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.

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-1^high monocytes show downregulation of proinflammatory genes, and T cells cocultured with stabilin-1^high monocytes showed decreased antigen recall, suggesting that monocyte stabilin-1 suppresses T-cell activation (Palani et al., 2016). Thus, one possibility is that stabilin-1^high 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.

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).

1. 1. Moncunill G
   2. Carnes J
   3. Young WC
   4. Carpp LN
   5. Rosa SD
   6. Campo JJ
   7. Nhabomba AJ
   8. Mpina M
   9. Jairoce C
   10. Finak G
   11. Haas P
   12. Murie C
   13. Van P
   14. Sanz H
   15. Dutta S
   16. Mordmüller B
   17. Agnandji ST
   18. Díez-Padrisa N
   19. Williams NA
   20. Aponte JJ
   21. Valim C
   22. Neafsey DE
   23. Daubenberger C
   24. McElrath J
   25. Dobaño C
   26. Stuart K
   27. Gottardo R

   (2021) NCBI Gene Expression Omnibus

   ID GSE176156. A baseline transcriptional signature associates with clinical malaria risk in RTS,S/AS01-vaccinated African children.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE176156

1. 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).

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE18323
2. 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.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89292
3. 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.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE103401
4. 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.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE102288
5. 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.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE107672

1. 1. Adey A
   2. Morrison HG
   3. Xun X
   4. Kitzman JO
   5. Turner EH
   6. Stackhouse B
   7. MacKenzie AP
   8. Caruccio NC
   9. Zhang X
   10. Shendure J

   (2010) Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition

   Genome Biology 11:R119.

   https://doi.org/10.1186/gb-2010-11-12-r119

   • PubMed
   • Google Scholar
2. 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

   • PubMed
   • Google Scholar
3. 1. Andersen-Nissen E
   2. Fiore-Gartland A
   3. Ballweber Fleming L
   4. Carpp LN
   5. Naidoo AF
   6. Harper MS
   7. Voillet V
   8. Grunenberg N
   9. Laher F
   10. Innes C
   11. Bekker L-G
   12. Kublin JG
   13. Huang Y
   14. Ferrari G
   15. Tomaras GD
   16. Gray G
   17. Gilbert PB
   18. McElrath MJ

   (2021) Innate immune signatures to a partially-efficacious HIV vaccine predict correlates of HIV-1 infection risk

   PLOS Pathogens 17:e1009363.

   https://doi.org/10.1371/journal.ppat.1009363

   • PubMed
   • Google Scholar
4. 1. Antwi-Baffour S
   2. Kyeremeh R
   3. Buabeng D
   4. Adjei JK
   5. Aryeh C
   6. Kpentey G
   7. Seidu MA

   (2018) Correlation of malaria parasitaemia with peripheral blood monocyte to lymphocyte ratio as indicator of susceptibility to severe malaria in Ghanaian children

   Malaria Journal 17:419.

   https://doi.org/10.1186/s12936-018-2569-x

   • PubMed
   • Google Scholar
5. 1. Baldridge JR
   2. McGowan P
   3. Evans JT
   4. Cluff C
   5. Mossman S
   6. Johnson D
   7. Persing D

   (2004) Taking a Toll on human disease: Toll-like receptor 4 agonists as vaccine adjuvants and monotherapeutic agents

   Expert Opinion on Biological Therapy 4:1129–1138.

   https://doi.org/10.1517/14712598.4.7.1129

   • PubMed
   • Google Scholar
6. 1. Beeson JG
   2. Kurtovic L
   3. Dobaño C
   4. Opi DH
   5. Chan JA
   6. Feng G
   7. Good MF
   8. Reiling L
   9. Boyle MJ

   (2019) Challenges and strategies for developing efficacious and long-lasting malaria vaccines

   Science Translational Medicine 11:474.

   https://doi.org/10.1126/scitranslmed.aau1458

   • PubMed
   • Google Scholar
7. 1. Benjamini Y
   2. Hochberg Y

   (1995) Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

   Journal of the Royal Statistical Society 57:289–300.

   https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

   • Google Scholar
8. 1. Bueno LL
   2. Fujiwara RT
   3. Soares IS
   4. Braga EM

   (2008) Direct effect of Plasmodium vivax recombinant vaccine candidates AMA-1 and MSP-119 on the innate immune response

   Vaccine 26:1204–1213.

   https://doi.org/10.1016/j.vaccine.2007.12.031

   • PubMed
   • Google Scholar
9. 1. Chaudhury S
   2. Ockenhouse CF
   3. Regules JA
   4. Dutta S
   5. Wallqvist A
   6. Jongert E
   7. Waters NC
   8. Lemiale F
   9. Bergmann-Leitner E

   (2016) The biological function of antibodies induced by the RTS,S/AS01 malaria vaccine candidate is determined by their fine specificity

   Malaria Journal 15:301.

   https://doi.org/10.1186/s12936-016-1348-9

   • PubMed
   • Google Scholar
10. 1. Dennison SM
    2. Reichartz M
    3. Abraha M
    4. Spreng RL
    5. Wille-Reece U
    6. Dutta S
    7. Jongert E
    8. Alam SM
    9. Tomaras GD

    (2021) Magnitude, Specificity, and Avidity of Sporozoite-Specific Antibodies Associate With Protection Status and Distinguish Among RTS,S/AS01 Dose Regimens

    Open Forum Infectious Diseases 8:ofaa644.

    https://doi.org/10.1093/ofid/ofaa644

    • Google Scholar
11. 1. Didierlaurent AM
    2. Laupèze B
    3. Di Pasquale A
    4. Hergli N
    5. Collignon C
    6. Garçon N

    (2017) Adjuvant system AS01: helping to overcome the challenges of modern vaccines

    Expert Review of Vaccines 16:55–63.

    https://doi.org/10.1080/14760584.2016.1213632

    • PubMed
    • Google Scholar
12. 1. Dobaño C
    2. Sanz H
    3. Sorgho H
    4. Dosoo D
    5. Mpina M
    6. Ubillos I
    7. Aguilar R
    8. Ford T
    9. Díez-Padrisa N
    10. Williams NA
    11. Ayestaran A
    12. Traore O
    13. Nhabomba AJ
    14. Jairoce C
    15. Waitumbi J
    16. Agnandji ST
    17. Kariuki S
    18. Abdulla S
    19. Aponte JJ
    20. Mordmüller B
    21. Asante KP
    22. Owusu-Agyei S
    23. Tinto H
    24. Campo JJ
    25. Moncunill G
    26. Gyan B
    27. Valim C
    28. Daubenberger C

    (2019a) Concentration and avidity of antibodies to different circumsporozoite epitopes correlate with RTS,S/AS01E malaria vaccine efficacy

    Nature Communications 10:2174.

    https://doi.org/10.1038/s41467-019-10195-z

    • PubMed
    • Google Scholar
13. 1. Dobaño C
    2. Ubillos I
    3. Jairoce C
    4. Gyan B
    5. Vidal M
    6. Jiménez A
    7. Santano R
    8. Dosoo D
    9. Nhabomba AJ
    10. Ayestaran A
    11. Aguilar R
    12. Williams NA
    13. Díez-Padrisa N
    14. Lanar D
    15. Chauhan V
    16. Chitnis C
    17. Dutta S
    18. Gaur D
    19. Angov E
    20. Asante KP
    21. Owusu-Agyei S
    22. Valim C
    23. Gamain B
    24. Coppel RL
    25. Cavanagh D
    26. Beeson JG
    27. Campo JJ
    28. Moncunill G

    (2019b) RTS,S/AS01E immunization increases antibody responses to vaccine-unrelated Plasmodium falciparum antigens associated with protection against clinical malaria in African children: a case-control study

    BMC Medicine 17:157.

    https://doi.org/10.1186/s12916-019-1378-6

    • PubMed
    • Google Scholar
14. 1. Du Y
    2. Thompson EG
    3. Muller J
    4. Valvo J
    5. Braun J
    6. Shankar S
    7. van den Berg RA
    8. Jongert E
    9. Dover D
    10. Sadoff J
    11. Hendriks J
    12. Gardner MJ
    13. Ballou WR
    14. Regules JA
    15. van der Most R
    16. Aderem A
    17. Ockenhouse CF
    18. Hill AV
    19. Wille-Reece U
    20. Zak DE

    (2020) The Ratiometric Transcript Signature MX2/GPR183 Is Consistently Associated With RTS,S-Mediated Protection Against Controlled Human Malaria Infection

    Frontiers in Immunology 11:669.

    https://doi.org/10.3389/fimmu.2020.00669

    • PubMed
    • Google Scholar
15. 1. Eisele TP

    (2019) Mass drug administration can be a valuable addition to the malaria elimination toolbox

    Malaria Journal 18:281.

    https://doi.org/10.1186/s12936-019-2906-8

    • PubMed
    • Google Scholar
16. 1. Fletcher HA
    2. Snowden MA
    3. Landry B
    4. Rida W
    5. Satti I
    6. Harris SA
    7. Matsumiya M
    8. Tanner R
    9. O’Shea MK
    10. Dheenadhayalan V
    11. Bogardus L
    12. Stockdale L
    13. Marsay L
    14. Chomka A
    15. Harrington-Kandt R
    16. Manjaly-Thomas Z-R
    17. Naranbhai V
    18. Stylianou E
    19. Darboe F
    20. Penn-Nicholson A
    21. Nemes E
    22. Hatherill M
    23. Hussey G
    24. Mahomed H
    25. Tameris M
    26. McClain JB
    27. Evans TG
    28. Hanekom WA
    29. Scriba TJ
    30. McShane H

    (2016) T-cell activation is an immune correlate of risk in BCG vaccinated infants

    Nature Communications 7:11290.

    https://doi.org/10.1038/ncomms11290

    • PubMed
    • Google Scholar
17. 1. Fourati S
    2. Cristescu R
    3. Loboda A
    4. Talla A
    5. Filali A
    6. Railkar R
    7. Schaeffer AK
    8. Favre D
    9. Gagnon D
    10. Peretz Y
    11. Wang I-M
    12. Beals CR
    13. Casimiro DR
    14. Carayannopoulos LN
    15. Sékaly R-P

    (2016) Pre-vaccination inflammation and B-cell signalling predict age-related hyporesponse to hepatitis B vaccination

    Nature Communications 7:10369.

    https://doi.org/10.1038/ncomms10369

    • PubMed
    • Google Scholar
18. 1. Gordon DM
    2. McGovern TW
    3. Krzych U
    4. Cohen JC
    5. Schneider I
    6. LaChance R
    7. Heppner DG
    8. Yuan G
    9. Hollingdale M
    10. Slaoui M

    (1995) Safety, immunogenicity, and efficacy of a recombinantly produced Plasmodium falciparum circumsporozoite protein-hepatitis B surface antigen subunit vaccine

    The Journal of Infectious Diseases 171:1576–1585.

    https://doi.org/10.1093/infdis/171.6.1576

    • PubMed
    • Google Scholar
19. 1. Hawkes N

    (2015) European Medicines Agency approves first malaria vaccine

    BMJ 351:h4067.

    https://doi.org/10.1136/bmj.h4067

    • PubMed
    • Google Scholar
20. 1. Haynes BF
    2. Gilbert PB
    3. McElrath MJ
    4. Zolla-Pazner S
    5. Tomaras GD
    6. Alam SM
    7. Evans DT
    8. Montefiori DC
    9. Karnasuta C
    10. Sutthent R
    11. Liao H-X
    12. DeVico AL
    13. Lewis GK
    14. Williams C
    15. Pinter A
    16. Fong Y
    17. Janes H
    18. DeCamp A
    19. Huang Y
    20. Rao M
    21. Billings E
    22. Karasavvas N
    23. Robb ML
    24. Ngauy V
    25. de Souza MS
    26. Paris R
    27. Ferrari G
    28. Bailer RT
    29. Soderberg KA
    30. Andrews C
    31. Berman PW
    32. Frahm N
    33. De Rosa SC
    34. Alpert MD
    35. Yates NL
    36. Shen X
    37. Koup RA
    38. Pitisuttithum P
    39. Kaewkungwal J
    40. Nitayaphan S
    41. Rerks-Ngarm S
    42. Michael NL
    43. Kim JH

    (2012) Immune-correlates analysis of an HIV-1 vaccine efficacy trial

    The New England Journal of Medicine 366:1275–1286.

    https://doi.org/10.1056/NEJMoa1113425

    • PubMed
    • Google Scholar
21. 1. Healer J
    2. Cowman AF
    3. Kaslow DC
    4. Birkett AJ

    (2017) Vaccines to Accelerate Malaria Elimination and Eventual Eradication

    Cold Spring Harbor Perspectives in Medicine 7:a025627.

    https://doi.org/10.1101/cshperspect.a025627

    • PubMed
    • Google Scholar
22. 1. Hill DL
    2. Carr EJ
    3. Rutishauser T
    4. Moncunill G
    5. Campo JJ
    6. Innocentin S
    7. Mpina M
    8. Nhabomba A
    9. Tumbo A
    10. Jairoce C
    11. Moll HA
    12. van Zelm MC
    13. Dobaño C
    14. Daubenberger C
    15. Linterman MA

    (2020) Immune system development varies according to age, location, and anemia in African children

    Science Translational Medicine 12:529.

    https://doi.org/10.1126/scitranslmed.aaw9522

    • PubMed
    • Google Scholar
23. 1. HIPC-CHI Signatures Project Team
    2. HIPC-I Consortium

    (2017) Multicohort analysis reveals baseline transcriptional predictors of influenza vaccination responses

    Science Immunology 2:eaal4656.

    https://doi.org/10.1126/sciimmunol.aal4656

    • PubMed
    • Google Scholar
24. 1. Hoffman SL
    2. Vekemans J
    3. Richie TL
    4. Duffy PE

    (2015) The march toward malaria vaccines

    Vaccine 33 Suppl 4:D13–D23.

    https://doi.org/10.1016/j.vaccine.2015.07.091

    • PubMed
    • Google Scholar
25. 1. Hornung V
    2. Rothenfusser S
    3. Britsch S
    4. Krug A
    5. Jahrsdörfer B
    6. Giese T
    7. Endres S
    8. Hartmann G

    (2002) Quantitative expression of toll-like receptor 1-10 mRNA in cellular subsets of human peripheral blood mononuclear cells and sensitivity to CpG oligodeoxynucleotides

    Journal of Immunology 168:4531–4537.

    https://doi.org/10.4049/jimmunol.168.9.4531

    • PubMed
    • Google Scholar
26. 1. Kazmin D
    2. Nakaya HI
    3. Lee EK
    4. Johnson MJ
    5. van der Most R
    6. van den Berg RA
    7. Ballou WR
    8. Jongert E
    9. Wille-Reece U
    10. Ockenhouse C
    11. Aderem A
    12. Zak DE
    13. Sadoff J
    14. Hendriks J
    15. Wrammert J
    16. Ahmed R
    17. Pulendran B

    (2017) Systems analysis of protective immune responses to RTS,S malaria vaccination in humans

    PNAS 114:2425–2430.

    https://doi.org/10.1073/pnas.1621489114

    • PubMed
    • Google Scholar
27. 1. Kester KE
    2. Cummings JF
    3. Ofori-Anyinam O
    4. Ockenhouse CF
    5. Krzych U
    6. Moris P
    7. Schwenk R
    8. Nielsen RA
    9. Debebe Z
    10. Pinelis E
    11. Juompan L
    12. Williams J
    13. Dowler M
    14. Stewart VA
    15. Wirtz RA
    16. Dubois M-C
    17. Lievens M
    18. Cohen J
    19. Ballou WR
    20. Heppner DG Jr
    21. RTS,S Vaccine Evaluation Group

    (2009) Randomized, double-blind, phase 2a trial of falciparum malaria vaccines RTS,S/AS01B and RTS,S/AS02A in malaria-naive adults: safety, efficacy, and immunologic associates of protection

    The Journal of Infectious Diseases 200:337–346.

    https://doi.org/10.1086/600120

    • PubMed
    • Google Scholar
28. 1. Kotliarov Y
    2. Sparks R
    3. Martins AJ
    4. Mulè MP
    5. Lu Y
    6. Goswami M
    7. Kardava L
    8. Banchereau R
    9. Pascual V
    10. Biancotto A
    11. Chen J
    12. Schwartzberg PL
    13. Bansal N
    14. Liu CC
    15. Cheung F
    16. Moir S
    17. Tsang JS

    (2020) Broad immune activation underlies shared set point signatures for vaccine responsiveness in healthy individuals and disease activity in patients with lupus

    Nature Medicine 26:618–629.

    https://doi.org/10.1038/s41591-020-0769-8

    • PubMed
    • Google Scholar
29. 1. Kzhyshkowska J
    2. Gratchev A
    3. Goerdt S

    (2006) Stabilin-1, a homeostatic scavenger receptor with multiple functions

    Journal of Cellular and Molecular Medicine 10:635–649.

    https://doi.org/10.1111/j.1582-4934.2006.tb00425.x

    • PubMed
    • Google Scholar
30. 1. Law CW
    2. Chen Y
    3. Shi W
    4. Smyth GK

    (2014) voom: Precision weights unlock linear model analysis tools for RNA-seq read counts

    Genome Biology 15:R29.

    https://doi.org/10.1186/gb-2014-15-2-r29

    • PubMed
    • Google Scholar
31. 1. Li S
    2. Rouphael N
    3. Duraisingham S
    4. Romero-Steiner S
    5. Presnell S
    6. Davis C
    7. Schmidt DS
    8. Johnson SE
    9. Milton A
    10. Rajam G
    11. Kasturi S
    12. Carlone GM
    13. Quinn C
    14. Chaussabel D
    15. Palucka AK
    16. Mulligan MJ
    17. Ahmed R
    18. Stephens DS
    19. Nakaya HI
    20. Pulendran B

    (2014) Molecular signatures of antibody responses derived from a systems biology study of five human vaccines

    Nature Immunology 15:195–204.

    https://doi.org/10.1038/ni.2789

    • PubMed
    • Google Scholar
32. 1. Lin L
    2. Finak G
    3. Ushey K
    4. Seshadri C
    5. Hawn TR
    6. Frahm N
    7. Scriba TJ
    8. Mahomed H
    9. Hanekom W
    10. Bart P-A
    11. Pantaleo G
    12. Tomaras GD
    13. Rerks-Ngarm S
    14. Kaewkungwal J
    15. Nitayaphan S
    16. Pitisuttithum P
    17. Michael NL
    18. Kim JH
    19. Robb ML
    20. O’Connell RJ
    21. Karasavvas N
    22. Gilbert P
    23. C De Rosa S
    24. McElrath MJ
    25. Gottardo R

    (2015) COMPASS identifies T-cell subsets correlated with clinical outcomes

    Nature Biotechnology 33:610–616.

    https://doi.org/10.1038/nbt.3187

    • PubMed
    • Google Scholar
33. 1. Liu C
    2. Martins AJ
    3. Lau WW
    4. Rachmaninoff N
    5. Chen J
    6. Imberti L
    7. Mostaghimi D
    8. Fink DL
    9. Burbelo PD
    10. Dobbs K
    11. Delmonte OM
    12. Bansal N
    13. Failla L
    14. Sottini A
    15. Quiros-Roldan E
    16. Han KL
    17. Sellers BA
    18. Cheung F
    19. Sparks R
    20. Chun T-W
    21. Moir S
    22. Lionakis MS
    23. NIAID COVID Consortium
    24. COVID Clinicians
    25. Rossi C
    26. Su HC
    27. Kuhns DB
    28. Cohen JI
    29. Notarangelo LD
    30. Tsang JS

    (2021) Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19

    Cell 184:1836–1857.

    https://doi.org/10.1016/j.cell.2021.02.018

    • PubMed
    • Google Scholar
34. 1. Lu P
    2. Guerin DJ
    3. Lin S
    4. Chaudhury S
    5. Ackerman ME
    6. Bolton DL
    7. Wallqvist A

    (2021) Immunoprofiling Correlates of Protection Against SHIV Infection in Adjuvanted HIV-1 Pox-Protein Vaccinated Rhesus Macaques

    Frontiers in Immunology 12:625030.

    https://doi.org/10.3389/fimmu.2021.625030

    • PubMed
    • Google Scholar
35. 1. Mitchell LA
    2. Henderson AJ
    3. Dow SW

    (2012) Suppression of vaccine immunity by inflammatory monocytes

    Journal of Immunology 189:5612–5621.

    https://doi.org/10.4049/jimmunol.1202151

    • PubMed
    • Google Scholar
36. 1. Moncunill G
    2. Han H
    3. Dobaño C
    4. McElrath MJ
    5. De Rosa SC

    (2014) OMIP-024: pan-leukocyte immunophenotypic characterization of PBMC subsets in human samples

    Cytometry. Part A 85:995–998.

    https://doi.org/10.1002/cyto.a.22580

    • PubMed
    • Google Scholar
37. 1. Moncunill G
    2. Dobaño C
    3. McElrath MJ
    4. De Rosa SC

    (2015) OMIP-025: evaluation of human T- and NK-cell responses including memory and follicular helper phenotype by intracellular cytokine staining

    Cytometry. Part A 87:289–292.

    https://doi.org/10.1002/cyto.a.22590

    • PubMed
    • Google Scholar
38. 1. Moncunill G
    2. De Rosa SC
    3. Ayestaran A
    4. Nhabomba AJ
    5. Mpina M
    6. Cohen KW
    7. Jairoce C
    8. Rutishauser T
    9. Campo JJ
    10. Harezlak J
    11. Sanz H
    12. Díez-Padrisa N
    13. Williams NA
    14. Morris D
    15. Aponte JJ
    16. Valim C
    17. Daubenberger C
    18. Dobaño C
    19. McElrath MJ

    (2017a) RTS,S/AS01E Malaria Vaccine Induces Memory and Polyfunctional T Cell Responses in a Pediatric African Phase III Trial

    Frontiers in Immunology 8:1008.

    https://doi.org/10.3389/fimmu.2017.01008

    • PubMed
    • Google Scholar
39. 1. Moncunill G
    2. Mpina M
    3. Nhabomba AJ
    4. Aguilar R
    5. Ayestaran A
    6. Sanz H
    7. Campo JJ
    8. Jairoce C
    9. Barrios D
    10. Dong Y
    11. Díez-Padrisa N
    12. Fernandes JF
    13. Abdulla S
    14. Sacarlal J
    15. Williams NA
    16. Harezlak J
    17. Mordmüller B
    18. Agnandji ST
    19. Aponte JJ
    20. Daubenberger C
    21. Valim C
    22. Dobaño C

    (2017b) Distinct Helper T Cell Type 1 and 2 Responses Associated With Malaria Protection and Risk in RTS,S/AS01E Vaccinees

    Clinical Infectious Diseases 65:746–755.

    https://doi.org/10.1093/cid/cix429

    • PubMed
    • Google Scholar
40. 1. Moncunill G
    2. Scholzen A
    3. Mpina M
    4. Nhabomba A
    5. Hounkpatin AB
    6. Osaba L
    7. Valls R
    8. Campo JJ
    9. Sanz H
    10. Jairoce C
    11. Williams NA
    12. Pasini EM
    13. Arteta D
    14. Maynou J
    15. Palacios L
    16. Duran-Frigola M
    17. Aponte JJ
    18. Kocken CHM
    19. Agnandji ST
    20. Mas JM
    21. Mordmüller B
    22. Daubenberger C
    23. Sauerwein R
    24. Dobaño C

    (2020) Antigen-stimulated PBMC transcriptional protective signatures for malaria immunization

    Science Translational Medicine 12:543.

    https://doi.org/10.1126/scitranslmed.aay8924

    • PubMed
    • Google Scholar
41. 1. Moris P
    2. Jongert E
    3. van der Most RG

    (2018) Characterization of T-cell immune responses in clinical trials of the candidate RTS,S malaria vaccine

    Human Vaccines & Immunotherapeutics 14:17–27.

    https://doi.org/10.1080/21645515.2017.1381809

    • PubMed
    • Google Scholar
42. 1. Norris BA
    2. Uebelhoer LS
    3. Nakaya HI
    4. Price AA
    5. Grakoui A
    6. Pulendran B

    (2013) Chronic but not acute virus infection induces sustained expansion of myeloid suppressor cell numbers that inhibit viral-specific T cell immunity

    Immunity 38:309–321.

    https://doi.org/10.1016/j.immuni.2012.10.022

    • PubMed
    • Google Scholar
43. 1. Ockenhouse CF
    2. Regules J
    3. Tosh D
    4. Cowden J
    5. Kathcart A
    6. Cummings J
    7. Paolino K
    8. Moon J
    9. Komisar J
    10. Kamau E
    11. Oliver T
    12. Chhoeu A
    13. Murphy J
    14. Lyke K
    15. Laurens M
    16. Birkett A
    17. Lee C
    18. Weltzin R
    19. Wille-Reece U
    20. Sedegah M
    21. Hendriks J
    22. Versteege I
    23. Pau MG
    24. Sadoff J
    25. Vanloubbeeck Y
    26. Lievens M
    27. Heerwegh D
    28. Moris P
    29. Guerra Mendoza Y
    30. Jongert E
    31. Cohen J
    32. Voss G
    33. Ballou WR
    34. Vekemans J

    (2015) Ad35.CS.01-RTS,S/AS01 Heterologous Prime Boost Vaccine Efficacy against Sporozoite Challenge in Healthy Malaria-Naïve Adults

    PLOS ONE 10:e0131571.

    https://doi.org/10.1371/journal.pone.0131571

    • PubMed
    • Google Scholar
44. 1. Palani S
    2. Elima K
    3. Ekholm E
    4. Jalkanen S
    5. Salmi M

    (2016) Monocyte Stabilin-1 Suppresses the Activation of Th1 Lymphocytes

    Journal of Immunology 196:115–123.

    https://doi.org/10.4049/jimmunol.1500257

    • PubMed
    • Google Scholar
45. 1. Pallikkuth S
    2. Chaudhury S
    3. Lu P
    4. Pan L
    5. Jongert E
    6. Wille-Reece U
    7. Pahwa S

    (2020) A delayed fractionated dose RTS,S AS01 vaccine regimen mediates protection via improved T follicular helper and B cell responses

    eLife 9:e51889.

    https://doi.org/10.7554/eLife.51889

    • PubMed
    • Google Scholar
46. 1. Pichichero ME

    (2014) Challenges in vaccination of neonates, infants and young children

    Vaccine 32:3886–3894.

    https://doi.org/10.1016/j.vaccine.2014.05.008

    • PubMed
    • Google Scholar
47. 1. Regules JA
    2. Cicatelli SB
    3. Bennett JW
    4. Paolino KM
    5. Twomey PS
    6. Moon JE
    7. Kathcart AK
    8. Hauns KD
    9. Komisar JL
    10. Qabar AN
    11. Davidson SA
    12. Dutta S
    13. Griffith ME
    14. Magee CD
    15. Wojnarski M
    16. Livezey JR
    17. Kress AT
    18. Waterman PE
    19. Jongert E
    20. Wille-Reece U
    21. Volkmuth W
    22. Emerling D
    23. Robinson WH
    24. Lievens M
    25. Morelle D
    26. Lee CK
    27. Yassin-Rajkumar B
    28. Weltzin R
    29. Cohen J
    30. Paris RM
    31. Waters NC
    32. Birkett AJ
    33. Kaslow DC
    34. Ballou WR
    35. Ockenhouse CF
    36. Vekemans J

    (2016) Fractional Third and Fourth Dose of RTS,S/AS01 Malaria Candidate Vaccine: A Phase 2a Controlled Human Malaria Parasite Infection and Immunogenicity Study

    The Journal of Infectious Diseases 214:762–771.

    https://doi.org/10.1093/infdis/jiw237

    • PubMed
    • Google Scholar
48. 1. Ritchie ME
    2. Phipson B
    3. Wu D
    4. Hu Y
    5. Law CW
    6. Shi W
    7. Smyth GK

    (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies

    Nucleic Acids Research 43:e47.

    https://doi.org/10.1093/nar/gkv007

    • PubMed
    • Google Scholar
49. 1. Robinson MD
    2. Oshlack A

    (2010) A scaling normalization method for differential expression analysis of RNA-seq data

    Genome Biology 11:R25.

    https://doi.org/10.1186/gb-2010-11-3-r25

    • PubMed
    • Google Scholar
50. 1. RTS,S Clinical Trials Partnership

    (2012) A phase 3 trial of RTS, S/AS01 malaria vaccine in African infants

    New England Journal of Medicine 367:2284–2295.

    https://doi.org/10.1056/NEJMoa1208394

    • PubMed
    • Google Scholar
51. 1. RTS,S Clinical Trials Partnership

    (2014) Efficacy and safety of the RTS,S/AS01 malaria vaccine during 18 months after vaccination: a phase 3 randomized, controlled trial in children and young infants at 11 African sites

    PLOS Medicine 11:e1001685.

    https://doi.org/10.1371/journal.pmed.1001685

    • PubMed
    • Google Scholar
52. 1. RTS,S Clinical Trials Partnership

    (2015) 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 386:31–45.

    https://doi.org/10.1016/S0140-6736(15)60721-8

    • PubMed
    • Google Scholar
53. 1. Sammicheli S
    2. Kuka M
    3. Di Lucia P
    4. de Oya NJ
    5. De Giovanni M
    6. Fioravanti J
    7. Cristofani C
    8. Maganuco CG
    9. Fallet B
    10. Ganzer L
    11. Sironi L
    12. Mainetti M
    13. Ostuni R
    14. Larimore K
    15. Greenberg PD
    16. de la Torre JC
    17. Guidotti LG
    18. Iannacone M

    (2016) Inflammatory monocytes hinder antiviral B cell responses

    Science Immunology 1:eaah6789.

    https://doi.org/10.1126/sciimmunol.aah6789

    • PubMed
    • Google Scholar
54. 1. Simon AK
    2. Hollander GA
    3. McMichael A

    (2015) Evolution of the immune system in humans from infancy to old age

    Proceedings. Biological Sciences 282:20143085.

    https://doi.org/10.1098/rspb.2014.3085

    • PubMed
    • Google Scholar
55. 1. Smyth GK

    (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments

    Statistical Applications in Genetics and Molecular Biology 3:e1027.

    https://doi.org/10.2202/1544-6115.1027

    • PubMed
    • Google Scholar
56. Preprint

    1. Soumillon M
    2. Cacchiarelli D
    3. Semrau S
    4. Oudenaarden A
    5. Mikkelsen TS

    (2014) Characterization of Directed Differentiation by High-Throughput Single-Cell RNA-Seq

    bioRxiv.

    https://doi.org/10.1101/003236

    • Google Scholar
57. 1. Subramanian A
    2. Tamayo P
    3. Mootha VK
    4. Mukherjee S
    5. Ebert BL
    6. Gillette MA
    7. Paulovich A
    8. Pomeroy SL
    9. Golub TR
    10. Lander ES
    11. Mesirov JP

    (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles

    PNAS 102:15545–15550.

    https://doi.org/10.1073/pnas.0506580102

    • PubMed
    • Google Scholar
58. 1. Suscovich TJ
    2. Fallon JK
    3. Das J
    4. Demas AR
    5. Crain J
    6. Linde CH
    7. Michell A
    8. Natarajan H
    9. Arevalo C
    10. Broge T
    11. Linnekin T
    12. Kulkarni V
    13. Lu R
    14. Slein MD
    15. Luedemann C
    16. Marquette M
    17. March S
    18. Weiner J
    19. Gregory S
    20. Coccia M
    21. Flores-Garcia Y
    22. Zavala F
    23. Ackerman ME
    24. Bergmann-Leitner E
    25. Hendriks J
    26. Sadoff J
    27. Dutta S
    28. Bhatia SN
    29. Lauffenburger DA
    30. Jongert E
    31. Wille-Reece U
    32. Alter G

    (2020) Mapping functional humoral correlates of protection against malaria challenge following RTS,S/AS01 vaccination

    Science Translational Medicine 12:553.

    https://doi.org/10.1126/scitranslmed.abb4757

    • Google Scholar
59. 1. Tsang JS
    2. Schwartzberg PL
    3. Kotliarov Y
    4. Biancotto A
    5. Xie Z
    6. Germain RN
    7. Wang E
    8. Olnes MJ
    9. Narayanan M
    10. Golding H
    11. Moir S
    12. Dickler HB
    13. Perl S
    14. Cheung F
    15. Baylor HIPC Center
    16. CHI Consortium

    (2014) Global analyses of human immune variation reveal baseline predictors of postvaccination responses

    Cell 157:499–513.

    https://doi.org/10.1016/j.cell.2014.03.031

    • PubMed
    • Google Scholar
60. 1. Tsang JS
    2. Dobaño C
    3. VanDamme P
    4. Moncunill G
    5. Marchant A
    6. Othman RB
    7. Sadarangani M
    8. Koff WC
    9. Kollmann TR

    (2020) Improving Vaccine-Induced Immunity: Can Baseline Predict Outcome?

    Trends in Immunology 41:457–465.

    https://doi.org/10.1016/j.it.2020.04.001

    • PubMed
    • Google Scholar
61. 1. Ubillos I
    2. Ayestaran A
    3. Nhabomba AJ
    4. Dosoo D
    5. Vidal M
    6. Jiménez A
    7. Jairoce C
    8. Sanz H
    9. Aguilar R
    10. Williams NA
    11. Díez-Padrisa N
    12. Mpina M
    13. Sorgho H
    14. Agnandji ST
    15. Kariuki S
    16. Mordmüller B
    17. Daubenberger C
    18. Asante KP
    19. Owusu-Agyei S
    20. Sacarlal J
    21. Aide P
    22. Aponte JJ
    23. Dutta S
    24. Gyan B
    25. Campo JJ
    26. Valim C
    27. Moncunill G
    28. Dobaño C

    (2018) Baseline exposure, antibody subclass, and hepatitis B response differentially affect malaria protective immunity following RTS,S/AS01E vaccination in African children

    BMC Medicine 16:197.

    https://doi.org/10.1186/s12916-018-1186-4

    • PubMed
    • Google Scholar
62. 1. Vahey MT
    2. Wang Z
    3. Kester KE
    4. Cummings J
    5. Heppner DG Jr
    6. Nau ME
    7. Ofori-Anyinam O
    8. Cohen J
    9. Coche T
    10. Ballou WR
    11. Ockenhouse CF

    (2010) Expression of genes associated with immunoproteasome processing of major histocompatibility complex peptides is indicative of protection with adjuvanted RTS,S malaria vaccine

    The Journal of Infectious Diseases 201:580–589.

    https://doi.org/10.1086/650310

    • PubMed
    • Google Scholar
63. 1. van den Berg RA
    2. Coccia M
    3. Ballou WR
    4. Kester KE
    5. Ockenhouse CF
    6. Vekemans J
    7. Jongert E
    8. Didierlaurent AM
    9. van der Most RG

    (2017) Predicting RTS,S Vaccine-Mediated Protection from Transcriptomes in a Malaria-Challenge Clinical Trial

    Frontiers in Immunology 8:557.

    https://doi.org/10.3389/fimmu.2017.00557

    • PubMed
    • Google Scholar
64. 1. Vaure C
    2. Liu YQ

    (2014) A comparative review of toll-like receptor 4 expression and functionality in different animal species

    Frontiers in Immunology 5:316.

    https://doi.org/10.3389/fimmu.2014.00316

    • PubMed
    • Google Scholar
65. 1. Warimwe GM
    2. Fletcher HA
    3. Olotu A
    4. Agnandji ST
    5. Hill AVS
    6. Marsh K
    7. Bejon P

    (2013a) 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 Medicine 11:184.

    https://doi.org/10.1186/1741-7015-11-184

    • PubMed
    • Google Scholar
66. 1. Warimwe GM
    2. Murungi LM
    3. Kamuyu G
    4. Nyangweso GM
    5. Wambua J
    6. Naranbhai V
    7. Fletcher HA
    8. Hill AVS
    9. Bejon P
    10. Osier FHA
    11. Marsh K

    (2013b) The ratio of monocytes to lymphocytes in peripheral blood correlates with increased susceptibility to clinical malaria in Kenyan children

    PLOS ONE 8:e57320.

    https://doi.org/10.1371/journal.pone.0057320

    • PubMed
    • Google Scholar
67. Website

    1. World Health Organization

    (2019) Q&A on the malaria vaccine implementation programme (MVIP

    Accessed November 29, 2019.

    https://www.who.int/malaria/media/malaria-vaccine-implementation-qa/en
68. Website

    1. World Health Organization

    (2021a) World Malaria Report

    Accessed January 9, 2021.

    https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2021
69. Website

    1. World Health Organization

    (2021b) WHO recommends groundbreaking malaria vaccine for children at risk

    News Release. Accessed October 6, 2021.

    https://www.who.int/news/item/06-10-2021-who-recommends-groundbreaking-malaria-vaccine-for-children-at-risk
70. 1. Wu D
    2. Smyth GK

    (2012) Camera: a competitive gene set test accounting for inter-gene correlation

    Nucleic Acids Research 40:17.

    https://doi.org/10.1093/nar/gks461

    • PubMed
    • Google Scholar
71. 1. Yang GG
    2. Kim D
    3. Pham A
    4. Paul CJ

    (2018) A Meta-Regression Analysis of the Effectiveness of Mosquito Nets for Malaria Control: The Value of Long-Lasting Insecticide Nets

    International Journal of Environmental Research and Public Health 15:E546.

    https://doi.org/10.3390/ijerph15030546

    • PubMed
    • Google Scholar
72. 1. Young WC
    2. Carpp LN
    3. Chaudhury S
    4. Regules JA
    5. Bergmann-Leitner ES
    6. Ockenhouse C
    7. Wille-Reece U
    8. deCamp AC
    9. Hughes E
    10. Mahoney C
    11. Pallikkuth S
    12. Pahwa S
    13. Dennison SM
    14. Mudrak SV
    15. Alam SM
    16. Seaton KE
    17. Spreng RL
    18. Fallon J
    19. Michell A
    20. Ulloa-Montoya F
    21. Coccia M
    22. Jongert E
    23. Alter G
    24. Tomaras GD
    25. Gottardo R

    (2021) 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

    Frontiers in Big Data 4:672460.

    https://doi.org/10.3389/fdata.2021.672460

    • PubMed
    • Google Scholar

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

   "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

   "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

    "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

    "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

    "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

    "This ORCID iD identifies the author of this article:" 0000-0002-3867-0232

• 998

  views

• 161

  downloads

• 3

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.