
    97. Abdul O
    98. Kilavo H
    99. Mahende C
    100. Liheluka E
    101. Lemnge M
    102. Theander T
    103. Drakeley C
    104. Ansong D
    105. Agbenyega T
    106. Adjei S
    107. Boateng HO
    108. Rettig T
    109. Bawa J
    110. Sylverken J
    111. Sambian D
    112. Agyekum A
    113. Owusu L
    114. Martinson F
    115. Hoffman I
    116. Mvalo T
    117. Kamthunzi P
    118. Nkomo R
    119. Msika A
    120. Jumbe A
    121. Chome N
    122. Nyakuipa D
    123. Chintedza J
    124. Ballou WR
    125. Bruls M
    126. Cohen J
    127. Guerra Y
    128. Jongert E
    129. Lapierre D
    130. Leach A
    131. Lievens M
    132. Ofori-Anyinam O
    133. Vekemans J
    134. Carter T
    135. Leboulleux D
    136. Loucq C
    137. Radford A
    138. Savarese B
    139. Schellenberg D
    140. Sillman M
    141. Vansadia P
    (2011) First results of phase 3 trial of RTS,S/AS01 malaria vaccine in African children
    The New England Journal of Medicine 365:1863–1875.
    https://doi.org/10.1056/NEJMoa1102287

Decision letter

  1. Richard B Kennedy
    Reviewing Editor; Mayo Clinic, United States
  2. Betty Diamond
    Senior Editor; The Feinstein Institute for Medical Research, United States
  3. Wiebke Nahrendorf
    Reviewer; University of Edinburgh, United Kingdom

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "A baseline transcriptional signature associates with clinical malaria risk in RTS,S/AS01-vaccinated African children" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Betty Diamond as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Wiebke Nahrendorf (Reviewer #1).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Reviewer #1 (Recommendations for the authors):

The authors use a systems vaccinology approach to find a circulating immune signature that can predict RTS,S vaccine efficacy. It is perfectly possible that such a signature simply does not exist (the null hypothesis). I would recommend throughout the paper for the authors to check if their results (in the way they were generated and analysed) support or reject the null hypothesis – both of which are equally valid outcomes!

The MAL067 trial set up is fantastic (age-matched children receiving RTS,S or comparator vaccine who do or do not develop clinical malaria), but unfortunately isolating PBMCs 1-month after final vaccination to assess recall responses did not really work: whilst unstimulated PBMCs are transcriptional different between RTS,S and comparator, no signifiant signature of RTS,S vaccination after CSP and HBS stimulation was observed (Figure 2A). It is biologically hard to explain why differences were not maintained after stimulation. Together with the inconsistencies (opposite correlations) when analysing AMA-1 stimulated PBMCs or matched pre- and post RTS,S vaccination samples (Figure 2B), and allowing for 20% false discoveries it raises questions about the experimental and statistical robustness of this approach. Since antigen-stimulation of PBMCs is not informative the authors could focus their analysis on unstimulated PBMCs from Figure 3 onwards, which would massively streamline their message.

Figure 4: include Figure S2 as a panels as they convey crucial information: explicitly draw the conclusion that the production of multiple cytokines after 1-month after final RTS,S vaccination does not predict protection from clinical malaria (reflect in relevant results subheading and figure legend title).

Figure 5: this Figure is too dense which makes it hard to find the interesting information – it could be simplified by only including unstimulated PBMCs. It is surprising that antibodies do not correlate with B cell modules – causing some doubt about this correlation approach (not clear from line 229 – 232 if Spearman or Pearson? very relaxed p-value of 0.2).

Figure 6: A the five post vaccination modules identified in Figure 3 do not associate with risk in RTS,S CHMI studies – why might this be? young African children in malaria endemic environment vs malaria-naive US adults? or because these modules were not very strongly associated with risk in the first place? B – this panel is what the title and main conclusions of the paper are based on – it is interesting to speculate that the innate responses before vaccination may shape vaccine efficacy. Drilling down into the genes in the modules (functional transcriptomics) would be useful to get a glimpse into how this might work and to guide future work where e.g monocytes could be directly isolated. As it stands the conclusion that "inflammatory monocytes may inhibit protective RTS,S/AS01-induced responses" (line 58/59) is not well supported – especially since the authors published a paper last year which finds an inverse association (Moncunill G, Scholzen A, Mpina M, Nhabomba A, Hounkpatin AB, Osaba L, Valls R, Campo JJ, Sanz H, Jairoce C, et al: Antigen-stimulated PBMC transcriptional protective signatures for malaria immunization. Sci Transl Med 2020). The explanations offered in the discussion (621 – 633) are technical rather than biological again raising some questions about the robustness of the here presented approach.

General remarks

The decision to use blood transcriptional module analysis to reduce data dimensionality is a good one, but once candidate modules are identified the authors should drill down into the nitty gritty of which genes are up and downregulated and infer cell function to provide testible hypothesis for future functional and mechanistic studies.

Use figure legend headings/ results subheadings to summarise results.

Include n in Figure legends.

"month 3" could be called "post vaccination" for a more intuitive read.

Unstimulated PBMCs: sometimes called vehicle, sometimes DMSO – standardise.

Specifics

Line 319 "tended to correlate in opposite directions with risk" : no talk about malaria risk yet.

Line 466 – 475 move trial details from Results section to methods.

Supplementary methods are 3 lines long – include in text.

Reviewer #3 (Recommendations for the authors):

I thank the authors for presenting and interesting and well-written manuscript. In addition to my comments above, I have the following suggestions which I believe would improve the clarity and transparency of the paper

1. It would be helpful to provide an explanation of how the sample sizes were derived. Since the main objective was to identify BTMs associated with developing malaria after RTS,S/AS01 vaccine, it would be useful to report the malaria incidence in the main Phase 3 trial for the age groups studied at each site in the follow-up period of this study in the RTS,S and comparator vaccine arms. This will help to address the question of whether receipt of RTS,S vaccine is the main reason for the difference between cases and controls, or whether it is more related to exposure to infectious bites. If the EIR is low, then one might expect that the vaccine explains rather little of the difference between cases and controls, and therefore a large sample size would be needed to detect any significant association between gene expression and vaccine efficacy.

2. It would be helpful to include more data in table 1 to show how well the matching process for cases and controls worked. Table 1 could be reformatted to be easier to read, with separate columns for each site (rather than the current presentation of this information in parentheses) and could include rows for age, sex, time of vaccination and duration of follow-up.

3. In view of the perplexing result mentioned in the previous review section for DMSO stimulation, I think further detail about the PBMC stimulation is needed. I cannot find the DMSO concentration used for vehicle or antigen stimulation in either this paper or the "previously described" reference (24). The authors do not describe that they have subtracted or adjusted for the vehicle-induced gene expression in assessing antigen induced gene expression, but this might be one explanation for this odd result. If this is not the explanation, I worry that different concentrations of DMSO may have been used in the vehicle and antigen stimulation conditions. As this results appears anomalous, it requires further explanation of the methods or discussion as to why this result may have occurred.

4. The choice of an FDR significance threshold of 0.2 has not been justified. As mentioned in the previous section of the review this is extremely liberal, and diminishes confidence in the significance of the findings. In most analyses there are BTMs with FDRs below 0.05, so I think it is essential that the authors explain why they chose to use an FDR of 0.2 throughout the paper, and that they discuss the implications of selecting such a liberal threshold.

5. The authors have described quite a lot of different statistical approaches in the methods (line 227-256) but it is currently difficult for the reader to understand where each of these has been applied to the data that are presented (particularly in the figures). I think it is essential that figure legends include the number of subjects from each group included in analyses and the type of analysis which has been performed and the criteria for statistical significance. There are several elements in the current description of these analyses which did not fully make sense or which I could not see used in the results that have been presented:

a. Line 229-231 state that Spearman's rank correlation was used and then in the next sentence that Pearson correlation was used – please clarify as only one or the other should be used for each analysis.

b. Line 235-239 states that adjustment was made for clinical and experimental covariates – these covariates do not appear to be reported anywhere and it is unlcear in the Results section which analyses (if any) included this adjustment.

c. Line 247-250: the results of this logistic regression are not obviously presented (or at least this method of analysis is not reported explicitly in the Results section) and the stratification variables do not appear to have been reported anywhere.

6. The authors have decided to include combine rabies and meningococcal C vaccine arms into a single "comparator" vaccine group. One might expect these to elicit quite different effects on the immune system and I think it is important to present data to justify the decision to include them together. For example, a series of PCA plots of the DMSO-stimulated transcriptional responses, with subjects coloured by vaccine, by age group, and by site could be very informative for interpretation of the rest of the data in the paper.

7. In all figures the use of a log10 FDR colour scale in the heatmaps makes it very difficult to identify the FDR values. I appreciate that these are included in the supplementary tables, but is it possible to add the FDR value in each cell of the heatmap for the significant values, or if not, perhaps to adopt a categorical approach to colouring them (eg. FDR<0.01; FDR<0.05; FDR <0.2)? This would enhance interpretation.

https://doi.org/10.7554/eLife.70393.sa1

Author response

Reviewer #1 (Recommendations for the authors):

The authors use a systems vaccinology approach to find a circulating immune signature that can predict RTS,S vaccine efficacy. It is perfectly possible that such a signature simply does not exist (the null hypothesis). I would recommend throughout the paper for the authors to check if their results (in the way they were generated and analysed) support or reject the null hypothesis – both of which are equally valid outcomes!

The MAL067 trial set up is fantastic (age-matched children receiving RTS,S or comparator vaccine who do or do not develop clinical malaria), but unfortunately isolating PBMCs 1-month after final vaccination to assess recall responses did not really work: whilst unstimulated PBMCs are transcriptional different between RTS,S and comparator, no signifiant signature of RTS,S vaccination after CSP and HBS stimulation was observed (Figure 2A). It is biologically hard to explain why differences were not maintained after stimulation.

The fact that bulk transcriptional profiling of Ag-stimulated PBMCs (and specifically to CSP) did not identify large significant differences in BTM expression between the RTS,S vs. comparator group could be due to several factors. First of all, the frequency of antigen-specific CD4+ T cells was very low among CD4+ T cells (Figure 4 of the manuscript shows that CSP-specific CD4+ T cells comprise < 0.004% of all CD4+ T cells). This low frequency of CSP-specific T cells is consistent with other RTS,S studies [e.g. as we state on line 385, we have previously found that CSP-specific T-cells in RTS,S/AS01 vaccinees comprise < 0.10% of all CD4+ T cells (1)]. Moreover, CD4+ T cells themselves comprise approximately 45-57% of all PBMCs (2). Thus, finding an expression signal between the RTS,S vs. comparator group would require the signal to be high enough to be detected in only 0.002% of all PBMCs [0.004% (% CSP-specific CD4+ T cells out of total CD4+ T cells) x 51% (average % of CD4+ T cells out of all PBMCs) = 0.002%]. Thus, lack of detectable recall response does not mean lack of recall response. Moreover, as suggested below, we opted not to focus the rest of the manuscript on the Ag-stimulation results.

Second of all, the PBMCs were stimulated on site for 12h and then cryopreserved. This stimulation time was chosen based on the kinetics of IFN-g and IL-2 mRNA response (3), but other responses may have had different kinetics and thus have already resolved or have not yet occurred by the 12-h cryopreservation. We have added text in the manuscript to discuss these caveats (“Another potential reason for why no BTMs were found to associate with the response to RTS,S/AS01 vaccination or with protection when analyzing CSP-stimulated PBMC is that all PBMC were stimulated on site for 12 hours (this stimulation time was chosen based on the kinetics of the IFN-γ transcriptional response) and then cryopreserved. Thus, we were unable to detect earlier transient responses that had already resolved by 12 hours, as well as more delayed response that had not yet initiated by 12 hours, if such responses occurred.; lines 599-605).

It should be noted that in all our analyses, the stimulated results were adjusted for DMSO to focus on the antigen-specific response only. This would explain why we detect signal in the DMSO samples but not in response to stimulation. We have realized that this was not very well described in the figure captions and the Methods section and have added more details, including the model description in Methods section. As such, we do not believe that these results impact all downstream conclusions. We believe that the unstimulated results provide significant new insights into the immune and molecular mechanisms of RTS,S vaccine efficacy, not necessarily directly related to the RTS,S-specific acquired immune response. Finally, we would like to highlight the fact that we have improved our model specification to directly account for the pairing of some of the samples using a random effect using the limma package. This has slightly increased statistical power, and as such the number of significantly differentially expressed BTMs in response to stimulation is a bit higher (but still much less than that for the DMSO). Originally, we had decided against the use of a random effect due to the computational cost of estimating the random effect.

In addition, one clarification, we investigated whether a circulating immune signature associates with individual-level clinical malaria case/control status, not RTS,S vaccine efficacy.

Together with the inconsistencies (opposite correlations) when analysing AMA-1 stimulated PBMCs or matched pre- and post RTS,S vaccination samples (Figure 2B), and allowing for 20% false discoveries it raises questions about the experimental and statistical robustness of this approach.

Our response explains the opposite correlations. Since AMA1 is actually looking at the effect of AMA1-DMSO.

As for the FDR rate, it is not uncommon to use a threshold of 20% for immune correlates studies [e.g. (6-11)]. We agree with you that it is important to clearly state the chosen FDR rate and to discuss conclusions in the context of the FDR rate used. We see we could improve our manuscript in this respect. We have added the following:

Results: “Compared to the 45 BTMs whose baseline levels significantly associated with clinical malaria risk in RTS,S/AS01-vaccinated African children, fewer BTMs (seven) had levels at one month post-final RTS,S/AS01 dose that significantly associated with clinical malaria risk. Moreover, if a more stringent FDR cutoff had been used (i.e. 5%), six of these seven BTMs would not have been identified. Thus it is entirely possible that, at one month post-final RTS,S/AS01 dose, there is no circulating immune transcriptomic signature predictive of risk…” (lines 617-628)

Discussion: “Finally, while it is not uncommon to use an FDR cutoff of 20% in high-dimensional immune correlates studies [e.g. (65-70)], our results should be interpreted with the requisite level of caution. However, we do note that many of our significant modules in the baseline risk analysis would have survived even lower FDR cutoffs (in many cases even a 1% cutoff), giving us a fair degree of confidence in our results. For example, of the seven monocyte-related BTMs whose baseline levels associated with risk, all would have survived a 5% FDR cut-off, and three even a 1% cut-off; likewise, of the four dendritic cell-related BTMs whose baseline levels associated with risk, all would have survived a 5% FDR cut-off, and three even a 1% cut-off.” (lines 644-651)

Moreover, we have revised Figures 2, 3, and 6 so that it is easy to discern whether a specific BTM correlation would also pass more stringent FDR cutoffs, through the addition of 1, 2, or 3 asterisks where appropriate: “|FDR| < 0.2 (*), < 0.05 (**), < 0.01 (***).” Note that, most central to the key message of the paper, many of the monocyte-related, DC-related, and cell cycle-related BTMs would have passed more stringent FDR cutoffs, with many even passing a 1% FDR cutoff (as discussed above).

Since antigen-stimulation of PBMCs is not informative the authors could focus their analysis on unstimulated PBMCs from Figure 3 onwards, which would massively streamline their message.

We believe that the stimulation results should be reported in Figure 3 but agree that the manuscript could be streamlined by removing subsequent antigen stimulation results. We have made the following revisions to the manuscript:

– Removed all data from Ag-stimulated PBMC from Figure 5

– Removed related discussion of Ag-stimulation results

– Removed all data from Ag-stimulated PBMC from Figure S2

– Removed all data from Ag-stimulated PBMC from Figure S11

Figure 4: include Figure S2 as a panels as they convey crucial information: explicitly draw the conclusion that the production of multiple cytokines after 1-month after final RTS,S vaccination does not predict protection from clinical malaria (reflect in relevant results subheading and figure legend title).

We have modified Figure 4 as suggested and made the requested edits to the results subheading and figure title.

Figure 5: this Figure is too dense which makes it hard to find the interesting information – it could be simplified by only including unstimulated PBMCs. It is surprising that antibodies do not correlate with B cell modules – causing some doubt about this correlation approach (not clear from line 229 – 232 if Spearman or Pearson? very relaxed p-value of 0.2).

Thank you for this good suggestion. We have modified Figure 5 as requested.

The fact that expression levels of B-cell-related modules in PBMC circulating at month 3 do not correlate with antibodies circulating at the same time is not surprising given that antibodies are secreted by antigen-specific B cells that constitute a very small percentage of total B cells (and thus an even smaller percentage of PBMC).

Moreover, the samples were collected 1 month post vaccination, which is past the plasma cell response. Typically, plasmablast response and B cell activation occurs about one week post-vaccination [e.g. (12-15)]; the effect on circulating antibodies is seen later, e.g. Nakaya et al. showed that, in influenza vaccine recipients, the frequency of influenza-specific IgG secreting plasmablasts at day 7 correlates significantly with the influenza antibody response at day 28 (12). In the RTS,S study in naïve adults (4), activation was detected after 1 and 2 days after the administration of RTS,S vaccine doses. In that study, antigen-specific plasmablasts were detected after 6 days after vaccination but frequencies of these cells did not correlate with the antibody response.

Figure 6: A the five post vaccination modules identified in Figure 3 do not associate with risk in RTS,S CHMI studies – why might this be? young African children in malaria endemic environment vs malaria-naive US adults? or because these modules were not very strongly associated with risk in the first place?

It is true that in panel A there is no clear pattern for the whole 7 modules across all the RTS,S CHMI studies (see discussion below and the manuscript text for potential explanations of this). In the text related to Figure 6A we state:

“No BTM was consistently associated with malaria risk (or non-protection) across all four studies. The most consistent result was for the monocyte-related BTM “enriched in monocytes (II) (M11.0),” whose month 3 expression was significantly associated with risk in two of the three CHMI studies (Figure 6A, Figure 6-source data 1).” (lines 480-483).

As to the differences between study populations and whether this may explain the lack of a clear pattern for the whole 7 modules in Figure 6A across all the CHMI studies, the study populations clearly differ with respect to a variety of factors including but not limited to age, genetics, malaria exposure/malaria history, and other infection history. Thus, yes, we believe that these differences are one of the main reasons for the observed differences between studies. In addition, samples from the Phase 3 trial were collected a month after the 3rd dose, whereas samples in the other studies were collected at different times (21 or 14 days post-vaccination), which could result also in some differences between studies. Finally, there were differences in the formulation of the RTS,S vaccine (AS01E adjuvant vs AS02A or AS01B adjuvants). All these differences are already explained in the manuscript.

As to the question of whether these seven modules were “not very strongly associated with risk in the first place”, this is also possible. As discussed above, only one of these seven modules would have passed a more stringent FDR cutoff of 5%. We now state in the Discussion:

“Compared to the 45 BTMs whose baseline levels significantly associated with clinical malaria risk in RTS,S/AS01-vaccinated African children, fewer BTMs (seven) had levels at one month post-final RTS,S/AS01 dose that significantly associated with clinical malaria risk. Moreover, if a more stringent FDR cutoff had been used (i.e. 5%), six of these seven BTMs would not have been identified. Thus it is entirely possible that, at one month post-final RTS,S/AS01 dose, there is no circulating immune transcriptomic signature predictive of risk. Such a conclusion would not be surprising, given that in malaria-naïve adults, the transcriptional response to the third RTS,S/AS01 dose has been shown to peak at Day 1 post-injection, with some decline by Day 6 and approximately 90% of the response having waned by Day 21 (17). Therefore, it is likely that the sampling scheme in this study (one month post-final dose) misses the majority of the transcriptional response to RTS,S/AS01. Future studies with dense PBMC sampling during the transcriptional peak of the vaccine-induced response could be useful for further investigating RTS,S transcriptional immune correlates.” (lines 617-628)

B – this panel is what the title and main conclusions of the paper are based on – it is interesting to speculate that the innate responses before vaccination may shape vaccine efficacy. Drilling down into the genes in the modules (functional transcriptomics) would be useful to get a glimpse into how this might work and to guide future work where e.g monocytes could be directly isolated. As it stands the conclusion that "inflammatory monocytes may inhibit protective RTS,S/AS01-induced responses" (line 58/59) is not well supported – especially since the authors published a paper last year which finds an inverse association (Moncunill G, Scholzen A, Mpina M, Nhabomba A, Hounkpatin AB, Osaba L, Valls R, Campo JJ, Sanz H, Jairoce C, et al: Antigen-stimulated PBMC transcriptional protective signatures for malaria immunization. Sci Transl Med 2020). The explanations offered in the discussion (621 – 633) are technical rather than biological again raising some questions about the robustness of the here presented approach.

Thank you for this excellent suggestion. We have discussed above the gene-level analyses we have done and the new supplementary figures and supplementary file that have been added to the manuscript.

Please note that the hypothesis that inflammatory monocytes specifically inhibit RTS,S vaccine efficacy was originally put forth by Warimwe et al. in 2013 “It is plausible that RTS,S vaccine efficacy is specifically inhibited by inflammatory monocytes, thus confounding induction of an effective adaptive response, but further studies in both animal models and humans will be needed to confirm this.” (16) We mention this in our Discussion:

“Based on previous evidence in various mouse models of viral infection that inflammatory monocytes inhibit T cell proliferation (54, 55), T cell activation (55), and B cell responses (56), Warimwe et al. proposed that inflammatory monocytes may inhibit RTS,S-induced protective adaptive responses.” (lines 564-567).

Prior to performing the gene-level analyses, the fact that we did observe upregulation of many monocyte-related BTMs at baseline in cases vs controls (Figure 6B), as well as many inflammatory-related BTMs at baseline in cases vs controls (Figure 6B), did seem to support this hypothesis.

We agree that our previous hypothesis that “inflammatory monocytes may inhibit protective RTS,S/AS01-induced responses” should be revised in light of our stabilin-1 (clever-1) findings, discussed above. In fact, if the increased expression of STAB1 at baseline in PBMC in cases vs controls (Figure 6—figure supplement 1, 2, and 6) is reflective of an increase in circulating stabilin-1high immunosuppressive monocytes (this would of course need to be confirmed experimentally), this would support an opposite hypothesis that immunosuppressive monocytes may inhibit protective RTS,S/AS01-induced responses.

We have made the following edits to remove the mentions of “inflammatory monocytes”:

Abstract (lines 71-72): “suggesting that inflammatory monocytes may inhibit protective RTS,S/AS01-induced responses” γ edited to “suggests that certain monocyte subsets may inhibit protective RTS,S/AS01-induced responses”.

We have also removed the discussion of Mitchell et al. showing a potential association of inflammatory monocytes and decreased vaccine immunity, as well as removing the suggestion of potentially modulating monocyte populations via chemokine receptor antagonists to help boost RTS,S efficacy.

We have added the following to the Discussion (lines 567-578):

“Our gene-level correlates analyses suggest an alternative hypothesis, however. With the caveat that the gene-level analyses were performed post hoc, high baseline expression of STAB1 (which is present in DC-related, monocyte-related, and cell cycle-related modules) was found to positively associate with clinical malaria risk. STAB1 encodes stabilin-1 (also called Clever-1), a transmembrane glycoprotein scavenger receptor that links extracellular signals to intracellular vesicle trafficking pathways (17). Interestingly, stabilin-1high monocytes show downregulation of proinflammatory genes, and T cells co-cultured with stabilin-1high monocytes showed decreased antigen recall, suggesting that monocyte stabilin-1 suppresses T cell activation (18). Thus one possibility is that stabilin-1high immunosuppressive monocytes circulating at baseline could decrease protective RTS,S-induced T-cell responses, or inhibit another aspect of adaptive immunity. Single-cell transcriptomic profiling of PBMC in future RTS,S trials in African children in malaria-endemic areas could help test this hypothesis.”

Please note that we stress that this is a “possibility” and a “hypothesis” rather than a definitive conclusion of the work; as we state at the beginning of the Discussion:

“Our main finding is the identification of a baseline blood transcriptional module (BTM) signature that associates with clinical malaria risk in RTS,S/AS01-vaccinated African children. In a cross-study comparison, much of this baseline risk signature – specifically, dendritic cell- and monocyte-related BTMs – was also recapitulated in two of the three CHMI studies in healthy, malaria-naïve adults.” (lines 543-546)

This conclusion is well-supported by our data.

Regarding how the results of the present manuscript relate to the results of Moncunill et al. 2020 STM: We see how the two sets of results may seem discrepant; however, when we examine the specific details of our previous and current analyses, we see that they are in fact compatible.

Author response table 1 summarizes the technical differences and similarities between the relevant work described in the present manuscript and the work described in Moncunill et al. 2020 STM.

Author response table 1
Present manuscript: Baseline signature associated with riskMoncunill et al. 2020 STM: Protective signature
PBMC sampling timepointBaseline1 month post-third vaccination
PBMC stimulationVehicle (DMSO); stimulated on site before cryopreservation of cell pellets for subsequent RNA extraction.24-hour antigen (CSP) stimulation adjusted by vehicle (DMSO); PBMC were cryopreserved before stimulation
Background correctionN/AYes, subtraction of expression in vehicle-stimulated PBMC (thus the observed response is specific to antigen stimulation, i.e. recall response)
Presence of monocyte-related BTMsYes, of the 45 BTMs, 7 were monocyte-related: M81, M118.1, M11.0, M118.0, S4, M73, M4.15Yes, of the 24 BTMs, 3 were monocyte-related: M81, M118.1, M11.0
Gene expression measurementRNA-seqMicroarray
Hypothesis:Stabilin-1high immunosuppressive monocytes circulating at baseline may inhibit protective RTS,S-induced T-cell responses [supported by (18)] or another RTS,S-induced protective adaptive response.Protected individuals may have monocytes that are qualitatively superior in mediating, e.g., Fc receptor/antibody-dependent responses (reflected by an altered transcriptional profile), resulting in improved control of infection.

We have added the following text to our Discussion (lines 584-605):

“We have also previously reported that interferon, NF-κB, TLR, and monocyte-related BTMs were associated with protection in children and infants in the RTS,S/AS01 phase 3 trial (64). […] Thus, we were unable to detect earlier transient responses that had already resolved by 12 hours, as well as more delayed response that had not yet initiated by 12 hours, if such responses occurred.”

General remarks

The decision to use blood transcriptional module analysis to reduce data dimensionality is a good one, but once candidate modules are identified the authors should drill down into the nitty gritty of which genes are up and downregulated and infer cell function to provide testible hypothesis for future functional and mechanistic studies.

We have added new supplementary figures (Figure 6—figure supplements 1-8) looking at individual genes. These analyses did yield interesting results, especially with respect to STAB1 (stabilin-1, clever-1). We have significantly revised our Discussion in the light of these new results.

Use figure legend headings/ results subheadings to summarise results

Currently, our results subheadings are written in conclusion form e.g. “RTS,S/AS01 vaccination is associated with month 3 downregulation of B cell- and monocyte-related BTMs, along with upregulation of T cell-related BTMs”, “Monocyte-related RTS,S/AS01 signature BTMs associate with clinical malaria risk”, “RTS,S/AS01 vaccination elicits polyfunctional CSP-specific CD4+ T-cell responses elicited that do not correlate with malaria risk”, “Month 3 levels of RTS,S/AS01 signature BTMs tend to correlate directly with month 3 IgM antibody responses and inversely with month 3 IgG responses”, and “Cross-study immune correlates analysis reveals a mostly consistent association in RTS,S/AS01-vaccinees between baseline expression of DC- and monocyte-related BTMs and risk”.

We prefer to keep our figure titles descriptive for two reasons: (1) avoiding redundancy with the results subheadings, and (2) we think it is most straightforward to describe what analysis is shown in the figure, and leave the interpretation/result from that figure to the “Results” text.

Include n in Figure legends.

We have included the “n”s in the legends of Figures 2, 3, 4, 5 and 6.

Reviewer #3 (Recommendations for the authors):

I thank the authors for presenting and interesting and well-written manuscript. In addition to my comments above, I have the following suggestions which I believe would improve the clarity and transparency of the paper

1. It would be helpful to provide an explanation of how the sample sizes were derived. Since the main objective was to identify BTMs associated with developing malaria after RTS,S/AS01 vaccine, it would be useful to report the malaria incidence in the main Phase 3 trial for the age groups studied at each site in the follow-up period of this study in the RTS,S and comparator vaccine arms. This will help to address the question of whether receipt of RTS,S vaccine is the main reason for the difference between cases and controls, or whether it is more related to exposure to infectious bites. If the EIR is low, then one might expect that the vaccine explains rather little of the difference between cases and controls, and therefore a large sample size would be needed to detect any significant association between gene expression and vaccine efficacy.

Malaria transmission intensity of each site in 2007 (2 years before the start of the study) and the malaria incidence during the Phase 3 Clinical Trial can be found in the final Phase 3 Clinical trial paper (21). Malaria transmission intensity in Bagamoyo and Manhiça sites was low/moderate. Therefore, we used a case-control design for the study instead of a cohort design. Sample sizes were based on availability of samples and malaria cases. We used all samples available from malaria cases and selected 2 to 4 matched controls for each case for RTS,S vaccinees and 2 controls for comparators. Higher numbers of controls were used to consider the heterogeneity and the lack of specificity of the controls as we do not know if these were truly protected children or they had not been exposed. Adding more controls would not increase statistical power.

We have made the following revisions to the text in this light:

Materials and methods: “As malaria transmission intensity at the Bagamoyo and Manhiça sites was low/moderate (11), we used a case-control design for the study instead of a cohort design. Sample sizes were based on availability of samples and malaria cases. We used all samples available from malaria cases and selected 2 to 4 matched controls for each case for RTS,S vaccinees and 2 controls for comparators. Higher numbers of controls were used to consider the heterogeneity and the lack of specificity of the controls as we do not know if these were truly protected children (they may have been sick but not gone to a health post, or they may not have been exposed during follow-up). In selecting controls, we prioritized participants who had samples at both month 0 and month 3 and in whom the complete set of antigen stimulations was conducted.” (lines 166-170)

Discussion (of limitations): “Third, we do not know whether the controls were truly protected or whether they were never exposed to malaria in the first place. This limitation highlights the importance of our cross-study analysis, where all participants are known to be exposed.” (lines 637-641)

2. It would be helpful to include more data in table 1 to show how well the matching process for cases and controls worked. Table 1 could be reformatted to be easier to read, with separate columns for each site (rather than the current presentation of this information in parentheses) and could include rows for age, sex, time of vaccination and duration of follow-up.

Thank you for the good suggestion to reformat Table 1. We have redone Table 1 to include separate columns for each site and agree that readability has improved substantially. In parallel, we have also reformatted Supplementary File 2 for improved readability.

Moreover, we now also provide the new Supplementary File 1, which provides complete information on participant match ID, site, age cohort, sex assigned at birth, and time of vaccination.

3. In view of the perplexing result mentioned in the previous review section for DMSO stimulation, I think further detail about the PBMC stimulation is needed. I cannot find the DMSO concentration used for vehicle or antigen stimulation in either this paper or the "previously described" reference (24). The authors do not describe that they have subtracted or adjusted for the vehicle-induced gene expression in assessing antigen induced gene expression, but this might be one explanation for this odd result. If this is not the explanation, I worry that different concentrations of DMSO may have been used in the vehicle and antigen stimulation conditions. As this results appears anomalous, it requires further explanation of the methods or discussion as to why this result may have occurred.

DMSO (D2650, Σ) was used at a final dilution of 1/322, the same concentration of DMSO as used for CSP peptide pool. We have now added this information to the manuscript. (lines 184-1485) As discussed above, we have also added details on how the stimulation data were analyzed (i.e. correcting for DMSO).

4. The choice of an FDR significance threshold of 0.2 has not been justified. As mentioned in the previous section of the review this is extremely liberal, and diminishes confidence in the significance of the findings. In most analyses there are BTMs with FDRs below 0.05, so I think it is essential that the authors explain why they chose to use an FDR of 0.2 throughout the paper, and that they discuss the implications of selecting such a liberal threshold.

While it is not uncommon to use a threshold of 20% for immune correlates studies [e.g. (6-11)], we agree with you that it is important to clearly state the chosen FDR rate and to discuss conclusions in the context of the FDR rate used. We see we could improve our manuscript in this respect. We have added the following:

Results: “Compared to the 45 BTMs whose baseline levels significantly associated with clinical malaria risk in RTS,S/AS01-vaccinated African children, fewer BTMs (seven) had levels at one month post-final RTS,S/AS01 dose that significantly associated with clinical malaria risk. Moreover, if a more stringent FDR cutoff had been used (i.e. 5%), six of these seven BTMs would not have been identified. Thus it is entirely possible that, at one month post-final RTS,S/AS01 dose, there is no circulating immune transcriptomic signature predictive of risk…” (lines 617-628)

Discussion: “Finally, while it is not uncommon to use an FDR cutoff of 20% in high-dimensional immune correlates studies [e.g. (65-70)], our results should be interpreted with the requisite level of caution. However, we do note that many of our significant modules in the baseline risk analysis would have survived even lower FDR cutoffs (in many cases even a 1% cutoff), giving us a fair degree of confidence in our results. For example, of the seven monocyte-related BTMs whose baseline levels associated with risk, all would have survived a 5% FDR cut-off, and three even a 1% cut-off; likewise, of the four dendritic cell-related BTMs whose baseline levels associated with risk, all would have survived a 5% FDR cut-off, and three even a 1% cut-off.” (lines 644-651)

Moreover, we have revised Figures 2, 3, and 6 so that it is easy to discern whether a specific BTM correlation would also pass more stringent FDR cutoffs, through the addition of 1, 2, or 3 asterisks where appropriate: “|FDR| < 0.2 (*), < 0.05 (**), < 0.01 (***).” Note that, most central to the key message of the paper, many of the monocyte-related, DC-related, and cell cycle-related BTMs would have passed more stringent FDR cutoffs, with many even passing a 1% FDR cutoff (as discussed above).

5. The authors have described quite a lot of different statistical approaches in the methods (line 227-256) but it is currently difficult for the reader to understand where each of these has been applied to the data that are presented (particularly in the figures). I think it is essential that figure legends include the number of subjects from each group included in analyses and the type of analysis which has been performed and the criteria for statistical significance. There are several elements in the current description of these analyses which did not fully make sense or which I could not see used in the results that have been presented:

a. Line 229-231 state that Spearman's rank correlation was used and then in the next sentence that Pearson correlation was used – please clarify as only one or the other should be used for each analysis.

b. Line 235-239 states that adjustment was made for clinical and experimental covariates – these covariates do not appear to be reported anywhere and it is unlcear in the Results section which analyses (if any) included this adjustment.

c. Line 247-250: the results of this logistic regression are not obviously presented (or at least this method of analysis is not reported explicitly in the Results section) and the stratification variables do not appear to have been reported anywhere.

We have significantly improved our method description both in the results and methods section, as detailed below:

Figure legends: Numbers of participants included in each analysis have been included. Methods: Full equations for all analyses have been provided (“plain English” versions of these are now in the figure legends). Criteria for statistical significance are already present in the figure legends.

a) “Pearson” changed to “Spearman”

b) Edited to “All analyses controlled for plate, total reads, and age.” In addition, the full equations have been provided in Methods.

c) We have removed the results of this analysis and the related Methods text. Instead, for testing for significant differences in controls vs cases for each of the monocyte-related variables in Figure 6—figure supplement 10, we have added the following text to the legend of Figure 6—figure supplement 10:

“The p values at the bottom of each panel are from testing for a significant difference in controls vs cases within each panel, and were modeled using a mixed-effects model (using lmer) with match id as a random effect.”

Each panel also contains a p value underneath for the significance of the difference in cases vs controls.

6. The authors have decided to include combine rabies and meningococcal C vaccine arms into a single "comparator" vaccine group. One might expect these to elicit quite different effects on the immune system and I think it is important to present data to justify the decision to include them together. For example, a series of PCA plots of the DMSO-stimulated transcriptional responses, with subjects coloured by vaccine, by age group, and by site could be very informative for interpretation of the rest of the data in the paper.

While PCA plots could be generated as the reviewer suggests, there is a complete overlap of vaccine in comparators and age cohort (all infants received meningococcal vaccine and all children received rabies vaccine). Thus, we are not sure how informative such plots would be as they would be confounded by age group.

The impact of combining both vaccines in comparator recipients on the study results and conclusions is minimal since the main results are based on baseline gene expression and its association with malaria risk within RTS,S vaccinees. Correlates of malaria risk in comparators are done separately. Comparator vaccination may be a confounding factor for age cohort, but we are not analyzing the effect of age cohort on the transcriptional profile. Comparators are only included in the analysis of RTS,S immunogenicity at post-vaccination (RTS,S vs Comparators, Figure 2A, Comparison (1)) and we have adjusted analyses by age cohort and hence by comparator vaccine. The fact that the comparators received different control vaccines only stresses that the BTMs found to be associated with RTS,S vaccination are specific to the RTS,S vaccine.

Moreover, as an alternative way to identify RTS,S-specific transcriptional responses, we also include Comparison (2), which compares Month 3 to Month 0 transcription levels within RTS,S vaccinees.

7. In all figures the use of a log10 FDR colour scale in the heatmaps makes it very difficult to identify the FDR values. I appreciate that these are included in the supplementary tables, but is it possible to add the FDR value in each cell of the heatmap for the significant values, or if not, perhaps to adopt a categorical approach to colouring them (eg. FDR<0.01; FDR<0.05; FDR <0.2)? This would enhance interpretation.

Yes, Figures 2, 3, 3—figure supplement 1, and 6 now have asterisks to denote three different FDR cutoffs: |FDR| < 0.2 (*), < 0.05 (**), < 0.01 (***).

References:

1. Moncunill G, De Rosa SC, Ayestaran A, Nhabomba AJ, Mpina M, Cohen KW, Jairoce C, Rutishauser T, Campo JJ, Harezlak J, Sanz H, Diez-Padrisa N, Williams NA, Morris D, Aponte JJ, Valim C, Daubenberger C, Dobano C, McElrath MJ. RTS,S/AS01E Malaria Vaccine Induces Memory and Polyfunctional T Cell Responses in a Pediatric African Phase III Trial. Front Immunol. 2017;8:1008.

2. Kleiveland CR. Peripheral Blood Mononuclear Cells. In: Verhoeckx K, Cotter P, López-Expósito I, Kleiveland C, Lea T, Mackie A, et al., editors. The Impact of Food Bioactives on Health: in vitro and ex vivo models. Cham: Springer International Publishing; 2015. p. 161-7.

3. Schultz-Thater E, Frey DM, Margelli D, Raafat N, Feder-Mengus C, Spagnoli GC, Zajac P. Whole blood assessment of antigen specific cellular immune response by real time quantitative PCR: a versatile monitoring and discovery tool. J Transl Med. 2008;6:58.

4. Kazmin D, Nakaya HI, Lee EK, Johnson MJ, van der Most R, van den Berg RA, Ballou WR, Jongert E, Wille-Reece U, Ockenhouse C, Aderem A, Zak DE, Sadoff J, Hendriks J, Wrammert J, Ahmed R, Pulendran B. Systems analysis of protective immune responses to RTS,S malaria vaccination in humans. Proc Natl Acad Sci U S A. 2017;114(9):2425-30.

5. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545-50.

6. Liu C, Martins AJ, Lau WW, Rachmaninoff N, Chen J, Imberti L, Mostaghimi D, Fink DL, Burbelo PD, Dobbs K, Delmonte OM, Bansal N, Failla L, Sottini A, Quiros-Roldan E, Han KL, Sellers BA, Cheung F, Sparks R, Chun TW, Moir S, Lionakis MS, Consortium NC, Clinicians C, Rossi C, Su HC, Kuhns DB, Cohen JI, Notarangelo LD, Tsang JS. Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19. Cell. 2021;184(7):1836-57 e22.

7. Andersen-Nissen E, Fiore-Gartland A, Ballweber Fleming L, Carpp LN, Naidoo AF, Harper MS, Voillet V, Grunenberg N, Laher F, Innes C, Bekker LG, Kublin JG, Huang Y, Ferrari G, Tomaras GD, Gray G, Gilbert PB, McElrath MJ. Innate immune signatures to a partially-efficacious HIV vaccine predict correlates of HIV-1 infection risk. PLoS Pathog. 2021;17(3):e1009363.

8. Lu P, Guerin DJ, Lin S, Chaudhury S, Ackerman ME, Bolton DL, Wallqvist A. Immunoprofiling Correlates of Protection Against SHIV Infection in Adjuvanted HIV-1 Pox-Protein Vaccinated Rhesus Macaques. Front Immunol. 2021;12:625030.

9. Haynes BF, Gilbert PB, McElrath MJ, Zolla-Pazner S, Tomaras GD, Alam SM, Evans DT, Montefiori DC, Karnasuta C, Sutthent R, Liao HX, DeVico AL, Lewis GK, Williams C, Pinter A, Fong Y, Janes H, DeCamp A, Huang Y, Rao M, Billings E, Karasavvas N, Robb ML, Ngauy V, de Souza MS, Paris R, Ferrari G, Bailer RT, Soderberg KA, Andrews C, Berman PW, Frahm N, De Rosa SC, Alpert MD, Yates NL, Shen X, Koup RA, Pitisuttithum P, Kaewkungwal J, Nitayaphan S, Rerks-Ngarm S, Michael NL, Kim JH. Immune-correlates analysis of an HIV-1 vaccine efficacy trial. N Engl J Med. 2012;366(14):1275-86.

10. Fletcher HA, Snowden MA, Landry B, Rida W, Satti I, Harris SA, Matsumiya M, Tanner R, O'Shea MK, Dheenadhayalan V, Bogardus L, Stockdale L, Marsay L, Chomka A, Harrington-Kandt R, Manjaly-Thomas ZR, Naranbhai V, Stylianou E, Darboe F, Penn-Nicholson A, Nemes E, Hatherill M, Hussey G, Mahomed H, Tameris M, McClain JB, Evans TG, Hanekom WA, Scriba TJ, McShane H. T-cell activation is an immune correlate of risk in BCG vaccinated infants. Nat Commun. 2016;7:11290.

11. Young WC, Carpp LN, Chaudhury S, Regules JA, Bergmann-Leitner ES, Ockenhouse C, Wille-Reece U, deCamp AC, Hughes E, Mahoney C, Pallikkuth S, Pahwa S, Dennison SM, Mudrak SV, Alam SM, Seaton KE, Spreng RL, Fallon J, Michell A, Ulloa-Montoya F, Coccia M, Jongert E, Alter G, Tomaras GD, Gottardo R. Comprehensive Data Integration Approach to Assess Immune Responses and Correlates of RTS,S/AS01-Mediated Protection From Malaria Infection in Controlled Human Malaria Infection Trials. Front Big Data. 2021;4:672460.

12. Nakaya HI, Wrammert J, Lee EK, Racioppi L, Marie-Kunze S, Haining WN, Means AR, Kasturi SP, Khan N, Li GM, McCausland M, Kanchan V, Kokko KE, Li S, Elbein R, Mehta AK, Aderem A, Subbarao K, Ahmed R, Pulendran B. Systems biology of vaccination for seasonal influenza in humans. Nat Immunol. 2011;12(8):786-95.

13. Frolich D, Giesecke C, Mei HE, Reiter K, Daridon C, Lipsky PE, Dorner T. Secondary immunization generates clonally related antigen-specific plasma cells and memory B cells. J Immunol. 2010;185(5):3103-10.

14. Li S, Sullivan NL, Rouphael N, Yu T, Banton S, Maddur MS, McCausland M, Chiu C, Canniff J, Dubey S, Liu K, Tran V, Hagan T, Duraisingham S, Wieland A, Mehta AK, Whitaker JA, Subramaniam S, Jones DP, Sette A, Vora K, Weinberg A, Mulligan MJ, Nakaya HI, Levin M, Ahmed R, Pulendran B. Metabolic Phenotypes of Response to Vaccination in Humans. Cell. 2017;169(5):862-77 e17.

15. Wrammert J, Smith K, Miller J, Langley WA, Kokko K, Larsen C, Zheng NY, Mays I, Garman L, Helms C, James J, Air GM, Capra JD, Ahmed R, Wilson PC. Rapid cloning of high-affinity human monoclonal antibodies against influenza virus. Nature. 2008;453(7195):667-71.

16. Warimwe GM, Fletcher HA, Olotu A, Agnandji ST, Hill AV, Marsh K, Bejon P. Peripheral blood monocyte-to-lymphocyte ratio at study enrollment predicts efficacy of the RTS,S malaria vaccine: analysis of pooled phase II clinical trial data. BMC Med. 2013;11:184.

17. Kzhyshkowska J, Gratchev A, Goerdt S. Stabilin-1, a homeostatic scavenger receptor with multiple functions. J Cell Mol Med. 2006;10(3):635-49.

18. Palani S, Elima K, Ekholm E, Jalkanen S, Salmi M. Monocyte Stabilin-1 Suppresses the Activation of Th1 Lymphocytes. J Immunol. 2016;196(1):115-23.

19. Moncunill G, Scholzen A, Mpina M, Nhabomba A, Hounkpatin AB, Osaba L, Valls R, Campo JJ, Sanz H, Jairoce C, Williams NA, Pasini EM, Arteta D, Maynou J, Palacios L, Duran-Frigola M, Aponte JJ, Kocken CHM, Agnandji ST, Mas JM, Mordmuller B, Daubenberger C, Sauerwein R, Dobano C. Antigen-stimulated PBMC transcriptional protective signatures for malaria immunization. Sci Transl Med. 2020;12(543).

20. Moodie Z, Juraska M, Huang Y, Zhuang Y, Fong Y, Carpp LN, Self SG, Chambonneau L, Small R, Jackson N, Noriega F, Gilbert PB. Neutralizing Antibody Correlates Analysis of Tetravalent Dengue Vaccine Efficacy Trials in Asia and Latin America. J Infect Dis. 2018;217(5):742-53.

21. RTS, S Clinical Trials Partnership. Efficacy and safety of RTS,S/AS01 malaria vaccine with or without a booster dose in infants and children in Africa: final results of a phase 3, individually randomised, controlled trial. Lancet. 2015;386(9988):31-45.

https://doi.org/10.7554/eLife.70393.sa2

Article and author information

Author details

  1. Gemma Moncunill

    1. ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain
    2. CIBER de Enfermedades Infecciosas, Madrid, Spain
    Contribution
    Conceptualization, Funding acquisition, Investigation, Resources, Supervision, Visualization, Writing – original draft, Writing – review and editing, Methodology
    For correspondence
    gemma.moncunill@isglobal.org
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5105-9836
  2. Jason Carnes

    Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, United States
    Contribution
    Investigation, Methodology, Resources, Writing – review and editing
    Contributed equally with
    William Chad Young and Lindsay Carpp
    Competing interests
    No competing interests declared
  3. William Chad Young

    Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    Contribution
    Data curation, Formal analysis, Investigation, Project administration, Visualization, Writing – review and editing
    Contributed equally with
    Jason Carnes and Lindsay Carpp
    Competing interests
    No competing interests declared
  4. Lindsay Carpp

    Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    Contribution
    Investigation, Visualization, Writing – original draft, Writing – review and editing, Conceptualization
    Contributed equally with
    Jason Carnes and William Chad Young
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0333-5925
  5. Stephen De Rosa

    Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    Contribution
    Conceptualization, Investigation, Methodology, Resources, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
  6. Joseph J Campo

    Antigen Discovery Inc, Irvine, United States
    Contribution
    Conceptualization, Funding acquisition, Investigation, Resources, Writing – review and editing
    Competing interests
    is an employee of Antigen Discovery Inc. The author declare that no other competing interests exist
  7. Augusto Nhabomba

    Centro de Investigação em Saúde de Manhiça (CISM), Rua 12, Cambeve, Vila de Manhiça, Maputo, Mozambique
    Contribution
    Investigation, Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  8. Maxmillian Mpina

    Ifakara Health Institute. Bagamoyo Research and Training Centre, Bagamoyo, United Republic of Tanzania
    Contribution
    Investigation, Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  9. Chenjerai Jairoce

    Centro de Investigação em Saúde de Manhiça (CISM), Rua 12, Cambeve, Vila de Manhiça, Maputo, Mozambique
    Contribution
    Investigation, Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  10. Greg Finak

    Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    Present address
    Ozette Technologies, Seattle, United States
    Contribution
    Data curation, Formal analysis, Visualization, Writing – review and editing
    Competing interests
    No competing interests declared
  11. Paige Haas

    Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, United States
    Present address
    Department of Cellular and Molecular Pharmacology, University of California, San Francisco, United States
    Contribution
    Investigation, Writing – review and editing, Methodology
    Competing interests
    No competing interests declared
  12. Carl Muriel

    Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    Contribution
    Data curation, Formal analysis, Resources, Visualization, Writing – review and editing
    Competing interests
    No competing interests declared
  13. Phu Van

    Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    Contribution
    Formal analysis, Investigation, Resources, Visualization, Writing – review and editing
    Competing interests
    No competing interests declared
  14. Héctor Sanz

    ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain
    Contribution
    Data curation, Formal analysis, Writing – review and editing
    Competing interests
    No competing interests declared
  15. Sheetij Dutta

    Walter Reed Army Institute of Research (WRAIR), Silver Spring, United States
    Contribution
    Project administration, Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  16. Benjamin Mordmüller

    1. CIBER de Enfermedades Infecciosas, Madrid, Spain
    2. Institute of Tropical Medicine and German Center for Infection Research, Tubingen, Germany
    Present address
    Department of Medical Microbiology, Radboudumc, Nijmegen, Netherlands
    Contribution
    Conceptualization, Funding acquisition, Investigation, Project administration, Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  17. Selidji T Agnandji

    1. Institute of Tropical Medicine and German Center for Infection Research, Tubingen, Germany
    2. Centre de Recherches Médicales de Lambaréné (CERMEL), BP 242, Lambaréné, Gabon
    Contribution
    Conceptualization, Funding acquisition, Investigation, Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  18. Núria Díez-Padrisa

    ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain
    Contribution
    Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  19. Nana Aba Williams

    ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain
    Contribution
    Funding acquisition, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  20. John J Aponte

    ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Resources, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
  21. Clarissa Valim

    Department of Global Health, Boston University School of Public Health, Boston, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
  22. Daniel E Neafsey

    1. Broad Institute of Massachusetts Institute of Technology and Harvard, Boston, United States
    2. Harvard T.H. Chan School of Public Health, Boston, United States
    Contribution
    Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review and editing
    Contributed equally with
    Claudia Daubenberger
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1665-9323
  23. Claudia Daubenberger

    1. Swiss Tropical and Public Health Institute, Basel, Switzerland
    2. University of Basel, Basel, Switzerland
    Contribution
    Conceptualization, Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing – review and editing
    Contributed equally with
    Daniel E Neafsey
    Competing interests
    No competing interests declared
  24. M Juliana McElrath

    1. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    2. Departments of Laboratory Medicine and Medicine, University of Washington, Seattle, United States
    Contribution
    Conceptualization, Funding acquisition, Investigation, Supervision, Writing – review and editing, Project administration
    Contributed equally with
    Carlota Dobaño, Ken Stuart and Raphael Gottardo
    Competing interests
    No competing interests declared
  25. Carlota Dobaño

    1. ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain
    2. CIBER de Enfermedades Infecciosas, Madrid, Spain
    Contribution
    Conceptualization, Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing – review and editing
    Contributed equally with
    M Juliana McElrath, Ken Stuart and Raphael Gottardo
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6751-4060
  26. Ken Stuart

    1. Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, United States
    2. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    3. Department of Pediatrics, University of Washington, Seattle, United States
    4. Department of Global Health, University of Washington, Seattle, United States
    Contribution
    Conceptualization, Funding acquisition, Investigation, Project administration, Supervision, Writing – review and editing
    Contributed equally with
    M Juliana McElrath, Carlota Dobaño and Raphael Gottardo
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4064-9758
  27. Raphael Gottardo

    1. Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    2. University of Lausanne and Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Project administration, Supervision, Methodology, Writing – review and editing
    Contributed equally with
    M Juliana McElrath, Carlota Dobaño and Ken Stuart
    For correspondence
    Raphael.Gottardo@chuv.ch
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3867-0232

Funding

National Institute of Allergy and Infectious Diseases (R01AI095789)

  • Carlota Dobaño

National Institute of Allergy and Infectious Diseases (U19AI128914)

  • Julie McElrath
  • Raphael Gottardo
  • Ken Stuart

PATH Malaria Vaccine Initiative (Research Contract)

  • Carlota Dobaño

Instituto de Salud Carlos III (PI11/00423)

  • Carlota Dobaño

National Institute of Allergy and Infectious Diseases (U19AI110818)

  • Daniel E Neafsey

Departament de Salut, Generalitat de Catalunya (SLT006/17/00109)

  • Gemma Moncunill

Instituto de Salud Carlos III (PI14/01422)

  • Carlota Dobaño

Bill and Melinda Gates Foundation (INV-008576/OPP1154739)

  • Raphael Gottardo

Ministerio de Economía, Industria y Competitividad, Gobierno de España (RYC-2008-02631)

  • Carlota Dobaño

Instituto de Salud Carlos III (CD010/00156)

  • Gemma Moncunill

The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.

Acknowledgements

We are very grateful to study participants, their families, and vaccine trial site field and lab staff. We thank the phase 3 trial sites PIs Salim Abdulla, Pedro Alonso, Jahit Sacarlal, and Pedro Aide; the investigators involved in the generation of immunology data used here, including providers of antigens for antibody assays (Itziar Ubillos, Marta Vidal, Alfons Jimenez, Ruth Aguilar, Diana Barrios, Laura Puyol, Aintzane Ayestaran, Luis Izquierdo, David Cavanagh, James Beeson, David Lanar, Vir Chauhan, Chetan Chitnis, Deepak Gaur, Evelina Angov, Benoit Gamain, and Ross Coppel); the MAL067 Vaccine Immunology Consortium investigators and Working Groups; and Fergal Duffy for valuable comments on the manuscript. We thank GlaxoSmithKline Biologicals SA for their support in the conduct of the MAL067 study.

Ethics

Clinical trial registration ClinicalTrials.gov identifier NCT00866619.

The study protocol was approved by the Ethical Committee of the Hospital Clínic in Barcelona (CEIC, Spain), the National Health and Bioethics Committee (CNBS, Mozambique), the Ethikkommission Beider Basel (EKBB, Switzerland), the National Institutional Review Board (NIMR, Tanzania), the Ifakara Health Institute IRB (IHIIRB, Tanzania), the Lambaréné Independent Regional Ethics Committee (CERIL, Gabon), and the Research Ethics Committee (REC, USA). The study teams complied with the Declaration of Helsinki and Good Clinical Practice including monitoring of data. Written informed consent was obtained from children's parents or guardians before recruitment.

Senior Editor

  1. Betty Diamond, The Feinstein Institute for Medical Research, United States

Reviewing Editor

  1. Richard B Kennedy, Mayo Clinic, United States

Reviewer

  1. Wiebke Nahrendorf, University of Edinburgh, United Kingdom

Publication history

  1. Received: May 15, 2021
  2. Preprint posted: May 19, 2021 (view preprint)
  3. Accepted: December 20, 2021
  4. Version of Record published: January 21, 2022 (version 1)

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Metrics

  • 563
    Page views
  • 92
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Gemma Moncunill
  2. Jason Carnes
  3. William Chad Young
  4. Lindsay Carpp
  5. Stephen De Rosa
  6. Joseph J Campo
  7. Augusto Nhabomba
  8. Maxmillian Mpina
  9. Chenjerai Jairoce
  10. Greg Finak
  11. Paige Haas
  12. Carl Muriel
  13. Phu Van
  14. Héctor Sanz
  15. Sheetij Dutta
  16. Benjamin Mordmüller
  17. Selidji T Agnandji
  18. Núria Díez-Padrisa
  19. Nana Aba Williams
  20. John J Aponte
  21. Clarissa Valim
  22. Daniel E Neafsey
  23. Claudia Daubenberger
  24. M Juliana McElrath
  25. Carlota Dobaño
  26. Ken Stuart
  27. Raphael Gottardo
(2022)
Transcriptional correlates of malaria in RTS,S/AS01-vaccinated African children: a matched case–control study
eLife 11:e70393.
https://doi.org/10.7554/eLife.70393

Further reading

    1. Immunology and Inflammation
    Mimmi LE Lundahl, Morgane Mitermite ... Ed C Lavelle
    Research Article

    Macrophages are a highly adaptive population of innate immune cells. Polarization with IFNγ and LPS into the 'classically activated' M1 macrophage enhances pro-inflammatory and microbicidal responses, important for eradicating bacteria such as Mycobacterium tuberculosis. By contrast, 'alternatively activated' M2 macrophages, polarized with IL-4, oppose bactericidal mechanisms and allow mycobacterial growth. These activation states are accompanied by distinct metabolic profiles, where M1 macrophages favor near exclusive use of glycolysis, whereas M2 macrophages up-regulate oxidative phosphorylation (OXPHOS). Here we demonstrate that activation with IL-4 and IL-13 counterintuitively induces protective innate memory against mycobacterial challenge. In human and murine models, prior activation with IL-4/13 enhances pro-inflammatory cytokine secretion in response to a secondary stimulation with mycobacterial ligands. In our murine model, enhanced killing capacity is also demonstrated. Despite this switch in phenotype, IL-4/13 trained murine macrophages do not demonstrate M1-typical metabolism, instead retaining heightened use of OXPHOS. Moreover, inhibition of OXPHOS with oligomycin, 2-deoxy glucose or BPTES all impeded heightened pro-inflammatory cytokine responses from IL-4/13 trained macrophages. Lastly, this work identifies that IL-10 attenuates protective IL-4/13 training, impeding pro-inflammatory and bactericidal mechanisms. In summary, this work provides new and unexpected insight into alternative macrophage activation states in the context of mycobacterial infection.

    1. Immunology and Inflammation
    Xin-Zi Tang, Lieselotte S M Kreuk ... Christopher D C Allen
    Research Article

    In allergic asthma, allergen inhalation leads to local Th2 cell activation and peribronchial inflammation. However, the mechanisms for local antigen capture and presentation remain unclear. By two-photon microscopy of the mouse lung, we established that soluble antigens in the bronchial airway lumen were efficiently captured and presented by a population of CD11c+ interstitial macrophages with high CX3CR1-GFP and MHC class II expression. We refer to these cells as Bronchus-Associated Macrophages (BAMs) based on their localization underneath the bronchial epithelium. BAMs were enriched in collagen-rich regions near some airway branchpoints, where inhaled antigens are likely to deposit. BAMs engaged in extended interactions with effector Th2 cells and promoted Th2 cytokine production. BAMs were also often in contact with dendritic cells (DCs). After exposure to inflammatory stimuli, DCs migrated to draining lymph nodes, whereas BAMs remained lung resident. We propose that BAMs act as local antigen presenting cells in the lung and also transfer antigen to DCs.