Malaria is a parasitic life-threatening disease caused by species of the Plasmodium genus. In 2021, there were an estimated 619,000 deaths due to malaria, with children under 5 accounting for 77% of these (WHO, 2022). Plasmodium falciparum causes the greatest disease burden and most severe outcomes, but our efforts to combat the disease are challenged by its complex life cycle and its sophisticated immune evasion strategies. P. falciparum has several highly polymorphic variant surface antigens encoded by multi-gene families, with the best studied high molecular weight P. falciparum erythrocyte membrane protein 1 (PfEMP1) family of proteins known to play a major role in the pathogenesis of malaria (Leech et al., 1984; Wahlgren et al., 2017). About 60 polymorphic var genes per parasite genome encode different PfEMP1 variants, which are exported to the surface of parasite-infected erythrocytes, where they mediate cytoadherence to host endothelial cells (Leech et al., 1984; Su et al., 1995; Smith et al., 1995; Baruch et al., 1995; Rask et al., 2010). Var genes are expressed in a mutually exclusive pattern, resulting in each parasite expressing only one var gene, and therefore one PfEMP1 protein, at a time (Scherf et al., 1998). Due to the exposure of PfEMP1 proteins to the host immune system, switching expression between the approximately 60 var genes in the genome is an effective immune evasion strategy, which can result in selection and dominance of parasites expressing particular var genes within each host (Smith et al., 1995).

Despite their sequence polymorphism, var genes could be classified into four categories (A, B, C, and E) according to their chromosomal location, transcriptional direction, type of 5'-upstream sequence (UPSA–E), and encoded protein domains with associated binding phenotype (Figure 1; Lavstsen et al., 2003; Kraemer and Smith, 2003; Kyes et al., 2007; Rask et al., 2010). PfEMP1 proteins have up to 10 extracellular domains, with the N-terminal domains forming a semi-conserved head structure complex typically containing the N-terminal segment (NTS), a Duffy binding-like domain of class α (DBLα) coupled to a cysteine-rich interdomain region (CIDR). C-terminally to this head structure, PfEMP1 proteins exhibit a varying but semi-ordered composition of additional DBL and CIDR domains of different subtypes (Figure 1c). The PfEMP1 family divides into three main groups based on the receptor specificity of the N-terminal CIDR domain: (i) PfEMP1 proteins with CIDRα1 domains bind endothelial protein C receptor (EPCR), while (ii) PfEMP1 proteins with CIDRα2–6 domains bind CD36 and (iii) the atypical VAR2CSA PfEMP1 proteins bind placental chondroitin sulphate A (CSA) (Salanti et al., 2004). In addition to these, a subset of PfEMP1 proteins have N-terminal CIDRβ/γ/δ domains of unknown function. This functional diversification correlates with the genetic organisation of the var genes. Thus, UPSA var genes encode PfEMP1 proteins with domain sub-variants NTSA-DBLα1-CIDRα1/β/γ/δ, whereas UPSB and UPSC var genes encode PfEMP1 proteins with NTSB-DBLα0-CIDRα2–6. One exception to this rule is the B/A chimeric var genes, which encode NTSB-DBLα2-CIDRα1 domains. The different receptor binding specificities are associated with different clinical outcomes of infection. Pregnancy-associated malaria is linked to parasites expressing VAR2CSA, whereas parasites expressing EPCR-binding PfEMP1 are linked to severe malaria and parasites expressing CD36-binding PfEMP1 are linked to uncomplicated malaria (Turner et al., 2013; Lavstsen et al., 2012; Avril et al., 2012; Claessens et al., 2012; Tonkin-Hill et al., 2018; Wichers et al., 2021). The clinical relevance of PfEMP1 proteins with unknown binding phenotypes of the N-terminal head structure and C-terminal PfEMP1 domains is largely unknown, albeit specific interactions with endothelial receptors and plasma proteins have been described (Tuikue Ndam et al., 2017; Quintana et al., 2019; Stevenson et al., 2015). Each parasite genome carries a similar repertoire of var genes, which in addition to the described variants include a highly conserved var1 variant of either type 3D7 or IT, which in most genomes occurs with a truncated or absent exon 2. Also, most genomes carry the unusually small and highly conserved var3 genes, of unknown function (Figure 1c; Otto et al., 2019).

Figure 1

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Comprehensive characterisation and quantification of var gene expression in field samples have been complicated by biological and technical challenges. The extreme polymorphism of var genes precludes a reference var sequence. Var genes can be lowly expressed or not expressed at all, contain repetitive domains, and can have large duplications (Otto et al., 2019). Consequently, most studies relating var gene expression to severe malaria have relied on primers with restricted coverage of the var family, use of laboratory-adapted parasite strains, or have predicted the downstream sequence from DBLα domains (Sahu et al., 2021; Storm et al., 2019; Shabani et al., 2017; Mkumbaye et al., 2017; Kessler et al., 2017; Bernabeu et al., 2016; Jespersen et al., 2016; Lavstsen et al., 2012). This has resulted in incomplete var gene expression quantification and the inability to elucidate specific or detect atypical var sequences. RNA-sequencing has the potential to overcome these limitations and provide a better link between var expression and PfEMP1 phenotype in in vitro assays, co-expression with other genes or gene families and epigenetics. While approaches for var assembly and quantification based on RNA-sequencing have recently been proposed (Wichers et al., 2021; Stucke et al., 2021; Andrade et al., 2020; Tonkin-Hill et al., 2018; Duffy et al., 2016 ), these still produce inadequate assembly of the biologically important N-terminal domain region, have a relatively high number of misassemblies, and do not provide an adequate solution for handling the conserved var variants (Table 1).

Plasmodium parasites from human blood samples are often adapted to or expanded through in vitro culture to provide sufficient parasites for subsequent investigation of parasite biology and phenotype (Brown and Guler, 2020). This is also the case for several studies assessing the PfEMP1 phenotype of parasites isolated from malaria-infected donors (Pickford et al., 2021; Joste et al., 2020; Storm et al., 2019; Tuikue Ndam et al., 2017; Bruske et al., 2016; Claessens et al., 2012; Lavstsen et al., 2005; Jensen et al., 2004; Kirchgatter and Hernando del, 2002; Dimonte et al., 2016; Hoo et al., 2019). However, in vitro conditions are considerably different to those found in vivo, e.g., in terms of different nutrient availability and lack of a host immune response (Brown and Guler, 2020). Previous studies found inconsistent results in terms of whether var gene expression is impacted by culture and, if so, which var groups were the most affected (Zhang et al., 2011; Peters et al., 2007). Similar challenges apply to the understanding of changes in P. falciparum non-polymorphic core genes in culture, with the focus previously being on long-term laboratory-adapted parasites (Claessens et al., 2017; Mackinnon et al., 2009). Consequently, direct interpretation of a short-term cultured parasite’s transcriptome remains a challenge. It is fundamental to understand var genes in context with the parasite’s core transcriptome. This could provide insights into var gene regulation and phenomena such as the proposed lower level of var gene expression in asymptomatic individuals (Almelli et al., 2014; Andrade et al., 2020).

Here, we present an improved method for assembly, characterisation, and quantification of var gene expression from RNA-sequencing data. This new approach overcomes previous limitations and outperforms current methods, enabling a much greater understanding of the var transcriptome. We demonstrate the power of this new approach by evaluating changes in var gene expression of paired samples from clinical isolates of P. falciparum during their early transition to in vitro culture, across several generations. The use of paired samples, which are genetically identical and hence have the same var gene repertoire, allows validation of assembled transcripts and direct comparisons of expression. We complement this with a comparison of changes which occur in the non-polymorphic core transcriptome over the same transition into culture. We find a background of modest changes in var gene expression with unpredictable patterns of var gene switching, favouring an apparent convergence towards var2csa expression. More extensive changes were observed in the core transcriptome during the first cycle of culture, suggestive of a parasite stress response.

To extend our ability to characterise var gene expression profiles and changes over time in clinical P. falciparum isolates, we set out to improve current assembly methods. Previous methods for assembling var transcripts have focussed on assembling whole transcripts (Tonkin-Hill et al., 2018; Wichers et al., 2021; Guillochon et al., 2022; Andrade et al., 2020). However, due to the diversity within PfEMP1 domains, their associations with disease severity, and the fact different domain types are not inherited together, a method focussing on domain assembly was first developed. In addition, a novel whole transcript approach, using a different de novo assembler, was developed and their performance compared to the method of Wichers et al. (hereafter termed ‘original approach’, Figure 2; Wichers et al., 2021). The new approaches made use of the MalariaGEN P. falciparum dataset, which led to the identification of additional multi-mapping non-core reads (a median of 3955 reads per sample) prior to var transcript assembly (Ahouidi et al., 2021). We incorporated read error correction and improved large scaffold construction with fewer misassemblies (see Materials and methods). We then applied this pipeline to paired ex vivo and short-term in vitro cultured parasites to enhance our understanding of the impact of short-term culturing on the var transcriptome (Figure 3). The var transcriptome was assessed at several complementary levels: first, changes in the dominantly expressed var gene and the homogeneity of the var expression profile in paired samples were investigated; second, changes in var domain expression through culture were assessed; and third, var group and global var gene expression changes were evaluated. All these analyses on var expression were accompanied by analysis of the core transcriptome at the transition to short-term culture.

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Longitudinal analysis of var transcriptome from ex vivo to in vitro samples

To assess the changes in the var transcriptome induced by parasite culturing, we performed a series of analyses, all of which addressed different aspects: (i) changes in individual var gene expression pattern and var expression homogeneity (VEH) (‘per patient analysis’), (ii) changes in the expression of var variants conserved between strains, (iii) changes in the expression of PfEMP1 domains, (iv) changes in expression at the var group level, and (v) at the overall var expression level. We validated our results using the DBLα-tag approach and complemented the var-specific analysis by also examining changes in the core transcriptome.

To investigate whether dominant var gene expression changes through in vitro culture, rank analysis of var transcript expression was performed (Figure 4, Figure 4—figure supplement 1). In most cases a single dominant var transcript was detected. The dominant var gene did not change in most patient samples and the ranking of var gene expression remained similar. However, we observed a change in the dominant var gene being expressed through culture in isolates from 3 of 13 (23%) patients (#6, #17, and #26). Changes in the dominant var gene expression were also observed in the DBLα-tag data for these patients (described below). In parasites from three additional patients, #1, #7, and #14, the top expressed var gene remained the same, however we observed a change in the ranking of other highly expressed var genes in the cultured samples compared to the ex vivo sample. Interestingly, in patient #26 we observed a switch from a dominant group A var gene to a group B and C var gene. This finding was also observed in the DBLα-tag analysis (results below). A similar finding was seen in patient #7. In the ex vivo sample, the second most expressed var transcript was a group A transcript. However, in the cultured samples expression of this transcript was reduced and we observed an increase in the expression of group B and C var transcripts. A similar pattern was observed in the DBLα-tag analysis for patient #7, whereby the expression of a group A transcript was reduced during the first cycle of cultivation. Overall, the data suggest that some patient samples underwent a larger var transcriptional change when cultured compared to the other patient samples and that culturing parasites can lead to an unpredictable var transcriptional change.

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In line with these results, VEH on a per patient basis showed in some patients a clear change, with the ex vivo sample diversity curve distinct from those of in vitro generation 1 and generation 2 samples (patients #1, #2, #4) (Figure 4—figure supplement 2). Similarly, in other patient samples, we observed a clear difference in the curves of ex vivo and generation 1 samples (patients #25 and #26, both from first-time infected severe malaria patients). Some of these samples (#1 and #26, both from first-time infected severe malaria patients) also showed changes in their dominant var gene expression during culture, taken together indicating much greater var transcriptional changes in vitro compared to the other samples.

Expression of conserved var gene variants through short-term in vitro culture

Due to the relatively high level of conservation observed in var1, var2csa, and var3, they do not present with the same limitations as regular var genes. Therefore, changes in their expression through short-term culture was investigated across all samples together. We observed no significant differences in the expression of conserved var gene variants, var1-IT (p_adj = 0.61, paired Wilcoxon test), var1-3D7 (p_adj = 0.93, paired Wilcoxon test), and var2csa (p_adj = 0.54, paired Wilcoxon test) between paired ex vivo and generation 1 parasites, but var2csa was significantly differentially expressed between generation 1 and generation 2 parasites (p_adj = 0.029, paired Wilcoxon test) (Figure 4—figure supplement 3). However, var2csa expression previously appeared to have decreased in some paired samples during the first cycle of cultivation (Figure 4—figure supplement 3).

Differential expression of var domains from ex vivo to in vitro samples

There is overlap in PfEMP1 domain subtypes of different parasite isolates which can be associated with var gene groups and receptor binding phenotypes. This allows performing differential expression analysis on the level of encoded PfEMP1 domain subtypes, as done in previous studies (Tonkin-Hill et al., 2018; Wichers et al., 2021). PCA on var domain expression (Figure 5a) showed some patients’ ex vivo samples clustering away from their respective generation 1 sample (patients #1, #2, #4, #12, #17, #25), again indicating a greater var transcriptional change relative to the other samples during the first cycle of cultivation. However, in the pooled comparison of the generation 1 vs ex vivo of all isolates, a single domain was significantly differentially expressed, CIDRα2.5 associated with B-type PfEMP1 proteins and CD36 binding (Figure 5b). In the generation 2 vs ex vivo comparison, there were no domains significantly differentially expressed, however we observed large log₂FC values in similar domains to those changing most in the ex vivo vs generation 1 comparison (Figure 5c). No differentially expressed domains were found in the generation 1 vs generation 2 comparison. These results suggest that individual changes in var expression are not reflected in the pooled analysis and the per patient approach is more suitable.

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Var group expression analysis

A previous study found group A var genes to have a rapid transcriptional decline in culture compared to group B var genes, however another study found a decrease in both group A and group B var genes in culture (Zhang et al., 2011; Peters et al., 2007). These studies were limited as the var type was determined by analysing the sequence diversity of DBLα domains, and by quantitative PCR (qPCR) methodology which restricts analysis to quantification of known/conserved sequences. Due to these results, the expression of group A var genes vs. group B and C var genes was investigated using a paired analysis on all the DBLα (DBLα1 vs DBLα0 and DBLα2) and NTS (NTSA vs NTSB) sequences assembled from ex vivo samples and across multiple generations in culture. A linear model was created with group A expression as the response variable, the generation and life cycle stage as independent variables, and the patient information included as a random effect. The same was performed using group B and C expression levels.

In both approaches, DBLα and NTS, we found no significant changes in total group A or group B and C var gene expression levels (Figure 6). We observed high levels of group B and C var gene expression compared to group A in all patients, both in the ex vivo samples and in the in vitro samples. In some patients we observed a decrease in group A var genes from ex vivo to generation 1 (patients #1, #2, #5, #6, #9, #12, #17, #26) (Figure 6a), however in all but four patients (patient #1, #2, #5, #6) the levels of group B and C var genes remained consistently high from ex vivo to generation 1 (Figure 6b). Interestingly, patients #6 and #17 also had a change in the dominant var gene expression through culture. Taken together with the preceding results, it appears that observed differences in var transcript expression occurring with transition to short-term culture are not due to modulation of recognised var classes, but due to differences in expression of particular var transcripts.

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Quantification of total var gene expression

We observed a trend of decreasing total var gene expression between generations irrespective of the assembler used in the analysis (Figure 6—figure supplement 1). A similar trend is seen with the LARSFADIG count, which is commonly used as a proxy for the number of different var genes expressed (Otto et al., 2019). A linear model was created (using only paired samples from ex vivo and generation 1) (Supplementary file 4) with proportion of total gene expression dedicated to var gene expression as the response variable, the generation and life cycle stage as independent variables, and the patient information included as a random effect. This model showed no significant differences between generations, suggesting that differences observed in the raw data may be a consequence of small changes in developmental stage distribution in culture.

Validation of var expression profiling by DBLα-tag sequencing

Deep sequencing of RT-PCR-amplified DBLα expressed sequence tags combined with prediction of the associated transcripts and their encoded domains using the Varia tool (Mackenzie et al., 2022) was performed to supplement the RNA-sequencing analysis. The raw Varia output file is given in Supplementary file 5. Overall, we found a high agreement between the detected DBLα-tag sequences and the de novo assembled var transcripts. A median of 96% (IQR: 93–100%) of all unique DBLα-tag sequences detected with >10 reads were found in the RNA-sequencing approach. This is a significant improvement on the original approach (p=0.0077, paired Wilcoxon test), in which a median of 83% (IQR: 79–96%) was found (Wichers et al., 2021). To allow for a fair comparison of the >10 reads threshold used in the DBLα-tag approach, the upper 75th percentile of the RNA-sequencing-assembled DBLα domains were analysed. A median of 77.4% (IQR: 61–88%) of the upper 75th percentile of the assembled DBLα domains were found in the DBLα-tag approach. This is a lower median percentage than the median of 81.3% (IQR: 73–98%) found in the original analysis (p=0.28, paired Wilcoxon test) and suggests the new assembly approach is better at capturing all expressed DBLα domains.

The new whole transcript assembly approach also had high consistency with the domain annotations predicted from Varia. Varia predicts var sequences and domain annotations based on short sequence tags, using a database of previously defined var sequences and annotations (Mackenzie et al., 2022). A median of 85% of the DBLα annotations and 73% of the DBLα-CIDR domain annotations, respectively, identified using the DBLα-tag approach were found in the RNA-sequencing approach. This further confirms the performance of the whole transcript approach and it was not restricted by the pooled approach of the original analysis. We also observed consistent results with the per patient analysis, in terms of changes in the dominant var gene expression (described above) (Supplementary file 5). In line with the RNA-sequencing data, the DBLα-tag approach revealed no significant differences in group A and groups B and C during short-term culture, further highlighting the agreement of both methods (Figure 6—figure supplement 2).

Differential expression analysis of the core transcriptome between ex vivo and in vitro samples

Given the modest changes in var gene expression repertoire upon culture, we wanted to investigate the extent of any accompanying changes in the core parasite transcriptome. PCA was performed on core gene (var, rif, stevor, surf, and rRNA genes removed) expression, adjusted for life cycle stage. We observed distinct clustering of ex vivo, generation 1, and generation 2 samples, with patient identity having much less influence (Figure 7a). There was also a change from the heterogeneity between the ex vivo samples to more uniform clustering of the generation 1 samples (Figure 7a), suggesting that during the first cycle of cultivation the core transcriptomes of different parasite isolates become more alike.

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In total, 920 core genes (19% of the core transcriptome) were found to be differentially expressed after adjusting for life cycle stages using the mixture model approach between ex vivo and generation 1 samples (Supplementary file 6). The majority were upregulated, indicating a substantial transcriptional change during the first cycle of in vitro cultivation (Figure 7b). 74 genes were found to be upregulated in generation 2 when compared to the ex vivo samples, many with log₂FC greater than those in the ex vivo vs generation 1 comparison (Figure 7c). No genes were found to be significantly differentially expressed between generation 1 and generation 2. However, five genes had a log₂FC ≥ 2 and were all upregulated in generation 2 compared to generation 1. Interestingly, the gene with the greatest fold change, encoding ROM3 (PF3D7_0828000), was also found to be significantly downregulated in generation 1 parasites in the ex vivo vs generation 1 analysis. The other four genes were also found to be non-significantly downregulated in generation 1 parasites in the ex vivo vs generation 1 analysis. This suggests that changes in gene expression during the first cycle of cultivation are the greatest compared to the other cycles.

The most significantly upregulated genes (in terms of fold change) in generation 1 contained several small nuclear RNAs, splicesomal RNAs, and non-coding RNAs (ncRNAs). 16 ncRNAs were found upregulated in generation 1, with several RNA-associated proteins having large fold changes (log₂FC >7). Significant gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) pathways for the core genes upregulated in generation 1 included ‘entry into host’, ‘movement into host’, and ‘cytoskeletal organisation’ suggesting the parasites undergo a change in invasion efficiency, which is connected to the cytoskeleton, during their first cycle of in vitro cultivation (Figure 7—figure supplement 1). We observed eight AP2 transcription factors upregulated in generation 1 (PF3D7_0404100/AP2-SP2, PF3D7_0604100/SIP2, PF3D7_0611200/AP2-EXP2, PF3D7_0613800, PF3D7_0802100/AP2-LT, PF3D7_1143100/AP2-O, PF3D7_1239200, PF3D7_1456000/AP2-HC) with no AP2 transcription factors found to be downregulated in generation 1. To confirm the core gene expression changes identified were not due to the increase in parasite age during culture, as indicated by upregulation of many schizont-related genes, core gene differential expression analysis was performed on paired ex vivo and generation 1 samples that contained no schizonts or gametocytes in generation 1. The same genes were identified as significantly differentially expressed with a Spearman’s rank correlation of 0.99 for the log₂FC correlation between this restricted sample approach and those produced using all samples (Figure 7—figure supplement 2).

Multiple lines of evidence point to PfEMP1 as a major determinant of malaria pathogenesis, but previous approaches for characterising var expression profiles in field samples have limited in vivo studies of PfEMP1 function, regulation, and association with clinical symptoms (Tarr et al., 2018; Lee et al., 2018; Warimwe et al., 2013; Rorick et al., 2013; Zhang et al., 2011; Taylor et al., 2002). A more recent approach, based on RNA-sequencing, overcame many of the limitations imposed by the previous primer-based methods (Tonkin-Hill et al., 2018; Wichers et al., 2021). However, depending on the expression level and sequencing depth, var transcripts were found to be fragmented and only a partial reconstruction of the var transcriptome was achieved (Tonkin-Hill et al., 2018; Wichers et al., 2021; Andrade et al., 2020; Guillochon et al., 2022; Yamagishi et al., 2014). The present study developed a novel approach for var gene assembly and quantification that overcomes many of these limitations.

Our new approach used the most geographically diverse reference of var gene sequences to date, which improved the identification of reads derived from var transcripts. This is crucial when analysing patient samples with low parasitaemia where var transcripts are hard to assemble due to their low abundancy (Guillochon et al., 2022). Our approach has wide utility due to stable performance on both laboratory-adapted and clinical samples. Concordance in the different var expression profiling approaches (RNA-sequencing and DBLα-tag) on ex vivo samples increased using the new approach by 13%, when compared to the original approach (96% in the whole transcript approach compared to 83% in Wichers et al., 2021). This suggests the new approach provides a more accurate method for characterising var genes, especially in samples collected directly from patients. Ultimately, this will allow a deeper understanding of relationships between var gene expression and clinical manifestations of malaria.

Having a low number of long contigs is desirable in any de novo assembly. This reflects a continuous assembly, as opposed to a highly fragmented one where polymorphic and repeat regions could not be resolved (Lischer and Shimizu, 2017). An excessive number of contigs cannot be reasonably handled computationally and results from a high level of ambiguity in the assembly (Yang et al., 2012). We observed a greater than 50% reduction in the number of contigs produced in our new approach, which also had a 21% increase in the maximum length of the assembled var transcripts, when compared to the original approach. It doubled the assembly continuity and assembled an average of 13% more of the var transcripts. This was particularly apparent in the N-terminal region, which has often been poorly characterised by existing approaches. The original approach failed to assemble the N-terminal region in 58% of the samples, compared to just 4% in the new approach with assembly consistently achieved with an accuracy >90%. This is important because the N-terminal region is known to contribute to the adhesion phenotypes of most PfEMP1 proteins.

The new approach allows for var transcript reconstruction across a range of expression levels, which is required when characterising var transcripts from multi-clonal infections. Assembly completeness of the lowly expressed var genes increased fivefold using the new approach. Biases towards certain parasite stages have been observed in non-severe and severe malaria cases, so it is valuable to assemble the var transcripts from different life cycle stages (Tonkin-Hill et al., 2018). Our new approach is not limited by parasite stage. It was able to assemble the whole var transcript, in a single contig, at later stages in the P. falciparum 3D7 intra-erythrocytic cycle, something previously unachievable. The new approach allows for a more accurate and complete picture of the var transcriptome. It provides new perspectives for relating var expression to regulation, co-expression, epigenetics, and malaria pathogenesis. It can be applied, for example, in the analysis of patient samples with different clinical outcomes and longitudinal tracking of infections in vivo. It represents a crucial improvement for quantifying the var transcriptome. In this work, the improved approach for var gene assembly and quantification was used to characterise var gene expression during transition from in vivo to short-term culture.

This study had substantial power through the use of paired samples. However, many var gene expression studies do not have longitudinal sampling. Future work should focus on identifying the best approach for analysing the var transcripts in cross-sectional samples. Higher-level var classification systems, such as the PfEMP1 predicted binding phenotype or domain cassettes, could be applied to test for over-representation of different var gene features in different groups of interest, because the assumption of overlapping var repertoires at these levels of classification would be more realistic. This was briefly explored in our analysis through var domain differential expression analysis, which found minimal changes in var domain expression through short-term culture, supporting the per patient analysis results. This could be further improved by advancing the classifications of domain subtypes. This has recently been studied using MEME to identify short nucleotide motifs that are representative of domain subtypes (Otto et al., 2019). Other research could investigate clustering var transcripts based on sequence identity and testing for clusters associated with specific malaria disease groups.

Studies have been performed investigating differences between long-term laboratory-adapted clones and clinical isolates, with hundreds of genes found to be differentially expressed (Hoo et al., 2019; Tarr et al., 2018; Mackinnon et al., 2009). Surprisingly, studies investigating the impact of short-term culture on parasites are extremely limited, despite it being commonly undertaken for making inferences about the in vivo transcriptome (Vignali et al., 2011). Using the new var assembly approach, we found that var gene expression remains relatively stable during transition to culture. However, the conserved var2csa had increased expression from generation 1 to generation 2. It has previously been suggested that long-term cultured parasites converge to expressing var2csa, but our findings suggest this begins within two cycles of cultivation (Zhang et al., 2022; Mok et al., 2008). Switching to var2csa has been shown to be favourable and is suggested to be the default var gene upon perturbation to var-specific heterochromatin (Ukaegbu et al., 2015). These studies also suggested that var2csa has a unique role in var gene switching and our results are consistent with the role of var2csa as the dominant ‘sink node’ (Zhang et al., 2022; Ukaegbu et al., 2015; Ukaegbu et al., 2014; Mok et al., 2008). A previous study suggested that in vitro cultivation of controlled human malaria infection samples resulted in dramatic changes in var gene expression (Lavstsen et al., 2005; Peters et al., 2007). Almost a quarter of samples in our analysis showed more pronounced and unpredictable changes. In these individuals, the dominant var gene being expressed changed within one cycle of cultivation. This implies that short-term culture can result in unpredictable var gene expression as observed previously using a semi-quantitative RT-PCR approach (Bachmann et al., 2011) and that one would need to confirm in vivo expression matches in vitro expression. This can be achieved using the assembly approach described here.

We observed no generalised pattern of up- or downregulation of specific var groups following transition to culture. This implies that there is probably not a selection event occurring during culture but may represent a loss of selection that is present in vivo. A global downregulation of certain var groups might only occur as a selective process over many cycles in extended culture. Determining changes in var group expression levels are difficult using degenerate qPCR primers bias and previous studies have found conflicting results in terms of changes of expression of var groups through cultivation. Zhang et al., 2011, found a rapid transcriptional decline of group A and group B var genes, however Peters et al., 2007, found group A var genes to have a high rate of downregulation, when compared to group B var genes. These studies differed in the stage distribution of the parasites and were limited in measuring enough variants through their use of primers. Our new approach allowed for the identification of more sequences, with 26.6% of assembled DBLα domains not found via the DBLα-tag approach. This better coverage of the expressed var diversity was not possible in these previous studies and may explain discrepancies observed.

Generally, there was a high consensus between all levels of var gene analysis and changes observed during short-term in vitro cultivation. However, the impact of short-term culture was the most apparent at the var transcript level and became less clear at the var domain, var type and global var gene expression level. This highlights the need for accurate characterisation of full-length var transcripts and analysis of the var transcriptome at different levels, both of which can be achieved with the new approach developed here.

We saw striking changes in the core gene transcriptomes between ex vivo and generation 1 parasites with 19% of the core genome being differentially expressed. A previous study showed that expression of 18% of core genes were significantly altered after ~50 cycles through culture (Mackinnon et al., 2009), but our data suggest that much of this change occurs early in the transition to culture. We observed that genes with functions unrelated to ring-stage parasites were among those most significantly expressed in the generation 1 vs ex vivo analysis, suggesting the culture conditions may temporarily dysregulate stage-specific expression patterns or result in the parasites undergoing a rapid adaptation response (Andreadaki et al., 2020; Beeson et al., 2016). Several AP2 transcription factors (AP2-SP2, AP2-EXP2, AP2-LT, AP2-O, and AP2-HC) were upregulated in generation 1. AP2-HC has been shown to be expressed in asexual parasites (Carrington et al., 2021). AP2-O is thought to be specific for the ookinete stage and AP2-SP2 plays a key role in sporozoite stage-specific gene expression (Kaneko et al., 2015; Yuda et al., 2010). Our findings are consistent with another study investigating the impact of long-term culture (Mackinnon et al., 2009) which also found genes like merozoite surface proteins differentially expressed, however they were downregulated in long-term cultured parasites, whereas we found them upregulated in generation 1. This suggests that short-term cultured parasites might be transcriptionally different from long-term cultured parasites, especially in their invasion capabilities, something previously unobserved. Several genes involved in the stress response of parasites were upregulated in generation 1, e.g., DnaJ proteins, serine proteases, and ATP-dependent CLP proteases (Oakley et al., 2007). The similarity of the core transcriptomes of the in vitro samples compared to the heterogeneity seen in the ex vivo samples could be explained by a stress response upon entry to culture. Studies investigating whether the dysregulation of stage-specific expression and the expression of stress-associated genes persist in long-term culture are required to understand whether they are important for growth in culture. Critically, the marked differences presented here suggest that the impact of short-term culture can override differences observed in both the in vivo core and var transcriptomes of different disease manifestations.

In summary, we present an enhanced approach for var transcript assembly which allows for var gene expression to be studied in connection to P. falciparum’s core transcriptome through RNA-sequencing. This will be useful for expanding our understanding of var gene regulation and function in in vivo samples. As an example of the capabilities of the new approach, the method was used to quantify differences in gene expression upon short-term culture adaptation. This revealed that inferences from clinical isolates of P. falciparum put into short-term culture must be made with a degree of caution. Whilst var gene expression is often maintained, unpredictable switching does occur, necessitating that the similarity of in vivo and in vitro expression should be confirmed. The more extreme changes in the core transcriptome could have much bigger implications for understanding other aspects of parasite biology such as growth rates and drug susceptibility and raise a need for additional caution. Further work is needed to examine var and core transcriptome changes during longer-term culture on a larger sample size. Understanding the ground truth of the var expression repertoire of Plasmodium field isolates still presents a unique challenge and this work expands the database of var sequences globally. The increase in long-read sequencing and the growing size of var gene databases containing isolates from across the globe will help overcome this issue in future studies.

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials. Additionally, the raw sequencing data are openly available in National Center for Biotechnology Information (NCBI) at BioProject ID: PRJNA679547 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA679547). The already published laboratory dataset (3D7 time course RNA-sequencing data) was deposited in the European nucleotide archive (ENA): PRJEB31535 (https://www.ebi.ac.uk/ena/browser/view/PRJEB31535; Wichers et al., 2019) and is also available on plasmoDB. All scripts are available in the GitHub repository (https://github.com/ClareAndradiBrown/varAssembly copy archived at Brown, 2023).

1. 1. Wichers JS
   2. Tonkin-Hill G
   3. Thye T
   4. Krumkamp R
   5. Kreuels B
   6. Strauss J
   7. von Thien H
   8. Scholz JA
   9. Smedegaard Hansson H
   10. Weisel Jensen R
   11. Turner L
   12. Lorenz FR
   13. Schöllhorn A
   14. Bruchhaus I
   15. Tannich E
   16. Fendel R
   17. Otto TD
   18. Lavstsen T
   19. Gilberger TW
   20. Duffy MF
   21. Bachmann A

   (2020) NCBI BioProject

   ID PRJNA679547. Common virulence gene expression in adult first-time infected malaria patients and severe cases.

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA679547

1. 1. Wichers JS
   2. Scholz JAM
   3. Strauss J
   4. Witt S
   5. Lill A
   6. Ehnold LI
   7. Neupert N
   8. Liffner B
   9. Lühken R
   10. Petter M
   11. Lorenzen S
   12. Wilson DW
   13. Löw C
   14. Lavazec C
   15. Bruchhaus I
   16. Tannich E
   17. Gilberger TW
   18. Bachmann A

   (2019) European nucleotide archive (ENA)

   ID PRJEB31535. RNA-seq of the malaria parasite Plasmodium falciparum 3D7 during its intraerythrocytic development cycle.

   https://www.ebi.ac.uk/ena/browser/view/PRJEB31535

1. 1. Ahouidi A
   2. Ali M
   3. Almagro-Garcia J
   4. Amambua-Ngwa A
   5. Amaratunga C
   6. Amato R
   7. Amenga-Etego L
   8. Andagalu B
   9. Anderson TJC
   10. Andrianaranjaka V
   11. Apinjoh T
   12. Ariani C
   13. Ashley EA
   14. Auburn S
   15. Awandare GA
   16. Ba H
   17. Baraka V
   18. Barry AE
   19. Bejon P
   20. Bertin GI
   21. Boni MF
   22. Borrmann S
   23. Bousema T
   24. Branch O
   25. Bull PC
   26. Busby GBJ
   27. Chookajorn T
   28. Chotivanich K
   29. Claessens A
   30. Conway D
   31. Craig A
   32. D’Alessandro U
   33. Dama S
   34. Day NP
   35. Denis B
   36. Diakite M
   37. Djimdé A
   38. Dolecek C
   39. Dondorp AM
   40. Drakeley C
   41. Drury E
   42. Duffy P
   43. Echeverry DF
   44. Egwang TG
   45. Erko B
   46. Fairhurst RM
   47. Faiz A
   48. Fanello CA
   49. Fukuda MM
   50. Gamboa D
   51. Ghansah A
   52. Golassa L
   53. Goncalves S
   54. Hamilton WL
   55. Harrison GLA
   56. Hart L
   57. Henrichs C
   58. Hien TT
   59. Hill CA
   60. Hodgson A
   61. Hubbart C
   62. Imwong M
   63. Ishengoma DS
   64. Jackson SA
   65. Jacob CG
   66. Jeffery B
   67. Jeffreys AE
   68. Johnson KJ
   69. Jyothi D
   70. Kamaliddin C
   71. Kamau E
   72. Kekre M
   73. Kluczynski K
   74. Kochakarn T
   75. Konaté A
   76. Kwiatkowski DP
   77. Kyaw MP
   78. Lim P
   79. Lon C
   80. Loua KM
   81. Maïga-Ascofaré O
   82. Malangone C
   83. Manske M
   84. Marfurt J
   85. Marsh K
   86. Mayxay M
   87. Miles A
   88. Miotto O
   89. Mobegi V
   90. Mokuolu OA
   91. Montgomery J
   92. Mueller I
   93. Newton PN
   94. Nguyen T
   95. Nguyen T-N
   96. Noedl H
   97. Nosten F
   98. Noviyanti R
   99. Nzila A
   100. Ochola-Oyier LI
   101. Ocholla H
   102. Oduro A
   103. Omedo I
   104. Onyamboko MA
   105. Ouedraogo J-B
   106. Oyebola K
   107. Pearson RD
   108. Peshu N
   109. Phyo AP
   110. Plowe CV
   111. Price RN
   112. Pukrittayakamee S
   113. Randrianarivelojosia M
   114. Rayner JC
   115. Ringwald P
   116. Rockett KA
   117. Rowlands K
   118. Ruiz L
   119. Saunders D
   120. Shayo A
   121. Siba P
   122. Simpson VJ
   123. Stalker J
   124. Su X-Z
   125. Sutherland C
   126. Takala-Harrison S
   127. Tavul L
   128. Thathy V
   129. Tshefu A
   130. Verra F
   131. Vinetz J
   132. Wellems TE
   133. Wendler J
   134. White NJ
   135. Wright I
   136. Yavo W
   137. Ye H
   138. MalariaGEN

   (2021) An open dataset of Plasmodium falciparum genome variation in 7,000 worldwide samples

   Wellcome Open Research 6:42.

   https://doi.org/10.12688/wellcomeopenres.16168.2

   • PubMed
   • Google Scholar
2. 1. Almelli T
   2. Nuel G
   3. Bischoff E
   4. Aubouy A
   5. Elati M
   6. Wang CW
   7. Dillies M-A
   8. Coppée J-Y
   9. Ayissi GN
   10. Basco LK
   11. Rogier C
   12. Ndam NT
   13. Deloron P
   14. Tahar R

   (2014) Differences in gene transcriptomic pattern of Plasmodium falciparum in children with cerebral malaria and asymptomatic carriers

   PLOS ONE 9:e114401.

   https://doi.org/10.1371/journal.pone.0114401

   • PubMed
   • Google Scholar
3. 1. Anders S
   2. Pyl PT
   3. Huber W

   (2015) HTSeq--a python framework to work with high-throughput sequencing data

   Bioinformatics 31:166–169.

   https://doi.org/10.1093/bioinformatics/btu638

   • PubMed
   • Google Scholar
4. 1. Andrade CM
   2. Fleckenstein H
   3. Thomson-Luque R
   4. Doumbo S
   5. Lima NF
   6. Anderson C
   7. Hibbert J
   8. Hopp CS
   9. Tran TM
   10. Li S
   11. Niangaly M
   12. Cisse H
   13. Doumtabe D
   14. Skinner J
   15. Sturdevant D
   16. Ricklefs S
   17. Virtaneva K
   18. Asghar M
   19. Homann MV
   20. Turner L
   21. Martins J
   22. Allman EL
   23. N’Dri M-E
   24. Winkler V
   25. Llinás M
   26. Lavazec C
   27. Martens C
   28. Färnert A
   29. Kayentao K
   30. Ongoiba A
   31. Lavstsen T
   32. Osório NS
   33. Otto TD
   34. Recker M
   35. Traore B
   36. Crompton PD
   37. Portugal S

   (2020) Increased circulation time of Plasmodium falciparum underlies persistent asymptomatic infection in the dry season

   Nature Medicine 26:1929–1940.

   https://doi.org/10.1038/s41591-020-1084-0

   • PubMed
   • Google Scholar
5. 1. Andreadaki M
   2. Pace T
   3. Grasso F
   4. Siden-Kiamos I
   5. Mochi S
   6. Picci L
   7. Bertuccini L
   8. Ponzi M
   9. Currà C

   (2020) Plasmodium berghei Gamete Egress Protein is required for fertility of both genders

   MicrobiologyOpen 9:e1038.

   https://doi.org/10.1002/mbo3.1038

   • PubMed
   • Google Scholar
6. 1. Avril M
   2. Tripathi AK
   3. Brazier AJ
   4. Andisi C
   5. Janes JH
   6. Soma VL
   7. Sullivan DJ
   8. Bull PC
   9. Stins MF
   10. Smith JD

   (2012) A restricted subset of var genes mediates adherence of Plasmodium falciparum-infected erythrocytes to brain endothelial cells

   PNAS 109:E1782–E1790.

   https://doi.org/10.1073/pnas.1120534109

   • PubMed
   • Google Scholar
7. 1. Bachmann A
   2. Predehl S
   3. May J
   4. Harder S
   5. Burchard GD
   6. Gilberger TW
   7. Tannich E
   8. Bruchhaus I

   (2011) Highly co-ordinated var gene expression and switching in clinical Plasmodium falciparum isolates from non-immune malaria patients

   Cellular Microbiology 13:1397–1409.

   https://doi.org/10.1111/j.1462-5822.2011.01629.x

   • Google Scholar
8. 1. Baruch DI
   2. Pasloske BL
   3. Singh HB
   4. Bi X
   5. Ma XC
   6. Feldman M
   7. Taraschi TF
   8. Howard RJ

   (1995) Cloning the P. falciparum gene encoding PfEMP1, a malarial variant antigen and adherence receptor on the surface of parasitized human erythrocytes

   Cell 82:77–87.

   https://doi.org/10.1016/0092-8674(95)90054-3

   • PubMed
   • Google Scholar
9. 1. Beeson JG
   2. Drew DR
   3. Boyle MJ
   4. Feng G
   5. Fowkes FJI
   6. Richards JS

   (2016) Merozoite surface proteins in red blood cell invasion, immunity and vaccines against malaria

   FEMS Microbiology Reviews 40:343–372.

   https://doi.org/10.1093/femsre/fuw001

   • PubMed
   • Google Scholar
10. 1. Bernabeu M
    2. Danziger SA
    3. Avril M
    4. Vaz M
    5. Babar PH
    6. Brazier AJ
    7. Herricks T
    8. Maki JN
    9. Pereira L
    10. Mascarenhas A
    11. Gomes E
    12. Chery L
    13. Aitchison JD
    14. Rathod PK
    15. Smith JD

    (2016) Severe adult malaria is associated with specific PfEMP1 adhesion types and high parasite biomass

    PNAS 113:E3270–E3279.

    https://doi.org/10.1073/pnas.1524294113

    • PubMed
    • Google Scholar
11. 1. Boetzer M
    2. Henkel CV
    3. Jansen HJ
    4. Butler D
    5. Pirovano W

    (2011) Scaffolding pre-assembled contigs using SSPACE

    Bioinformatics 27:578–579.

    https://doi.org/10.1093/bioinformatics/btq683

    • PubMed
    • Google Scholar
12. 1. Bozdech Z
    2. Llinás M
    3. Pulliam BL
    4. Wong ED
    5. Zhu J
    6. DeRisi JL

    (2003) The transcriptome of the intraerythrocytic developmental cycle of Plasmodium falciparum

    PLOS Biology 1:E5.

    https://doi.org/10.1371/journal.pbio.0000005

    • PubMed
    • Google Scholar
13. 1. Brown AC
    2. Guler JL

    (2020) From circulation to cultivation: plasmodium in vivo versus in vitro

    Trends in Parasitology 36:914–926.

    https://doi.org/10.1016/j.pt.2020.08.008

    • PubMed
    • Google Scholar
14. Software

    1. Brown CA

    (2023) Varassembly, version swh:1:rev:1f7aeeca5d97427dbd54cb6cfb54c9e6322a8fea

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:9cfd6ffe0a27f5a09e256cb56dbcffb60d1930e4;origin=https://github.com/ClareAndradiBrown/varAssembly;visit=swh:1:snp:9a6f70069bf9e9ed6a54b05372518a60a724cb7b;anchor=swh:1:rev:1f7aeeca5d97427dbd54cb6cfb54c9e6322a8fea
15. 1. Bruske EI
    2. Dimonte S
    3. Enderes C
    4. Tschan S
    5. Flötenmeyer M
    6. Koch I
    7. Berger J
    8. Kremsner P
    9. Frank M

    (2016) In vitro variant surface antigen expression in Plasmodium falciparum parasites from a semi-immune individual is not correlated with var gene transcription

    PLOS ONE 11:e0166135.

    https://doi.org/10.1371/journal.pone.0166135

    • PubMed
    • Google Scholar
16. 1. Bushmanova E
    2. Antipov D
    3. Lapidus A
    4. Prjibelski AD

    (2019) rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data

    GigaScience 8:giz100.

    https://doi.org/10.1093/gigascience/giz100

    • PubMed
    • Google Scholar
17. 1. Carrington E
    2. Cooijmans RHM
    3. Keller D
    4. Toenhake CG
    5. Bártfai R
    6. Voss TS

    (2021) The ApiAP2 factor PfAP2-HC is an integral component of heterochromatin in the malaria parasite Plasmodium falciparum

    iScience 24:102444.

    https://doi.org/10.1016/j.isci.2021.102444

    • PubMed
    • Google Scholar
18. 1. Claessens A
    2. Adams Y
    3. Ghumra A
    4. Lindergard G
    5. Buchan CC
    6. Andisi C
    7. Bull PC
    8. Mok S
    9. Gupta AP
    10. Wang CW
    11. Turner L
    12. Arman M
    13. Raza A
    14. Bozdech Z
    15. Rowe JA

    (2012) A subset of group A-like var genes encodes the malaria parasite ligands for binding to human brain endothelial cells

    PNAS 109:E1772–E1781.

    https://doi.org/10.1073/pnas.1120461109

    • PubMed
    • Google Scholar
19. 1. Claessens A
    2. Affara M
    3. Assefa SA
    4. Kwiatkowski DP
    5. Conway DJ

    (2017) Culture adaptation of malaria parasites selects for convergent loss-of-function mutants

    Scientific Reports 7:41303.

    https://doi.org/10.1038/srep41303

    • PubMed
    • Google Scholar
20. 1. Danecek P
    2. Bonfield JK
    3. Liddle J
    4. Marshall J
    5. Ohan V
    6. Pollard MO
    7. Whitwham A
    8. Keane T
    9. McCarthy SA
    10. Davies RM
    11. Li H

    (2021) Twelve years of SAMtools and BCFtools

    GigaScience 10:giab008.

    https://doi.org/10.1093/gigascience/giab008

    • PubMed
    • Google Scholar
21. 1. Dara A
    2. Drábek EF
    3. Travassos MA
    4. Moser KA
    5. Delcher AL
    6. Su Q
    7. Hostelley T
    8. Coulibaly D
    9. Daou M
    10. Dembele A
    11. Diarra I
    12. Kone AK
    13. Kouriba B
    14. Laurens MB
    15. Niangaly A
    16. Traore K
    17. Tolo Y
    18. Fraser CM
    19. Thera MA
    20. Djimde AA
    21. Doumbo OK
    22. Plowe CV
    23. Silva JC

    (2017) New var reconstruction algorithm exposes high var sequence diversity in a single geographic location in Mali

    Genome Medicine 9:30.

    https://doi.org/10.1186/s13073-017-0422-4

    • PubMed
    • Google Scholar
22. 1. Dimonte S
    2. Bruske EI
    3. Hass J
    4. Supan C
    5. Salazar CL
    6. Held J
    7. Tschan S
    8. Esen M
    9. Flötenmeyer M
    10. Koch I
    11. Berger J
    12. Bachmann A
    13. Sim BKL
    14. Hoffman SL
    15. Kremsner PG
    16. Mordmüller B
    17. Frank M

    (2016) Sporozoite route of infection influences in vitro var gene transcription of Plasmodium falciparum parasites from controlled human infections

    The Journal of Infectious Diseases 214:884–894.

    https://doi.org/10.1093/infdis/jiw225

    • PubMed
    • Google Scholar
23. 1. Dimonte S
    2. Bruske EI
    3. Enderes C
    4. Otto TD
    5. Turner L
    6. Kremsner P
    7. Frank M

    (2020) Identification of a conserved var gene in different Plasmodium falciparum strains

    Malaria Journal 19:194.

    https://doi.org/10.1186/s12936-020-03257-x

    • PubMed
    • Google Scholar
24. 1. Dong M
    2. Thennavan A
    3. Urrutia E
    4. Li Y
    5. Perou CM
    6. Zou F
    7. Jiang Y

    (2021) SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references

    Briefings in Bioinformatics 22:416–427.

    https://doi.org/10.1093/bib/bbz166

    • PubMed
    • Google Scholar
25. 1. Duffy MF
    2. Noviyanti R
    3. Tsuboi T
    4. Feng Z-P
    5. Trianty L
    6. Sebayang BF
    7. Takashima E
    8. Sumardy F
    9. Lampah DA
    10. Turner L
    11. Lavstsen T
    12. Fowkes FJI
    13. Siba P
    14. Rogerson SJ
    15. Theander TG
    16. Marfurt J
    17. Price RN
    18. Anstey NM
    19. Brown GV
    20. Papenfuss AT

    (2016) Differences in PfEMP1s recognized by antibodies from patients with uncomplicated or severe malaria

    Malaria Journal 15:258.

    https://doi.org/10.1186/s12936-016-1296-4

    • PubMed
    • Google Scholar
26. 1. Finn RD
    2. Clements J
    3. Eddy SR

    (2011) HMMER web server: interactive sequence similarity searching

    Nucleic Acids Research 39:W29–W37.

    https://doi.org/10.1093/nar/gkr367

    • PubMed
    • Google Scholar
27. 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
28. 1. Ge SX
    2. Jung D
    3. Yao R

    (2020) ShinyGO: a graphical gene-set enrichment tool for animals and plants

    Bioinformatics 36:2628–2629.

    https://doi.org/10.1093/bioinformatics/btz931

    • PubMed
    • Google Scholar
29. 1. Guillochon E
    2. Fraering J
    3. Joste V
    4. Kamaliddin C
    5. Vianou B
    6. Houzé L
    7. Baudrin LG
    8. Faucher JF
    9. Aubouy A
    10. Houzé S
    11. Cot M
    12. Argy N
    13. Taboureau O
    14. Bertin GI
    15. NeuroCM group

    (2022) Transcriptome analysis of Plasmodium falciparum isolates from benin reveals specific gene expression associated with cerebral malaria

    The Journal of Infectious Diseases 225:2187–2196.

    https://doi.org/10.1093/infdis/jiac086

    • PubMed
    • Google Scholar
30. Conference

    1. Henschel R
    2. Lieber M
    3. Wu LS
    4. Nista PM
    5. Haas BJ
    6. LeDuc RD

    (2012) Trinity RNA-Seq assembler performance optimization

    XSEDE12. pp. 1–8.

    https://doi.org/10.1145/2335755.2335842

    • Google Scholar
31. 1. Hoo R
    2. Bruske E
    3. Dimonte S
    4. Zhu L
    5. Mordmüller B
    6. Sim BKL
    7. Kremsner PG
    8. Hoffman SL
    9. Bozdech Z
    10. Frank M
    11. Preiser PR

    (2019) Transcriptome profiling reveals functional variation in Plasmodium falciparum parasites from controlled human malaria infection studies

    EBioMedicine 48:442–452.

    https://doi.org/10.1016/j.ebiom.2019.09.001

    • PubMed
    • Google Scholar
32. 1. Howick VM
    2. Russell AJC
    3. Andrews T
    4. Heaton H
    5. Reid AJ
    6. Natarajan K
    7. Butungi H
    8. Metcalf T
    9. Verzier LH
    10. Rayner JC
    11. Berriman M
    12. Herren JK
    13. Billker O
    14. Hemberg M
    15. Talman AM
    16. Lawniczak MKN

    (2019) The malaria cell atlas: single parasite transcriptomes across the complete plasmodium life cycle

    Science 365:eaaw2619.

    https://doi.org/10.1126/science.aaw2619

    • PubMed
    • Google Scholar
33. 1. Huang X
    2. Madan A

    (1999) CAP3: A DNA sequence assembly program

    Genome Research 9:868–877.

    https://doi.org/10.1101/gr.9.9.868

    • PubMed
    • Google Scholar
34. 1. Jensen ATR
    2. Magistrado P
    3. Sharp S
    4. Joergensen L
    5. Lavstsen T
    6. Chiucchiuini A
    7. Salanti A
    8. Vestergaard LS
    9. Lusingu JP
    10. Hermsen R
    11. Sauerwein R
    12. Christensen J
    13. Nielsen MA
    14. Hviid L
    15. Sutherland C
    16. Staalsoe T
    17. Theander TG

    (2004) Plasmodium falciparum associated with severe childhood malaria preferentially expresses PfEMP1 encoded by group A var genes

    The Journal of Experimental Medicine 199:1179–1190.

    https://doi.org/10.1084/jem.20040274

    • Google Scholar
35. 1. Jespersen JS
    2. Wang CW
    3. Mkumbaye SI
    4. Minja DT
    5. Petersen B
    6. Turner L
    7. Petersen JE
    8. Lusingu JP
    9. Theander TG
    10. Lavstsen T

    (2016) Plasmodium falciparum var genes expressed in children with severe malaria encode CIDRα1 domains

    EMBO Molecular Medicine 8:839–850.

    https://doi.org/10.15252/emmm.201606188

    • Google Scholar
36. 1. Joste V
    2. Guillochon E
    3. Fraering J
    4. Vianou B
    5. Watier L
    6. Jafari-Guemouri S
    7. Cot M
    8. Houzé S
    9. Aubouy A
    10. Faucher JF
    11. Argy N
    12. Bertin GI

    (2020) PfEMP1 A-type ICAM-1-binding domains are not associated with cerebral malaria in beninese children

    mBio 11:e02103-20.

    https://doi.org/10.1128/mBio.02103-20

    • PubMed
    • Google Scholar
37. 1. Kaneko I
    2. Iwanaga S
    3. Kato T
    4. Kobayashi I
    5. Yuda M

    (2015) Genome-wide identification of the target gnes of AP2-O, a pasmodium AP2-family transcription factor

    PLOS Pathogens 11:e1004905.

    https://doi.org/10.1371/journal.ppat.1004905

    • PubMed
    • Google Scholar
38. 1. Kessler A
    2. Dankwa S
    3. Bernabeu M
    4. Harawa V
    5. Danziger SA
    6. Duffy F
    7. Kampondeni SD
    8. Potchen MJ
    9. Dambrauskas N
    10. Vigdorovich V
    11. Oliver BG
    12. Hochman SE
    13. Mowrey WB
    14. MacCormick IJC
    15. Mandala WL
    16. Rogerson SJ
    17. Sather DN
    18. Aitchison JD
    19. Taylor TE
    20. Seydel KB
    21. Smith JD
    22. Kim K

    (2017) LInking epcr-binding pfemp1 to brain swelling in pediatric cerebral malaria

    Cell Host & Microbe 22:601–614.

    https://doi.org/10.1016/j.chom.2017.09.009

    • PubMed
    • Google Scholar
39. 1. Kirchgatter K
    2. Hernando del AP

    (2002)

    Association of severe noncerebral Plasmodium falciparum malaria in Brazil with expressed PfEMP1 DBL1 alpha sequences lacking cysteine residues

    Molecular Medicine 8:16–23.

    • PubMed
    • Google Scholar
40. 1. Kraemer SM
    2. Smith JD

    (2003) Evidence for the importance of genetic structuring to the structural and functional specialization of the Plasmodium falciparum var gene family

    Molecular Microbiology 50:1527–1538.

    https://doi.org/10.1046/j.1365-2958.2003.03814.x

    • PubMed
    • Google Scholar
41. 1. Kyes SA
    2. Kraemer SM
    3. Smith JD

    (2007) Antigenic variation in Plasmodium falciparum: gene organization and regulation of the var multigene family

    Eukaryotic Cell 6:1511–1520.

    https://doi.org/10.1128/EC.00173-07

    • PubMed
    • Google Scholar
42. 1. Lavstsen T
    2. Salanti A
    3. Jensen ATR
    4. Arnot DE
    5. Theander TG

    (2003) Sub-grouping of Plasmodium falciparum 3D7 var genes based on sequence analysis of coding and non-coding regions

    Malaria Journal 2:27.

    https://doi.org/10.1186/1475-2875-2-27

    • PubMed
    • Google Scholar
43. 1. Lavstsen T
    2. Magistrado P
    3. Hermsen CC
    4. Salanti A
    5. Jensen ATR
    6. Sauerwein R
    7. Hviid L
    8. Theander TG
    9. Staalsoe T

    (2005) Expression of Plasmodium falciparum erythrocyte membrane protein 1 in experimentally infected humans

    Malaria Journal 4:21.

    https://doi.org/10.1186/1475-2875-4-21

    • PubMed
    • Google Scholar
44. 1. Lavstsen T
    2. Turner L
    3. Saguti F
    4. Magistrado P
    5. Rask TS
    6. Jespersen JS
    7. Wang CW
    8. Berger SS
    9. Baraka V
    10. Marquard AM
    11. Seguin-Orlando A
    12. Willerslev E
    13. Gilbert MTP
    14. Lusingu J
    15. Theander TG

    (2012) Plasmodium falciparum erythrocyte membrane protein 1 domain cassettes 8 and 13 are associated with severe malaria in children

    PNAS 109:E1791–E1800.

    https://doi.org/10.1073/pnas.1120455109

    • PubMed
    • Google Scholar
45. 1. Lee HJ
    2. Georgiadou A
    3. Walther M
    4. Nwakanma D
    5. Stewart LB
    6. Levin M
    7. Otto TD
    8. Conway DJ
    9. Coin LJ
    10. Cunnington AJ

    (2018) Integrated pathogen load and dual transcriptome analysis of systemic host-pathogen interactions in severe malaria

    Science Translational Medicine 10:eaar3619.

    https://doi.org/10.1126/scitranslmed.aar3619

    • PubMed
    • Google Scholar
46. 1. Leech JH
    2. Barnwell JW
    3. Miller LH
    4. Howard RJ

    (1984) Identification of a strain-specific malarial antigen exposed on the surface of Plasmodium falciparum-infected erythrocytes

    The Journal of Experimental Medicine 159:1567–1575.

    https://doi.org/10.1084/jem.159.6.1567

    • PubMed
    • Google Scholar
47. 1. Lewis TE
    2. Sillitoe I
    3. Lees JG

    (2019) cath-resolve-hits: a new tool that resolves domain matches suspiciously quickly

    Bioinformatics 35:1766–1767.

    https://doi.org/10.1093/bioinformatics/bty863

    • PubMed
    • Google Scholar
48. Preprint

    1. Li H

    (2013) Aligning Sequence Reads, Clone Sequences and Assembly Contigs with BWA-MEM

    arXiv.

    https://arxiv.org/abs/1303.3997

    • Google Scholar
49. 1. Liao Y
    2. Smyth GK
    3. Shi W

    (2013) The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote

    Nucleic Acids Research 41:e108.

    https://doi.org/10.1093/nar/gkt214

    • PubMed
    • Google Scholar
50. 1. Liao Y
    2. Smyth GK
    3. Shi W

    (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features

    Bioinformatics 30:923–930.

    https://doi.org/10.1093/bioinformatics/btt656

    • PubMed
    • Google Scholar
51. 1. Lischer HEL
    2. Shimizu KK

    (2017) Reference-guided de novo assembly approach improves genome reconstruction for related species

    BMC Bioinformatics 18:474.

    https://doi.org/10.1186/s12859-017-1911-6

    • PubMed
    • Google Scholar
52. 1. Love MI
    2. Huber W
    3. Anders S

    (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

    Genome Biology 15:550.

    https://doi.org/10.1186/s13059-014-0550-8

    • PubMed
    • Google Scholar
53. 1. Mackenzie G
    2. Jensen RW
    3. Lavstsen T
    4. Otto TD

    (2022) Varia: a tool for prediction, analysis and visualisation of variable genes

    BMC Bioinformatics 23:52.

    https://doi.org/10.1186/s12859-022-04573-6

    • PubMed
    • Google Scholar
54. 1. Mackinnon MJ
    2. Li J
    3. Mok S
    4. Kortok MM
    5. Marsh K
    6. Preiser PR
    7. Bozdech Z

    (2009) Comparative transcriptional and genomic analysis of Plasmodium falciparum field isolates

    PLOS Pathogens 5:e1000644.

    https://doi.org/10.1371/journal.ppat.1000644

    • PubMed
    • Google Scholar
55. 1. Mkumbaye SI
    2. Wang CW
    3. Lyimo E
    4. Jespersen JS
    5. Manjurano A
    6. Mosha J
    7. Kavishe RA
    8. Mwakalinga SB
    9. Minja DTR
    10. Lusingu JP
    11. Theander TG
    12. Lavstsen T

    (2017) The severity of Plasmodium falciparum infection Is associated with transcript levels of var genes encoding endothelial protein C receptor-binding: falciparum Erythrocyte Membrane Protein 1

    Infection and Immunity 85:e00841-16.

    https://doi.org/10.1128/IAI.00841-16

    • PubMed
    • Google Scholar
56. 1. Mok BW
    2. Ribacke U
    3. Rasti N
    4. Kironde F
    5. Chen Q
    6. Nilsson P
    7. Wahlgren M

    (2008) Default pathway of var2csa switching and translational repression in Plasmodium falciparum

    PLOS ONE 3:e1982.

    https://doi.org/10.1371/journal.pone.0001982

    • PubMed
    • Google Scholar
57. 1. Oakley MSM
    2. Kumar S
    3. Anantharaman V
    4. Zheng H
    5. Mahajan B
    6. Haynes JD
    7. Moch JK
    8. Fairhurst R
    9. McCutchan TF
    10. Aravind L

    (2007) Molecular factors and biochemical pathways induced by febrile temperature in intraerythrocytic Plasmodium falciparum parasites

    Infection and Immunity 75:2012–2025.

    https://doi.org/10.1128/IAI.01236-06

    • PubMed
    • Google Scholar
58. 1. Otto TD
    2. Assefa SA
    3. Böhme U
    4. Sanders MJ
    5. Kwiatkowski D
    6. Pf3k consortium
    7. Berriman M
    8. Newbold C

    (2019) Evolutionary analysis of the most polymorphic gene family in falciparum malaria

    Wellcome Open Research 4:193.

    https://doi.org/10.12688/wellcomeopenres.15590.1

    • PubMed
    • Google Scholar
59. 1. Patro R
    2. Duggal G
    3. Love MI
    4. Irizarry RA
    5. Kingsford C

    (2017) Salmon provides fast and bias-aware quantification of transcript expression

    Nature Methods 14:417–419.

    https://doi.org/10.1038/nmeth.4197

    • PubMed
    • Google Scholar
60. 1. Peters JM
    2. Fowler EV
    3. Krause DR
    4. Cheng Q
    5. Gatton ML

    (2007) Differential changes in Plasmodium falciparum var transcription during adaptation to culture

    The Journal of Infectious Diseases 195:748–755.

    https://doi.org/10.1086/511436

    • PubMed
    • Google Scholar
61. 1. Pickford AK
    2. Michel-Todó L
    3. Dupuy F
    4. Mayor A
    5. Alonso PL
    6. Lavazec C
    7. Cortés A
    8. Wellems TE

    (2021) Expression patterns of Plasmodium falciparum clonally variant genes at the onset of a blood infection in malaria-naive humans

    mBio 12:e0163621.

    https://doi.org/10.1128/mBio.01636-21

    • PubMed
    • Google Scholar
62. 1. Quintana MDP
    2. Ecklu-Mensah G
    3. Tcherniuk SO
    4. Ditlev SB
    5. Oleinikov AV
    6. Hviid L
    7. Lopez-Perez M

    (2019) Comprehensive analysis of Fc-mediated IgM binding to the Plasmodium falciparum erythrocyte membrane protein 1 family in three parasite clones

    Scientific Reports 9:6050.

    https://doi.org/10.1038/s41598-019-42585-0

    • PubMed
    • Google Scholar
63. 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
64. 1. Robert F
    2. Ntoumi F
    3. Angel G
    4. Candito D
    5. Rogier C
    6. Fandeur T
    7. Sarthou JL
    8. Mercereau-Puijalon O

    (1996) Extensive genetic diversity of Plasmodium falciparum isolates collected from patients with severe malaria in dakar, senegal

    Transactions of the Royal Society of Tropical Medicine and Hygiene 90:704–711.

    https://doi.org/10.1016/s0035-9203(96)90446-0

    • PubMed
    • Google Scholar
65. 1. Rorick MM
    2. Rask TS
    3. Baskerville EB
    4. Day KP
    5. Pascual M

    (2013) Homology blocks of Plasmodium falciparum var genes and clinically distinct forms of severe malaria in a local population

    BMC Microbiology 13:244.

    https://doi.org/10.1186/1471-2180-13-244

    • PubMed
    • Google Scholar
66. 1. Sahu PK
    2. Duffy FJ
    3. Dankwa S
    4. Vishnyakova M
    5. Majhi M
    6. Pirpamer L
    7. Vigdorovich V
    8. Bage J
    9. Maharana S
    10. Mandala W
    11. Rogerson SJ
    12. Seydel KB
    13. Taylor TE
    14. Kim K
    15. Sather DN
    16. Mohanty A
    17. Mohanty RR
    18. Mohanty A
    19. Pattnaik R
    20. Aitchison JD
    21. Hoffmann A
    22. Mohanty S
    23. Smith JD
    24. Bernabeu M
    25. Wassmer SC

    (2021) Determinants of brain swelling in pediatric and adult cerebral malaria

    JCI Insight 6:e145823.

    https://doi.org/10.1172/jci.insight.145823

    • PubMed
    • Google Scholar
67. 1. Salanti A
    2. Dahlbäck M
    3. Turner L
    4. Nielsen MA
    5. Barfod L
    6. Magistrado P
    7. Jensen ATR
    8. Lavstsen T
    9. Ofori MF
    10. Marsh K
    11. Hviid L
    12. Theander TG

    (2004) Evidence for the involvement of VAR2CSA in pregnancy-associated malaria

    The Journal of Experimental Medicine 200:1197–1203.

    https://doi.org/10.1084/jem.20041579

    • PubMed
    • Google Scholar
68. 1. Scherf A
    2. Hernandez-Rivas R
    3. Buffet P
    4. Bottius E
    5. Benatar C
    6. Pouvelle B
    7. Gysin J
    8. Lanzer M

    (1998) Antigenic variation in malaria: in situ switching, relaxed and mutually exclusive transcription of var genes during intra-erythrocytic development in Plasmodium falciparum

    The EMBO Journal 17:5418–5426.

    https://doi.org/10.1093/emboj/17.18.5418

    • PubMed
    • Google Scholar
69. 1. Schulz MH
    2. Zerbino DR
    3. Vingron M
    4. Birney E

    (2012) Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels

    Bioinformatics 28:1086–1092.

    https://doi.org/10.1093/bioinformatics/bts094

    • PubMed
    • Google Scholar
70. 1. Shabani E
    2. Hanisch B
    3. Opoka RO
    4. Lavstsen T
    5. John CC

    (2017) Plasmodium falciparum EPCR-binding PfEMP1 expression increases with malaria disease severity and is elevated in retinopathy negative cerebral malaria

    BMC Medicine 15:183.

    https://doi.org/10.1186/s12916-017-0945-y

    • PubMed
    • Google Scholar
71. 1. Smith JD
    2. Chitnis CE
    3. Craig AG
    4. Roberts DJ
    5. Hudson-Taylor DE
    6. Peterson DS
    7. Pinches R
    8. Newbold CI
    9. Miller LH

    (1995) Switches in expression of Plasmodium falciparum var genes correlate with changes in antigenic and cytoadherent phenotypes of infected erythrocytes

    Cell 82:101–110.

    https://doi.org/10.1016/0092-8674(95)90056-x

    • PubMed
    • Google Scholar
72. 1. Stevenson L
    2. Laursen E
    3. Cowan GJ
    4. Bandoh B
    5. Barfod L
    6. Cavanagh DR
    7. Andersen GR
    8. Hviid L

    (2015) α2-Macroglobulin can crosslink multiple Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) molecules and may facilitate adhesion of parasitized erythrocytes

    PLOS Pathogens 11:e1005022.

    https://doi.org/10.1371/journal.ppat.1005022

    • PubMed
    • Google Scholar
73. 1. Storm J
    2. Jespersen JS
    3. Seydel KB
    4. Szestak T
    5. Mbewe M
    6. Chisala NV
    7. Phula P
    8. Wang CW
    9. Taylor TE
    10. Moxon CA
    11. Lavstsen T
    12. Craig AG

    (2019) Cerebral malaria is associated with differential cytoadherence to brain endothelial cells

    EMBO Molecular Medicine 11:e9164.

    https://doi.org/10.15252/emmm.201809164

    • PubMed
    • Google Scholar
74. 1. Stucke EM
    2. Dara A
    3. Dwivedi A
    4. Hodges TK
    5. Ott S
    6. Coulibaly D
    7. Koné AK
    8. Traoré K
    9. Guindo B
    10. Tangara BM
    11. Niangaly A
    12. Daou M
    13. Diarra I
    14. Tolo Y
    15. Sissoko M
    16. Tallon LJ
    17. Sadzewicz L
    18. Zhou AE
    19. Laurens MB
    20. Ouattara A
    21. Kouriba B
    22. Doumbo OK
    23. Takala-Harrison S
    24. Serre D
    25. Plowe CV
    26. Thera MA
    27. Travassos MA
    28. Silva JC

    (2021) Successful Profiling of Plasmodium falciparum var Gene Expression in Clinical Samples via a Custom Capture Array

    mSystems 6:e00226-21.

    https://doi.org/10.1128/mSystems.00226-21

    • PubMed
    • Google Scholar
75. 1. Su XZ
    2. Heatwole VM
    3. Wertheimer SP
    4. Guinet F
    5. Herrfeldt JA
    6. Peterson DS
    7. Ravetch JA
    8. Wellems TE

    (1995) The large diverse gene family var encodes proteins involved in cytoadherence and antigenic variation of Plasmodium falciparum-infected erythrocytes

    Cell 82:89–100.

    https://doi.org/10.1016/0092-8674(95)90055-1

    • PubMed
    • Google Scholar
76. 1. Tarr SJ
    2. Díaz-Ingelmo O
    3. Stewart LB
    4. Hocking SE
    5. Murray L
    6. Duffy CW
    7. Otto TD
    8. Chappell L
    9. Rayner JC
    10. Awandare GA
    11. Conway DJ

    (2018) Schizont transcriptome variation among clinical isolates and laboratory-adapted clones of the malaria parasite Plasmodium falciparum

    BMC Genomics 19:894.

    https://doi.org/10.1186/s12864-018-5257-x

    • PubMed
    • Google Scholar
77. 1. Taylor HM
    2. Grainger M
    3. Holder AA

    (2002) Variation in the expression of a Plasmodium falciparum protein family implicated in erythrocyte invasion

    Infection and Immunity 70:5779–5789.

    https://doi.org/10.1128/IAI.70.10.5779-5789.2002

    • PubMed
    • Google Scholar
78. 1. Tebben K
    2. Dia A
    3. Serre D

    (2022) Determination of the stage composition of plasmodium infections from bulk gene expression data

    mSystems 7:e0025822.

    https://doi.org/10.1128/msystems.00258-22

    • PubMed
    • Google Scholar
79. 1. Tonkin-Hill GQ
    2. Trianty L
    3. Noviyanti R
    4. Nguyen HHT
    5. Sebayang BF
    6. Lampah DA
    7. Marfurt J
    8. Cobbold SA
    9. Rambhatla JS
    10. McConville MJ
    11. Rogerson SJ
    12. Brown GV
    13. Day KP
    14. Price RN
    15. Anstey NM
    16. Papenfuss AT
    17. Duffy MF

    (2018) The Plasmodium falciparum transcriptome in severe malaria reveals altered expression of genes involved in important processes including surface antigen-encoding var genes

    PLOS Biology 16:e2004328.

    https://doi.org/10.1371/journal.pbio.2004328

    • PubMed
    • Google Scholar
80. 1. Tuikue Ndam N
    2. Moussiliou A
    3. Lavstsen T
    4. Kamaliddin C
    5. Jensen ATR
    6. Mama A
    7. Tahar R
    8. Wang CW
    9. Jespersen JS
    10. Alao JM
    11. Gamain B
    12. Theander TG
    13. Deloron P

    (2017) Parasites causing cerebral falciparum malaria bind multiple endothelial receptors and express EPCR and ICAM-1-Binding PfEMP1

    The Journal of Infectious Diseases 215:1918–1925.

    https://doi.org/10.1093/infdis/jix230

    • PubMed
    • Google Scholar
81. 1. Turner L
    2. Lavstsen T
    3. Berger SS
    4. Wang CW
    5. Petersen JEV
    6. Avril M
    7. Brazier AJ
    8. Freeth J
    9. Jespersen JS
    10. Nielsen MA
    11. Magistrado P
    12. Lusingu J
    13. Smith JD
    14. Higgins MK
    15. Theander TG

    (2013) Severe malaria is associated with parasite binding to endothelial protein C receptor

    Nature 498:502–505.

    https://doi.org/10.1038/nature12216

    • PubMed
    • Google Scholar
82. 1. Ukaegbu UE
    2. Kishore SP
    3. Kwiatkowski DL
    4. Pandarinath C
    5. Dahan-Pasternak N
    6. Dzikowski R
    7. Deitsch KW

    (2014) Recruitment of PfSET2 by RNA polymerase II to variant antigen encoding loci contributes to antigenic variation in P: falciparum

    PLOS Pathogens 10:e1003854.

    https://doi.org/10.1371/journal.ppat.1003854

    • PubMed
    • Google Scholar
83. 1. Ukaegbu UE
    2. Zhang X
    3. Heinberg AR
    4. Wele M
    5. Chen Q
    6. Deitsch KW

    (2015) A unique virulence gene occupies a principal position in immune evasion by the malaria parasite Plasmodium falciparum

    PLOS Genetics 11:e1005234.

    https://doi.org/10.1371/journal.pgen.1005234

    • PubMed
    • Google Scholar
84. 1. Vignali M
    2. Armour CD
    3. Chen J
    4. Morrison R
    5. Castle JC
    6. Biery MC
    7. Bouzek H
    8. Moon W
    9. Babak T
    10. Fried M
    11. Raymond CK
    12. Duffy PE

    (2011) NSR-seq transcriptional profiling enables identification of a gene signature of Plasmodium falciparum parasites infecting children

    The Journal of Clinical Investigation 121:1119–1129.

    https://doi.org/10.1172/JCI43457

    • PubMed
    • Google Scholar
85. 1. Wagner BD
    2. Grunwald GK
    3. Zerbe GO
    4. Mikulich-Gilbertson SK
    5. Robertson CE
    6. Zemanick ET
    7. Harris JK

    (2018) On the ue of diversity measures in longitudinal sequencing studies of microbial communities

    Frontiers in Microbiology 9:1037.

    https://doi.org/10.3389/fmicb.2018.01037

    • PubMed
    • Google Scholar
86. 1. Wahlgren M
    2. Goel S
    3. Akhouri RR

    (2017) Variant surface antigens of Plasmodium falciparum and their roles in severe malaria

    Nature Reviews. Microbiology 15:479–491.

    https://doi.org/10.1038/nrmicro.2017.47

    • PubMed
    • Google Scholar
87. Preprint

    1. Wang W
    2. Chen EZ
    3. Li H

    (2021) Truncated rank-based tests for two-part models with excessive zeros and applications to microbiome data

    arXiv.

    https://arxiv.org/abs/2110.05368

    • Google Scholar
88. 1. Warimwe GM
    2. Recker M
    3. Kiragu EW
    4. Buckee CO
    5. Wambua J
    6. Musyoki JN
    7. Marsh K
    8. Bull PC

    (2013) Plasmodium falciparum var gene expression homogeneity as a marker of the host-parasite relationship under different levels of naturally acquired immunity to malaria

    PLOS ONE 8:e70467.

    https://doi.org/10.1371/journal.pone.0070467

    • PubMed
    • Google Scholar
89. Report

    1. WHO

    (2022)

    World malaria report 2022

    Geneva: World Health Organization.

    • Google Scholar
90. 1. Wichers JS
    2. Scholz JAM
    3. Strauss J
    4. Witt S
    5. Lill A
    6. Ehnold LI
    7. Neupert N
    8. Liffner B
    9. Lühken R
    10. Petter M
    11. Lorenzen S
    12. Wilson DW
    13. Löw C
    14. Lavazec C
    15. Bruchhaus I
    16. Tannich E
    17. Gilberger TW
    18. Bachmann A

    (2019) Dissecting the gene expression, localization, membrane topology, and function of the Plasmodium falciparum STEVOR protein family

    mBio 10:e01500-19.

    https://doi.org/10.1128/mBio.01500-19

    • PubMed
    • Google Scholar
91. 1. Wichers JS
    2. Tonkin-Hill G
    3. Thye T
    4. Krumkamp R
    5. Kreuels B
    6. Strauss J
    7. von Thien H
    8. Scholz JA
    9. Smedegaard Hansson H
    10. Weisel Jensen R
    11. Turner L
    12. Lorenz F-R
    13. Schöllhorn A
    14. Bruchhaus I
    15. Tannich E
    16. Fendel R
    17. Otto TD
    18. Lavstsen T
    19. Gilberger TW
    20. Duffy MF
    21. Bachmann A

    (2021) Common virulence gene expression in adult first-time infected malaria patients and severe cases

    eLife 10:e69040.

    https://doi.org/10.7554/eLife.69040

    • PubMed
    • Google Scholar
92. 1. Xie Y
    2. Wu G
    3. Tang J
    4. Luo R
    5. Patterson J
    6. Liu S
    7. Huang W
    8. He G
    9. Gu S
    10. Li S
    11. Zhou X
    12. Lam TW
    13. Li Y
    14. Xu X
    15. Wong GKS
    16. Wang J

    (2014) SOAPdenovo-trans: de novo transcriptome assembly with short RNA-Seq reads

    Bioinformatics 30:1660–1666.

    https://doi.org/10.1093/bioinformatics/btu077

    • PubMed
    • Google Scholar
93. 1. Yamagishi J
    2. Natori A
    3. Tolba MEM
    4. Mongan AE
    5. Sugimoto C
    6. Katayama T
    7. Kawashima S
    8. Makalowski W
    9. Maeda R
    10. Eshita Y
    11. Tuda J
    12. Suzuki Y

    (2014) Interactive transcriptome analysis of malaria patients and infecting Plasmodium falciparum

    Genome Research 24:1433–1444.

    https://doi.org/10.1101/gr.158980.113

    • Google Scholar
94. 1. Yang X
    2. Charlebois P
    3. Gnerre S
    4. Coole MG
    5. Lennon NJ
    6. Levin JZ
    7. Qu J
    8. Ryan EM
    9. Zody MC
    10. Henn MR

    (2012) De novo assembly of highly diverse viral populations

    BMC Genomics 13:475.

    https://doi.org/10.1186/1471-2164-13-475

    • PubMed
    • Google Scholar
95. 1. Yuda M
    2. Iwanaga S
    3. Shigenobu S
    4. Kato T
    5. Kaneko I

    (2010) Transcription factor AP2-Sp and its target genes in malarial sporozoites

    Molecular Microbiology 75:854–863.

    https://doi.org/10.1111/j.1365-2958.2009.07005.x

    • PubMed
    • Google Scholar
96. 1. Zerbino DR
    2. Birney E

    (2008) Velvet: algorithms for de novo short read assembly using de bruijn graphs

    Genome Research 18:821–829.

    https://doi.org/10.1101/gr.074492.107

    • PubMed
    • Google Scholar
97. 1. Zhang Q
    2. Zhang Y
    3. Huang Y
    4. Xue X
    5. Yan H
    6. Sun X
    7. Wang J
    8. McCutchan TF
    9. Pan W
    10. Hviid L

    (2011) From in vivo to in vitro: dynamic analysis of Plasmodium falciparum var gene expression patterns of patient isolates during adaptation to culture

    PLOS ONE 6:e20591.

    https://doi.org/10.1371/journal.pone.0020591

    • PubMed
    • Google Scholar
98. 1. Zhang X
    2. Florini F
    3. Visone JE
    4. Lionardi I
    5. Gross MR
    6. Patel V
    7. Deitsch KW

    (2022) A coordinated transcriptional switching network mediates antigenic variation of human malaria parasites

    eLife 11:e83840.

    https://doi.org/10.7554/eLife.83840

    • PubMed
    • Google Scholar
99. 1. Zimin AV
    2. Marçais G
    3. Puiu D
    4. Roberts M
    5. Salzberg SL
    6. Yorke JA

    (2013) The MaSuRCA genome assembler

    Bioinformatics 29:2669–2677.

    https://doi.org/10.1093/bioinformatics/btt476

    • PubMed
    • Google Scholar

Author details

1. Clare Andradi-Brown

   1. Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, United Kingdom
   2. Department of Life Sciences, Imperial College London, South Kensington, London, United Kingdom
   3. Centre for Paediatrics and Child Health, Imperial College London, London, United Kingdom

   Contribution

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

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2461-5256

2. Jan Stephan Wichers-Misterek

   1. Bernhard Nocht Institute for Tropical Medicine, Bernhard-Nocht-Strasse, Hamburg, Germany
   2. Centre for Structural Systems Biology, Hamburg, Germany
   3. Biology Department, University of Hamburg, Hamburg, Germany

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0599-1742

3. Heidrun von Thien

   1. Bernhard Nocht Institute for Tropical Medicine, Bernhard-Nocht-Strasse, Hamburg, Germany
   2. Centre for Structural Systems Biology, Hamburg, Germany
   3. Biology Department, University of Hamburg, Hamburg, Germany

   Contribution

   Investigation

   Competing interests

   No competing interests declared

4. Yannick D Höppner

   1. Bernhard Nocht Institute for Tropical Medicine, Bernhard-Nocht-Strasse, Hamburg, Germany
   2. Centre for Structural Systems Biology, Hamburg, Germany
   3. Biology Department, University of Hamburg, Hamburg, Germany

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Judith AM Scholz

   Bernhard Nocht Institute for Tropical Medicine, Bernhard-Nocht-Strasse, Hamburg, Germany

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Helle Hansson

   1. Center for Medical Parasitology, Department of Immunology and Microbiology, University of Copenhagen, Copenhagen, Denmark
   2. Department of Infectious Diseases, Copenhagen University Hospital, Copenhagen, Denmark

   Contribution

   Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6484-1165

7. Emma Filtenborg Hocke

   1. Center for Medical Parasitology, Department of Immunology and Microbiology, University of Copenhagen, Copenhagen, Denmark
   2. Department of Infectious Diseases, Copenhagen University Hospital, Copenhagen, Denmark

   Contribution

   Investigation

   Competing interests

   No competing interests declared

8. Tim Wolf Gilberger

   1. Bernhard Nocht Institute for Tropical Medicine, Bernhard-Nocht-Strasse, Hamburg, Germany
   2. Centre for Structural Systems Biology, Hamburg, Germany
   3. Biology Department, University of Hamburg, Hamburg, Germany

   Contribution

   Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7965-8272

9. Michael F Duffy

   Department of Microbiology and Immunology, University of Melbourne, Melbourne, Australia

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

10. Thomas Lavstsen

    1. Center for Medical Parasitology, Department of Immunology and Microbiology, University of Copenhagen, Copenhagen, Denmark
    2. Department of Infectious Diseases, Copenhagen University Hospital, Copenhagen, Denmark

    Contribution

    Resources, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-3044-4249

11. Jake Baum

    1. Department of Life Sciences, Imperial College London, South Kensington, London, United Kingdom
    2. School of Biomedical Sciences, Faculty of Medicine & Health, UNSW, Kensington, Sydney, United Kingdom

    Contribution

    Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

12. Thomas D Otto

    School of Infection & Immunity, MVLS, University of Glasgow, Glasgow, United Kingdom

    Contribution

    Conceptualization, Supervision, Methodology, Writing – original draft, Writing – review and editing

    Contributed equally with

    Aubrey J Cunnington and Anna Bachmann

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-1246-7404

13. Aubrey J Cunnington

    1. Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, United Kingdom
    2. Centre for Paediatrics and Child Health, Imperial College London, London, United Kingdom

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

    Contributed equally with

    Thomas D Otto and Anna Bachmann

    For correspondence

    a.cunnington@imperial.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-1305-3529

14. Anna Bachmann

    1. Bernhard Nocht Institute for Tropical Medicine, Bernhard-Nocht-Strasse, Hamburg, Germany
    2. Centre for Structural Systems Biology, Hamburg, Germany
    3. Biology Department, University of Hamburg, Hamburg, Germany
    4. German Center for Infection Research (DZIF), partner site Hamburg-Borstel-Lübeck-Riems, Hamburg, Germany

    Contribution

    Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Validation, Investigation, Writing – original draft, Project administration, Writing – review and editing

    Contributed equally with

    Thomas D Otto and Aubrey J Cunnington

    For correspondence

    bachmann@bni-hamburg.de

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8397-7308

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