arise from R. Thomson-Luce et al. Nature Communications https://doi.org/10.1038/s41467-021-25062-z (2021)
We have previously shown that circulating in Papua Plasmodium falciparum Patients who develop severe malaria are younger than those who develop uncomplicated malaria1Thomson-Luke et al.2 Subsequently, in our dataset and others, circulating parasitemia was inversely correlated with estimated parasite age, which is Plasmodium falciparum They express PfEMP1, which causes severe malaria with high cell adhesion, leading to premature sequestration of the parasite in the microvasculature and decreased splenic clearance. Here, the data do not support the hypothesis, as they show that circulating parasitemia and the percentage of total circulating parasites in the dataset are not correlated with age of circulating parasites.
Thomson Luque et al.confirmed our findings that it circulates Plasmodium falciparum Parasites in severe malaria are younger than those that cause uncomplicated malaria1 And they suggested that this difference confounds comparisons between the transcriptomes of parasites that cause severe and uncomplicated malaria. This is precisely why we developed and applied a mathematical approach to control for parasite stage variation before identifying upregulated genes in severe malaria by differential gene expression (DGE) analysis. Like Thomson-Luque et al. We missed these details outlined in our methods and results. Therefore, they may have misunderstood the differentially expressed gene set in the reanalysis. Thomson Luque et al. We showed that the genes we identified as upregulated in severe malaria were not expressed earlier than those upregulated in uncomplicated malaria (Thomson-Luque et al. Fig. 3a,b). Thus, our analysis successfully controlled for parasite stage variation that did not perturb differentially expressed gene sets.
Thomson-Luque showed that genes upregulated in severe malaria in differentially expressed gene sets were not expressed earlier than those upregulated in uncomplicated malaria. They argued that this similarity in onset timing was due to no difference in parasitemia between patients with severe and uncomplicated malaria. patient samples were analyzed. was We compared gene de novo assembly with these parasite densities, but only 35 of these were used for DGE due to prehospital drug treatment or insufficient sequencing coverage1Parasitemia was more common in 16 severe malaria cases used for DGE (median 2.071%, IQR 0.422–12.83) was significantly higher. p= 0.0136 two-tailed Mann-Whitney test cormorant = 78. Therefore, not providing individual parasite densities, Thomson-Luque et al. Of course, it would be wrong to assume that the samples used for DGE also did not differ in parasitemia.
Thomson Luque et al. We used mixed models to estimate parasite stage in multiple datasets and showed an inverse correlation between these estimates of parasite age and circulating parasitism. They speculated that isolated parasite burden correlated with circulating parasitemia and thus inversely correlated with age of circulating parasites (Thomson-Luque et al., Fig. 5). However, our data do not support these correlations. Although the publication did not provide individual patient parasitemia, we reanalyzed the data and compared the proportion of parasite stages in the samples to parasitemia. There is no correlation between parasitemia and rates of ring-stage parasites in our dataset (Spearman r = 0.2033 95% CI −0.1494 is 0.5101 p = 0.2415) or mixed models (Spearman r= -0.2088 95% CI -0.5143 is 0.1438 p = 0.2288) (Figure 1). Consequently, parasitemia does not correlate with parasite age in our sample.
In their Fig. 1c, Thomson-Luque et al. Instead of individual parasitemia, we ranked 41 patient samples by RNAseq read counts for the single glycine tRNA ligase gene PF3D7_1420400. This gene has been described as a housekeeping gene and its expression level is speculated to represent parasitemia. However, Tonkin-Hill et al.1 Levels of normalized readings of PF3D7_1420400 and parasitemia (Spearman r = −0.1908 95% CI −0.4687 is 0.1213, p = 0.2147). Levels of normalized reads and percentages of ring stages in PF3D7_1420400 (Spearman r = 0.4934, 95% CI 0.1432 to 0.6634, p = 0.004) or the proportion of asexual non-ring stages (Spearman r = -0.4608 95% CI -0.6782 to -0.1693 p = 0.0024). The fact that the normalized glycine tRNA ligase readout correlates positively with the percentage of ring stages and negatively correlates with the percentage of non-ring asexual stages indicates that levels of glycine tRNA ligase transcripts are associated with parasitemia. suggests that it better reflects the youth of the parasite than the The majority of RNAseq datasets were prepared using the same approach as our own and are available in plasmodb3,4,5,6,7Thus, a putative relationship between raw reads of glycine tRNA ligase and circulating parasitemia in the samples of Tonkin-Hill et al. is not correct, the trend observed in gene expression by Tonkin-Hill et al. Sample of Fig. 1c from Thomson-Luque et al. It is not associated with increased parasitemia.
Thomson Luque et al. Using transcriptomic staging of circulating parasites, we infer a relationship between parasite stage and circulating parasite density, suggesting that early sequestration occurs in hyperparasitemia/severe malaria. I suggested. However, using the estimated total parasite biomass, we present data suggesting that this is not the case in our samples (part) calculated from the HRP2 level. partEstimates both isolated and circulating parasites8 Therefore, rather than inferring cell adhesion from differences in circulating parasitemia, we measure parasite clearance from circulation by cell adhesion more directly. partVariations in parasite growth rate, duration of infection, distribution volume, and inter-individual variability in PfHRP2 half-life may also influence, but nevertheless, clinical outcome and severity are more important than peripheral blood parasitemia. better univariate correlation with the prognostic index of8. partIt can be calculated for 21 simple malaria samples and 16 severe malaria samples.1√Ptot is the number of these samples (two-sided unpaired t-test p= 0.0094, t= 2.749, df = 35, the raw Ptot was right-skewed, so the data were square root transformed and normality was confirmed by the D’Agostino & Pearson normality test). However, the circulating parasitemia/Ptot ratio, a relative measure of the proportion of circulating parasites that make up the total body parasite load, did not correlate with the proportion of circulating parasites that were rings (Spearman et al. r= −0.0767 95% CI −0.3994 is 0.2629, p= 0.6520) or asexual non-ring stage (Spearman r= 0.0923 95% CI −0.2483 is 0.4125. p= 0.5871) (Figure 2). This highlights that in our data set, the percentage of total parasites isolated is not related to the circulating parasite stage. These results do not support the model of hyperparasitemia proposed by Thomson-Luque, which causes severe disease due to earlier sequestration and consequently younger circulating parasites.
Our data support some of the hypotheses of Thomson-Luque et al., particularly the well-established role of PfEMP1-mediated sequestration in severe disease. Although that was the main focus of our study, Thomson-Luque et al.interpreted our manuscript as a report wasIn fact, when we reported that “there was no difference in overall var gene expression between severe and uncomplicated malaria,” gene expression was reduced in severe cases.1Thomson-Luke et al. You seem to have misunderstood our statement thatwasGene expression was regulated”, which refers to histone methyltransferases involved in wasGene silencing and switching downregulated in severe malaria.So we were referring to potentially modified wasGene switching or silencing. wasGene expressed but not at total level wasgene expression.
In summary, we previously reported that circulating parasites in severe malaria are younger than those in uncomplicated malaria.1Our interpretation of these data differs significantly from that of Thomson-Luque et al. This is because, in our data, neither parasitemia (Fig. 1) nor direct evidence for the percentage of total circulating parasites (Fig. 2) correlated with age of circulating parasites. . Thomson-Luque et al. are of potential importance in understanding malaria pathogenesis, our data do not support the hypothesis of Thomson-Luque et al. Do not support. Both our study and that of Thomson-Luke et al. Bulk RNAseq data were used to estimate the distribution of life cycle stages in circulating parasites.Although this leads to broad conclusions that parasites age differently, single-cell RNAseq analysis is required to fully resolve the distribution of life cycle stages, and expression could also be improved. increase wasgene assembly. Identification of surface-expressed PfEMP1 requires the development of PfEMP1 variant-specific detection reagents.
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