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).

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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

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