Malaria, a disease caused by the single-celled eukaryotic parasite Plasmodium, caused an estimated 241 million cases and 627,000 deaths in 2020, mostly by the species Plasmodium falciparum (P. falciparum; WHO Report 2021). Various strategies have been adopted for elimination of malaria, focusing on vector control, chemoprevention and vaccines. In 2021, the World Health Organization (WHO) made its first recommendation for widespread use of a malaria vaccine, RTS,S. While this is an encouraging step, there is nevertheless need for improvement as the efficacy of RTS,S is only 30–40% and protection wanes in a few months despite a four-dose regimen (Clinical Trials Partnership, 2015; Olotu et al., 2016). To design more effective vaccines, a deeper understanding of the nature of acquired immunity to malaria is critical.

Natural protection against malaria, particularly protection from uncomplicated malaria and ability to control parasitemia, requires multiple exposures and wanes upon cessation of exposure (Doolan et al., 2009). This naturally acquired immunity develops gradually with age and increasing cumulative exposure to P. falciparum in endemic settings, where adults may obtain substantial protection from disease, and children under 5 face the highest risk of death (Doolan et al., 2009; WHO Report 2020). While a comprehensive understanding of the factors influencing the slow development of immunity is still lacking, certain properties of parasite antigens have been proposed to contribute (Portugal et al., 2013). These include, amongst other properties, antigenic variation, antigens containing repeat elements and cross-reactive epitopes (Anders, 1986; Reeder and Brown, 1996; Schofield, 1991). While antigenic variation has been extensively studied in malaria, a systematic investigation of repeat-containing antigens and cross-reactive epitopes has been lacking.

Repeat elements are those where identical or similar motifs are repeated in tandem or with spaces within a protein. Repeat elements are widely prevalent in the proteome of P. falciparum and have been described to be highly immunogenic in a few antigens (Davies et al., 2017), such as the short, linear ‘NANP’ repeats from circumsporozoite protein (CSP) present in the RTS,S and R21 vaccines (Cockburn and Seder, 2018). Due to increased valency, epitopes in repeat elements can behave differently in comparison to the presentation of the same epitope as a single copy and have the potential to alter the nature of the resulting response. For instance, increased valency may lead to increased plasmablast formation by increasing the strength of the antigen-B-cell receptor (BCR) interaction, potentially altering the T-dependent response and inducing a T-independent response (Feldmann and Easten, 1971; Kato et al., 2020; O’Connor et al., 2006; Ochiai et al., 2013; Paus et al., 2006; Schofield, 1991; Schwickert et al., 2011). Although a few repeat antigens in P. falciparum have been well characterized, there has not been a comprehensive investigation of repeat elements with respect to their seroreactivity and their associations with humoral development.

The presence of biochemically similar epitopes can lead to cross-reactivity with antibodies and BCR. While non-identical repeat elements may represent such potential cross-reactive epitopes within a protein, similar epitopes may also be present across different proteins. How the quality of humoral response may be impacted by the presence of cross-reactive epitopes remains largely unexplored, although a study with viral variant antigens points to a frustrated affinity maturation process due to conflicting selection forces from variant epitopes (Wang et al., 2015). A handful of cross-reactive epitopes have been reported in P. falciparum (Wåhlin et al., 1992) and have been proposed to negatively impact the affinity maturation process, although direct evidence is lacking (Anders, 1986). To obtain a deeper understanding of how cross-reactive epitopes influence B cell immunity to malaria, a comprehensive atlas of cross-reactive epitopes across the P. falciparum proteome is first needed.

A systematic proteome-wide investigation of the humoral response to P. falciparum would provide important insights to our understanding of malaria immunity, including features such as repeat elements and cross-reactive epitopes. Specific technical challenges have impeded progress in this area. Although high-throughput approaches like protein arrays and alpha screens have reached a high coverage of the P. falciparum proteome (Camponovo et al., 2020; Morita et al., 2017), they do not allow for high-resolution, characterization of regions within antigenic proteins. In contrast, peptide arrays offer high-resolution antigenic profiling but are inherently limited to the numbers of targets that can be produced and printed on an array, usually in the order of tens of proteins (Hou et al., 2020; Jaenisch et al., 2019).

Here, we customized a programmable phage display system (PhIP-seq; Larman et al., 2011), previously used for antigenic profiling in many diseases, including autoimmune disorders and viral infections (Mandel-Brehm et al., 2019; Rajan et al., 2021; Vazquez et al., 2020; Zamecnik et al., 2020), for interrogation of the humoral response to P. falciparum infection. We designed a custom library (‘Falciparome’) that features over 238,000 individual 62 amino acid peptides encoded in T7 Phage, tiled every 25 amino acids across all annotated P. falciparum open reading frames from 3D7/IT genomes with additional variant antigenic sequences. Importantly, PhIP-seq leverages advances in next-generation sequencing to effectively convert serological assays into digital sequence counts. Furthermore, programmable phage display allows iterative enrichment, driving a high signal to background ratio with high specificity and sensitivity (O’Donovan et al., 2020).

We performed PhIP-seq with the Falciparome phage library to characterize the targets of the naturally acquired antibody response to P. falciparum in high-resolution, leveraging well-defined cohorts composed of 198 Ugandan children and adults from two different transmission settings and compared these to a large set of US anonymous blood donors. The resulting high-resolution atlas of seroreactive peptides suggest that antibody responses to repeat-containing regions are more exposure-dependent and/or less durable in children, compared to antibody responses to regions without repeats. Further, an extensive presence of potential cross-reactive motifs was identified among antigenic peptides from many proteins highly targeted by the immune system. These results have important implications for understanding the nature of humoral response in malaria and the future vaccine designs.

PhIP-seq was performed on plasma samples selected from two Ugandan cohorts with household level data on entomologic exposure as well as detailed individual characteristics (Table 1). For this study, we selected a single sample from each of 200 age-stratified individuals (children aged 2–11 years and adults) from two different sites in Uganda: Tororo, a region which had very high malaria transmission at the time of sampling (annual entomological inoculation rate [EIR] - 49 infective bites per person) and Kanungu, a region of moderately high transmission (EIR - 5 infective bites per person; Kamya et al., 2015). While the majority of individuals from Tororo were positive for infection at the time of sampling, those from Kanungu were sampled at a median of 100 days after their previous infection. We have previously shown that children acquire clinical immunity to malaria more rapidly in Tororo than Kanungu, consistent with higher rates of exposure (Rodriguez-Barraquer et al., 2018), and that adults at both sites have substantial immunity (Rek et al., 2016).

Falciparome library design

We constructed a T7 phage display library programmed to display the entire proteome of P. falciparum in 62-amino acid peptides with 25-amino acid step size, resulting in 37-amino acid tiling, referred to as the Falciparome. (Methods, Figure 1). The complete design files are available at Dryad doi:10.7272/Q69S1P9G and protocol at 10.17504/protocols.io.j8nlkkrr5l5r/v1. Overall, the library includes 238,068 peptides from 8980 protein sequences, including all known protein sequences from 2 reference strains (3D7 and IT) and extensive diversity of variant sequences from key antigens including PfEMP1s, RIFINs and STEVORs (Table 2, Figure 1—figure supplement 1, Methods). Of these, 223,145 peptides from 7577 proteins are from P. falciparum. Greater than 99.5% of the programmed peptides were represented in the final packaged phage library with relatively uniform distribution of abundance, with 90% of the peptides within a 16-fold difference (Figure 2—figure supplement 1).

Figure 1 with 1 supplement see all

Download asset Open asset

Table 2

	Input sequences before collapsing on similarity	Identity threshold for collapsing byCD-HIT	# Final collapsed Protein sequences
P. falciparum reference proteome	3D7, IT (10,771 total)	99%	6372
P. falciparum variant sequences	PfEMP1 (431 from 3D7, IT, IGH, RAJ116, PFCLIN, IT4, DD2 genomes) RIFIN (all 3D7+IT) STEVOR (all 3D7+IT) SURFIN (all 3D7+IT + 15) AMA1 (2) CSP (6) MSPDBL1 (6) MSPDBL2 (5) PfMC2TM (all 3D7+IT)	100% (90% for CSP)	1205
Other variants	P. reichnowi PfEMP1 (PFREICH) Anopheles - CE5 (5), SG6 (5)
Anopheles salivary proteins	53 proteins from 19 Anopheles species as described in Figure 1 of Arcà et al., 2017	98%	708
Vaccine/Viral/Toxin sequences	Tetanus Diphtheria Pertussis EBV Measles Mumps Rubella Polio RotoAB	98% (90% for RotoAB)	684
Laboratory positive controls	GFAP GFP Gephryn MYC, NR1 Tubulin (alpha/beta)	98%	11
TOTAL PROTEINS			8,980
TOTAL PEPTIDES			238,068

Overview of the malaria-specific seroreactive peptides identified with PhIP-seq

The 9927 seroreactive peptides identified by the pipeline were derived from 1648 parasite proteins (‘seroreactive proteins’) and antigenic variants, representing approximately 30% of the 5400 member proteome of P. falciparum, many of which showed broad seroreactivity across pediatric and adult Ugandan samples, whereas these same peptides showed no or rare seroreactivity in US controls (Figure 2a). The number of peptides enriched (‘breadth’) in children from moderate transmission settings was less than half of that in children in high transmission settings or adults in either setting (Figure 2b), an observation that we examined in greater detail below.

Figure 2 with 6 supplements see all

Download asset Open asset

Figure 2—source data 1

GO analysis of top seroreactive proteins.

https://cdn.elifesciences.org/articles/81401/elife-81401-fig2-data1-v2.xlsx

Download elife-81401-fig2-data1-v2.xlsx

The 1648 seroreactive proteins identified here have reported expression across the lifecycle stages occurring in the human – sporozoite, asexual, and sexual blood stages (Figure 2c). Although liver stage proteomic P. falciparum datasets were not available for comparison, several known liver stage antigens in the dataset (LSA1, LSA3, etc.) were detected. Notably, very few of the proteins were exclusively detected in the mosquito oocyst stage. Among the 40 seroreactive proteins with the highest seropositivity (percent of people enriching for at least one seroreactive peptide in that protein), protective antibodies have been reported for 20 of them (Supplementary file 2). Moreover, as expected, and consistent with previous studies, the top seroreactive proteins (Supplementary file 3) were enriched for those at the host-parasite interface (GO analysis – Figure 2—source data 1).

The proteins identified here overlapped substantially with antigenic proteins identified in previous protein array screens (28%, 49%, and 44% of those reported in Camponovo et al., 2020; Crompton et al., 2010; Helb et al., 2015 respectively). However, this whole-proteome approach also identified 952 proteins not identified in the above studies. Antigens identified in previous studies may have not been enriched here because of a known limitation of PhIP-seq – it detects predominantly linear epitopes, as opposed to conformational epitopes, which would account for loss of sensitivity with respect to particular proteins. In addition, prior studies were performed in different populations and may include false positives, for example by not using a large number of unexposed human samples to account for non-specific cross-reactivity. Furthermore, in vitro protein production for arrays may not guarantee full length, or correct folding.

Tiled design of library facilitates high-resolution characterization of seroreactive proteins

The short length and tiling design of the peptides in this library facilitated high-resolution characterization of antigenic regions within seroreactive proteins. Representative examples from previously characterized proteins, such as Falciparum Interspersed Repeat Antigen (FIRA), Circumsporozoite Protein (CSP) and Liver Stage Antigen (LSA3) are shown (Figure 3a), where known epitopes consisted of short amino-acid motifs repeated multiple times within the proteins (‘repeat elements’). Comparison with a previous study using a high-density linear peptide array covering a subset of antigens showed substantial overlap of the seroreactive regions within these antigens (Jaenisch et al., 2019; Figure 3—figure supplement 1), although some differences were apparent. Differences in the length of peptides as well as nature of display (linear 15-aa peptides on an array versus phage display of 62-aa peptides) are potential explanations for these discrepancies.

Figure 3 with 1 supplement see all

Download asset Open asset

Importantly, high-resolution maps of seroreactivity for over 1000 proteins were characterized for the first time in our dataset (Figure 3b). A notable example is, PHISTc (PF3D7_0801000), which has previously been described as an antigenic protein, but not dissected at high resolution (Baum et al., 2013; Dent et al., 2015). It is exported from the parasite during the asexual blood stage and has unknown function, although mildly protective antibodies have been described against the N-terminal segment (Nagaoka et al., 2021). Another example is RON4 (Rhoptry Neck Protein 4), part of the moving junction during merozoite invasion of the host (Morahan et al., 2009) and is also critical for sporozoite invasion of hepatocytes (Giovannini et al., 2011).

Beyond the overall breadth of seroreactive peptides, the dataset facilitated a high-resolution lens for investigating the effect of age and exposure on seroreactivity to individual proteins. For instance, as expected (Kazmin et al., 2017), we observed exposure-dependent seropositivity at the B-cell epitope in CSP targeted by the RTS,S vaccine (NANP repeating sequence; Figure 3a). The magnitude of exposure- and age-related differences in proportion seropositive varied by individual protein and even within different regions of specific proteins (Figure 3, Supplementary file 4), highlighting the importance of dissecting responses to different antigenic regions within seroreactive proteins.

Seroreactive proteins contain more repeat elements than non-seroreactive proteins

A prominent feature that stood out following high-resolution characterization of seroreactive regions was the presence of repeat elements, where identical or similar motifs were repeated in tandem or with gaps within a given protein (Figure 3). Previous studies focused on individual or targeted subsets of antigens in P. falciparum have highlighted the immunogenic nature of short amino acid repeat sequences (Davies et al., 2017). The proteome of P. falciparum is notoriously rich in such sequences; however, their functions have remained enigmatic and their properties have been difficult to characterize. To systematically investigate the association of seroreactivity with these elements, repeats throughout all coding sequences were first identified using RADAR (Rapid Automatic Detection and Alignment of Repeats) (Madeira et al., 2019) and then compared to both PhIP-seq seroreactive and non-seroreactive proteins. The number of repeats in each protein sequence was significantly higher in the seroreactive proteins in comparison to non-seroreactive proteins (median number of repeats per protein: seroreactive proteins – 20; non-seroreactive proteins – 6; p-value = <0.001, based on 1000 iterations [1636 proteins per iteration] of random sampling of the non-seroreactive set; Figure 4a).

Figure 4 with 1 supplement see all

Download asset Open asset

Seropositivity is more exposure-dependent and short-lived in children for peptides containing repeat elements than those without repeats

To investigate whether the breadth of seroreactive repeat-containing peptides differed depending on exposure-setting and age, seroreactive peptides were first binned into two categories: those with repeats, and those without. Specifically, seroreactive peptides with a 7-mer repeat index ≥ 3 were binned together as ‘repeat-containing peptides’ and those with a 7-mer repeat index ≤ 2 were binned as ‘non-repeat peptides’. For the set of non-repeat containing peptides, breadth (number of non-repeat peptides enriched per person) was significantly higher in adults than children in both exposure settings (percent increase in median breadth in adults over 4- to 6-year old children: moderate setting – 28%; high setting – 20%; Figure 5a). However, within each set of age groups, there was no significant difference in breadth between the two exposure settings.

Figure 5 with 4 supplements see all

Download asset Open asset

For repeat-containing seroreactive peptides, breadth was calculated as follows. Each repeat-containing seroreactive peptide was defined by the 7-mer (repeat element) that was used to calculate its repeat index as described above. To avoid redundant counting, all repeat-containing peptides from a given protein defined by the same repeat element were collapsed and counted only once. Similar to non-repeat peptides, breadth of these peptides was higher in adults than children, reaching a similar level in both exposure settings (percent increase in median breadth in adults over 4- to 6-year-old children: moderate setting – 193%; high setting – 56%). In contrast to non-repeat peptides however, there was an exposure dependence in the responses to repeat-containing peptides with age, such that children living in the high versus moderate exposure setting had twice the breadth of repeat-containing peptides, reaching the same level in adulthood in both settings (Figure 5b). These results were consistently observed with different thresholds for categorizing repeat-containing peptides (repeat index ≥ 4 or 5; Figure 5—figure supplement 1). Investigation of individual repeat elements recapitulated this trend and showed higher seropositivity in the high exposure setting compared to moderate exposure in children, but not adults (Figure 5—figure supplement 2). There were a small number of notable exceptions, including repeat elements from PHISTc (PF3D7_0801000), LSA3 (PF3D7_0220000), and FIRA (PF3D7_0501400), none of which showed a transmission setting-dependent response in children (Supplementary file 5).

Samples from the two exposure settings differed not just by exposure, but also with respect to time since most recent infection, reflecting the differing epidemiology of infection in these settings. In the moderate exposure setting, the median number of days since last infection was 100, whereas over 65% of the samples from the high exposure setting were taken during periods of active infection. The difference in seroreactivity to repeat-containing peptides observed here between the settings could therefore emerge from two related mechanisms. In the first, the difference could be driven by a requirement for a minimum level of cumulative exposure to the target repeats to generate a robust response. In the second, the antibody response to repeats may be inherently less durable, leading to rapid waning in the absence of frequent exposure. Each of these two possibilities were investigated below.

First, to determine the effect of exposure while controlling for infection status, children (ages 2–11 years) were combined to afford sufficient statistical power and were classified as actively infected or 60–120 days from infection (those infected 1–59 days prior were excluded from this analysis to look at a time point well past infection). For repeat containing peptides, breadth was significantly increased in the high exposure setting relative to moderate exposure, regardless of infection status at the time of sampling. This difference was not observed for non-repeat containing peptides (Figure 5—figure supplement 3). These data suggest a dependency on cumulative exposure for the observed differential antibody responses in children to repeat-containing peptides relative to non-repeat peptides.

Next, durability of responses was investigated by characterizing differences in breadth with, time since last infection, in each exposure setting (Figure 5—figure supplement 4). For repeat containing peptides, a significant decrease in breadth was evident between the most recent infections (0–30 days) and the longest (240–360 days) in the moderate exposure setting, while the difference in the high exposure setting between 0–30 days and 60–120 days was not significant; notably everyone in this setting was infected within the prior 120 days. No significant difference was evident for non-repeat peptides in either setting. Together, these results are consistent with the notion that the antibody response to repeat containing peptides are short lived relative to non-repeat peptides. Overall, the above data show that antibody responses to repeat-containing peptides may be less efficiently acquired and/or maintained in children than non-repeat peptides, but plateau at the same level of prevalence in adulthood.

Extensive sharing of motifs observed between seroreactive proteins, particularly the PfEMP1 family

While repeat elements within individual proteins were explored in the previous section, similar or identical motifs may also be shared among different proteins. If these motifs are a part of an epitope, then antibodies and BCR specific to a motif can potentially cross-react with the motif variants in different proteins, depending on accessibility and other factors. Identifying such shared motifs serves as the first step in exploring potential cross-reactivity between individual seroreactive proteins, and to identify them, a systematic investigation was performed.

First, enriched kmers (6–9 amino acids) were identified by collecting those present in a significantly (FDR-adjusted p-value <0.001) higher number of seroreactive peptides (9927) than a random sampling (1000 iterations) of 9927 peptides from the whole library. From this collection, enriched kmers that were shared by different seroreactive proteins were identified as ‘inter-protein motifs’ (Figure 6a). Using a kmer size of 7, and allowing for up to two biochemically conservative substitutions, a total of 911 significantly enriched inter-protein motifs were identified, representing 509 seroreactive proteins (Supplementary file 6). Limiting the selection of inter-protein motifs to only the most significantly enriched motif per seroreactive peptide (the motif with the lowest p-value among all motifs in each peptide) yielded 417 significant inter-protein motifs, from a similar number of proteins (507). As expected, increasing the kmer size, or further constraining the number of allowed substitutions resulted in fewer identified motifs (Supplementary file 7). For the subsequent analysis, we show results with a kmer size of 7, which is in the range of average length of a linear B-cell epitope (Buus et al., 2012). As expected, previously described cross-reactive epitopes between antigens were well represented, such as the glutamate-rich motifs in Pf11-1 and Ag332 (Mattei et al., 1989), among others (Figure 6a). Taken as a group, the collected motifs had a lower hydrophobicity index (mean Kyle-Doolittle=–1.95), a lower net charge (mean = –0.47; at pH 7), enrichment of charged glutamate, lysine, asparagine, and aspartate residues and depletion of cysteine and hydrophobic residues than a random set of motifs in the proteome (Figure 6—figure supplement 1). These biochemical characteristics are consistent with those observed in prior studies of residues in B cell epitopes (Akbar et al., 2021; Rubinstein et al., 2008).

Figure 6 with 5 supplements see all

Download asset Open asset

The design of the programmable phage display library used here features 62 amino acid peptides tiled with a 25 amino acid step size, yielding an overlap of 37 amino acids for sequential fragments, and 12 amino acids for every second fragment (Figure 6—figure supplement 2a). The design provides for localization of seroreactive sequences to the region of overlap when considering adjacent fragments. For all except the first and last two peptides in each protein (85% of peptides in the library), the seroreactive region can theoretically be narrowed down to a 12-13aa segment within the peptide. Given that B cell linear epitopes are typically 5–12 amino acids in length (Buus et al., 2012), the 12-13aa mapping provides a near-epitope resolution.

To test the notion that the inter-protein motifs within each peptide are actually the elements associated with the observed seroreactivity, we leveraged the tiled peptide library design by comparing inter-protein motif carrying peptides with overlapping and adjacent peptides (Figure 6—figure supplement 2b). The maximum seropositivity among peptides containing an inter-protein motif was on average 54-fold higher than the maximum seropositivity among overlapping peptides not containing the motif (using a pseudo-seropositivity of 0.1% for peptides with 0% seropositivity to facilitate fold change calculation), suggesting a strong association between seroreactivity and the inter-protein motif itself, not just the whole peptide within which it resides (comparison of median seropositivity yielded a similar result). Furthermore, a similar result was observed when the same analysis was done with all the significantly enriched kmers (Figure 6—figure supplement 2c).

Then, to evaluate potential cross-reactivity, we measured the co-occurrence of reactivity to peptides containing inter-protein motifs from different proteins within the same individual. A cumulative profile was then created from the aggregation of the results across all individuals in the study, and then compared to a background distribution drawn by random sampling of peptides without inter-protein motifs (Figure 6—figure supplement 3). We observed that shared seroreactivity of inter-protein motifs within individuals was significantly higher than in the randomly drawn peptides lacking these motifs (KS test p-value <0.01). Overall, these data suggest a possibility for cross-reactivity to these motifs within individuals. However, it is important to note that these data were derived from complex polyclonal responses in each person, and do not represent direct evaluation of the cross-reactivity of single antibodies.

On average, each inter-protein motif was shared by 3 seroreactive proteins (median = 2). Among the 509 seroreactive proteins, each of them shared inter-protein motifs with six other proteins on average (median = 3), (Supplementary file 8, Figure 6—figure supplement 4). Visualized as a network (Figure 6b), the PfEMP1 family of proteins formed a central hub to which a large number of other seroreactive proteins were connected. The PfEMP1 family of proteins possessed at least 90 shared inter-protein motifs, and this family shared those motifs with the greatest number of other seroreactive proteins (57) compared to all other proteins in this analysis. Approximately five times as many proteins shared connections with PfEMP1 than would be expected by chance (PfEMP1 shared motifs with 12–16 other proteins using a set of 9927 peptides consisting of PfEMP1 seroreactive peptides + random non-PfEMP1 peptides). Seroreactive proteins sharing motifs with PfEMP1 included many of the proteins with the highest measured seropositivity, such as RIFINs, SURFINs, FIRA, and PHISTc. This extent of sharing was driven, in part, by the number of PfEMP1 sequences included in the analysis. This was apparent when the same analysis performed with a reduced diversity of PfEMP1 sequences in the seroreactive peptide set (using PfEMP1 peptides from only PF3D7 and PFIT genomes instead of 7 genomes) resulted in PfEMP1 sharing motifs with 32 seroreactive proteins instead of 57. This suggests that the extent of sharing for PfEMP1 observed in this study may only be a small fraction of that occurring in the extensive natural diversity of PfEMP1 variants in circulating parasites.

Outside the main network driven by PfEMP1, 495 seroreactive proteins were also found to be highly connected to each other through motif sharing (Figure 6—figure supplement 5a). A large proportion of proteins with high seropositivity were connected (80% and 58% of proteins with >30% and 10–30% seropositivity, respectively). This included proteins like GARP, LSA3, Pf332, Pf11-1, and MESA (Figure 6b, Figure 6—figure supplement 5b). As observed for the full set of inter protein motifs, motifs shared by the subset of proteins with >30% seropositivity also consisted predominantly of charged glutamate, lysine, asparagine, and aspartate residues (Figure 6—figure supplement 5b). Since the analysis used here to identify inter-protein motifs allowed only up to two conservative substitutions in 7-mer motifs, the similarity of motifs in the network in Figure 6—figure supplement 5b suggests that with a less stringent threshold of identifying motifs, these proteins would be even more highly connected. Moreover, 80% of proteins in this network had reported expression in the asexual blood stage of the lifecycle of P. falciparum (PlasmoDB), suggesting temporally concordant presence of proteins sharing motifs within their seroreactive regions.

These results indicate that the interprotein motifs are strongly associated with seroreactivity and are extensively shared across seroreactive proteins, including among regions highly targeted by the antibodies. Furthermore, PfEMP1 shares motifs with the greatest number of other seroreactive proteins, partly driven by the sequence diversity of PfEMP1 variants.

Using a customized programmable phage display (PhIP-seq) library, we have evaluated the proteome-wide antigenic landscape of the malaria parasite P. falciparum, using the sera of 198 individuals living in two distinct malaria endemic areas. This approach readily identified previously known antigens, including proteins that are targets of protective antibodies, as well as novel antigens. In our study, we characterized features of P. falciparum antigens that could potentially contribute to the inefficient acquisition and maintenance of immunity to malaria. Repeat elements were found to be commonly targeted by antibodies, and had patterns of seropositivity that were less durable and more dependent on exposure than non-repeat peptides. Furthermore, extensive sharing of motifs associated with seroreactivity was observed among hundreds of parasite proteins, indicating potential for extensive cross-reactivity among antigens in P. falciparum. These data suggest that repeat elements– a common feature of the P. falciparum proteome, and shared motifs between antigenic proteins could have important roles in shaping the nature and development of the immune response to malaria.

To map the antigenic landscape, PhIP-seq for P. falciparum offers several attractive advantages. The library described here contains >99.5% of the proteome, including variants for several antigenic families, surpassing the coverage of other existing proteome-wide tools for P. falciparum (Camponovo et al., 2020; Morita et al., 2017), while simultaneously providing high-resolution characterization of antigens (up to 12–13 aa regions within peptides). Unlike peptide arrays, the platform converts a proteomic assay into a genomic assay, leveraging the massive scale and low-cost nature of next-generation short-read sequencing. The result is a cost-effective and scalable system, allowing for the processing of hundreds of samples in parallel. Finally, an important aspect of all phage display systems is the ability to sequentially enrich, release phage, and repeat, thus greatly amplifying the signal to noise (O’Donovan et al., 2020; Smith and Petrenko, 1997). Only one published study to date evaluates responses to more than a quarter of the proteome (Camponovo et al., 2020), inherently limiting the scope of potential targets interrogated. Twenty-eight percent of the hits described in this study of individuals living in Tanzania and exposed to various doses of PfSpz vaccine overlapped with the hit proteins described in our study (Camponovo et al., 2020). The limited overlap may be due to multiple factors, including differences in the characteristics of individuals sampled, the use of vaccine, and determination of seropositivity based on technical as opposed to biological controls (sera from unexposed individuals).

The near-epitope resolution provided by this platform allowed a systematic investigation of targets of antibodies. Targets with high seropositivity were observed to be significantly enriched for repeat elements. In some previous reports, the elevated antigenic potential of repeat elements has been noted (Davies et al., 2017); however, the proteome-wide approach described here demonstrates that a large collection of proteins containing these elements are highly immunogenic. The high immunogenicity of repeat sequences observed here may be the result of competitive advantages that B cell clones could encounter when binding to higher valency epitopes, as opposed to single copy epitopes. Evidence from experimental inoculations of antigens with differing repeat numbers support this notion (Kato et al., 2020). Moreover, tandem repeat regions are predicted to be intrinsically disordered, which in turn have favorable predictions as linear B cell epitopes (Guy et al., 2015). Notably, this high immunogenicity can potentially restrict responses to other epitopes within the antigen, as has been reported for responses to protective non-repeat epitopes in the circumsporozoite protein (CSP; Chatterjee et al., 2021).

A key finding of this study is the difference in seroreactivity to repeat-containing peptides in children vs. other peptides, with the breadth of seroreactivity to repeat-containing peptides increasing more quickly with age in the high versus moderate exposure setting and decreasing with increase in time since infection. These results suggest that antibody responses to repeats are more likely to be exposure-dependent and/or short-lived in children than responses to non-repeats. There were a few exceptions, including repeats from FIRA, PHISTc, and LSA3, that did not show an exposure-setting dependent difference in seropositivity, suggesting that factors beyond the repeated nature of the epitope influence the nature of the response. Regardless, the predominance of repeat containing peptides in antibody targets, along with the remarkable abundance of these peptides in the P. falciparum proteome, suggests a possible strategy evolved by the parasite for the purpose of diverting the humoral response towards short-lived or exposure-dependent responses.

The hypothesis of less durable antibody responses to repeat antigens in P. falciparum can be reconciled with a model in which repeating epitopes favor extrafollicular B cell responses, which are typically short-lived (Cockburn and Seder, 2018; Schofield, 1991). This is based on the potential of repeat epitopes in an antigen to interact with multiple BCRs on naive B-cells, thereby conferring high binding strength and sufficient activation to direct these cells into an early extrafollicular response and production of short-lived plasmablasts. Several studies provide support to this model, where strong binding of BCR to the antigen, including through increased valency, increases the production of plasmablasts (Kato et al., 2020; O’Connor et al., 2006; Ochiai et al., 2013; Paus et al., 2006; Schwickert et al., 2011). This could also happen via a T-cell-independent response, as has been reported for some repeat antigenic structures (Schofield, 1991; Schofield and Uadia, 1990). On the other hand, the effect on germinal centers (GC), which result in long-lived plasma cells (LLPCs) and isotype-switched memory cells, is unclear. While some studies have reported no change or a decrease in the formation of GCs (O’Connor et al., 2006; Ochiai et al., 2013; Paus et al., 2006) with increased strength of interaction between antigen and B-cells, some have reported an increase (Kato et al., 2020; Schwickert et al., 2011), although it is unclear whether the latter were productive GCs. More insights come from a few studies that measured the outcome of GCs following increased strength of BCR-antigen interaction and these have reported a decrease in LLPCs (Fink et al., 2007) and IgG-switched memory cells (Pape et al., 2018; Taylor et al., 2015). If repeat antigens in P. falciparum follow this pattern, an expected outcome would be defective formation of LLPCs and memory B cells.

Another major finding of this study is the extensive presence of inter-protein motifs among seroreactive proteins. Since a strong association with seroreactivity was observed for these motifs and there was evidence of shared reactivity among peptides containing the motif from different proteins, they may potentially represent cross-reactive epitopes. Definitive confirmation of cross-reactivity could be demonstrated with targeted studies using monoclonal antibodies. Furthermore, whether these inter-protein motifs are cross-reactive in vivo is unclear and may depend on expression timing and accessibility to the immune system, among other factors.

Analogously, seroreactive repeat elements with non-identical repeating units could represent cross-reactive epitopes within proteins. Extensive presence of potential cross-reactive epitopes in P. falciparum antigens may play an important role in influencing the quality of the immune response to malaria. While it could be advantageous for the host if multiple parasite proteins could be targeted by antibodies through cross-reactivity, simultaneous presence of cross-reactive epitopes could alternatively frustrate the affinity maturation process due to conflicting selection forces, as was observed for variant HIV antigens (Wang et al., 2015). Further, recurrent exposure may be necessary for the generation of strong cross-binding antibodies to cross-reactive epitopes (Murugan et al., 2020). Thus, the extensive presence of cross-reactive epitopes, both within and between antigenic proteins in P. falciparum, could represent an evolutionary strategy aimed at limiting high-affinity antibodies in favor of lower affinity, cross reactive antibodies. In essence, the large number of shared seroreactive sequences in P. falciparum may represent a complex immune counter measure, resulting in inefficient immunity acquisition which requires extensive exposure. The atlas of seroreactive repeat elements and inter-protein motifs from this study will be useful for future investigations in understanding their impact on the quality of immune response to malaria.

The PfEMP1 family shared inter-protein motifs with the greatest number of other antigens in this study. This was driven in part by the wide diversity of PfEMP1 variants, indicating that as one becomes naturally exposed to different PfEMP1 variants (Cham et al., 2009), there may be an increase in not only the sequence diversity, but the number of cross-reactive epitopes that the immune system encounters. Possessing cross-reactive epitopes with other antigens could result in binding of pre-existing antibodies to the new variants, which could be disadvantageous to the host if binding strength is weak. Further, cross-reactivity may inhibit generation of antibodies specific to the new variant due to original antigenic sin (Vatti et al., 2017). Thus, the mechanism of evolving PfEMP1 variants within a network of shared sequences with other antigens could be another strategy evolved by the parasite for immune evasion. On the other hand, binding of new variants to pre-existing antibodies may be advantageous to the host if those antibodies are effective against the new variants.

While phage display of small peptides yields high-resolution discrimination of linear epitopes, this approach may not capture antibodies binding to conformational epitopes. Therefore, such epitopes are likely to be missed by this assay, although polyclonal responses are frequently a mixture of linear, partially linear, and conformational epitopes. Reassuringly, we observed a large-scale enrichment of P. falciparum peptide sequences in exposed individuals when compared with control sera from the US. This suggests that the humoral immune system of exposed individuals acquires an extensive and diverse set of P. falciparum targets, including thousands of linear sequences. The bias towards linear epitopes may have increased the relative detectability of repeat regions by this assay since they often form intrinsically disordered regions. However, that would not account for the observed differences between exposure settings for children and adults. Furthermore, antibodies that depend on epitopes with disulfide linkages and post-translational modifications for binding would likely not be enriched using phage-presented peptides. Another limitation of our study is that the PhIP-seq technique does not inherently provide quantitative measures of antibody affinity or titer, as many factors influence the actual number of reads recovered after immunoprecipitation, such as starting copy number in the library and non-specific interactions with beads. Instead, our analysis relies on per peptide relative (ratiometric) enrichments, using non-malaria control (n=86) sera as the basis for comparison, which also serves to remove non-specific enrichments. We imposed a stringent filter to minimize false positives by requiring that each seroreactive peptide be enriched in at least five Ugandan samples over control sera. While this excluded possible seroreactive peptides unique to a single sample, the resulting sequences that passed were those that exhibited a minimum level of sharing among multiple individuals, thereby enriching for those seroreactive peptides that represent common serological responses to malaria.

Findings from this study could have important implications on malaria vaccine design. Results from our study suggest that that in natural infections in children, repeat regions in P. falciparum could lead to an exposure-dependent and/or short-lived antibody response to a higher degree than for non-repeat regions. While we recognize that vaccine induced immunity is distinct from naturally acquired immunity, this potential limitation should be considered when evaluating repeat-containing antigens as vaccine targets. Further, given that highly immunogenic regions in natural immunity to malaria are predominantly repeats and there is widespread presence of potential cross-reactive epitopes across many proteins, whole-parasite vaccines may also be susceptible to similar limitations. If the findings from this study translate to vaccine-induce immune responses, non-repetitive, unique antigenic regions may be more effective targets.

All data generated or analyzed during this study are included in the manuscript, supporting files and in the Dryad repository with the https://doi.org/10.7272/Q69S1P9G.

1. 1. Raghavan M
   2. Kalantar K
   3. Duarte E
   4. Teyssier N
   5. Takahashi S
   6. Kung A
   7. Rajan J
   8. Rek J
   9. Tetteh K
   10. Drakeley C
   11. Ssewanyana I
   12. Rodriguez-Barraquer I
   13. Greenhouse B
   14. DeRisi J

   (2022) Dryad Digital Repository

   Proteome-wide antigenic profiling in Ugandan cohorts identifies associations between age, exposure intensity, and responses to repeat-containing antigens in Plasmodium falciparum.

   https://doi.org/10.7272/Q69S1P9G

1. 1. Akbar R
   2. Robert PA
   3. Pavlović M
   4. Jeliazkov JR
   5. Snapkov I
   6. Slabodkin A
   7. Weber CR
   8. Scheffer L
   9. Miho E
   10. Haff IH
   11. Haug DTT
   12. Lund-Johansen F
   13. Safonova Y
   14. Sandve GK
   15. Greiff V

   (2021) A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

   Cell Reports 34:108856.

   https://doi.org/10.1016/j.celrep.2021.108856

   • PubMed
   • Google Scholar
2. 1. Amos B
   2. Aurrecoechea C
   3. Barba M
   4. Barreto A
   5. Basenko EY
   6. Bażant W
   7. Belnap R
   8. Blevins AS
   9. Böhme U
   10. Brestelli J
   11. Brunk BP
   12. Caddick M
   13. Callan D
   14. Campbell L
   15. Christensen MB
   16. Christophides GK
   17. Crouch K
   18. Davis K
   19. DeBarry J
   20. Doherty R
   21. Duan Y
   22. Dunn M
   23. Falke D
   24. Fisher S
   25. Flicek P
   26. Fox B
   27. Gajria B
   28. Giraldo-Calderón GI
   29. Harb OS
   30. Harper E
   31. Hertz-Fowler C
   32. Hickman MJ
   33. Howington C
   34. Hu S
   35. Humphrey J
   36. Iodice J
   37. Jones A
   38. Judkins J
   39. Kelly SA
   40. Kissinger JC
   41. Kwon DK
   42. Lamoureux K
   43. Lawson D
   44. Li W
   45. Lies K
   46. Lodha D
   47. Long J
   48. MacCallum RM
   49. Maslen G
   50. McDowell MA
   51. Nabrzyski J
   52. Roos DS
   53. Rund SSC
   54. Schulman SW
   55. Shanmugasundram A
   56. Sitnik V
   57. Spruill D
   58. Starns D
   59. Stoeckert CJ
   60. Tomko SS
   61. Wang H
   62. Warrenfeltz S
   63. Wieck R
   64. Wilkinson PA
   65. Xu L
   66. Zheng J

   (2022) VEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center

   Nucleic Acids Research 50:D898–D911.

   https://doi.org/10.1093/nar/gkab929

   • PubMed
   • Google Scholar
3. 1. Anders RF

   (1986) Multiple cross-reactivities amongst antigens of Plasmodium falciparum impair the development of protective immunity against malaria

   Parasite Immunology 8:529–539.

   https://doi.org/10.1111/j.1365-3024.1986.tb00867.x

   • PubMed
   • Google Scholar
4. 1. Arcà B
   2. Lombardo F
   3. Struchiner CJ
   4. Ribeiro JMC

   (2017) Anopheline salivary protein genes and gene families: An evolutionary overview after the whole genome sequence of sixteen anopheles species

   BMC Genomics 18:153.

   https://doi.org/10.1186/s12864-017-3579-8

   • PubMed
   • Google Scholar
5. 1. Barry AE
   2. Trieu A
   3. Fowkes FJI
   4. Pablo J
   5. Kalantari-Dehaghi M
   6. Jasinskas A
   7. Tan X
   8. Kayala MA
   9. Tavul L
   10. Siba PM
   11. Day KP
   12. Baldi P
   13. Felgner PL
   14. Doolan DL

   (2011) The stability and complexity of antibody responses to the major surface antigen of Plasmodium falciparum are associated with age in a malaria endemic area

   Molecular & Cellular Proteomics 10:M111.

   https://doi.org/10.1074/mcp.M111.008326

   • PubMed
   • Google Scholar
6. 1. Baum E
   2. Badu K
   3. Molina DM
   4. Liang X
   5. Felgner PL
   6. Yan G

   (2013) Protein microarray analysis of antibody responses to Plasmodium falciparum in western kenyan highland sites with differing transmission levels

   PLOS ONE 8:e82246.

   https://doi.org/10.1371/journal.pone.0082246

   • PubMed
   • Google Scholar
7. 1. Buus S
   2. Rockberg J
   3. Forsström B
   4. Nilsson P
   5. Uhlen M
   6. Schafer-Nielsen C

   (2012) High-resolution mapping of linear antibody epitopes using ultrahigh-density peptide microarrays

   Molecular & Cellular Proteomics 11:1790–1800.

   https://doi.org/10.1074/mcp.M112.020800

   • PubMed
   • Google Scholar
8. 1. Camponovo F
   2. Campo JJ
   3. Le TQ
   4. Oberai A
   5. Hung C
   6. Pablo JV
   7. Teng AA
   8. Liang X
   9. Sim BKL
   10. Jongo S
   11. Abdulla S
   12. Tanner M
   13. Hoffman SL
   14. Daubenberger C
   15. Penny MA

   (2020) Proteome-wide analysis of a malaria vaccine study reveals personalized humoral immune profiles in tanzanian adults

   eLife 9:e53080.

   https://doi.org/10.7554/eLife.53080

   • PubMed
   • Google Scholar
9. 1. Cham GKK
   2. Turner L
   3. Lusingu J
   4. Vestergaard L
   5. Mmbando BP
   6. Kurtis JD
   7. Jensen ATR
   8. Salanti A
   9. Lavstsen T
   10. Theander TG

   (2009) Sequential, ordered acquisition of antibodies to Plasmodium falciparum erythrocyte membrane protein 1 domains

   Journal of Immunology 183:3356–3363.

   https://doi.org/10.4049/jimmunol.0901331

   • PubMed
   • Google Scholar
10. 1. Chatterjee D
    2. Lewis FJ
    3. Sutton HJ
    4. Kaczmarski JA
    5. Gao X
    6. Cai Y
    7. McNamara HA
    8. Jackson CJ
    9. Cockburn IA

    (2021) Avid binding by B cells to the plasmodium circumsporozoite protein repeat suppresses responses to protective subdominant epitopes

    Cell Reports 35:108996.

    https://doi.org/10.1016/j.celrep.2021.108996

    • PubMed
    • Google Scholar
11. 1. Clinical Trials Partnership S

    (2015) Efficacy and safety of RTS,S/AS01 malaria vaccine with or without a booster dose in infants and children in africa: final results of a phase 3, individually randomised, controlled trial

    Lancet 386:31–45.

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

    • PubMed
    • Google Scholar
12. 1. Cockburn IA
    2. Seder RA

    (2018) Malaria prevention: From immunological concepts to effective vaccines and protective antibodies

    Nature Immunology 19:1199–1211.

    https://doi.org/10.1038/s41590-018-0228-6

    • PubMed
    • Google Scholar
13. 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

    • PubMed
    • Google Scholar
14. 1. Davies HM
    2. Nofal SD
    3. McLaughlin EJ
    4. Osborne AR

    (2017) Repetitive sequences in malaria parasite proteins

    FEMS Microbiology Reviews 41:923–940.

    https://doi.org/10.1093/femsre/fux046

    • PubMed
    • Google Scholar
15. 1. Dent AE
    2. Nakajima R
    3. Liang L
    4. Baum E
    5. Moormann AM
    6. Sumba PO
    7. Vulule J
    8. Babineau D
    9. Randall A
    10. Davies DH
    11. Felgner PL
    12. Kazura JW

    (2015) Plasmodium falciparum protein microarray antibody profiles correlate with protection from symptomatic malaria in kenya

    Journal of Infectious Diseases 212:1429–1438.

    https://doi.org/10.1093/infdis/jiv224

    • PubMed
    • Google Scholar
16. 1. Doolan DL
    2. Dobaño C
    3. Baird JK

    (2009) Acquired immunity to malaria

    Clinical Microbiology Reviews 22:13–36.

    https://doi.org/10.1128/CMR.00025-08

    • PubMed
    • Google Scholar
17. 1. Edwards RJ
    2. Davey NE
    3. Shields DC
    4. Thattai M

    (2007) SLiMFinder: A probabilistic method for identifying over-represented, convergently evolved, short linear motifs in proteins

    PLOS ONE 2:e967.

    https://doi.org/10.1371/journal.pone.0000967

    • PubMed
    • Google Scholar
18. 1. Feldmann M
    2. Easten A

    (1971) The relationship between antigenic structure and the requirement for thymus-derived cells in the immune response

    The Journal of Experimental Medicine 134:103–119.

    https://doi.org/10.1084/jem.134.1.103

    • PubMed
    • Google Scholar
19. 1. Fink K
    2. Manjarrez-Orduño N
    3. Schildknecht A
    4. Weber J
    5. Senn BM
    6. Zinkernagel RM
    7. Hengartner H

    (2007) B cell activation state-governed formation of germinal centers following viral infection

    Journal of Immunology 179:5877–5885.

    https://doi.org/10.4049/jimmunol.179.9.5877

    • PubMed
    • Google Scholar
20. 1. Fu L
    2. Niu B
    3. Zhu Z
    4. Wu S
    5. Li W

    (2012) CD-HIT: Accelerated for clustering the next-generation sequencing data

    Bioinformatics 28:3150–3152.

    https://doi.org/10.1093/bioinformatics/bts565

    • PubMed
    • Google Scholar
21. 1. Giovannini D
    2. Späth S
    3. Lacroix C
    4. Perazzi A
    5. Bargieri D
    6. Lagal V
    7. Lebugle C
    8. Combe A
    9. Thiberge S
    10. Baldacci P
    11. Tardieux I
    12. Ménard R

    (2011) Independent roles of apical membrane antigen 1 and rhoptry neck proteins during host cell invasion by apicomplexa

    Cell Host & Microbe 10:591–602.

    https://doi.org/10.1016/j.chom.2011.10.012

    • PubMed
    • Google Scholar
22. 1. Guy AJ
    2. Irani V
    3. MacRaild CA
    4. Anders RF
    5. Norton RS
    6. Beeson JG
    7. Richards JS
    8. Ramsland PA

    (2015) Insights into the immunological properties of intrinsically disordered malaria proteins using proteome scale predictions

    PLOS ONE 10:e0141729.

    https://doi.org/10.1371/journal.pone.0141729

    • PubMed
    • Google Scholar
23. 1. Helb DA
    2. Tetteh KKA
    3. Felgner PL
    4. Skinner J
    5. Hubbard A
    6. Arinaitwe E
    7. Mayanja-Kizza H
    8. Ssewanyana I
    9. Kamya MR
    10. Beeson JG
    11. Tappero J
    12. Smith DL
    13. Crompton PD
    14. Rosenthal PJ
    15. Dorsey G
    16. Drakeley CJ
    17. Greenhouse B
    18. Rayner JC

    (2015) Novel serologic biomarkers provide accurate estimates of recent Plasmodium falciparum exposure for individuals and communities

    PNAS 112:E4438–E4447.

    https://doi.org/10.1073/pnas.1501705112

    • PubMed
    • Google Scholar
24. 1. Hou N
    2. Jiang N
    3. Ma Y
    4. Zou Y
    5. Piao X
    6. Liu S
    7. Chen Q

    (2020) Low-complexity repetitive epitopes of Plasmodium falciparum are decoys for humoural immune responses

    Frontiers in Immunology 11:610.

    https://doi.org/10.3389/fimmu.2020.00610

    • PubMed
    • Google Scholar
25. 1. Jaenisch T
    2. Heiss K
    3. Fischer N
    4. Geiger C
    5. Bischoff FR
    6. Moldenhauer G
    7. Rychlewski L
    8. Sié A
    9. Coulibaly B
    10. Seeberger PH
    11. Wyrwicz LS
    12. Breitling F
    13. Loeffler FF

    (2019) High-density peptide arrays help to identify linear immunogenic B-cell epitopes in individuals naturally exposed to malaria infection

    Molecular & Cellular Proteomics 18:642–656.

    https://doi.org/10.1074/mcp.RA118.000992

    • PubMed
    • Google Scholar
26. 1. Joannin N
    2. Abhiman S
    3. Sonnhammer EL
    4. Wahlgren M

    (2008) Sub-grouping and sub-functionalization of the RIFIN multi-copy protein family

    BMC Genomics 9:19.

    https://doi.org/10.1186/1471-2164-9-19

    • PubMed
    • Google Scholar
27. 1. Kamya MR
    2. Arinaitwe E
    3. Wanzira H
    4. Katureebe A
    5. Barusya C
    6. Kigozi SP
    7. Kilama M
    8. Tatem AJ
    9. Rosenthal PJ
    10. Drakeley C
    11. Lindsay SW
    12. Staedke SG
    13. Smith DL
    14. Greenhouse B
    15. Dorsey G

    (2015) Malaria transmission, infection, and disease at three sites with varied transmission intensity in uganda: Implications for malaria control

    The American Journal of Tropical Medicine and Hygiene 92:903–912.

    https://doi.org/10.4269/ajtmh.14-0312

    • PubMed
    • Google Scholar
28. 1. Kato Y
    2. Abbott RK
    3. Freeman BL
    4. Haupt S
    5. Groschel B
    6. Silva M
    7. Menis S
    8. Irvine DJ
    9. Schief WR
    10. Crotty S

    (2020) Multifaceted effects of antigen valency on B cell response composition and differentiation in vivo

    Immunity 53:548–563.

    https://doi.org/10.1016/j.immuni.2020.08.001

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

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

    PNAS 114:2425–2430.

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

    • PubMed
    • Google Scholar
30. 1. Kilama M
    2. Smith DL
    3. Hutchinson R
    4. Kigozi R
    5. Yeka A
    6. Lavoy G
    7. Kamya MR
    8. Staedke SG
    9. Donnelly MJ
    10. Drakeley C
    11. Greenhouse B
    12. Dorsey G
    13. Lindsay SW

    (2014) Estimating the annual entomological inoculation rate for Plasmodium falciparum transmitted by anopheles gambiae s.l. using three sampling methods in three sites in uganda

    Malaria Journal 13:1–13.

    https://doi.org/10.1186/1475-2875-13-111

    • PubMed
    • Google Scholar
31. 1. Larman HB
    2. Zhao Z
    3. Laserson U
    4. Li MZ
    5. Ciccia A
    6. Gakidis MAM
    7. Church GM
    8. Kesari S
    9. Leproust EM
    10. Solimini NL
    11. Elledge SJ

    (2011) Autoantigen discovery with a synthetic human peptidome

    Nature Biotechnology 29:535–541.

    https://doi.org/10.1038/nbt.1856

    • PubMed
    • Google Scholar
32. 1. Li W
    2. Godzik A

    (2006) Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences

    Bioinformatics 22:1658–1659.

    https://doi.org/10.1093/bioinformatics/btl158

    • PubMed
    • Google Scholar
33. 1. Madeira F
    2. Park YM
    3. Lee J
    4. Buso N
    5. Gur T
    6. Madhusoodanan N
    7. Basutkar P
    8. Tivey ARN
    9. Potter SC
    10. Finn RD
    11. Lopez R

    (2019) The EMBL-EBI search and sequence analysis tools apis in 2019

    Nucleic Acids Research 47:W636–W641.

    https://doi.org/10.1093/nar/gkz268

    • PubMed
    • Google Scholar
34. 1. Mandel-Brehm C
    2. Dubey D
    3. Kryzer TJ
    4. O’Donovan BD
    5. Tran B
    6. Vazquez SE
    7. Sample HA
    8. Zorn KC
    9. Khan LM
    10. Bledsoe IO
    11. McKeon A
    12. Pleasure SJ
    13. Lennon VA
    14. DeRisi JL
    15. Wilson MR
    16. Pittock SJ

    (2019) Kelch-like protein 11 antibodies in seminoma-associated paraneoplastic encephalitis

    The New England Journal of Medicine 381:47–54.

    https://doi.org/10.1056/NEJMoa1816721

    • PubMed
    • Google Scholar
35. 1. Martin M

    (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads

    EMBnet.Journal 17:10.

    https://doi.org/10.14806/ej.17.1.200

    • Google Scholar
36. 1. Mattei D
    2. Berzins K
    3. Wahlgren M
    4. Udomsangpetch R
    5. Perlmann P
    6. Griesser HW
    7. Scherf A
    8. Müller-Hill B
    9. Bonnefoy S
    10. Guillotte M

    (1989) Cross-reactive antigenic determinants present on different Plasmodium falciparum blood-stage antigens

    Parasite Immunology 11:15–29.

    https://doi.org/10.1111/j.1365-3024.1989.tb00645.x

    • PubMed
    • Google Scholar
37. 1. Monaco DR
    2. Kottapalli SV
    3. Breitwieser FP
    4. Anderson DE
    5. Wijaya L
    6. Tan K
    7. Chia WN
    8. Kammers K
    9. Caturegli P
    10. Waugh K
    11. Roederer M
    12. Petri M
    13. Goldman DW
    14. Rewers M
    15. Wang LF
    16. Larman HB

    (2021) Deconvoluting virome-wide antibody epitope reactivity profiles

    EBioMedicine 75:103747.

    https://doi.org/10.1016/j.ebiom.2021.103747

    • PubMed
    • Google Scholar
38. 1. Morahan BJ
    2. Sallmann GB
    3. Huestis R
    4. Dubljevic V
    5. Waller KL

    (2009) Plasmodium falciparum: genetic and immunogenic characterisation of the rhoptry neck protein pfron4

    Experimental Parasitology 122:280–288.

    https://doi.org/10.1016/j.exppara.2009.04.013

    • PubMed
    • Google Scholar
39. 1. Morita M
    2. Takashima E
    3. Ito D
    4. Miura K
    5. Thongkukiatkul A
    6. Diouf A
    7. Fairhurst RM
    8. Diakite M
    9. Long CA
    10. Torii M
    11. Tsuboi T

    (2017) Immunoscreening of Plasmodium falciparum proteins expressed in a wheat germ cell-free system reveals a novel malaria vaccine candidate

    Scientific Reports 7:46086.

    https://doi.org/10.1038/srep46086

    • PubMed
    • Google Scholar
40. 1. Murugan R
    2. Scally SW
    3. Costa G
    4. Mustafa G
    5. Thai E
    6. Decker T
    7. Bosch A
    8. Prieto K
    9. Levashina EA
    10. Julien JP
    11. Wardemann H

    (2020) Evolution of protective human antibodies against Plasmodium falciparum circumsporozoite protein repeat motifs

    Nature Medicine 26:1135–1145.

    https://doi.org/10.1038/s41591-020-0881-9

    • PubMed
    • Google Scholar
41. 1. Nagaoka H
    2. Kanoi BN
    3. Morita M
    4. Nakata T
    5. Palacpac NMQ
    6. Egwang TG
    7. Horii T
    8. Tsuboi T
    9. Takashima E

    (2021) Characterization of a Plasmodium falciparum phistc protein, pf3d7_0801000, in blood- stage malaria parasites

    Parasitology International 80:102240.

    https://doi.org/10.1016/j.parint.2020.102240

    • PubMed
    • Google Scholar
42. 1. Niang M
    2. Bei AK
    3. Madnani KG
    4. Pelly S
    5. Dankwa S
    6. Kanjee U
    7. Gunalan K
    8. Amaladoss A
    9. Yeo KP
    10. Bob NS
    11. Malleret B
    12. Duraisingh MT
    13. Preiser PR

    (2014) STEVOR is a Plasmodium falciparum erythrocyte binding protein that mediates merozoite invasion and rosetting

    Cell Host & Microbe 16:81–93.

    https://doi.org/10.1016/j.chom.2014.06.004

    • PubMed
    • Google Scholar
43. 1. Ochiai K
    2. Maienschein-Cline M
    3. Simonetti G
    4. Chen J
    5. Rosenthal R
    6. Brink R
    7. Chong AS
    8. Klein U
    9. Dinner AR
    10. Singh H
    11. Sciammas R

    (2013) Transcriptional regulation of germinal center B and plasma cell fates by dynamical control of IRF4

    Immunity 38:918–929.

    https://doi.org/10.1016/j.immuni.2013.04.009

    • PubMed
    • Google Scholar
44. 1. O’Connor BP
    2. Vogel LA
    3. Zhang W
    4. Loo W
    5. Shnider D
    6. Lind EF
    7. Ratliff M
    8. Noelle RJ
    9. Erickson LD

    (2006) Imprinting the fate of antigen-reactive B cells through the affinity of the B cell receptor

    The Journal of Immunology 177:7723–7732.

    https://doi.org/10.4049/jimmunol.177.11.7723

    • PubMed
    • Google Scholar
45. 1. O’Donovan B
    2. Mandel-Brehm C
    3. Vazquez SE
    4. Liu J
    5. Parent AV
    6. Anderson MS
    7. Kassimatis T
    8. Zekeridou A
    9. Hauser SL
    10. Pittock SJ
    11. Chow E
    12. Wilson MR
    13. DeRisi JL

    (2020) High-resolution epitope mapping of anti-hu and anti-yo autoimmunity by programmable phage display

    Brain Communications 2:fcaa059.

    https://doi.org/10.1093/braincomms/fcaa059

    • PubMed
    • Google Scholar
46. 1. O’Leary NA
    2. Wright MW
    3. Brister JR
    4. Ciufo S
    5. Haddad D
    6. McVeigh R
    7. Rajput B
    8. Robbertse B
    9. Smith-White B
    10. Ako-Adjei D
    11. Astashyn A
    12. Badretdin A
    13. Bao Y
    14. Blinkova O
    15. Brover V
    16. Chetvernin V
    17. Choi J
    18. Cox E
    19. Ermolaeva O
    20. Farrell CM
    21. Goldfarb T
    22. Gupta T
    23. Haft D
    24. Hatcher E
    25. Hlavina W
    26. Joardar VS
    27. Kodali VK
    28. Li W
    29. Maglott D
    30. Masterson P
    31. McGarvey KM
    32. Murphy MR
    33. O’Neill K
    34. Pujar S
    35. Rangwala SH
    36. Rausch D
    37. Riddick LD
    38. Schoch C
    39. Shkeda A
    40. Storz SS
    41. Sun H
    42. Thibaud-Nissen F
    43. Tolstoy I
    44. Tully RE
    45. Vatsan AR
    46. Wallin C
    47. Webb D
    48. Wu W
    49. Landrum MJ
    50. Kimchi A
    51. Tatusova T
    52. DiCuccio M
    53. Kitts P
    54. Murphy TD
    55. Pruitt KD

    (2016) Reference sequence (refseq) database at NCBI: Current status, taxonomic expansion, and functional annotation

    Nucleic Acids Research 44:D733–D745.

    https://doi.org/10.1093/nar/gkv1189

    • PubMed
    • Google Scholar
47. 1. Olotu A
    2. Fegan G
    3. Wambua J
    4. Nyangweso G
    5. Leach A
    6. Lievens M
    7. Kaslow DC
    8. Njuguna P
    9. Marsh K
    10. Bejon P

    (2016) Seven-year efficacy of RTS, S/AS01 malaria vaccine among young african children

    The New England Journal of Medicine 374:2519–2529.

    https://doi.org/10.1056/NEJMoa1515257

    • PubMed
    • Google Scholar
48. 1. Pape KA
    2. Maul RW
    3. Dileepan T
    4. Paustian AS
    5. Gearhart PJ
    6. Jenkins MK

    (2018) Naive B cells with high-avidity germline-encoded antigen receptors produce persistent igm+ and transient igg+ memory B cells

    Immunity 48:1135–1143.

    https://doi.org/10.1016/j.immuni.2018.04.019

    • Google Scholar
49. 1. Paus D
    2. Phan TG
    3. Chan TD
    4. Gardam S
    5. Basten A
    6. Brink R

    (2006) Antigen recognition strength regulates the choice between extrafollicular plasma cell and germinal center B cell differentiation

    The Journal of Experimental Medicine 203:1081–1091.

    https://doi.org/10.1084/jem.20060087

    • PubMed
    • Google Scholar
50. 1. Portugal S
    2. Pierce SK
    3. Crompton PD

    (2013) Young lives lost as B cells falter: What we are learning about antibody responses in malaria

    Journal of Immunology 190:3039–3046.

    https://doi.org/10.4049/jimmunol.1203067

    • PubMed
    • Google Scholar
51. Software

    1. Raghavan M

    (2023) Phage-malaria-uganda200, version swh:1:rev:b49a3ec15d86048dd570b340487cee139fbeb445

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:b5eb627d24bae8bb5a30f89157db391dd95c080c;origin=https://github.com/madhura-raghavan/phage-malaria-uganda200;visit=swh:1:snp:905b76d7f2c528466e2faac9643cc288ab30115e;anchor=swh:1:rev:b49a3ec15d86048dd570b340487cee139fbeb445
52. 1. Rajan JV
    2. McCracken M
    3. Mandel-Brehm C
    4. Gromowski G
    5. Pollett S
    6. Jarman R
    7. DeRisi JL

    (2021) Phage display demonstrates durable differences in serological profile by route of inoculation in primary infections of non-human primates with dengue virus 1

    Scientific Reports 11:10823.

    https://doi.org/10.1038/s41598-021-90318-z

    • PubMed
    • Google Scholar
53. 1. Rask TS
    2. Hansen DA
    3. Theander TG
    4. Gorm Pedersen A
    5. Lavstsen T

    (2010) Plasmodium falciparum erythrocyte membrane protein 1 diversity in seven genomes -- divide and conquer

    PLOS Computational Biology 6:e1000933.

    https://doi.org/10.1371/journal.pcbi.1000933

    • PubMed
    • Google Scholar
54. 1. Reeder JC
    2. Brown GV

    (1996) Antigenic variation and immune evasion in Plasmodium falciparum malaria

    Immunology and Cell Biology 74:546–554.

    https://doi.org/10.1038/icb.1996.88

    • PubMed
    • Google Scholar
55. 1. Rek J
    2. Katrak S
    3. Obasi H
    4. Nayebare P
    5. Katureebe A
    6. Kakande E
    7. Arinaitwe E
    8. Nankabirwa JI
    9. Jagannathan P
    10. Drakeley C
    11. Staedke SG
    12. Smith DL
    13. Bousema T
    14. Kamya M
    15. Rosenthal PJ
    16. Dorsey G
    17. Greenhouse B

    (2016) Characterizing microscopic and submicroscopic malaria parasitaemia at three sites with varied transmission intensity in uganda

    Malaria Journal 15:470.

    https://doi.org/10.1186/s12936-016-1519-8

    • PubMed
    • Google Scholar
56. 1. Rodriguez-Barraquer I
    2. Arinaitwe E
    3. Jagannathan P
    4. Kamya MR
    5. Rosenthal PJ
    6. Rek J
    7. Dorsey G
    8. Nankabirwa J
    9. Staedke SG
    10. Kilama M
    11. Drakeley C
    12. Ssewanyana I
    13. Smith DL
    14. Greenhouse B

    (2018) Quantification of anti-parasite and anti-disease immunity to malaria as a function of age and exposure

    eLife 7:e35832.

    https://doi.org/10.7554/eLife.35832

    • PubMed
    • Google Scholar
57. 1. Rubinstein ND
    2. Mayrose I
    3. Halperin D
    4. Yekutieli D
    5. Gershoni JM
    6. Pupko T

    (2008) Computational characterization of B-cell epitopes

    Molecular Immunology 45:3477–3489.

    https://doi.org/10.1016/j.molimm.2007.10.016

    • PubMed
    • Google Scholar
58. 1. Saito F
    2. Hirayasu K
    3. Satoh T
    4. Wang CW
    5. Lusingu J
    6. Arimori T
    7. Shida K
    8. Palacpac NMQ
    9. Itagaki S
    10. Iwanaga S
    11. Takashima E
    12. Tsuboi T
    13. Kohyama M
    14. Suenaga T
    15. Colonna M
    16. Takagi J
    17. Lavstsen T
    18. Horii T
    19. Arase H

    (2017) Immune evasion of Plasmodium falciparum by RIFIN via inhibitory receptors

    Nature 552:101–105.

    https://doi.org/10.1038/nature24994

    • PubMed
    • Google Scholar
59. 1. Schofield L
    2. Uadia P

    (1990)

    Lack of IR gene control in the immune response to malaria. I. A thymus-independent antibody response to the repetitive surface protein of sporozoites

    Journal of Immunology 144:2781–2788.

    • PubMed
    • Google Scholar
60. 1. Schofield L

    (1991) On the function of repetitive domains in protein antigens of plasmodium and other eukaryotic parasites

    Parasitology Today 7:99–105.

    https://doi.org/10.1016/0169-4758(91)90166-L

    • Google Scholar
61. 1. Schwickert TA
    2. Victora GD
    3. Fooksman DR
    4. Kamphorst AO
    5. Mugnier MR
    6. Gitlin AD
    7. Dustin ML
    8. Nussenzweig MC

    (2011) A dynamic T cell-limited checkpoint regulates affinity-dependent B cell entry into the germinal center

    The Journal of Experimental Medicine 208:1243–1252.

    https://doi.org/10.1084/jem.20102477

    • PubMed
    • Google Scholar
62. 1. Shannon P
    2. Markiel A
    3. Ozier O
    4. Baliga NS
    5. Wang JT
    6. Ramage D
    7. Amin N
    8. Schwikowski B
    9. Ideker T

    (2003) Cytoscape: A software environment for integrated models of biomolecular interaction networks

    Genome Research 13:2498–2504.

    https://doi.org/10.1101/gr.1239303

    • PubMed
    • Google Scholar
63. 1. Smith GP
    2. Petrenko VA

    (1997) Phage display

    Chemical Reviews 97:391–410.

    https://doi.org/10.1021/cr960065d

    • PubMed
    • Google Scholar
64. 1. Tan J
    2. Pieper K
    3. Piccoli L
    4. Abdi A
    5. Perez MF
    6. Geiger R
    7. Tully CM
    8. Jarrossay D
    9. Maina Ndungu F
    10. Wambua J
    11. Bejon P
    12. Fregni CS
    13. Fernandez-Rodriguez B
    14. Barbieri S
    15. Bianchi S
    16. Marsh K
    17. Thathy V
    18. Corti D
    19. Sallusto F
    20. Bull P
    21. Lanzavecchia A

    (2015) A LAIR1 insertion generates broadly reactive antibodies against malaria variant antigens

    Nature 529:105–109.

    https://doi.org/10.1038/nature16450

    • PubMed
    • Google Scholar
65. 1. Taylor JJ
    2. Pape KA
    3. Steach HR
    4. Jenkins MK

    (2015) Humoral immunity. apoptosis and antigen affinity limit effector cell differentiation of a single naïve B cell

    Science 347:784–787.

    https://doi.org/10.1126/science.aaa1342

    • PubMed
    • Google Scholar
66. 1. Vatti A
    2. Monsalve DM
    3. Pacheco Y
    4. Chang C
    5. Anaya JM
    6. Gershwin ME

    (2017) Original antigenic sin: A comprehensive review

    Journal of Autoimmunity 83:12–21.

    https://doi.org/10.1016/j.jaut.2017.04.008

    • PubMed
    • Google Scholar
67. 1. Vazquez SE
    2. Ferré EM
    3. Scheel DW
    4. Sunshine S
    5. Miao B
    6. Mandel-Brehm C
    7. Quandt Z
    8. Chan AY
    9. Cheng M
    10. German M
    11. Lionakis M
    12. DeRisi JL
    13. Anderson MS

    (2020) Identification of novel, clinically correlated autoantigens in the monogenic autoimmune syndrome aps1 by proteome-wide phip-seq

    eLife 9:e55053.

    https://doi.org/10.7554/eLife.55053

    • PubMed
    • Google Scholar
68. 1. Wåhlin B
    2. Sjölander A
    3. Ahlborg N
    4. Udomsangpetch R
    5. Scherf A
    6. Mattei D
    7. Berzins K
    8. Perlmann P

    (1992) Involvement of pf155/RESA and cross-reactive antigens in Plasmodium falciparum merozoite invasion in vitro

    Infection and Immunity 60:443–449.

    https://doi.org/10.1128/iai.60.2.443-449.1992

    • PubMed
    • Google Scholar
69. 1. Wang S
    2. Mata-Fink J
    3. Kriegsman B
    4. Hanson M
    5. Irvine DJ
    6. Eisen HN
    7. Burton DR
    8. Wittrup KD
    9. Kardar M
    10. Chakraborty AK

    (2015) Manipulating the selection forces during affinity maturation to generate cross-reactive HIV antibodies

    Cell 160:785–797.

    https://doi.org/10.1016/j.cell.2015.01.027

    • PubMed
    • Google Scholar
70. 1. Wu TD
    2. Nacu S

    (2010) Fast and SNP-tolerant detection of complex variants and splicing in short reads

    Bioinformatics 26:873–881.

    https://doi.org/10.1093/bioinformatics/btq057

    • PubMed
    • Google Scholar
71. 1. Xie Y
    2. Li X
    3. Chai Y
    4. Song H
    5. Qi J
    6. Gao GF

    (2021) Structural basis of malarial parasite RIFIN-mediated immune escape against LAIR1

    Cell Reports 36:109600.

    https://doi.org/10.1016/j.celrep.2021.109600

    • PubMed
    • Google Scholar
72. 1. Yeka A
    2. Nankabirwa J
    3. Mpimbaza A
    4. Kigozi R
    5. Arinaitwe E
    6. Drakeley C
    7. Greenhouse B
    8. Kamya MR
    9. Dorsey G
    10. Staedke SG

    (2015) Factors associated with malaria parasitemia, anemia and serological responses in a spectrum of epidemiological settings in uganda

    PLOS ONE 10:e0118901.

    https://doi.org/10.1371/journal.pone.0118901

    • PubMed
    • Google Scholar
73. 1. Zamecnik CR
    2. Rajan JV
    3. Yamauchi KA
    4. Mann SA
    5. Loudermilk RP
    6. Sowa GM
    7. Zorn KC
    8. Alvarenga BD
    9. Gaebler C
    10. Caskey M
    11. Stone M
    12. Norris PJ
    13. Gu W
    14. Chiu CY
    15. Ng D
    16. Byrnes JR
    17. Zhou XX
    18. Wells JA
    19. Robbiani DF
    20. Nussenzweig MC
    21. DeRisi JL
    22. Wilson MR

    (2020) ReScan, a multiplex diagnostic pipeline, pans human sera for SARS-cov-2 antigens

    Cell Reports. Medicine 1:100123.

    https://doi.org/10.1016/j.xcrm.2020.100123

    • PubMed
    • Google Scholar

Decision letter after peer review:

Thank you for submitting your article "Proteome-wide antigenic profiling in Ugandan cohorts identifies associations between age, exposure intensity, and responses to repeat-containing antigens in Plasmodium falciparum" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Dominique Soldati-Favre as the Senior Editor. The reviewers have opted to remain anonymous.

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

Essential revisions:

1) Please conduct analyses of the data stratified by BS/PCR status.

2) Please include analyses of antibody data to confirm your hypothesis that "inter-protein motifs" do cross-react.

3) Please revise the manuscript according to the additional suggestion indicated by the numbered lines corresponding to the text in the manuscript.

Reviewer #1 (Recommendations for the authors):

Specific comments

Interprotein motifs as potential cross-reactive epitopes:

Line 59: "short motifs associated with seroreactivity were extensively shared among hundreds of antigens, potentially representing cross-reactive epitopes." Please present analyses showing whether reactivity to these shared motifs indeed correlates between different proteins within the same individual. One presumes so but this can be assessed with available data.

Lines 396-479 – an extended passage is dedicated to the analysis of interprotein motifs, and their association with seroreactive proteins. The authors should analyze whether seroreactivities to different peptides containing the same/similar interprotein motif correlate or not within individuals. This would test the authors' hypothesis on L 399 "If these motifs are a part of an epitope, then antibodies and B-cell receptors (BCR) specific to a motif can potentially cross-react with the motif variants in different proteins".

L 608: "Another limitation of our study is that it did not provide quantitative measures of absolute antibody reactivity to individual peptides per person." This is a bit confusing, since Figure 2 —figure supplement 2 shows that "Technical replicates are well correlated" for seroreactivity to individual peptides, indicating the assay is quantitative (but perhaps not for "antibody reactivity", although that is the underlying selective force?). In any case, it should be possible to compare RP5K between peptides that share the same interprotein motif, assuming that these motifs represent important B cell epitopes as the authors hypothesize.

Effects of concurrent parasitemia on antibody:

Line 61: "PfEMP1 shared motifs with the greatest number of other antigens". This echoes prior studies showing short-term responses against different (heterologous) PfEMP1 variants during/after infection, which is an important potential confounder in the analyses presented here. The authors should address/analyze this issue of concurrent malaria infection effects more thoroughly, otherwise, any comparison between the two cohorts is hard to interpret.

Line 174 – please clarify if there was a rationale for the different characteristics of the sample sets between the two sites – "the majority of Tororo samples were positive for infection", while those in Kanungu were "100 days after their last infection". This will bias any comparisons between the two cohorts. For example, Helb et al. previously observed, with sera from these same study sites, that "Pf-Specific Antibody Profiles Showed Decreased Responses with Increased Days Since Infection."

Line 254 – "children in the higher transmission setting had a significantly higher breadth than children in the moderate transmission setting." Here and elsewhere in the Results, the analyses could be confounded by concurrent parasitemia during sampling (which is more common in the high transmission cohort), since acute parasitemia has acute effects on antibody profiles including cross-reactive antibodies (for example, see https://pubmed.ncbi.nlm.nih.gov/15175751/). The authors should compare individuals with patent parasitemia (by microscopy), subpatent parasitemia (by PCR), and without parasitemia at the time of sample collection, to understand these potential confounding effects.

L 532-536: "The difference in seroreactivity to repeat-containing peptides observed here between the [high versus moderate transmission] settings could therefore emerge from two related mechanisms. In the first, the difference could be driven by a requirement for a minimum level of cumulative exposure to the target repeats to generate a robust response. In the second, the antibody response to repeats may be inherently less durable, leading to rapid waning in the absence of frequent exposure." Please test the possibility that these differences among children are simply related to concurrent parasitemia at the time of sample collection.

Platform limitations:

Line 228 – "none of the proteins expressed in the mosquito oocyst stage were identified as seroreactive" Kindly clarify this statement, since for example CSP is expressed during the oocyst stage.

Line 239 – "a known limitation of PhIP-seq – it detects predominantly linear epitopes". The passage including this statement may leave readers with the impression that the previous protein array studies cited here (which appear to have all used the same in vitro protein translation system) were generated using properly folded proteins with conformationally correct epitopes. However, this may be unlikely given the difficulty to express properly folded malaria antigens. Many of the antigens targeted by protective immunity are highly folded proteins and functional antibodies against these antigens are frequently conformation-dependent. As such, one might expect the PhIP-seq platform (and the other protein array platforms they cite) to perform poorly at studying such antibodies. The authors should clarify these issues a bit more as context for readers. The limitation of this PhIP approach may be indicated by "Figure 2 —figure supplement 7 Breadth of seroreactivity in the variable regions of RIFIN and PfEMP1" which shows modest reactivity of adult sera to peptides of PfEMP1, whereas prior studies have established that adult sera in Africa cross-react to many/all blood-stage parasite surface variants circulating in a community (eg, see Figure 1 in https://pubmed.ncbi.nlm.nih.gov/10882604/ ), presumably reflecting seroreactivity to the immunodominant PfEMP1 proteins.

Line 267 – "In the moderate transmission setting, adults had a significantly higher breadth of PfEMP1 variants recognized than children, suggesting an age and/or cumulative exposure dependent increase in PfEMP1 breadth in this setting, as previously observed in (Cham et al., 2009)." Please provide numbers and results of statistical analysis to substantiate this statement. This difference looking at the figure is not striking-it appears reasonably clear for CIDR a/b/g, but less so for DBL domains.

Line 274: "a decline in responses to variants as children develop into adults in this [high transmission] setting. This is consistent with observations from a previous study investigating antibody responses to PfEMP1 DBLa domains in Papua New Guinea (Barry et al., 2011)". While the results are consistent, it's worth noting that Barry used an array of in vitro translated proteins that may not be properly folded-this was never tested in Barry's study and hence would have similar limitations to the study here that uses only linear epitopes.

Figure 1: please state in the figure the total number of falciparum proteins and corresponding peptides in the library-the number 8980 stated here includes >1400 non-malaria proteins (and these were apparently excluded from analysis-line 725).

Reviewer #2 (Recommendations for the authors):

Overall, this paper was excellent. The writing was careful, and the experiment is exciting.

"Slow development of immunity" is a motivation described in the introduction for investigating repeat-containing elements. I would recommend adding some nuance to this description of natural immunity to avoid confusion for some readers. Immunity to severe disease in children (and in pregnancy) is acquired quite rapidly after only one or two infections. This is relevant here because it may differentiate the immunity to the types of antigens focused on in this paper from variant antigens and genes with completely different, but discrete numbers, of alleles-e.g., MSP1, EBA175, etc.

A potential major confounder in the interpretation of results indicating responses to repeat elements that are shared amongst multiple proteins is that the assay wouldn't differentiate antibodies derived from one or many of these loci. The conclusions about the importance of inter-protein repeats on immunity are based on the assumption that any signal in these regions is based on antibodies derived from those proteins, rather than Ab's to one region causing the immunoprecipitation of the others. This seems especially relevant where seroreactivity is discussed. Is it possible to look at regions adjacent to the inter-protein repeats to discern this? I understand this was done when investigating seroreactivity within proteins, but this would be to confirm signals from different proteins. If it's just that I'm missing something obvious, some discussion clarifying why this isn't a confounder would be a good addition.

I thought the focus on repeat regions was important and wonderful to see, but some comparison to polymorphic loci feels like a lost opportunity to add significant value. It would be particularly interesting to know if an immunity to repeats was anticorrelated within individuals to other loci, and how this compares across transmission intensity. For example, does high repeat-element seroreactivity stymie development of antibodies to invasion receptors?

Another potential confounder would be if the parasites in one of your cohorts was more similar to 3D7/IT than the other. This might be assessed with a PCA plot of SNPs from your NGS reads, or perhaps with MalariaGen or PlasmoDB tools comparing different locations in Uganda.

Reviewer #3 (Recommendations for the authors):

Lines 218-220: "The 9,927 seroreactive peptides identified by the pipeline were derived from 1,648 parasite proteins and antigenic variants, many of which showed broad seroreactivity across pediatric and adult Ugandan samples"

The 9,927 seroreactive peptides were identified from 238,068 total peptides corresponding to 4.2% of the peptides. Similarly, 1,648 seroreactive proteins were identified from 8,980 total proteins corresponding to 18.4% of the proteins. Taken together, the seropositivity in this study was lower than the previous studies using protein array and α screen. Please discuss why this difference has happened.

The reader of this paper will be interested in the seropositivity ranking among the top 100 proteins (not only the list of Top protein names; Supplementary table 2). Please provide the ranking of the seropositive proteins and add a discussion about the selected proteins.

As I listed in the public review 1), the phage display system is the ability to sequentially enrich and amplify the signal to noise. Although more rounds of IP definitely increase the S/N, an increased number of rounds of IP will have disadvantages such as biased results. Please add a description of why the authors defined the methods to round 2 of IP, not 1 or 3 or more.

Essential revisions:

1) Please conduct analyses of the data stratified by BS/PCR status.

We thank the reviewers for this suggestion. We have added this analysis, taking into account both exposure and infection status. (lines 409-435). Two new supplementary figures (Figure 5 Figure Supplement 3, Figure 5 Figure Supplement 4), also accompany the new text to support these analyses.

2) Please include analyses of antibody data to confirm your hypothesis that "inter-protein motifs" do cross-react.

We thank the reviewers for this suggestion. We have added this analysis to the manuscript (lines 495- 503). A new supplementary figure (Figure 6 Figure Supplement 3) also accompanies the new text to support this analysis.

3) Please revise the manuscript according to the additional suggestion indicated by the numbered lines corresponding to the text in the manuscript.

We have revised the manuscript as indicated by the numbered line suggestions.

Reviewer #1 (Recommendations for the authors):

Specific comments

Interprotein motifs as potential cross-reactive epitopes:

Line 59: "short motifs associated with seroreactivity were extensively shared among hundreds of antigens, potentially representing cross-reactive epitopes." Please present analyses showing whether reactivity to these shared motifs indeed correlates between different proteins within the same individual. One presumes so but this can be assessed with available data.

We have performed the suggested analysis. This has been added to the Results section (lines 495-503), as well as a new supplemental figure (Figure 6, Supplement 3) comparing seroreactivity of inter-protein motifs within individuals. We find significantly more sharing of seroreactivity to peptides with inter-protein motifs compared to peptides without inter-protein motifs, supporting the notion of cross-reactivity. Despite this association, we believe it prudent to be conservative about the conclusions, since the response of every individual is complex and polyclonal. Thus, we maintain our position that the data suggests wide-spread cross reactivity, but does not prove it. We have emphasized this point in the discussion.

Lines 396-479 – an extended passage is dedicated to the analysis of interprotein motifs, and their association with seroreactive proteins. The authors should analyze whether seroreactivities to different peptides containing the same/similar interprotein motif correlate or not within individuals. This would test the authors' hypothesis on L 399 "If these motifs are a part of an epitope, then antibodies and B-cell receptors (BCR) specific to a motif can potentially cross-react with the motif variants in different proteins".

As noted above, we have included a new analysis to address this question.

L 608: "Another limitation of our study is that it did not provide quantitative measures of absolute antibody reactivity to individual peptides per person." This is a bit confusing, since Figure 2 —figure supplement 2 shows that "Technical replicates are well correlated" for seroreactivity to individual peptides, indicating the assay is quantitative (but perhaps not for "antibody reactivity", although that is the underlying selective force?). In any case, it should be possible to compare RP5K between peptides that share the same interprotein motif, assuming that these motifs represent important B cell epitopes as the authors hypothesize.

While PhIPseq technical replicates are highly correlated, the assay is not considered quantitative in terms of titer or antibody affinity. As discussed in previous papers using PhIPseq ((Vazquez et al. 2020; Yuan et al. 2018), the actual number of reads recovered from an IP may vary due to many factors. This includes the starting copy number of phage targets in the library, position of the epitope in the peptide, non-specific interactions with the magnetic beads, and so on. In this work, and previous papers, the fold enrichment (by RPK) of any given peptide species is evaluated relative to the same library applied to control samples (non-malaria exposed plasma in this case), allowing a semi-quantitative, relative assessment of seroreactivity. An important aspect of this work is our inclusion of a large number (n=86) of (non-malaria) US controls, which forms the basis for measuring the relative enrichment in the Ugandan cohort. We have added a clarification in the limitations section of the discussion to hopefully avoid confusion (Lines 677-681).

As noted above, we have also included the requested analysis of per individual interprotein motif comparisons for cross-reactivity.

Effects of concurrent parasitemia on antibody:

Line 61: "PfEMP1 shared motifs with the greatest number of other antigens". This echoes prior studies showing short-term responses against different (heterologous) PfEMP1 variants during/after infection, which is an important potential confounder in the analyses presented here. The authors should address/analyze this issue of concurrent malaria infection effects more thoroughly, otherwise, any comparison between the two cohorts is hard to interpret.

We thank the reviewer for this comment. The analysis on inter-protein motifs was processed using the entire set of 9927 seroreactive peptides, regardless of cohort, to obtain a landscape of potential cross-reactive sequences. Note that the quoted statement refers to a comparison of responses to PfEMP1 to those of other, non-PfEMP1 proteins, mitigating the potential effect of broad responses to heterologous PfEMP1 variants. Therefore, the result on motif sharing with PfEMP1 only suggests that with the widely diverse PfEMP1 variants, there is a potential for cross-reactivity with different non-PfEMP1 antigens depending on the variant expressed within an individual. We claim only that the potential for cross reactivity exists, by sequence similarity alone, among the seroreactive peptide sequences. We have made the limitations of this claim clear in the discussion (Line 632-635).

We have however tested the important confounder of concurrent malaria with respect to breadth of reactivity to repeats as suggested by the reviewer, as described in the public review.

Line 174 – please clarify if there was a rationale for the different characteristics of the sample sets between the two sites – "the majority of Tororo samples were positive for infection", while those in Kanungu were "100 days after their last infection". This will bias any comparisons between the two cohorts. For example, Helb et al. previously observed, with sera from these same study sites, that "Pf-Specific Antibody Profiles Showed Decreased Responses with Increased Days Since Infection."

The samples were selected based on age stratification and not based on positivity for infection per se – the descriptive characteristics of the sample sets mentioned by the reviewer partially reflect the fact that the individuals in Tororo were much more often infected and thus many more will be infected at the time of sampling.

Line 254 – "children in the higher transmission setting had a significantly higher breadth than children in the moderate transmission setting." Here and elsewhere in the Results, the analyses could be confounded by concurrent parasitemia during sampling (which is more common in the high transmission cohort), since acute parasitemia has acute effects on antibody profiles including cross-reactive antibodies (for example, see https://pubmed.ncbi.nlm.nih.gov/15175751/). The authors should compare individuals with patent parasitemia (by microscopy), subpatent parasitemia (by PCR), and without parasitemia at the time of sample collection, to understand these potential confounding effects.

As suggested by the reviewer and described in public review, we have added new analyses with respect to current and recent parasitemia. The practical limitations in sample size with this study, did not allow us to stratify by patent/subpatent parasitimea however.

L 532-536: "The difference in seroreactivity to repeat-containing peptides observed here between the [high versus moderate transmission] settings could therefore emerge from two related mechanisms. In the first, the difference could be driven by a requirement for a minimum level of cumulative exposure to the target repeats to generate a robust response. In the second, the antibody response to repeats may be inherently less durable, leading to rapid waning in the absence of frequent exposure." Please test the possibility that these differences among children are simply related to concurrent parasitemia at the time of sample collection.

This is included in our new analyses, as described above.

Platform limitations:

Line 228 – "none of the proteins expressed in the mosquito oocyst stage were identified as seroreactive" Kindly clarify this statement, since for example CSP is expressed during the oocyst stage.

This is correct. Our original analysis was based on mass spectrometry data deposited in PlasmoDB until 2019, and using a spectral count >= 2 for calling expression, CSP was not called as expressed in the oocyst stage. Since then, newer datasets have been deposited and we have re-analyzed using all the currently available datasets, and we reduced the threshold to spectral count >= 1 to improve sensitivity, at the potential cost of specificity. Using this revised analysis, we now observe some oocyst sporozoite proteins (including CSP) identified in the seroreactive set. Notably though, the number of oocyst-only proteins is small, consistent with less representation of mosquito stage-specific proteins in the seroreactive set. We have updated the manuscript accordingly (lines 231-233 and Figure 2c).

Line 239 – "a known limitation of PhIP-seq – it detects predominantly linear epitopes". The passage including this statement may leave readers with the impression that the previous protein array studies cited here (which appear to have all used the same in vitro protein translation system) were generated using properly folded proteins with conformationally correct epitopes. However, this may be unlikely given the difficulty to express properly folded malaria antigens. Many of the antigens targeted by protective immunity are highly folded proteins and functional antibodies against these antigens are frequently conformation-dependent. As such, one might expect the PhIP-seq platform (and the other protein array platforms they cite) to perform poorly at studying such antibodies. The authors should clarify these issues a bit more as context for readers. The limitation of this PhIP approach may be indicated by "Figure 2 —figure supplement 7 Breadth of seroreactivity in the variable regions of RIFIN and PfEMP1" which shows modest reactivity of adult sera to peptides of PfEMP1, whereas prior studies have established that adult sera in Africa cross-react to many/all blood-stage parasite surface variants circulating in a community (eg, see Figure 1 in https://pubmed.ncbi.nlm.nih.gov/10882604/ ), presumably reflecting seroreactivity to the immunodominant PfEMP1 proteins.

We agree and thank the reviewer for pointing out this important point that the previous protein array studies may suffer from similar limitations of PhIP-seq in not displaying the conformations properly, especially for malaria proteins. We have modified the text to convey this point (lines 247).

With regards to PfEMP1, we acknowledge that our findings could just indicate a decrease in breadth of variant PfEMP1 recognition through linear epitopes in adults, and perhaps there is an increase in breadth of recognition through conformational epitopes that cannot be captured in our assay. This point has been added in lines 293-295.

Line 267 – "In the moderate transmission setting, adults had a significantly higher breadth of PfEMP1 variants recognized than children, suggesting an age and/or cumulative exposure dependent increase in PfEMP1 breadth in this setting, as previously observed in (Cham et al., 2009)." Please provide numbers and results of statistical analysis to substantiate this statement. This difference looking at the figure is not striking-it appears reasonably clear for CIDR a/b/g, but less so for DBL domains.

We have included this analysis in the Results section as suggested by the reviewer (lines 273-274) and we added additional detail to Figure 2, Supplement 6, including the ATS domain. In the moderate setting, the median number of PfEMP1 recognized in adults was 44, whereas the median in children was 26. For the moderate setting, the majority of seroreactivity was localized to the ATS domain, which is unsurprising, since this domain is conserved. The variable domains are inherently unlikely to be perfectly reflected between the PhIPseq library and the actual domains present in parasites from this cohort, which may result in less overall sensitivity in this assay. The text has been amended to clarify this point (lines 276-279).

Line 274: "a decline in responses to variants as children develop into adults in this [high transmission] setting. This is consistent with observations from a previous study investigating antibody responses to PfEMP1 DBLa domains in Papua New Guinea (Barry et al., 2011)". While the results are consistent, it's worth noting that Barry used an array of in vitro translated proteins that may not be properly folded-this was never tested in Barry's study and hence would have similar limitations to the study here that uses only linear epitopes.

Yes, as noted above, we have pointed out this limitation in the revision.

Figure 1: please state in the figure the total number of falciparum proteins and corresponding peptides in the library-the number 8980 stated here includes >1400 non-malaria proteins (and these were apparently excluded from analysis-line 725).

We have changed Figure 1 now to represent the total number of malaria proteins and peptides instead of overall total and added this to the text (lines 189-190).

Reviewer #2 (Recommendations for the authors):

Overall, this paper was excellent. The writing was careful, and the experiment is exciting.

We thank the reviewer for these comments.

"Slow development of immunity" is a motivation described in the introduction for investigating repeat-containing elements. I would recommend adding some nuance to this description of natural immunity to avoid confusion for some readers. Immunity to severe disease in children (and in pregnancy) is acquired quite rapidly after only one or two infections. This is relevant here because it may differentiate the immunity to the types of antigens focused on in this paper from variant antigens and genes with completely different, but discrete numbers, of alleles-e.g., MSP1, EBA175, etc.

Agreed. We have added a modification to the Introduction, as suggested by the reviewer (lines 101-102).

A potential major confounder in the interpretation of results indicating responses to repeat elements that are shared amongst multiple proteins is that the assay wouldn't differentiate antibodies derived from one or many of these loci. The conclusions about the importance of inter-protein repeats on immunity are based on the assumption that any signal in these regions is based on antibodies derived from those proteins, rather than Ab's to one region causing the immunoprecipitation of the others. This seems especially relevant where seroreactivity is discussed. Is it possible to look at regions adjacent to the inter-protein repeats to discern this? I understand this was done when investigating seroreactivity within proteins, but this would be to confirm signals from different proteins. If it's just that I'm missing something obvious, some discussion clarifying why this isn't a confounder would be a good addition.

Yes, thank you. There are two points here. First, we did indeed examine the regions immediately adjacent to the inter-protein repeat motifs, which is possible since our library consists of overlapping peptides, giving us an effective resolution of 12-13 amino acids (Figure 6, Supplement 2). Here, we observed that seroreactivity is indeed localized to the part of the peptide with the interprotein motif, as opposed to other parts of the peptide. Second, we have added a new analysis of cross-reactivity between shared interprotein motifs within each individual (see Lines 495-503, and response to Reviewers #1 and #2). There are limitations to this analysis, which we have made clear in the manuscript as well (Lines 632-635).

I thought the focus on repeat regions was important and wonderful to see, but some comparison to polymorphic loci feels like a lost opportunity to add significant value. It would be particularly interesting to know if an immunity to repeats was anticorrelated within individuals to other loci, and how this compares across transmission intensity. For example, does high repeat-element seroreactivity stymie development of antibodies to invasion receptors?

This is an interesting question, although difficult to directly address with this dataset. The question is whether repeats stymie or interfere with the development of immune responses to particular classes of proteins (like invasion proteins). As a whole, we observe more seroreactivity to both repeats and non-repeats together as a function of exposure and age (Author response image 1). That said, individuals vary widely with respect to seropositivity to specific repeats and non-repeats, and therefore it would require a much larger dataset to establish co-occurrence, or interference, since no two individuals are alike. This is graphically depicted in Figure 2, Supplement 2 (top panel). The Ugandan samples are clearly more correlated than the US samples, due to malaria exposure, but high correlation values between individuals are lacking. Furthermore, interference may be relieved in the form of antibody feedback – for instance, it has been shown that feedback from antibodies to the immunodominant repeat epitopes in CSP can lead to diversification of responses to subdominant epitopes in a vaccine setting (Mcnamara et al. 2020). A correlation analysis using this dataset is unlikely to capture these nuances.

Author response image 1

Download asset Open asset

However, within proteins, it is clear that the linear peptide regions that are immuno-dominant are the repeats themselves (Figure 3, and also Figure 4), which is consistent with the notion that repeats prevent or shield against the development of seroreactivity to the non-repeat portions of the same protein. This effect has been posited in ((Schofield 1991)) and elsewhere. As we’ve pointed out, one limitation on this conclusion is that PhIPseq detects largely non-conformational epitopes.

Another potential confounder would be if the parasites in one of your cohorts was more similar to 3D7/IT than the other. This might be assessed with a PCA plot of SNPs from your NGS reads, or perhaps with MalariaGen or PlasmoDB tools comparing different locations in Uganda.

In this dataset, the NGS reads are from our phage library, and not from Ugandan parasite sequences themselves, and thus we do not have the direct data to answer this question. However, parasite diversity in Uganda (and sub-saharan Africa in general) is amongst the highest in the world, with little population structure within Uganda, as we have previously shown (Chang et al. 2017)

We thus expect any differences in genetic composition of parasites between the sites overall to be quite small in comparison to the extensive variation within each site and the large differences between parasites to which people are exposed and the reference parasites.

Reviewer #3 (Recommendations for the authors):

Lines 218-220: "The 9,927 seroreactive peptides identified by the pipeline were derived from 1,648 parasite proteins and antigenic variants, many of which showed broad seroreactivity across pediatric and adult Ugandan samples"

The 9,927 seroreactive peptides were identified from 238,068 total peptides corresponding to 4.2% of the peptides. Similarly, 1,648 seroreactive proteins were identified from 8,980 total proteins corresponding to 18.4% of the proteins. Taken together, the seropositivity in this study was lower than the previous studies using protein array and α screen. Please discuss why this difference has happened.

We thank the reviewer for this comment. We would like to point out that the 238,068 peptides and 8,980 proteins in the library represent the 3D7 and IT proteomes along with variant proteins as well as viral and experimental control proteins, as described in Table 2 (the complete peptide list and sequences are also available at Dryad doi:10.7272/Q69S1P9G). The set of 1,648 seroreactive proteins identified here represent unique proteins, to avoid double counting from 3D7/IT or variant antigens. Thus, these 1,648 proteins represent ~30% of the 5,400 proteome of P. falciparum, and we have clarified this in the Results (Lines 221-222).

We note that we implemented a highly conservative inclusion criteria for proteins in this set to minimize false positives (Figure 2, Supplement 4) by leveraging the large number of US control samples and enforcing a minimum level of 5 individuals sharing. We note that previous studies either did not have a large control (unexposed) set and/or did not require sharing among at least 5 individuals. The comparison of the 1,648 proteins identified here with previous screens are discussed in detail in both the Results (Lines 239-247) and Discussion section (Lines 565-571).

The reader of this paper will be interested in the seropositivity ranking among the top 100 proteins (not only the list of Top protein names; Supplementary table 2). Please provide the ranking of the seropositive proteins and add a discussion about the selected proteins.

Yes, agreed. A “top ranking” summary is now provided (Supplementary table 2b), in addition to the full dataset.

As I listed in the public review 1), the phage display system is the ability to sequentially enrich and amplify the signal to noise. Although more rounds of IP definitely increase the S/N, an increased number of rounds of IP will have disadvantages such as biased results. Please add a description of why the authors defined the methods to round 2 of IP, not 1 or 3 or more.

We and others have measured the gain in signal as a function of IP round (O’Donovan et al., “High-resolution epitope mapping of anti-Hu and anti-Yo autoimmunity by programmable page display. Brain Communications 2020). As shown in the above reference, multiple rounds amplifies the signal. Pilot experiments on Ugandan samples done with 1, 2 or 3 rounds using the Falciparome library showed that the largest fold-gain in S/N for known antibody targets in P. falciparum was realized between round 1 and 2, and thus we proceeded with 2 rounds to avoid potential bias from phage amplification.

References

Chang, Hsiao Han, Colin J. Worby, Adoke Yeka, Joaniter Nankabirwa, Moses R. Kamya, Sarah G. Staedke, Grant Dorsey, et al. 2017. “THE REAL McCOIL: A Method for the Concurrent Estimation of the Complexity of Infection and SNP Allele Frequency for Malaria Parasites.” PLoS Computational Biology 13 (1). https://doi.org/10.1371/JOURNAL.PCBI.1005348.

Mcnamara, Hayley A, Azza H Idris, Henry J Sutton, Mattia Bonsignori, Robert A Seder, Ian A Cockburn Correspondence, Rachel Vistein, et al. 2020. “Antibody Feedback Limits the Expansion of B Cell Responses to Malaria Vaccination but Drives Diversification of the Humoral Response.” https://doi.org/10.1016/j.chom.2020.07.001.

Schofield, L. 1991. “On the Function of Repetitive Domains in Protein Antigens of Plasmodium and Other Eukaryotic Parasites.” Parasitology Today (Personal Ed.) 7 (5): 99–105. https://doi.org/10.1016/0169-4758(91)90166-L.

Vazquez, Sara E., Elise M.N. Ferré, David W. Scheel, Sara Sunshine, Brenda Miao, Caleigh Mandel-Brehm, Zoe Quandt, et al. 2020. “Identification of Novel, Clinically Correlated Autoantigens in the Monogenic Autoimmune Syndrome APS1 by Proteome-Wide Phip-Seq.” eLife 9 (May). https://doi.org/10.7554/ELIFE.55053.

Yuan, Tiezheng, Divya Mohan, Uri Laserson, Ingo Ruczinski, Alan N Baer, H Benjamin Larman, and Corresponding. 2018. “Improved Analysis of Phage ImmunoPrecipitation Sequencing (PhIP-Seq) Data Using a Z-Score Algorithm.” BioRxiv, April, 285916. https://doi.org/10.1101/285916.

Author details

1. Madhura Raghavan

   University of California, San Francisco, San Francisco, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8566-9189

2. Katrina L Kalantar

   Chan Zuckerberg Initiative, Redwood City, United States

   Contribution

   Software, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Elias Duarte

   University of California, Berkeley, Berkeley, United States

   Contribution

   Validation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2541-5504

4. Noam Teyssier

   University of California, San Francisco, San Francisco, United States

   Contribution

   Software, Formal analysis

   Competing interests

   No competing interests declared

5. Saki Takahashi

   University of California, San Francisco, San Francisco, United States

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

6. Andrew F Kung

   University of California, San Francisco, San Francisco, United States

   Contribution

   Software, Writing – review and editing

   Competing interests

   No competing interests declared

7. Jayant V Rajan

   University of California, San Francisco, San Francisco, United States

   Contribution

   Methodology

   Competing interests

   No competing interests declared

8. John Rek

   Infectious Diseases Research Collaboration, Kampala, Uganda

   Contribution

   Resources

   Competing interests

   No competing interests declared

9. Kevin KA Tetteh

   London School of Hygiene and Tropical Medicine, London, United Kingdom

   Contribution

   Resources

   Competing interests

   No competing interests declared

10. Chris Drakeley

    London School of Hygiene and Tropical Medicine, London, United Kingdom

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4863-075X

11. Isaac Ssewanyana

    1. Infectious Diseases Research Collaboration, Kampala, Uganda
    2. London School of Hygiene and Tropical Medicine, London, United Kingdom

    Contribution

    Resources

    Competing interests

    No competing interests declared

12. Isabel Rodriguez-Barraquer

    1. University of California, San Francisco, San Francisco, United States
    2. Chan Zuckerberg Biohub, San Francisco, United States

    Contribution

    Conceptualization, Formal analysis, Investigation, Visualization, Writing – review and editing

    Competing interests

    Reviewing editor, eLife

    "This ORCID iD identifies the author of this article:" 0000-0001-6784-1021

13. Bryan Greenhouse

    1. University of California, San Francisco, San Francisco, United States
    2. Chan Zuckerberg Biohub, San Francisco, United States

    Contribution

    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    bryan.greenhouse@ucsf.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-0287-9111

14. Joseph L DeRisi

    1. University of California, San Francisco, San Francisco, United States
    2. Chan Zuckerberg Biohub, San Francisco, United States

    Contribution

    Conceptualization, Resources, Software, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    joe@derisilab.ucsf.edu

    Competing interests

    Paid scientific advisor for Allen & Co. Paid scientific advisor for the Public Health Company, Inc and holds stock options. Founder and holding stock options in VeriPhi Health, Inc

    "This ORCID iD identifies the author of this article:" 0000-0002-4611-9205

• 702

  Page views

• 168

  Downloads

• 1

  Citations

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