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Limited antigenic diversity of Plasmodium falciparum apical membrane antigen 1 supports the development of effective multi-allele vaccines

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      Abstract

      Background

      Polymorphism in antigens is a common mechanism for immune evasion used by many important pathogens, and presents major challenges in vaccine development. In malaria, many key immune targets and vaccine candidates show substantial polymorphism. However, knowledge on antigenic diversity of key antigens, the impact of polymorphism on potential vaccine escape, and how sequence polymorphism relates to antigenic differences is very limited, yet crucial for vaccine development. Plasmodium falciparum apical membrane antigen 1 (AMA1) is an important target of naturally-acquired antibodies in malaria immunity and a leading vaccine candidate. However, AMA1 has extensive allelic diversity with more than 60 polymorphic amino acid residues and more than 200 haplotypes in a single population. Therefore, AMA1 serves as an excellent model to assess antigenic diversity in malaria vaccine antigens and the feasibility of multi-allele vaccine approaches. While most previous research has focused on sequence diversity and antibody responses in laboratory animals, little has been done on the cross-reactivity of human antibodies.

      Methods

      We aimed to determine the extent of antigenic diversity of AMA1, defined by reactivity with human antibodies, and to aid the identification of specific alleles for potential inclusion in a multi-allele vaccine. We developed an approach using a multiple-antigen-competition enzyme-linked immunosorbent assay (ELISA) to examine cross-reactivity of naturally-acquired antibodies in Papua New Guinea and Kenya, and related this to differences in AMA1 sequence.

      Results

      We found that adults had greater cross-reactivity of antibodies than children, although the patterns of cross-reactivity to alleles were the same. Patterns of antibody cross-reactivity were very similar between populations (Papua New Guinea and Kenya), and over time. Further, our results show that antigenic diversity of AMA1 alleles is surprisingly restricted, despite extensive sequence polymorphism. Our findings suggest that a combination of three different alleles, if selected appropriately, may be sufficient to cover the majority of antigenic diversity in polymorphic AMA1 antigens. Antigenic properties were not strongly related to existing haplotype groupings based on sequence analysis.

      Conclusions

      Antigenic diversity of AMA1 is limited and a vaccine including a small number of alleles might be sufficient for coverage against naturally-circulating strains, supporting a multi-allele approach for developing polymorphic antigens as malaria vaccines.

      Electronic supplementary material

      The online version of this article (doi:10.1186/s12916-014-0183-5) contains supplementary material, which is available to authorized users.

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      Most cited references 58

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      Gamma-globulin and acquired immunity to human malaria.

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        Breadth and magnitude of antibody responses to multiple Plasmodium falciparum merozoite antigens are associated with protection from clinical malaria.

        Individuals living in areas where malaria is endemic are repeatedly exposed to many different malaria parasite antigens. Studies on naturally acquired antibody-mediated immunity to clinical malaria have largely focused on the presence of responses to individual antigens and their associations with decreased morbidity. We hypothesized that the breadth (number of important targets to which antibodies were made) and magnitude (antibody level measured in a random serum sample) of the antibody response were important predictors of protection from clinical malaria. We analyzed naturally acquired antibodies to five leading Plasmodium falciparum merozoite-stage vaccine candidate antigens, and schizont extract, in Kenyan children monitored for uncomplicated malaria for 6 months (n = 119). Serum antibody levels to apical membrane antigen 1 (AMA1) and merozoite surface protein antigens (MSP-1 block 2, MSP-2, and MSP-3) were inversely related to the probability of developing malaria, but levels to MSP-1(19) and erythrocyte binding antigen (EBA-175) were not. The risk of malaria was also inversely associated with increasing breadth of antibody specificities, with none of the children who simultaneously had high antibody levels to five or more antigens experiencing a clinical episode (17/119; 15%; P = 0.0006). Particular combinations of antibodies (AMA1, MSP-2, and MSP-3) were more strongly predictive of protection than others. The results were validated in a larger, separate case-control study whose end point was malaria severe enough to warrant hospital admission (n = 387). These findings suggest that under natural exposure, immunity to malaria may result from high titers antibodies to multiple antigenic targets and support the idea of testing combination blood-stage vaccines optimized to induce similar antibody profiles.
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          The Relationship between Anti-merozoite Antibodies and Incidence of Plasmodium falciparum Malaria: A Systematic Review and Meta-analysis

          Introduction Malaria caused by Plasmodium falciparum is a leading cause of mortality and morbidity globally, particularly among young children. After repeated exposure, individuals develop effective immunity that controls blood-stage parasitaemia, thereby reducing clinical symptoms and life-threatening complications (reviewed in [1]). Antibodies are important mediators of acquired immunity to malaria as evidenced by experimental animal models and, most importantly, passive transfer studies in which antibodies from malaria-immune adults were successfully used to treat patients with severe malaria [2],[3]. Antibodies to merozoite antigens are considered important targets of protective antibodies and are thought to function in vivo by inhibiting merozoite invasion of erythrocytes, opsonizing merozoites for phagocytosis, and antibody-dependent cellular inhibition [4]–[7]. However, it is unclear which merozoite antigens are important targets of naturally acquired immunity. A number of merozoite antigens have established roles in erythrocyte invasion and some have been identified as targets of human invasion-inhibition antibodies or antibody-dependent cellular inhibition in vitro [8]–[15]. Merozoite surface proteins (MSPs) are thought to be involved in the initial attachment of the merozoite to the erythrocyte surface (e.g., MSP-1) and apical membrane antigen-1 (AMA-1) has been implicated in apical reorientation of the merozoite prior to invasion. Two invasion ligand families present in the apical organelles, the erythrocyte binding antigens (e.g., EBA175, EBA181, EBA140) and P. falciparum reticulocyte-binding homologues are also required for invasion [16]. There are numerous surface proteins with no known function including MSP-2, MSP-3, MSP-4, and glutamate-rich protein (GLURP) [16]. Genetic polymorphisms exist in most antigens and some can be grouped into major allelic types. Many of these antigens are currently being evaluated or developed as candidates for inclusion in an erythrocytic-stage malaria vaccine [17]. There are several criteria that can be used to objectively prioritize known and predicted antigens for vaccine development [17]. These include the demonstration that antibodies against these antigens inhibit P. falciparum growth in vitro, or are protective in animal models, and the demonstration that naturally acquired antibodies are associated with protection from symptomatic disease in malaria endemic populations. Consequently, numerous epidemiological studies have investigated the role of merozoite surface antigens as targets of human immunity. However, the epidemiological evidence of the protective effect of naturally acquired anti-merozoite responses is conflicting. There are numerous potential reasons for the inconsistencies in the estimates of protection. In malaria endemic areas the rate at which natural immunity develops is dependent on the intensity and stability of exposure to P. falciparum, with immunity to severe and mild disease developing more rapidly in areas with higher transmission [1],[18]. Differences in the acquisition of immunity may influence associations between specific responses and immunity. Furthermore, the prevalence of the major allelic types of specific antigens and subsequent acquisition of allele-specific immunity may be different across populations. The alleles represented in recombinant proteins used for determining antibody responses varies between studies in addition to the preparation of antigens used in immunoassays. Most importantly, the study designs used to investigate the associations between antibody responses and P. falciparum malaria studies vary considerably among the published literature. Evidence quoted in the literature regarding the protective role of antigen-specific antibodies is often based on data from cross-sectional or case-control studies. Examining the association of antibody responses with parasitological and clinical outcomes determined at a single time point, or in individuals who have already developed disease, makes the establishment of causality problematic. The highest level of evidence of causality in observational studies comes from prospective cohort studies in which a temporal relationship can be established between exposure and outcome. We performed a systematic review, with meta-analyses, of cohort studies to determine the association of antibody responses to merozoite surface antigens with incidence of P. falciparum malaria in naturally exposed populations, and to identify factors that may account for differences in reported findings. The broad aim of this study was to advance our understanding of naturally acquired immunity to malaria and to contribute to rational vaccine development. Materials and Methods We performed a systematic review of the published literature according to the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) guidelines for the conduct of meta-analyses of observational studies [19]. Results are reported according to the recently published PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses; http://www.prisma-statement.org; Text S1). The study protocol was developed by FJIF, JAS, and JGB. Search Methods for Identification of Studies PubMed, Web of Science, Scopus, Google Scholar, African Index Medicus, and LILACS (Latin American and Caribbean Health Sciences Literature) (all years, ending 31 January 2009) were searched for studies examining the association of antibody responses to merozoite antigens with P. falciparum malaria. Key words included: MSP, merozoite surface protein, MSA, merozoite surface antigen, GLURP, glutamate-rich protein, serine repeat antigen, SERA, S-Antigen, ABRA, AMA, apical membrane antigen, EBA, erythrocyte binding antigen, rhoptry, malaria, P. falciparum, immunity, antibodies, IgG, cohort, longitudinal, incidence, risk, epidemiology, vaccine. The key words variant surface antigen (VSA) were also used because merozoite antigens are sometimes used as comparative antigens in studies investigating VSAs. The reference lists of obtained papers were searched for further studies. Studies reported in languages other than English were included. Criteria for Considering Studies for This Review Study designs The criteria for inclusion of studies were population-based prospective studies and population-based treatment to reinfection studies. Population-based cross-sectional studies to determine prevalence were excluded because causality cannot be established. Case-control studies, hospital-based studies, and vaccine efficacy trials of blood-stage vaccines were also excluded because of the rigorous inclusion and exclusion criteria applied during these studies, such that the participants would not be representative of the general population. Study participants The criterion for inclusion of participants was individuals living in malaria endemic areas. Studies restricted to pregnant women and/or children 20%, which is often the case in highly malaria endemic areas [22]. Thus, RR, HR, and IRR were extracted or calculated where possible, or unadjusted ORs were converted to RR using the method of Zhang and Yu [23]. RR, HR, and IRR are hereinafter denoted as RR. A RR equal to 1 occurs when the incidence risk of malaria is equal for those with antibody responses (responders) and those without (nonresponders), and when the incidence risk is unchanged for 2-fold increases in the antibody levels. Where possible, estimates adjusted for demographic variables, spatial confounders, P. falciparum parasitaemia (at baseline or preseason), and/or bed net use are reported. Estimates adjusted for other anti-merozoite antibodies (including antibodies to schizont protein extract) are not reported because antibody responses are typically highly correlated making it difficult to estimate their individual regression coefficients reliably; in these cases unadjusted estimates are reported. For all malaria outcomes the study-assigned P. falciparum definitions were used. Our aim was to obtain a single RR estimate for each study. If antibody responses to the same antigen, in the same population-based study, were reported in several publications, results from the largest sample size were used. Separate estimates were obtained for the RR associated with AMA-1 (full-length ectodomains of FVO [pro-DI-DII-DIII] and 3D7 [DI-DII-DIII]), EBA-175 (all regions including F1 and F2), GLURP (R0, R1, and R2 fragments), MSP-2 (full length 3D7, full length FC27, and C terminus), MSP-3 (full length 3D7, full length K1, and the conserved C terminus). For MSP-1, separate analyses were done for each region and allelic type (MSP-1-block 1 [MAD20], MSP-1-block 2 [K1-like (3D7), MAD20-like [MAD20], and RO33-like [RO33]), and processing fragments (MSP-142, MSP-119 [including MSP-1-EGF1, MSP-1-EGF2]). Estimates from the above-mentioned regions/alleles were used to ensure maximum comparability between studies. Separate analyses were not done for MSP-3-Ct or MSP-119 alleles because of the conserved nature of MSP-3-Ct and MSP-119 (similarly EGF domains). For these antigens, if responses to multiple alleles were investigated in the same study, the most common circulating allele in the population was included in the meta-analysis. Meta-analysis Where there were sufficient data, a pooled summary statistic for each malaria outcome was calculated using either a fixed-effect or random-effects model. The standard error of the natural logarithm (ln) of the RR was calculated using the formula (ln[upper limit of CI]−ln[RR])/1.96. Heterogeneity between studies was tested with the I 2 statistic [24]. If the I 2 statistic was ≤30%, a meta-analysis based on a fixed-effect model was conducted; otherwise the random-effects model was used. When the I 2 statistic was >75% and/or the lower 95% confidence limit was between 50%–100%, the studies were not combined [25]. When statistical heterogeneity was noted it was evaluated by fitting meta-regression models to the log-transformed individual study RRs. Clinical and methodological heterogeneity was explored using prespecified variables to minimize spurious findings. Variables evaluated included study design (prospective cohort, treatment-to-reinfection), length of follow-up, age of study participants (dichotomous variable: adults and children, children only), malaria endemicity (perennial, seasonal, perennial with seasonal peaks), source of malaria cases (dichotomous variable: ACD only, PCD, and ACD), definition of symptomatic malaria, preparation of antigen (allele, expression vector, tag), and method of antibody determination (ELISA, microarray). Influence analysis was also performed whereby pooled estimates were calculated omitting one study at a time. Where possible, publication bias was assessed visually by plotting a funnel plot [26]; publication bias is unlikely if the funnel plots shows a symmetrical inverted V shape [27]. All analyses were performed using the open source statistical package, R 2.9.0 (R Foundation for Statistical Computing). Results Identification and Description of Included Studies Figure 1 outlines identification of studies for this systematic review. The literature search identified 73 potentially relevant studies, of which only 30 fulfilled the inclusion and quality criteria (details of excluded studies can be found in Text S2) [28]–[57]. We obtained further data from three studies after contacting authors (Figure 1), giving a total of 33 studies to be included in the systematic review [58]–[60]. 10.1371/journal.pmed.1000218.g001 Figure 1 Flow chart of study identification. Details of excluded studies can be found in Text S2. aDefinition of symptomatic malaria did not meet protocol definition; bAnalysed retro- and prospectively collected clinical data (n = 3), analysed antibody levels as outcome (n = 4,) and data presented on P. falciparum positive individuals only (n = 1); cReasons for exclusion: Data from seroprevalence surveys (n = 15); hospital-based study/recruited cases based on clinical/parasitemic status (n = 6); did not include malaria outcome of interest (n = 5); mother/infant studies (n = 3); measured IgG responses to undefined regions of antigens (n = 1); dScopel et al. (2007) provided data using a definition of symptomatic malaria that met our quality criteria, Sarr et al. (2006) provided data so P. falciparum could be analysed as outcome, and Osier et al. (2008) provided estimates for the whole cohort, whereas the manuscript originally presented data from P. falciparum-positive individuals only [58]–; eThe characteristics of included studies are given in Table 1. The 33 studies reported data obtained from 14 separate prospective cohort studies and six separate treatment-to-reinfection population studies (Tables 1 and 2, respectively) indicating that multiple publications arise from a single population-based study. For the purpose of this review we shall refer to each publication as a study. The majority of studies report data from Africa (28/33; 84.8%), with three in Papua New Guinea, one in Asia, and one from South America. Study size ranged from 80 to 1,071 participants (median = 280) and duration of participant follow-up ranged from 3 to 18 mo (median = 6). The association of antibody responses to MSP-1 (including processing fragments and defined blocks), MSP-2, and MSP-3, AMA-1, EBA-175, and GLURP with incidence risk of P. falciparum malaria was examined in 19, eight, seven, five, three, and six studies, respectively. Details of recombinant proteins and sero-prevalences can be found in Text S2 (Tables A and B). All studies measured total IgG by ELISA with the exception of Gray et al. (2007), who measured IgG by microarray [40]. Symptomatic P. falciparum malaria during follow-up was the most common outcome, examined in 29 studies; with reinfection and high density infection during follow-up examined in five and three studies, respectively. No study examined the association of anti-merozoite responses with incidence risk of severe P. falciparum malaria or P. falciparum malaria-associated mortality. 10.1371/journal.pmed.1000218.t001 Table 1 Characteristics of prospective studies included in the systematic review by country. Country Study: Author, Year [Reference] Province Follow-up (mo) Population Merozoite IgG Response P. falciparum Outcome Sample Size Age (y) Source Incidence Outcome (Cumulative Incidence %) Brazil Scopel, 2007 [58] Acre 15 356 5–65 MSP-2 ACD, PCD Symptomatic Pf c (6.5) Burkina Faso Meraldi, 2004 [28] Kadiogo 7 293 0.5–9 GLURP, MSP-3 ACD Symptomatic Pf d (49) Nebie, 2008 [29] Bazega 4 286 0.5–15 AMA-1, GLURP, MSP-119, MSP-3 ACD Symptomatic Pf d (41) Nebie, 2008 [30] Bazega 4 360 0.5–10 GLURP, MSP-3 ACD Symptomatic Pf d (DNS) The Gambia Conway, 2000 [31] a Upper River 5 337 3–7 MSP-119, MSP-1-BL1, MSP-1-BL2 ACD, PCD Symptomatic Pf d (19) Polley, 2003 [32] a Upper River 5 334 3–7 MSP-1-BL2 ACD, PCD Symptomatic Pf d (19) Metzger, 2003 [33] a Upper River 5 329 3–7 MSP-2 ACD, PCD Symptomatic Pf d (19) Polley, 2007 [34] a Upper River 5 319 3–7 MSP-3 ACD, PCD Symptomatic Pf d (19) Dziegiel, 1993 [35] a North Bank 6 385 3–8 GLURP ACD Symptomatic Pf d (35) Egan, 1996 [36] a North Bank 6 327 3–8 MSP-119, MSP-1-EGF ACD Symptomatic Pf d (35) Taylor, 1998 [37] a North Bank 6 355 3–8 MSP-2 ACD Symptomatic Pf d (35) Okenu, 2000 [38] a North Bank 6 284 3–8 EBA-175 ACD Symptomatic Pf d (35) Okech, 2004 [39] a North Bank 6 260 3–8 MSP-119 b ACD Symptomatic Pf d (35) Gray, 2007 [40] a North Bank 6 189 3–8 AMA-1, MSP-119,b MSP-1-BL2, MSP-2,b MSP-3 ACD Symptomatic Pf d (35) Ghana Dodoo, 1999 [41] a Greater Accra 18 266 3–15 MSP-119,b MSP-1-EGF ACD, PCD Symptomatic Pf e (41) Dodoo, 2000 [42] a Greater Accra 18 115 3–15 GLURP ACD, PCD Symptomatic Pf e (41) Cavanagh, 2004 [43] a Greater Accra 18 280 3–15 MSP-119, MSP-1-BL1, MSP-1-BL2 ACD, PCD Symptomatic Pf e (41) Dodoo, 2008 [44] Greater Accra 9 352 3–10 AMA-1, GLURP, MSP-119, MSP-3 ACD, PCD Symptomatic Pf e (19) Kenya Polley, 2004 [45] a Coast 6 1,071 0.1–85 AMA-1 ACD, PCD Symptomatic Pf f (15, 26) Polley, 2006 [46] a Coast 6 1,068 0.1–85 MSP-2 ACD, PCD Symptomatic Pf f (15, 26) Osier, 2007 [47] a Coast 6 536 0.1–85 MSP-3 ACD, PCD Symptomatic Pf f (15) Osier, 2008 [59] a Coast 6 280 0.1–85 EBA-175, MSP-119, MSP-1-BL2 ACD, PCD Symptomatic Pf f (24) Papua New Guinea Al-Yaman, 1995 [48] a East Sepik 12 230 0.5–15 MSP-2 ACD, PCD Symptomatic Pf e (DNS) Al-Yaman, 1996 [49] a East Sepik 12 230 0.5–15 MSP-142 ACD, PCD Symptomatic Pf e (DNS) Senegal Perraut, 2005 [50] Fatick 5 205 3–75 MSP-119 ACD, PCD Symptomatic Pf g (60) Sarr, 2006 [60] Fatick 6 169 2–10 MSP-2 ACD Symptomatic Pf h (53) Sierra-Leone Egan, 1996 [36] Southern 12 645 0–8 MSP-119, MSP-1-EGF ACD Symptomatic Pf d (42) Tanzania Lusingu, 2005 [51] Tanga 6 171 0–19 GLURP ACD, PCD Symptomatic Pf e (32) Sample size refers to number of participants whose serology was determined. IgG responses measured by ELISA with the exception of Gray et al. [40] who used microarray immunoassays. Manuscripts by Egan et al. [36] and Okech et al. [39] report studies performed in two countries and feature twice in Table 1 and once in Tables 1 and 2, respectively. Studies by Polley et al. [45],[46] in the Kenyan coast were done at two study sites. a Indicates that the different antibody association studies were performed in the same cohort for the specified country and province. In The Gambia, the “Upper River” and “North Bank” studies were separate cohorts. b Antigen was not included in meta-analysis (as per protocol). Malaria definitions: c History of fever plus P. falciparum >300/µl. d Fever plus P. falciparum ≥5,000/µl or fever plus P. falciparum >5,000/µl. e Fever or history of fever (within the past 72 h) plus P. falciparum ≥5,000/µl. f Fever plus an age-dependent threshold of P. falciparum. g Fever plus >30 P. falciparum trophozoites/100 leukocytes. h Fever plus P. falciparum >2,500/µl. Pf, P. falciparum. 10.1371/journal.pmed.1000218.t002 Table 2 Characteristics of prospective treatment-to-reinfection studies included in the systematic review by country. Country Study: Author, Year [Reference] Province Antimalarial Follow-up (mo) Population Merozoite IgG Response P. falciparum Outcome Sample Size Age (y) Source Incidence Outcome (Cumulative Incidence %) Kenya John, 2004 [52] a Rift Valley SP 3 84 1–80 MSP-119 ACD, PCD Reinfection (45) John, 2005 [53] a Rift Valley SP 3 84 1–80 AMA-1, EBA-175, MSP-119 b ACD, PCD Reinfection (45) Mali Tolle, 1993 [54] Bamako CQ 7 191 1 to >15 MSP-1-BL2 ACD High Pf density [c] (DNS) Papua New Guinea Stanisic, 2009 [55] Madang A 6 206 5–14 AMA-1, MSP-119, MSP-2 ACD, PCD Reinfection (95), High Pf density [c] (52), Symptomatic Pf [d] (49) Senegal Perraut, 2003 [56] Fatick Q 5 110 2–73 MSP-119 ACD Reinfection (93), Symptomatic Pf[e] (66) Uganda Okech, 2004 [39] Northern Region SP 5 156 7–16 MSP-119 ACD High Pf density [c] (18) Vietnam Wang, 2001 [57] Khanh-Hoa Q+D+P 6 112 9–55 MSP-119, MSP-4 ACD Reinfection (42) Sample size refers to number of participants whose serology was determined. IgG responses measured by ELISA. Okech (2004) [39] performed studies in two countries and also features in Table 1. a Indicates that the different antibody association studies were performed in the same cohort for the specified country and province. b Antigen was not included in meta-analysis (as per protocol). Malaria definitions: c P. falciparum >5,000/µl. d Fever plus P. falciparum ≥5,000/µl or fever plus P. falciparum >5,000/µl. e Fever plus >30 P. falciparum trophozoites/100 leukocytes. A, artesunate; CQ, chloroquine; D, Doxycyline; MSP-1-BL2r, Block 2 repeats; P, Primaquine; Pf, P. falciparum; Q, quinine; SP, sulfadoxine-pyrimethamine. Association between Anti-MSP-1 Responses and Incidence of P. falciparum Malaria MSP-1 C-terminal (Ct)–processing fragments MSP-1 is a high molecular mass protein (Mr≈180 kDa) that is proteolytically processed into 83 kDa, 30 kDa, 38 kDa, and C-terminal 42 kDa (MSP-142) fragments [61]. During invasion, MSP-142 is further processed into MSP-119 and MSP-133 fragments. Both MSP-119 and MSP-142 are regarded as potential vaccine candidates and have been shown to be protective in animal models [17]. Meta-analysis of five studies showed that MSP-119 IgG responders had an 18% reduction in the risk of symptomatic P. falciparum malaria compared to nonresponders (pooled RR using random-effects [reRR] 0.82, 95% CI 0.7–0.96, p = 0.012; Figure 2) [31],[36] [43] [50] [59]. Meta-regression analysis revealed heterogeneity between allelic groups (p = 0.0223) with the greatest magnitude of effect seen with MAD20 and Palo Alto alleles (29% and 33% relative reduction in symptomatic disease, respectively; Figure 2). Because the methods for the preparation of each antigen was the same for each allelic variant (Text S2, Table A) similar results were obtained when grouping according to expression system and tag used to make the recombinant antigen. Other methodological and clinical characteristics of the studies did not influence estimates and there was no evidence of publication bias. 10.1371/journal.pmed.1000218.g002 Figure 2 Forest plot of the association of MSP-119 responses with incidence of symptomatic P. falciparum malaria. RRs correspond to risk of symptomatic P. falciparum malaria for MSP-119 responders versus nonresponders and per doubling of antibody responses (log base 2). RR 1 indicate susceptibility. aEstimates are calculated by authors from data in the paper; bdata supplied by original authors and calculated by current authors; cestimates are published estimates. All estimates are unadjusted with the exception of estimates from Nebie et al. (2008) and Dodoo et al. (2008), which are adjusted for age, and estimates from Stanisic (2009) are adjusted for age and spatial confounders [29],[44],[55]. W, weight. Note: Egan, 1996 had two study sites *Sierra-Leone and **The Gambia, and their analysis only included those with clinical disease versus asymptomatics, i.e., excluded those uninfected as they were assumed to be unexposed [36]. Data were obtained from a further three studies and pooled to examine the dose-response association of MSP-119 levels (log base 2) and the risk of malaria [29],[44],[55]. A 15% reduction in symptomatic P. falciparum per doubling of antibody levels was observed (reRR 0.85, 95% CI 0.74–0.97, p = 0.019; Figure 2). With only three studies in the meta-analysis, further subgroup analysis was not feasible. One additional study examined the association of antibody levels (excluded from meta-analysis because transformation, if any, was not stated in the original manuscript) with symptomatic P. falciparum and found weak evidence of a protective effect (RR 0.97, 95% CI 0.94–1.00, p = 0.0713) [56]. There was no conclusive evidence to support an association between anti-MSP-119 responses with P. falciparum high density infection or reinfection (see Text S2). MSP-119 is made up of two epidermal growth factor-like modules (EGF-1 and EGF-2). Meta-analyses showed no association between the presence of responses to either MSP-1-EGF1 or MSP-1-EGF2 with protection against symptomatic P. falciparum (RR 1.06, 95% CI 0.88–1.26, p = 0.56 and reRR 0.59, 95% CI 0.19–1.84, p = 0.37; I 2 = 71.4%, 95% CI 2.8–91.6%, respectively) [36],[41]. For individual study estimates see Text S2. Only one study examined the association of MSP-142 levels (log base 2) with incidence risk of symptomatic P. falciparum and found a reduced risk (RR 0.76, 95% CI data not shown in original manuscript [DNS], p≤0.001) [49]. MSP-1 polymorphic N-terminal regions MSP-1 block 2 can be grouped into three allelic types, K1-like, RO33-like, and MAD20 like. The association of incidence risk of symptomatic P. falciparum with allelic specific MSP-1 block 2 responders compared to nonresponders was examined in four studies [31],[40],[43],[59]. Pooled results were done separately for each allelic type. Meta-analysis revealed no evidence of an association with the K1-like (reRR 0.88, 95% CI 0.67, 1.17, p = 0.39) or RO33-like allele (RR 0.99, 95% CI 0.81, 1.21, p = 0.91) (Figure 3). There was weak evidence of a protective effect of MAD20-like responses with incidence risk of symptomatic P. falciparum (reRR 0.79, 95% CI 0.6, 1.04, p = 0.093; Figure 3). 10.1371/journal.pmed.1000218.g003 Figure 3 Forest plot of the association of MSP-1 block 2 and block 1 responses with incidence of symptomatic P. falciparum malaria. RRs represent the risk of symptomatic P. falciparum malaria in IgG responders relative to nonresponders. RR 1 indicate susceptibility. aEstimates are published estimates; bestimates are calculated by authors from data in the paper; cdata supplied by original authors and calculated by current authors. All reported estimates are unadjusted. W, weight. The K1-like and MAD20-like types of MSP-1 block 2 differ in the length of tri-peptide repeats in the middle of the block as well as the flanking nonrepetitive sequences. Meta-analysis was performed on three studies investigating the association between responses to MSP-1 block 2 repeats and flanking regions (responders versus nonresponders) and incidence risk of symptomatic P. falciparum [32],[40],[43]. There was some evidence of an association for MSP-1 block 2 K1-like repeats (RR 0.72, 95% CI 0.54–0.97, p = 0.031) but not MSP-1 block 2 MAD20-like repeats (reRR 0.79, 95% CI 0.48–1.3, p = 0.35) (Figure 4). There was also no evidence of an association between MSP-1 block 2 flanking regions with risk of symptomatic P. falciparum (K1-like RR 0.87, 95% CI 0.66–1.14, p = 0.31 and MAD20-like reRR 0.84, 95% CI 0.52–1.34, p = 0.46; Figure 4). 10.1371/journal.pmed.1000218.g004 Figure 4 Forest plot of the association of MSP-1-block 2 repeats and flanking region responses with incidence of symptomatic P. falciparum malaria. RRs represent the risk of symptomatic P. falciparum malaria in IgG responders relative to nonresponders. RR 1 indicate susceptibility. aEstimates are published estimates; bestimates are calculated by authors from data in the paper. All reported estimates are unadjusted. W, weight. Combined results from two studies showed no evidence of an association of MSP-1 block 1 responses with risk of symptomatic falciparum malaria (responders versus nonresponders RR 0.96, 95% CI 0.57–1.62, p = 0.88; Figure 4) [31],[43]. Association between Anti-MSP-2 Responses and Incidence of P. falciparum Malaria The single msp2 locus of P. falciparum is highly polymorphic but can be grouped into two major allelic types, 3D7 and FC27. Meta-analysis of six studies investigating MSP-23D7 and MSP-2FC27 showed no evidence of a reduced risk of symptomatic P. falciparum in those with responses compared to those without responses (MSP-23D7, reRR 0.92, 95% CI 0.75–1.13, p = 0.43; MSP-2FC27, reRR 0.82, 95% CI 0.62–1.08, p = 0.16, Figure 5) [33],[37],[46],[55],[58],[60]. Methodological and clinical characteristics of the studies did not influence estimates and there was no evidence of publication bias. One additional study found a dose-dependent response with MSP-23D7 antibody levels (log base 2) and risk of symptomatic P. falciparum (RR 0.81, 95% CI DNS, p = 0.003) but not MSP-2FC27 (RR 0.99, 95% CI DNS, p = 0.86) [48]. Another study examined the effect of MSP-2-Ct (responders versus nonresponders) and found no evidence of an association with symptomatic P. falciparum (RR 0.55, 95% CI 0.27, 1.14, p = 0.11) [33]. Only one study examined the association of MSP-2 antibodies with reinfection and high density infection and found no association (see Text S2) [55]. 10.1371/journal.pmed.1000218.g005 Figure 5 Forest plot of the association of MSP-2 responses with incidence of symptomatic P. falciparum malaria. RR 1 indicate susceptibility. aEstimates are published estimates; bconverted published estimate; cestimates are calculated by authors from data supplied by original author; destimates are calculated by authors from data in the paper. W, weight. Estimates reported are unadjusted with the exception of Stanisic (2009) (adjusted for spatial confounders and age) and Metzger (2003) (adjusted for age and preseason parasitaemia) [33],[55]. Note that estimates for Taylor (1998) are based on clinical and asymptomatic cases only (i.e., those uninfected were excluded on the basis they were unexposed) [37]. Polley (2006) stratified for two study sites in Coastal Kenya, *Chonyi and **Ngerenya [46]. Association between Anti-MSP-3 Responses and Incidence of P. falciparum Malaria The C-terminal region of MSP-3 (MSP-3-Ct) is highly conserved whereas the remainder of the sequence is defined by two major allelic types, 3D7 and K1 [62]. Meta-analyses of four studies [28],[30],[34],[47] examining antibodies to MSP-3-Ct responses showed a 54% reduction in symptomatic P. falciparum in responders versus nonresponders (RR 0.46, 95% CI 0.32–0.67, p 1 indicate susceptibility in responders versus nonresponders or per doubling of antibody responses. Estimates reported are unadjusted with the exception of Nebie (2008) (adjusted for age, sex, and village) [30] and Nebie (2008) and Dodoo (2008) (adjusted for age) [29],[44]. aEstimates are calculated by authors from data in the paper; bestimates are published estimates. All reported estimates are unadjusted. W, weight. Three studies examined the association of full length MSP-33D7 and MSP-3K1 responses (responders versus nonresponders) with risk of symptomatic P. falciparum [34],[40],[47]. Meta-analysis showed no evidence of an association with anti-MSP-33D7 responses (reRR 0.92, 95% CI 0.64–1.31, p = 0.63; Figure 6), but a large amount of heterogeneity was observed (I 2 = 67.6%, 95% CI 0–90.6%). A high degree of heterogeneity was also seen for MSP-3K1 associations (I 2 = 76.8%, 95% CI 24.4–92.9) so results were not combined (Figure 6). Due to the small number of studies in these meta-analyses, exploration of heterogeneity by subgroup analysis was not feasible. Association between anti-AMA-1 Responses and Incidence of P. falciparum Malaria There are currently two different AMA-1 strains of the full-length ectodomain under development as vaccine candidates (FVO and 3D7) [17]. There was evidence of reduced risk of symptomatic P. falciparum with AMA-13D7 responders versus nonresponders (RR 0.79, 95% CI 0.65–0.96, p = 0.015), and there was also a tendency towards a protective effect in the study that examined tertiles (Figure 7) [40],[45],[55]. For AMA-1FVO, one study showed a reduced risk of symptomatic P. falciparum in AMA-1FVO responders compared to nonresponders (RR 0.66 95% CI 0.52–0.84, p = 0.0007), but combined results of two studies showed no association of anti-AMA-1FVO levels (log base 2) with incidence risk of symptomatic P. falciparum (RR 0.99, 95% CI 0.9–1.08, p = 0.76; Figure 7) [29],[44],[45]. There was insufficient evidence to show an association between AMA-1 responses with risk of reinfection and high density P. falciparum (see Text S2). 10.1371/journal.pmed.1000218.g007 Figure 7 Forest plot of the association of AMA-1 responses with incidence of symptomatic P. falciparum malaria. RRs correspond to risk of symptomatic P. falciparum malaria for AMA1 responders versus nonresponders, High (H) and medium (M) versus low (L) responders (based on tertiles because sero-prevalence was high) and per doubling of antibody responses (log base 2). RR 1 indicate susceptibility. aEstimates are calculated by authors from data in the paper; bestimates are published estimates; cestimates supplied by the original authors. All estimates are unadjusted with the exception of Dodoo (2008) and Nebie (2008) with adjustments for age and Stanisic (2009) with adjustments for age and spatial confounders [29],[44],[55]. Polley (2004) stratified for two study sites in Coastal Kenya, *Chonyi and **Ngerenya. Association between Anti-GLURP Responses and Incidence of P. falciparum Malaria GLURP can be divided into an N-terminal nonrepeat region (R0), a central repeat region (R1), and a C-terminal repeat region (R2). A reduced risk of symptomatic P. falciparum was shown in GLURP-R0 responders compared to nonresponders (RR 0.69, 95% CI 0.48–0.97, p = 0.032) and per doubling of antibody levels (RR 0.79, 95% CI 0.69–0.91, p = 0.0006; Figure 8) [29],[30],[44]. Dodoo et al. (2000) also reported that anti-GLURP-R0 levels were associated with protection (p 1 indicate susceptibility. aEstimates are published estimates with adjustments for age, Nebie (2008) responder versus nonresponder analysis also adjusted for sex and village [30]; bestimates are calculated by authors from data in the paper. GLURP-R2 estimates were not combined because I 2>75%. W, weight. GLURP-R2 was associated with protection to varying degrees. Meraldi et al. and Nebie et al. showed a 90% (RR 0.1, 95% CI 0.05–0.23, p 75 percentile versus 300 parasites/µl to >5,000 parasites/µl and the sensitivity of these definitions would vary across populations. In addition, we would expect reduced specificity of the definition for the one study that reanalysed data with a high density cut-off for inclusion in our review [58]. A causal relationship between anti-merozoite antibodies and P. falciparum malaria is strengthened by the consistent demonstration of findings under different circumstances. Consistent findings were demonstrated for some antigens despite differences in the preparation of antigens, malaria endemicity, study participants, and study area. Interestingly, we found very few published studies that were performed outside Africa. Of the 32 included studies, only one was performed in Asia (excluding Papua New Guinea) and only one in South America (see Table 1). The generalizability of our findings to populations living in these less-represented regions is unknown. Additionally, we only identified two studies that investigated allele-specific immunity (both studies MSP-2 only), whereby the allele-specific antibody response was related to the strain causing the malaria episode [55],[58]. If protection is purely allele-specific then the true causal protective effect will be underestimated in studies that do not use allele-specific P. falciparum outcomes. Another important limitation in published literature is that data generated by ELISA does not produce a common metric measurement thereby restricting the standardization of exposure variables. In meta-analyses we were able to pool RR for responders versus nonresponders and RR derived from log base 2 antibody levels, which represent the change in risk per doubling of antibody levels. However, antibody concentrations vary across populations according to the level of exposure to malaria. Therefore the magnitude of effect according to quantified responses may vary significantly across studies. This was evident by the dose-dependent relationships between some antibody responses and level of protection and would suggest that antibody responses need to be quantified. Furthermore, knowledge on how long specific merozoite antibody responses last, how they are boosted, and the duration of any protection from responses is presently limited. The duration of the follow up in observational studies may therefore have an impact on the strength and direction of an association, an effect we explored in meta-regression. Further studies that measure responses at multiple time points are needed to better understand these issues. The definition of “protected” individuals (i.e., those who did not have symptomatic malaria) varied across studies. For most studies this definition included all participants who had no recorded episodes of symptomatic P. falciparum malaria. Three studies excluded individuals who did not have any detected parasitaemia during follow-up from the “protected” group, on the basis that these individuals were unexposed [35]–[37]. Only the six treatment-to-reinfection studies had regular blood collection for detection of parasitaemia; all other included studies only collected blood slides during follow-up when an individual was febrile, so accurately determining true “unexposed” individuals in areas where asymptomatic parasitaemia is prevalent will be problematic. Recent analyses by Bejon et al. (2009) of anti-VSA antibodies in individuals living in Kilifi, Kenya, showed that by removing unexposed children from conventional analyses, the magnitude of effect was greater between those with high and low responses [71]. This is consistent with other studies in Kilifi that showed that associations between specific merozoite antibody responses and protection were stronger in children who were asymptomatic at baseline [45],[46],[59]. A further consideration is that studies in malaria-endemic areas typically compare individuals with different levels of immunity, not individuals with complete immunity versus individuals with no immunity. Therefore, the reported effect size may not accurately reflect the true magnitude of the response in the study population. Conclusion and Guidelines for Future Research IgG responses to some, but not all, merozoite surface antigens were associated with protection against symptomatic P. falciparum in malaria endemic areas. We identified very few antigens that had been well studied and a deficiency of studies done outside Africa. More studies in different populations, examining multiple antigens at multiple time-points, are needed to better determine the role of anti-merozoite antibodies in protection against malaria, with prospective cohort studies as the preferred study design to establish temporal causality. In the future, there should be as much uniformity between studies as possible to ensure maximum comparability. This could be improved by the quantification and standardization of IgG responses, which could be achieved by establishing a reference reagent for determining antibody concentrations. Furthermore, the protective effects of anti-merozoite responses observed epidemiologically must also be supported by evidence of the function of the antibodies. Development and application of functional assays rather than standard immunoassays would also be highly valuable. Presently, data on the function of antibodies against merozoite antigens is very limited [8],[12],[15],[72]. Lastly, there is a need to incorporate strain-specific responses and endpoints to address whether protective responses against particular antigens are strain-transcending or strain-specific. A challenging aspect of this systematic review was the standardization of exposure and outcome measurements as there is no consistent approach to reporting of data. To facilitate the standardization of results in future studies, we propose guidelines for the reporting of malaria immuno-epidemiology studies adapted from the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement (Table 3) [73],[74]. Standardizing studies, and removing as much methodological heterogeneity as possible, will help obtain more comparable results in the future. By doing so, we will then be in a more favourable position to assess the relative contribution of responses to certain antigens, thereby informing vaccine candidate choices. 10.1371/journal.pmed.1000218.t003 Table 3 Proposed guidelines of the reporting of Malaria Immuno-epidemiology Observational Studies (MIOS guidelines). Report Section Topics Recommended Inclusions Title and abstract — Indicate the study design and the study population — Provide in the abstract an informative and balanced summary of what was done and the main findings. Indicate immune response measured, antigens used, and all Plasmodium and clinical end-points examined. Present key estimates of associations with measures of variability. Introduction — Explain the scientific background and rationale for the antigens and Plasmodium end-points chosen. — State objectives, including any prespecified hypotheses (i.e., protection, no effect). — State how the current study will add to the malaria immuno-epidemiology literature and briefly state how it compares to previous studies. Methods Epidemiological study A description of the setting, including location, Plasmodium spp. found in the area, rate of malaria transmission, dates of transmission. Mention any recent changes in endemicity. — Study design, describe exactly how and when immune response, Plasmodium and clinical data collection took place. For longitudinal studies discriminate between serial cross-sectional studies and longitudinal cohort studies. — Relevant dates such as participant recruitment, measurement of immune responses, follow-up, and Plasmodium and clinical data collection. — Eligibility criteria and sources and methods of selection of participants. Justification of criteria. Methods of follow-up and data collection. Indicate intervals for ACD and the appropriateness of the use of PCD in the setting. Indicate how presumptive malaria diagnosis was dealt with in data collection. — A description of any efforts to address potential sources of bias. — Sample size calculations. Include the level of precision and power, the expected size of differences to be measured (e.g., in antibody levels, risk/odds of malaria), and the minimum difference you wish to detect. Variables Definitions of all Plasmodium outcomes (i.e., parasitaemia, symptomatic malaria), detail parasitological cut-offs and fever definitions. State whether Plasmodium speciation was done and how this was incorporated into definitions. Mention the sensitivity and specificity of malaria definitions in the population. Indicate how “unexposed” individuals were defined, if relevant. — Definitions of all immunological variables. Explain how responders and nonresponders were defined. Explain how continuous variables were handled in the analyses such as the use of transformations and groupings. Describe which groupings were chosen and why, and state the cut-offs used for each group and the category mean or median values. For each antigen indicate the allele, amino acid position, expression system, and tag. Provide gene accession numbers. — A list of all potential confounders and effect modifiers that were considered with justification. These should at least include age, Plasmodium status at baseline, and variables that represent level of transmission/exposure (e.g., spatial confounders). Statistical analysis Rationale for statistical approach considering study design and distribution of immunological and Plasmodium data. Make particular note of any collinearity issues with immunological data. — Description of all statistical methods, including those used to control for confounding, examine subgroups and interactions (particularly with age) and any sensitivity analyses. Explain how missing data were addressed if relevant. — Details and justification of all data transformations explored during analysis. State any assumptions of linearity in immunological data. State whether categories generated from continuous antibody variables were used as a nominal or ordinal variable (i.e., classified into unordered or ordered qualitative categories). Results Study participants The numbers of individuals at each stage of the study and any groups excluded from analysis. — The demographic and clinical characteristics of the participants and information on exposures and potential confounders. Indicate the number of participants with missing data for each variable of interest. Summarize follow-up times if applicable and mention changes in incidence of Plasmodium over follow-up. Consider presenting clinical and immunology data according to age group to give the reader a sense of the acquisition of immunity in the study population or by immunological response categories so they can be related to confounders. Immunological responses and malaria measures Mean (standard deviation) or median (percentiles/range) of values to describe measures of central tendency and the spread of data measured in the study. Do not use inferential measures such as standard errors or confidence intervals. — Details of any quantification of antibody or other concentrations (i.e., titres in µg/ml). — Counts of cases, controls, person-time at risk, risk etc. for each immune response category in addition to effect-measure estimates and results of model fitting. Risk estimates Unadjusted and adjusted estimates of risk and their precision, e.g., 95% CIs. This will allow the reader to judge by how much, and in what direction, they changed. Make clear which confounders were adjusted for and why they were included. Provide risk estimates for all immunology variables investigated (i.e., responders versus nonresponders and any dose-dependent variables). — Separate estimates for each immune response. Also assess joint effects and interactions between immune responses. Consider both additive and multiplicative scales (i.e., does the combined effect of response A and B add (a+b)% or (a×b)% to risk?). This will help assess the relative contribution of each immune response to protection. — Separate estimates for different lengths of follow-up. E.g., 1, 3, 6, 9, 12 mo. — Report all other analyses done such as subgroups, interactions, and sensitivity analysis. Discussion — Summarise key results in relation to study objectives — Provide limitations of your study. — Give a balanced interpretation of the results considering limitations. Discuss both direction and magnitude of effects and pay particular attention to evidence of no effect versus no evidence of an effect. Outline possible methodological reasons for why the current results may differ from other studies. — Discuss the generalisability of results to other malaria endemic areas. Items should be addressed in the main body of the manuscript and/or supplementary material. This table has been adapted from the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement, which contains a checklist of items that should be addressed in reports of observational studies [74]. The STROBE statement and explanation [73],[74] should also be consulted. Supporting Information Text S1 PRISMA checklist. (0.07 MB DOC) Click here for additional data file. Text S2 Excluded studies, supplementary tables, and analyses. (0.54 MB DOC) Click here for additional data file.
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            Author and article information

            Affiliations
            [ ]The Burnet Institute of Medical Research and Public Health, 85 Commercial Road, Melbourne, Victoria 3004 Australia
            [ ]Department of Medicine, University of Melbourne, Melbourne, Victoria Australia
            [ ]Walter and Eliza Hall Institute, Melbourne, Australia
            [ ]Centre for Geographic Medicine, Coast, Kenya Medical Research Institute, Kilifi, Kenya
            [ ]Department of Medical Biology, University of Melbourne, Melbourne, Victoria Australia
            [ ]La Trobe University, Melbourne, Australia
            [ ]Walter Reed Army Institute, Silver Spring, MD USA
            [ ]Papua New Guinea Institute of Medical Research, Madang, Papua New Guinea
            [ ]Swiss Tropical and Public Health Institute, Basel, Switzerland
            [ ]Department of Microbiology, Monash University, Clayton, Victoria Australia
            Contributors
            terheggen@gmx.de
            damiendrew@burnet.edu.au
            hodder@wehi.edu.au
            cross.nadia@gmail.com
            ckmugyenyi@gmail.com
            barry@wehi.edu.au
            r.anders@latrobe.edu.au
            sheetij.dutta@us.army.mil
            sheetij.dutta@us.army.mil
            salennaelliott@yahoo.com
            nicolas.senn@gmail.com
            d.stanisic@griffith.edu.au
            kmarsh@kemri-wellcome.org
            peter.siba@pngimr.org.pg
            mueller@wehi.edu.au
            Richards@burnet.edu.au
            beeson@burnet.edu.au
            Journal
            BMC Med
            BMC Med
            BMC Medicine
            BioMed Central (London )
            1741-7015
            16 October 2014
            16 October 2014
            2014
            : 12
            : 1
            25319190 4212128 183 10.1186/s12916-014-0183-5
            © Terheggen et al.; licensee BioMed Central Ltd. 2014

            This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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