A complex Plasmodium falciparum cryptotype circulating at low frequency across the African continent

Introduction

Plasmodium falciparum is one of the protozoa species that causes malaria in humans. In this preprint—using a public database of P. falciparum genome variation, “Pf7” —the authors identified a subset of parasites that do not seem to cluster within their geographic parasite populations. They discovered this set of parasites when running a population structure analysis of African samples. The separate cluster is named a “cryptotype” and is found at about ~1% frequency in 13 African countries. These parasites appear to carry specific variants across their genomes, unlike their con-specifics, and this pattern is characterized in detail in the preprint. The authors also speculate on the origin and maintenance of the identified cryptotype, suggesting that it may represent an adaptation to an as of yet unidentified host niche. The implications of this preprint’s findings are yet to be determined but the preprint does highlight the need for new methods to find track or find other rare circulating cryptotypes.

 

Key findings

• Population structure analysis of African falciparum parasites

The 4,376 African samples from the Pf7 dataset were grouped into three regions: West, Central, and East Africa (labeled WAF, CAF, and EAF, respectively). PoCA of pairwise genetic distance for 743,584 SNPs showed that PC1 separated samples according to geographic region and the second largest component, PC2, clustered parasites from all three regions, this cluster was named “AF1” (Figure1). This initial result was intriguing because AF1 included countries with both high and low transmission, suggesting clustering is not due to parasite population structure, which might be expected with low transmission. The 47 samples above the authors’ threshold of PC2 ≥ 0.025 were labeled as members of the AF1 cryptotype.

• Characterization of the AF1 cryptotype

To describe the genetic makeup of AF1 parasites, the authors estimated allele frequencies and calculated Fst (a measure of allele frequency differentiation) between AF1 and non-AF1 in each one of the three main populations: WAF, CAF, and EAF. The authors identified a total of 199 nonsynonymous coding SNPs with mean Fst > 0.5; and 69 with mean Fst > 0.75. In addition, these variants were found not to be distributed randomly across the genome but restricted to particular chromosomal loci. Analysis of linkage disequilibrium (LD) revealed a high correlation between many pairs of SNPs in AF1, even across chromosomes (mean r2 >0.2; Figure 2 in the preprint). Six of these loci showed a high LD with at least one other SNP with SNPs at all other loci (r2 >0.4).

Furthermore, an analysis of IBD (identity by descent) was carried out by the authors to determine if parasites from this AF1 cluster share ancestry. The results showed that pairs of AF1 parasites are IBD at a much higher fraction of their genomes compared to non-AF1 (median 22.6%) and regions of IBD that it coincided with regions of high Fst (see Figure 3 in preprint). Across the genome, the authors identified 23 “high-IBD” regions where at least 50% of all AF1 pairs were IBD. However, the authors think this is not maintained due to clonality (i.e. effect of selfing) because sympatric AF and non-AF1 parasites shared higher IBD overall; for example, WAF AF1 shared more sequence IBD with WAF non-AF1 genomes than with EAF non-AF1 genomes (median=0.56% vs 0.12%), and vice versa (median=0.55% vs 0.0%).  Thus, this led the authors to infer that AF1 parasites have recombined with sympatric non-AF1 parasites.

• Characterization of top highest scores alleles, IBD, Fst

Chromosomes 9 and 10 showed the highest Fst values and IBD among AF1 samples. The authors described the types of genes and genetic variation in these regions. On chromosome 10, sequence coverage analysis revealed a putative large deletion including loss of the genes MSP6 and H10. Also, the DBLMSP gene showed a complex pattern of coverage, possibly due to mismapping with the near paralog DBLMSP2. After comparison to different strains, the authors found that the DBLMSP allele common in AF1 showed similarity with the DBLMSP2 sequence from a South American strain (PfIT reference sequence) at the 5’ end, returning to match the DBLMSP sequence downstream of position 1010, suggesting gene conversion from DBLMSP2 to DBLMSP and identifying the recombination breakpoint. On chromosome 9, the variants with the highest Fst were within the MSP1 gene. Other variants with high Fst were found to belong to notable functional categories, including invasion (e.g. MSP, EBA-175) and surface antigens (e.g. REX, SURFIN), but the authors did not quantify an enrichment.

Why did we choose this preprint & why do we think this work is important?

We chose this preprint because the findings are quite unexpected and could be due to an interesting biological process. In addition, it highlights that these protozoa parasites can always surprise us and that we should be prepared to question our assumptions, as there is always more to learn about them.

Future directions and questions for the authors

• What is next for this project and what do the authors think their findings mean for the field of malariology?
• We would have liked to see how other samples—like South American/Asian samples—would relate to this cryptotype. Although there is a supplementary table where it seems most variants are not present, it was not clear if these were rounded to 0 or actually 0. And some are present in South America, so we wondered if they shared IBD with the African AF1 parasites.
• We would have liked to see some go-enrichment analysis or statistical tests to provide some support for the functional hypotheses about variation at the identified unique AF1 loci. It was unclear how enriched the gene categories highlighted were and whether there were also potential variants in uncharacterized genes that could play a role. Another enrichment analysis that would help support functional interpretation is whether 199 NS SNPs was more than expected if the regions were randomly distributed.
• Relating the results back to the introduction, do you have a hypothesis to reconcile the shared co-inheritance with the outbreeding, not by selfing? It seems somewhat contradictory that these will maintain identity, but the same niche selects for this parasite phenotype. In both low- and high-transmission regions, these AF1 parasites seem to recombine with non-AF parasites to a similar extent, according to the supplementary figure.
• Do you see some variants in the gametocyte-related genes that could explain mating preferences despite their frequency potentially explaining similar crossbreeding with non-AF1 independent of different transmission settings? In other words, similar relatedness with non-AF1 in low and high transmission is quite unexpected if mixing is driven by the probability of encounter (frequency-driven) we would expect the differences of outbreeding (mix with nonAF1) to differ between both settings?
• Given the preprint’s initial hypothesis about transmission and population structure introduction, could you speculate whether there is a pattern between the transmission intensity and population structure across countries?
• We were curious about the parasites that fall between non-AF1 and AF1 parasites on PC2. If you include these or analyze them separately, would the same AF1 loci be highlighted? Do they tend to include a particular subset of the differentiated loci?
• Do you think other environmental factors—like differences in vector species—could explain the maintenance of a low-frequency cryptotype or do you know if there is a low-frequency hemoglobinopathy present that could match the geographic pattern and frequency?

 

Posted on: 3 April 2024 , updated on: 4 April 2024

doi: https://doi.org/10.1242/prelights.37000

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