The burden of Plasmodium falciparum malaria is greatest in young children and pregnant women (World Health Organisation, 2019). Among women with lifelong malaria exposure, pregnant women have increased susceptibility to malaria, and complications include maternal mortality, severe maternal anemia, miscarriage and stillbirth, and impaired fetal growth or prematurity leading to low birth weight delivery (Steketee et al., 2001; Guyatt and Snow, 2001; Desai et al., 2007; Moore et al., 2017a; Moore et al., 2017b). The prevalence, intensity, and consequences of infection decline with increasing gravidity (Guyatt and Snow, 2001).

The susceptibility of pregnant women to P. falciparum malaria is in part due to the ability of infected erythrocytes (IEs) to sequester in the maternal blood spaces of the placenta (Rogerson et al., 2007), where IEs that express VAR2CSA adhere to chondroitin sulfate A (CSA), a glycosaminoglycan chain on syndecan-1 expressed by the placental syncytiotrophoblast (Salanti et al., 2004; Ayres Pereira et al., 2016). VAR2CSA is a unique 350 kDa protein that is a member of the P. falciparum erythrocyte membrane protein 1 (PfEMP1) family of variant surface antigens (VSA). PfEMP1s are encoded by var genes and are composed of multiple Duffy binding-like (DBL) domains and cysteine-rich interdomain regions (CIDR). VAR2CSA has six DBL domains, with CSA adhesion being strongly associated with the DBL2 domain, which is present in the N-terminal region of the protein (Doritchamou et al., 2013). Two constructs based on this region have entered clinical trials as vaccine candidates to prevent placental malaria (Mordmüller et al., 2019; Sirima et al., 2020).

Antibodies to VAR2CSA, in its native form on IE or expressed as recombinant protein, develop in a parity-dependent manner, block adhesion to CSA, and opsonize IE for phagocytosis by monocytic cells (Fried et al., 1998; Ricke et al., 2000; Salanti et al., 2004; Keen et al., 2007). Some studies report associations between measures of single antibody features and protection from malaria complications such as placental infection, impaired fetal growth, prematurity, or anemia (Duffy and Fried, 2003; Staalsoe et al., 2004; Feng et al., 2009). However, identifying targets and features of protective antibody responses to malaria is challenging because exposure to infection is accompanied by the production of antibody to many parasite antigens, only a fraction of which contributes to protective immunity (Crompton et al., 2010). The large size and multidomain structure of VAR2CSA adds complexity to the identification of targets and features of protective immune response. In a recent systematic review, antibody responses to VAR2CSA were often correlated with exposure to placental malaria, rather than protection from infection (Cutts et al., 2020), although some studies indicate an association between antibody to IE and protection from low birth weight in subsets of pregnant women (Duffy and Fried, 2003; Staalsoe et al., 2004; Feng et al., 2009).

To identify antibody responses that protect women from placental malaria, an alternative approach that examines multiple antibody features and controls for exposure may be needed. Placental malaria mostly occurs when antigenic variation (Roberts et al., 1992) leads to a parasite that has infected a pregnant woman, expressing VAR2CSA. These parasites can flourish by sequestering in the placenta in women who lack VAR2CSA-specific immunity (Hviid, 2004). By contrast, some pregnant women with peripheral blood infection remain free of placental malaria, and we postulate that these women have antibody responses that specifically protect them against VAR2CSA and placental malaria.

Antibody measures towards the IE have traditionally focused on the quantity of bound antibodies and their ability to inhibit IE binding to CSA (Cutts et al., 2020), but there is increasing interest in how antibodies that recognize IE engage with innate immune cells and activate complement (Aitken et al., 2020), which are both determined by biophysical features of the Fab and Fc regions of antibody (reviewed in Arnold and Chung, 2018). In recent years, detailed functional and biophysical characterization of antibody responses has led to the identification of specific antibody determinants that correlate with vaccine-induced protection from HIV (Chung et al., 2015), control of latent tuberculosis infection (Lu et al., 2016), transplacental transfer of antibody (Martinez et al., 2019; Jennewein et al., 2019), and correlates of vaccine-induced protection in human malaria challenge models (Suscovich et al., 2020). This approach, which has been termed systems serology, is well suited to placental malaria because there is a clear relationship between a single antigen (VAR2CSA) and a specific pathology (the sequestration of parasites in the placenta, reviewed in Ataíde et al., 2014).

The objective of this study was develop a prognostic model using antibody features to identify women protected from placental malaria. In this prospective study, we identified features, functions, and targets of naturally acquired antibody to VAR2CSA that contribute to protection against placental malaria. Pregnant Papua New Guinean women who were participating in a trial of intermittent preventive treatment against malaria (Unger et al., 2015) were categorized at delivery as currently uninfected, having placental malaria, or having a non-placental infection. Using plasma samples collected at enrollment in mid-pregnancy, we performed extensive profiling of antibody responses to VAR2CSA recombinant proteins and IEs expressing VAR2CSA. Machine learning techniques revealed that 6 of 169 antibody features measured mid-pregnancy were able to discriminate between women who had placental malaria and non-placental infection at delivery with high accuracy. Five of the six leading antibody features we found were related to inhibition of placental sequestration and/or opsonization for phagocytic clearance.

We used a systems serology approach to broadly characterize antibody responses to the VAR2CSA protein that mediates protection from placental malaria. This involved measuring a wide range of antibody features to this protein followed by employing the machine learning techniques elastic net-regularized regression and PLSDA to identify those antibody features that protect pregnant women with P. falciparum infection from placental malaria. The combination of a prospective study design and a systems serology approach provided an unbiased way to identify the key features of the antibody response to VAR2CSA that mediates protection. Using this approach, we identified six antibody features of 169 tested, which were able to correctly differentiate, on average, 86% of the pregnant women with placental malaria and non-placental infection. The antibody features identified fell into broad functional groups: antibody that blocks placental binding of IEs and opsonizing antibody that result in phagocytic clearance of IEs (see Figure 6 for a schematic of mechanisms of protection of the six selected antibody features).

Figure 6

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We hypothesized that pregnant women who had P. falciparum peripheral blood parasitemia at delivery but who did not have placental sequestration were protected from placental malaria by antibody immunity targeting VAR2CSA, and we sought to determine the characteristics of this protective antibody response. We frequency matched groups on participant characteristics such as gravidity and urban or rural residence to minimize confounding by these variables that are known to affect exposure and immunity. Antibody features at enrollment did not differ between women with placental malaria and those who were uninfected at delivery, but they did differ between infected women with and without placental malaria at delivery.

Unlike previously published studies (Aitken et al., 2010; Fried et al., 2018), the inclusion of an infected group without placental malaria allowed us to accurately control for exposure to the parasite at the time point of outcome. The lack of differences in antibody features between the non-infected and placental malaria groups was quite striking and mirrors previous observations (reviewed by Cutts et al., 2020). It may in part be attributable to the heterogeneous nature of the non-infected group, which probably includes both women who are exposed but protected by antibody and women who have not been exposed to malaria in pregnancy and therefore lack pregnancy-specific antibodies. It is possible that the non-infected group is protected by antibodies towards other antigens. That differences were seen only when comparing the two infected groups, clearly highlighting the importance for controlling for exposure when studying antibody responses and protection from malaria. In Cameroon, multigravid women studied in early pregnancy who did not go on to have placental malaria had a greater breadth and intensity of antibody responses to DBL domains than those with placental malaria at delivery (Tutterrow et al., 2012), and higher avidity antibodies to full-length VAR2CSA, suggesting that a broad, high-affinity IgG response to DBL proteins may also correlate with protection.

In this study, fitting the ENLR model to resampled subsets of data allowed us to rank antibody features by their selection frequency. We generated a ranking of the 20 most frequently selected features, and then used PLSDA to determine a minimal set of antibody features that classify the pregnant women with a high accuracy. These six features were selected by ENLR more than 70% of the time, and the inclusion of these six features in the PLSDA showed an ability to accurately distinguish 86% of the women in the two groups. Notably this ability to distinguish the two groups did not increase if more than six features were included in the model. The six antibody features that best distinguished the groups were all more common in women with non-placental infection, and they included previously identified and novel responses.

Among the features we identified as associated with protection against placental malaria were adhesion inhibition and cytophilic antibodies that mediate ADCP of IE. The ability to block IE binding to CSA has been associated with protection against placental malaria, maternal anemia, low birth weight, or prematurity in subsets of pregnant women (Duffy and Fried, 2003; Ndam et al., 2015). IgG1 and IgG3 antibody responses to VAR2CSA are dominant and mediate opsonic clearance of IE (Tebo et al., 2002; Elliott et al., 2005a; Megnekou et al., 2005; Keen et al., 2007; Damelang et al., 2019). ADCP using THP-1 cells has been correlated with protection from malaria or its complications in pregnant women and in adults (reviewed in Teo et al., 2016). ADNP of IE, which has not previously been systematically studied in malaria immunity, also correlated with placental infection status. Antibodies that block adhesion and mediate ADCP by THP-1 cells or ADNP are important correlates of protection that should be included in future studies of naturally acquired and vaccine-induced antibody to malaria in pregnancy.

Although only 11% of features measured antibody to IE, four of the six top antibody features involved assays using IE. Similarly, both features towards recombinant proteins involved the DBL2 domain, which is the principal DBL domain involved in placental adhesion. The overrepresentation of both IE and DBL2 in the selected features supports the notion that antibody features directed towards functionally important targets are key to protection from placental malaria.

Novel features identified using the system serology approach include the antibody feature IgA2. Of note, IgA2 levels in serum are generally low (most serum IgA is IgA1) (Macpherson et al., 2008). Interestingly a recent study looking for correlates of malaria protection from vaccine-induced immunity also identified IgA2 as a variable that helped predict those protected from P. falciparum infection (Suscovich et al., 2020). IgA2 can elicit myeloid cell effector functions, including opsonic phagocytosis (mediated by FcαRI) (Breedveld and van Egmond, 2019), and as it recognized the CSA binding DBL domain DBL2, it may also inhibit IE binding to CSA. However, since IgA2 levels in serum are low, we hypothesize that rather than being directly involved in protection IgA2 might instead be a marker of a protective antibody response. Further investigation of VAR2CSA-specific B cells and whether a protective response is associated with increased IgA2 is warranted.

Our analysis suggests that features determined by measuring antibody to VAR2CSA-expressing IE are important indicators of protection from placental malaria. Four features measured antibody to IE using representatives of the dominant 3D7-like and FCR3-like clades (Patel et al., 2017; Benavente et al., 2018) whose N-terminal regions are used in two VAR2CSA-based pregnancy malaria vaccines (Sirima et al., 2020; Mordmüller et al., 2019). While three of these four assays targeted FCR3 or the isogenic CS2 parasite line, studies of global diversity in VAR2CSA to date have included few sequences from Papua New Guinea (Benavente et al., 2018), and the relative importance of immunity to specific clades may vary by region. Vaccination offered limited heterologous protection (Sirima et al., 2020), and it is unclear whether effective naturally acquired protective immunity needs to target multiple clades or different clades in different regions.

Antibody responses to VAR2CSA domains serve as excellent markers of exposure to malaria in pregnant women (Cutts et al., 2020), but few were selected as correlates of protection in our analysis. This may reflect the fact that the responses to different VARCSA domains were generally highly correlated, as revealed by their clustering together in the center of our network analysis, or it might be because of the longevity of antibody that promotes ADCP of IE compared to that of antibody responses to recombinant proteins (Teo et al., 2014; Teo et al., 2015; Chandrasiri et al., 2014). Antibody to VAR2CSA recombinant proteins, and particularly to specific VAR2CSA peptides, appears to be a good correlate of exposure and transmission intensity (Fonseca et al., 2019), but these responses appear to be less useful as correlates of protection.

The correlation network showed close correlations between different antibody responses to recombinant DBL domains (which are predominantly located in the central cluster) but the protective antibody features were dispersed, suggesting that distinct pathways contribute to the development of protection against placental malaria. The analysis of functions identified in the selected features further supports this, with most protected women (and few unprotected women) having significant antibody that promoted both binding inhibition to CSA and phagocytosis; however, no consistent pattern of antibody functions in protected women was seen. Similar associations have been seen in the relationships between antibody features and protection from viral pathogens, and there is a growing awareness that polyfunctional antibody responses, rather than a single functional correlate, contribute to protection from disease (Gunn et al., 2018; Chung et al., 2014). None of the antibody features targeted more than one variant of the protein or parasite strain. One possible explanation is that epitopes recognized by antibodies mediating each function may vary between strains (as suggested by Doritchamou et al., 2019). This could in turn mean that protective functional features could vary with different populations (as there is variation in global distribution of the different VAR2CSA domain clades; Benavente et al., 2018). Future studies should ideally include multiple VAR2CSA clades and study diverse populations.

We used systems serology to identify protective features of the naturally acquired antibody response to VAR2CSA, a PfEMP1 variant that is specifically associated with placental malaria, complementing a recent study that used systems serology to identify functional antibody correlates of vaccine-induced immunity, in subjects vaccinated with RTS,S (Suscovich et al., 2020). A systems serology approach could also be used to identify the characteristics of protective immunity to other malaria syndromes, such as cerebral malaria, which is often associated with a subset of PfEMP1s that are potential vaccine candidates (reviewed in Jensen et al., 2020). It will be important to determine whether protective features of the cerebral malaria-associated PfEMP1 antibody response analogous to the ones that we identified in this study can be identified.

We were interested in measuring antibodies to placental binding parasites and defined placental malaria by the detection of IE in the placenta by histology because previous studies have shown that these are the infections associated with adverse pregnancy outcomes (Menendez et al., 2000; Rogerson et al., 2003). Ring stage parasites in the peripheral blood will circulate through the placenta, and, in some cases, these could be detected by PCR of placental blood; however, we did not consider these submicroscopic infections as placental malaria as they were not shown to be sequestering there.

Study strengths include the application of 169 novel and established assays; minimal missing predictor and no missing outcome data, the ability to control for exposure with the identification of pregnant women with P. falciparum infection with, and without, placental malaria; the prospective study design; and the use of native antigens on IEs to complement the data we acquired using recombinant protein. Together, this allowed us to discover the determinants of naturally acquired protective immunity. Weaknesses include the lack of inclusion of full-length VAR2CSA in our Luminex and bead-based assays, and an imbalance in numbers between women with parasitemia but no placental infection relative to the other groups. Samples from all women in the former group were selected for this study (and statistical analysis controlled for this imbalance between groups), but this group is relatively rare, with placental infection in the absence of peripheral infection being substantially more common (Desai et al., 2007). High levels of collinearity between some variables make it difficult to dissect out individual protective responses. Demonstration that our selected variables correlate with lack of placental malaria in other cohorts would further strengthen the findings. Antibody glycosylation (Lu et al., 2016) is an additional feature that could be included in future studies.

The present study identified leading candidate antibody features that mediate naturally acquired protection from placental malaria and demonstrated we could use these features to identify women in this cohort protected from placental malaria with high accuracy (though this should be validated in a second cohort). Our results suggest that multiple pathways exist that lead to the development of protective immunity against placental malaria. Next steps will include validating this set of features in other sample sets from different geographical settings and also determining whether these antibody features predict protection from adverse pregnancy outcomes such as low birth weight. The immune responses identified are relevant to further development of pregnancy malaria-specific therapies such as vaccines or monoclonal antibodies and possibly to the identification of protective antibody against malaria in non-pregnant populations. Multiple antibody functions and characteristics may be required to develop robust protective immunity to placental malaria.

Dataset used for Figures 1–6 has been uploaded to Dryad: doi:10.5061/dryad.wpzgmsbkx.

All antibody feature data has been deposited in Dryad.

1. 1. Aitken EH
   2. Damelang T
   3. Ortega-Pajares A
   4. Alemu A
   5. Hasang W
   6. Dini S
   7. Unger HW
   8. Ome-Kaius M
   9. Nielsen MA
   10. Salanti A
   11. Smith J
   12. Kent SJ
   13. Hogarth PM
   14. Wines BD
   15. Simpson JA
   16. Chung AW
   17. Rogerson SJ

   (2021) Dryad Digital Repository

   Antibody features towards VAR2CSA and CSA binding infected erythrocytes in a cohort of pregnant women from PNG.

   https://doi.org/10.5061/dryad.wpzgmsbkx

1. 1. Ackerman ME
   2. Moldt B
   3. Wyatt RT
   4. Dugast AS
   5. McAndrew E
   6. Tsoukas S
   7. Jost S
   8. Berger CT
   9. Sciaranghella G
   10. Liu Q
   11. Irvine DJ
   12. Burton DR
   13. Alter G

   (2011) A robust, high-throughput assay to determine the phagocytic activity of clinical antibody samples

   Journal of Immunological Methods 366:8–19.

   https://doi.org/10.1016/j.jim.2010.12.016

   • PubMed
   • Google Scholar
2. 1. Aitken EH
   2. Mbewe B
   3. Luntamo M
   4. Maleta K
   5. Kulmala T
   6. Friso MJ
   7. Fowkes FJ
   8. Beeson JG
   9. Ashorn P
   10. Rogerson SJ

   (2010) Antibodies to chondroitin sulfate A-binding infected erythrocytes: dynamics and protection during pregnancy in women receiving intermittent preventive treatment

   The Journal of Infectious Diseases 201:1316–1325.

   https://doi.org/10.1086/651578

   • PubMed
   • Google Scholar
3. 1. Aitken EH
   2. Mahanty S
   3. Rogerson SJ

   (2020) Antibody effector functions in malaria and other parasitic diseases: a few needles and many haystacks

   Immunology & Cell Biology 98:264–275.

   https://doi.org/10.1111/imcb.12320

   • PubMed
   • Google Scholar
4. 1. Arnold KB
   2. Chung AW

   (2018) Prospects from systems serology research

   Immunology 153:279–289.

   https://doi.org/10.1111/imm.12861

   • PubMed
   • Google Scholar
5. 1. Ataíde R
   2. Hasang W
   3. Wilson DW
   4. Beeson JG
   5. Mwapasa V
   6. Molyneux ME
   7. Meshnick SR
   8. Rogerson SJ

   (2010) Using an improved phagocytosis assay to evaluate the effect of HIV on specific antibodies to pregnancy-associated malaria

   PLOS ONE 5:e10807.

   https://doi.org/10.1371/journal.pone.0010807

   • PubMed
   • Google Scholar
6. 1. Ataíde R
   2. Mayor A
   3. Rogerson SJ

   (2014) Malaria, Primigravidae, and antibodies: knowledge gained and future perspectives

   Trends in Parasitology 30:85–94.

   https://doi.org/10.1016/j.pt.2013.12.007

   • PubMed
   • Google Scholar
7. 1. Avril M
   2. Cartwright MM
   3. Hathaway MJ
   4. Smith JD

   (2011) Induction of strain-transcendent antibodies to placental-type isolates with VAR2CSA DBL3 or DBL5 recombinant proteins

   Malaria Journal 10:36.

   https://doi.org/10.1186/1475-2875-10-36

   • PubMed
   • Google Scholar
8. 1. Ayres Pereira M
   2. Mandel Clausen T
   3. Pehrson C
   4. Mao Y
   5. Resende M
   6. Daugaard M
   7. Riis Kristensen A
   8. Spliid C
   9. Mathiesen L
   10. E Knudsen L
   11. Damm P
   12. G Theander T
   13. R Hansson S
   14. A Nielsen M
   15. Salanti A

   (2016) Placental sequestration of plasmodium falciparum malaria parasites is mediated by the interaction between VAR2CSA and chondroitin sulfate A on Syndecan-1

   PLOS Pathogens 12:e1005831.

   https://doi.org/10.1371/journal.ppat.1005831

   • PubMed
   • Google Scholar
9. 1. Barker M
   2. Rayens W

   (2003) Partial least squares for discrimination

   Journal of Chemometrics 17:166–173.

   https://doi.org/10.1002/cem.785

   • Google Scholar
10. 1. Barua P
    2. Beeson JG
    3. Maleta K
    4. Ashorn P
    5. Rogerson SJ

    (2019) The impact of early life exposure to plasmodium falciparum on the development of naturally acquired immunity to malaria in young malawian children

    Malaria Journal 18:11.

    https://doi.org/10.1186/s12936-019-2647-8

    • PubMed
    • Google Scholar
11. 1. Benavente ED
    2. Oresegun DR
    3. de Sessions PF
    4. Walker EM
    5. Roper C
    6. Dombrowski JG
    7. de Souza RM
    8. Marinho CRF
    9. Sutherland CJ
    10. Hibberd ML
    11. Mohareb F
    12. Baker DA
    13. Clark TG
    14. Campino S

    (2018) Global genetic diversity of var2csa in plasmodium falciparum with implications for malaria in pregnancy and vaccine development

    Scientific Reports 8:15429.

    https://doi.org/10.1038/s41598-018-33767-3

    • PubMed
    • Google Scholar
12. 1. Breedveld A
    2. van Egmond M

    (2019) IgA and fcαri: pathological roles and therapeutic opportunities

    Frontiers in Immunology 10:553.

    https://doi.org/10.3389/fimmu.2019.00553

    • PubMed
    • Google Scholar
13. 1. Bulmer JN
    2. Rasheed FN
    3. Francis N
    4. Morrison L
    5. Greenwood BM

    (1993) Placental malaria. I. pathological classification

    Histopathology 22:211–218.

    https://doi.org/10.1111/j.1365-2559.1993.tb00110.x

    • PubMed
    • Google Scholar
14. 1. Buuren S
    2. Groothuis-Oudshoorn K

    (2011)

    Mice : multivariate imputation by chained equations in R

    Journal of Statistical Software 45:1–67.

    • Google Scholar
15. 1. Chandrasiri UP
    2. Randall LM
    3. Saad AA
    4. Bashir AM
    5. Rogerson SJ
    6. Adam I

    (2014) Low antibody levels to pregnancy-specific malaria antigens and heightened cytokine responses associated with severe malaria in pregnancy

    The Journal of Infectious Diseases 209:1408–1417.

    https://doi.org/10.1093/infdis/jit646

    • PubMed
    • Google Scholar
16. 1. Chung AW
    2. Ghebremichael M
    3. Robinson H
    4. Brown E
    5. Choi I
    6. Lane S
    7. Dugast AS
    8. Schoen MK
    9. Rolland M
    10. Suscovich TJ
    11. Mahan AE
    12. Liao L
    13. Streeck H
    14. Andrews C
    15. Rerks-Ngarm S
    16. Nitayaphan S
    17. de Souza MS
    18. Kaewkungwal J
    19. Pitisuttithum P
    20. Francis D
    21. Michael NL
    22. Kim JH
    23. Bailey-Kellogg C
    24. Ackerman ME
    25. Alter G

    (2014) Polyfunctional Fc-effector profiles mediated by IgG subclass selection distinguish RV144 and VAX003 vaccines

    Science Translational Medicine 6:228ra38.

    https://doi.org/10.1126/scitranslmed.3007736

    • PubMed
    • Google Scholar
17. 1. Chung AW
    2. Kumar MP
    3. Arnold KB
    4. Yu WH
    5. Schoen MK
    6. Dunphy LJ
    7. Suscovich TJ
    8. Frahm N
    9. Linde C
    10. Mahan AE
    11. Hoffner M
    12. Streeck H
    13. Ackerman ME
    14. McElrath MJ
    15. Schuitemaker H
    16. Pau MG
    17. Baden LR
    18. Kim JH
    19. Michael NL
    20. Barouch DH
    21. Lauffenburger DA
    22. Alter G

    (2015) Dissecting polyclonal Vaccine-Induced humoral immunity against HIV using systems serology

    Cell 163:988–998.

    https://doi.org/10.1016/j.cell.2015.10.027

    • PubMed
    • Google Scholar
18. 1. Crompton PD
    2. Kayala MA
    3. Traore B
    4. Kayentao K
    5. Ongoiba A
    6. Weiss GE
    7. Molina DM
    8. Burk CR
    9. Waisberg M
    10. Jasinskas A
    11. Tan X
    12. Doumbo S
    13. Doumtabe D
    14. Kone Y
    15. Narum DL
    16. Liang X
    17. Doumbo OK
    18. Miller LH
    19. Doolan DL
    20. Baldi P
    21. Felgner PL
    22. Pierce SK

    (2010) A prospective analysis of the ab response to plasmodium falciparum before and after a malaria season by protein microarray

    PNAS 107:6958–6963.

    https://doi.org/10.1073/pnas.1001323107

    • Google Scholar
19. 1. Cutts JC
    2. Agius PA
    3. Lin Z
    4. Powell R
    5. Moore K
    6. Draper B
    7. Simpson JA
    8. Fowkes FJI

    (2020) Pregnancy-specific malarial immunity and risk of malaria in pregnancy and adverse birth outcomes: a systematic review

    BMC Medicine 18:14.

    https://doi.org/10.1186/s12916-019-1467-6

    • PubMed
    • Google Scholar
20. 1. Damelang T
    2. Rogerson SJ
    3. Kent SJ
    4. Chung AW

    (2019) Role of IgG3 in infectious diseases

    Trends in Immunology 40:197–211.

    https://doi.org/10.1016/j.it.2019.01.005

    • PubMed
    • Google Scholar
21. 1. Desai M
    2. ter Kuile FO
    3. Nosten F
    4. McGready R
    5. Asamoa K
    6. Brabin B
    7. Newman RD

    (2007) Epidemiology and burden of malaria in pregnancy

    The Lancet Infectious Diseases 7:93–104.

    https://doi.org/10.1016/S1473-3099(07)70021-X

    • PubMed
    • Google Scholar
22. 1. Doritchamou J
    2. Bigey P
    3. Nielsen MA
    4. Gnidehou S
    5. Ezinmegnon S
    6. Burgain A
    7. Massougbodji A
    8. Deloron P
    9. Salanti A
    10. Ndam NT

    (2013) Differential adhesion-inhibitory patterns of antibodies raised against two major variants of the NTS-DBL2X region of VAR2CSA

    Vaccine 31:4516–4522.

    https://doi.org/10.1016/j.vaccine.2013.07.072

    • PubMed
    • Google Scholar
23. 1. Doritchamou JYA
    2. Herrera R
    3. Aebig JA
    4. Morrison R
    5. Nguyen V
    6. Reiter K
    7. Shimp RL
    8. MacDonald NJ
    9. Narum DL
    10. Fried M
    11. Duffy PE

    (2016) VAR2CSA Domain-Specific Analysis of Naturally Acquired Functional Antibodies to Plasmodium falciparum Placental Malaria

    Journal of Infectious Diseases 214:577–586.

    https://doi.org/10.1093/infdis/jiw197

    • Google Scholar
24. 1. Doritchamou J
    2. Teo A
    3. Morrison R
    4. Arora G
    5. Kwan J
    6. Manzella-Lapeira J
    7. Medina-Maldonado S
    8. Langhorne J
    9. Hviid L
    10. Narum DL
    11. Dicko A
    12. Fried M
    13. Duffy PE

    (2019) Functional antibodies against placental malaria parasites are variant dependent and differ by geographic region

    Infection and Immunity 87:00865-18.

    https://doi.org/10.1128/IAI.00865-18

    • Google Scholar
25. 1. Duffy PE
    2. Fried M

    (2003) Antibodies That Inhibit Plasmodium falciparum Adhesion to Chondroitin Sulfate A Are Associated with Increased Birth Weight and the Gestational Age of Newborns

    Infection and Immunity 71:6620–6623.

    https://doi.org/10.1128/IAI.71.11.6620-6623.2003

    • Google Scholar
26. 1. Elliott SR
    2. Brennan AK
    3. Beeson JG
    4. Tadesse E
    5. Molyneux ME
    6. Brown GV
    7. Rogerson SJ

    (2005a) Placental malaria induces variant-specific antibodies of the cytophilic subtypes immunoglobulin G1 (IgG1) and IgG3 that correlate with adhesion inhibitory activity

    Infection and Immunity 73:5903–5907.

    https://doi.org/10.1128/IAI.73.9.5903-5907.2005

    • PubMed
    • Google Scholar
27. 1. Elliott SR
    2. Duffy MF
    3. Byrne TJ
    4. Beeson JG
    5. Mann EJ
    6. Wilson DW
    7. Rogerson SJ
    8. Brown GV

    (2005b) Cross-reactive surface epitopes on chondroitin sulfate A-adherent plasmodium falciparum-infected erythrocytes are associated with transcription of var2csa

    Infection and Immunity 73:2848–2856.

    https://doi.org/10.1128/IAI.73.5.2848-2856.2005

    • PubMed
    • Google Scholar
28. 1. Epskamp S
    2. Cramer AOJ
    3. Waldorp LJ
    4. Schmittmann VD
    5. Borsboom D

    (2012) Qgraph : Network Visualizations of Relationships in Psychometric Data

    Journal of Statistical Software 48:1–18.

    https://doi.org/10.18637/jss.v048.i04

    • Google Scholar
29. 1. Feng G
    2. Aitken E
    3. Yosaatmadja F
    4. Kalilani L
    5. Meshnick SR
    6. Jaworowski A
    7. Simpson JA
    8. Rogerson SJ

    (2009) Antibodies to variant surface antigens of plasmodium falciparum-infected erythrocytes are associated with protection from treatment failure and the development of Anemia in pregnancy

    The Journal of Infectious Diseases 200:299–306.

    https://doi.org/10.1086/599841

    • PubMed
    • Google Scholar
30. 1. Fonseca AM
    2. González R
    3. Bardají A
    4. Jairoce C
    5. Rupérez M
    6. Jiménez A
    7. Quintó L
    8. Cisteró P
    9. Vala A
    10. Sacoor C
    11. Gupta H
    12. Hegewisch-Taylor J
    13. Brew J
    14. Ndam NT
    15. Kariuki S
    16. López M
    17. Dobaño C
    18. Chitnis CE
    19. Ouma P
    20. Ramharter M
    21. Abdulla S
    22. Aponte JJ
    23. Massougbodji A
    24. Briand V
    25. Mombo-Ngoma G
    26. Desai M
    27. Cot M
    28. Nhacolo A
    29. Sevene E
    30. Macete E
    31. Menéndez C
    32. Mayor A

    (2019) VAR2CSA serology to detect plasmodium falciparum transmission patterns in pregnancy

    Emerging Infectious Diseases 25:1851–1860.

    https://doi.org/10.3201/eid2510.181177

    • PubMed
    • Google Scholar
31. 1. Fried M
    2. Nosten F
    3. Brockman A
    4. Brabin BJ
    5. Duffy PE

    (1998) Maternal antibodies block malaria

    Nature 395:851–852.

    https://doi.org/10.1038/27570

    • PubMed
    • Google Scholar
32. 1. Fried M
    2. Kurtis JD
    3. Swihart B
    4. Morrison R
    5. Pond-Tor S
    6. Barry A
    7. Sidibe Y
    8. Keita S
    9. Mahamar A
    10. Andemel N
    11. Attaher O
    12. Dembele AB
    13. Cisse KB
    14. Diarra BS
    15. Kanoute MB
    16. Narum DL
    17. Dicko A
    18. Duffy PE

    (2018) Antibody levels to recombinant VAR2CSA domains vary with plasmodium falciparum parasitaemia, gestational age, and Gravidity, but do not predict pregnancy outcomes

    Malaria Journal 17:106.

    https://doi.org/10.1186/s12936-018-2258-9

    • PubMed
    • Google Scholar
33. 1. Goodyer ID
    2. Johnson J
    3. Eisenthal R
    4. Hayes DJ

    (1994) Purification of mature-stage plasmodium falciparum by gelatine flotation

    Annals of Tropical Medicine and Parasitology 88:209–211.

    https://doi.org/10.1080/00034983.1994.11812859

    • PubMed
    • Google Scholar
34. 1. Gunn BM
    2. Yu WH
    3. Karim MM
    4. Brannan JM
    5. Herbert AS
    6. Wec AZ
    7. Halfmann PJ
    8. Fusco ML
    9. Schendel SL
    10. Gangavarapu K
    11. Krause T
    12. Qiu X
    13. He S
    14. Das J
    15. Suscovich TJ
    16. Lai J
    17. Chandran K
    18. Zeitlin L
    19. Crowe JE
    20. Lauffenburger D
    21. Kawaoka Y
    22. Kobinger GP
    23. Andersen KG
    24. Dye JM
    25. Saphire EO
    26. Alter G

    (2018) A role for fc function in therapeutic monoclonal Antibody-Mediated protection against ebola virus

    Cell Host & Microbe 24:221–233.

    https://doi.org/10.1016/j.chom.2018.07.009

    • PubMed
    • Google Scholar
35. 1. Guyatt HL
    2. Snow RW

    (2001) The epidemiology and burden of plasmodium falciparum-related Anemia among pregnant women in sub-Saharan africa

    The American Journal of Tropical Medicine and Hygiene 64:36–44.

    https://doi.org/10.4269/ajtmh.2001.64.36

    • PubMed
    • Google Scholar
36. 1. Hviid L

    (2004) The immuno-epidemiology of pregnancy-associated plasmodium falciparum malaria: a variant surface antigen-specific perspective

    Parasite Immunology 26:477–486.

    https://doi.org/10.1111/j.0141-9838.2004.00733.x

    • PubMed
    • Google Scholar
37. 1. Jegaskanda S
    2. Weinfurter JT
    3. Friedrich TC
    4. Kent SJ

    (2013) Antibody-dependent cellular cytotoxicity is associated with control of pandemic H1N1 influenza virus infection of macaques

    Journal of Virology 87:5512–5522.

    https://doi.org/10.1128/JVI.03030-12

    • PubMed
    • Google Scholar
38. 1. Jegaskanda S
    2. Vandenberg K
    3. Laurie KL
    4. Loh L
    5. Kramski M
    6. Winnall WR
    7. Kedzierska K
    8. Rockman S
    9. Kent SJ

    (2014) Cross-Reactive Influenza-Specific Antibody-Dependent cellular cytotoxicity in intravenous immunoglobulin as a potential therapeutic against emerging influenza viruses

    Journal of Infectious Diseases 210:1811–1822.

    https://doi.org/10.1093/infdis/jiu334

    • Google Scholar
39. 1. Jennewein MF
    2. Goldfarb I
    3. Dolatshahi S
    4. Cosgrove C
    5. Noelette FJ
    6. Krykbaeva M
    7. Das J
    8. Sarkar A
    9. Gorman MJ
    10. Fischinger S
    11. Boudreau CM
    12. Brown J
    13. Cooperrider JH
    14. Aneja J
    15. Suscovich TJ
    16. Graham BS
    17. Lauer GM
    18. Goetghebuer T
    19. Marchant A
    20. Lauffenburger D
    21. Kim AY
    22. Riley LE
    23. Alter G

    (2019) Fc Glycan-Mediated regulation of placental antibody transfer

    Cell 178:202–215.

    https://doi.org/10.1016/j.cell.2019.05.044

    • Google Scholar
40. 1. Jensen AR
    2. Adams Y
    3. Hviid L

    (2020) Cerebral plasmodium falciparum malaria: the role of PfEMP1 in its pathogenesis and immunity, and PfEMP1-based vaccines to prevent it

    Immunological Reviews 293:230–252.

    https://doi.org/10.1111/imr.12807

    • PubMed
    • Google Scholar
41. 1. Keen J
    2. Serghides L
    3. Ayi K
    4. Patel SN
    5. Ayisi J
    6. van Eijk A
    7. Steketee R
    8. Udhayakumar V
    9. Kain KC

    (2007) HIV impairs opsonic phagocytic clearance of pregnancy-associated malaria parasites

    PLOS Medicine 4:e181.

    https://doi.org/10.1371/journal.pmed.0040181

    • PubMed
    • Google Scholar
42. 1. Kuhn M

    (2008)

    Building predictive models in R Using the caret Package

    Journal of Statistical Software 28:1–26.

    • Google Scholar
43. 1. Lambros C
    2. Vanderberg JP

    (1979) Synchronization of Plasmodium falciparum erythrocytic stages in culture

    The Journal of Parasitology 65:418–420.

    https://doi.org/10.2307/3280287

    • PubMed
    • Google Scholar
44. 1. Lu LL
    2. Chung AW
    3. Rosebrock TR
    4. Ghebremichael M
    5. Yu WH
    6. Grace PS
    7. Schoen MK
    8. Tafesse F
    9. Martin C
    10. Leung V
    11. Mahan AE
    12. Sips M
    13. Kumar MP
    14. Tedesco J
    15. Robinson H
    16. Tkachenko E
    17. Draghi M
    18. Freedberg KJ
    19. Streeck H
    20. Suscovich TJ
    21. Lauffenburger DA
    22. Restrepo BI
    23. Day C
    24. Fortune SM
    25. Alter G

    (2016) A functional role for antibodies in tuberculosis

    Cell 167:433–443.

    https://doi.org/10.1016/j.cell.2016.08.072

    • PubMed
    • Google Scholar
45. 1. Lufele E
    2. Umbers A
    3. Ordi J
    4. Ome-Kaius M
    5. Wangnapi R
    6. Unger H
    7. Tarongka N
    8. Siba P
    9. Mueller I
    10. Robinson L
    11. Rogerson S

    (2017) Risk factors and pregnancy outcomes associated with placental malaria in a prospective cohort of papua new guinean women

    Malaria Journal 16:427.

    https://doi.org/10.1186/s12936-017-2077-4

    • PubMed
    • Google Scholar
46. 1. Macpherson AJ
    2. McCoy KD
    3. Johansen FE
    4. Brandtzaeg P

    (2008) The immune geography of IgA induction and function

    Mucosal Immunology 1:11–22.

    https://doi.org/10.1038/mi.2007.6

    • PubMed
    • Google Scholar
47. 1. Martinez DR
    2. Fong Y
    3. Li SH
    4. Yang F
    5. Jennewein MF
    6. Weiner JA
    7. Harrell EA
    8. Mangold JF
    9. Goswami R
    10. Seage GR
    11. Alter G
    12. Ackerman ME
    13. Peng X
    14. Fouda GG
    15. Permar SR

    (2019) Fc characteristics mediate selective placental transfer of IgG in HIV-Infected women

    Cell 178:190–201.

    https://doi.org/10.1016/j.cell.2019.05.046

    • Google Scholar
48. 1. Megnekou R
    2. Staalsoe T
    3. Taylor DW
    4. Leke R
    5. Hviid L

    (2005) Effects of pregnancy and intensity of plasmodium falciparum transmission on immunoglobulin G subclass responses to variant surface antigens

    Infection and Immunity 73:4112–4118.

    https://doi.org/10.1128/IAI.73.7.4112-4118.2005

    • PubMed
    • Google Scholar
49. 1. Menendez C
    2. Ordi J
    3. Ismail MR
    4. Ventura PJ
    5. Aponte JJ
    6. Kahigwa E
    7. Font F
    8. Alonso PL

    (2000) The impact of placental malaria on gestational age and birth weight

    The Journal of Infectious Diseases 181:1740–1745.

    https://doi.org/10.1086/315449

    • PubMed
    • Google Scholar
50. 1. Mevik B-H
    2. Wehrens R

    (2007)

    The pls package: principal component and partial least squares regression in R

    Journal of Statistical Software 18:1–23.

    • Google Scholar
51. 1. Moore KA
    2. Simpson JA
    3. Scoullar MJL
    4. McGready R
    5. Fowkes FJI

    (2017a) Quantification of the association between malaria in pregnancy and stillbirth: a systematic review and meta-analysis

    The Lancet Global Health 5:e1101–e1112.

    https://doi.org/10.1016/S2214-109X(17)30340-6

    • PubMed
    • Google Scholar
52. 1. Moore KA
    2. Simpson JA
    3. Wiladphaingern J
    4. Min AM
    5. Pimanpanarak M
    6. Paw MK
    7. Raksuansak J
    8. Pukrittayakamee S
    9. Fowkes FJI
    10. White NJ
    11. Nosten F
    12. McGready R

    (2017b) Influence of the number and timing of malaria episodes during pregnancy on prematurity and small-for-gestational-age in an area of low transmission

    BMC Medicine 15:117.

    https://doi.org/10.1186/s12916-017-0877-6

    • Google Scholar
53. 1. Mordmüller B
    2. Sulyok M
    3. Egger-Adam D
    4. Resende M
    5. de Jongh WA
    6. Jensen MH
    7. Smedegaard HH
    8. Ditlev SB
    9. Soegaard M
    10. Poulsen L
    11. Dyring C
    12. Calle CL
    13. Knoblich A
    14. Ibáñez J
    15. Esen M
    16. Deloron P
    17. Ndam N
    18. Issifou S
    19. Houard S
    20. Howard RF
    21. Reed SG
    22. Leroy O
    23. Luty AJF
    24. Theander TG
    25. Kremsner PG
    26. Salanti A
    27. Nielsen MA

    (2019) First-in-human, randomized, Double-blind clinical trial of differentially adjuvanted PAMVAC, A vaccine candidate to prevent Pregnancy-associated malaria

    Clinical Infectious Diseases 69:1509–1516.

    https://doi.org/10.1093/cid/ciy1140

    • PubMed
    • Google Scholar
54. 1. Ndam NT
    2. Denoeud-Ndam L
    3. Doritchamou J
    4. Viwami F
    5. Salanti A
    6. Nielsen MA
    7. Fievet N
    8. Massougbodji A
    9. Luty AJ
    10. Deloron P

    (2015) Protective antibodies against placental malaria and poor outcomes during pregnancy, Benin

    Emerging Infectious Diseases 21:813–823.

    https://doi.org/10.3201/eid2105.141626

    • PubMed
    • Google Scholar
55. 1. Nielsen MA
    2. Pinto VV
    3. Resende M
    4. Dahlbäck M
    5. Ditlev SB
    6. Theander TG
    7. Salanti A

    (2009) Induction of adhesion-inhibitory antibodies against placental plasmodium falciparum parasites by using single domains of VAR2CSA

    Infection and Immunity 77:2482–2487.

    https://doi.org/10.1128/IAI.00159-09

    • PubMed
    • Google Scholar
56. 1. Nielsen MA
    2. Salanti A

    (2015) High-Throughput testing of Antibody-Dependent binding inhibition of placental malaria parasites

    Methods in Molecular Biology 1325:241–253.

    https://doi.org/10.1007/978-1-4939-2815-6_20

    • PubMed
    • Google Scholar
57. 1. Patel JC
    2. Hathaway NJ
    3. Parobek CM
    4. Thwai KL
    5. Madanitsa M
    6. Khairallah C
    7. Kalilani-Phiri L
    8. Mwapasa V
    9. Massougbodji A
    10. Fievet N
    11. Bailey JA
    12. Ter Kuile FO
    13. Deloron P
    14. Engel SM
    15. Taylor SM
    16. Juliano JJ
    17. Tuikue Ndam N
    18. Meshnick SR

    (2017) Increased risk of low birth weight in women with placental malaria associated with P. falciparum VAR2CSA clade

    Scientific Reports 7:7768.

    https://doi.org/10.1038/s41598-017-04737-y

    • PubMed
    • Google Scholar
58. Software

    1. R Development Core Team

    (2019) R: A Language and Environment for Statistical Computing, version 2.6.2

    R Foundation for Statistical Computing, Vienna, Austria.

    https://www.R-project.org/
59. 1. Ricke CH
    2. Staalsoe T
    3. Koram K
    4. Akanmori BD
    5. Riley EM
    6. Theander TG
    7. Hviid L

    (2000) Plasma antibodies from malaria-exposed pregnant women recognize variant surface antigens on plasmodium falciparum-infected erythrocytes in a parity-dependent manner and block parasite adhesion to chondroitin sulfate A

    The Journal of Immunology 165:3309–3316.

    https://doi.org/10.4049/jimmunol.165.6.3309

    • PubMed
    • Google Scholar
60. 1. Roberts DJ
    2. Craig AG
    3. Berendt AR
    4. Pinches R
    5. Nash G
    6. Marsh K
    7. Newbold CI

    (1992) Rapid switching to multiple antigenic and adhesive phenotypes in malaria

    Nature 357:689–692.

    https://doi.org/10.1038/357689a0

    • PubMed
    • Google Scholar
61. 1. Rogerson SJ
    2. Pollina E
    3. Getachew A
    4. Tadesse E
    5. Lema VM
    6. Molyneux ME

    (2003) Placental monocyte infiltrates in response to plasmodium falciparum malaria infection and their association with adverse pregnancy outcomes

    The American Journal of Tropical Medicine and Hygiene 68:115–119.

    https://doi.org/10.4269/ajtmh.2003.68.1.0680115

    • PubMed
    • Google Scholar
62. 1. Rogerson SJ
    2. Hviid L
    3. Duffy PE
    4. Leke RF
    5. Taylor DW

    (2007) Malaria in pregnancy: pathogenesis and immunity

    The Lancet Infectious Diseases 7:105–117.

    https://doi.org/10.1016/S1473-3099(07)70022-1

    • PubMed
    • Google Scholar
63. 1. Rohart F
    2. Gautier B
    3. Singh A
    4. Lê Cao KA

    (2017) mixOmics: an R package for 'omics feature selection and multiple data integration

    PLOS Computational Biology 13:e1005752.

    https://doi.org/10.1371/journal.pcbi.1005752

    • PubMed
    • Google Scholar
64. 1. Salanti A
    2. Dahlbäck M
    3. Turner L
    4. Nielsen MA
    5. Barfod L
    6. Magistrado P
    7. Jensen ATR
    8. Lavstsen T
    9. Ofori MF
    10. Marsh K
    11. Hviid L
    12. Theander TG

    (2004) Evidence for the involvement of VAR2CSA in Pregnancy-associated malaria

    Journal of Experimental Medicine 200:1197–1203.

    https://doi.org/10.1084/jem.20041579

    • Google Scholar
65. 1. Sirima SB
    2. Richert L
    3. Chêne A
    4. Konate AT
    5. Campion C
    6. Dechavanne S
    7. Semblat JP
    8. Benhamouda N
    9. Bahuaud M
    10. Loulergue P
    11. Ouédraogo A
    12. Nébié I
    13. Kabore M
    14. Kargougou D
    15. Barry A
    16. Ouattara SM
    17. Boilet V
    18. Allais F
    19. Roguet G
    20. Havelange N
    21. Lopez-Perez E
    22. Kuppers A
    23. Konaté E
    24. Roussillon C
    25. Kanté M
    26. Belarbi L
    27. Diarra A
    28. Henry N
    29. Soulama I
    30. Ouédraogo A
    31. Esperou H
    32. Leroy O
    33. Batteux F
    34. Tartour E
    35. Viebig NK
    36. Thiebaut R
    37. Launay O
    38. Gamain B

    (2020) PRIMVAC vaccine adjuvanted with alhydrogel or GLA-SE to prevent placental malaria: a first-in-human, randomised, double-blind, placebo-controlled study

    The Lancet Infectious Diseases 20:585–597.

    https://doi.org/10.1016/S1473-3099(19)30739-X

    • PubMed
    • Google Scholar
66. 1. Staalsoe T
    2. Shulman CE
    3. Bulmer JN
    4. Kawuondo K
    5. Marsh K
    6. Hviid L

    (2004) Variant surface antigen-specific IgG and protection against clinical consequences of pregnancy-associated plasmodium falciparum malaria

    The Lancet 363:283–289.

    https://doi.org/10.1016/S0140-6736(03)15386-X

    • Google Scholar
67. 1. Steketee RW
    2. Nahlen BL
    3. Parise ME
    4. Menendez C

    (2001) The burden of malaria in pregnancy in malaria-endemic Areas

    The American Journal of Tropical Medicine and Hygiene 64:28–35.

    https://doi.org/10.4269/ajtmh.2001.64.28

    • PubMed
    • Google Scholar
68. 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:eabb4757.

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

    • PubMed
    • Google Scholar
69. 1. Tebo AE
    2. Kremsner PG
    3. Luty AJ

    (2002) Fcgamma receptor-mediated phagocytosis of plasmodium falciparum-infected erythrocytes in vitro

    Clinical and Experimental Immunology 130:300–306.

    https://doi.org/10.1046/j.1365-2249.2002.01972.x

    • PubMed
    • Google Scholar
70. 1. Teo A
    2. Hasang W
    3. Randall LM
    4. Feng G
    5. Bell L
    6. Unger H
    7. Langer C
    8. Beeson JG
    9. Siba PM
    10. Mueller I
    11. Molyneux ME
    12. Brown GV
    13. Rogerson SJ

    (2014) Decreasing malaria prevalence and its potential consequences for immunity in pregnant women

    Journal of Infectious Diseases 210:1444–1455.

    https://doi.org/10.1093/infdis/jiu264

    • Google Scholar
71. 1. Teo A
    2. Hasang W
    3. Boeuf P
    4. Rogerson S

    (2015) A robust phagocytosis assay to evaluate the opsonic activity of antibodies against plasmodium falciparum-Infected erythrocytes

    Methods in Molecular Biology 1325:145–152.

    https://doi.org/10.1007/978-1-4939-2815-6_12

    • PubMed
    • Google Scholar
72. 1. Teo A
    2. Feng G
    3. Brown GV
    4. Beeson JG
    5. Rogerson SJ

    (2016) Functional antibodies and protection against Blood-stage malaria

    Trends in Parasitology 32:887–898.

    https://doi.org/10.1016/j.pt.2016.07.003

    • PubMed
    • Google Scholar
73. 1. Tutterrow YL
    2. Avril M
    3. Singh K
    4. Long CA
    5. Leke RJ
    6. Sama G
    7. Salanti A
    8. Smith JD
    9. Leke RG
    10. Taylor DW

    (2012) High levels of antibodies to multiple domains and strains of VAR2CSA correlate with the absence of placental malaria in cameroonian women living in an area of high plasmodium falciparum transmission

    Infection and Immunity 80:1479–1490.

    https://doi.org/10.1128/IAI.00071-12

    • PubMed
    • Google Scholar
74. 1. Unger HW
    2. Ome-Kaius M
    3. Wangnapi RA
    4. Umbers AJ
    5. Hanieh S
    6. Suen CS
    7. Robinson LJ
    8. Rosanas-Urgell A
    9. Wapling J
    10. Lufele E
    11. Kongs C
    12. Samol P
    13. Sui D
    14. Singirok D
    15. Bardaji A
    16. Schofield L
    17. Menendez C
    18. Betuela I
    19. Siba P
    20. Mueller I
    21. Rogerson SJ

    (2015) Sulphadoxine-pyrimethamine plus azithromycin for the prevention of low birthweight in Papua New Guinea: a randomised controlled trial

    BMC Medicine 13:9.

    https://doi.org/10.1186/s12916-014-0258-3

    • PubMed
    • Google Scholar
75. 1. Wines BD
    2. Vanderven HA
    3. Esparon SE
    4. Kristensen AB
    5. Kent SJ
    6. Hogarth PM

    (2016) Dimeric fcγr ectodomains as probes of the fc receptor function of Anti-Influenza virus IgG

    The Journal of Immunology 197:1507–1516.

    https://doi.org/10.4049/jimmunol.1502551

    • PubMed
    • Google Scholar
76. Report

    1. World Health Organisation

    (2019) World Malaria Report 2019

    Global malaria programme.

    https://www.who.int/publications/i/item/9789241565721

    • Google Scholar
77. 1. Yosaatmadja F
    2. Andrews KT
    3. Duffy MF
    4. Brown GV
    5. Beeson JG
    6. Rogerson SJ

    (2008) Characterization of VAR2CSA-deficient plasmodium falciparum-infected erythrocytes selected for adhesion to the BeWo placental cell line

    Malaria Journal 7:51.

    https://doi.org/10.1186/1475-2875-7-51

    • PubMed
    • Google Scholar
78. 1. Zou H
    2. Hastie T

    (2005)

    Regularization and variable selection via the elastic net

    Journal of the Royal Statistical Society: Series B 301:20.

    • Google Scholar

Author details

1. Elizabeth H Aitken

   Department of Medicine, University of Melbourne, the Doherty Institute, Melbourne, Australia

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Contributed equally with

   Timon Damelang and Amaya Ortega-Pajares

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2677-6208

2. Timon Damelang

   Department of Microbiology and Immunology, University of Melbourne, the Doherty Institute, Melbourne, Australia

   Contribution

   Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - review and editing

   Contributed equally with

   Elizabeth H Aitken and Amaya Ortega-Pajares

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6150-4435

3. Amaya Ortega-Pajares

   Department of Medicine, University of Melbourne, the Doherty Institute, Melbourne, Australia

   Contribution

   Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - review and editing

   Contributed equally with

   Elizabeth H Aitken and Timon Damelang

   Competing interests

   No competing interests declared

4. Agersew Alemu

   Department of Medicine, University of Melbourne, the Doherty Institute, Melbourne, Australia

   Contribution

   Validation, Investigation, Methodology

   Competing interests

   No competing interests declared

5. Wina Hasang

   Department of Medicine, University of Melbourne, the Doherty Institute, Melbourne, Australia

   Contribution

   Supervision, Validation, Investigation, Methodology, Project administration

   Competing interests

   No competing interests declared

6. Saber Dini

   Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

7. Holger W Unger

   1. Department of Medicine, University of Melbourne, the Doherty Institute, Melbourne, Australia
   2. Department of Obstetrics and Gynaecology, Royal Darwin Hospital, Darwin, Australia
   3. Menzies School of Health Research, Darwin, Australia

   Contribution

   Data curation, Writing - review and editing

   Competing interests

   No competing interests declared

8. Maria Ome-Kaius

   Walter and Eliza Hall Institute of Medical Research, Parkville, Australia

   Contribution

   Data curation

   Competing interests

   No competing interests declared

9. Morten A Nielsen

   Centre for Medical Parasitology, Department of Microbiology and immunology, University of Copenhagen, Copenhagen, Denmark

   Contribution

   Resources, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2668-4992

10. Ali Salanti

    1. Centre for Medical Parasitology, Department of Microbiology and immunology, University of Copenhagen, Copenhagen, Denmark
    2. Department of Infectious Disease, Copenhagen University Hospital, Copenhagen, Denmark

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

11. Joe Smith

    1. Seattle Children’s Research Institute, Seattle, United States
    2. Department of Pediatrics, University of Washington, Seattle, United States

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7915-6360

12. Stephen Kent

    Department of Microbiology and Immunology, University of Melbourne, the Doherty Institute, Melbourne, Australia

    Contribution

    Resources, Supervision, Writing - review and editing

    Competing interests

    No competing interests declared

13. P Mark Hogarth

    1. Seattle Children’s Research Institute, Seattle, United States
    2. Immune Therapies Group, Centre for Biomedical Research, Burnet Institute, Melbourne, Australia
    3. Department of Clinical Pathology, University of Melbourne, Melbourne, Australia
    4. Department of Immunology and Pathology, Monash University, Melbourne, Australia

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

14. Bruce D Wines

    1. Immune Therapies Group, Centre for Biomedical Research, Burnet Institute, Melbourne, Australia
    2. Department of Clinical Pathology, University of Melbourne, Melbourne, Australia
    3. Department of Immunology and Pathology, Monash University, Melbourne, Australia

    Contribution

    Resources, Writing - review and editing

    Competing interests

    No competing interests declared

15. Julie A Simpson

    Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia

    Contribution

    Formal analysis, Supervision, Methodology, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-2660-2013

16. Amy Chung

    Department of Microbiology and Immunology, University of Melbourne, the Doherty Institute, Melbourne, Australia

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing

    Contributed equally with

    Stephen J Rogerson

    Competing interests

    No competing interests declared

17. Stephen J Rogerson

    Department of Medicine, University of Melbourne, the Doherty Institute, Melbourne, Australia

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing - original draft, Project administration, Writing - review and editing

    Contributed equally with

    Amy Chung

    For correspondence

    sroger@unimelb.edu.au

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4287-1982

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