PREreview of Genetic Diversity of the Malaria Vaccine Candidate PfRIPR in a High Transmission Region of Senegal

Summary:

This preprint fills a gap in the literature to date by investigating the genetic diversity of RH5-Interacting Protein (PfRIPR) using 89 blood samples from Senegal. This is a highly relevant and timely study given that PfRIPR, which is part of the PTRAMP—CSS—RIPR—CyRPA—RH5 (PCRCR) complex that the parasite relies on to invade red blood cells, is currently in Phase 1a clinical trials as a novel malaria vaccine candidate.

Deep targeted amplicon Next Generation Sequencing was used to identify RIPR mutations in Plasmodium falciparum clinical isolates with a sensitivity threshold of 2%. This approach was particularly appropriate to detect rare mutations present in minority parasite strains in the context of polygenomic infections.

In this study, PfRIPR shows moderate genetic diversity in Senegal, with 71.9% of samples containing at least one non-synonymous SNP. The researchers identified 26 non-synonymous single nucleotide polymorphisms (SNPs), 11 of which were previously described in literature and 15 of which are novel. Structural modeling predicted that several mutations destabilize the protein, which may affect parasite invasion ability and therefore potentially impact vaccine efficacy. However, most of the reported variants occur outside the known neutralizing antibody-binding regions. T738K and V840L, 2 out of 3 of the relevant SNPs which are localized in the vaccine targeting region (the epidermal growth factor-like (EGF) domains 5-8)  and may enable immune evasion, have low variant read frequencies (3.6% and 3.7% respectively).

The findings of this preprint indicate that PfRIPR is highly conserved and remains a viable malaria vaccine candidate. The authors are appropriately cautious by acknowledging that the specific epitopes targeted by leading anti-PfRIPR antibodies have yet to be identified and claim that epitope mapping studies, biochemical antibody-protein binding studies and phenotypic assays are needed to make a comprehensive assessment about whether or not the identified variants compromise vaccine efficacy.

Overall, this work is very interesting and could be even more impactful should the authors consider the suggested revisions below.

Introduction

·      The introduction effectively establishes the urgency and public health relevance of the problem addressed in this preprint.

·      The authors provide the relevant context for understanding this research and its significance and do so in a way that is accessible to broad readership while maintaining scientific rigor.

·      The authors appropriately cite relevant prior work (ongoing clinical trials, failed vaccine history, foundational PCRCR biology), acknowledging other researchers' contribution to the field and positioning their own work within the broader vaccine development effort.

·      The section suitably ends with a statement about the study objectives.

·      The researchers state that they utilized “structure modeling to determine the impact of the identified mutations on RIPR stability and the binding of neutralizing antibodies" (page 3, lines 31-32). This statement is later proven untrue in the ‘Structural Modeling of SNPs’ section of the paper when the authors explain that the cryo-EM structure of PfRIPR in complex with PfRH5 and PfCyRPA they used “did not have density for PfRIPR beyond residue 716” (lines 28-29) and that they “were unable to thread SNPs located on EGFs 5-8 onto the PfRIPR structure” (lines 33-34).

·      I recommend revising the Introduction to accurately state that structural modeling was performed to assess stability effects on the protein core but antibody-binding regions could not be modeled.

Methods

·      The PCR amplification strategy differs by year: the 2019 samples (n=15) were amplified in two parts while 2022-2023 samples (n=74) used full-length amplification (page 16, lines 16-17). Please answer the following questions if possible:

o   What regions of the protein did each 2019 amplicon cover? Was the entire sequence covered? Was there any overlap or did the amplicons span adjacent/non-overlapping regions?

o   What was the rationale for changing amplification strategy?

o   Were the PfRIPR variants consistent across years or were there notable differences between 2019 and later years? If no year-specific SNP pattern differences were observed, a brief statement to this effect would address any potential concerns for methodological bias.

·      The authors acknowledge that their sensitivity threshold choice matters, stating “If we increase our SNP threshold to 5%, most of these SNPs would not be called.” (Discussion, page 10, lines 38-39). Yet, they provide no rationale for choosing 2% as the variant frequency threshold for calling SNPs. They should provide a justification for this cutoff percentage.

·      The sequencing error rate is not reported. The authors say they used the 3D7 control to "confirm the absence of SNPs arising from PCR or sequencing errors" (page 16, lines 33-34) but do not state at what frequency false variants were called in the control. I recommend reporting this to empirically establish a sequencing error rate.

·      Most of the SNPs (9/15) being presented as novel genetic diversity of PfRIPR in this study have very low variant read frequencies (2-5% VRF). Given this and their singleton occurrence, what evidence supports them as true biological variants rather than sequencing artifacts? Empirically establishing a sequencing error rate using the 3D7 control would help the authors justify their 2% sensitivity threshold and help the readers assess whether the low-frequency SNPs reported are reliably distinguishable from artifacts/technical noise.

Results

·      The statement “All SNPs were only identified in a single sample, with the exception of D454G which was identified in two samples” (Page 4, lines 16-17) is ambiguous and confusing on multiple levels:

o   By a “single sample”, do the authors mean that all SNPs but 1 were identified in the same sample (unlikely) or are they saying all SNPs but 1 were identified in only 1 sample (not necessarily the same; different SNPs in different samples  but each was found in no more than one sample)?

o   Does "all SNPs" refer to all 26 non-synonymous SNPs (clearly contradicted by Figure 1 showing Y259H in 37 samples and V190A in 21 samples) or does it refer to the 15 novel SNPs (more plausible given frequency data)

I suggest revising this sentence to avoid reader confusion and clearly delineate between novel and previously reported SNP frequency patterns and clarify whether the SNPs were found in the same or different samples.

·      Table 1 shows statistically significant complexity of infection (COI) differences between sites (p-value = 0.0016), but the authors do not explore the potential implications of this. COI being an important parasitological parameter, this doesn’t seem like a minor oversight. I would recommend stratifying their SNP analysis by site or finding an alternative way to show this factor was taken into consideration.

·      Table 2 shows the structural effects of 33 PfRIPR SNPs but only 26 were found in this study. The accompanying text doesn't distinguish which structural predictions are for the novel SNPs vs. previously reported SNPs vs. SNPs described in literature that were not identified in this study. The authors should make those distinctions clear in the text.

·      The authors state that “the low prevalence of H191N, (identified in one patient sample) may suggest that it is not a favorable SNP for parasite invasion or survival” (page 7, lines 41-43) due to its destabilizing effect. But V190A has a similar destabilizing effect on the RIPR/CyRPA interaction and is 15 times more common. How is that? Moreover, the researchers propose that V190A's destabilization provides "flexibility" advantage to the parasite (page 8) but don't explain how a weakened RIPR/CyRPA interaction specifically enhances invasion.

·      This interpretive inconsistency (similar ΔΔG values receiving opposite functional explanations) also applies to other mutation pairs (e.g., Y259H vs H191N) in the dataset. The authors should either explain the mechanistic differences to make it clear they are not doing post-hoc rationalization or explicitly flag this paradox as requiring functional validation studies.

Discussion

·      The vast majority of the structural modeling and discussion sections is spent on mutations in EGF1-4, which are not antibody-binding regions. While this provide information about PfRIPRR protein stability and possibly parasite invasion ability, it does not inform vaccine escape potential. The statement that “most of the identified mutations...are localized outside of known neutralizing antibody-binding regions” (page 13, lines 11-13) describes the most important finding for vaccine development, yet it receives minimal emphasis.

·      Consider reorganizing and rebalancing the presentation of the results and discussion to emphasize that (1) only 3/26 SNPs are in vaccine-relevant regions, (2) these could not be structurally modeled, and (3) the extensive stability analysis informs understanding of protein function but not vaccine escape potential.

Competing interests

The authors declare that they have no competing interests.

Use of Artificial Intelligence (AI)

The authors declare that they did not use generative AI to come up with new ideas for their review.